An Epidemiological Approach

Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN

UPDATED 7/10/2022

New Epidemiological Data on new COVID BA.5 Variant: Is this the most transmissable and  virulent form?

New versions of Omicron are masters of immune evasion

Vaccines and prior infection still prevent severe disease from new SARS-CoV-2 strains

A health care worker gives a woman a COVID-19 vaccine through the window of a car
A woman receives a COVID-19 vaccine in New Delhi in June 2021. The continuing evolution of SARS-CoV-2 is a challenge for vaccine developers.ATUL LOKE/PANOS PICTURES/REDUX
Table of contents
A version of this story appeared in Science, Vol 376, Issue 6594.Download PDF

Once again, South Africa is at the forefront of the changing COVID-19 pandemic. Epidemiologists and virologists are watching closely as cases there rise sharply again, just 5 months after the Omicron variant caused a dramatic surge. This time, the drivers are two new subvariants of Omicron named BA.4 and BA.5, which the Network for Genomic Surveillance in South Africa first detected in January.

The new strains didn’t have much of an impact initially, but over the past few weeks case numbers in South Africa jumped from roughly 1000 per day on 17 April to nearly 10,000 on 7 May. A third subvariant called BA.2.12.1 is spreading in the United States, driving increases along the East Coast.

It’s still unclear whether the new subvariants will cause another global COVID-19 wave. But like the earlier versions of Omicron, they have a remarkable ability to evade immunity from vaccines, previous infection, or both—a disturbing portent for the future of the pandemic and a potentially serious complication for vaccine developers.

In most cases, vaccination or earlier infection still seem to provide protection from severe disease. “There’s no reason to freak out,” says John Moore, an immunologist at Weill Cornell Medicine. The new strains are “an additional hassle,” he says, but “there’s no indication that they’re more dangerous or more pathogenic.”

Hospitalizations in South Africa, for example, have increased, “but because it is starting from a very low level, it’s not cause for alarm,” says virologist Tulio de Oliveira of Stellenbosch University, who helped identify BA.4 and BA.5. Numbers of patients in intensive care units are as low as they have been since the start of the pandemic, he says. “At the moment, we expect something similar to the Omicron BA.1 wave,” when hospitalization rates stayed manageable.

The new superspreaders do, however, showcase the restless virus’ ability to find ways around the “immunity wall” built up over the past 2 years and to continue to circulate at high levels. Even if the new variants cause relatively little severe disease, “it’s a numbers game,” says Leif Erik Sander, an infectious disease expert at the Charité University Hospital in Berlin; enough new infections could still overwhelm health systems.

All three new strains share key mutations with the BA.2 strain of Omicron, which, like BA.1, emerged in southern Africa in October 2021. Initial studies by de Oliveira and Alex Sigal, an infectious disease expert at the Africa Health Research Institute in Durban, suggest BA.4 and BA.5 can elude the immunity of patients who were infected with the BA.1 strain, which in South Africa caused a much larger wave than BA.2. That may be in part because immunity has waned since South Africa’s BA.1 wave peaked in December. People who were both vaccinated and infected had somewhat stronger protection, de Oliveira and Sigal reported in a 2 May preprint.

All three new variants have mutations that alter a key amino acid called L452, which may help explain their ability to dodge immunity. L452 is part of the receptor-binding domain, the part of the spike protein that locks onto cells, enabling infection. The domain is also a key target for protective antibodies.

The Delta variant that caused devastating surges around the world in 2021 had mutations in L452 as well, so many scientists have been watching this hot spot carefully, including immunologist Yunlong Richard Cao of Peking University. On 11 April, Cao says, he and his

colleagues noticed a pattern: New Omicron sublineages from New York, Belgium, France, and South Africa all had changes in L452. “The independent appearance of four different mutations at the same site? That’s not normal,” Cao says. The researchers suspected it was the virus’ response to the high levels of immunity generated by the huge Omicron waves.

They immediately started to make copies of the spike protein based on the new sequences and test how well different antibodies could block those proteins, preventing them from binding to cells. They used sera from 156 vaccinated and boosted subjects, including some who had recovered from either BA.1 or severe acute respiratory syndrome (SARS), the coronavirus disease that caused a deadly global outbreak almost 2 decades ago. Like the South African team, they found that blood from patients who had been infected with BA.1 had only weak ability to neutralize BA.4 and BA.5; the same was true for BA.2.12.1. Even less effective were sera from people who had previously been infected with SARS and then vaccinated against COVID-19, they reported in a 2 May preprint.

The latter finding was surprising. Previous work by Linfa Wang, a bat coronavirus researcher at the Duke-NUS Medical School in Singapore, had shown patients who had recovered from SARS and were then vaccinated had strong protection against earlier SARS-CoV-2 variants—and even some related animal viruses—a finding that seemed to hold clues to developing vaccines effective against multiple coronaviruses, including those that might trigger the next pandemic. But the new mutations apparently helped the Omicron subvariants evade those previously powerful antibodies.

Wang notes, however, that the subjects in the new study were all vaccinated with CoronaVac, a Chinese vaccine made from inactivated virus. Subjects in his study were vaccinated with messenger RNA (mRNA) vaccines, which might provide a more potent response to the new strains, he says. But Wang agrees that Omicron’s knack for immune escape is dramatic. Based on its immunological profile, it  “should be called SARS-3,” he says—an entirely distinct virus.

Omicron’s rapid evolution creates difficult decisions for vaccine- and policymakers about whether to shift to a new set of vaccines or stick with the current formulations, which are based on the virus that emerged in Wuhan, China, more than 
2 years ago. Moderna has tested two versions of its mRNA vaccine, containing the ancestral strain and either the Beta variant—which spread in South Africa for a while in 2021 but is now gone—or the Omicron BA.1 variant. The company has not yet reported data on how well they might protect against the new subvariants.

Pfizer, the other mRNA vaccine producer, has tested the efficacy of a booster and a primary vaccine based on BA.1. Results are expected by the end of June. The U.S. Food and Drug Administration has scheduled a meeting for 28 June to analyze available data and make vaccine recommendations for the fall.

The limited protection that BA.1 infection provided against the new subvariants in lab studies has already raised questions about how useful the new Omicron-specific vaccines might be. Wang says the virus is evolving too quickly for strain-specific vaccines to keep up. Instead, a broad cocktail of monoclonal antibodies targeting different strains might be the best way forward, he says.

Such a shot could prevent infections for several months in those vulnerable to severe disease, including immunocompromised people who don’t respond to vaccines. Protecting that group is crucial, he notes, because many researchers suspect new variants emerge during long-term infections in people whose immune systems fail to clear the virus. The main hurdle, Wang says, is cost: A dose of monoclonal antibodies is about $1000 per patient, he notes, “but if someone could find a way to lower that to $50 or $100,” the approach could be cheaper than constantly updating vaccines.

Kristian Andersen, who studies viral evolution at Scripps Research, draws a sobering lesson from the newest Omicron variants. Although we don’t know what future variants will look like, he says, “we can be certain that they’ll continue to be more and more capable of immune escape,” possibly leading to lower protection against not just infection, but also against severe disease. “We need to focus on broadening our immunity,” he says.


Source: https://www.science.org/content/article/new-versions-omicron-are-masters-immune-evasion

Implications of the emergence and spread of the SARS-CoV-2 variants of concern BA.4 and BA.5 for the EU/EEA

Epidemiological update

Most European Union/European Economic Area (EU/EEA) countries have detected low proportions of the SARS-CoV-2 variants BA.4 and BA.5, however many have seen an increase in recent weeks. In Portugal, BA.5 has become the dominant SARS-CoV-2 variant and the increasing proportions of BA.5 have been accompanied by a surge in COVID-19 cases. The growth advantage reported for BA.4 and BA.5 suggest that these variants will become dominant throughout the EU/EEA, probably resulting in an increase in COVID-19 cases in coming weeks.

The extent of the increase in COVID-19 cases will depend on various factors, including immune protection against infection influenced by the timing and coverage of COVID-19 vaccination regimes, and the extent, timing and variant landscape of previous SARS-CoV-2 pandemic waves. Based on limited data, there is no evidence of BA.4 and BA.5 being associated with increased infection severity compared to the circulating variants BA.1 and BA.2. However, as in previous waves, an increase in COVID-19 cases overall can result in an increase in hospitalisations, ICU admissions and deaths.

Countries should remain vigilant for signals of BA.4 and BA.5 emergence and spread; maintain sensitive and representative testing and genomic surveillance with timely sequence reporting, and strengthen sentinel surveillance systems (primary care ILI/ARI and SARI). Countries should continue to monitor COVID-19 case rates – especially in people aged 65 and older – and severity indicators such as hospitalisations, ICU admissions, ICU occupancy and death.

Improving COVID-19 vaccine uptake of the primary course and first booster dose in populations who are yet to receive them remains a priority. It is expected that additional booster doses will be needed for those groups most at risk of severe disease, in anticipation of future waves.

Event background

The SARS-CoV-2 variants BA.4 and BA.5 were first detected in South Africa in January and February 2022, respectively. They became the dominant variants in that country in May 2022 [1] and a parallel increasing trend in epidemiological indicators, such as case notification rates and test positivity rates [2], suggested that these two variants were responsible for the surge in cases observed in South Africa in April−May 2022. As of 12 May 2022, ECDC reclassified Omicron sub-lineages BA.4 and BA.5 from variants of interest to variants of concern [3].

BA.4 and BA.5 are two sub-lineages of the Omicron clade (B.1.1.529). They share the same mutation profile in the spike gene, while they have different sets of mutations in their remaining genome. The defining mutations in the spike protein of BA.4 and BA.5 compared to BA.2 include Δ69-70, L452R, F486V and R493Q (reversion). Given the current epidemiological background where BA.2 is the dominant variant in the EU/EEA, laboratories can use the presence of the spike changes L452R or F486V or S-gene target failure [4] as a pre-screening for the variants. To be able to differentiate between BA.4 and BA.5, assays targeting discordant parts outside of the spike gene or whole genome sequencing (WGS) are required. Since BA.4 and BA.5 only emerged recently, it has not yet been possible to include the variants in a rapid antigen detection test (RADT) evaluation study.

There is currently no indication of any change in severity for BA.4 or BA.5 compared to previous Omicron lineages. Severity indicators in Portugal (hospitalisation, ICU admissions and deaths) as of 1 June are below the levels reached in the previous Omicron peak, however, week-on-week increases continue to be observed. Over the past six weeks, both hospitalisations and ICU admission increases have mainly been among those aged 60 years and above [5].

The current growth advantage for the BA.4 and BA.5 variants of concern (mainly observed in South Africa [4] and Portugal [6]) compared to the dominant variant BA.2, is probably due to their ability to evade immune protection against infection induced by prior infection and/or vaccination, particularly if this has waned over time. Based on preliminary in-vitro data from preprints, BA.4 and BA.5 are antigenically distant from the ancestral virus [7,8] and,  compared to BA.1 and BA.2, they are less efficiently neutralised by sera from individuals vaccinated with three doses of COVID-19 vaccine (Vaxzevria or Comirnaty), or by sera from BA.1 vaccine breakthrough infections [8,9]. In addition, there has been an increased rate of re-infection in Portugal [5]. Overall, this raises concerns about more frequent BA.4/BA.5 vaccine breakthrough infections than for BA.1/BA.2, and for Omicron reinfections. Nevertheless, as of the end of week 22, 2022, overall transmission continues to decline in most EU/EEA countries, as shown by both overall case notification rates and case rates among people aged 65 years and above [10].

More evidence is needed to elucidate the efficiency of monoclonal antibodies (mAb) against the BA.4 and BA.5 variants, but evidence so far suggests that BA.4 and BA.5 have shown a reduced or broadly similar pattern of mAb sensitivity to that of BA.2 [7,11,12].

At present, there are no data available on vaccine effectiveness against different clinical outcomes for Omicron sub-lineages BA.4 and BA.5. Data on vaccine effectiveness against the Omicron variants BA.1 and BA.2 are described in a recently published ECDC technical report on the second mRNA COVID-19 vaccine booster dose [13].

BA.4 and BA.5 in the EU/EEA: update as of 13 June 2022

BA.4 and BA.5 were first detected in the EU/EEA in March 2022. Portugal was the first country in the EU/EEA to observe a significant increase in cases and in the proportion of one of these two variants (BA.5). As of 30 May 2022, BA.5 is the dominant SARS-CoV-2 variant in Portugal, with an estimated proportion of around 87% [6,14]. Between week 19 and 20, 2022, case numbers in Portugal declined and became stable, indicating that the peak of a BA.5 wave in Portugal may have been reached. In recent weeks (week 17−21 of 2022), an increase in the proportion of BA.4 and BA.5 infections was observed in many EU/EEA countries including Austria, Belgium, Denmark, France, Germany, Ireland, Italy, Netherlands, Spain and Sweden [15,16].

In particular, in Belgium BA.5 reached an estimated proportion of 19% and BA.4 accounted for 7.5% of the sequenced genomes during weeks 21−22. In Spain, BA.4 and BA.5 accounted for more than 10% of the samples analysed by variant-specific PCR in 10 autonomous communities during weeks 21−22, with a wide variation between the communities. In the Netherlands, BA.5 reached a proportion of 8% in week 20, while BA.4 was detected in a proportion close to 5%.

The growth advantage reported for BA.4 and BA.5 suggest that these variants will become dominant in the whole EU/EEA, probably resulting in an increase in COVID-19 cases in the coming weeks. The extent of the increase will depend on various factors, including immune protection against infection influenced by the timing and coverage of COVID-19 vaccination regimes and the extent, timing and variant landscape of previous SARS-CoV-2 pandemic waves. Based on limited data, there is no evidence of BA.4 and BA.5 being associated with increased infection severity compared to the circulating variants BA.1 and BA.2. However, as in previous waves, an increase in COVID-19 cases can result in a rise in hospitalisations, ICU admissions and deaths.

Monitoring and reporting

ECDC encourages countries to remain vigilant for signals of BA.4 and BA.5 emergence. Sensitive and representative testing policies and genomic surveillance are required to accurately determine the extent to which these variants may contribute to any observed increases in severe outcomes in the population (e.g. increases in hospital or ICU admissions).

Countries should therefore strengthen sentinel systems (primary care ILI/ARI and SARI) and continue to collect data on laboratory-confirmed cases (from non-sentinel sites) and on hospitalisations/ICU admissions and hospital bed occupancy [17]. Countries should remain vigilant and scale up testing and sequencing if required. Countries should continue to sequence positive specimens and share sequence data in a timely manner [18,19]. SARS-CoV-2 consensus sequences should be deposited in the GISAID database and, if available, raw data of SARS-CoV-2 sequences should be deposited in the COVID-19 data portal through the European Nucleotide Archive (ENA) [20]. If possible, antigenic characterisation should be undertaken as this will contribute to an understanding of the properties of these variants and specimens/isolates could also be shared for characterisation with the WHO reference laboratories [21].

Minimum metadata should be reported to TESSy case-based record type NCOV or in aggregated form to NCOVVariant and, if possible, GISAID accession numbers of sequenced cases should be reported. ECDC has invited nominated users of countries to use the EpiPulse event on BA.4 and BA.5 to informally discuss and share information on these two variants, specifically on virus characterisation and evidence on changes in disease severity, virus transmissibility, immune evasion and effects on diagnostics and therapeutics.



Improving COVID-19 vaccine uptake of the primary course and first booster dose in populations remains a priority for all age groups in order to reduce the COVID-19 hospitalisation and death burden. Detailed information on COVID-19 vaccine doses administered and vaccine uptake rates are reported on the ECDC Vaccine Tracker [22].

Depending on the evolving epidemiology, data on vaccine effectiveness over time, and other factors such as seasonality, it will be necessary to re-assess recommendations on timing and the populations that may benefit from additional booster doses.

The public health benefit of administering a second mRNA COVID-19 booster dose was recently assessed by ECDC to be clearest in those aged 80 years and above and immediate administration of a second booster dose in this population was found to be optimal in situations where viral circulation was high or increasing. Mathematical modelling also suggests that a second booster roll-out including those aged 60−79 years who are immunocompetent in the EU/EEA will probably be beneficial in terms of averted deaths, although the best timing for the roll-out is uncertain and will depend on future waves [13].

Additional booster doses in anticipation of future waves, or in advance of the autumn/winter season, are expected to be needed for rapid deployment in those groups most at risk of severe disease (e.g. adults aged 60 years and above and medically vulnerable populations). These additional doses will have the greatest impact if administered closer to expected periods of increased viral circulation, but before it reaches high levels.

COVID-19 spike protein illustration

How to protect yourself from COVID subvariant BA.5

(SACRAMENTO)Forget Delta, Alpha and the original Omicron variant.

The latest Omicron subvariant BA.5 is a whole different animal. Its most defining factor? It is the most easily transmissible COVID variant to date, able to evade previous immunity from COVID infection and vaccination.

According to the Centers for Disease Control and Prevention (CDC), as of last Saturday, BA.5 accounted for more than half of the country’s new COVID cases. The numbers continue to grow. The CDC reports an average of 106,549 new cases of COVID daily. The CDC also reports daily averages of 5,093 new hospital admissions and 273 new deaths from COVID.

“The main reason this variant has become the predominant one that is now circulating is that it is able to evade previous immunity,” said Dean Blumberg, chief of pediatric infectious diseases at UC Davis Children’s Hospital. “Even people who have partial immunity from a previous infection or vaccination can still have a breakthrough infection.”

That means even if you were infected in 2020 with Delta or even Omicron BA.1 last winter, you can still get BA.5. Your previous immunity does not protect you from the latest strain.

Reported symptoms of BA.5 are similar to previous COVID variants: fever, runny nose, coughing, sore throat, headaches, muscle pain and fatigue.

“The good news is that the vast majority of breakthrough infections now are outpatient illnesses. They are not resulting in the kind of severe illness that we saw earlier in the pandemic when no one had immunity, which led to increased hospitalizations and deaths.”

But the bad news is that even if you have been infected with other strains, including previous Omicron strains, you can still get infected with BA.5.

Emerging research is finding that with each repeat COVID infection – even asymptomatic infection — you increase your risk for complications including stroke, heart attack, diabetes, digestive and kidney disorders and long-term cognitive impairment, including dementia.

Each reinfection also carries with it the risk of Long COVID, a syndrome with ongoing COVID symptoms that can last for weeks or months after infection.

So, what can people do to defend against the latest variants?

  1. Ensure you are up-to-date with your COVID vaccinations and boosters. “There is abundant evidence that being vaccinated and getting all of the boosters that you are eligible for helps protect you against severe disease,” Blumberg said. The CDC recently released data that showed the risk of death from COVID was four times higher for those over 50 who had just the first booster, compared with those who had two boosters. This is especially key for those who are over 50 and immunocompromised.
  2. Continue to wear a well-fitted face covering (N95 or KN95, if possible) when you are indoors and you’re not able to socially distance from people outside of your household. “Continue to mask if you are at risk for severe disease or if you are worried about that,” Blumberg said.

Source: https://health.ucdavis.edu/coronavirus/news/headlines/how-to-protect-yourself-from-covid-subvariant-ba5/2022/07 

UPDATED on 12/23/2021

Dr. John Campbell Daily Discussion on COVID-19 and Omicron Variant

Source: https://www.youtube.com/watch?v=GDhIL9o3vHg

Highlights Include:

  • Omicron variant is replacing the delta variant as major variant in US
  • they still have alot of delta variant in UK so outlook may be worse for UK than US
  • South Africa hospitlization are all omicron variant but hospitilizations are relatively small number compared to previous COVID waves
  • There is a disagreement between CDC and worldwide epidemiologists as CDC says not enough US data on omicron
  • about 33% of South Africa is vaccinated while US has higher vaccination rates so we will have to see how important this is, although majority of US hospitilations are unvaccinated patients (> 95%)

UPDATED on 12/22/2021

South Africa has passed its Omicron outbreak peak, top researcher says

“In South Africa, variants, even highly mutated ones, will run out of people pretty quickly,” he said. “Pretty much by the end of last week it was running out of steam; there just aren’t enough people left to infect.”

Two new preprint papers add to growing evidence that the Omicron coronavirus variant may be less likely to cause severe disease and hospitalization compared to the Delta variant.

Omicron is associated with a two-thirds reduction in the risk of Covid-19 hospitalization compared with Delta, suggests one study, released online Wednesday as a working paper by researchers at the University of Edinburgh in the United Kingdom. That research was based out of Scotland.

The other paper, posted Tuesday to the online server medrxiv.org, suggests that people with Omicron infections have had 80% lower odds of being admitted to the hospital compared with Delta infections. But once a patient was hospitalized, there was no difference in the risk of severe disease, according to that research, based out of South Africa.

Both studies include preliminary data and have not yet been published in a peer-reviewed journal.

The study out of Scotland included data on 23,840 Omicron cases and 126,511 Delta cases, from Nov. 1 to Dec. 19. The researchers – from the University of Edinburgh, University of Strathclyde and Public Health Scotland – took a close look at the health outcomes among those Omicron infections compared with Delta infections. There were 15 hospital admissions among those with Omicron infections and 856 hospital admissions among Delta.

“Although small in number, the study is good news. The two thirds reduction in hospitalisation of double vaccinated young people compared to Delta indicates that Omicron will be milder for more people,” James Naismith, director of the Rosalind Franklin Institute and professor of structural biology at the University of Oxford, who was not involved in either study, said in a written statement distributed by the UK-based Science Media Centre on Wednesday.

The researchers found that the proportion of Omicron cases that were likely reinfections in people who have had Covid-19 before was more than 10 times that of Delta. The data also showed that having received a third dose of vaccine, or booster shot, was associated with a 57% reduction in the risk of symptomatic Omicron infection when compared with being at least 25 weeks out from completing a second dose.

“These early national data suggest that Omicron is associated with a two-thirds reduction in the risk of COVID-19 hospitalisation when compared to Delta. Whilst offering the greatest protection against Delta, the third/booster dose of vaccination offers substantial additional protection against the risk of symptomatic COVID-19 for Omicron,” the researchers wrote in the paper.

Read the full story on the studies here.

However some caution is warranted in translating this news to the Omicron outbreak in other countries, as highlighted in this interview with Veronica Uekermann, head of the COVID-19 response team at Steve Biko Academic Hospital.

“In terms of the massive everyday doubling that we were seeing just over a week ago with huge numbers, that seems to have settled, said Professor Veronica Uekermann, head of the COVID-19 response team at Steve Biko Academic Hospital.

But it is way too early to suggest that we have passed the peak. There are too many external factors, including the movement during the holiday season and the general behavior during this period, she said, noting that infections spiked last year after the holiday break.

It’s summertime in South Africa and many gatherings are outdoors, which may make a difference between the omicron-driven wave here and the surges in Europe and North America, where people tend to gather indoors.

Another unknown factor is how much omicron has spread among South Africans without causing disease.

Some health officials in New York have suggested that because South Africa appears to have experienced a quick, mild wave of omicron, the variant may behave similarly there and elsewhere in the U.S. But Nunes cautions against jumping to those conclusions.

Each setting, each country is different. The populations are different. The demographics of the population, the immunity is different in different countries, she said. South Africa’s population, with an average age of 27, is more youthful than many Western countries, for instance.

Most of the patients currently being treated for COVID-19 in hospitals are unvaccinated, Uekermann emphasized. About 40per cent of adult South Africans have been inoculated with two doses.

All my patients in ICU are unvaccinated,” Uekermann said. So our vaccinated people are doing better in this wave, for sure. We have got some patients who are very ill with severe COVID, and these are unvaccinated patients.

UPDATED on 11/22/2021

Defective viral RNA sensing gene OAS1 linked to severe COVID-19

Reporter: Stephen J. Williams, Ph.D

Defective viral RNA sensing gene OAS1 linked to severe COVID-19

UPDATED on 11/05/2021

Preparing for “Disease X”

SCIENCE•13 Oct 2021•Vol 374Issue 6566•p. 377•DOI: 10.1126/science.abm7796
The past 30 years have exposed the global public health and economic threats posed by the emergence of infectious pathogens with epidemic and pandemic potential. Severe acute respiratory syndrome (SARS), middle east respiratory syndrome (MERS), influenza, Ebola, Marburg, Lassa, Nipah, Zika, and now SARS coronavirus 2 (SARS-CoV-2) each have been the “Disease X” of their time. The risk of future emergence is driven by multiple forces, including climate change, ecosystem changes, and increasing urbanization. The next Disease X could appear at any time, and the world needs to be better prepared.
The newly established World Health Organization (WHO) Scientific Advisory Group on the Origins of Novel Pathogens (SAGO) presents an unprecedented opportunity to better guide studies that specifically investigate high-threat pathogens. Its mandate is to advise the WHO on developing a framework to define comprehensive studies on the origins of such pathogens, including SARS-CoV-2—information that is essential for developing policies and enhancing preparedness to reduce the possibility of future zoonotic spillover events (transmission of a pathogen from animals to humans) and the chance that those events become major outbreaks.
This is not the first time that international studies on the origin of a new virus have been conducted. Yet each time, scientists at the WHO and elsewhere faced challenges—not only scientific, but also logistical and political. These hurdles have also hampered efforts to understand the origins of COVID-19.
Since the beginning of this pandemic, scientists from around the world have worked together to understand the events that led to the first human infections. In May 2020, WHO’s member states passed a unanimous resolution giving WHO a mandate to bring together international experts for scientific and collaborative studies on the virus origins. But it’s clear that the scientific processes have been hurt by politicization, which is why the global scientific community must redouble efforts to drive the scientific process forward. In forming SAGO, experts were selected (from an open call for applicants) with diverse technical expertise from countries in all six WHO regions.
Building on the March 2021 findings from the Joint WHO-China study, as well as other findings published since then, SAGO will quickly assess the status of SARSCoV-2 origin studies and advise the WHO on what is known, the outstanding gaps, and next steps. All hypotheses must continue to be examined and, as WHO has said from the outset, a fully open and transparent scientific process is essential. Recent findings on the potential for zoonotic spillover of SARS-CoV-2 to humans, either directly from bats or through other animals, include, but are not limited to, studies of wildlife sold in markets in and around Wuhan, China (where cases of COVID-19 were first reported in December 2019); studies of SARS-like coronaviruses circulating in bats in China and Southeast Asia; studies on prepandemic biological sampling around the world; and other animal susceptibility studies. However, detailed investigations of the earliest known and suspected cases in China prior to December 2019 are still urgently needed, including analyses of stored blood samples from 2019 in Wuhan and surrounding areas and retrospective searches of hospital and mortality data for earlier cases.
As well, laboratory hypotheses must be examined carefully, with a focus on labs in the location where the first reports of human infections emerged in Wuhan. A lab accident cannot be ruled out until there is sufficient evidence to do so and those results are openly shared.
COVID-19 will not be the last Disease X. We need scientific collaboration, data sharing, and implementation of a robust “one health” approach that brings together the human, animal, and environmental spheres to boost risk identification, reduction, and surveillance in animals and at the human-animal-environment interface. This must be linked to early action to investigate, characterize, and contain threats. In parallel, the world needs systematic processes to study the emergence of these pathogens and their routes of transmission from natural reservoirs to humans. Laboratory protocols around the world must be monitored and strengthened.
Globally, at least 4.8 million people have died from COVID-19. They and their families are owed answers as to where and how the virus originated. It’s in everyone’s interest to better prepare for the next Disease X.

Why is Delta so infectious? New lab tool spotlights little noticed mutation that speeds viral spread

“Viruslike particles” open the way to safely study nucleocapsid and other viral proteins

Source:  https://www.science.org/content/article/why-delta-so-infectious-new-lab-tool-spotlights-little-noticed-mutation-speeds-viral-spread

An illustrated cross section of a Covid-19 coronavirus particle
The infectiousness of the Delta variant may be driven by a mutation in SARS-CoV-2’s nucleocapsid protein, shown as orange spirals binding the virus’s purple RNA inside a spherical viral particle.DESIGN CELLS/SCIENCE SOURCE

As the world has learned to its cost, the Delta variant of the pandemic coronavirus is more than twice as infectious as previous strains. Just what drives Delta’s ability to spread so rapidly hasn’t been clear, however. Now, a new lab strategy that makes it possible to quickly and safely study the effects of mutations in SARS-CoV-2 variants has delivered one answer: a little-noticed mutation in Delta that allows the virus to stuff more of its genetic code into host cells, thus boosting the chances that each infected cell will spread the virus to another cell.

That discovery, published today in Science, is “a big deal,” says Michael Summers, a structural biologist at the University of Maryland, Baltimore County—not just because it helps explain Delta’s ravages. The new system, developed by Nobel Prize winner Jennifer Doudna of the University of California (UC), Berkeley, and her colleagues, is a powerful tool for understanding current SARS-CoV-2 variants and exploring how future variants might affect the pandemic, he says. “The system she has developed allows you to look at any mutation and its influence on key parts of viral replication. … That can now be studied in a much easier way by a lot more scientists.”

Researchers analyzing how mutations in the coronavirus’ genome affect its activity have concentrated on the spike protein, which studs the virus’ surface and allows it to invade human cells. That’s partly because, short of deliberately mutating the virus and testing it—research that requires high-level biosafety facilities—the best tool for probing individual mutations has been what’s called a “pseudovirus,” a construct made from a different virus, often a lentivirus, that can express a coronavirus protein on its surface. But lentiviruses only express spike, not SARS-CoV-2’s other three structural proteins.

Doudna and her team made the new tool by tweaking lab constructs called viruslike particles (VLPs), which contain all the virus’ structural proteins but lack its genome. From the outside, a SARS-CoV-2 VLP looks exactly like the full-fledged virus. It can bind with cells in a laboratory and invade them. But because it is stripped of the virus’ RNA genome, it can’t hijack a cell’s machinery to replicate and burst out of the host cell to infect more cells. “It’s a one-way ticket. It doesn’t spread,” says Charles Rice, a molecular virologist at Rockefeller University.

Doudna and her colleagues, including co–senior author Melanie Ott, a virologist and director of the Gladstone Institute of Virology, added a new innovation to the VLP system. They inserted a snippet of messenger RNA (mRNA) that causes cells invaded by the VLPs to light up and glow. The brighter the cells glow after being infected with the VLPs, the more mRNA the VLPs have successfully delivered.

Next, the researchers tweaked the VLP’s proteins with various mutations. One was R203M, a mutation found in Delta that alters the nucleocapsid (N), a protein tucked inside the virus that packages its RNA genome. The N protein is a central player in viral replication, with roles that include stabilizing and releasing the virus’ genetic material. And it contains a mutational hot spot: a seven–amino acid stretch that is mutated in every SARS-CoV-2 variant of interest or concern in most samples studied. R203M is one mutation in this hot spot.

That work “revealed a surprise,” Doudna says. According to the intensity of the VLP’s glow, “A single amino acid change found in Delta’s nucleocapsid protein supercharged the particles with 10 times more mRNA compared with the original virus!” Cells infected with VLPs carrying N mutations found in the Alpha and Gamma variants glowed 7.5 and 4.2 times brighter, respectively.

The scientists next tested a real coronavirus engineered to include the R203M mutation, in appropriate lab biosafety conditions. After invading lung cells in the lab, the mutated virus produced 51 times more infectious virus than an original SARS-CoV-2 strain.

In people infected with the coronavirus, a very small proportion of viral particles produced by a cell actually go on to infect another cell, in part because many viral particles lack parts or all of the viral RNA genome. So mutations that make the virus more efficient at putting RNA inside host cells can boost the number of infectious particles produced.

“This mutation that’s found in Delta … makes the virus better at making infectious particles and because of that, it spreads more quickly,” says Abdullah Syed, a biomedical engineer at the Gladstone Institute of Data Science and Biotechnology, and one of the paper’s first authors.

The finding has implications for treatments, says Shan Lu, a cell biologist at UC San Diego who studies the N protein. “The field could think more about targeting the nucleocapsid protein to really help control infection and help treat patients.”

The researchers are now trying to understand just how Delta’s R203M mutation and others in N improve the assembly of viral particles and their mRNA delivery to host cells. They will probe whether a host protein is involved. If so, targeting it with a drug could be an effective way to stall Delta’s spread.

Scientists are also excited by the new VLP system, which will allow researchers without high-level biosafety access to study how all four coronavirus structural proteins work to assemble the virus, help it bud from cells, and invade other cells. Jasmine Cubuk, a biochemist and biophysicist at Washington University in St. Louis who studies the SARS-CoV-2 N protein, calls it “a fascinating and a very powerful tool.”

Rice cautions the new VLPs are a model system that may not always mimic the real thing. Researchers will still need to work with the real virus in advanced biosafety labs. “At the end of the day if you really want to understand how these mutations are affecting basic viral replicative processes, you have to put [a mutation] in the virus and study it.”

But he praises the new tool. “It really provides a wonderful system to study coronavirus assembly and also to look for drugs, for inhibitors that interfere with these processes.”

UPDATED on 12/21/2020

New COVID-19 Variant Spreading Rapidly in the UK

The following video for Dr. John Campbell explains the latest information of the new COVID-19 variant which is spreading rapidly in the UK and has been identified in many major European Union countries.  Some highlights of the video include:

  • New variant, VUI-202012/01 is a mutation (N501Y) in the spike protein
  • this new mutant displays stronger transmission than previous forms of COVID19
  • no real data on the virulence
  • variant is defined by a set of 17 mutations, of which N501Y in spike protein is most common
  • 70% more infections, increasing R value to 0.4 (R of 1.0 means not infectious)
  • as of 12/20/2020 60% of new cases in London are this new mutant

Please watch the following VIDEO for more details

UPDATED on 12/12/2020

From the Journal Science
Cryptic transmission of SARS-CoV-2 in Washington state

Source: https://science.sciencemag.org/content/370/6516/571?_ga=2.262386124.1159634523.1607784280-778044524.1607784280

A recent paper in the journal Science discusses the appropriate actions and failures in community surveillance to the emergence of the pandemic in the state of Washington in early spring of 2020.  Analysis of genome sequencing from 453 SARS-CoV2 infected individuals collected in the early stage of the Washington state COVID-19 outbreak presents a more accurate timeline of the spread of the disease, and suggests the importance of community surveillance in controlling a local spread of COVID-19.

A series of unfortunate events

The history of how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread around the planet has been far from clear. Several narratives have been propagated by social media and, in some cases, national policies were forged in response. Now that many thousands of virus sequences are available, two studies analyzed some of the key early events in the spread of SARS-CoV-2. Bedford et al. found that the virus arrived in Washington state in late January or early February. The viral genome from the first case detected had mutations similar to those found in Chinese samples and rapidly spread and dominated subsequent undetected community transmission. The other viruses detected had origins in Europe. Worobey et al. found that early introductions into Germany and the west coast of the United States were extinguished by vigorous public health efforts, but these successes were largely unrecognized. Unfortunately, several major travel events occurred in February, including repatriations from China, with lax public health follow-up. Serial, independent introductions triggered the major outbreaks in the United States and Europe that still hold us in the grip of control measures.

Abstract: After its emergence in Wuhan, China, in late November or early December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus rapidly spread globally. Genome sequencing of SARS-CoV-2 allows the reconstruction of its transmission history, although this is contingent on sampling. We analyzed 453 SARS-CoV-2 genomes collected between 20 February and 15 March 2020 from infected patients in Washington state in the United States. We find that most SARS-CoV-2 infections sampled during this time derive from a single introduction in late January or early February 2020, which subsequently spread locally before active community surveillance was implemented.

Fig. 1 Maximum-likelihood phylogeny of 455 SARS-CoV-2 viruses collected from Washington state on a background of 493 globally collected viruses.

Viruses collected from Washington state are shown as red circles. Tips and branches are colored on the basis of location, branch lengths are proportional to the number of mutations along a branch, and the xaxis is labeled with the number of substitutions relative to the root of the phylogeny—here equivalent to basal Wuhan outbreak viruses. The clustering of related viruses indicates community transmission after an introduction event. Branch locations are estimated on the basis of a discrete traits model. We observe a single introduction leading to a large outbreak clade of 384 sampled viruses from Washington state (marked by the larger arrow), and we observe a second introduction leading to a smaller outbreak clade of 39 viruses (marked by the smaller arrow). An interactive version of this figure is available at https://nextstrain.org/community/blab/ncov-cryptic-transmission/introductions.

Fig. 2 Maximum-likelihood phylogeny of the Washington state outbreak clade and immediately ancestral variants containing 448 SARS-CoV-2 viruses and Bayesian estimates of the date of the outbreak common ancestor and outbreak doubling time.

(A) Maximum-likelihood phylogeny. Tips are colored on the basis of location, branch lengths are proportional to the number of mutations between viruses, and the x axis is labeled with the number of substitutions relative to the root of the phylogeny—here equivalent to the WA1 haplotype. This comb-like phylogenetic structure of the Washington state outbreak clade is consistent with rapid exponential growth of the virus population. An interactive version of this figure is available at https://nextstrain.org/community/blab/ncov-cryptic-transmission/wa-clade. (B) Highest posterior density estimates for the date of the common ancestor of viruses from the Washington state outbreak clade (top) as well as the doubling time in days of the growth of this clade (bottom).

Our results highlight the critical need for widespread surveillance for community transmission of SARS-CoV-2 throughout the United States and the rest of the world, even after the current pandemic is brought under control. The broad spectrum of disease severity (23) makes such surveillance challenging (24). The combination of traditional public health surveillance and genomic epidemiology can provide actionable insights, as happened in this instance: Upon sequencing the initial community case on 29 February 2020, results were immediately shared with national, state, and local public health agencies, which resulted in the rapid rollout of social distancing policies as Seattle and Washington state came to grips with the extent of existing COVID-19 spread. The confirmation of local transmission in Seattle prompted a change in testing criteria to emphasize individuals with no travel history. From 29 February onward, genomic data were immediately posted to the GISAID EpiCoV sequence database (910) and analyzed alongside other public SARS-CoV-2 genomes by means of the Nextstrain online platform (25) to provide immediate and public situational awareness. We see the combination of community surveillance, genomic analysis, and public real-time sharing of results as a pathway to empower infectious disease surveillance systems.

UPDATED on 12/03/2020

Science ViewPoint: The dampened course of COVID-19 in Africa might suggest some innovative solutions for dealing with pandemic

Reporter: Stephen J. Williams, PhD

As the COVID-19 pandemic enters a more serious wave 2 in many countries, one country, Africa, has seemed to fare rather well with respect to mortality and prevalence of the viral pandemic.  This certainly appears curious as we, in other posts, have discussed the disparate health response and extreme health disparities which occur on the African continent, particularly with respect to cancer and other chronic diseases.  A recent Science Perspectives article by Moustapha Mhow et al.  [1] discusses how, more than 4 months after the first cases of COVID-19 found in Africa, prevalence and mortality from the infection are still low.  Interestingly, in another post discussed here, certain regions of Italy appear to have much lower infectivity and morbidity than other regions of the same and surrounding countries.

Some explanations for this difference between the African continent and other highly affected countries may include unreliable reporting and death registration, lockdown stringency, demography, sociocultural aspects, environmental exposures, genetics, and a different and potentially stronger immune system among Africans.

Africa faces major health and socioeconomic challenges that should have made the COVID-19 problem worse in Africa like:

  • A weak and geographically challenging health system
  • Population densities are very high in many regions

Interestingly in spite of projections, community transmission rates in highly populated areas have been actually much lower than expected, and resultant predicted deaths had not been observed.  This may be that African nations implemented testing much earlier than many other countries and more tests per number of cases were carried out than in other countries.

Measures such as curfews, school closings, and travel restrictions were implemented very early in the course of the epidemic in Africa.

Some of these early responses may have been a result of the vast experience that the African continent has had recently with infectious epidemics like Ebola, Zika, and Lassa fever.

Another factor contributing to the low morbidity is the demographics.  Elderly people are heavily at risk for COVID related deaths than younger people and, in Africa, the median population is 19.7 years compared to 37 for the US.  So Africa’s younger population may be protective against the severity seen with COVID-19.

However, something else is in play here as, it would be expected based on age demographics that Africa’s morbidity would be four times lower, not the observed forty times lower than Europe or the US.

Source: https://science.sciencemag.org/content/369/6504/624?_ga=2.22258611.940768937.1607032673-1406008116.1607032673



  1. Mbow, M.; Lell, B.; Jochems, S.P.; Cisse, B.; Mboup, S.; Dewals, B.G.; Jaye, A.; Dieye, A.; Yazdanbakhsh, M. COVID-19 in Africa: Dampening the storm? Science 2020, 369, 624-626, doi:10.1126/science.abd3902.

UPDATED on 11/25/2020 

Reporter: Aviva Lev-Ari, PhD, RN

Credit…Mary Turner for The New York Times

Excerpts from

Evidence Builds That an Early Mutation Made the Pandemic Harder to Stop


Kristian Andersen, a geneticist at Scripps Research, La Jolla, said the research did show that the variant is more transmissible, but he believes the difference is subtle.

Even so, Dr. Andersen said that the variant’s higher transmissibility could help explain why some countries that were initially successful in containing the virus became susceptible to it later. The virus may have been “harder to contain than the first time around,” he said.

“What you used to do may not be quite enough to control it,” Dr. Andersen said. “Don’t necessarily expect that the enemy of two months ago is the enemy you have the next time.”

Around the world, the emergence of 614G has generated both serious scientific debate and largely political blame dodging. Government officials in Vietnam and Thailand, which fared well in containing the ancestral strain despite an influx of Chinese visitors early in the year, have suggested that the later outbreaks may have been partly the result of the 614G virus.

Image Source: 

By Sarah Almukhtar
Note: Countries shown reported at least five months of data since January and at least 75 sequences in one of those months.



Original Study

SARS-CoV-2 D614G variant exhibits efficient replication ex vivo and transmission in vivo

 See all authors and affiliations

Science  12 Nov 2020:
DOI: 10.1126/science.abe8499


The spike D614G substitution is prevalent in global SARS-CoV-2 strains, but its effects on viral pathogenesis and transmissibility remain unclear. We engineered a SARS-CoV-2 variant containing this substitution. The variant exhibits more efficient infection, replication, and competitive fitness in primary human airway epithelial cells, but maintains similar morphology and in vitro neutralization properties, compared with the ancestral wild-type virus. Infection of human angiotensin-converting enzyme 2 (ACE2) transgenic mice and Syrian hamsters with both viruses resulted in similar viral titers in respiratory tissues and pulmonary disease. However, the D614G variant transmits significantly faster and displayed increased competitive fitness than the wild-type virus in hamsters. These data show that the D614G substitution enhances SARS-CoV-2 infectivity, competitive fitness, and transmission in primary human cells and animal models.



a1 f5


The engines of SARS-CoV-2 spread: A Viewpoint article in the journal Science

From Science: https://science.sciencemag.org/content/370/6515/406?_ga=2.144739188.198351659.1604874521-577951357.1604874521

  1. Elizabeth C. Lee1,2
  2. Nikolas I. Wada2
  3. M. Kate Grabowski1,2,3
  4. Emily S. Gurley1,2
  5. Justin Lessler1,2  

Science  23 Oct 2020:
Vol. 370, Issue 6515, pp. 406-407
DOI: 10.1126/science.abd8755

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly across the globe, causing epidemics that range from quickly controlled local outbreaks (such as New Zealand) to large ongoing epidemics infecting millions (such as the United States). A tremendous volume of scientific literature has followed, as has vigorous debate about poorly understood facets of the disease, including the relative importance of various routes of transmission, the roles of asymptomatic and presymptomatic infections, and the susceptibility and transmissibility of specific age groups. This discussion may create the impression that our understanding of transmission is frequently overturned. Although our knowledge of SARS-CoV-2 transmission is constantly deepening in important ways, the fundamental engines that drive the pandemic are well established and provide a framework for interpreting this new information.

The majority of SARS-CoV-2 infections likely occur within households and other residential settings (such as nursing homes). This is because most individuals live with other people, and household contacts include many forms of close, high-intensity, and long-duration interaction. Both early contact tracing studies and a large study of more than 59,000 case contacts in South Korea found household contacts to be greater than six times more likely to be infected with SARS-CoV-2 than other close contacts (12). Household contacts accounted for 57% of identified secondary infections in the South Korean study, despite exhaustive tracking of community contacts. Globally, the proportion of cases attributable to household transmission will vary because of multiple factors, including household size. Contact studies suggest that 17 to 38% of contacts occur in households, implying that 46 to 66% of transmission is household-based (using the standard formula for attributable fraction) (3). This is consistent with household contact being a key driver of transmission for other respiratory viruses.

Even among close contacts within households, there are considerable heterogeneities in transmission risk. Spouses of index cases are more than twice as likely to be infected as other adult household members, and symptomatic index cases may be more likely to transmit the virus (4). Moreover, older age is associated with increased susceptibility to infection, increased transmissibility, and severe disease (4). Older members may face extra risk in multigenerational households if younger members have unavoidable work or school obligations, although young children may be less susceptible to infection and transmit the virus less readily (4).

Just as in households, those who live in congregate residences such as prisons, worker dormitories, and long-term care facilities have intense, long-duration, close contact. There are more potential contacts in these settings, which are often among older age groups. The confluence of these factors can lead to high infection rates in outbreaks (attack rate); for example, 66% of residents were infected in a homeless shelter, 62% in a nursing home, and 80% in a prison dormitory (56). Even when residents rarely leave, these facilities are highly connected to communities through workers and guests.

Although transmission may be easiest and most frequent in households and congregate residences, community transmission connects these settings and is, therefore, essential to sustain the epidemic, even if it directly causes fewer cases. Inevitably, “community contacts” include a heterogeneous mix of interactions. The probability that any of these interactions results in transmission stems from a complex interplay of pathogen attributes, host characteristics, timing, and setting. Hence, the properties of community transmission are difficult to measure, and this is where much of the remaining debate around SARS-CoV-2 transmission occurs.

A crucial factor in community transmission is that infected individuals not experiencing symptoms can transmit SARS-CoV-2. Infectiousness may peak before symptom onset (7). Viral loads appear to be similar between asymptomatic and symptomatic patients (8), although the implications for infectiousness are unclear. People experiencing symptoms may self-isolate or seek medical care, but those with no or mild symptoms may continue to circulate in the community. Because of this, those without severe symptoms have the potential to be “superspreaders” and may have an outsized influence on maintaining the epidemic.

Superspreading events, in which one person infects many, are often as much the result of setting as host characteristics. Apparent superspreading events of SARS-CoV-2 have occurred during choir practice (9), in department stores, at church events, and in health care settings (5). These are all settings where one individual can have many close contacts over a short period of time. Transmission can also be amplified if multiple subsequent infections occur in rapid succession, and outbreaks with high attack rates have occurred in close-contact settings such as schools (14%), meat processing plants (36%), and churches (38%) (510).

Both superspreading events and transmission-amplifying settings are part of a more general phenomenon: overdispersion in transmission. Overdispersion means that there is more variation than expected if cases exhibit homogeneity in transmissibility and number of contacts; hence, a small number of individuals are responsible for the majority of infections. This phenomenon has been described for diseases as diverse as measles, influenza, and pneumonic plague (11). For SARS-CoV-2, studies suggest that 10% of cases cause 80% of infections (1). Overdispersion is characterized by a large number of people who infect no one, and most people who do transmit infect a low-to-moderate number of individuals. Large superspreading events (such as those infecting 10 or more people) are likely quite rare, although they are far more likely to be detected and reported.

Such events have driven much of the debate around the relative importance of different modes of transmission. In household settings, contacts are so long and intense that it matters little whether large droplets, fomites (contaminated surfaces), or aerosolized particles are driving spread; all have ample opportunity. In community settings, where there is greater variety in the nature of infectious contacts, these distinctions become more important, particularly because they affect policy. Aerosolization of fecal matter caused one of the largest superspreading events of the 2003 SARS-CoV epidemic (12), and aerosolizing medical procedures facilitate the spread of coronaviruses (1213). Several SARS-CoV-2 transmission events suggest that aerosolized viral particles may play a role in transmission in everyday settings. Although the frequency of aerosolized transmission is uncertain, extended close contact and sharing of spaces poses the greatest risk. It is also difficult to generalize the importance of different modes of transmission across settings because their relative contributions can be modified by environmental conditions. For example, low–absolute humidity environments are associated with influenza virus transmission in temperate regions, probably because these conditions facilitate small droplet spread, yet influenza outbreaks are still common in tropical regions, with fomites potentially playing a larger role (14).

A mode of transmission need not be frequent to be important, and regardless of the cause, overdispersion has considerable implications. First, overdispersion means that most infected individuals who enter a community will not transmit, so many introductions may occur before an epidemic takes hold; likewise, overdispersion also increases the probability of disease extinction as the epidemic recedes and fewer people are infected (11). When large transmission events do occur, epidemics can expand rapidly, but as the epidemic grows, overdispersion will matter less to the trajectory until incidence decreases and case counts are low again. Second, overdispersion gives transmission networks “scale-free” properties, in which connectivity in the network is dominated by a few highly connected nodes. Compared with networks with more evenly distributed connections, the connectivity of scale-free networks is less sensitive to random node removal but more susceptible to targeting of highly connected nodes (11).

If transmission is highly overdispersed, broad and untargeted interventions may be less effective than expected, whereas interventions targeted at settings conducive to superspreading (such as mass gatherings and hospitals) may have an outsized effect. Although some determinants of overdispersion may not be amenable to targeted interventions, others related to occupation or setting could be. For example, rapidly improved infection control procedures within health care facilities played a critical role in containing the nascent SARS-CoV pandemic of 2003.

Intercity, interregional, and international spread are also essential to sustain the pandemic, even if long-distance transmission events are rare (see the figure). Only a small number of such long-distance connections are needed to create a “small world” network in which only a few infection events can transmit the virus between any two individuals worldwide. This is one reason why early travel bans could not stop the global spread of SARS-CoV-2, although they may have slowed the pandemic. However, travel restrictions can work: Extreme measures in China played an important part in achieving domestic suppression of the virus.

Science Magazine

SARS-CoV-2 spread across spatial scales

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is mostly transmitted within households and household-like settings. A decreasing proportion of transmission events take place at increasing spatial scales, but these events become more critical for sustaining the pandemic.


Phylogenetic data provide some insight into global connectivity and the scale at which intercommunity mixing is most relevant to spread. Major SARS-CoV-2 clades are present in all global regions. Within the United States, where interstate travel continued during lockdowns, the mix of viral lineages was similar across states (15). This suggests that viral lineages spread quickly throughout the country and that reintroductions are highly probable should an area achieve local elimination of the virus.

References and Notes

    1. Q. Bi et al

., Lancet Infect. Dis. 20, 911 (2020).

CrossRefPubMedGoogle Scholar

    1. Y. J. Park et al

., Emerg. Infect. Dis. 26, 10 (2020).

Google Scholar

    1. J. M. Read et al

., Proc. Biol. Sci. 281, 1785 (2014).

Google Scholar

    1. Z. J. Madewell et al

., medRxiv 2020.07.29.20164590 [Preprint] 1 August 2020.

Google Scholar

    1. Q. J. Leclerc et al

., Dataset, Figshare (2020).

Google Scholar

    1. H. Njuguna et al

., MMWR Morb. Mortal. Wkly. Rep. 69, 26 (2020).

Google Scholar

    1. X. He et al

., Nat. Med. 26, 672 (2020).

PubMedGoogle Scholar

    1. S. Lee et al

., JAMA Intern. Med. 10.1001/jamainternmed.2020.3862 (2020).

Google Scholar

    1. L. Hamner et al

., MMWR Morb. Mortal. Wkly. Rep. 69, 19 (2020).

Google Scholar

    1. C. Stein-Zamir et al

., Euro Surveill. 25, 29 (2020).

Google Scholar

    1. J. O. Lloyd-Smith et al

., Nature 438, 355 (2005).

CrossRefPubMedWeb of ScienceGoogle Scholar

    1. B. Gamage et al

., Am. J. Infect. Control 33, 114 (2005).

CrossRefPubMedWeb of ScienceGoogle Scholar

    1. D. A. T. Cummings et al

., Clin. Infect. Dis. ciaa900 (2020).

Google Scholar

    1. S. Paynter

, Epidemiol. Infect. 143, 1110 (2015).

CrossRefGoogle Scholar

    1. J. Hadfield et al

., Bioinformatics 34, 4121 (2018).

PubMedGoogle Scholar

Acknowledgments: E.C.L. and N.I.W. contributed equally to this article.


Reporter: Stephen J. Williams, Ph.D

WHO Reports on new COVID-19 mutation found propagating and transmissible from Danish minks

Source: https://www.who.int/csr/don/06-november-2020-mink-associated-sars-cov2-denmark/en/

SARS-CoV-2 mink-associated variant strain – Denmark

Disease Outbreak News
6 November 2020

Since June 2020, 214 human cases of COVID-19 have been identified in Denmark with SARS-CoV-2 variants associated with farmed minks, including 12 cases with a unique variant, reported on 5 November. All 12 cases were identified in September 2020 in North Jutland, Denmark. The cases ranged in age from 7 to 79 years, and eight had a link to the mink farming industry and four cases were from the local community.

Initial observations suggest that the clinical presentation, severity and transmission among those infected are similar to that of other circulating SARS-CoV-2 viruses. However, this variant, referred to as the “cluster 5” variant, had a combination of mutations, or changes that have not been previously observed. The implications of the identified changes in this variant are not yet well understood. Preliminary findings indicate that this particular mink-associated variant identified in both minks and the 12 human cases has moderately decreased sensitivity to neutralizing antibodies. Further scientific and laboratory-based studies are required to verify preliminary findings reported and to understand any potential implications of this finding in terms of diagnostics, therapeutics and vaccines in development. In the meantime, actions are being taken by Danish authorities to limit the further spread of this variant of the virus among mink and human populations.

SARS-CoV-2, the virus which causes COVID-19, was first identified in humans in December 2019. As of 6 November, it has affected more than 48 million people causing over 1.2 million deaths worldwide. Although the virus is believed to be ancestrally linked to bats, the virus origin and intermediate host(s) of SARS-CoV-2 have not yet been identified.

Available evidence suggests that the virus is predominantly transmitted between people through respiratory droplets and close contact, but there are also examples of transmission between humans and animals. Several animals that have been in contact with infected humans, such as minks, dogs, domestic cats, lions and tigers, have tested positive for SARS-CoV-2.

Minks were infected following exposure from infected humans. Minks can act as a reservoir of SARS-CoV-2, passing the virus between them, and pose a risk for virus spill-over from mink to humans. People can then transmit this virus within the human population. Additionally, spill-back (human to mink transmission) can occur. It remains a concern when any animal virus spills in to the human population, or when an animal population could contribute to amplifying and spreading a virus affecting humans. As viruses move between human and animal populations, genetic modifications in the virus can occur. These changes can be identified through whole genome sequencing, and when found, experiments can study the possible implications of these changes on the disease in humans.

To date, six countries, namely Denmark, the Netherlands, Spain, Sweden, Italy and the United States of America have reported SARS-CoV-2 in farmed minks to the World Organisation for Animal Health (OIE).

Please watch VIDEO from Dr. John Campbell

Dr. Campbell discusses this finding in detail.


From AAAS Science News on COVID19: New CRISPR based diagnostic may shorten testing time to 5 minutes

Reporter: Stephen J. Williams, Ph.D.

at https://pharmaceuticalintelligence.com/2020/10/11/from-aaas-science-news-on-covid19-new-crispr-based-diagnostic-may-shorten-testing-time-to-5-minutes/

Researchers have used CRISPR gene-editing technology to come up with a test that detects the pandemic coronavirus in just 5 minutes. The diagnostic doesn’t require expensive lab equipment to run and could potentially be deployed at doctor’s offices, schools, and office buildings.


New SARS-CoV2 mutant discovered in Houston area from a large sequencing study

From Washington Post at https://www.washingtonpost.com/health/2020/09/23/houston-coronavirus-mutations/?arc404=true

Scientists in Houston on Wednesday released a study of more than 5,000 genetic sequences of the coronavirus that reveals the virus’s continual accumulation of mutations, one of which may have made it more contagious. The new report, however, did not find that these mutations have made the virus deadlier or changed clinical outcomes. All viruses accumulate genetic mutations, and most are insignificant, scientists say.

The new study, which has not been peer-reviewed, was posted Wednesday on the preprint server MedRxiv. It appears to be the largest single aggregation of genetic sequences of the virus in the United States thus far. A larger batch of sequences was published earlier this month by scientists in the United Kingdom, and, like the Houston study, concluded that a mutation that changes the structure of the “spike protein” on the surface of the virus may be driving the outsized spread of that particular strain.

Article in MedRxiV preprint server

Molecular Architecture of Early Dissemination and Massive Second Wave of the SARS-CoV-2 Virus in a Major Metropolitan Area
Scott Wesley LongRandall J OlsenPaul A. ChristensenDavid W BernardJames J. DavisMaulik ShuklaMarcus NguyenMatthew Ojeda SaavedraPrasanti YerramilliLayne PruittSishir SubediHung-Che KuoHeather HendricksonGhazaleh EskandariHoang A.T. NguyenJames Hunter LongMuthiah KumaraswamiJule GoikeDaniel BoutzJimmy GolliharJason S. McLellanChia-Wei ChouKamyab JavanmardiIlya J. FinkelsteinJames Musser

We sequenced the genomes of 5,085 SARS-CoV-2 strains causing two COVID-19 disease waves in metropolitan Houston, Texas, an ethnically diverse region with seven million residents. The genomes were from viruses recovered in the earliest recognized phase of the pandemic in Houston, and an ongoing massive second wave of infections. The virus was originally introduced into Houston many times independently. Virtually all strains in the second wave have a Gly614 amino acid replacement in the spike protein, a polymorphism that has been linked to increased transmission and infectivity. Patients infected with the Gly614 variant strains had significantly higher virus loads in the nasopharynx on initial diagnosis. We found little evidence of a significant relationship between virus genotypes and altered virulence, stressing the linkage between disease severity, underlying medical conditions, and host genetics. Some regions of the spike protein – the primary target of global vaccine efforts – are replete with amino acid replacements, perhaps indicating the action of selection. We exploited the genomic data to generate defined single amino acid replacements in the receptor binding domain of spike protein that, importantly, produced decreased recognition by the neutralizing monoclonal antibody CR30022. Our study is the first analysis of the molecular architecture of SARS-CoV-2 in two infection waves in a major metropolitan region. The findings will help us to understand the origin, composition, and trajectory of future infection waves, and the potential effect of the host immune response and therapeutic maneuvers on SARS-CoV-2


Online Event: Vaccine matters: Can we cure coronavirus? An AAAS Webinar

Reporter: Stephen J. Williams, Ph.D.


UPDATED on 8/08/2020

Recent Grim COVID-19 Statistics in U.S. and Explanation from Dr. John Campbell: Why We Need to be More Proactive

Reporter: Stephen J. Williams, PhD

Summary: Model projections from University of Washington’s Institute for Health Metrics and Evaluation suggest U.S. could see almost 300,00 COVID related deaths by December first and, if everyone started right now to wear a mask that number would only be reduced by 66,000.  I summary all experts agree that more, consistent proactive measures need to be enacted. Additional data from UK, India, China, and Africa are presented.

at https://pharmaceuticalintelligence.com/2020/08/08/recent-grim-covid-19-statistics-in-u-s-and-explanation-from-dr-john-campbell-why-we-need-to-be-more-proactive/

UPDATED on 8/04/2020

Is SARS-COV2 Hijacking the Complement and Coagulation Systems?

Reporter: Stephen J. Williams, PhD

at https://pharmaceuticalintelligence.com/2020/08/04/is-sars-cov2-hijacking-the-complement-and-coagulation-systems/

UPDATED on 7/19/2020

National Public Radio interview with Dr. Anthony Fauci on his optimism on a COVID-19 vaccine by early 2021

Reporter: Stephen J. Williams, PhD

Below I am giving a link to an important interview by NPR’s Judy Woodruff with Dr. Anthony Fauci on his thoughts regarding the recent spikes in cases, the potential for a COVID-19 vaccine by next year, and promising therapeutics in the pipeline.  The interview link is given below however I will summarize a few of the highlights of the interview.

See Video and interview at https://pharmaceuticalintelligence.com/2020/07/19/national-public-radio-interview-with-dr-anthony-fauci-on-his-optimism-on-a-covid-19-vaccine-by-early-2021/

UPDATED on 7/03/2020

From Cell Press:  New Insights on the D614G Strain of COVID: Will a New Mutated Strain Delay Vaccine Development?


Reporter: Stephen J. Williams, PhD

UPDATED ON 6/09/2020

The Wide Variability in Reported COVID-19 Epidemiologic Data May Suggest That Personalized Omic Testing May Be Needed to Identify At-Risk Populations

at https://pharmaceuticalintelligence.com/2020/06/09/the-wide-variability-in-reported-covid-19-epidemiologic-data-may-suggest-that-personalized-omic-testing-may-be-needed-to-identify-at-risk-populations/

Curator: Stephen J. Williams, PhD

UPDATED ON 5/25/2020

Mass. reports 44 new deaths, 596 new cases of coronavirus on Monday

Pedestrians wore masks as they walked along Nantasket Beach boardwalk on Sunday. It was largely empty due to a combination of chilly weather and the coronavirus outbreak.
Pedestrians wore masks as they walked along Nantasket Beach boardwalk on Sunday. It was largely empty due to a combination of chilly weather and the coronavirus outbreak.JESSICA RINALDI/GLOBE STAFF

The state reported Monday afternoon 44 new deaths from the coronavirus, bringing Massachusetts’ death toll to 6,416, while the number of people who have tested positive for COVID-19 also rose to 93,271, with 596 newly reported cases.

The state’s three-day average of COVID-19 deaths was 67 as of May 22, which marked the eighth straight day of decline.

Another measure — the state’s seven-day weighted average positive test rate — was 8.9 percent Sunday, the state reported, a drop of one-tenth of a percent from the previous day.

Hospitalizations also dropped as of Sunday, according to the state. The three-day average number of hospitalized COVID-19 patients fell to 2,179, from 2,243 on Saturday, the state reported.


The number of hospitals using surge capacity to treat patients with the coronavirus also fell to eight Sunday, from nine on Saturday.

Within the state’s long-term care facilities, including nursing homes, there have been 3,924 COVID-19 deaths, the state reported, along with 19,742 cases of the disease.

Monday’s report comes as many businesses that had been shuttered because of the coronavirus were allowed to reopen, as part of an effort by Governor Charlie Baker to loosen restrictions imposed more than two months ago to combat the coronavirus.

On Monday, regulations that shuttered many “nonessential” businesses were loosened, allowing businesses like hair salons, barbers, and pet groomers, to reopen by appointment only. Retailers could also resume, though limited to curbside pickup, according to the state’s plan.

John Hilliard can be reached at john.hilliard@globe.com.


The History of Infectious Diseases and Epidemiology in the late 19th and 20th Century

posted by Larry H. Bernstein, MD PhD FACS larryhbern

The Unfolding of the Pandemic:

The pandemic of 1918-1919 occurred in three waves. The first
wave had occurred when mild influenza erupted in the late
spring and summer of 1918. The second wave occurred with an
outbreak of severe influenza in the fall of 1918 and the final wave
occurred in the spring of 1919.

In its wake, the pandemic would leave about twenty million dead
across the world. In America alone, about 675,000 people in
a population of 105 million would die from the disease.

Find out what happened in your state during the Pandemic

Mobilizing to Fight Influenza:

Although taken unaware by the pandemic, federal, state and local
authorities quickly mobilized to fight the disease.

On September 27th, influenza became a reportable disease. However,
influenza had become so widespread by that time that most states
were unable to keep accurate records. Many simply failed to
report to the Public Health Service during the pandemic, leaving
epidemiologists to guess at the impact the disease may have
had in different areas.

World War I had left many communities with a shortage of trained
medical personnel. As influenza spread, local officials urgently
requested the Public Health Service to send nurses and doctors.
With less than 700 officers on duty, the Public Health Service was
unable to meet most of these requests. On the rare occasions when
the PHS was able to send physicians and nurses, they often became
ill en route. Those who did reach their destination safely often found
themselves both unprepared and unable to provide real assistance.

In October, Congress appropriated a million dollars for the Public
Health Service. The money enabled the PHS to recruit and pay
for additional doctors and nurses. The existing shortage of doctors
and nurses, caused by the war, made it difficult for the PHS to locate and hire qualified practitioners. The virulence of the disease also meant that many nurses and doctors contracted influenza
within days of being hired.

Confronted with a shortage of hospital beds, many local officials
ordered that community centers and local schools be transformed
into emergency hospitals. In some areas, the lack of doctors meant
that nursing and medical students were drafted to staff these
makeshift hospitals.

The Pandemic Hits:

Entire families became ill. In Philadelphia, a city especially hard hit,
so many children were orphaned that the Bureau of Child Hygiene
found itself overwhelmed and unable to care for them.

As the disease spread, schools and businesses emptied. Telegraph
and telephone services collapsed as operators took to their
beds. Garbage went uncollected as garbage men reported sick.
The mail piled up as postal carriers failed to come to work.

State and local departments of health also suffered from high
absentee rates. No one was left to record the pandemic’s spread
and the Public Health Service’s requests for information went

As the bodies accumulated, funeral parlors ran out of caskets
and bodies went uncollected in morgues.

This is a very comprehensive read of multiple pandemics throughout history and the mitigations used at those times in history to control those outbreaks.  However, as in the past, the virus seems to win in the beginning and humans once again are at its mercy,


Great News from the South Korean CDC!  Published paper shows patients who recovered are showing immunity and are not reinfected!

Reporter: Stephen J. Williams, PhD

In the video below, Dr. John Campbell, internationally renowned immunologist and virologist from Fox Chase Cancer Center, discusses a recent paper from the south Korean CDC (not published in English yet) that shows patients who had previously been infected with COVID19 and have recovered (recovered from illness) are retesting positive merely because the high sensitivity of the PCR test used was actually detecting “dead” viral particles which were still being shed from upper airway epithelium.  These patients were showing immunity to COVID19 and were not shedding live viral particles (infectious virus).  In addition, the Korean CDC feels that there was not a reactivation of the virus nor was this reinfection by new virus particles, as patients had antibodies and appeared immune to further COVID19 infection.  Dr. Campbell describes this as very good news.  In addition he says that COVID19, unlike some influenza viruses, is a slow mutating virus and explains why this is important with respect to pathogenecity and herd immunity.

However we don’t really know how long the immunity lasts, if memory immune cells have been detected, or if this immunity is dependent on exposure to a certain viral load.

In summary, 290 patients that had recovered from COVID19 that retested positive after 10 weeks post recovery

  1. probably retested positive due to the sensitivity of the PCR test used to detect dead virus (a sort of false positive) and
  2.  they developed immunity and
  3. did not appear to have capacity to infect others



The Genesis of the 1918 Spanish Influenza Pandemic

May 1, 2014

29.4K subscribers

Michael Worobey, Professor, Ecology and Evolutionary Biology, The University of Arizona The Spanish influenza pandemic of 1918 was the most intense outbreak of disease in human history. It killed upwards of 50 million people (most in a six-week period) casting a long shadow of fear and mystery: nearly a century later, scientists have been unable to explain why, unlike all other influenza outbreaks, it killed young adults in huge numbers. I will describe how analyses of large numbers of influenza virus genomes are revealing the pathway traveled by the genes of this virus before it exploded in 1918. What emerges is a surprising tale with many players and plot lines, in which echoes of prior pandemics, imprinted in the immune responses of those alive in 1918, set the stage for the catastrophe. I will also discuss how resolving the mysteries of 1918 could help to prevent future pandemics and to control seasonal influenza, which quietly kills millions more every decade.


The Philadelphia area has been home to many vaccine development and discovery.  Included here is a previous post with an interview with the CEO of PhillImune, Inc., a Philadelphia area vaccine development consultancy firm.  As this interview was conducted years ago, before the current pandemic, it feels fitting to repost as the interview contains many of the issues and hindrances to vaccine development that the field saw a few years ago, and potentially how we can learn from the past lessons.

The Vibrant Philly Biotech Scene: Focus on Vaccines and Philimmune, LLC


The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak


A SARS-CoV-2 epidemiological model reveals the effects of travel restrictions and transmission reduction efforts on the spread of this novel virus.

Outbreak to pandemic

In response to global dispersion of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2), quarantine measures have been implemented around the world. To understand how travel and quarantine influence the dynamics of the spread of this novel human virus, Chinazzi et al. applied a global metapopulation disease transmission model to epidemiological data from China. They concluded that the travel quarantine introduced in Wuhan on 23 January 2020 only delayed epidemic progression by 3 to 5 days within China, but international travel restrictions did help to slow spread elsewhere in the world until mid-February. Their results suggest that early detection, hand washing, self-isolation, and household quarantine will likely be more effective than travel restrictions at mitigating this pandemic.

Science, this issue p. 395


Motivated by the rapid spread of coronavirus disease 2019 (COVID-19) in mainland China, we use a global metapopulation disease transmission model to project the impact of travel limitations on the national and international spread of the epidemic. The model is calibrated on the basis of internationally reported cases and shows that, at the start of the travel ban from Wuhan on 23 January 2020, most Chinese cities had already received many infected travelers. The travel quarantine of Wuhan delayed the overall epidemic progression by only 3 to 5 days in mainland China but had a more marked effect on the international scale, where case importations were reduced by nearly 80% until mid-February. Modeling results also indicate that sustained 90% travel restrictions to and from mainland China only modestly affect the epidemic trajectory unless combined with a 50% or higher reduction of transmission in the community.

Source: https://science.sciencemag.org/content/368/6489/395


Singapore in the Midst of its Second Wave of Coronavirus Incidence: What Went Wrong and What Will a Second Wave Look Like

Curator: Stephen J. Williams, Ph.D.

In a recent development, Singapore, which was once touted as a model system for containing the COVID-19 epidemic, is now experiencing its second wave of infections.  As the following article from CNN highlights, Singapore, although did a wonderful job on testing, failed to test some migrant worker cohorts, as well as relaxing social distancing and lockdown measures.  It appears that statisitics need to incorporate more population density metrics as well as migration metrics in their evaluations.

The CNN article is given below:

Singapore had a model coronavirus response, then cases spiked. What happened?

World Population Density Map from NASA

Note: Higher densities in red represent Indonesia as well as eastern China, northeast USA, areas in India and some subsaharan African areas.


AAAS Science Podcast: Why some diseases are seasonal and some are not: Coronaviruses and more


The following podcast from the American Association for Advancement of Science (AAAS) discusses the seasonality of some viruses while other viruses are able to manifest themselves in different seasons over the globe.

Please Play



Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period

 Hide authors and affiliations

Science  14 Apr 2020:
DOI: 10.1126/science.abb5793


It is urgent to understand the future of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) transmission. We used estimates of seasonality, immunity, and cross-immunity for betacoronaviruses OC43 and HKU1 from time series data from the USA to inform a model of SARS-CoV-2 transmission. We projected that recurrent wintertime outbreaks of SARS-CoV-2 will probably occur after the initial, most severe pandemic wave. Absent other interventions, a key metric for the success of social distancing is whether critical care capacities are exceeded. To avoid this, prolonged or intermittent social distancing may be necessary into 2022. Additional interventions, including expanded critical care capacity and an effective therapeutic, would improve the success of intermittent distancing and hasten the acquisition of herd immunity. Longitudinal serological studies are urgently needed to determine the extent and duration of immunity to SARS-CoV-2. Even in the event of apparent elimination, SARS-CoV-2 surveillance should be maintained since a resurgence in contagion could be possible as late as 2024.

In summary, the total incidence of COVID-19 illness over the next five years will depend critically upon whether or not it enters into regular circulation after the initial pandemic wave, which in turn depends primarily upon the duration of immunity that SARS-CoV-2 infection imparts. The intensity and timing of pandemic and post-pandemic outbreaks will depend on the time of year when widespread SARS-CoV-2 infection becomes established and, to a lesser degree, upon the magnitude of seasonal variation in transmissibility and the level of cross-immunity that exists between the betacoronaviruses. Social distancing strategies could reduce the extent to which SARS-CoV-2 infections strain health care systems. Highly-effective distancing could reduce SARS-CoV-2 incidence enough to make a strategy based on contact tracing and quarantine feasible, as in South Korea and Singapore. Less effective one-time distancing efforts may result in a prolonged single-peak epidemic, with the extent of strain on the healthcare system and the required duration of distancing depending on the effectiveness. Intermittent distancing may be required into 2022 unless critical care capacity is increased substantially or a treatment or vaccine becomes available. The authors are aware that prolonged distancing, even if intermittent, is likely to have profoundly negative economic, social, and educational consequences. Our goal in modeling such policies is not to endorse them but to identify likely trajectories of the epidemic under alternative approaches, identify complementary interventions such as expanding ICU capacity and identifying treatments to reduce ICU demand, and to spur innovative ideas (55) to expand the list of options to bring the pandemic under long-term control. Our model presents a variety of scenarios intended to anticipate possible SARS-CoV-2 transmission dynamics under specific assumptions. We do not take a position on the advisability of these scenarios given the economic burden that sustained distancing may impose, but we note the potentially catastrophic burden on the healthcare system that is predicted if distancing is poorly effective and/or not sustained for long enough. The model will have to be tailored to local conditions and updated as more accurate data become available. Longitudinal serological studies are urgently required to determine the extent and duration of immunity to SARS-CoV-2, and epidemiological surveillance should be maintained in the coming years to anticipate the possibility of resurgence.

References and Notes



Comprehensive List of References used for the creation of the following document predicting the Excess in Hospital Utilization Resources is provided below as a resource on the Epidemiological Aspects specific to the 2020 Coronavirus Pandemic

Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator days and deaths by US state in the next 4 months

IHME COVID-19 health service utilization forecasting team




  1. Hui DS, I Azhar E, Madani TA, et al. The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health—The latest 2019 novel coronavirus outbreak in Wuhan, China. Int J Infect Dis. 2020;91:264–266.
  2. Coronavirus disease 2019 (COVID-19) Situation Report – 62. March 2020. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports. Accessed March 22, 2020.
  3. Li R, Pei S, Chen B, et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2). Science. 2020;First release. doi:DOI: 10.1126/science.abb3221
  4. Ferguson NM, Laydon D, Nedjati-Gilani G, et al. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. Imp Coll COVID-19 Response Team. March 2020:20. doi:https://doi.org/10.25561/77482
  5. Binti Hamza F, Lau C, Nazri H, et al. CoronaTracker: World-wide COVID-19 Outbreak Data Analysis and Prediction. Bull World Health Organ. March 2020. doi:(http://dx.doi.org/10.2471/BLT.20.251561
  6. Tsai TC, Jacobson B, Jha AK. American hospital capacity and projected need for COVID-19 patient care. Health Aff (Millwood). March 2020. doi:10.1377/hblog20200317.457910
  7. Kucharski AJ, Russell TW, Diamond C, et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect Dis. 2020;Online First. doi:10.1016/S1473-3099(20)30144-4
  8. Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet. 2020;395(10225):689-697. doi:10.1016/S0140-6736(20)30260-9
  9. Predictive Healthcare Team, Penn Medicine. COVID-19 hospital impact model for epidemics. https://penn-chime.phl.io/. Published March 24, 2020. Accessed March 24,
  10. Wilson C. Exclusive: here’s how fast the coronavirus could infect over 1 million Americans. Time. March 2020. https://time.com/5801726/coronavirus-models-forecast/. Accessed March 24, 2020.
  11. Anastassopoulou, C, Russo L, Tsakris A, Siettos C. Data-based analysis, modelling and forecasting of the COVID-19 outbreak. MedRxiv. March 2020. doi:https://doi.org/10.1101/2020.02.11.20022186
  12. Roosa K, Lee Y, Luo R, et al. Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infect Dis Model. 2020;5:256-263. doi:10.1016/j.idm.2020.02.002
  13. Roosa K, Lee Y, Luo R, et al. Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13–23, 2020. J Clin Med. 2020;9(2):596.
  14. Georgia State University, School of Public Health. Coronavirus Incidence Forecasts. Wkly Incid Rep. https://publichealth.gsu.edu/research/coronavirus/. Accessed March 24,
  15. Carnegie Mellon University. Mathematical model shows heterogeneous approach might be best for reducing COVID-19 deaths. Mellon College of Science. http://www.cmu.edu/mcs/news-events/2020/0318_covid-19-math-model.html. Published March 17, 2020. Accessed March 24,
  16. Massonnaud C, Roux J, Crépey P. COVID-19: Forecasting short term hospital needs in France. medRxiv. March 2020. doi:https://doi.org/10.1101/2020.03.16.20036939
  17. Alsinglawi B, Elkhodr M, Mubin O. COVID-19 death toll estimated to reach 3,900 by next Friday, according to AI modelling. The Conversatio http://theconversation.com/covid-19- death-toll-estimated-to-reach-3-900-by-next-friday-according-to-ai-modelling-133052. Accessed March 24, 2020.
  18. Thomala LL. Projected worst impact on China’s GDP growth by COVID-19 outbreak 2020. Statista. https://statista.com/statistics/1102691/china-estimated-coronavirus-covid-19- impact-on-gdp-growth/. Accessed March 24, 2020.
  19. Hao K. This is how the CDC is trying to forecast coronavirus’s spread. MIT Technol Rev. March 2020. https://technologyreview.com/s/615360/cdc-cmu-forecasts-coronavirus- spread/. Accessed March 24, 2020.
  20. Danner C. CDC’s worst-case coronavirus Model: 214M infected, 1.7M dead. N Y Mag Intell. March 2020. https://nymag.com/intelligencer/2020/03/cdcs-worst-case-coronavirus-model- 210m-infected-1-7m-dead.html. Accessed March 24,
  21. Presidenza del Consiglio dei Ministri – Dipartimento della Protezione Civile. COVID-19 data Italy. https://github.com/pcm-dpc/COVID-19. Published March 24, 2020. Accessed March 23, 2020.
  22. Robert Koch Institut. Current situation report of the Robert Koch Institute on COVID-19. https://rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Situationsberichte/Gesamt .html. Published March 23, 2020. Accessed March 23, 2020.
  1. Ministry of Health, Consumption and Social Welfare. Novel coronavirus disease, COVID-19 in Spain. Current situation Coronavirus. https://mscbs.gob.es/profesionales/saludPublica/ccayes/alertasActual/nCov- China/situacionActual.htm. Published March 23, 2020. Accessed March 23, 2020.
  2. Health Commission of Hubei Province. Epidemic situation of new crown pneumonia in Hubei Province. http://wjw.hubei.gov.cn/fbjd/dtyw/index_1.shtml. Accessed March 23, 2020.
  3. JHU CSSE. 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE. GitHub. https://github.com/CSSEGISandData/COVID-19. Published March 24, 2020. Accessed March 23,
  4. New Zealand Government. COVID-19 Alert System. Unite against COVID-19. https://covid19.govt.nz/government-actions/covid-19-alert-system/. Published March 24, 2020. Accessed March 23,
  5. Health Forum, LLC. By Special Request from the AHA Annual Report, 2020. American Hospital Association; 2020. Available at http://www.ahaonlinestore.com.
  6. Halpern NA, Goldman DA, Tan KS, Pastores SM. Trends in critical care beds and use among population groups and Medicare and Medicaid beneficiaries in the United States: 2000–2010. Crit Care Med. 2016;44(8):1490-1499. doi:10.1097/CCM.0000000000001722
  7. CDC COVID-19 Response Team. Severe outcomes among patients with coronavirus disease 2019 (COVID-19) — United States, February 12–March 16, 2020. MMWR Morb Mortal Wkly Rep. 2020;69. https://cdc.gov/mmwr/volumes/69/wr/pdfs/mm6912e2-H.pdf.
  8. Novel Coronavirus Pneumonia Emergency Response Epidemiology Team. The epidemiological characteristics of an outbreak of 2019 novel Coronavirus diseases (COVID-19) — China, 2020. China CDC Wkly. 2020;2.
  1. Xu B, Gutierrez B, Mekaru S, et al. Epidemiological data from the COVID-19 outbreak, real-time case information. Sci Data. 2020;7(1):1-6. doi:10.1038/s41597-020-0448-0
  2. Task force COVID-19 del Dipartimento Malattie Infettive e Servizio di Sorveglianza Integrata COVID-19 in Italia. Istituto Superiore di Sanità; 2020. https://www.docdroid.net/xxzxtmG/infografica-17marzo-ita.pdf.
  3. Korea CDC. The Updates of COVID-19 in Republic of Korea. Press Release. http://www.cdc.go.kr. Published March 18, 2020. Accessed March 23,
  4. McMichael TM, Clark S, Pgosjans S, et al. COVID-19 in a long-term care facility — King County, Washington, February 27–March 9, 2020. MMWR Morb Mortal Wkly Rep. 2020;69. doi:10.15585/mmwr.mm6912e1
  5. Healy J, Kovaleski S. The coronavirus’s rampage through a suburban nursing home. The New York Times. https://nytimes.com/2020/03/21/us/coronavirus-nursing-home- kirkland-life-care.html. Published March 21, 2020. Accessed March 22, 2020.
  6. Guan W, Ni Z, Hu Y, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. February 2020. doi:DOI: 1056/NEJMoa2002032
  7. Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet.
  8. Governor’s Office. Proclamation by the Govenor amending proclamation 20-05; 20-24 Restrictions on Non Urgent Medical Procedures. March 2020. https://governor.wa.gov/node/495945.
  9. Ohio Department of Health. Director’s Order for the management of non-essential surgeries and procedures throughout Ohio | Director Amy Acton. March 2020. https://documentcloud.org/documents/6816633-Director-s-Order-Non-Essential- Surgery.html.
  10. Governor’s Office. Colorado continues to take action in response to COVID-19 | Colorado Governor Jared Polis. Press Release. https://colorado.gov/governor/news/colorado- continues-take-action-response-covid-19. Published March 19, 2020. Accessed March 22, 2020.
  11. O’Donnell J. Elective surgeries continue at some US hospitals during coronavirus outbreak despite supply and safety worries. USA Today. https://usatoday.com/story/news/health/2020/03/21/hospitals-doing-elective-surgery- despite-covid-19-risk-short-supplies/2881141001/. Published March 21, 2020. Accessed March 22, 2020.
  12. Governor’s Office. Inslee letter requests the U.S.S. Mercy be sent to Puget Sound region | Governor Jay Inslee. Press Release. https://governor.wa.gov/news-media/inslee-letter- requests-uss-mercy-be-sent-puget-sound-region. Published March 19, 2020. Accessed March 22, 2020.
  13. National Guard COVID-19 Response. https://nationalguard.mil/coronavirus/. Accessed March 25, 2020.
  14. Ankel S. Photos show the National Guard converting New York City’s Javits Center into a disaster hospital for coronavirus patients. Bus Insid. https://www.businessinsider.com/photos-emergency-coronavirus-hospital-built-in-nyc-javits- center-2020-3. Accessed March 25, 2020.
  1. Wang J, Zhu E, Umlauf T. How China built two Coronavirus hospitals in just over a week. The Wall Street Journal. https://wsj.com/articles/how-china-can-build-a-coronavirus- hospital-in-10-days-11580397751. Published February 26, 2020. Accessed March 22, 2020.
  2. Bush E. King County to put 200-bed field hospital on Shoreline soccer field amid coronavirus outbreak. The Seattle Times. https://seattletimes.com/seattle-news/health/king-county-to-put-200-bed-field-hospital-on-shoreline-soccer-field-amid- coronavirus-outbreak/. Published March 18, 2020.
  1. Governor’s Office. Governor Cuomo Announces Four Sites Identified by Army Corps of Engineers on Initial List of Temporary Hospitals. Press Release. https://governor.ny.gov/news/governor-cuomo-announces-four-sites-identified-army- corps-engineers-initial-list-temporary. Published March 21, 2020.


Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator days and deaths by US state in the next 4 months

IHME COVID-19 health service utilization forecasting team

Key Points Question:

Assuming social distancing measures are maintained, what are the forecasted gaps in available health service resources and number of deaths from the COVID-19 pandemic for each state in the United States?


Using a statistical model, we predict excess demand will be 64,175 (95% UI 7,977 to 251,059) total beds and 17,380 (95% UI 2,432 to 57,955) ICU beds at the peak of COVID-19. Peak ventilator use is predicted to be 19,481 (95% UI 9,767 to 39,674) ventilators. Peak demand will be in the second week of April. We estimate 81,114 (95% UI 38,242 to 162,106) deaths in the United States from COVID-19 over the next 4 months.


Even with social distancing measures enacted and sustained, the peak demand for hospital services due to the COVID-19 pandemic is likely going to exceed capacity substantially. Alongside the implementation and enforcement of social distancing measures, there is an urgent need to develop and implement plans to reduce non-COVID-19 demand for and temporarily increase capacity of health facilities.



This study presents the first set of estimates of predicted health service utilization and deaths due to COVID-19 by day for the next 4 months for each state in the US.

Objective: To determine the extent and timing of deaths and excess demand for hospital services due to COVID-19 in the US.

Design, Setting, and Participants:

This study used data on confirmed COVID-19 deaths by day from WHO websites and local and national governments; data on hospital capacity and utilization for US states; and observed COVID-19 utilization data from select locations to develop a statistical model forecasting deaths and hospital utilization against capacity by state for the US over the next 4 months.



Main outcome(s) and measure(s):

Deaths, bed and ICU occupancy, and ventilator use.


Compared to licensed capacity and average annual occupancy rates, excess demand from COVID-19 at the peak of the pandemic in the second week of April is predicted to be 64,175 (95% UI 7,977 to 251,059) total beds and 17,380 (95% UI 2,432 to 57,955) ICU beds. At the peak of the pandemic, ventilator use is predicted to be 19,481 (95% UI 9,767 to 39,674). The date of peak excess demand by state varies from the second week of April through May. We estimate that there will a total of 81,114 (95% UI 38,242 to 162,106) deaths from COVID-19 over the next 4 months in the US. Deaths from COVID-19 are estimated to drop below 10 deaths per day between May 31 and June 6.

Conclusions and Relevance:

In addition to a large number of deaths from COVID-19, the epidemic in the US will place a load well beyond the current capacity of hospitals to manage, especially for ICU care. These estimates can help inform the development and implementation of Preprint submitted to MedRxiv 03.25.2020 – tracking ID MEDRXIV/2020/043752 strategies to mitigate this gap, including reducing non-COVID-19 demand for services and temporarily increasing system capacity. These are urgently needed given that peak volumes are estimated to be only three weeks away.

The estimated excess demand on hospital systems is predicated on the enactment of social distancing measures in all states that have not done so already within the next week and maintenance of these measures throughout the epidemic, emphasizing the importance of implementing, enforcing, and maintaining these measures to mitigate hospital system overload and prevent deaths.

Data availability statement:

A full list of data citations are available by contacting the corresponding author.

Funding Statement: Bill & Melinda Gates Foundation and the State of Washington

 List of Figures and Tables

Figure 1. Normalized age-pattern of death based on data from Italy, South Korea, China, and the US

Figure 2. Death rate data age-standardized to California as a function of time since a threshold death rate of 0.3 per million

Figure 3. Age-specific ratio of admissions to deaths based on data from Italy, China, and the US

Figure 4. Estimates of hospitalization utilization and deaths by day, US

Figure 5. Date of peak hospital bed usage by state

Figure 6. Excess demand for services above capacity available currently.

Figure 7. Peak % excess demand by state for total beds.

Figure 8. Peak % excess demand by state for ICU beds

Figure 9. Expected cumulative death numbers with 95% uncertainty intervals

Figure 10. Probability by date that the number of daily deaths in the US will be below 10 deaths.

Figure 11. Date at which the daily death rate is projected to drop below 0.3 per million by state.

Table 1. Summary information on deaths, peak demand, peak excess demand, and aggregate demand, by state




A special page at NEJM.org presents a collection of articles and other resources on the Coronavirus (Covid-19) outbreak, including clinical reports, management guidelines, and commentary. All articles are freely available.

Visit NEJM.org/coronavirus today >>


Epidemiology of Covid-19 in a Long-Term Care Facility in King County, Washington

T.M. McMichael and Others


Undocumented U.S. Immigrants and Covid-19

K.R. Page and Others


Clinical Characteristics of Covid-19 in China

A.P. Zavascki and Others


Epidemiology of Covid-19

X. Zhang and Others


Responses to the #COVID-19 outbreak from Oncologists, Cancer Societies and the NCI: Important information for cancer patients

Curator: Stephen J. Williams, Ph.D.

at https://pharmaceuticalintelligence.com/2020/03/19/responses-to-the-covid-19-outbreak-from-oncologists-cancer-societies-and-the-nci-important-information-for-cancer-patients/


Confirmed Cases and Deaths by Country, Territory, or Conveyance

The coronavirus COVID-19 is affecting 179 countries and territories around the world and 1 international conveyance (the Diamond Princess cruise ship harbored in Yokohama, Japan). The day is reset after midnight GMT+0.




Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Published:March 11, 2020DOI:https://doi.org/10.1016/S0140-6736(20)30566-3


813 adult patients were hospitalised in Jinyintan Hospital or Wuhan Pulmonary Hospital with COVID-19 before Jan 31, 2020. After excluding 613 patients that were still hospitalised or not confirmed by SARS-CoV-2 RNA detection as of Jan 31, 2020, and nine inpatients without available key information in their medical records, we included 191 inpatients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) in the final analysis. 54 patients died during hospitalisation and 137 were discharged. The median age of the 191 patients was 56·0 years (IQR 46·0–67·0), ranging from 18 years to 87 years, and most patients were male (table 1). Comorbidities were present in nearly half of patients, with hypertension being the most common comorbidity, followed by diabetes and coronary heart disease (table 1). The most common symptoms on admission were fever and cough, followed by sputum production and fatigue (table 1). Lymphocytopenia occurred in 77 (40%) patients. 181 (95%) patients received antibiotics and 41 (21%) received antivirals (lopinavir/ritonavir; table 2). Systematic corticosteroid and intravenous immunoglobulin use differed significantly between non-survivors and survivors (table 2). The comparison of characteristics, treatment, and outcomes of patients from the two hospitals are shown in the appendix (pp 2–4).

Coronavirus: Why You Must Act Now – Tomas Pueyo – Medium https://medium.com/@tomaspueyo/coronavirus-act-today-or-people-will-die-f4d3d9cd99ca
The Global Geographic Distribution of the Virus Infection 

The Coronavirus Pandemic