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Archive for the ‘Seasonality & Environmental Factors in Resurgence’ Category


Sex Differences in Immune Responses that underlie COVID-19 Disease Outcomes

Reporter: Aviva Lev-Ari, PhD, RN – color and bold face added

 

This is an unedited manuscript that has been accepted for publication. Nature Research are providing this early version of the manuscript as a service to our authors and readers. The manuscript will undergo copyediting, typesetting and a proof review before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers apply.

Sex differences in immune responses that underlie COVID-19 disease outcomes

Abstract

A growing body of evidence indicates sex differences in the clinical outcomes of coronavirus disease 2019 (COVID-19)1–5. However, whether immune responses against SARS-CoV-2 differ between sexes, and whether such differences explain male susceptibility to COVID-19, is currently unknown. In this study, we examined sex differences in

  • viral loads,
  • SARS-CoV-2-specific antibody titers,
  • plasma cytokines, as well as
  • blood cell phenotyping in COVID-19 patients.

By focusing our analysis on patients with moderate disease who had not received immunomodulatory medications, our results revealed that

  • male patients had higher plasma levels of innate immune cytokines such as IL-8 and IL-18 along with more robust induction of non-classical monocytes. In contrast,
  • female patients mounted significantly more robust T cell activation than male patients during SARS-CoV-2 infection, which was sustained in old age.
  • Importantly, we found that a poor T cell response negatively correlated with patients’ age and was associated with worse disease outcome in male patients, but not in female patients.
  • Conversely, higher innate immune cytokines in female patients associated with worse disease progression, but not in male patients.
  • These findings reveal a possible explanation underlying observed sex biases in COVID-19, and provide an important basis for the development of
  • a sex-based approach to the treatment and care of men and women with COVID-19.

Author information

Affiliations

Consortia

Corresponding author

Correspondence to Akiko Iwasaki.

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Clustering of Country-Based Data in COVID-19 Infections by Coronavirus outbreak features – First wave, Data up to date 28/5/2020

Authors: Akad Doha, Markman Ofer and Lefkort Jared

 

This study investigated connections between the infection cycles of countries around the world. Utilizing factors such as the Day of Maximum Infections, the Total Infections and the Day of Maximum Infections, and Deaths and Recoveries per Million. In addition, countries that have completed the infection cycle were compared to understand similarities and differences amongst the aforementioned factors and others.

Note: All variables are reportedly up to date 28/5.

The variables:

Country

State / status – The state of the outbreak

Daily_peak – Maximum number of new daily infections

Total_at_daily_peak – The number of infections from the beginning of the outbreak to the maximum day of the new infections.

Death_per_m – The deaths per million people

Recovered_per_m – The recovery cases per million people

Continent – Continent

Time_to_peak- Time from day to the maximum day of new infections.

Break_time – Time in days from the maximum day for new infections until fading (only in countries that have significantly decreased the number of infections, which means that they can be considered in the end)

Total_time- Time from the day of first outbreak to the end.

 

 

Clustering:

Figure 1. Classification 1, Clustering Based on the variables – the number of new daily infections , the number of infections from the beginning of the outbreak to the maximum day of infections , the deaths per million people , the recoveries per million people , the time to the maximum day for new infections.

Cluster 1 – red – characterized by:

  • The number of new daily maximum infections below average
  • The number of infections from the beginning of the outbreak to the maximum daily infections below average
  • Deaths per million persons below average
  • Recoveries per million less than the number of deaths and below average.

Cluster 2 – blue – characterized by:

  • The number of new daily infections usually above average Deaths per million people above average
  • Recoveries per million above average yet less than deaths
  • Time to the maximum day for new infections less than average.

 

Figure 2. Classification 2, Clustering Based on the variables – the number of new daily infections, the number of infections from the beginning of the outbreak to the maximum day of infections, the deaths per million people , the recovery cases per million people.

Cluster 1 (red): The number of new daily infections is less than average, the number of infections from the beginning of the outbreak to the maximum day of the new infections is almost average, deaths to one million people on average, recovery cases per million people above average

Cluster 2 ( green): the number of new daily maximum infections above average, the number of infections from the beginning of the outbreak to the maximum daily infections most often above average, yet less than the maximum daily new infections, the deaths per million above average, the recoveries per million above average, but less than deaths.

Cluster 3 (blue): maximum number of new daily infections smaller than average and smaller than cluster 1 , the number of infections since the beginning of the outbreak to the maximum new infections below the average, deaths per million people below average, recoveries per million people under the average and lower than deaths.

 

Figure 3. Classification 3, Cluster (clustering) Based on all variables for countries that have already completed the outbreak cycle.

Cluster 1 (red): maximum number of daily new infections above average, number of infections from the initial outbreak to the maximum day of new infections above average, recoveries per million people below average, the fading time below average, and total time to completion of outbreak circle below average.

Cluster 2 ( blue ): maximum number of daily new infections below average, number of infections from the initial outbreak to the maximum day of new infections less than average, fading time usually above average and not necessarily over cluster 1, and the total time to the end of the outbreak cycle above average.

This classification is done based on a small number of countries since there are a lack of countries who have completed the outbreak circle, so we will use it only to understand what kinds of classifications we receive if there is a fading time and total time.

Figure 4. World map by classification 1:

The map shows that the countries of Asia, Northeastern Europe, Africa, Central America and South America, and some of North America are classified by Cluster 1, which means that they have Cluster 1 characteristics.

Western Europe, Eastern South America, part of North America belongs to Cluster 2. (Please refer to Cluster properties in explanation of Figure 3)

 

Figure 5. World Map by Classification 2:

Northern North America, South America, the Middle East, parts of Europe, and North Asia are classified as Cluster 3.

Western Europe, Southeastern America, and some of North America are classified as Cluster 2.

East Asia, Africa, parts of Northern Europe, parts of South America and Central America are classified into Cluster 3. (Please refer to Cluster properties in explanation of Figure 2).

 

Figure 6. Summary Classification – Combining the two classifications 1 and 2:

Cluster 1 (red) is characterized by a maximum number of new infections larger than average (highest number of maximum daily infections), the number of infections since the beginning of the outbreak to the day of maximum new daily infections more than or equal to the average, deaths above average and above cluster 4, recoveries per million people over the average, yet less than deaths.

Cluster 2 (green) is characterized by the maximum number of daily new infections close to average and tends to be above average in most cases, the number of infections since the beginning of the outbreak to the day of maximum new daily infections almost average, deaths mostly at or above average, but below cluster 1, recoveries per million above average and greater than the deaths.

Cluster 3 (blue) is characterized by a maximum number of new infections below average, the number of infections since the beginning of the outbreak to the day of maximum new daily infections less than or equal to the average, deaths below average (lowest deaths) , recoveries per million people below average and less than deaths.

Cluster 4 (Purple) is characterized by a maximum number of new infections below average, the number of infections since the beginning of the outbreak to the day of maximum new daily infections below average, deaths above average and above clusters 2 and 3, recoveries per million above average and above deaths (greatest amount of recoveries)

 

Figure 7. Distribution of time until the maximum day of New infections by the summary classification.

Cluster 3 has the highest average time up to the maximum day for new infections, followed by Cluster 1, then Cluster 2 and Cluster 4 with the lowest average.

 

Figure 8. The world map is classified according to the summery classification:

Southern South America, parts of North America, and Western Europe are classified as Cluster 1.

Table 1. countries in first cluster:

Status Country
Ongoing USA
Subsiding Belgium
Subsiding UK
Subsiding Italy
Ongoing Brazil
Subsiding France
Subsiding Spain

 

Western South America, parts of North America, the Middle East, North Asia and some parts of Europe are classified as Cluster 2.

Table 2. countries in second cluster:

status country status country
ongoing Panama ongoing Russia
completed Norway subsiding Turkey
subsiding Germany reemerged Iran
ongoing Peru ongoing Canada
subsiding Netherlands ongoing Saudi Arabia
ongoing Sweden ongoing Chile
completed Israel subsiding Portugal
completed Austria subsiding Ecuador
    subsiding Denmark

 

Parts of America, Africa, East Asia and parts of Europe are classified into Cluster 3.

Table 3. countries in second cluster:

status country status country
ongoing South Africa ongoing Poland
ongoing Philippines ongoing Mexico
ongoing Dominican Republic ongoing India
ongoing Egypt ongoing Pakistan
completed South Korea ongoing Bangladesh
subsiding Czechia ongoing Ukraine
ongoing Argentina ongoing Indonesia
ongoing Algeria subsiding Romania
subsiding Finland completed Japan
subsiding Hungary ongoing Colombia
    completed China

 

Small parts of Western Europe are classified into Cluster 4. (Please refer to Cluster properties in explanation of Figures 6 and 7)

Table 4. countries in second cluster:

status country
completed Switzerland
completed Ireland

 

Interesting discovery:

While searching the variables that contribute to a clearer picture of the world situation, some countries were found to have a day that repeats every week, characterized by the minimum number of deceased from coronavirus. These countries include: The United States, Brazil, the Netherlands, Sweden, and Israel.

In addition, India had a day characterized by a maximum number of new infections that repeats every week.

Peru had a devoted day that repeats every week characterized by a minimum number of new infections.

Statistical insights appendix:

 

Figure 9. The quantum of the quantitative variables

We can see that:

  1. The maximum number of new daily infections in most countries is less than 10000 people. In individual cases over 10000.
  2. The number of deaths from the virus in most countries is less than 200 people per million.
  3. The number of people who have recovered from the virus in most countries are under 2000 people per million.
  4. The maximum time to date for new infections varies by country and there is no common reservation for a number of days, but from the chart it can be assumed that most countries are below 80 days for maximum full outbreak.
  5. The number of infections from the beginning of the outbreak to the maximum day for new infections in most countries does not exceed 250000 infections.

 

Relationships and adjustments between variables:

 

Figure 10. Correlation between the different variables

The most prominent correlations between the variables are:

  1. The number of new daily infections in the maximum day for new infections and the number of infections from the beginning of the outbreak to the maximum day for new infections. Indicates a strong positive correlation.
  2. Between the number of deaths and the number of recoveries a moderate positive correlation exists.
  3. Between the number recoveries per million and the time to maximum day of new infections a moderate negative correlation exists.

 

Figure 11. Correlation of all variables Countries that completed the outbreak cycle:

The most prominent correlations between the variables are:

  1. The number of new daily infections in the maximum day for new infections and the number of infections from the beginning of the outbreak to the maximum day for new infections. Indicates a very strong positive correlation.
  2. Between the number of deaths and the number of recoveries correlates strong positive.
  3. The number of infections that have healed, the maximum number of new daily cases and the number of infections from the beginning of the outbreak to the maximum day of new infections has a negative medium correlation.
  4. Between the time of the outbreak fading and the time of the complete outbreak cycle there is a very strong positive correlation.
  5. The maximum number of daily new infections and outbreak fading time and all the time of outbreak cycle has a strong negative correlation.
  6. Between the number of infections from the onset of the outbreak to the maximum day for new infections, the time of outbreak fading and the whole time of the complete outbreak cycle has a very strong negative correlation.

* consider that the correlations are based on a small number of countries, so there may be biases in the correctness of adjustment with the true situation. If there were more countries that have completed the outbreak cycle would have been more precise – recommends future research.

 

Figure 12. Diagram of the correlation between variables by PCA analysis (For all countries)

 

The diagram shows the relationships between all variables, they can be interpreted as follows:

  • As the total number of infections from the onset of the outbreak to the maximum day for new infections increases, the number of maximum new daily infections increases.
  • As the number of deaths increases, the number of recovered patients also increases.
  • As the time to the maximum day for new infections decreases, the number of recovered patients increases.
  • The variables depicted in red represent those that are significant to understanding the world data, and conversely, the variables in blue are less significant, but are also necessary in understanding the data. Therefore, subsequently, one analysis was performed including the maximum day for new infections variable, and one was performed without it.

 

Figure 13. Diagram of the correlation between variables by PCA analysis (Countries that have completed the outbreak cycle)

Chart is prepared to show the connections of the variables with two variables that were found only in countries that have completed the outbreak cycle, 1. Fading time 2. The total time to completion.

  • As the time between the reduction of infection rates and the day of maximum infections increases, so does the total length of the infection cycle. And it seems that a negative relationship exists between this relationship and time to the maximum day of new infections.
  • As the fading time and time to end decreases, the total number of infections in the maximum day of new infections and new daily infections number increases (very interesting).

Reference:

The data was collected from:

https://ourworldindata.org/covid-deaths

 

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Apr 22, 2020

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Dmitry Korkin is a professor of bioinformatics and computational biology at Worcester Polytechnic Institute, where he specializes in bioinformatics of complex disease, computational genomics, systems biology, and biomedical data analytics. I came across Dmitry’s work when in February his group used the viral genome of the COVID-19 to reconstruct the 3D structure of its major viral proteins and their interactions with human proteins, in effect creating a structural genomics map of the coronavirus and making this data open and available to researchers everywhere. We talked about the biology of COVID-19, SARS, and viruses in general, and how computational methods can help us understand their structure and function in order to develop antiviral drugs and vaccines.
This conversation is part of the Artificial Intelligence podcast.
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Study with important implications when considering widespread serological testing, Ab protection against re-infection with SARS-CoV-2 and the durability of vaccine protection

Reporter: Aviva Lev-Ari, PhD, RN

Serological Testing WordCloud

Longitudinal evaluation and decline of antibody responses in SARS-CoV-2 infection

Jeffrey SeowCarl GrahamBlair MerrickSam AcorsKathryn J.A. SteelOliver HemmingsAoife O’BryneNeophytos KouphouSuzanne PickeringRui GalaoGilberto BetancorHarry D WilsonAdrian W SignellHelena WinstoneClaire KerridgeNigel TempertonLuke SnellKaren BisnauthsingAmelia MooreAdrian GreenLauren MartinezBrielle StokesJohanna HoneyAlba Izquierdo-BarrasGill ArbaneAmita PatelLorcan OConnellGeraldine O HaraEithne MacMahonSam DouthwaiteGaia NebbiaRahul BatraRocio Martinez-NunezJonathan D. EdgeworthStuart J.D. NeilMichael H. MalimKatie Doores

Abstract

Antibody (Ab) responses to SARS-CoV-2 can be detected in most infected individuals 10-15 days following the onset of COVID-19 symptoms. However, due to the recent emergence of this virus in the human population it is not yet known how long these Ab responses will be maintained or whether they will provide protection from re-infection. Using sequential serum samples collected up to 94 days post onset of symptoms (POS) from 65 RT-qPCR confirmed SARS-CoV-2-infected individuals, we show seroconversion in >95% of cases and neutralizing antibody (nAb) responses when sampled beyond 8 days POS. We demonstrate that the magnitude of the nAb response is dependent upon the disease severity, but this does not affect the kinetics of the nAb response. Declining nAb titres were observed during the follow up period. Whilst some individuals with high peak ID50 (>10,000) maintained titres >1,000 at >60 days POS, some with lower peak ID50 had titres approaching baseline within the follow up period. A similar decline in nAb titres was also observed in a cohort of seropositive healthcare workers from Guy′s and St Thomas′ Hospitals. We suggest that this transient nAb response is a feature shared by both a SARS-CoV-2 infection that causes low disease severity and the circulating seasonal coronaviruses that are associated with common colds. This study has important implications when considering widespread serological testing, Ab protection against re-infection with SARS-CoV-2 and the durability of vaccine protection.

SOURCE

https://www.medrxiv.org/content/10.1101/2020.07.09.20148429v1

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The Complexity of Estimation of the Economic Impact of an Outbreak | Panel Discussion | BC Woods College

Reporter: Ofer Markman, PhD

Economic Impact of an Outbreak | Panel Discussion | BC Woods College

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May 21, 2020

Prominent economists, all faculty of the Boston College M.S. in Applied Economics degree program in the Woods College of Advancing Studies, presented a virtual panel discussion on the impact of the coronavirus outbreak on the health care system and the global economy. For more information about the M.S. program, visit https://on.bc.edu/MSAppliedEcon

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COVID-19’s seasonal cycle to be estimated at Lawrence Berkeley National Laboratory (Berkeley Lab) by Artificial Intelligence and Machine Learning Algorithms: Will A Fall and Winter resurgence be Likely??

Reporter: Aviva Lev-Ari, PhD, RN

Using machine learning to estimate COVID-19’s seasonal cycle

Woman walks down empty city street wearing a mask

Credit: Ivan Marc/Shutterstock

Berkeley Lab researchers have launched a project to determine if the novel coronavirus might be seasonal, waning in summer months and resurging in fall and winter.

One of the many unanswered scientific questions about COVID-19 is whether it is seasonal like the flu — waning in warm summer months then resurging in the fall and winter.

Now scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) are launching a project to apply machine-learning methods to a plethora of health and environmental datasets, combined with high-resolution climate models and seasonal forecasts, to tease out the answer.

“Environmental variables, such as temperature, humidity, and UV [ultraviolet radiation] exposure, can have an effect on the virus directly, in terms of its viability. They can also affect the transmission of the virus and the formation of aerosols,” said Berkeley Lab scientist Eoin Brodie, the project lead. “We will use state-of-the-art machine-learning methods to separate the contributions of social factors from the environmental factors to attempt to identify those environmental variables to which disease dynamics are most sensitive.

The research team will take advantage of an abundance of health data available at the county level — such as the severity, distribution and duration of the COVID-19 outbreak, as well as what public health interventions were implemented when — along with demographics, climate and weather factors, and, thanks to smartphone data, population mobility dynamics. The initial goal of the research is to predict — for each county in the United States — how environmental factors influence the transmission of the SARS-CoV-2 virus, which causes COVID-19.

Multidisciplinary team for a complex problem

Untangling environmental factors from social and health factors is a knotty problem with a large number of variables, all interacting in different ways. On top of that, climate and weather affect not only the virus but also human physiology and behavior. For example, people may spend more or less time indoors, depending on the weather; and their immune systems may also change with the seasons.

It’s a complex data problem similar to others tackled by Berkeley Lab’s researchers studying systems like watersheds and agriculture; the challenge involves integrating data across scales to make predictions at the local level. “Downscaling of climate information is something that we routinely do to understand how climate impacts ecosystem processes,” Brodie said. “It involves the same types of variables — temperature, humidity, solar radiation.”

Brodie, deputy director of Berkeley Lab’s Climate and Ecosystem Sciences Division, is leading a cross-disciplinary team of Lab scientists with expertise in climate modeling, data analytics, machine learning, and geospatial analytics. Ben Brown, a computational biologist in Berkeley Lab’s Biosciences Area, is leading the machine-learning analysis. One of their main aims is to understand how climate and weather interact with societal factors.

“We don’t necessarily expect climate to be a massive or dominant effect in and of itself. It’s not going to trump which city shut down when,” Brown said. “But there may be some really important interactions [between the variables]. Looking at New York and California for example, even accounting for the differences between the timing of state-instituted interventions, the death rate in New York may be four times higher than in California — though additional testing on random samples of the population is needed to know for sure. Understanding the environmental interactions may help explain why these patterns appear to be emerging. This is a quintessential problem for machine learning and AI [artificial intelligence].”

The computing work will be conducted at the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science user facility located at Berkeley Lab.

Signs of climatic influences

map of the worldwide incidence rate of COVID-19
The worldwide incidence rate of COVID-19.
Credit: Center for Systems Science and Engineering at Johns Hopkins University

Already, geographical differences in how the disease behaves have been reported, the researchers point out. Temperature, humidity, and the UV Index have all been statistically associated with rates of COVID-19 transmission — although contact rates are still the dominant influence on the spread of disease. In the southern hemisphere, for example, where it’s currently fall, disease spread has been slower than in the northern hemisphere. “There’s potentially other factors associated with that,” Brodie said. “The question is, when the southern hemisphere moves into winter, will there be an increase in transmission rate, or will fall and winter 2020 lead to a resurgence across the U.S. in the absence of interventions?”

India is another place where COVID-19 does not yet appear to be as virulent. “There are cities where it behaves as if it’s the most infectious disease in recorded history. Then there are cities where it behaves more like influenza,” Brown said. “It is really critical to understand why we see those massive differences.”

Brown notes other experiments suggesting the SARS-CoV-2 virus could be seasonal. In particular, the National Biodefense Analysis and Countermeasures Center (NBACC) assessed the longevity of the virus on various surfaces. “Under sunlight and humidity, they found that the virus loses viability in under 60 minutes,” Brown said. “But in darkness and low temperatures it’s stable for eight days. There’s some really serious differences that need investigating.”

The Berkeley Lab team believes that enough data may now be available to determine what environmental factors may influence the virulence of the virus. “Now we should have enough data from around the world to really make an assessment,” Brown said.

The team hopes to have the first phase of their analysis available by late summer or early fall. The next phase will be to make projections under different scenarios, which could aid in public health decisions.

“We would use models to project forward, with different weather scenarios, different health intervention scenarios — such as continued social distancing or whether there are vaccines or some level of herd immunity — in different parts of the country. For example, we hope to be able to say, if you have kids going back to school under this type of environment, the climate and weather in this zone will influence the potential transmission by this amount,” Brodie explained. “That will be a longer-term task for us to accomplish.”

This research is supported by Berkeley Lab’s Laboratory Directed Research and Development (LDRD) program. Other team members include Dan Feldman, Zhao Hao, Chaincy Kuo, Haruko Wainwright, and Nicola Falco. Berkeley Lab mobilized quickly to provide LDRD funding for several research projects to address the COVID-19 pandemic, including one on text mining scientific literature and another on indoor transmission of the virus.

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