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

Key Immune System Genes Identified to Explain High COVID Deaths and Spread in Northern Italy Versus Fewer Cases and Deaths in the South

Posted in Biomarkers: Inflammation, Coronavirus Gene Expression, COVID-19, COVID-19: Pandemic Surgery Guidance, Economic Impact of Coronavirus Pandemic, number of asymptomatic infections, Population Health Management, Genetics & Pharmaceutical, Population Health Management, Nutrition and Phytochemistry, SARS-CoV-2, Seasonality & Environmental Factors in Resurgence, Serology tests for coronavirus antibodies, Treatment Protocols for COVID-19, Vaccinology, Virus Infective Acute Respiratory Syndrome: SARS-CoV on July 29, 2020| Leave a Comment »

Key Immune System Genes Identified to Explain High COVID Deaths and Spread in Northern Italy Versus Fewer Cases and Deaths in the South

Reporter: Stephen J. Williams, PhD

via Key Immune System Genes Identified to Explain High COVID Deaths and Spread in Northern Italy Versus Fewer Cases and Deaths in the South

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The Inequality and Health Disparity seen with the COVID-19 Pandemic Is Similar to Past Pandemics

Posted in and Bioethics, Coronavirus Gene Expression, COVID-19, Economic Impact of Coronavirus Pandemic, Health Care System by Country, Health Economics and Outcomes Research, Health Law & Patient Safety, Healthcare costs and reimbursement, Healthcare Reform, Income Disparity, Infectious Disease Immunodiagnostics, number of asymptomatic infections, Population Health Management, Population Health Management, Genetics & Pharmaceutical, SARS-CoV-2, Seasonality & Environmental Factors in Resurgence, Treatment Protocols for COVID-19, United States, Vaccinology, Viral diseases, Virus Infective Acute Respiratory Syndrome: SARS-CoV, Women Health, tagged #COVID-19, African American, black death, health disparities, income inequality, Pandemic, plague, SARS-CoV-2, socioeconomic, spanish flu, wealth gap on July 20, 2020| Leave a Comment »

The Inequality and Health Disparity seen with the COVID-19 Pandemic Is Similar to Past Pandemics

Curator: Stephen J. Williams, PhD

2019-nCoV-CDC-23311

It has become very evident, at least in during this pandemic within the United States, that African Americans and poorer communities have been disproportionately affected by the SARS-CoV2 outbreak . However, there are many other diseases such as diabetes, heart disease, and cancer in which these specific health disparities are evident as well :

Diversity and Health Disparity Issues Need to be Addressed for GWAS and Precision Medicine Studies

Personalized Medicine, Omics, and Health Disparities in Cancer: Can Personalized Medicine Help Reduce the Disparity Problem?

Disease like cancer have been shown to have wide disparities based on socioeconomic status, with higher incidence rates seen in poorer and less educated sub-populations, not just here but underdeveloped countries as well (see Opinion Articles from the Lancet: COVID-19 and Cancer Care in China and Africa) and graphics below)

In an article in Science by Lizzie Wade, these disparities separated on socioeconomic status, have occurred in many other pandemics throughout history, and is not unique to the current COVID19 outbreak. The article, entitled “An Unequal Blow”, reveal how

in past pandemics, people on the margins suffered the most.

Source: https://science.sciencemag.org/content/368/6492/700.summary

Health Disparities during the Black Death Bubonic Plague Pandemic in the 14^th Century (1347-1351)

During the mid 14^th century, all of Europe was affected by a plague induced by the bacterium Yersinia pestis, and killed anywhere between 30 – 60% of the European population. According to reports by the time the Black Death had reached London by January 1349 there had already been horrendous reports coming out of Florence Italy where the deadly disease ravished the population there in the summer of 1348 (more than half of the city’s population died). And by mid 1349 the Black Death had killed more than half of Londoners. It appeared that no one was safe from the deadly pandemic, affecting the rich, the poor, the young, the old.

However, after careful and meticulous archaeological and historical analysis in England and other sites, revealed a distinct social and economic inequalities that predominated and most likely guided the pandemics course throughout Europe. According to Dr. Gwen Robbins Schug, a bio-archaeologist at Appalachian State University,

Bio-archaeology and other social sciences have repeatedly demonstrated that these kinds of crises play out along the preexisting fault lines of each society. The people at greatest risk were often those already marginalized- the poor and minorities who faced discrimination in ways that damaged their health or limited their access to medical care even in pandemic times.

At the start of the Black Death, Europe had already gone under a climactic change with erratic weather. As a result, a Great Famine struck Europe between 1315-17. Wages fell and more people fell into poverty while the wealthiest expanded their riches, leading to an increased gap in wealth and social disparity. In fact according to recordkeeping most of Englanders were living below the poverty line.

Author Lizzie Wade also interviewed Dr. Sharon, DeWitte, a biological anthropologist at University of South Carolina, who looks at skeletal remains of Black Death victims to get evidence on their health status, like evidence of malnutrition, osteoporosis, etc. And it appears that most of the victims may have had preexisting health conditions indicative of poorer status. And other evidence show that wealthy landowners had a lower mortality rate than poorer inner city dwellers.

1918 Spanish Flu

Socioeconomic and demographic studies have shown that both Native American Indians and African Americans on the lower end of the socioeconomic status were disproportionately affected by the 1918 Spanish flu pandemic. According to census records, the poorest had a 50% higher mortality rate than wealthy areas in the city of Oslo. In the US, minors and factory workers died at the highest rates. In the US African Americans had already had bouts with preexisting issues like tuberculosis and may have contributed to the higher mortality. In addition Jim Crow laws in the South, responsible for widespread discrimination, also impacted the ability of African Americans to seek proper medical care.

From the Atlantic

Source: https://www.theatlantic.com/politics/archive/2016/05/americas-health-segregation-problem/483219/

America’s Health Segregation Problem

Has the country done enough to overcome its Jim Crow health care history?

VANN R. NEWKIRK II

MAY 18, 2016

Like other forms of segregation, health-care segregation was originally a function of explicitly racist black codes and Jim Crow laws. Many hospitals, clinics, and doctor’s offices were totally segregated by race, and many more maintained separate wings or staff that could never intermingle under threat of law. The deficit of trained black medical professionals (itself caused by a number of factors including education segregation) meant that no matter where black people received health-care services, they would find their care to be subpar compared to that of whites. While there were some deaths that were directly attributable to being denied emergency service, most of the damage was done in establishing the same cumulative health disparities that plague black people today as a societal fate. The descendants of enslaved people lived much more dangerous and unhealthy lives than white counterparts, on disease-ridden and degraded environments. Within the confines of a segregated health-care system, these factors became poor health outcomes that shaped black America as if they were its genetic material.

https://twitter.com/time4equity/status/1175080469425266688?s=20

R.A.Hahn^a B.I.Truman^b D.R.Williams^c.Civil rights as determinants of public health and racial and ethnic health equity: Health care, education, employment, and housing in the United States.

SSM – Population Health: Volume 4, April 2018, Pages 17-24

Highlights

Civil rights are characterized as social determinants of health.
Four domains in civil rights history since 1950 are explored in—health care, education, employment, and housing.
Health care, education, employment show substantial benefits when civil rights are enforced.
Housing shows an overall failure to enforce existing civil rights and persistent discrimination.
Civil rights and their enforcement may be considered a powerful arena for public health theorizing, research, policy, and action.

For more articles on COVID-19 Please go to our Coronovirus Portal

https://pharmaceuticalintelligence.com/coronavirus-portal/

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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

Posted in COVID-19, Immune-Mediation (independent immunopathology: lung and reticuloendothelial system), Population Health Management, Genetics & Pharmaceutical, SARS-CoV-2, Seasonality & Environmental Factors in Resurgence, Serology tests for coronavirus antibodies, Treatment Protocols for COVID-19, Vaccinology, Virus Infective Acute Respiratory Syndrome: SARS-CoV on July 14, 2020| Leave a Comment »

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

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

Posted in BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biomarkers: Inflammation, Biotechnology, coronavirus, Coronavirus Gene Expression, COVID-19, COVID-19: Pandemic Surgery Guidance, Drug Development Process, Economic Impact of Coronavirus Pandemic, Immune-Mediation (independent immunopathology: lung and reticuloendothelial system), Infectious Disease & New Antibiotic Targets, number of asymptomatic infections, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmaceutical R&D Investment, Population Health Management, Nutrition and Phytochemistry, SARS-CoV-2, Seasonality & Environmental Factors in Resurgence, Serology tests for coronavirus antibodies, Treatment Protocols for COVID-19, Vaccinology, Viral diseases, Virology - Vector-borne DIsease, Virus Infective Acute Respiratory Syndrome: SARS-CoV, tagged coronavirus, Coronavirus Vaccines, coronoavirus, SARS-CoV-2, variants on July 3, 2020| Leave a Comment »

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

Reporter: Stephen J. Williams, PhD

Two recent articles in Cell Press, both peer reviewed, discuss the emergence and potential dominance of a new mutated strain of COVID-19, in which the spike protein harbors a D614G mutation.

In the first article “Making Sense of Mutation: What D614G means for the COVID-19 pandemic Remains Unclear”[1] , authors Drs. Nathan Grubaugh, William Hanage, and Angela Rasmussen discuss the recent findings by Korber et al. 2020 [2] which describe the potential increases in infectivity and mortality of this new mutant compared to the parent strain of SARS-CoV2. For completeness sake I will post this article as to not defer from their interpretations of this important paper by Korber and to offer some counter opinion to some articles which have surfaced this morning in the news.

Making sense of mutation: what D614G means for the COVID-19 pandemic remains unclear

Nathan D. Grubaugh1 *, William P. Hanage2 *, Angela L. Rasmussen3 * 1Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA 2Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA 3Center for Infection and Immunity, Columbia Mailman School of Public Health, New York, NY 10032, USA Correspondence: grubaughlab@gmail.com

Abstract: Korber et al. (2020) found that a SARS-CoV-2 variant in the spike protein, D614G, rapidly became dominant around the world. While clinical and in vitro data suggest that D614G changes the virus phenotype, the impact of the mutation on transmission, disease, and vaccine and therapeutic development are largely unknown.

Introduction: Following the emergence of SARS-CoV-2 in China in late 2019, and the rapid expansion of the COVID19 pandemic in 2020, questions about viral evolution have come tumbling after. Did SARS-CoV-2 evolve to become better adapted to humans? More infectious or transmissible? More deadly? Virus mutations can rise in frequency due to natural selection, random genetic drift, or features of recent epidemiology. As these forces can work in tandem, it’s often hard to differentiate when a virus mutation becomes common through fitness or by chance. It is even harder to determine if a single mutation will change the outcome of an infection, or a pandemic. The new study by Korber et al. (2020) sits at the heart of this debate. They present compelling data that an amino acid change in the virus’ spike protein, D614G, emerged early during the pandemic, and viruses containing G614 are now dominant in many places around the world. The crucial questions are whether this is the result of natural selection, and what it means for the COVID-19 pandemic. For viruses like SARS-CoV-2 transmission really is everything – if they don’t get into another host their lineage ends. Korber et al. (2020) hypothesized that the rapid spread of G614 was because it is more infectious than D614. In support of their hypothesis, the authors provided evidence that clinical samples from G614 infections have a higher levels of viral RNA, and produced higher titers in pseudoviruses from in vitro experiments; results that now seem to be corroborated by others [e.g. (Hu et al., 2020; Wagner et al., 2020)]. Still, these data do not prove that G614 is more infectious or transmissible than viruses containing D614. And because of that, many questions remain on the potential impacts, if any, that D614G has on the COVID-19 pandemic.

The authors note that this new G614 variant has become the predominant form over the whole world however in China the predominant form is still the D614 form. As they state

“over the period that G614 became the global majority variant, the number of introductions from China where D614 was still dominant were declining, while those from Europe climbed. This alone might explain the apparent success of G614.”

Grubaugh et al. feel there is not enough evidence that infection with this new variant will lead to higher mortality. Both Korber et al. and the Seattle study (Wagner et al) did not find that the higher viral load of this variant led to a difference in hospitalizations so apparently each variant might be equally as morbid.

In addition, Grubaugh et al. believe this variant would not have much affect on vaccine development as, even though the mutation lies within the spike protein, D614G is not in the receptor binding domain of the spike protein. Korber suggest that there may be changes in glycosylation however these experiments will need to be performed. In addition, antibodies from either D614 or G614 variant infected patients could cross neutralize.

Conclusions: While there has already been much breathless commentary on what this mutation means for the COVID19 pandemic, the global expansion of G614 whether through natural selection or chance means that this variant now is the pandemic. As a result its properties matter. It is clear from the in vitro and clinical data that G614 has a distinct phenotype, but whether this is the result of bonafide adaptation to human ACE2, whether it increases transmissibility, or will have a notable effect, is not clear. The work by Korber et al. (2020) provides an early base for more extensive epidemiological, in vivo experimental, and diverse clinical investigations to fill in the many critical gaps in how D614G impacts the pandemic.

The link to the Korber Cell paper is here: https://www.cell.com/cell/fulltext/S0092-8674(20)30820-5

Tracking changes in SARS-CoV-2 Spike: evidence that D614G increases infectivity of the COVID-19 virus

DOI: https://doi.org/10.1016/j.cell.2020.06.043

Keypoints

The consistent increase of G614 at regional levels may indicate a fitness advantage

G614 is associated with lower RT PCR Ct’s, suggestive of higher viral loads in patients

The G614 variant grows to higher titers as pseudotyped virions

Summary

A SARS-CoV-2 variant carrying the Spike protein amino acid change D614G has become the most prevalent form in the global pandemic. Dynamic tracking of variant frequencies revealed a recurrent pattern of G614 increase at multiple geographic levels: national, regional and municipal. The shift occurred even in local epidemics where the original D614 form was well established prior to the introduction of the G614 variant. The consistency of this pattern was highly statistically significant, suggesting that the G614 variant may have a fitness advantage. We found that the G614 variant grows to higher titer as pseudotyped virions. In infected individuals G614 is associated with lower RT-PCR cycle thresholds, suggestive of higher upper respiratory tract viral loads, although not with increased disease severity. These findings illuminate changes important for a mechanistic understanding of the virus, and support continuing surveillance of Spike mutations to aid in the development of immunological interventions.

References

Grubaugh, N.D., Hanage, W.P., Rasmussen, A.L., Making sense of mutation: what D614G means for the COVID-19 pandemic remains unclear, Cell (2020), doi: https:// doi.org/10.1016/j.cell.2020.06.040.
Korber, B., Fischer, W.M., Gnanakaran, S., Yoon, H., Theiler, J., Abfalterer, W., Hengartner, N., Giorgi, E.E., Bhattacharya, T., Foley, B., et al. (2020). Tracking changes in SARS-CoV-2 Spike: evidence that D614G increases infectivity of the COVID-19 virus. Cell 182.
Endo, A., Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Abbott, S., Kucharski, A.J., and Funk, S. (2020). Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China. Wellcome Open Res 5, 67.
Hu, J., He, C.-L., Gao, Q.-Z., Zhang, G.-J., Cao, X.-X., Long, Q.-X., Deng, H.-J., Huang, L.-Y., Chen, J., Wang, K., et al. (2020). The D614G mutation of SARS-CoV-2 spike protein enhances viral infectivity and decreases neutralization sensitivity to individual convalescent sera. bioRxiv 2020.06.20.161323.
Wagner, C., Roychoudhury, P., Hadfield, J., Hodcroft, E.B., Lee, J., Moncla, L.H., Müller, N.F., Behrens, C., Huang, M.-L., Mathias, P., et al. (2020). Comparing viral load and clinical outcomes in Washington State across D614G mutation in spike protein of SARS-CoV-2. Https://github.com/blab/ncov-D614G.

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

Posted in COVID-19, Economic Impact of Coronavirus Pandemic, Population Health Management, Population Health Management, Genetics & Pharmaceutical, SARS-CoV-2, Seasonality & Environmental Factors in Resurgence, Serology tests for coronavirus antibodies, Treatment Protocols for COVID-19, U.S. Employment-to-Population Ratio, Vaccinology on June 10, 2020| Leave a Comment »

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

197 views

•May 21, 2020

Boston College

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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??

Posted in COVID-19, Evolution of Biology Through Culture, Population Health Management, Genetics & Pharmaceutical, SARS-CoV-2, Seasonality & Environmental Factors in Resurgence, Vaccinology, Virus Infective Acute Respiratory Syndrome: SARS-CoV on June 1, 2020| Leave a Comment »

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

By Julie Chao, Berkeley LabThursday, May 21, 2020

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

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.

SOURCE

https://www.universityofcalifornia.edu/news/using-machine-learning-estimate-covid-19-seasonal-cycle

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Crowdsourcing Difficult-to-Collect Epidemiological Data in Pandemics: Lessons from Ebola to the current COVID-19 Pandemic

Posted in Biomarkers: Inflammation, coronavirus, Coronavirus Gene Expression, COVID-19, Curation, Curation methodology, Economic Impact of Coronavirus Pandemic, Evidence-based decision-making, Evolution of Biology Through Culture, Immune-Mediation (independent immunopathology: lung and reticuloendothelial system), number of asymptomatic infections, Population Health Management, Population Health Management, Genetics & Pharmaceutical, SAR-Cov-2 a vasculotropic (blood vessels) RNA Virus, SARS-CoV-2, SARS-COV2 Hijacking the Complement and Coagulation Systems, Seasonality & Environmental Factors in Resurgence, Serology tests for coronavirus antibodies, Treatment Protocols for COVID-19, Vaccinology, Virus Infective Acute Respiratory Syndrome: SARS-CoV, tagged China, coronavirus, Ebola virus, Epidemiology, Pandemic, Population Health Management, SARS-CoV-2 on May 15, 2020| Leave a Comment »

Crowdsourcing Difficult-to-Collect Epidemiological Data in Pandemics: Lessons from Ebola to the current COVID-19 Pandemic

Curator: Stephen J. Williams, Ph.D.

At the onset of the COVID-19 pandemic, epidemiological data from the origin of the Sars-Cov2 outbreak, notably from the Wuhan region in China, was sparse. In fact, official individual patient data rarely become available early on in an outbreak, when that data is needed most. Epidemiological data was just emerging from China as countries like Italy, Spain, and the United States started to experience a rapid emergence of the outbreak in their respective countries. China, made of 31 geographical provinces, is a vast and complex country, with both large urban and rural areas.

Geographic Regions of China. Source: https://www.unicef.cn/en/figure-11-geographic-regions-china

As a result of this geographical diversity and differences in healthcare coverage across the country, epidemiological data can be challenging. For instance, cancer incidence data for regions and whole country is difficult to calculate as there are not many regional cancer data collection efforts, contrasted with the cancer statistics collected in the United States, which is meticulously collected by cancer registries in each region, state and municipality. Therefore, countries like China must depend on hospital record data and autopsy reports in order to back-extrapolate cancer incidence data. This is the case in some developed countries like Italy where cancer registry is administered by a local government and may not be as extensive (for example in the Napoli region of Italy).

Population density China by province. Source https://www.unicef.cn/en/figure-13-population-density-province-2017

Epidemiologists, in areas in which data collection may be challenging, are relying on alternate means of data collection such as using devices connected to the internet-of-things such as mobile devices, or in some cases, social media is becoming useful to obtain health related data. Such as effort to acquire pharmacovigilance data, patient engagement, and oral chemotherapeutic adherence using the social media site Twitter has been discussed in earlier posts: (see below)

Twitter is Becoming a Powerful Tool in Science and Medicine at https://pharmaceuticalintelligence.com/2014/11/06/twitter-is-becoming-a-powerful-tool-in-science-and-medicine/

Now epidemiologists are finding crowd-sourced data from social media and social networks becoming useful in collecting COVID-19 related data in those countries where health data collection efforts may be sub-optimal. In a recent paper in The Lancet Digital Health [1], authors Kaiyuan Sun, Jenny Chen, and Cecile Viboud present data from the COVID-19 outbreak in China using information collected over social network sites as well as public news outlets and find strong correlations with later-released government statistics, showing the usefulness in such social and crowd-sourcing strategies to collect pertinent time-sensitive data. In particular, the authors aim was to investigate this strategy of data collection to reduce the time delays between infection and detection, isolation and reporting of cases.

The paper is summarized below:

Kaiyuan Sun, PhD Jenny Chen, BScn Cécile Viboud, PhD . (2020). Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study. The Lancet: Digital Health; Volume 2, Issue 4, E201-E208.

Summary

Background

As the outbreak of coronavirus disease 2019 (COVID-19) progresses, epidemiological data are needed to guide situational awareness and intervention strategies. Here we describe efforts to compile and disseminate epidemiological information on COVID-19 from news media and social networks.

Methods

In this population-level observational study, we searched DXY.cn, a health-care-oriented social network that is currently streaming news reports on COVID-19 from local and national Chinese health agencies. We compiled a list of individual patients with COVID-19 and daily province-level case counts between Jan 13 and Jan 31, 2020, in China. We also compiled a list of internationally exported cases of COVID-19 from global news media sources (Kyodo News, The Straits Times, and CNN), national governments, and health authorities. We assessed trends in the epidemiology of COVID-19 and studied the outbreak progression across China, assessing delays between symptom onset, seeking care at a hospital or clinic, and reporting, before and after Jan 18, 2020, as awareness of the outbreak increased. All data were made publicly available in real time.

Findings

We collected data for 507 patients with COVID-19 reported between Jan 13 and Jan 31, 2020, including 364 from mainland China and 143 from outside of China. 281 (55%) patients were male and the median age was 46 years (IQR 35–60). Few patients (13 [3%]) were younger than 15 years and the age profile of Chinese patients adjusted for baseline demographics confirmed a deficit of infections among children. Across the analysed period, delays between symptom onset and seeking care at a hospital or clinic were longer in Hubei province than in other provinces in mainland China and internationally. In mainland China, these delays decreased from 5 days before Jan 18, 2020, to 2 days thereafter until Jan 31, 2020 (p=0·0009). Although our sample captures only 507 (5·2%) of 9826 patients with COVID-19 reported by official sources during the analysed period, our data align with an official report published by Chinese authorities on Jan 28, 2020.

Interpretation

News reports and social media can help reconstruct the progression of an outbreak and provide detailed patient-level data in the context of a health emergency. The availability of a central physician-oriented social network facilitated the compilation of publicly available COVID-19 data in China. As the outbreak progresses, social media and news reports will probably capture a diminishing fraction of COVID-19 cases globally due to reporting fatigue and overwhelmed health-care systems. In the early stages of an outbreak, availability of public datasets is important to encourage analytical efforts by independent teams and provide robust evidence to guide interventions.

A Few notes on Methodology:

The authors used crowd-sourced reports from DXY.cn, a social network for Chinese physicians, health-care professionals, pharmacies and health-care facilities. This online platform provides real time coverage of the COVID-19 outbreak in China
More data was curated from news media, television and includes time-stamped information on COVID-19 cases
These reports are publicly available, de-identified patient data
No patient consent was needed and no ethics approval was required
Data was collected between January 20, 2020 and January 31,2020
Sex, age, province of identification, travel history, dates of symptom development was collected
Additional data was collected for other international sites of the pandemic including Cambodia, Canada, France, Germany, Hong Kong, India, Italy, Japan, Malaysia, Nepal, Russia, Singapore, UK, and USA
All patients in database had laboratory confirmation of infection

Results

507 patient data was collected with 153 visited and 152 resident of Wuhan
Reported cases were skewed toward males however the overall population curve is skewed toward males in China
Most cases (26%) were from Beijing (urban area) while an equal amount were from rural areas combined (Shaanzi and Yunnan)
Age distribution of COVID cases were skewed toward older age groups with median age of 45 HOWEVER there were surprisingly a statistically high amount of cases less than 5 years of age
Outbreak progression based on the crowd-sourced patient line was consistent with the data published by the China Center for Disease Control
Median reporting delay in the authors crowd-sourcing data was 5 days
Crowd-sourced data was able to detect apparent rapid growth of newly reported cases during the collection period in several provinces outside of Hubei province, which is consistent with local government data

The following graphs show age distribution for China in 2017 and predicted for 2050.

Source: https://www.indexmundi.com/china/age_structure.html

projected age distribution China 2050. Source https://chinapower.csis.org/aging-problem/

The authors have previously used this curation of news methodology to analyze the Ebola outbreak[2].

A further use of the crowd-sourced database was availability of travel histories for patients returning from Wuhan and onset of symptoms, allowing for estimation of incubation periods.

The following published literature has also used these datasets:

Backer JA, Klinkenberg D, Wallinga J: Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020. Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin 2020, 25(5).

Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, Azman AS, Reich NG, Lessler J: The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of internal medicine 2020, 172(9):577-582.

Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, Ren R, Leung KSM, Lau EHY, Wong JY et al: Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. The New England journal of medicine 2020, 382(13):1199-1207.

Dataset is available on the Laboratory for the Modeling of Biological and Socio-technical systems website of Northeastern University at https://www.mobs-lab.org/.

References

Sun K, Chen J, Viboud C: Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study. The Lancet Digital health 2020, 2(4):e201-e208.
Cleaton JM, Viboud C, Simonsen L, Hurtado AM, Chowell G: Characterizing Ebola Transmission Patterns Based on Internet News Reports. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2016, 62(1):24-31.

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Powerful Artificial Intelligence (AI) Tools Being Developed for the COVID-19 Fight

Posted in Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, Big Data, Biomarkers: Inflammation, Cancer Researchers Fighting COVID-19, coronavirus, Coronavirus Gene Expression, COVID-19, COVID-19 effects on Human Heart, COVID-19: Pandemic Surgery Guidance, Disease Biology, Economic Impact of Coronavirus Pandemic, Health Economics and Outcomes Research, HealthCare IT, Image Processing/Computing, Imaging-based Cancer Patient Management, Immune-Mediation (independent immunopathology: lung and reticuloendothelial system), Infectious Disease & New Antibiotic Targets, Infectious Disease Immunodiagnostics, Intelligent Information Systems, number of asymptomatic infections, SAR-Cov-2 a vasculotropic (blood vessels) RNA Virus, SARS-CoV-2, SARS-COV2 Hijacking the Complement and Coagulation Systems, Seasonality & Environmental Factors in Resurgence, Serology tests for coronavirus antibodies, Treatment Protocols for COVID-19, Vaccinology, Viral diseases, Virology - Vector-borne DIsease, tagged #COVID-19, AI, Artificial intelligence, Artificial Intelligence & Clinical Trials, biotech innovation, COVID-19, DiA, IBM Watson, imaging, Pandemic, SARS-CoV-2, Technion – Israel Institute of Technology on May 13, 2020| Leave a Comment »

Powerful AI Tools Being Developed for the COVID-19 Fight

Curator: Stephen J. Williams, Ph.D.

Source: https://www.ibm.com/blogs/research/2020/04/ai-powered-technologies-accelerate-discovery-covid-19/

IBM Releases Novel AI-Powered Technologies to Help Health and Research Community Accelerate the Discovery of Medical Insights and Treatments for COVID-19

April 3, 2020 | Written by: Dario Gil

IBM Research has been actively developing new cloud and AI-powered technologies that can help researchers across a variety of scientific disciplines accelerate the process of discovery. As the COVID-19 pandemic unfolds, we continue to ask how these technologies and our scientific knowledge can help in the global battle against coronavirus.

Today, we are making available multiple novel, free resources from across IBM to help healthcare researchers, doctors and scientists around the world accelerate COVID-19 drug discovery: from gathering insights, to applying the latest virus genomic information and identifying potential targets for treatments, to creating new drug molecule candidates.

Though some of the resources are still in exploratory stages, IBM is making them available to qualifying researchers at no charge to aid the international scientific investigation of COVID-19.

Today’s announcement follows our recent leadership in launching the U.S. COVID-19 High Performance Computing Consortium, which is harnessing massive computing power in the effort to help confront the coronavirus.

Streamlining the Search for Information

Healthcare agencies and governments around the world have quickly amassed medical and other relevant data about the pandemic. And, there are already vast troves of medical research that could prove relevant to COVID-19. Yet, as with any large volume of disparate data sources, it is difficult to efficiently aggregate and analyze that data in ways that can yield scientific insights.

To help researchers access structured and unstructured data quickly, we are offering a cloud-based AI research resource that has been trained on a corpus of thousands of scientific papers contained in the COVID-19 Open Research Dataset (CORD-19), prepared by the White House and a coalition of research groups, and licensed databases from the DrugBank, Clinicaltrials.gov and GenBank. This tool uses our advanced AI and allows researchers to pose specific queries to the collections of papers and to extract critical COVID-19 knowledge quickly. Please note, access to this resource will be granted only to qualified researchers. To learn more and request access, please click here.

Aiding the Hunt for Treatments

The traditional drug discovery pipeline relies on a library of compounds that are screened, improved, and tested to determine safety and efficacy. In dealing with new pathogens such as SARS-CoV-2, there is the potential to enhance the compound libraries with additional novel compounds. To help address this need, IBM Research has recently created a new, AI-generative framework which can rapidly identify novel peptides, proteins, drug candidates and materials.

We have applied this AI technology against three COVID-19 targets to identify 3,000 new small molecules as potential COVID-19 therapeutic candidates. IBM is releasing these molecules under an open license, and researchers can study them via a new interactive molecular explorer tool to understand their characteristics and relationship to COVID-19 and identify candidates that might have desirable properties to be further pursued in drug development.

To streamline efforts to identify new treatments for COVID-19, we are also making the IBM Functional Genomics Platform available for free for the duration of the pandemic. Built to discover the molecular features in viral and bacterial genomes, this cloud-based repository and research tool includes genes, proteins and other molecular targets from sequenced viral and bacterial organisms in one place with connections pre-computed to help accelerate discovery of molecular targets required for drug design, test development and treatment.

Select IBM collaborators from government agencies, academic institutions and other organizations already use this platform for bacterial genomic study. And now, those working on COVID-19 can request the IBM Functional Genomics Platform interface to explore the genomic features of the virus. Access to the IBM Functional Genomics Platform will be prioritized for those conducting COVID-19 research. To learn more and request access, please click here.

Drug and Disease Information

Clinicians and healthcare professionals on the frontlines of care will also have free access to hundreds of pieces of evidence-based, curated COVID-19 and infectious disease content from IBM Micromedex and EBSCO DynaMed. Using these two rich decision support solutions, users will have access to drug and disease information in a single and comprehensive search. Clinicians can also provide patients with consumer-friendly patient education handouts with relevant, actionable medical information. IBM Micromedex is one of the largest online reference databases for medication information and is used by more than 4,500 hospitals and health systems worldwide. EBSCO DynaMed provides peer-reviewed clinical content, including systematic literature reviews in 28 specialties for comprehensive disease topics, health conditions and abnormal findings, to highly focused topics on evaluation, differential diagnosis and management.

The scientific community is working hard to make important new discoveries relevant to the treatment of COVID-19, and we’re hopeful that releasing these novel tools will help accelerate this global effort. This work also outlines our long-term vision for the future of accelerated discovery, where multi-disciplinary scientists and clinicians work together to rapidly and effectively create next generation therapeutics, aided by novel AI-powered technologies.

Learn more about IBM’s response to COVID-19: IBM.com/COVID19.

Source: https://www.ibm.com/blogs/research/2020/04/ai-powered-technologies-accelerate-discovery-covid-19/

DiA Imaging Analysis Receives Grant to Accelerate Global Access to its AI Ultrasound Solutions in the Fight Against COVID-19

Source: https://www.grantnews.com/news-articles/?rkey=20200512UN05506&filter=12337

Grant will allow company to accelerate access to its AI solutions and use of ultrasound in COVID-19 emergency settings

TEL AVIV, Israel, May 12, 2020 /PRNewswire-PRWeb/ — DiA Imaging Analysis, a leading provider of AI based ultrasound analysis solutions, today announced that it has received a government grant from the Israel Innovation Authority (IIA) to develop solutions for ultrasound imaging analysis of COVID-19 patients using Artificial Intelligence (AI).Using ultrasound in point of care emergency settings has gained momentum since the outbreak of COVID-19 pandemic. In these settings, which include makeshift hospital COVID-19 departments and triage “tents,” portable ultrasound offers clinicians diagnostic decision support, with the added advantage of being easier to disinfect and eliminating the need to transport patients from one room to another.However, analyzing ultrasound images is a process that it is still mostly done visually, leading to a growing market need for automated solutions and decision support.As the leading provider of AI solutions for ultrasound analysis and backed by Connecticut Innovations, DiA makes ultrasound analysis smarter and accessible to both new and expert ultrasound users with various levels of experience. The company’s flagship LVivo Cardio Toolbox for AI-based cardiac ultrasound analysis enables clinicians to automatically generate objective clinical analysis, with increased accuracy and efficiency to support decisions about patient treatment and care.

The IIA grant provides a budget of millions NIS to increase access to DiA’s solutions for users in Israel and globally, and accelerate R&D with a focus on new AI solutions for COVID-19 patient management. DiA solutions are vendor-neutral and platform agnostic, as well as powered to run in low processing, mobile environments like handheld ultrasound.Recent data highlights the importance of looking at the heart during the progression of COVID-19, with one study citing 20% of patients hospitalized with COVID-19 showing signs of heart damage and increased mortality rates in those patients. DiA’s LVivo cardiac analysis solutions automatically generate objective, quantified cardiac ultrasound results to enable point-of-care clinicians to assess cardiac function on the spot, near patients’ bedside.

According to Dr. Ami Applebaum, the Chairman of the Board of the IIA, “The purpose of IIA’s call was to bring solutions to global markets for fighting COVID-19, with an emphasis on relevancy, fast time to market and collaborations promising continuity of the Israeli economy. DiA meets these requirements with AI innovation for ultrasound.”DiA has received several FDA/CE clearances and established distribution partnerships with industry leading companies including GE Healthcare, IBM Watson and Konica Minolta, currently serving thousands of end users worldwide.”We see growing use of ultrasound in point of care settings, and an urgent need for automated, objective solutions that provide decision support in real time,” said Hila Goldman-Aslan, CEO and Co-founder of DiA Imaging Analysis, “Our AI solutions meet this need by immediately helping clinicians on the frontlines to quickly and easily assess COVID-19 patients’ hearts to help guide care delivery.”

About DiA Imaging Analysis:
DiA Imaging Analysis provides advanced AI-based ultrasound analysis technology that makes ultrasound accessible to all. DiA’s automated tools deliver fast and accurate clinical indications to support the decision-making process and offer better patient care. DiA’s AI-based technology uses advanced pattern recognition and machine-learning algorithms to automatically imitate the way the human eye detects image borders and identifies motion. Using DiA’s tools provides automated and objective AI tools, helps reduce variability among users, and increases efficiency. It allows clinicians with various levels of experience to quickly and easily analyze ultrasound images.

For additional information, please visit http://www.dia-analysis.com.

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