Lag Between COVID-19 Cases And Subsequent Deaths (preprint)

Background: Observed COVID-19 cases and deaths are frequently used as independent factors to justify the use or de-escalation of non-pharmaceutical interventions. To investigate and describe the temporal relationship between changes in epidemic curves for COVID-19 cases and deaths, a longitudinal analysis of these metrics was performed for 16 states in the United States and three countries.Results: The analysis performed demonstrates a considerable, consistent lag between a surge in COVID-19 cases and the subsequent rise in attributable deaths, with approximately a month-long lag observed.Conclusions: The time lag identified must be incorporated in health emergency decision making in order to avoid the pre-mature de-escalation of disease mitigation measures. 

Pilot program for test-based SARS-CoV-2 screening and environmental monitoring in an urban public school district (preprint)

Importance. Scalable programs for school-based SARS-CoV-2 testing and surveillance are needed to guide in-person learning practices and inform risk assessments in K-12 settings. Objectives. To characterize SARS-CoV-2 infections in staff and students in an urban public school setting and evaluate test-based strategies to support ongoing risk assessment and mitigation for K-12 in-person learning. Design, Setting, and Participants. The pilot program engaged three schools for weekly saliva PCR testing of staff and students participating in in-person learning over a 5-week period. Wastewater, air, and surface samples were collected weekly and tested for SARS-CoV-2 RNA to determine surrogacy for case detection and interrogate transmission risk of in-building activities. Main Outcomes and Measures. SARS-CoV-2 detection in saliva and environmental samples and risk factors for SARS-CoV-2 infection. Results. 2,885 supervised self-collected saliva samples were tested from 773 asymptomatic staff and students during November and December, 2020. 46 cases (22 students, 24 staff) were detected, representing a 5.8- and 2.5-fold increase in case detection rates among students and staff, respectively, compared to conventional reporting mechanisms. SARS-CoV-2 RNA was detected in wastewater samples from all pilot schools, as well as in air samples collected from two choir rooms. Sequencing of 21 viral genomes in saliva specimens demonstrated minimal clustering associated with one school. Geographic analysis of SARS-CoV-2 cases reported district-wide demonstrated higher community risk in zip codes proximal to the pilot schools. Conclusions and Relevance. Weekly screening of asymptomatic staff and students by saliva PCR testing dramatically increased SARS-CoV-2 case detection in an urban public-school setting, exceeding infection rates reported at the county level. Experiences differed among schools, and virus sequencing and geographic analyses suggest a dynamic interplay of school-based and community-derived transmission risk. Environmental testing for SARS-CoV-2 RNA in air and surface samples enabled real-time risk assessment of in-school activities and allowed for interventions in choir classes. Wastewater testing demonstrated the utility of school building-level SARS-CoV-2 surveillance. Collectively, these findings provide insight into the performance and community value of test-based SARS-CoV-2 screening and surveillance strategies in the K-12 educational setting.

Zoonotic Risk Technology Enters the Viral Emergence Toolkit (preprint)

In light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programs will identify hundreds of novel viruses that might someday pose a threat to humans. Our capacity to identify which viruses are capable of zoonotic emergence depends on the existence of a technology—a machine learning model or other informatic system—that leverages available data on known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions: What are the prerequisites, in terms of open data, equity, and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it, and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges?

Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-analysis (preprint)

Objectives This systematic review and meta-analysis synthesized the evidence on the impact of demographics and comorbidities with clinical outcomes of COVID-19, including severe illness, admission to the intensive care unit (ICU), and death.MethodsThe PRISMA guidelines were followed to conduct and report this meta-analysis. The protocol is registered in PROSPERO International prospective register of systematic reviews (ID=CRD42020184440). Two authors independently searched literature from PubMed, Embase, Cochrane library and CINHAL on May 6, 2020; removed duplicates; screened titles, abstracts and full text using criteria; and extracted data from eligible articles. A random-effects model was used to estimate the summary odds ratio (OR). Variations among studies were examined using Cochrane Q and I2.ResultsOut of 4,275 articles obtained from the databases and screened, 71 studies that involved 216,843 patients were abstracted and then, where appropriate, analyzed by meta-analysis. The COVID-19 related outcomes reported were death in 26 studies, severe illness in 41 studies, and admission to ICU in 11 studies. Death was significantly correlated with hypertension (OR 2.60, 95% CI 1.95–3.25, I2 = 52.6%, n= 13 studies), cardiovascular disease (5.16, 4.10–6.22, 0.0%, 6), diabetes (2.11, 1.35–2.87, 67.4%, 12), chronic respiratory disease (2.83, 2.14–3.51, 0.0%, 9), cerebrovascular diseases (5.14, 1.08–9.19, 0.0%, 2), male sex (1.34, 1.18 – 1.50, 38.7%, 16), age older than 60 (6.09, 3.53 – 8.66, 95.5%, 6) or 65 years (3.56, 1.21 – 5.90, 18.2%, 6). Severe illness was also significantly associated with hypertension (1.70, 1.30 –2.10, 47.8%, 21), cardiovascular diseases (2.04, 1.01–3.08, 30.6%, 10), diabetes (1.65, 1.23–2.08, 24.9%, 18), male sex (1.35, 1.23 – 1.47, 0.0%, 32) and age at least 60 (4.91, 1.35 – 8.47, 0.0%, 4) or 65 (2.55,1.94 – 3.17, 24.5%, 9) years. Among hospitalized patients, the odds of admission to ICU was greater in individuals who had cardiovascular diseases (1.36,1.04–1.69, 0.0%, 4), diabetes (1.55, 1.20–1.90, 0.0%, 5) and chronic respiratory disease (1.52, 1.09–1.94, 0.0%, 5) than those who were not having these comorbidities. ConclusionsOlder age and chronic diseases increase the risk of developing severe illness, admission to ICU and death among COVID-19 patients. Special strategies are warranted to prevent SARS-CoV-2 infection and manage COVID-19 cases in those with vulnerabilities.

Risk factors for severe illness and death in COVID-19: a systematic review and meta-analysis (preprint)

ObjectivesThis systematic review and meta-analysis synthesized the evidence on the impact of demographics and comorbidities with clinical outcomes of COVID-19, including severe illness, admission to the intensive care unit (ICU), and death. MethodsThe PRISMA guidelines were followed to conduct and report this meta-analysis. The protocol is registered in PROSPERO International prospective register of systematic reviews (ID=CRD42020184440). Two authors independently searched literature from PubMed, Embase, Cochrane library and CINHAL on May 6, 2020; removed duplicates; screened titles, abstracts and full text using criteria; and extracted data from eligible articles. A random-effects model was used to estimate the summary odds ratio (OR). Variations among studies were examined using Cochrane Q and I2. ResultsOut of 4,275 articles obtained from the databases and screened, 71 studies that involved 216,843 patients were abstracted and then, where appropriate, analyzed by meta-analysis. The COVID-19 related outcomes reported were death in 26 studies, severe illness in 41 studies, and admission to ICU in 11 studies. Death was significantly correlated with hypertension (OR 2.60, 95% CI 1.95-3.25, I2 = 52.6%, n= 13 studies), cardiovascular disease (5.16, 4.10-6.22, 0.0%, 6), diabetes (2.11, 1.35-2.87, 67.4%, 12), chronic respiratory disease (2.83, 2.14-3.51, 0.0%, 9), cerebrovascular diseases (5.14, 1.08-9.19, 0.0%, 2), male sex (1.34, 1.18 1.50, 38.7%, 16), age older than 60 (6.09, 3.53 8.66, 95.5%, 6) or 65 years (3.56, 1.21 5.90, 18.2%, 6). Severe illness was also significantly associated with hypertension (1.70, 1.30 -2.10, 47.8%, 21), cardiovascular diseases (2.04, 1.01-3.08, 30.6%, 10), diabetes (1.65, 1.23-2.08, 24.9%, 18), male sex (1.35, 1.23 1.47, 0.0%, 32) and age at least 60 (4.91, 1.35 8.47, 0.0%, 4) or 65 (2.55,1.94 3.17, 24.5%, 9) years. Among hospitalized patients, the odds of admission to ICU was greater in individuals who had cardiovascular diseases (1.36,1.04-1.69, 0.0%, 4), diabetes (1.55, 1.20-1.90, 0.0%, 5) and chronic respiratory disease (1.52, 1.09-1.94, 0.0%, 5) than those who were not having these comorbidities. ConclusionsOlder age and chronic diseases increase the risk of developing severe illness, admission to ICU and death among COVID-19 patients. Special strategies are warranted to prevent SARS-CoV-2 infection and manage COVID-19 cases in those with vulnerabilities.

Identifying Non-Traditional Epidemic Disease Risk Factors Associated to Major Health Events from WHO and World Bank Open Data (preprint)

Objectives: Health events emerge from a multifactorial milieu involving host, community, environment, and pathogen factors. Therefore, developing accurate forecasting models to improve epidemic prediction towards better prevention and capabilities management is a complex task. Here, we describe an exploratory analysis to identify non-health risk factors that could improve the forecast and events risk signals using a feasible and practical approach by combining surveillance report data with non-health data from open data sources. Methods: A line listing was developed using the World Health Organization Disease Outbreaks News from 2016-2018 and merged with non-health indicators data from the World Bank. Poisson regression models employing forward imputations were used to establish relationships and predict values over the dependent variable (health event frequency). Findings: The resulting regression model provided evidence that changes in non-health factors important to community experiences impact the risk of the number of major health events that a country could experience. Three non-health indicators (extrinsic factors) were associated significantly to event frequency (population urban change, gross domestic product change per capita—a novel factor, and average forest area). An exploratory analysis of the current COVID-19 pandemic suggested similar associations, but confounding by global disease burden is likely. Interpretation: Continued development of forecasting approaches capturing available whole-of-society extrinsic factors (non-health factors); could improve the risk management process through earlier hazard identification, and as importantly inform strategic decision processes in multisectoral strategies to preventing, detecting, and responding to pandemic-threat events.Funding: USUHS intramural funding to a Henry M. Jackson Foundation post-doctoral fellowship award.Declaration of Interests: The authors declare no conflicts of interest.Ethics Approval Statement: This manuscript was approved by USUHS clearance publication committee.

Identifying Non-traditional Epidemic Disease Risk Factors associated to major Health Events from WHO and World Bank Open Data (preprint)

Objectives Health events emerge from a multifactorial milieu involving host, community, environment, and pathogen factors. Therefore, developing accurate forecasting models to improve epidemic prediction towards better prevention and capabilities management is a complex task. Here, we describe an exploratory analysis to identify non-health risk factors that could improve the forecast and events risk signals using a feasible and practical approach by combining surveillance report data with non-health data from open data sources. Methods A line listing was developed using information from the World Health Organization Disease Outbreaks News from 2016-2018. A database was created merging the line listing data with non-health indicators from the World Bank. Poisson regression models employing forward imputations were used to establish relationships and predict values over the dependent variable (health event frequency); which are the health events reported by each country to WHO during 2016-2018. Findings The resulting regression model provided evidence that changes in non-health factors important to community experiences impact the risk of the number of major health events that a country could experience. Three non-health indicators (extrinsic factors) were associated significantly to event frequency (population urban change, gross domestic product change per capita—a novel factor, and average forest area). An exploratory analysis of the current COVID-19 pandemic suggested similar associations, but confounding by global disease burden is likely. Conclusion Continued development of forecasting approaches capturing available whole-of-society extrinsic factors (non-health factors); could improve the risk management process through earlier hazard identification, and as importantly inform strategic decision processes in multisectoral strategies to preventing, detecting, and responding to pandemic-threat events.

Transmission Potential of SARS-CoV-2 in Viral Shedding Observed at the University of Nebraska Medical Center (preprint)

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) originated in Wuhan, China in late 2019, and its resulting coronavirus disease, COVID-19, was declared a pandemic by the World Health Organization on March 11, 2020. The rapid global spread of COVID-19 represents perhaps the most significant public health emergency in a century. As the pandemic progressed, a continued paucity of evidence on routes of SARS-CoV-2 transmission has resulted in shifting infection prevention and control guidelines between clasically-defined airborne and droplet precautions. During the initial isolation of 13 individuals with COVID-19 at the University of Nebraska Medical Center, we collected air and surface samples to examine viral shedding from isolated individuals. We detected viral contamination among all samples, indicating that SARS-CoV-2 may spread through both direct (droplet and person-to-person) as well as indirect mechanisms (contaminated objects and airborne transmission). Taken together, these finding support the use of airborne isolation precautions when caring for COVID-19 patients.