Estimates of the severity of coronavirus disease 2019: a model-based analysis

COVID-19 antibody seroprevalence in Santa Clara County, California

BACKGROUND

Measuring the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is central to understanding infection risk and fatality rates. We studied Coronavirus Disease 2019 (COVID-19)-antibody seroprevalence in a community sample drawn from Santa Clara County.

METHODS

On 3 and 4 April 2020, we tested 3328 county residents for immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies to SARS-CoV-2 using a rapid lateral-flow assay (Premier Biotech). Participants were recruited using advertisements that were targeted to reach county residents that matched the county population by gender, race/ethnicity and zip code of residence. We estimate weights to match our sample to the county by zip, age, sex and race/ethnicity. We report the weighted and unweighted prevalence of antibodies to SARS-CoV-2. We adjust for test-performance characteristics by combining data from 18 independent test-kit assessments: 14 for specificity and 4 for sensitivity.

RESULTS

The raw prevalence of antibodies in our sample was 1.5% [exact binomial 95% confidence interval (CI) 1.1-2.0%]. Test-performance specificity in our data was 99.5% (95% CI 99.2-99.7%) and sensitivity was 82.8% (95% CI 76.0-88.4%). The unweighted prevalence adjusted for test-performance characteristics was 1.2% (95% CI 0.7-1.8%). After weighting for population demographics, the prevalence was 2.8% (95% CI 1.3-4.2%), using bootstrap to estimate confidence bounds. These prevalence point estimates imply that 53 000 [95% CI 26 000 to 82 000 using weighted prevalence; 23 000 (95% CI 14 000-35 000) using unweighted prevalence] people were infected in Santa Clara County by late March-many more than the ∼1200 confirmed cases at the time.

CONCLUSION

The estimated prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that COVID-19 was likely more widespread than indicated by the number of cases in late March, 2020. At the time, low-burden contexts such as Santa Clara County were far from herd-immunity thresholds.

COVID-19 antibody seroprevalence in Santa Clara County, California

BACKGROUND

Measuring the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is central to understanding infection risk and fatality rates. We studied Coronavirus Disease 2019 (COVID-19)-antibody seroprevalence in a community sample drawn from Santa Clara County.

METHODS

On 3 and 4 April 2020, we tested 3328 county residents for immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies to SARS-CoV-2 using a rapid lateral-flow assay (Premier Biotech). Participants were recruited using advertisements that were targeted to reach county residents that matched the county population by gender, race/ethnicity and zip code of residence. We estimate weights to match our sample to the county by zip, age, sex and race/ethnicity. We report the weighted and unweighted prevalence of antibodies to SARS-CoV-2. We adjust for test-performance characteristics by combining data from 18 independent test-kit assessments: 14 for specificity and 4 for sensitivity.

RESULTS

The raw prevalence of antibodies in our sample was 1.5% [exact binomial 95% confidence interval (CI) 1.1-2.0%]. Test-performance specificity in our data was 99.5% (95% CI 99.2-99.7%) and sensitivity was 82.8% (95% CI 76.0-88.4%). The unweighted prevalence adjusted for test-performance characteristics was 1.2% (95% CI 0.7-1.8%). After weighting for population demographics, the prevalence was 2.8% (95% CI 1.3-4.2%), using bootstrap to estimate confidence bounds. These prevalence point estimates imply that 53 000 [95% CI 26 000 to 82 000 using weighted prevalence; 23 000 (95% CI 14 000-35 000) using unweighted prevalence] people were infected in Santa Clara County by late March-many more than the ∼1200 confirmed cases at the time.

CONCLUSION

The estimated prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that COVID-19 was likely more widespread than indicated by the number of cases in late March, 2020. At the time, low-burden contexts such as Santa Clara County were far from herd-immunity thresholds.

Intestinal permeability changes with bacterial translocation as key events modulating systemic host immune response to SARS-CoV-2: A working hypothesis

The microbiota-gut-liver-lung axis plays a bidirectional role in the pathophysiology of a number of infectious diseases. During the course of severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1) and 2 (SARS-CoV-2) infection, this pathway is unbalanced due to intestinal involvement and systemic inflammatory response. Moreover, there is convincing preliminary evidence linking microbiota-gut-liver axis perturbations, proinflammatory status, and endothelial damage in noncommunicable preventable diseases with coronavirus disease 2019 (Covid-19) severity. Intestinal damage due to SARS-CoV-2 infection, systemic inflammation-induced dysfunction, and IL-6-mediated diffuse vascular damage may increase intestinal permeability and precipitate bacterial translocation. The systemic release of damage- and pathogen-associated molecular patterns (e.g. lipopolysaccharides) and consequent immune-activation may in turn auto-fuel vicious cycles of systemic inflammation and tissue damage. Thus, intestinal bacterial translocation may play an additive/synergistic role in the cytokine release syndrome in Covid-19. This review provides evidence on gut-liver axis involvement in Covid-19 as well as insights into the hypothesis that intestinal endotheliitis and permeability changes with bacterial translocation are key pathophysiologic events modulating systemic inflammatory response. Moreover, it presents an overview of readily applicable measures for the modulation of the gut-liver axis and microbiota in clinical practice.

Costing the COVID-19 Pandemic: An Exploratory Economic Evaluation of Hypothetical Suppression Policy in the United Kingdom

OBJECTIVE

This study aims to cost and calculate the relative cost-effectiveness of the hypothetical suppression policies found in the Imperial College COVID-19 Response Team model.

METHODS

Key population-level disease projections in deaths, intensive care unit bed days, and non-intensive care unit bed days were taken from the Imperial College COVID-19 Response Team report of March 2020, which influenced the decision to introduce suppression policies in the United Kingdom. National income loss estimates were from a study that estimated the impact of a hypothetical pandemic on the UK economy, with sensitivity analyses based on projections that are more recent. Individual quality-adjusted life-year (QALY) loss and costed resource use inputs were taken from published sources.

RESULTS

Imperial model projected suppression polices compared to an unmitigated pandemic, even with the most pessimistic national income loss scenarios under suppression (10%), give incremental cost-effectiveness ratios below £50 000 per QALY. Assuming a maximum reduction in national income of 7.75%, incremental cost-effectiveness ratios for Imperial model projected suppression versus mitigation are below 60 000 per QALY.

CONCLUSIONS

Results are uncertain and conditional on the accuracy of the Imperial model projections; they are also sensitive to estimates of national income loss. Nevertheless, it would be difficult to claim that the hypothetical Imperial model-projected suppression policies are obviously cost-ineffective relative to the alternatives available. Despite evolving differences between government policy and Imperial model-projected suppression policy, it is hoped this article will provide some early insight into the trade-offs that are involved.

Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing

The newly emergent human virus SARS-CoV-2 (severe acute respiratory syndrome-coronavirus 2) is resulting in high fatality rates and incapacitated health systems. Preventing further transmission is a priority. We analyzed key parameters of epidemic spread to estimate the contribution of different transmission routes and determine requirements for case isolation and contact tracing needed to stop the epidemic. Although SARS-CoV-2 is spreading too fast to be contained by manual contact tracing, it could be controlled if this process were faster, more efficient, and happened at scale. A contact-tracing app that builds a memory of proximity contacts and immediately notifies contacts of positive cases can achieve epidemic control if used by enough people. By targeting recommendations to only those at risk, epidemics could be contained without resorting to mass quarantines ("lockdowns") that are harmful to society. We discuss the ethical requirements for an intervention of this kind.