In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility.

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In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility.

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Background The prevalence of hepatitis B virus (HBV) infection varies geographically around the world. However, the underlying reasons for this variation are unknown. Using a nationally representative population-based sample from all 58 administrative divisions in Cameroon, we examined the association between median maternal age at first childbirth in a preceding generation, a proxy for the frequency of mother-to-child transmission (MTCT) of HBV in a region, and the risk of chronic HBV infection, defined as positive surface antigen (HBsAg), in the index generation. Methods We estimated a division-specific median maternal age at first childbirth using historical data from Demographic Health Surveys (DHS) in 1991/1998/2004/2011. We tested HBsAg in 2011 DHS participants. We used maps to display spatial variation and spatial models for the analysis. Results In 14,150 participants (median 27 years old, 51% females), the overall weighted prevalence of HBsAg was 11.9% (95%CI: 11.0–12.8), with a wide geographical variation across the divisions (range: 6.3-23.7%). After adjusting for confounding factors and spatial dependency, lower maternal age at first childbirth was significantly associated with positive HBsAg at the division level (β: 1.89 [95%CI: 1.26-2.52], p<0.001), and at the individual level (OR: 1.20 [95%CI: 1.04-1.39], p=0.016). A similar ecological correlation was observed across other African countries. Conclusions The significant association between the maternal age at first childbirth and HBsAg-positivity suggests a crucial role of MTCT in maintaining high HBV endemicity in some areas in Cameroon. This underlines an urgent need to effectively prevent MTCT in order to achieve WHO’s global hepatitis elimination goals.

Social gatherings can be an important locus of transmission for many pathogens including SARS-CoV-2. During an outbreak, restricting the size of these gatherings is one of several non-pharmaceutical interventions available to policy-makers to reduce transmission. Often these restrictions take the form of prohibitions on gatherings above a certain size. While it is generally agreed that such restrictions reduce contacts, the specific size threshold separating "allowed" from "prohibited" gatherings often does not have a clear scientific basis, which leads to dramatic differences in guidance across location and time. Building on the observation that gathering size distributions are often heavy-tailed, we develop a theoretical model of transmission during gatherings and their contribution to general disease dynamics. We find that a key, but often overlooked, determinant of the optimal threshold is the distribution of gathering sizes. Using data on pre-pandemic contact patterns from several sources as well as empirical estimates of transmission parameters for SARS-CoV-2, we apply our model to better understand relationship between restriction threshold and reduction in cases. We find that, under reasonable transmission parameter ranges, restrictions may have to be set quite low to have any demonstrable effect on cases due to relative frequency of smaller gatherings. We compare our conceptual model with observed changes in reported contacts during lockdown in March of 2020.

Background: The COVID-19 epidemic in the United States has been characterized by two stark disparities. COVID-19 burden has been unequally distributed among racial and ethnic groups and at the same time the mortality rates have been sharply higher among older age groups. These disparities have led some to suggest that higher equity could be attained by vaccinating front-line workers before vaccinating older individuals, who in the US are disproportionately Non-Hispanic White. Methods: We compare the performance of two distribution policies, one allocating vaccines to front-line workers and another to older individuals aged 65-74-year-old. We estimate both the number of lives saved and the number of years of life saved under each of the policies, overall and in every race/ethnicity groups, in the United States and every state. Findings: We show that prioritizing COVID-19 vaccines for 65-74-year-olds saves both more lives and more years of life than attributing vaccines front-line workers in each racial/ethnic group, in the United States as a whole and in nearly every state. Interpretation: When evaluating equity in vaccine allocation policies, the overall benefit to impact of each population subgroup should be considered, not only the proportion of doses that is distributed to each subgroup. Further work can identify prioritization schemes that perform better on multiple equity metrics.