Introduction The infection fatality rate (IFR) of COVID-19 has been carefully measured and analysed in high-income countries, whereas there has been no systematic analysis of age-specific seroprevalence or IFR for developing countries. Methods We systematically reviewed the literature to identify all COVID-19 serology studies in developing countries that were conducted using representative samples collected by February 2021. For each of the antibody assays used in these serology studies, we identified data on assay characteristics, including the extent of seroreversion over time. We analysed the serology data using a Bayesian model that incorporates conventional sampling uncertainty as well as uncertainties about assay sensitivity and specificity. We then calculated IFRs using individual case reports or aggregated public health updates, including age-specific estimates whenever feasible. Results In most locations in developing countries, seroprevalence among older adults was similar to that of younger age cohorts, underscoring the limited capacity that these nations have to protect older age groups. Age-specific IFRs were roughly 2 times higher than in high-income countries. The median value of the population IFR was about 0.5%, similar to that of high-income countries, because disparities in healthcare access were roughly offset by differences in population age structure. Conclusion The burden of COVID-19 is far higher in developing countries than in high-income countries, reflecting a combination of elevated transmission to middle-aged and older adults as well as limited access to adequate healthcare. These results underscore the critical need to ensure medical equity to populations in developing countries through provision of vaccine doses and effective medications. 

https://gh.bmj.com/content/bmjgh/7/5/e008477.full.pdf

Assessing the burden of COVID-19 in developing countries: systematic review, meta-analysis and public policy implications

https://doi.org/10.1016/j.lana.2021.100178

The Role of Poverty and Racial Discrimination in Exacerbating the Health Consequences of COVID-19

https://doi.org/10.1016/j.scitotenv.2022.158056

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

http://manuscript.elsevier.com/S1047279722000588/pdf/S1047279722000588.pdf

Neighborhood disparities in COVID-19 outcomes in New York city over the first two waves of the outbreak

ABSTRACT When communities face infectious disease outbreaks such as COVID-19, their resilience is largely dependent on their social vulnerability. Housing, which functions as a precipitator and outcome of vulnerability, needs to be considered in this context. Using geospatial data, we developed a housing vulnerability index which demonstrates that COVID-19 transmission hotspots in Melbourne are potentially related to where and how people live – which in turn impacts their capacity to isolate. This analysis provides a means of both retrospectively and prospectively highlighting socio-spatial vulnerabilities that can impact transmission, suggesting that addressing some of Melbourne’s housing problems might reduce COVID-19 transmission.

Housing Vulnerability and COVID-19 Outbreaks: When Crises Collide

Association between overcrowded households, multigenerational households, and COVID-19: a cohort study.

Introduction The role of overcrowded and multigenerational households as a risk factor for COVID-19 remains unmeasured. The objective of this study is to examine and quantify the association between overcrowded and multigenerational households, and COVID-19 in New York City (NYC). Methods We conducted a Bayesian ecological time series analysis at the ZIP Code Tabulation Area (ZCTA) level in NYC to assess whether ZCTAs with higher proportions of overcrowded (defined as proportion of estimated number of housing units with more than one occupant per room) and multigenerational households (defined as the estimated percentage of residences occupied by a grandparent and a grandchild less than 18 years of age) were independently associated with higher suspected COVID-19 case rates (from NYC Department of Health Syndromic Surveillance data for March 1 to 30, 2020). Our main measure was adjusted incidence rate ratio (IRR) of suspected COVID-19 cases per 10,000 population. Our final model controlled for ZCTA-level sociodemographic factors (median income, poverty status, White race, essential workers), prevalence of clinical conditions related to COVID-19 severity (obesity, hypertension, coronary heart disease, diabetes, asthma, smoking status, and chronic obstructive pulmonary disease), and spatial clustering. Results 39,923 suspected COVID-19 cases presented to emergency departments across 173 ZCTAs in NYC. Adjusted COVID-19 case rates increased by 67% (IRR 1.67, 95% CI = 1.12, 2.52) in ZCTAs in quartile four (versus one) for percent overcrowdedness and increased by 77% (IRR 1.77, 95% CI = 1.11, 2.79) in quartile four (versus one) for percent living in multigenerational housing. Interaction between both exposures was not significant (βinteraction = 0.99, 95% CI: 0.99-1.00). Conclusions Over-crowdedness and multigenerational housing are independent risk factors for suspected COVID-19. In the early phase of surge in COVID cases, social distancing measures that increase house-bound populations may inadvertently but temporarily increase SARS-CoV-2 transmission risk and COVID-19 disease in these populations.

/pdf/association-between-overcrowded-households-multigenerational-2kw7wscplm.pdf

Association between overcrowded households, multigenerational households, and COVID-19: a cohort study

Time-course gene expression datasets provide insight into the dynamics of complex biological processes, such as immune response and organ development. It is of interest to identify genes with similar temporal expression patterns because such genes are often biologically related. However, this task is challenging due to the high dimensionality of such datasets and the nonlinearity of gene expression time dynamics. We propose an empirical Bayes approach to estimating ordinary differential equation (ODE) models of gene expression, from which we derive similarity metrics that can be used to identify groups of genes with co-moving or time-delayed expression patterns. These metrics, which we call the Bayesian lead-lag R2 values, can be used to construct clusters or networks of functionally-related genes. A key feature of this method is that it leverages biological databases that document known interactions between genes. This information is automatically used to define informative prior distributions on the ODE model9s parameters. We then derive data-driven shrinkage parameters from Stein9s unbiased risk estimate that optimally balance the ODE model9s fit to both the data and external biological information. Using real gene expression data, we demonstrate that our biologically-informed similarity metrics allow us to recover sparse, interpretable gene networks. These networks reveal new insights about the dynamics of biological systems.

/pdf/an-empirical-bayes-approach-to-estimating-dynamic-models-of-22hk8cuapr.pdf

Sara Venkatraman

Papers

Association between overcrowded households, multigenerational households, and COVID-19: a cohort study.

Association between overcrowded households, multigenerational households, and COVID-19: a cohort study

An Empirical Bayes Approach to Estimating Dynamic Models of Co-regulated Gene Expression