A Bayesian approach to estimating COVID-19 incidence and infection fatality rates

Tracking the Uneven Outcomes of COVID-19 on Racial and Ethnic Groups: Implications for Health Policy

The socioeconomic shocks of the first COVID-19 pandemic wave disproportionately affected vulnerable groups. But did that trend continue to hold during the Delta and Omicron waves? Leveraging data from the Johns Hopkins Coronavirus Resource Center, this paper examines whether demographic inequalities persisted across the waves of COVID-19 infections. The current study utilizes fixed effects regressions to isolate the marginal relationships between socioeconomic factors with case counts and death counts. Factors include levels of urbanization, age, gender, racial distribution, educational attainment, and household income, along with time- and state-specific COVID-19 restrictions and other time invariant controls captured via fixed effects controls. County-level health outcomes in large metropolitan areas show that despite higher incidence rates in suburban and exurban counties, urban counties still had disproportionately poor outcomes in the latter COVID-19 waves. Policy makers should consider health disparities when developing long-term public health regulatory policies to help shield low-income households from the adverse effects of COVID-19 and future pandemics.

A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater

Wastewater-based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID-19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibility of predicting COVID-19 infections from wastewater signals, there remain significant challenges for statistical modeling. Longitudinal observations of viral copies in municipal wastewater can be influenced by noisy datasets and missing values with irregular and sparse samplings. We propose an integrative Bayesian framework to predict daily positive cases from weekly wastewater observations with missing values via functional data analysis techniques. In a unified procedure, the proposed analysis models severe acute respiratory syndrome coronavirus-2 RNA wastewater signals as a realization of a smooth process with error and combines the smooth process with COVID-19 cases to evaluate the prediction of positive cases. We demonstrate that the proposed framework can achieve these objectives with high predictive accuracies through simulated and observed real data.