An integrated risk and epidemiological model to estimate risk-stratified COVID-19 outcomes for Los Angeles County: March 1, 2020-March 1, 2021.

Abigail L Horn,Emil Hvitfeldt,Kayla De La Haye,Maryann Pentz,William Nicholas,David V Conti,Wendy Cozen,Neeraj Sood,Faith Washburn,Lai Jiang,Paul Simon,Martial L Ndeffo Mbah

doi:10.1371/journal.pone.0253549

Abigail L Horn, Emil Hvitfeldt + Show 10 more

Open Access

https://doi.org/10.1371/journal.pone.0253549

Copy DOI

Abstract

The objective of this study was to use available data on the prevalence of COVID-19 risk factors in subpopulations and epidemic dynamics at the population level to estimate probabilities of severe illness and the case and infection fatality rates (CFR and IFR) stratified across subgroups representing all combinations of the risk factors age, comorbidities, obesity, and smoking status. We focus on the first year of the epidemic in Los Angeles County (LAC) (March 1, 2020-March 1, 2021), spanning three epidemic waves. A relative risk modeling approach was developed to estimate conditional effects from available marginal data. A dynamic stochastic epidemic model was developed to produce time-varying population estimates of epidemic parameters including the transmission and infection observation rate. The epidemic and risk models were integrated to produce estimates of subpopulation-stratified probabilities of disease progression and CFR and IFR for LAC. The probabilities of disease progression and CFR and IFR were found to vary as extensively between age groups as within age categories combined with the presence of absence of other risk factors, suggesting that it is inappropriate to summarize epidemiological parameters for age categories alone, let alone the entire population. The fine-grained subpopulation-stratified estimates of COVID-19 outcomes produced in this study are useful in understanding disparities in the effect of the epidemic on different groups in LAC, and can inform analyses of targeted subpopulation-level policy interventions.

Highlights

The two-step parameter estimation approach (broad grid search to select a single mode of each parameter, followed by approximate Bayesian computation (ABC) using a prior distribution specified around that single mode) achieved convergence in posterior densities
By specifying a narrow prior distribution around a mode chosen from the broad grid search sampling, convergence around a dominant single mode is achieved in the final posterior density returned by the ABC sampling step
To reflect the uncertain knowledge of many parameters and the understanding that in non-linear systems small variations to specific parameters can result in large impacts in outputs [37], we account for uncertainty in all results through the use of a stochastic epidemic model and a Bayesian approach to parameter estimation

Summary

Introduction

Health disparities have emerged with the COVID-19 epidemic because the risk of exposure to infection and the prevalence of risk factors for severe outcomes given infection vary within. Risk-stratified COVID-19 outcomes for Los Angeles County: March 1, 2020—March 1, 2021. GitHub page of the Los Angeles Times Data and Graphics Department. These data are provided in a public repository at: https://github. These data are provided in a public repository at: https://github. com/AbigailHorn/COV2-LA/tree/master/data

Objectives

Methods

Results

Conclusion