Pseudo-Likelihood Based Logistic Regression forEstimating COVID-19 Infection and Case FatalityRates by Gender, Race, and Age in California

摘要: In emerging epidemics, early estimates of key epidemiological characteristics of the disease are critical for guiding public policy. In particular, identifying high risk population subgroups aids policymakers and health officials in combatting the epidemic. This has been challenging during the coronavirus disease 2019 (COVID-19) pandemic, because governmental agencies typically release aggregate COVID-19 data as marginal summary statistics of patient demographics. These data may identify disparities in COVID-19 outcomes between broad population subgroups, but do not provide comparisons between more granular population subgroups defined by combinations of multiple demographics. We introduce a method that overcomes the limitations of aggregated summary statistics and yields estimates of COVID-19 infection and case fatality rates—key quantities for guiding public policy related to the control and prevention of COVID-19—for population subgroups across combinations of