A Bayesian latent variable model for the optimal identification of disease incidence rates given information constraints

We present an original approach for measuring infections as a latent variable and making use of serological and expert surveys to provide ground truth identification during the early pandemic period. Compared to existing approaches, our model relies more on empirical information than strong structural forms, permitting inference with relatively few assumptions of cumulative infections. We also incorporate a range of political, economic, and social covariates to richly parameterize the relationship between epidemic spread and human behaviour. To show the utility of the model, we provide robust estimates of total infections that account for biases in COVID-19 cases and tests counts in the U.S. from March to July of 2020, a period of time when accurate data about the nature of the SARS-CoV-2 virus was of limited availability. In addition, we can show how sociopolitical factors like the Black Lives Matter protests and support for President Donald Trump are associated with the spread of the virus via changes in fear of the virus and cell phone mobility. A reproducible version of this article is available as an Rmarkdown file at https://github.com/CoronaNetDataScience/covid_model.