Modeling the spread of COVID-19 in New York City

This paper proposes an ensemble predictor for the weekly increase in the number of confirmed COVID-19 cases in the city of New York at zip code level. Within a Bayesian model averaging framework, the baseline is a Poisson regression for count data. The set of covariates includes autoregressive terms, spatial effects, and demographic and socioeconomic variables. Our results for the second wave of the coronavirus pandemic show that these regressors are more significant to predict the number of new confirmed cases as the pandemic unfolds. Both pointwise and interval forecasts exhibit strong predictive ability in-sample and out-of-sample.

Automated summary

This study proposes an ensemble predictor for predicting the weekly increase in confirmed COVID-19 cases at the zip code level in New York City. Results indicate that various regression variables are important for predicting the number of new cases, and both pointwise and interval forecasts show strong predictive ability both in-sample and out-of-sample.