Probabilistic Programming Method for Time-Series Forecasting of COVID-19 Cases Based on Empirical Data

Abstract

During a pandemic, leaders and decision-makers are compelled to urgently make decisions due to public health concerns, public and expert opinions, and other factors. It is typical, particularly in the early phases of a pandemic, for decisions to depend on partial or missing information. A current trend in scientific publication is to present repeatable results, accompanied by data sources and artefacts. This results in complete transparency and auditability of the results, as well as a platform for future research. CoronaCaster is a tool based on the probabilistic programming method, built for transparent forecasting of pandemic cases, hospital capacity, and mortality rate. Probabilistic programming, i.e. Bayesian inference, has been shown to perform well with time series prediction challenges with small sample size and great uncertainty. CoronaCaster uses an advanced Bayesian method to obtain model parameters and their confidence intervals for the user-selected shape function (polynomial, exponential or sigmoid). They are obtained by sampling parameter space using the training period data.