Forecasting Upper Bounds for Daily New COVID-19 Infections Using Tolerance Limits

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first identified in Wuhan, China, in December 2019. Since then, it has evolved into a global pandemic. Forecasting the number of COVID-19 cases is a crucial task that can greatly aid management decisions. Numerous methods have been proposed in the literature to forecast COVID-19 case numbers; however, most do not yield highly accurate results. Rather than focusing solely on predicting exact case numbers, providing robust upper bounds may offer a more practical approach to support effective decision-making and resource preparedness. This study proposes the use of tolerance interval methods to construct upper bounds for daily new COVID-19 case numbers. The tolerance limits derived from the normal, Poisson, and negative binomial distributions are compared. These methods rely either on historical data alone or on a combination of historical data and auxiliary data from other regions. The results demonstrate that the proposed methods can generate informative upper bounds for COVID-19 case counts, offering a valuable alternative to traditional forecasting models that emphasize exact number estimation. This approach can improve pandemic preparedness through better equipment planning, resource allocation, and timely response strategies.