A Powerful Modelling Framework for Nowcasting and Forecasting COVID-19 and Other Diseases

TL;DR

This paper introduces a flexible hierarchical modeling framework that improves nowcasting and forecasting of COVID-19 and other diseases by effectively correcting for reporting delays, demonstrated through applications to UK and Brazil data.

Contribution

The paper presents a novel spatio-temporal hierarchical model that outperforms existing methods in correcting delayed reporting for disease surveillance.

Findings

Our approach achieves higher accuracy in nowcasting COVID-19 deaths.

The model reduces bias and improves precision compared to competing methods.

Demonstrated effectiveness in real-world data from UK and Brazil.

Abstract

The COVID-19 pandemic has highlighted delayed reporting as a significant impediment to effective disease surveillance and decision-making. In the absence of timely data, statistical models which account for delays can be adopted to nowcast and forecast cases or deaths. We discuss the four key sources of systematic and random variability in available data for COVID-19 and other diseases, and critically evaluate current state-of-the-art methods with respect to appropriately separating and capturing this variability. We present a general spatio-temporal hierarchical framework for correcting delayed reporting and demonstrate its application to daily English hospital deaths from COVID-19 and Severe Acute Respiratory Infection cases in Brazil. We compare our approach to competing models with respect to theoretical flexibility and quantitative metrics from a rolling nowcasting experiment imitating a realistic operational scenario. Based on consistent and compelling leads in nowcasting accuracy, bias, and precision, we demonstrate that our approach represents the current best-practice for correcting delayed reporting.