Regional now- and forecasting for data reported with delay: Towards surveillance of COVID-19 infections

TL;DR

This paper introduces a regional nowcasting and forecasting model for COVID-19 infections that accounts for reporting delays, providing policymakers with timely insights into current and future infection levels at the district level.

Contribution

The paper presents a novel statistical approach for real-time monitoring and short-term prediction of COVID-19 cases considering reporting delays at the regional level.

Findings

Effective nowcasting of unreported cases achieved

Accurate short-term infection forecasts demonstrated

Model applied successfully to German district data

Abstract

Governments around the world continue to act to contain and mitigate the spread of COVID-19. The rapidly evolving situation compels officials and executives to continuously adapt policies and social distancing measures depending on the current state of the spread of the disease. In this context, it is crucial for policymakers to have a firm grasp on what the current state of the pandemic is as well as to have an idea of how the infective situation is going to unfold in the next days. However, as in many other situations of compulsorily-notifiable diseases and beyond, cases are reported with delay to a central register, with this delay deferring an up-to-date view of the state of things. We provide a stable tool for monitoring current infection levels as well as predicting infection numbers in the immediate future at the regional level. We accomplish this through nowcasting of cases that have not yet been reported as well as through predictions of future infections. We apply our model to German data, for which our focus lies in predicting and explain infectious behavior by district.