Nowcasting Covid-19 statistics reported withdelay: a case-study of Sweden

TL;DR

This paper presents a statistical method to accurately nowcast COVID-19 statistics in Sweden despite reporting delays, improving real-time understanding of the pandemic's true impact.

Contribution

It introduces a novel delay-adjusted estimation approach based on the removal method, tailored for COVID-19 reporting data.

Findings

Effective correction for reporting delays in COVID-19 data

Improved real-time estimates of infections, hospitalizations, and deaths

Method applicable to other delayed reporting scenarios

Abstract

The new corona virus disease -- COVID-2019 -- is rapidly spreading through the world. The availability of unbiased timely statistics of trends in disease events are a key to effective responses. But due to reporting delays, the most recently reported numbers are frequently underestimating of the total number of infections, hospitalizations and deaths creating an illusion of a downward trend. Here we describe a statistical methodology for predicting true daily quantities and their uncertainty, estimated using historical reporting delays. The methodology takes into account the observed distribution pattern of the lag. It is derived from the removal method, a well-established estimation framework in the field of ecology.