Improving the Estimation of the COVID-19 Effective Reproduction Number using Nowcasting

TL;DR

This paper introduces a data-driven nowcasting method for estimating the COVID-19 effective reproduction number, $R_t$, leveraging symptom onset data to provide more accurate and timely epidemic indicators.

Contribution

It presents a novel approach that uses self-reported symptom onset data for nowcasting $R_t$, improving epidemic tracking accuracy over traditional methods.

Findings

The method improves the precision of $R_t$ estimates as more data becomes available.

Using symptom onset data offers advantages over daily report counts.

The approach can serve as a foundation for epidemic monitoring indicators.

Abstract

As the interactions between people increases, the impending menace of COVID-19 outbreaks materialize, and there is an inclination to apply lockdowns. In this context, it is essential to have easy-to-use indicators for people to use as a reference. The basic reproduction number of confirmed positives, $R_t$, fulfill such a role. This document proposes a data-driven approach to nowcast $R_t$ based on previous observations' statistical behavior. As more information arrives, the method naturally becomes more precise about the final count of confirmed positives. Our method's strength is that it is based on the self-reported onset of symptoms, in contrast to other methods that use the daily report's count to infer this quantity. We show that our approach may be the foundation for determining useful epidemy tracking indicators.