Modelling Recovered Cases and Death Probabilities for the COVID-19 Outbreak

TL;DR

This paper introduces a method to estimate missing recovered COVID-19 case data and calculates death and survival probabilities over time, addressing data gaps crucial for outbreak modeling.

Contribution

It provides a novel approach for estimating missing recovered case data and associated death/survival probabilities in COVID-19 modeling.

Findings

Effective estimation of missing recovered cases.

Probabilities of death and survival over days post-confirmation.

Validated method improves outbreak modeling accuracy.

Abstract

From March 23rd, the data for the recovered cases of COVID-19 are missing from the standard repository maintained by the Johns Hopkins University in collaboration with the WHO. But since data concerning recovered patients are extremely important for modelling the COVID-19 outbreak, a method for estimating the missing data is provided and tested. As a byproduct, it produces estimates for the probabilities to die $k$ days after confirmation, or to survive after $d$ days.