Artificial Neural Network Prediction of COVID-19 Daily Infection Count

TL;DR

This paper employs an artificial neural network to estimate the true COVID-19 infection count by integrating confirmed cases, testing data, and death data, calibrated with an SEIR model, addressing underreporting issues.

Contribution

It introduces a neural network approach to estimate true infection counts using death data and IFR, combined with SEIR model calibration, providing more accurate pandemic insights.

Findings

Neural network effectively estimates true infection counts.

Calibration with SEIR improves accuracy.

Method accounts for underreporting and testing variability.

Abstract

It is well known that the confirmed COVID-19 infection is only a fraction of the true fraction. In this paper we use an artificial neural network to learn the connection between the confirmed infection count, the testing data, and the true infection count. The true infection count in the training set is obtained by backcasting from the death count and the infection fatality ratio (IFR). Multiple factors are taken into consideration in the estimation of IFR. We also calibrate the recovered true COVID-19 case count with an SEIR model.