Artificial Neural Network Prediction of COVID-19 Daily Infection Count.

Publication date: Apr 01, 2024

This study addresses COVID-19 testing as a nonlinear sampling problem, aiming to uncover the dependence of the true infection count in the population on COVID-19 testing metrics such as testing volume and positivity rates. Employing an artificial neural network, we explore the relationship among daily confirmed case counts, testing data, population statistics, and the actual daily case count. The trained artificial neural network undergoes testing in in-sample, out-of-sample, and several hypothetical scenarios. A substantial focus of this paper lies in the estimation of the daily true case count, which serves as the output set of our training process. To achieve this, we implement a regularized backcasting technique that utilize death counts and the infection fatality ratio (IFR), as the death statistics and serological surveys (providing the IFR) as more reliable COVID-19 data sources. Addressing the impact of factors such as age distribution, vaccination, and emerging variants on the IFR time series is a pivotal aspect of our analysis. We expect our study to enhance our understanding of the genuine implications of the COVID-19 pandemic, subsequently benefiting mitigation strategies.

Concepts Keywords
Covid Artificial neural network
Daily Backcasting method
Fatality Covid-19 case count
Math Infection fatality ratio
Vaccination

Semantics

Type Source Name
disease MESH COVID-19
disease MESH Infection
disease VO population
disease VO volume
disease IDO process
disease MESH death
disease VO vaccination
disease VO time

Original Article

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