Estimating the real burden of disease under a pandemic situation: The SARS-CoV2 case

TL;DR

This paper presents a novel Hidden Markov Model incorporating epidemic dynamics and time-varying under-reporting to better estimate the true burden of COVID-19 from reported data.

Contribution

It introduces a new model that integrates epidemic spread information and adjusts for under-reporting in non-stationary count data.

Findings

Model effectively estimates true infection counts.

Incorporates epidemic dynamics into under-reporting estimation.

Adapts to seasonal and trend variations in data.

Abstract

The present paper introduces a new model used to study and analyse the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) epidemic-reported-data from Spain. This is a Hidden Markov Model whose hidden layer is a regeneration process with Poisson immigration, Po-INAR(1), together with a mechanism that allows the estimation of the under-reporting in non-stationary count time series. A novelty of the model is that the expectation of the innovations in the unobserved process is a time-dependent function defined in such a way that information about the spread of an epidemic, as modelled through a Susceptible-Infectious-Removed dynamical system, is incorporated into the model. In addition, the parameter controlling the intensity of the under-reporting is also made to vary with time to adjust to possible seasonality or trend in the data. Maximum likelihood methods are used to estimate the parameters of the model.