Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling

TL;DR

This paper enhances traditional COVID-19 epidemiological models by incorporating time-varying transmission and reporting rates, leading to more accurate parameter estimates and better predictions, revealing significant under-reporting during the pandemic.

Contribution

It introduces a Bayesian inference framework for SIR and SEIR models with dynamic parameters, improving estimation accuracy and accounting for under-reporting in COVID-19 data.

Findings

Models with time-varying rates outperform constant-rate models.

Detected consistent under-reporting of active cases.

Improved one-week ahead prediction accuracy.

Abstract

The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-reporting of active cases, thus introducing bias in the estimation of model parameters. To infer more accurate parameter estimates and to reduce the uncertainty of these estimates, we extend the SIR and SEIR epidemiological models with two time-varying parameters that capture the transmission rate and the rate at which active cases are reported to health officials. Using two real data sets of COVID-19 cases, we perform Bayesian inference via our SIR and SEIR models with time-varying transmission and reporting rates and via their standard counterparts with constant rates; our approach provides parameter estimates with more realistic interpretation, and one-week ahead predictions with reduced uncertainty. Furthermore, we find consistent under-reporting in the number of active cases in the data that we consider, suggesting that the initial phase of the pandemic was more widespread than previously reported.