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

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 1-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.

Automated summary

The role of epidemiological models in informing public health officials during a public health emergency is crucial. Traditional models fail to capture the time-varying effects of mitigation strategies and under-reporting of active cases, resulting in biased estimation of parameters. To address this, the researchers extended the SIR and SEIR models with two time-varying parameters and performed Bayesian inference using real COVID-19 data. Their approach provided more realistic parameter estimates and reduced uncertainty in 1-week ahead predictions.