Modelling COVID-19 Transmission Dynamics in Ghana

TL;DR

This study develops and analyzes a detailed mathematical model (CoVCom9) for COVID-19 transmission in Ghana, estimating the basic reproduction number and identifying key factors influencing disease spread.

Contribution

The paper introduces a novel nine-compartment SEIR model tailored for Ghana and provides a comprehensive mathematical analysis including stability and sensitivity assessments.

Findings

Estimated R0 of 3.110 with confidence interval 2.042–3.240.

Six key parameters significantly influence R0, including testing rate.

Model fits well with reported COVID-19 data in Ghana.

Abstract

In late 2019, a novel coronavirus, the SARS-CoV-2 outbreak was identified in Wuhan, China and later spread to every corner of the globe. Whilst the number of infection-induced deaths in Ghana, West Africa are minimal when compared with the rest of the world, the impact on the local health service is still significant. Compartmental models are a useful framework for investigating transmission of diseases in societies. To understand how the infection will spread and how to limit the outbreak. We have developed a modified SEIR compartmental model with nine compartments (CoVCom9) to describe the dynamics of SARS-CoV-2 transmission in Ghana. We have carried out a detailed mathematical analysis of the CoVCom9, including the derivation of the basic reproduction number, $\mathcal{R}_{0}$. In particular, we have shown that the disease-free equilibrium is globally asymptotically stable when $\mathcal{R}_{0}<1$ via a candidate Lyapunov function. Using the SARS-CoV-2 reported data for confirmed-positive cases and deaths from March 13 to August 10, 2020, we have parametrised the CoVCom9 model. The results of this fit show good agreement with data. We used Latin hypercube sampling-rank correlation coefficient (LHS-PRCC) to investigate the uncertainty and sensitivity of $\mathcal{R}_{0}$ since the results derived are significant in controlling the spread of SARS-CoV-2. We estimate that over this five month period, the basic reproduction number is given by $\mathcal{R}_{0} = 3.110$, with the 95\% confidence interval being $2.042 \leq \mathcal{R}_0 \leq 3.240$, and the mean value being $\mathcal{R}_{0}=2.623$. Of the 32 parameters in the model, we find that just six have a significant influence on $\mathcal{R}_{0}$, these include the rate of testing, where an increasing testing rate contributes to the reduction of $\mathcal{R}_{0}$.