Investigating the transmission dynamics of SARS-CoV-2 in Nigeria: A SEIR modelling approach

Abstract

This study was designed to investigate the transmission dynamics of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to inform policy advisory vital for managing the spread of the virus in Nigeria. We applied the Susceptible-Exposed-Infectious-Recovered (SEIR)-type predictive model to discern the transmission dynamics of SARS-CoV-2 at different stages of the pandemic; incidence, during and after the lockdown from 27th March 2020 to 22nd September 2020 in Nigeria. Our model was calibrated with the COVID-19 data (obtained from the Nigeria Centre for Disease Control) using the “lsqcurvefit” package in MATLAB to fit the “cumulative active cases” and “cumulative death” data. We adopted the Latin hypercube sampling with a partial rank correlation coefficient index to determine the measure of uncertainty in our parameter estimation at a 99% confidence interval (CI). At the incidence of SARS-CoV-2 in Nigeria, the basic reproduction number (R0) was 6.860; 99%CI [6.003, 7.882]. R0 decreased by half (3.566; 99%CI [3.503, 3.613]) during the lockdown, and R0 was 1.238; 99%CI [1.215, 1.262] after easing the lockdown. If all parameters are maintained (as in after easing the lockdown), our model forecasted a gradual and perpetual surge through the next 12 months or more. In the light of our results and available data, evidence of human-to-human transmission at higher rates is still very likely. A timely, proactive, and well-articulated effort should help mitigate the transmission of SARS-CoV-2 in Nigeria.

Keywords

• COVID-19
• Data fitting
• LHS/PRCC
• Basic reproduction number