MATHEMATICAL MODELING OF COVID-19 TRANSMISSION DYNAMICS IN NIGERIA

Abstract

The coronavirus disease (COVID-19) pandemic caused by Severe Acute Respiratory
Syndrome Corona virus-2 SARS-CoV-2, has posed significant health and socio-economic
challenges worldwide, including Nigeria. Understanding the disease's dynamics is essential
for effective public health interventions. This study develops a mathematical model to
analyze COVID-19 transmission in Nigeria, considering vaccination,
isolation/hospitalization, and recovery processes. A compartmental SVEIHR (Susceptible,
Vaccinated, Exposed, Infected asymptomatic, Infected symptomatic, Hospitalized, and
Recovered model was formulated, dividing the population into susceptible, vaccinated,
exposed, asymptomatic, symptomatic, hospitalized, and recovered groups. The model's
equilibrium points were analyzed mathematically for stability. Key epidemiological
parameters including the basic reproduction number R0, were derived to assess disease
progression. Numerical simulations were conducted using MAPLE 18.0 software to evaluate
vaccination and hospitalization impacts. The model demonstrated that solutions remained
non-negative and bounded under epidemiologically realistic conditions. A disease-free
equilibrium was stable when R0< 1, indicating the potential for eradication under controlled
conditions. Simulations showed that increased vaccination rates reduced susceptible and
infectious populations, while hospitalization effectively curtailed symptomatic and
asymptomatic cases. The SVEIHR model underscores the critical role of vaccination and
hospitalization in controlling COVID-19. These findings provide valuable insights for
policymakers to optimize intervention strategies and mitigate the pandemic's impact in
Nigeria.