Advanced panel data models in examining the impact of COVID-19 vaccines on the number of deaths in Sub-Saharan Africa
DOI:
https://doi.org/10.18203/2394-6040.ijcmph20254020Keywords:
COVID-19, Fixed effect, Pooled OLS regression, Random effect, VaccineAbstract
Background: The invention of vaccines to fight COVID-19 pandemic has been a great technical triumph as the pandemic became a big challenge around the globe. This study aimed to examine the impact of COVID-19 vaccines on the number of deaths in Sub-Saharan Africa.
Methods: The study employed advanced panel data model to evaluate the impact of COVID-19 vaccine from January 2020 to December 2023. The models used are pooled OLS regression, fixed and random effect models. The control factors investigated along with vaccine are COVID-19 cases, number of people vaccinated, cardiovascular death rate, population density and GDP per capita.
Results: The data findings from pooled regression, fixed and random effect models gave R Square of 0.99229, 0.97607 and 0.98307 respectively. Also, the results gave the root mean square error of 2088.69, 22637.08, 13301.06 for pooled regression, fixed and random effect model respectively. The three models indicate that vaccination is the substantial answer in minimizing the number of COVID-19 deaths in Sub-Saharan Africa.
Conclusions: The study found vaccine to be the most scientific strategy to combat the COVID-19 pandemic. There is a need for governments in Sub-Saharan Africa to invest more on vaccination programmes to safeguard people on the future pandemic eruption.
Metrics
References
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