Published online by Cambridge University Press: 2nd September 2020
1 Department of Statistics, Federal University of Technology, Akure, Nigeria
2 Population Study Center (NEPO), Universidade Estadual de Campinas, Campinas, Brazil
3 Department of Mathematics, Anchor University, Lagos, Nigeria
4 Institute of Mathematics and Statistics, Federal University of Bahia (UFBA), Salvador, Brazil
5 Faculty of Pharmaceutical Sciences, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
6 Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia
7 Department of Demography and Social Statistics, Covenant University, Ota, Nigeria
8 College of Medicine and Dentistry, James Cook University, Townsville, Australia
* Corresponding author: egayawan@futa.edu.ng
Abstract:
Corona virus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first detected in the city of Wuhan, China in December 2019. Although, the disease appeared in Africa later than other regions, it has now spread to virtually all countries on the continent. We provide early spatio-temporal dynamics of COVID-19 within the first 62 days of the disease’s appearance on the African continent. We used a two-parameter hurdle Poisson model to simultaneously analyze the zero counts and the frequency of occurrence. We investigate the effects of important healthcare capacities including hospital beds and number of medical doctors in the different countries. The results show that cases of the pandemic vary geographically across Africa with notably high incidence in neighboring countries particularly in West and North Africa. The burden of the disease (per 100,000) mostly impacted Djibouti, Tunisia, Morocco and Algeria. Temporally, during the first 4 weeks, the burden was highest in Senegal, Egypt and Mauritania, but by mid-April it shifted to Somalia, Chad, Guinea, Tanzania, Gabon, Sudan, and Zimbabwe. Currently, Namibia, Angola, South Sudan, Burundi and Uganda have the least burden. These findings could be useful in guiding epidemiological interventions and the allocation of scarce resources based on heterogeneity of the disease patterns.
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