The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa

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 analyse 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 different countries. The results show that cases of the pandemic vary geographically across Africa with notably high incidence in neighbouring 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.

Keywords: Africa; Bayesian analysis; COVID-19; hurdle Poisson; spatial analysis.

Fig. 1.

(a) Total number of confirmed COVID-19 cases as of 11 April 2020, (b) Distribution of the number of hospital beds (per 10 000), (C) Distribution of the number of physicians (per 10 000).

Fig. 2.

Scatter plot of number of confirmed cases of COVID-19 and healthcare capacities (Number of hospital beds/medical doctors).

Fig. 3.

Spatiotemporal pattern of COVID-19 in Africa based on expected value of the Poisson parameter (mu (μ) parameter). The scales indicate the range of the posterior mean estimates of the parameter.

Fig. 4.

Structured (a) and unstructured (b) spatial effects for the mean of COVID-19 (mu (μ) parameter) in Africa. The scales indicate the range of the posterior mean estimates of the parameter. Note: The structured spatial map was obtained from a component of the model that assumes spatial correlation among the countries implying that neighbouring countries can influence events among themselves which is not the case for two countries that are at distance and share no boundary. The unstructured map assumes independence among the countries.

Fig. 5.

Structured (a) and unstructured (b) spatial effects for the probability of no occurrence of COVID-19 (π parameter) in Africa. The scales indicate the range of the posterior mean estimates of the parameter.

Fig. 6.

Temporal trend of COVID-19 for (a) mean number of occurrence and (b) likelihood of no occurrence.

Fig. A1.

Trace plot for some of the parameters.

Fig. A2.

Plot of observed and expected (predicted) non-zero cases.

Fig. A3.

Burden (cases per 100,000 population) of COVID-19 across Africa as at 16th April 2020.