Demography, social contact patterns and the COVID-19 burden in different settings of Ethiopia: a modeling study

Abstract

BackgroundCOVID-19 spread may have a dramatic impact in countries with vulnerable economies and limited availability of, and access to, healthcare resources and infrastructures. However, in sub-Saharan Africa a low prevalence and mortality have been observed so far.MethodsWe collected data on individuals’ social contacts in Ethiopia across geographical contexts characterized by heterogeneous population density, work and travel opportunities, and access to primary care. We assessed how socio-demographic factors and observed mixing patterns can influence the COVID-19 disease burden, by simulating SARS-CoV-2 transmission in remote settlements, rural villages, and urban neighborhoods, under the current school closure mandate.ResultsFrom national surveillance data, we estimated a net reproduction number of 1.62 (95%CI 1.55-1.70). We found that, at the end of an epidemic mitigated by school closure alone, 10-15% of the overall population would have been symptomatic and 0.3-0.4% of the population would require mechanical ventilation and/or possibly result in a fatal outcome. Higher infection attack rates are expected in more urbanized areas, but the highest incidence of critical disease is expected in remote subsistence farming settlements.ConclusionsThe relatively low burden of COVID-19 in Ethiopia can be explained by the estimated mixing patterns, underlying demography and the enacted school closures. Socio-demographic factors can also determine marked heterogeneities across different geographical contexts within the same country. Our findings can contribute to understand why sub-Saharan Africa is experiencing a relatively lower attack rate of severe cases compared to high income countries.

A quantitative assessment of epidemiological parameters required to investigate COVID-19 burden

“…Mathematical modeling has been one of the cornerstones in the response to the COVID-19 pandemic [1][2][3][4][5][6][7][8][9][10][11]. To provide solid estimates, models need to be properly calibrated based on empirical evidence [7,[12][13][14][15].…”

Individual’s daily behaviour and intergenerational mixing in different social contexts of Kenya

“…These findings remark the importance of gathering further data to shed light on the differences in mixing patterns between different social contexts, especially in countries characterized by large socio-economic differentials at varying levels of urbanization. Indeed, individuals living in diverse settings along the rural-urban gradient are shown to suffer diverging morbidity and mortality paths because of the interplay of environmental and socio-economic factors [12][13][14][15] . is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint…”

“…Conversely, evidence from the current COVID-19 pandemic strongly suggests that older adults, along with people affected by chronic conditions, are at higher risk of experiencing severe disease and death [23][24][25][26] , while children are less susceptible to the infection and young adults are considered silent spreaders as they are more likely to be asymptomatic or paucisymptomatic compared to older ages 27,28 . To add to the clinical challenges, also the contextual complexities, especially in LMICs, have to be considered where the huge heterogeneity characterizing people mobility and access to work opportunities, and the daily routine of individuals living in different socio-economic conditions are critical determinants of the epidemiological outcomes 7,15,19 .…”