Kenya’s Waves Driven by Socio-economic Differences and Variants

Phot by Martin Sanchez on Unsplash

By combining COVID surveillance data with population mobility data from smartphones, infectious disease modellers have explained the evolution of the first three COVID waves to hit Kenya. 

Sequential waves of transmission through different socio-economic groups, followed by infection boosted by the introduction of new variants.

In order to forecast future outbreaks, the team had to develop a model to explain current waves. The work brought together COVID antibody survey data, PCR case data, genomic variant data and Google mobility data, seeking to find an explanation to the waves of COVID in Kenya. The aim was to then provide policy-based forecasts on future waves in the country based on the model findings.

Lower socio-economic groups have been identified as vulnerable to SARS-CoV-2 in the global South due to living in densely populated informal settlements, with reduced access to sanitation, and relying on daily mobility for informal employment. In contrast, those from higher socio-economic groups with job security can work from home, physically distance and readily access water and sanitation, thereby decreasing transmission.
The modelling results show that differences in mobility and contact rates between high and low socio-economic groups within Kenya explain the differences between the first and second waves. In the initial phase of the epidemic (from March 2020), individuals in high socio-economic groups could reduce their mobility and contact rates, but individuals in lower socio-economic groups could not. This resulted in transmission among individuals in lower socio-economic groups that was observed as the first wave in urban centres. As these individuals recovered from infection and became immune, at least temporarily, the first wave ended.

By the onset of the second wave (from October 2020), individuals in high socio-economic groups had increased their contact rates and mobility. This led to transmission among individuals in the high socio-economic groups, and also involved rural as well as urban areas. The second wave then appeared to end as individuals cleared the virus and became immune, at least for the time being. However, the advent of the more infectious Beta and Alpha variants resulted in a third wave among both high and low socio-economic groups (from March 2021).

 In many other African countries, there have been multiple waves that are not fully explained by timing of restrictions, and as they have similar urban socio-economic groupings, the researchers speculate that these explanations may have wider applicability. Understanding the causation of such multiple waves is critical for forecasting hospitalisation demand and the likely effectiveness of interventions including vaccination strategy.

Dr Samuel Brand from the University of Warwick said: “This is one of the first studies to consider detailed predictions of the dynamics of COVID across multiple waves in tropical sub-Saharan Africa. We believe this sets a new standard for the type of public health modelling work that can be conducted in real-time in developing countries.”

Dr John Ojal of KEMRI-Wellcome Trust Research Programme said: “There are highly detailed modelling studies of this nature in High Income Countries, but there have been none previously in tropical sub-Saharan Africa.”

The study has been published in the journal Science.

Source: University of Warwick

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