Published: 14 October 2021
Combining data on antibody prevalence, PCR test results, genomic surveillance and population mobility from smartphones has helped to explain the evolution of the first three Covid-19 waves that have affected Kenya since the start of the pandemic.
Infectious disease modelling jointly undertaken by the University of Warwick and Kenya’s KEMRI-Wellcome Trust Research Programme explains the COVID-19 pandemic in the country as sequential waves of transmission through different socio-economic groups, followed by infection boosted by the introduction of new variants.
The study, published in the journal Science, received funding from the NIHR Global Health Research programme and the Joint Initiative on Research in Epidemic Preparedness and Response, a partnership between Wellcome and the Foreign, Commonwealth and Development Office (FCDO).
Forecasting the future spread of COVID-19 requires an understanding of past patterns. The team used a mathematical model to test explanations for the first three COVID-19 epidemic waves in Kenya.
The model brought together COVID-19 antibody survey data, PCR case data, genomic variant data and Google mobility data for the first time in order to try and find an explanation for the waves of COVID-19 in Kenya. The aim was to then forecast future waves in the country based on the findings.
Lower socio-economic groups have been identified as particularly vulnerable to SARS-CoV-2 in the Global South due to being based in areas of high population density, having reduced access to sanitation and being dependent on informal employment requiring daily mobility. 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 results from the modelling show that the first and second waves of infections are explained by differences in mobility and contact rates between high and low socio-economic groups within Kenya. In the initial phase of the epidemic (from March 2020), individuals in high socio-economic groups were able to reduce their mobility and contact rates, but individuals in lower socio-economic groups were 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 time 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 that was observed as the second wave, which involved rural as well as urban areas. It appears that the second wave then ended as individuals cleared the virus and became, at least temporarily, immune. However, the new Beta and Alpha variants introduced into Kenya were more infectious and led to a third wave among all socio-economic groups (from March 2021).
Multiple waves have been observed in many other African countries that do not appear to be completely explained by the timing of restrictions, and since they also have in common similar socio-economic groupings in urban centres, the scientists speculate that these explanations may apply more widely. Understanding the causation of such multiple waves is critical for forecasting hospitalisation demand and the likely effectiveness of interventions including vaccination strategy.
Professor Matt Keeling, Director of the Zeeman Institute at the University of Warwick, said: “Studies in High Income Countries find the assumption of even mixing of the population works well in explaining the transmission of SARS-CoV-2 in those countries. Clearly, this is not always the case as shown in our study of Kenya, and variation in spread by socio-economic group might prevail in other low-income settings.”
Professor Edwine Barasa, Director of the Nairobi hub, KEMRI-Wellcome Trust Research Programme said: “I am not surprised by the findings of marked disparity of transmission by socio-economic group in Kenya where there is a very high proportion of the urban population working in the informal sector that do not have the luxury of reducing contacts but need to find work on a day-to-day basis.”
This project has been funded by the NIHR Global Health Research Programme
For more information, please see the project page