New modeling highlights role of socio-economic status in transmission of Covid-19 in Kenya


Combining data on antibody prevalence, PCR test results, genomic monitoring and population mobility from smartphones allowed infectious disease modellers to explain the evolution of the first three waves of Covid-19 that have affected Kenya since the start of the pandemic.

Modeling jointly conducted by the University of Warwick and the KEMRI-Wellcome Trust research program in Kenya explains the COVID-19 pandemic in Kenya as sequential waves of transmission across different socio-economic groups, followed by heightened infection by introducing new variants.

The study was published in the journal Science and received funding through the Joint Initiative on Research in Epidemic Preparedness and Response, a collaboration between Wellcome and the Foreign, Commonwealth and Development Office (FCDO), as well as funding from the Institute National Health Research (NIHR).

Predicting 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 waves of COVID-19 outbreaks in Kenya.

The work, undertaken jointly by scientists at the University of Warwick and the KEMRI-Wellcome Trust research program in Kenya, for the first time brought together investigative data on COVID-19 antibodies, PCR case data, genomic variant data and mobility data from Google, seeking an explanation for the waves of COVID-19 in Kenya. The aim was then to provide political forecasts on future waves in the country based on the results of the model.

Lower socio-economic groups have been identified as vulnerable to SARS-CoV-2 in countries of the South due to residence in informal settlements with high population density, reduced access to sanitation and dependency informal employment which requires daily mobility. In contrast, those from higher socioeconomic groups with job security can work from home, physically remotely and have easy access to water and sanitation, thereby decreasing transmission.

The modeling results show that the first and second waves of infections can be explained by differences in mobility and contact rates between high and low socioeconomic groups in Kenya. In the initial phase of the epidemic (from March 2020), individuals from higher socioeconomic groups were able to reduce their mobility and contact rates, but individuals from lower socioeconomic groups were not able to. This resulted in transmission among individuals of lower socioeconomic groups which was observed as the first wave in urban centers. When these people recovered from the infection and became immune, at least temporarily, the first wave ended.

By the time of the second wave (from October 2020), individuals from higher socioeconomic groups had increased their contact rates and mobility. This led to transmission among individuals of high socioeconomic groups which was observed as the second wave, and in addition the second wave involved rural areas as well as urban areas. It appears that the second wave then ended when individuals cleared the virus and became, at least temporarily, immune. However, the new beta and alpha variants introduced in Kenya were more contagious and led to a third wave among high and low socioeconomic groups (as of March 2021).

Multiple waves have been observed in many other African countries which do not appear to be fully explained by the timing of the restrictions, and since they also have similar socio-economic groupings in urban centers in common, scientists speculate that these explanations may apply more widely. Understanding the cause of these multiple waves is essential for predicting hospital demand and the likely effectiveness of interventions, including vaccination strategy.

Dr Samuel Brand of the Zeeman Institute for Systems Biology and Infectious Disease Epidemiological Research (SBIDER) and the School of Life Sciences at the University of Warwick said: “This is one of the first studies to consider predictions. details of the dynamics of Covid-19 over several waves in tropical sub-Saharan Africa. We believe this sets a new standard for the kind of public health modeling work that can be done in real time in developing countries.

Dr John Ojal of the KEMRI-Wellcome Trust research program said: “There are very detailed modeling studies of this nature in high-income countries, but none before in tropical sub-Saharan Africa.

Studies in high-income countries show that the uniform population mix hypothesis works well in explaining the transmission of SARS-CoV-2 in these countries. Obviously, this is not always the case, as our Kenya study shows, and variation in spread by socio-economic group might be prevalent in other low-income settings. “

Professor Matt Keeling, Director, Zeeman Institute, University of Warwick

Professor Edwine Barasa, Nairobi Center Director, KEMRI-Wellcome Trust Research Program, said: “I am not surprised by the results of a marked disparity in transmission by socio-economic group in Kenya where there is a very high proportion of the urban population working in the informal sector who do not have the luxury of reducing contacts but need to find work on a daily basis. “


Journal reference:

Brand, SPC, et al. (2021) Transmission dynamics of COVID-19 underlying epidemic waves in Kenya. Science.


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