Study population and setting
Authors constructed a data-driven dynamic transmission model to estimate the transmission and seroprevalence of SARS-CoV-2 virus infection in Kenya. The authors developed a simple but effective mathematical model to use the relatively sparse data available to model transmission, using seroprevalence estimates, mobility trends data, and national PCR testing data. In addition to estimating cumulative infections, the authors estimated an effective size parameter, which represents a percentage reduction in the size of the population at risk of infection due to population heterogeneity in connectivity and risk. A smaller effective population size lowers the expected herd immunity threshold, the theoretical threshold value where there is enough immunity in the population to provide indirect protection to susceptible individuals.
Summary of Main Findings
Forty-point-nine percent of Nairobi (95% CI, 24.3-54.7%), 33.8% (95% CI, 23.7-46.5%) of Mombasa are estimated to have been infected with SARS-CoV-2 by August 1, 2020. Authors inferred Nairobi’s effective population size to be 77% of the total population, while Mombasa’s was inferred to be 56% of the total population. These estimates suggest that the epidemic has largely peaked across Kenya, with Nairobi peaking in late July/early August 2020, Mombasa peaking in mid-June, and the rest of the country peaking between August and September. The authors also assume, based on the estimated effective population sizes, that Kenya is nearing herd immunity. The infection fatality ratio in Nairobi was estimated to be 0.014% (95% CI, 0.010-0.023%) and 0.02% (95% CI, 0.014-0.028%) in Mombasa, contrasting with the age-adjusted expected estimate of 0.26%.
This study developed an innovative yet relatively simple data-driven model for using multiple sources of available data to characterize the pandemic in Kenya. The authors validated the transmission estimates against reported deaths, demonstrating good fit.
The model relied on a strong assumption that infectious contact rates were fully correlated with mobility as estimated by Google mobility trends. Thus, their estimates of the time varying reproductive number (Rt) correlated fully with these mobility estimates. This is particularly problematic because transmission has been demonstrated to not fully correlate with increasing mobility trends, as people have improved their ability to limit transmission through social distancing, masks, and other means. As a result, the authors may be overestimating transmission following lockdown in Kenya, resulting in an overestimate of infections and seroprevalence. Additionally, this model is highly reliant on estimates of seroprevalence at two time points from a survey that took place in May and early June. Because of this time gap and limited population representation, it is very possible these two estimates are not generalizable and too long ago to truly inform infection later in the summer and across these populations. Finally, the authors do not account for COVID-19 death underreporting. While the authors state this as a limitation, their estimate of IFR is grounded in this assumption, thus the IFR is very likely underestimated.
This study provides the first complete assessment of SARS-CoV transmission for Kenya and one of the first for a country in Africa, where substantial questions remain as to why the pandemic has progressed differently than originally expected. This study cleverly combines mobility, seroprevalence, and PCR testing data to estimate transmission and seroprevalence, and provides an important and useful blueprint for doing this in other countries where sparse data have limited the understanding of the pandemic.
This review was posted on: 20 November 2020