COVID-19 Transmission Dynamics Underlying Epidemic Waves in Kenya

Brand, S. P. C.; Ojal, J.; Aziza, R.; Were, V.; Okiro, E.; Kombe, I.; Mburu, C.; Ogero, M.; Agweyu, A.; Warimwe, G. M.; Nyagwange, J.; Karanja, H.; Gitonga, J.; Mugo, D.; Uyoga, S.; Adetifa, I. M. O.; Scott, J. A. G.; Otieno, E.; Murunga, N.; Otiende, M.; Ochola-Oyier, L. I.; Agoti, C. N.; Githinji, G.; Kasera, K.; Amoth, P.; Mwangangi, M.; Aman, R.; Ng'ang'a, W.; Tsofa, B.; Bejon, P.; Keeling, M. J.; Nokes, D. J.; Barasa, E.

Policy decisions on COVID-19 interventions should be informed by a local, regional and national understanding of SARS-CoV-2 transmission. Epidemic waves may result when restrictions are lifted or poorly adhered to, variants with new phenotypic properties successfully invade, or when infection spreads to susceptible sub-populations. Three COVID-19 epidemic waves have been observed in Kenya. Using a mechanistic mathematical model we explain the first two distinct waves by differences in contact rates in high and low social-economic groups, and the third wave by the introduction of a new higher-transmissibility variant. Reopening schools led to a minor increase in transmission between the second and third waves. Our predictions of current population exposure in Kenya (~75% June 1st) have implications for a fourth wave and future control strategies.