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Modeling the effect of COVID-19 mortality shock on fertility rates in Kenya using negative binomial regression model
Everlyne Akoth Odero
Drinold Mbete
Abstract
Fertility plays a critical role in shaping population dynamics. Major mortality events, such as pandemics, often influence fertility trends through a variety of mechanisms including behavioral shifts, psychological responses, and socio-economic disruptions. This research applies a Negative Binomial Regression (NBR) model-suitable for overdispersed count data—to examine the influence of the COVID-19 mortality shock on Kenya’s fertility rates using the Kenya Demographic and Health Survey data from 2018 and 2022. Key factors analyzed include marital status, pregnancy loss, contraceptive uptake, sterility, and postpartum insusceptibility. The findings show a statistically significant rise in fertility post-COVID-19, with expected birth counts increasing from 7.146 to 7.488. The dispersion parameters of 1.537 and 1.572 had a minimal difference, indicating that both models captured overdispersion similarly. The study underscores the need to incorporate reproductive behavior into pandemic response frameworks.
