Modeling the effect of COVID-19 mortality shock on fertility rates in Kenya using negative binomial regression model

Linus Omina Wakhu; Everlyne Akoth Odero; Drinold Mbete

doi:10.51867/ajernet.maths.6.2.67

Authors

Linus Omina Wakhu Department of Mathematics, Masinde Muliro University of Science and Technology, P.O BOX 190-50100, Kakamega, Kenya
Everlyne Akoth Odero Department of Mathematics, Masinde Muliro University of Science and Technology, P.O BOX 190-50100, Kakamega, Kenya https://orcid.org/0000-0001-6633-7421
Drinold Mbete Department of Mathematics, Masinde Muliro University of Science and Technology, P. O. Box 190-50100, Kakamega, Kenya https://orcid.org/0000-0002-3975-2141

DOI:

https://doi.org/10.51867/ajernet.maths.6.2.67

Keywords:

Fertility rates, Mortality shock, Negative Binomial Regression

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.

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