Fitting COVID-19 Incidences in Kenya Using Fractional Polynomials and Linear Splines

Abstract

The study provides an in-depth analysis of COVID-19 infections in Kenya, aiming to model the non-linear trajectory of daily cases. The research explores two statistical techniques: fractional polynomials and linear splines, to fit the growth of infection rates over time. COVID-19, which first appeared in Kenya in March 2020, exhibited fluctuating trends in daily infections. The study utilizes infection data collected from March 13, 2020, to June 6, 2021. Descriptive statistics and exploratory data analysis revealed significant variability in daily cases, with the infection trajectory characterized by multiple waves. Fractional polynomial models, known for their flexibility in fitting non-linear relationships, were evaluated at varying degrees to identify the best model for COVID-19 incidence trends. The analysis showed that a second-degree fractional polynomial with powers (1, 2) provided the most accurate fit for the data. The closed test algorithm was applied to confirm the model's suitability. Additionally, linear spline models were employed, partitioning the data into segments and fitting linear splines at each knot point. The model with 19 knots demonstrated superior performance based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), outperforming the fractional polynomial model. The comparison of the two methods concluded that linear splines provided a more precise fit for the infection data, capturing the complex nature of COVID-19's spread in Kenya. The study's findings offer critical insights into the infection dynamics and can aid policymakers in resource allocation and mitigation planning during pandemics. The study recommends further analysis by incorporating more covariates and extending the models to other countries for a comparative understanding of pandemic management strategies.