Research Article
Fitting COVID-19 Incidences in Kenya Using Fractional Polynomials and Linear Splines
Damaris Njoroge,
Samuel Mwalili,
Anthony Wanjoya
Issue:
Volume 10, Issue 4, December 2024
Pages:
78-88
Received:
26 September 2024
Accepted:
28 October 2024
Published:
18 November 2024
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.
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 t...
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Research Article
Performance Assessment of Some Count Data Models to Immunization Coverage Data
Usman Aliyu*
,
Umar Usman,
Abubakar Umar Bashar,
Daha Umar Faruk
Issue:
Volume 10, Issue 4, December 2024
Pages:
89-100
Received:
11 July 2024
Accepted:
21 October 2024
Published:
22 November 2024
Abstract: This research evaluates the performance of various count data models, including Poisson Regression (PR), Zero-Inflated Poisson Regression (ZIP), Zero-Truncated Poisson Regression (ZTP), Truncated Negative Binomial Poisson Regression (TNBP), and Negative Binomial Poisson Regression (NBP), using immunization coverage data from the National Primary Health Care Development Agency (NPHCDA). The study focuses on children under 12 months, assessing model fit using Likelihood Ratio (LR), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) criteria. Analysis conducted with STATA indicates that the Truncated Negative Binomial Poisson Regression (TNBP) outperformed other models in fit and efficiency. Both the ZTeeP and TNBP models demonstrated the best fit, with lower AIC (1959.107) and BIC (2037.649) values and higher Pseudo R-squared values (0.0677 for ZTP and 0.0590 for TNBP), compared to standard models. Age was identified as a significant predictor, negatively associated with immunization status, implying that older infants in the under-12-month category are less likely to receive all vaccinations. The ZTP model showed significant positive effects for antigens such as HepB0, OPV0, BCG, and Measles, with age having a significant negative association. The findings highlight the importance of selecting appropriate statistical models for accurate public health data analysis, enhancing decision-making in immunization programs.
Abstract: This research evaluates the performance of various count data models, including Poisson Regression (PR), Zero-Inflated Poisson Regression (ZIP), Zero-Truncated Poisson Regression (ZTP), Truncated Negative Binomial Poisson Regression (TNBP), and Negative Binomial Poisson Regression (NBP), using immunization coverage data from the National Primary He...
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