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http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31124| Title: | Mathematical Modelling and Estimation of the Regional Variations in COVID-19 Infections Transmission in Nigeria: A Retrospective Data Analysis |
| Other Titles: | . |
| Authors: | Adeyemi, Rasheed Aliu, Rahima Jamiu, Olumoh Oguche, Samuel |
| Keywords: | Infectious Disease severe acute respiratory syndrome Reproduction number Log-linear mode |
| Issue Date: | 30-Mar-2025 |
| Publisher: | Journal of Science and Technology Research |
| Citation: | Rasheed A. Adeyemi, Rahima Aliu, Jamiu S. Olumoh and Samuel M. Oguche |
| Abstract: | This study estimated the reproduction number, which was used to determine the rate of spread of a communicable disease at sub-national levels in Nigeria and thereby provides state specific information needed to plan public health interventions. A Susceptible-Infectious-Recovered (SIR) model was first formulated from the compartmentalization of the whole disease population, and the SIR parameters for rate of spread of coronavirus (COVID)-19computedand the reproduction numbers of infection were estimated across statesin Nigeria. A log-linear model was also formulated from anexponential growth curve of COVID-19 infection, and thereafter thebasic reproduction numbers were as well determined.The SIR analysis yielded the median reproduction number of COVID-19 transmission rate across states in Nigeria of 0.04473, rangebetween 0.00082 and 1.2870, while the log-linear model yielded the median reproduction number of 2.003 ranges between 1.9759 and 2.0262 The results further reveals that there were significant disparities emerged when applying these models to the Nigerian context with notable under-estimation in some states perhaps due to under-reported cases at the early stage. The second approach, log-linear model with time-dependent transmission and removal rates to account for possible random errors across Nigeria states and estimates of reproduction numbers across states are greater than one (1tR), may be due to thespecifiedformula. The predictive ability of the log-linear model may be more suitable for modeling the incidence of COVID-19 and other infectious diseases in both the growth and decay phases, as well as for short-term predictions of the growth (or decay) of the number of new cases when no intervention measures hadbeen recently implemented before the advent of vaccines. The general findings may reflect the effectiveness of virus control strategies and non-pharmaceutical interventions |
| URI: | http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31124 |
| ISSN: | 2682-5821 2734-2352 |
| Appears in Collections: | Statistics |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Mathematical Modelling.pdf | 812.42 kB | Adobe PDF | View/Open |
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