Please use this identifier to cite or link to this item:
http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/29174
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Adeyemi, R.A. | - |
dc.contributor.author | Aliu, Rahima | - |
dc.contributor.author | Olumoh, Jamiu S. | - |
dc.contributor.author | Oguche, Samuel M. | - |
dc.date.accessioned | 2025-05-05T14:52:07Z | - |
dc.date.available | 2025-05-05T14:52:07Z | - |
dc.date.issued | 2025-03 | - |
dc.identifier.citation | Adeyemi et.al (2025) | en_US |
dc.identifier.issn | 2682-5821 | - |
dc.identifier.uri | http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/29174 | - |
dc.description | Mathematical Modelling of COVID-19 Infections in Nigeria | en_US |
dc.description.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)-19, and the reproduction numbers of infection were estimated across states in Nigeria. A log-linear model was also formulated for the exponential growth curve COVID-19 infection, and the basic reproduction numbers were determined as well as determine d. The SIR analysis yielded the median reproduction number of COVID-19 transmission rate across states in Nigeria of 0.04473, range (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 (1), may be due the formula, 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 have been recently implemented before the advent of vaccines. The general findings may reflect the effectiveness of virus control strategies and non-pharmaceutical interventions. | en_US |
dc.description.sponsorship | Self Funded | en_US |
dc.language.iso | en | en_US |
dc.publisher | NIPES Journal of Science and Technology Research | en_US |
dc.subject | Infectious Disease; | en_US |
dc.subject | severe acute respiratory syndrome; | en_US |
dc.subject | Reproduction number; L | en_US |
dc.subject | Log-linear model | en_US |
dc.title | Mathematical Modeling and Estimation of the Regional Variations in COVID-19 Infections Transmission in Nigeria: A Retrospective Data Analysis | en_US |
dc.type | Article | en_US |
Appears in Collections: | Statistics |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
adeyemi-2025 Mathematical Modeling and Estimation of the Regional Variations in COVID-19 .pdf | COVID-19 infections | 4.54 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.