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All Journal Jurnal Biometrika dan Kependudukan (Journal of Biometrics and Population) Jambura Journal of Biomathematics (JJBM)
Udom, Collins F.
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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UNDERREPORTING IN INFECTIOUS DISEASE CASES: A TIME SERIES REGRESSION-SIR APPROACH Akpan, Emmanuel Alphonsus; Ikono, Florence Gregory; Udom, Collins F.; Umondak, Uduakobong James; Ikeh, Onyekachukwu Henry; Nyong, Emem Godwin
Jurnal Biometrika dan Kependudukan Vol. 14 No. 1 (2025): JURNAL BIOMETRIKA DAN KEPENDUDUKAN
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jbk.v14i1.2025.22-33

Abstract

Failure to account for the underreporting of infectious disease cases distorts the understanding of infectious disease dynamics. Underreporting creates a false sense of security, allowing the disease to persist or resurge and undermining the effectiveness of public health interventions. This study aims to address underreporting and identify the underlying distribution that best describes the Coronavirus disease 2019 (COVID-19) cases in Nigeria. A Time Series Regression Susceptible-Infected-Recovered (TSIR) model, incorporating Poisson, Gaussian, and Quasi-Poisson distributions with various link functions, was applied to weekly cumulative COVID-19 case data. This dataset spans from February 28, 2020, to July 3, 2022, and includes 110 weekly records. It was sourced from the Nigerian Centre for Disease Control (NCDC) through publicly available weekly epidemiological reports. Microsoft Office Excel 2016 was utilized to collate the database, and the NCDC’s online platform served as the primary data source. The data were divided into two sets: training data from February 28, 2020, to March 20, 2022, comprising 100 cases for modeling TSIR, and testing data from March 27, 2022, to July 3, 2022, encompassing 10 weekly cases for model performance evaluation. These research findings revealed that the reporting rate of COVID-19 data under study is about 35%, indicating underreporting. When accounting for underreporting, the transmission rate was reduced by approximately 0.15. The quasi-Poisson distribution with the log function was the best at describing the distribution of the incidence cases. The study established that the COVID-19 incidence cases in Nigeria are underreported and follow a quasi-Poisson distribution.
Mathematical modelling for the transmission dynamics of Rift Valley fever virus with human host Oguntolu, Festus Abiodun; Yavalah, Deborah W.; Udom, Collins F.; Peter, Olumuyiwa James; Oshinubi, Kayode
Jambura Journal of Biomathematics (JJBM) Volume 3, Issue 1: June 2022
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjbm.v3i1.14160

Abstract

Rift Valley Fever (RVF) is a viral zoonosis spread primarily by mosquitos that primarily affects livestock but has the potential to affect humans. Because of its potential to spread quickly and become an epidemic, it has become a public concern. In this article, the transmission dynamics of RVF with mosquito, livestock and human host using a compartmental model is studied and analyzed. The basic reproduction number R0 is computed using next generation matrix and the disease-free equilibrium state is found to be locally asymptotically stable if R0 1 which implies that rift valley fever could be put under control in a population where the reproduction number is less than 1. The numerical simulations give insightful results to further explore the dynamics of the disease based on the effect of three interventions; efficacy of vaccination, culling of livestock and trapping of mosquitoes introduced in the model.
Co-Authors Akpan, Emmanuel Alphonsus Ikeh, Onyekachukwu Henry Ikono, Florence Gregory Nyong, Emem Godwin Oguntolu, Festus Abiodun Oshinubi, Kayode Peter, Olumuyiwa James Umondak, Uduakobong James Yavalah, Deborah W.