Conference Abstract | Volume 8, Abstract ELIC2025232 (Oral 106) | Published: 14 Aug 2025
Oluwafemi Lawal Bisiriyu1,&, Gloria Oluwaseun Olatunji2, Adetumi Adetunji Subulade3, Abimbola Morolayo Olusuyi4
1Department of Statistics, Obafemi Awolowo University, Ile-Ife, Nigeria2, Department of Epidemiology, School of Public Health, University of Medical Sciences, Ondo, Nigeria, 3Infectious Disease & Research Centre Unit, Community Health Dept., Federal Medical Centre, Owo, Ondo state, Nigeria, 4Department of Community Health, College of Health Sciences and Technology, Ijero-Ekiti, Ekiti State, Nigeria
&Corresponding author: Oluwafemi Lawal Bisiriyu, Department of Statistics, Obafemi Awolowo University, Ile-Ife, Nigeria, Email: Bisioluwafemi4@gmail.com
Received: 31 May 2025, Accepted: 09 Jul 2025, Published: 14 Aug 2025
Domain: Infectious Disease Epidemiology
Keywords: Lassa fever, Predictive analytics, Geographic Information Systems (GIS), Epidemic Forecasting, Geospatial mapping, Infectious disease modelling
©Oluwafemi Lawal Bisiriyu et al. Journal of Interventional Epidemiology and Public Health (ISSN: 2664-2824). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article: Oluwafemi Lawal Bisiriyu et al., The digital epidemic shield: Revolutionizing Lassa fever preparedness with predictive analytics. Journal of Interventional Epidemiology and Public Health. 2025;8(ConfProc5):00106. https://doi.org/10.37432/JIEPH-CONFPRO5-00106
Lassa fever is a critical public health concern in West African due to its high prevalence, frequent outbreaks, significant morbidity and mortality. Lassa fever poses a serious threat in endemic regions, particularly in Nigeria, Sierra Leone, Liberia and Guinea. This study used real-time data analytics, geospatial mapping and machine learning to detect and forecast the early stages of Lassa fever outbreaks.
A retrospective observational study using epidemiological data, environmental information and boundary data were taken from public sources. To spot clusters and map cases, spatial analysis was done in R. Weekly case incidence was forecast using Random Forest and Generalized Additive Models by examining historical data and weather variables. A Shiny dashboard linked to GIS technology was arranged to give users the ability to explore risk data interactively and in real time.
The study enhance the identification of Nigerian states and Local Government Areas (LGAs) with recurrent Lassa fever outbreaks, consistent with patterns observed in prior literature. States such as Edo, Ondo, Ebonyi, Bauchi, and Taraba continue exhibiting the highest infection rates. Within these states, LGAs including Esan West and Oredo (Edo State), Owo and Akure South (Ondo State), and Abakaliki (Ebonyi State) emerge as persistent hotspots. The predictive models demonstrate strong accuracy in forecasting new Lassa fever cases, particularly when environmental variables such as rainfall and temperature are integrated (p < 0.01).
Predictive analytics and GIS offer rapid, localized insights into epidemic progression, enabling scalable early warning systems in resource-limited settings. Integrating real-time data and environmental variables enhances forecasting accuracy for Lassa fever. An interactive geospatial platform supports targeted interventions by visualizing high-risk areas. Strengthening LGA-level reporting and upgrading diagnostic infrastructure will improve timely response and outbreak control.
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