Conference Abstract | Volume 8, Abstract ELIC2025429 (Poster 034) | Published: 30 Jul 2025
Kenneth Emeka Enwerem1,&, Yetunde Abioye2, Chijioke Mba1, Oluwaseun Badru1,3
1Institute of Human Virology, Abuja, Nigeria, 2Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria, 3Department of Community and Behavioral Health, College of Public Health, University of Iowa, Iowa, IA, USA
&Corresponding author: Kenneth Emeka Enwerem, Institute of Human Virology Nigeria (IHVN), Abuja, Nigeria. Email: kenwerem@ihvnigeria.org
Received: 30 Apr 2025, Accepted: 09 Jul 2025, Published: 30 Jul 2025
Domain: Infectious Disease Epidemiology
Keywords: Lassa fever diagnostics, outbreak response, test accuracy, disease modelling, epidemic control
©Kenneth E. Enwerem 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: Kenneth E. Enwerem et al., Mathematical modelling to optimize point-of-care diagnostic accuracy for Lassa fever control. Journal of Interventional Epidemiology and Public Health. 2025;8(ConfProc5):00178. https://doi.org/10.37432/JIEPH-CONFPRO5-00178
Lassa fever (LF), with a sero-prevalence of 21% and mortality rates of 15–20% in Nigeria, remains a critical public health challenge. Early diagnosis is hindered by non-specific symptoms and limitations in rapid point-of-care (POC) test accuracy, leading to delayed treatment and preventable deaths. This study utilized mathematical modeling to optimize POC diagnostic accuracy, addressing gaps in test sensitivity and coverage to curb transmission.
We adapted a Susceptible-Infectious-Recovered (SIR) compartmental-based model to simulate LF transmission in Ebonyi State, Nigeria. We derived key parameters from literature, including transmission rates, diagnostic positivity/negativity rates (77.4% baseline true positivity), and test coverage. Using scenario analyses we evaluated the impact of varying POC test accuracies (10% and 20% improvements) on disease spread. Geo-spatial and temporal data (2018–2022) informed our model calibration.
Our modeling revealed that improving rapid diagnostic test (RDT) accuracy directly reduces LF transmission. When test sensitivity increased from 77.4% to 97.4%, we detected 16% more cases, effectively removing these individuals from transmission chains. Even a 10% improvement (to 87.4% sensitivity) identified 4% additional cases. The model demonstrates that higher test accuracy leads to increased identification of infections, and with treatment, progressively shrinks the pool of infectious individuals. Conversely, we found that low testing coverage (<50%) delayed outbreak detection by 2-3 weeks, allowing more unchecked transmission. These results show that optimizing RDT sensitivity is crucial for breaking transmission cycles – with the greatest benefits seen when test accuracy approaches 97%.
Optimizing POC diagnostic accuracy is pivotal for interrupting LF transmission. We recommend that the Government of Ebonyi State, and by extension Nigeria, invest in next-generation POC tests with a sensitivity of ≥95%, pre-positioning testings in high-burden regions during peak seasons, and integrating modeling into national surveillance to inform test deployment. These strategies enhance laboratory networks by ensuring timely and accurate diagnostics, ultimately reducing mortality and outbreak risks.
Menu