Introduction: Lassa fever is a viral haemorrhagic illness that remains endemic in Nigeria, where it presents with symptoms that often resemble other common febrile diseases. Because clinical features alone are insufficient for reliable diagnosis, timely laboratory confirmation is essential for effective case detection and outbreak control. This study developed a mathematical modelling approach to evaluate how rapid point-of-care diagnostic tests could be optimised to improve early detection of Lassa fever in Nigeria. Methods: A cross-sectional analytical study was conducted using nationally available epidemiological data. A deterministic compartmental model based on the Susceptible–Exposed–Infectious–Recovered structure was adapted to incorporate the outcomes of rapid point-of-care testing. An individual-based modelling component was added to simulate diagnostic performance under varying epidemiological and operational conditions. Model simulations were conducted using R statistical software, and sensitivity analyses were performed to assess the influence of diagnostic accuracy parameters. Results: Model simulations showed that increasing the probability that a positive test correctly identifies an infected individual results in earlier and more reliable case detection. When the test had a high true-positive rate, the number of infectious individuals detected rose sharply at the onset of transmission but declined over time as immunity increased within the population. Enhanced diagnostic accuracy consistently reduced the pool of undetected infections and improved downstream control efforts. Conclusion: The modelling suggests that rapid diagnostic testing with high true-positive performance substantially strengthens early detection of Lassa fever and contributes to more effective outbreak management. Prioritising deployment of rapid point-of-care tests with strong predictive value will improve surveillance sensitivity, reduce diagnostic delays, and support national efforts to control Lassa fever transmission. Further operational research is recommended to evaluate implementation in real-world settings.
Awoyale et al. (Tue,) studied this question.