Mathematical modelling to forecast the optimization of rapid point-of-care diagnostic tests for Lassa fever in Nigeria

Abstract

Introduction Lassa fever (LF) is an acute viral haemorrhagic fever caused by Lassa virus. The first case of Lassa fever in Nigeria was discovered in Borno State in 1969. The seroprevalence in Nigeria is about 21%. Lassa fever can present nonspecific symptoms, so laboratory diagnosis is important. Mathematical models provide valuable insights into the dynamics of Lassa fever transmission and the impact of various interventions. The study aimed to develop a mathematical model to forecast the optimization of Rapid Point of Care Diagnostic Test (RPOCDT) tests for Lassa fever. Methods A cross-sectional analytical study using a novel deterministic compartmental mathematical model, Susceptible, Exposed, Infectious, and Recovered individuals (SEIR), slightly modified to include the outcomes of Rapid Point of Care Diagnostic Test, was used to represent the population dynamics of Lassa fever transmission in Nigeria. The novel compartmental model was parameterized using real-life and literature-driven data. The parameterized model was simulated using the R programming language, and an individual-based model was integrated to simulate test performance under various conditions. Results The model simulations showed that the Susceptible Population decreases slowly, while the Exposed Population shows a sharp peak early in the dynamics, followed by a rapid decline. RDT Positive Population shows a rapid increase, peaking before declining sharply, while RDT Negative Population shows a rapid increase, then a sharp decline. The Infectious Population shows a steady increase over time. The Uninfectious Population shows an initial rapid increase before stabilizing while Recovered Population rose consistently. Conclusion Higher testing rates (𝛼) and Predictive value positive, PVP (𝜑), represent more effective diagnostic testing, leading to a higher number of detected cases early, which gradually decline as the population becomes immune. Deployment of RPOCDT with a high PVP is recommended.