Skip to main content

Evaluation of rodent control to fight Lassa fever based on field data and mathematical modelling

The Natal multimammate mouse (Mastomys natalensis) is the reservoir host of Lassa virus, an arenavirus that causes Lassa haemorrhagic fever in humans in West Africa. Because no vaccine exists and therapeutic options are limited, preventing infection through rodent control and human behavioural measures is currently considered to be the only option. In order to assess the efficacy of rodent control, we performed a 4-year field experiment in rural Upper Guinea and developed a mathematical model to simulate different control strategies (annual density control, continuous density control, and rodent vaccination).

For the field study, rodenticide baits were placed each year in three rural villages, while three other villages were used as controls. Rodents were trapped before and after every treatment and their antibody status and age were determined. Data from the field study were used to parameterize the mathematical model. In the field study, we found a significant negative effect of rodent control on seroprevalence, but this effect was small especially given the effort. Furthermore, the rodent populations recovered rapidly after rodenticide application, leading us to conclude that an annual control strategy is unlikely to significantly reduce Lassa virus spillover to humans. In agreement with this finding, the mathematical model suggests that the use of continuous control or rodent vaccination is the only strategy that could lead to Lassa virus elimination. These field and model results can serve as a guide for determining how long and frequent rodent control should be done in order to eliminate Lassa virus in rural villages.

Categories: Rodent control
Geography: Guinea
Reference: Joachim Mariën, Benny Borremans, Fodé Kourouma, Jatta Baforday, Toni Rieger, Stephan Günther, N’Faly Magassouba, Herwig Leirs & Elisabeth Fichet-Calvet (2019) Evaluation of rodent control to fight Lassa fever based on field data and mathematical modelling, Emerging Microbes & Infections, 8:1, 640-649, DOI: 10.1080/22221751.2019.1605846