Impacts of disease surveillance frequency on understanding and controlling Rift Valley fever virus

Abstract

Effective livestock disease surveillance faces numerous socio-economic and disease-specific challenges, particularly within resource-limited settings. Rift Valley fever (RVF), a zoonotic vector-borne disease which is of growing threat to human and veterinary health, exemplifies these challenges, consequently leading to irregular or infrequent surveillance. While mathematical models frequently utilise these surveillance data to understand disease dynamics and assess the effectiveness of disease control measures, the impact of surveillance frequency on these model-based assessments is not clear. To address this, we used a livestock RVF virus infection model, which incorporates spatial and age structures, livestock movement, and environmental factors. We then generated synthetic cross-sectional seroprevalence surveys with varying frequencies from outbreak scenarios inferred from empirical serological data from the Comoros archipelago. By refitting the model to these synthetic data, we found that lower surveillance frequency was associated with increased uncertainties in understanding disease biology, inferring past outbreak timing, predicting future outbreak size, and recommending optimal control measures. In particular, once serological surveillance was less frequent than annually, the ability to distinguish spatio-temporal patterns of disease transmission and forecast trends diminished. These findings emphasise the need for adequately frequent surveillance data to ensure robust model-based analyses which inform effective preparedness and control strategies for livestock diseases within resource-limited settings. Author summaryDisease surveillance underpins mathematical models that are used to understand the emergence, spread and persistence of disease, and assess and compare the impacts of disease control measures. However, regular surveillance is a particular challenge for livestock diseases in resource-limited settings, such as those affected by Rift Valley fever. Here, our work provides an illustrative example of how surveillance frequency impacts our ability to understand disease outbreaks and to decide which control measures are most effective at combatting disease, using outbreak scenarios for Rift Valley fever in the Comoros archipelago. By simulating from a mathematical model informed by different surveillance frequencies, we found that less frequent surveillance was associated with greater uncertainty in model predictions, and demonstrated that this may distort epidemiological understanding and future control recommendations of a disease. Our work has crucial implications for animal health decision-making, highlighting the importance of consistent and frequent surveillance to improve disease management and prioritise disease control measures.