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

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.