Modelling the Impact of Reporting Rates on Outbreak Detection With Implications for Managing Emergency Animal Diseases

The detection and surveillance of emergency animal disease outbreaks play a crucial role in their management. However, monitoring such diseases can often rely on farmers self‐reporting animal infection. If there are disincentives for farmers to report disease, this can lead to delayed reporting, which could negatively impact outbreak management outcomes. To understand the range of impacts farmer reporting rates could have on the spread and management of outbreaks, we model the spread of animal disease using an agent‐based model. We investigate how varying the reporting rate for infected properties can impact firstly the total number of properties depopulated for various management strategies and secondly which strategy is optimal. Our model considers disease transmission occurring both within a property, through animal‐to‐animal transmission and between properties, through fomite dispersal and random movement of animals. Using this model, we compare the number of properties depopulated under four strategies: depopulating infected properties and animal movement restrictions combined with (1) ring culling, (2) ring testing, (3) ring vaccination (with a perfect vaccine) and (4) ring vaccination (with an imperfect vaccine). Given our assumptions and model structure, we find that outbreaks spread in clusters around an initially infected property, with additional clusters being seeded early by the random movement of animals between properties. We simulate the impact of reporting rates on a range of outbreak indicators, and we find it has the biggest impact on the time to detection. This time decreases as we increase the reporting rate, but with diminishing returns. We find the timing of outbreak detection impacts the total properties depopulated under all management strategies similarly, as outbreaks detected earlier ultimately lead to smaller outbreaks. As such, we find that varying the reporting rate had minimal impact on which strategy was optimal for a given simulation. Our modelling demonstrates how human behaviour, such as reporting rates, can impact the outcomes from managing emergency animal disease outbreaks. While exact behaviour cannot be predicted for future outbreaks, we can prepare for the next outbreak of emergency animal disease by investigating which management strategies can be robust to a variety of human behaviours.