Speaker
Description
Effective control of livestock diseases often produces a sub-critical phase – outbreaks occur but die out before becoming epidemic – in which standard epidemic models are poorly suited to characterising transmission dynamics. Foot-and-Mouth Disease (FMD) in India offers a clear example: national control programs have been in place and intensifying since 2012, yet the disease persists across multiple states, including Karnataka. Branching processes are a natural framework for this disease regime, capturing the stochastic extinction dynamics that define sub-critical spread.
We fit a branching process model to FMD outbreak data from Karnataka, using the offspring distribution to characterise how transmission intensity and individual-level variation have changed under successive control policies. Our best-fit model implies substantially smaller outbreak sizes and reduced variance in transmission compared to the pre-control period, consistent with a population-level reduction in R below 1. We further apply this model to derive the minimally-sufficient surveillance effort required to detect transient outbreaks before fade-out, with implications for establishing eradication and designing post-control monitoring programs.
