Early Decision Indicators for Foot-and-Mouth Disease Outbreaks in Non-Endemic Countries

Abstract

Disease managers face many challenges when deciding on the most effective control strategy to manage an outbreak of foot-and-mouth disease (FMD). Decisions have to be made under conditions of uncertainty and where the situation is continually evolving. In addition, resources for control are often limited. A modeling study was carried out to identify characteristics measurable during the early phase of a FMD outbreak that might be useful as predictors of the total number of infected places, outbreak duration, and the total area under control (AUC). The study involved two modeling platforms in two countries (Australia and New Zealand) and encompassed a large number of incursion scenarios. Linear regression, classification and regression tree, and boosted regression tree analyses were used to quantify the predictive value of a set of parameters on three outcome variables of interest: the total number of infected places, outbreak duration, and the total AUC. The number of infected premises (IPs), number of pending culls, AUC, estimated dissemination ratio, and cattle density around the index herd at days 7, 14, and 21 following first detection were associated with each of the outcome variables. Regression models for the size of the AUC had the highest predictive value (R2 = 0.51–0.9) followed by the number of IPs (R2 = 0.3–0.75) and outbreak duration (R2 = 0.28–0.57). Predictability improved at later time points in the outbreak. Predictive regression models using various cut-points at day 14 to define small and large outbreaks had positive predictive values of 0.85–0.98 and negative predictive values of 0.52–0.91, with 79–97% of outbreaks correctly classified. On the strict assumption that each of the simulation models used in this study provide a realistic indication of the spread of FMD in animal populations. Our conclusion is that relatively simple metrics available early in a control program can be used to indicate the likely magnitude of an FMD outbreak under Australian and New Zealand conditions.

Control fast or control smart: When should invading pathogens be controlled?

“…The closely-related first 14 days spatial spread parameter, FFS, was developed subsequently [ 55 ]. Outbreak simulations indicate that there is a strong positive correlation between FFI/FFS and variables indicative of outbreak severity such as final size and outbreak duration [ 56 , 57 ]. Further studies have strengthened the evidence that simple metrics available early in outbreaks are able to predict final epidemic characteristics [ 56 ].…”

“…Outbreak simulations indicate that there is a strong positive correlation between FFI/FFS and variables indicative of outbreak severity such as final size and outbreak duration [ 56 , 57 ]. Further studies have strengthened the evidence that simple metrics available early in outbreaks are able to predict final epidemic characteristics [ 56 ]. Such metrics can therefore form the basis of decision-making tools that present important information about the possible impacts of control interventions in a simplified format for policy-makers [ 57 ].…”

Evaluating vaccination strategies to control foot-and-mouth disease: a country comparison study

“…Nevertheless, our results support previous studies and outbreak findings [10, 11] that highlight the gains to be achieved from an early decision to vaccinate. Further studies to help understand the early predictors of large-scale outbreaks would assist decision makers when considering additional disease management strategies such as vaccination [33, 34].…”

Development of a transboundary model of livestock disease in Europe

“…As the EuFMDiS and AADIS modelling frameworks have a common underlying code baseline, EuFMDiS inherits from previous AADIS verification and validation activities, and modelling studies (Bradhurst, 2015;Bradhurst et al, 2015;Bradhurst et al, 2016;Garner et al, 2016;Van Andel et al, 2018;Bradhurst et al, 2019;Firestone et al, 2019;Firestone et al, 2020).…”