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Informing adaptive management strategies: Evaluating a mechanism to predict the likely qualitative size of foot-and-mouth disease outbreaks in New Zealand using data available in the early response phase of simulated outbreaks.

Robert L SansonZhidong D YuThomas G RawdonMary van Andel
Published in: Transboundary and emerging diseases (2020)
The objective of the study was to define and then evaluate an early decision indicator (EDI) trigger that operated within the first 5 weeks of a response that would indicate a large and/or long outbreak of FMD was developing, to be able to inform control options within an adaptive management framework. To define the EDI trigger, a previous dataset of 10,000 simulated FMD outbreaks in New Zealand, controlled by the standard stamping-out approach, was re-analysed at various time points between Days 11 and 35 of each response to find threshold values of cumulative detected infected premises (IPs) that indicated upper quartile sized outbreaks and estimated dissemination rate (EDR) values that indicated sustained spread. Both sets of thresholds were then parameterized within the InterSpread Plus modelling framework, such that if either the cumulative IPs or the EDR exceeded the defined thresholds, the EDI trigger would fire. A new series of simulations were then generated. The EDI trigger was like two diagnostic tests interpreted in parallel, with the diagnostic outcome positive if either test was positive at any time point between Days 11 and 35 inclusive. The diagnostic result was then compared to the final size of each outbreak, to see if the outbreak was an upper quartile outbreak in terms of cumulative IPs and/or final duration. The performance of the EDI trigger was then evaluated across the population of outbreaks, and the sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV) were calculated. The Se, Sp, PPV and NPV for predicting large outbreaks were 0.997, 0.513, 0.404 and 0.998, respectively. The study showed that the EDI trigger was very sensitive to detecting large outbreaks, although not all outbreaks predicted to be large were so, whereas outbreaks predicted to be small invariably were small. Therefore, it shows promise as a mechanism that could support an adaptive management approach to FMD control.
Keyphrases
  • infectious diseases
  • machine learning
  • deep learning
  • decision making