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Evidence-based controls for epidemics using spatio-temporal stochastic models in a Bayesian framework.

Hola K Adrakey1,2, George Streftaris2, Nik J Cunniffe3

  • 1Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK ha411@cam.ac.uk.

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|December 1, 2017
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Summary

Mathematical models aid in controlling infectious diseases by assessing host removal strategies. A "threat" measure, combining hazard and risk, proved most effective for disease control, especially with limited resources.

Keywords:
Bayesian inferencecontrol strategiesemerging epidemicnon-centred parametrizationspatio-temporal model

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Area of Science:

  • Epidemiological and ecological modeling
  • Mathematical and statistical modeling
  • Infectious disease control

Background:

  • Controlling infectious diseases in agriculture and livestock is challenging.
  • Mathematical models are increasingly used to inform disease control strategies.
  • Evidence-based decision-making is crucial for effective disease management.

Purpose of the Study:

  • To develop a general approach for selecting control strategies using spatio-temporal models within a Bayesian framework.
  • To assess methods for prioritizing individual host removal during epidemics.
  • To evaluate the effectiveness of different risk-based measures for disease containment.

Main Methods:

  • Utilized spatio-temporal stochastic models within a Bayesian framework.
  • Assessed control strategies based on pre-emptive removal of individual hosts.
  • Employed simulated and historic data for an Asiatic citrus canker epidemic in Florida.
  • Evaluated measures including hazard, risk, and a combined threat score for prioritizing removals.

Main Results:

  • The "threat" measure, combining host hazard and risk, typically resulted in the most effective control strategies.
  • This approach was particularly effective for clustered epidemics and scarce resources.
  • Functional-model representations reduced the variance of epidemic outcomes, decreasing simulation needs.

Conclusions:

  • Spatio-temporal modeling within a Bayesian framework provides a robust approach to selecting optimal disease control strategies.
  • The "threat" measure is a valuable tool for prioritizing interventions, especially under resource constraints.
  • The methodology offers a flexible framework for application to various infectious disease scenarios.