TY - JOUR
T1 - Optimal surveillance against foot-and-mouth disease
T2 - A sample average approximation approach
AU - Kompas, Tom
AU - Ha, Pham Van
AU - Nguyen, Hoa Thi Minh
AU - Garner, Graeme
AU - Roche, Sharon
AU - East, Iain
N1 - Publisher Copyright:
© 2020 Kompas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2020/7
Y1 - 2020/7
N2 - Decisions surrounding the presence of infectious diseases are typically made in the face of considerable uncertainty. However, the development of models to guide these decisions has been substantially constrained by computational difficulty. This paper focuses on the case of finding the optimal level of surveillance against a highly infectious animal disease where time, space and randomness are fully considered. We apply the Sample Average Approximation approach to solve our problem, and to control model dimension, we propose the use of an infection tree model, in combination with sensible 'tree-pruning' and parallel processing techniques. Our proposed model and techniques are generally applicable to a number of disease types, but we demonstrate the approach by solving for optimal surveillance levels against foot-and-mouth disease using bulk milk testing as an active surveillance protocol, during an epidemic, among 42,279 farms, fully characterised by their location, livestock type and size, in the state of Victoria, Australia.
AB - Decisions surrounding the presence of infectious diseases are typically made in the face of considerable uncertainty. However, the development of models to guide these decisions has been substantially constrained by computational difficulty. This paper focuses on the case of finding the optimal level of surveillance against a highly infectious animal disease where time, space and randomness are fully considered. We apply the Sample Average Approximation approach to solve our problem, and to control model dimension, we propose the use of an infection tree model, in combination with sensible 'tree-pruning' and parallel processing techniques. Our proposed model and techniques are generally applicable to a number of disease types, but we demonstrate the approach by solving for optimal surveillance levels against foot-and-mouth disease using bulk milk testing as an active surveillance protocol, during an epidemic, among 42,279 farms, fully characterised by their location, livestock type and size, in the state of Victoria, Australia.
UR - http://www.scopus.com/inward/record.url?scp=85087813022&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0235969
DO - 10.1371/journal.pone.0235969
M3 - Article
SN - 1932-6203
VL - 15
JO - PLoS ONE
JF - PLoS ONE
IS - 7 July
M1 - e0235969
ER -