EuFMD OS20

1. MISSPECIFYING OPERATIONAL DELAYS
MAY PRODUCE BIASED FORECASTS
A retrospective analysis of the 2001 FMDV outbreak in United Kingdom
Yun Tao1,2, William J.M. Probert3, Katriona Shea4,5, Michael C. Runge6, Kevin Lafferty2,6,7,
Michael Tildesley8, Matthew Ferrari4,5
1. Intelligence Community Postdoctoral Research Fellowship Program; 2. Department of Ecology, Evolution, and Marine Biology, University of California,
Santa Barbara; 3. Big Data Institute, University of Oxford; 4. Department of Biology, Pennsylvania State University; 5. The Center for Infectious Disease
Dynamics, Pennsylvania State University; 6. US Geological Survey; 7. Marine Science Institute, University of California, Santa Barbara; 8. The Zeeman
Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick.
Introduction & Methods
Achieving rapid responses to premises targeted for interventions is critical to the provision of strong biosecurity measures and
the overall success of FAST management programs. However, excess response delay has been a recurrent problem in past
cases of livestock epidemics when mass depopulation was put into effect. In many models, the scheduling of control actions on
individual farm was nevertheless assumed to closely mirror an idealized timeframe without reflecting the disconnect between
policy advice and management reality caused by common logistical constraints. Using survival analysis, we examined the 2001
UK Foot-and-mouth Disease Virus (FMDV) management timeline to reveal operational factors (i.e., farm size and control
demand) that impeded timely culling and disposal activities. We subsequently applied the Warwick model to evaluate how
increasingly accurate model representations of the response process can influence national outbreak predictions.
Are there substantial variations in the delays of
culling and disposal responses on infected premises?
The epidemic dynamics, shown in black (a-c), are conditional on variable
delays as a function of farm size, control demand, and policy timeframes
(OS2). The dynamics of (a) fixed, idealized response, (b) policy-agnostic
response (OS1), and (c) randomly drawn, approximated responses are
shown in green, orange, and blue, respectively. The management
outcomes of the model responses are shown in corresponding colors (d)
under three standard measures of control effectiveness.
Do delayed responses
affect outbreak and management success?
Results & Discussion
We identified farm size and control demand (i.e., number of
premises in the response queue) as key contributors to local
response delays. Our results further suggest that simple model
descriptions of outbreak management, e.g. fixed, policy-
conforming responses, may grossly underestimate outbreak
severity and its long-term consequences. Our results suggest the
value of basing expectations of response efficiency on time-
dependent, premises-specific logistical constraints. Including
such operational context in management models can help
improve real-time forecasts and inform decision-making.
Acknowledgement
YT was supported by an appointment to the Intelligence Community
Postdoctoral Research Fellowship Program at UC Santa Barbara,
administered by Oak Ridge Institute for Science and Education through an
interagency agreement between the U.S. Department of Energy and the
Office of the Director of National Intelligence. YT, MF, and KS were
supported by the National Institutes of Health: EEID award 1 R01
GM105247-01. KL and MCR were supported by the Ecosystem Mission
Area of the U.S. Geological Survey. Any use of trade, firm or product names
is for descriptive purposes only and does not imply endorsement by the
U.S. Government. MT was supported by the Biotechnology and Biological
Sciences Research Council (BB/T004312/1 and BB/S01750X/1). WP was
funded by the Li Ka Shing Foundation.