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Real-time decision-making during emergency disease outbreaks

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Real-time decision-making during emergency disease outbreaks. / Probert, William; Jewell, Christopher Parry; Werkman, Marleen; Fonnesbeck, Chris; Goto, Yoshitaka; Runge, Michael; Sekiguchi, Satoshi; Shea, Katriona; Keeling, Matthew J.; Ferrari, Matthew ; Tildesley, Michael.

In: PLoS Computational Biology, Vol. 14, No. 7, e1006202, 24.07.2018.

Research output: Contribution to journalJournal article

Harvard

Probert, W, Jewell, CP, Werkman, M, Fonnesbeck, C, Goto, Y, Runge, M, Sekiguchi, S, Shea, K, Keeling, MJ, Ferrari, M & Tildesley, M 2018, 'Real-time decision-making during emergency disease outbreaks', PLoS Computational Biology, vol. 14, no. 7, e1006202. https://doi.org/10.1371/journal.pcbi.1006202

APA

Probert, W., Jewell, C. P., Werkman, M., Fonnesbeck, C., Goto, Y., Runge, M., Sekiguchi, S., Shea, K., Keeling, M. J., Ferrari, M., & Tildesley, M. (2018). Real-time decision-making during emergency disease outbreaks. PLoS Computational Biology, 14(7), [e1006202]. https://doi.org/10.1371/journal.pcbi.1006202

Vancouver

Probert W, Jewell CP, Werkman M, Fonnesbeck C, Goto Y, Runge M et al. Real-time decision-making during emergency disease outbreaks. PLoS Computational Biology. 2018 Jul 24;14(7). e1006202. https://doi.org/10.1371/journal.pcbi.1006202

Author

Probert, William ; Jewell, Christopher Parry ; Werkman, Marleen ; Fonnesbeck, Chris ; Goto, Yoshitaka ; Runge, Michael ; Sekiguchi, Satoshi ; Shea, Katriona ; Keeling, Matthew J. ; Ferrari, Matthew ; Tildesley, Michael. / Real-time decision-making during emergency disease outbreaks. In: PLoS Computational Biology. 2018 ; Vol. 14, No. 7.

Bibtex

@article{720ba97cb6f64acbb73a251281742178,
title = "Real-time decision-making during emergency disease outbreaks",
abstract = "In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect. Our results show that the control policy that would have been chosen using all the data is also identified from an early stage in an outbreak using only the available data, despite high variability in projections of epidemic size. Critically, we find that it is an improved understanding of the locations of infected farms, rather than improved estimates of transmission parameters, that drives improved prediction of the relative performance of control interventions. However, the ability to estimate undetected infectious premises is a function of uncertainty in the transmission parameters. Here, we demonstrate the need for both real-time model fitting and generating projections to evaluate alternative control interventions throughout an outbreak. Our results highlight the use of using models at outbreak onset to inform policy and the importance of state-dependent interventions that adapt in response to additional information throughout an outbreak. ",
author = "William Probert and Jewell, {Christopher Parry} and Marleen Werkman and Chris Fonnesbeck and Yoshitaka Goto and Michael Runge and Satoshi Sekiguchi and Katriona Shea and Keeling, {Matthew J.} and Matthew Ferrari and Michael Tildesley",
year = "2018",
month = jul
day = "24",
doi = "10.1371/journal.pcbi.1006202",
language = "English",
volume = "14",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "7",

}

RIS

TY - JOUR

T1 - Real-time decision-making during emergency disease outbreaks

AU - Probert, William

AU - Jewell, Christopher Parry

AU - Werkman, Marleen

AU - Fonnesbeck, Chris

AU - Goto, Yoshitaka

AU - Runge, Michael

AU - Sekiguchi, Satoshi

AU - Shea, Katriona

AU - Keeling, Matthew J.

AU - Ferrari, Matthew

AU - Tildesley, Michael

PY - 2018/7/24

Y1 - 2018/7/24

N2 - In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect. Our results show that the control policy that would have been chosen using all the data is also identified from an early stage in an outbreak using only the available data, despite high variability in projections of epidemic size. Critically, we find that it is an improved understanding of the locations of infected farms, rather than improved estimates of transmission parameters, that drives improved prediction of the relative performance of control interventions. However, the ability to estimate undetected infectious premises is a function of uncertainty in the transmission parameters. Here, we demonstrate the need for both real-time model fitting and generating projections to evaluate alternative control interventions throughout an outbreak. Our results highlight the use of using models at outbreak onset to inform policy and the importance of state-dependent interventions that adapt in response to additional information throughout an outbreak.

AB - In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect. Our results show that the control policy that would have been chosen using all the data is also identified from an early stage in an outbreak using only the available data, despite high variability in projections of epidemic size. Critically, we find that it is an improved understanding of the locations of infected farms, rather than improved estimates of transmission parameters, that drives improved prediction of the relative performance of control interventions. However, the ability to estimate undetected infectious premises is a function of uncertainty in the transmission parameters. Here, we demonstrate the need for both real-time model fitting and generating projections to evaluate alternative control interventions throughout an outbreak. Our results highlight the use of using models at outbreak onset to inform policy and the importance of state-dependent interventions that adapt in response to additional information throughout an outbreak.

U2 - 10.1371/journal.pcbi.1006202

DO - 10.1371/journal.pcbi.1006202

M3 - Journal article

VL - 14

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 7

M1 - e1006202

ER -