41 results on '"Probert, William J. M."'
Search Results
2. Cost and cost-effectiveness of a universal HIV testing and treatment intervention in Zambia and South Africa: evidence and projections from the HPTN 071 (PopART) trial
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Thomas, Ranjeeta, Probert, William J M, Sauter, Rafael, Mwenge, Lawrence, Singh, Surya, Kanema, Sarah, Vanqa, Nosivuyile, Harper, Abigail, Burger, Ronelle, Cori, Anne, Pickles, Michael, Bell-Mandla, Nomtha, Yang, Blia, Bwalya, Justin, Phiri, Mwelwa, Shanaube, Kwame, Floyd, Sian, Donnell, Deborah, Bock, Peter, Ayles, Helen, Fidler, Sarah, Hayes, Richard J, Fraser, Christophe, and Hauck, Katharina
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- 2021
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3. Scenario Design for Infectious Disease Projections: Integrating Concepts from Decision Analysis and Experimental Design
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Runge, Michael C, primary, Shea, Katriona, additional, Howerton, Emily, additional, Yan, Katie, additional, Hochheiser, Harry, additional, Rosenstrom, Erik, additional, Probert, William J M, additional, Borchering, Rebecca, additional, Marathe, Madhav V, additional, Lewis, Bryan, additional, Venkatramanan, Srinivasan, additional, Truelove, Shaun A, additional, Lessler, Justin, additional, and Viboud, Cecile, additional
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- 2023
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4. Essential information : Uncertainty and optimal control of Ebola outbreaks
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Li, Shou-Li, Bjørnstad, Ottar N., Ferrari, Matthew J., Mummah, Riley, Runge, Michael C., Fonnesbeck, Christopher J., Tildesley, Michael J., Probert, William J. M., and Shea, Katriona
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- 2017
5. Multiple models for outbreak decision support in the face of uncertainty
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Shea, Katriona, primary, Borchering, Rebecca K., additional, Probert, William J. M., additional, Howerton, Emily, additional, Bogich, Tiffany L., additional, Li, Shou-Li, additional, van Panhuis, Willem G., additional, Viboud, Cecile, additional, Aguás, Ricardo, additional, Belov, Artur A., additional, Bhargava, Sanjana H., additional, Cavany, Sean M., additional, Chang, Joshua C., additional, Chen, Cynthia, additional, Chen, Jinghui, additional, Chen, Shi, additional, Chen, YangQuan, additional, Childs, Lauren M., additional, Chow, Carson C., additional, Crooker, Isabel, additional, Del Valle, Sara Y., additional, España, Guido, additional, Fairchild, Geoffrey, additional, Gerkin, Richard C., additional, Germann, Timothy C., additional, Gu, Quanquan, additional, Guan, Xiangyang, additional, Guo, Lihong, additional, Hart, Gregory R., additional, Hladish, Thomas J., additional, Hupert, Nathaniel, additional, Janies, Daniel, additional, Kerr, Cliff C., additional, Klein, Daniel J., additional, Klein, Eili Y., additional, Lin, Gary, additional, Manore, Carrie, additional, Meyers, Lauren Ancel, additional, Mittler, John E., additional, Mu, Kunpeng, additional, Núñez, Rafael C., additional, Oidtman, Rachel J., additional, Pasco, Remy, additional, Pastore y Piontti, Ana, additional, Paul, Rajib, additional, Pearson, Carl A. B., additional, Perdomo, Dianela R., additional, Perkins, T. Alex, additional, Pierce, Kelly, additional, Pillai, Alexander N., additional, Rael, Rosalyn Cherie, additional, Rosenfeld, Katherine, additional, Ross, Chrysm Watson, additional, Spencer, Julie A., additional, Stoltzfus, Arlin B., additional, Toh, Kok Ben, additional, Vattikuti, Shashaank, additional, Vespignani, Alessandro, additional, Wang, Lingxiao, additional, White, Lisa J., additional, Xu, Pan, additional, Yang, Yupeng, additional, Yogurtcu, Osman N., additional, Zhang, Weitong, additional, Zhao, Yanting, additional, Zou, Difan, additional, Ferrari, Matthew J., additional, Pannell, David, additional, Tildesley, Michael J., additional, Seifarth, Jack, additional, Johnson, Elyse, additional, Biggerstaff, Matthew, additional, Johansson, Michael A., additional, Slayton, Rachel B., additional, Levander, John D., additional, Stazer, Jeff, additional, Kerr, Jessica, additional, and Runge, Michael C., additional
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- 2023
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6. Participatory Mathematical Modeling Approach for Policymaking during the First Year of the COVID-19 Crisis, Jordan.
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Bellizzi, Saverio, Letchford, Nicholas, Adib, Keyrellous, Probert, William J. M., Hancock, Penelope, Alsawalha, Lora, Santoro, Alessio, Profili, Maria C., Aguas, Ricardo, Popescu, Christian, Ariqi, Lubna Al, White, Lisa, Hayajneh, Wail, Obeidat, Nathir, and Nabeth, Pierre
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COVID-19 pandemic ,SOCIAL distancing ,COVID-19 ,MATHEMATICAL models ,POLICY sciences - Abstract
We engaged in a participatory modeling approach with health sector stakeholders in Jordan to support government decision-making regarding implementing public health measures to mitigate COVID-19 disease burden. We considered the effect of 4 physical distancing strategies on reducing COVID-19 transmission and mortality in Jordan during March 2020–January 2021: no physical distancing; intermittent physical distancing where all but essential services are closed once a week; intermittent physical distancing where all but essential services are closed twice a week; and a permanent physical distancing intervention. Modeling showed that the fourth strategy would be most effective in reducing cases and deaths; however, this approach was only marginally beneficial to reducing COVID-19 disease compared with an intermittently enforced physical distancing intervention. Scenario-based model influenced policy-making and the evolution of the pandemic in Jordan confirmed the forecasting provided by the modeling exercise and helped confirm the effectiveness of the policy adopted by the government of Jordan. [ABSTRACT FROM AUTHOR]
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- 2023
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7. Context-dependent representation of within- and between-model uncertainty: aggregating probabilistic predictions in infectious disease epidemiology
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Howerton, Emily, primary, Runge, Michael C., additional, Bogich, Tiffany L., additional, Borchering, Rebecca K., additional, Inamine, Hidetoshi, additional, Lessler, Justin, additional, Mullany, Luke C., additional, Probert, William J. M., additional, Smith, Claire P., additional, Truelove, Shaun, additional, Viboud, Cécile, additional, and Shea, Katriona, additional
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- 2023
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8. Electronic Supplementary Material from Context-dependent representation of within- and between-model uncertainty: aggregating probabilistic predictions in infectious disease epidemiology
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Howerton, Emily, Runge, Michael C., Bogich, Tiffany L., Borchering, Rebecca K., Inamine, Hidetoshi, Lessler, Justin, Mullany, Luke C., Probert, William J. M., Smith, Claire P., Truelove, Shaun, Viboud, Cécile, and Shea, Katriona
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Additional information on theory and case studies discussed in Context-dependent representation of within- and between-model uncertainty: aggregating probabilistic predictions in infectious disease epidemiology
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- 2023
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9. Projected outcomes of universal testing and treatment in a generalised HIV epidemic in Zambia and South Africa (the HPTN 071 [PopART] trial): a modelling study
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Probert, William J M, primary, Sauter, Rafael, additional, Pickles, Michael, additional, Cori, Anne, additional, Bell-Mandla, Nomtha F, additional, Bwalya, Justin, additional, Abeler-Dörner, Lucie, additional, Bock, Peter, additional, Donnell, Deborah J, additional, Floyd, Sian, additional, Macleod, David, additional, Piwowar-Manning, Estelle, additional, Skalland, Timothy, additional, Shanaube, Kwame, additional, Wilson, Ethan, additional, Yang, Blia, additional, Ayles, Helen, additional, Fidler, Sarah, additional, Hayes, Richard J, additional, Fraser, Christophe, additional, Hayes, Richard, additional, Beyers, Nulda, additional, El-Sadr, Wafaa, additional, Cohen, Myron, additional, Eshleman, Susan, additional, Agyei, Yaw, additional, Bond, Virginia, additional, Hoddinott, Graeme, additional, Donnell, Deborah, additional, Emel, Lynda, additional, Noble, Heather, additional, Burns, David, additional, Sista, Nirupama, additional, Griffith, Sam, additional, Moore, Ayana, additional, Headen, Tanette, additional, White, Rhonda, additional, Miller, Eric, additional, Hargreaves, James, additional, Hauck, Katharina, additional, Thomas, Ranjeeta, additional, Limbada, Mohammed, additional, Sabapathy, Kalpana, additional, Schaap, Ab, additional, Dunbar, Rory, additional, Simwinga, Musonda, additional, Smith, Peter, additional, Vermund, Sten, additional, Mandla, Nomtha, additional, Makola, Nozizwe, additional, van Deventer, Anneen, additional, James, Anelet, additional, Jennings, Karen, additional, Kruger, James, additional, Phiri, Mwelwa, additional, Kosloff, Barry, additional, Mwenge, Lawrence, additional, Kanema, Sarah, additional, Probert, William, additional, Kumar, Ramya, additional, Sakala, Ephraim, additional, Silumesi, Andrew, additional, Skalland, Tim, additional, and Yuhas, Krista, additional
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- 2022
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10. Vote-processing rules for combining control recommendations from multiple models
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Probert, William J. M., primary, Nicol, Sam, additional, Ferrari, Matthew J., additional, Li, Shou-Li, additional, Shea, Katriona, additional, Tildesley, Michael J., additional, and Runge, Michael C., additional
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- 2022
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11. Minimizing the Cost of Keeping Options Open for Conservation in a Changing Climate
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MILLS, MORENA, NICOL, SAM, WELLS, JESSIE A., LAHOZ-MONFORT, JOSÉ J., WINTLE, BRENDAN, BODE, MICHAEL, WARDROP, MARTIN, WALSHE, TERRY, PROBERT, WILLIAM J. M., RUNGE, MICHAEL C., POSSINGHAM, HUGH P., and MADDEN, EVE MCDONALD
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- 2014
12. Supplementary Figures and Materials from Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models
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Hinch, Robert, Panovska-Griffiths, Jasmina, Probert, William J. M., Ferretti, Luca, Wymant, Chris, Lauro, Francesco Di, Baya, Nikolas, Ghafari, Mahan, Abeler-Dörner, Lucie, Consortium, The COVID-19 Genomics UK (COG-UK), and Fraser, Christophe
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ComputingMethodologies_SIMULATIONANDMODELING ,InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL ,ComputingMethodologies_DOCUMENTANDTEXTPROCESSING ,ComputingMilieux_COMPUTERSANDEDUCATION ,ComputerApplications_COMPUTERSINOTHERSYSTEMS - Abstract
All supplementary content referenced in the main article.
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- 2022
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13. Supplementary Figures and tables for 'Vote-processing rules for combining control recommendations from multiple models'
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Probert, William J. M., Nicol, Sam, Ferrari, Matthew J., Li, Shou-Li, Shea, Katriona, Tildesley, Michael J., and Runge, Michael C.
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Mathematical modelling is used during disease outbreaks to compare control interventions. Using multiple models, the best method to combine model recommendations is unclear. Existing methods weight model projections, then rank control interventions using the combined projections, presuming model outputs are directly comparable. However, the way each model represents the epidemiological system will vary. We apply electoral vote-processing rules to combine model-generated rankings of interventions. Combining rankings of interventions, instead of combining model projections, avoids assuming that projections are comparable as all comparisons of projections are made within each model. We investigate four rules: First-past-the-post, Alternative Vote (AV), Coombs Method and Borda Count. We investigate rule sensitivity by including models that favour only one action or including those that rank interventions randomly. We investigate two case studies: the 2014 Ebola outbreak in West Africa (37 compartmental models) and a hypothetical foot-and-mouth disease outbreak in UK (four individual-based models). The Coombs Method was least susceptible to adding models that favoured a single action, Borda Count and AV were most susceptible to adding models that ranked interventions randomly. Each rule chose the same intervention as when ranking interventions by mean projections, suggesting that combining rankings provides similar recommendations with fewer assumptions about model comparability.This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.
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- 2022
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14. Active adaptive conservation of threatened species in the face of uncertainty
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McDonald-Madden, Eve, Probert, William J. M., Hauser, Cindy E., Runge, Michael C., Possingham, Hugh P., Jones, Menna E., Moore, Joslin L., Rout, Tracy M., Vesk, Peter A., and Wintle, Brendan A.
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- 2010
15. Synergistic interventions to control COVID-19: Mass testing and isolation mitigates reliance on distancing
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Howerton, Emily, primary, Ferrari, Matthew J., additional, Bjørnstad, Ottar N., additional, Bogich, Tiffany L., additional, Borchering, Rebecca K., additional, Jewell, Chris P., additional, Nichols, James D., additional, Probert, William J. M., additional, Runge, Michael C., additional, Tildesley, Michael J., additional, Viboud, Cécile, additional, and Shea, Katriona, additional
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- 2021
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16. Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models.
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Hinch, Robert, Panovska-Griffiths, Jasmina, Probert, William J. M., Ferretti, Luca, Wymant, Chris, Di Lauro, Francesco, Baya, Nikolas, Ghafari, Mahan, Abeler-Dörner, Lucie, and Fraser, Christophe
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GEOLOGICAL statistics ,SARS-CoV-2 ,STATISTICAL models ,COUNTERFACTUALS (Logic) ,EPIDEMICS ,STAY-at-home orders - Abstract
The SARS-CoV-2 epidemic has been extended by the evolution of more transmissible viral variants. In autumn 2020, the B.1.177 lineage became the dominant variant in England, before being replaced by the B.1.1.7 (Alpha) lineage in late 2020, with the sweep occurring at different times in each region. This period coincided with a large number of non-pharmaceutical interventions (e.g. lockdowns) to control the epidemic, making it difficult to estimate the relative transmissibility of variants. In this paper, we model the spatial spread of these variants in England using a meta-population agent-based model which correctly characterizes the regional variation in cases and distribution of variants. As a test of robustness, we additionally estimated the relative transmissibility of multiple variants using a statistical model based on the renewal equation, which simultaneously estimates the effective reproduction number R. Relative to earlier variants, the transmissibility of B.1.177 is estimated to have increased by 1.14 (1.12-1.16) and that of Alpha by 1.71 (1.65-1.77). The vaccination programme starting in December 2020 is also modelled. Counterfactual simulations demonstrate that the vaccination programme was essential for reopening in March 2021, and that if the January lockdown had started one month earlier, up to 30 k (24 k-38 k) deaths could have been prevented. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'. [ABSTRACT FROM AUTHOR]
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- 2022
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17. OpenABM-Covid19—An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing
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Hinch, Robert, primary, Probert, William J. M., additional, Nurtay, Anel, additional, Kendall, Michelle, additional, Wymant, Chris, additional, Hall, Matthew, additional, Lythgoe, Katrina, additional, Bulas Cruz, Ana, additional, Zhao, Lele, additional, Stewart, Andrea, additional, Ferretti, Luca, additional, Montero, Daniel, additional, Warren, James, additional, Mather, Nicole, additional, Abueg, Matthew, additional, Wu, Neo, additional, Legat, Olivier, additional, Bentley, Katie, additional, Mead, Thomas, additional, Van-Vuuren, Kelvin, additional, Feldner-Busztin, Dylan, additional, Ristori, Tommaso, additional, Finkelstein, Anthony, additional, Bonsall, David G., additional, Abeler-Dörner, Lucie, additional, and Fraser, Christophe, additional
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- 2021
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18. Strategic testing approaches for targeted disease monitoring can be used to inform pandemic decision-making
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Nichols, James D., primary, Bogich, Tiffany L., additional, Howerton, Emily, additional, Bjørnstad, Ottar N., additional, Borchering, Rebecca K., additional, Ferrari, Matthew, additional, Haran, Murali, additional, Jewell, Christopher, additional, Pepin, Kim M., additional, Probert, William J. M., additional, Pulliam, Juliet R. C., additional, Runge, Michael C., additional, Tildesley, Michael, additional, Viboud, Cécile, additional, and Shea, Katriona, additional
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- 2021
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19. Temporal variations in logistical quantities from Causes of delayed outbreak responses and their impacts on epidemic spread
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Tao, Yun, Probert, William J. M., Shea, Katriona, Runge, Michael C., Lafferty, Kevin, Tildesley, Michael, and Ferrari, Matthew
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Temporal variations in quantities of logistical factors affecting control efforts on infected premises (IPs) during the 2001 foot-and-mouth epidemic in the UK. Top panel: Mean size of IPs across report dates. Middle panels: Mean levels of control demand on culling and disposal responses to IPs across report dates. Bottom panel: Mean value of farm density within a 5km radius from IPs across report dates.
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- 2021
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20. Simulated timeline of response delays from Causes of delayed outbreak responses and their impacts on epidemic spread
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Tao, Yun, Probert, William J. M., Shea, Katriona, Runge, Michael C., Lafferty, Kevin, Tildesley, Michael, and Ferrari, Matthew
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Mean daily delays to culling (purple) and disposal (green) over report dates, simulated as a function of farm size, control demand, and policy timeframes (time-dependent response).
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- 2021
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21. Mean observed and predicted response delays from Causes of delayed outbreak responses and their impacts on epidemic spread
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Tao, Yun, Probert, William J. M., Shea, Katriona, Runge, Michael C., Lafferty, Kevin, Tildesley, Michael, and Ferrari, Matthew
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Mean observed and predicted response delays on infected premises (IPs) grouped by farm size, control demand, and case report dates. IPs are separated into two size classes: farms that contain more than 500 livestock (red) and those with smaller populations (blue). Their response times are aggregated by the level of control demand, with the means calculated for each group (dots). From the same covariate values of farm size and control demand, delays are also simulated using the method of inverse probability integral transform. 1000 simulation replicates are run for each IP setting and their means are similarly aggregated and averaged by group (circles and lines). Note that the simulations of disposal delays on IPs reported prior to 1 April (c) depend only on farm size due to the insignificant effect of control demand (see Tables 1 and S2). The corresponding distribution of control demand is shown in each panel. Dashed line marks the 24 hours period.
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- 2021
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22. Causes of delayed outbreak responses and their impacts on epidemic spread
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Tao, Yun, primary, Probert, William J. M., additional, Shea, Katriona, additional, Runge, Michael C., additional, Lafferty, Kevin, additional, Tildesley, Michael, additional, and Ferrari, Matthew, additional
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- 2021
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23. OpenABM-Covid19 - an agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing
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Hinch, Robert, primary, Probert, William J M, additional, Nurtay, Anel, additional, Kendall, Michelle, additional, Wymant, Chris, additional, Hall, Matthew, additional, Lythgoe, Katrina, additional, Cruz, Ana Bulas, additional, Zhao, Lele, additional, Stewart, Andrea, additional, Ferretti, Luca, additional, Montero, Daniel, additional, Warren, James, additional, Mather, Nicole, additional, Abueg, Matthew, additional, Wu, Neo, additional, Finkelstein, Anthony, additional, Bonsall, David G, additional, Abeler-Dörner, Lucie, additional, and Fraser, Christophe, additional
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- 2020
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24. Harnessing multiple models for outbreak management
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Shea, Katriona, primary, Runge, Michael C., additional, Pannell, David, additional, Probert, William J. M., additional, Li, Shou-Li, additional, Tildesley, Michael, additional, and Ferrari, Matthew, additional
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- 2020
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25. PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial.
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Pickles, Michael, Cori, Anne, Probert, William J. M., Sauter, Rafael, Hinch, Robert, Fidler, Sarah, Ayles, Helen, Bock, Peter, Donnell, Deborah, Wilson, Ethan, Piwowar-Manning, Estelle, Floyd, Sian, Hayes, Richard J., and Fraser, Christophe
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HIV infection transmission ,BAYESIAN field theory ,INFECTIOUS disease transmission ,EPIDEMICS ,HIV infections ,SIMULATION methods & models ,HIV - Abstract
Mathematical models are powerful tools in HIV epidemiology, producing quantitative projections of key indicators such as HIV incidence and prevalence. In order to improve the accuracy of predictions, such models need to incorporate a number of behavioural and biological heterogeneities, especially those related to the sexual network within which HIV transmission occurs. An individual-based model, which explicitly models sexual partnerships, is thus often the most natural type of model to choose. In this paper we present PopART-IBM, a computationally efficient individual-based model capable of simulating 50 years of an HIV epidemic in a large, high-prevalence community in under a minute. We show how the model calibrates within a Bayesian inference framework to detailed age- and sex-stratified data from multiple sources on HIV prevalence, awareness of HIV status, ART status, and viral suppression for an HPTN 071 (PopART) study community in Zambia, and present future projections of HIV prevalence and incidence for this community in the absence of trial intervention. Author summary: In this paper we present PopART-IBM, an individual-based model used to simulate HIV transmission in communities in high prevalence settings. We show that PopART-IBM can simulate transmission over a span of decades in a large community in less than a minute. This computational efficiency allows us to calibrate the model within an inference framework, and we show an illustrative example of calibration using an adaptive population Monte Carlo Approximate Bayesian Computation algorithm for a community in Zambia that was part of the HPTN-071 (PopART) trial. We compare the detailed model output to real-world data collected during the trial from this community. Finally, we project how the HIV epidemic would have changed over time in this community if no intervention from the trial had occurred. [ABSTRACT FROM AUTHOR]
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- 2021
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26. Real-time decision-making during emergency disease outbreaks
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Koelle, Katia, Probert, William J. M., Jewell, Chris P., Werkman, Marleen, Fonnesbeck, Christopher J., Goto, Yoshitaka, Runge, Michael C., Sekiguchi, Satoshi, Shea, Katriona, Keeling, Matthew James, Ferrari, Matthew J., and Tildesley, Michael J.
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0301 basic medicine ,Time Factors ,Computer science ,Epidemiology ,IMPACT ,Swine ,Psychological intervention ,MOUTH-DISEASE ,Animal Diseases ,Disease Outbreaks ,0302 clinical medicine ,Japan ,Econometrics ,Medicine and Health Sciences ,Public and Occupational Health ,UK ,lcsh:QH301-705.5 ,Mammals ,Swine Diseases ,Ecology ,Simulation and Modeling ,Health Policy ,Eukaryota ,Agriculture ,Ruminants ,Vaccination and Immunization ,3. Good health ,Infectious Diseases ,Computational Theory and Mathematics ,Foot-and-Mouth Disease Virus ,Modeling and Simulation ,Animals, Domestic ,Vertebrates ,Life Sciences & Biomedicine ,Research Article ,Biochemistry & Molecular Biology ,Livestock ,Infectious Disease Control ,STRATEGIES ,FOOT ,Bioinformatics ,Immunology ,Foot and Mouth Disease ,Cattle Diseases ,Sheep Diseases ,Research and Analysis Methods ,Biochemical Research Methods ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,EPIDEMIC ,Genetics ,Animals ,Humans ,Time point ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,Health policy ,Decision Making, Organizational ,01 Mathematical Sciences ,Science & Technology ,Sheep ,Organisms ,Outbreak ,Biology and Life Sciences ,Viral Vaccines ,Models, Theoretical ,06 Biological Sciences ,Optimal control ,United Kingdom ,030104 developmental biology ,lcsh:Biology (General) ,Infectious disease (medical specialty) ,Foot-and-Mouth Disease ,Amniotes ,Cattle ,Preventive Medicine ,Mathematical & Computational Biology ,08 Information and Computing Sciences ,Epidemic model ,RA ,Zoology ,030217 neurology & neurosurgery ,Hindsight bias - 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 summary Mathematical and simulation models may be used to inform policy in the early stages of an infectious disease outbreak by evaluating which control strategies will minimize the impact of the epidemic. In these early stages, significant uncertainty can 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, what is most important is the selection of the optimal control intervention, rather than accuracy of model predictions. We fit 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 alternative control interventions. These are compared to policy recommendations generated in hindsight using data from the entire outbreak. Our results show that the optimal control policy is identified accurately from an early stage in an outbreak, despite high levels of uncertainty in projections of epidemic size, and that the relative performance of control strategies is strongly mediated by our understanding of the locations of infected farms, rather than improved estimates of transmission parameters.
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- 2018
27. A large-scale application of project prioritization to threatened species investment by a government agency
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Brazill-Boast, James, primary, Williams, Moira, additional, Rickwood, Beth, additional, Partridge, Thalie, additional, Bywater, Grant, additional, Cumbo, Bronwyn, additional, Shannon, Ian, additional, Probert, William J. M., additional, Ravallion, Julie, additional, Possingham, Hugh, additional, and Maloney, Richard F., additional
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- 2018
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28. Real-time decision-making during emergency disease outbreaks
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Probert, William J. M., primary, Jewell, Chris P., additional, Werkman, Marleen, additional, Fonnesbeck, Christopher J., additional, Goto, Yoshitaka, additional, Runge, Michael C., additional, Sekiguchi, Satoshi, additional, Shea, Katriona, additional, Keeling, Matt J., additional, Ferrari, Matthew J., additional, and Tildesley, Michael J., additional
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- 2018
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29. Quantifying the Value of Perfect Information in Emergency Vaccination Campaigns
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Bradbury, Naomi V., primary, Probert, William J. M., additional, Shea, Katriona, additional, Runge, Michael C., additional, Fonnesbeck, Christopher J., additional, Keeling, Matt J., additional, Ferrari, Matthew J., additional, and Tildesley, Michael J., additional
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- 2017
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30. Evaluating Trade-Offs between Target Persistence Levels and Numbers of Species Conserved
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Di Fonzo, Martina M. I., primary, Possingham, Hugh P., additional, Probert, William J. M., additional, Bennett, Joseph R., additional, Joseph, Liana N., additional, Tulloch, Ayesha I.T., additional, O'Connor, Shaun, additional, Densem, Jodie, additional, and Maloney, Richard F., additional
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- 2015
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31. Essential information: Uncertainty and optimal control of Ebola outbreaks.
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Shou-Li Li, Bjørnstad, Ottar N., Ferrari, Matthew J., Mummah, Riley, Runge, Michael C., Fonnesbeck, Christopher J., Tildesley, Michael J., Probert, William J. M., and Shea, Katriona
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EBOLA viral disease transmission ,EBOLA virus ,DECISION making ,HOSPITALS ,GENETIC algorithms - Abstract
Early resolution of uncertainty during an epidemic outbreak can lead to rapid and efficient decision making, provided that the uncertainty affects prioritization of actions. The wide range in caseload projections for the 2014 Ebola outbreak caused great concern and debate about the utility of models. By coding and running 37 published Ebolamodelswith five candidate interventions, we found that, despite this large variation in caseload projection, the ranking of management options was relatively consistent. Reducing funeral transmission and reducing community transmission were generally ranked as the two best options. Value of information (VoI) analyses show that caseloads could be reduced by 11% by resolving all model-specific uncertainties, with information about model structure accounting for 82% of this reduction and uncertainty about caseload only accounting for 12%. Our study shows that the uncertainty that is of most interest epidemiologically may not be the same as the uncertainty that is most relevant for management. If the goal is to improve management outcomes, then the focus of study should be to identify and resolve those uncertainties that most hinder the choice of an optimal intervention. Our study further shows that simplifying multiple alternative models into a smaller number of relevant groups (here, with shared structure) could streamline the decision-making process and may allow for a better integration of epidemiological modeling and decision making for policy. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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32. Evaluating Trade-Offs between Target Persistence Levels and Numbers of Species Conserved.
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Di Fonzo, Martina M. I., Possingham, Hugh P., Probert, William J. M., Bennett, Joseph R., Joseph, Liana N., Tulloch, Ayesha I.T., O'Connor, Shaun, Densem, Jodie, and Maloney, Richard F.
- Subjects
CONSERVATION biology ,SPECIES ,BIODIVERSITY research ,RESOURCE allocation ,PLANNING - Abstract
A focus of conservation planning is to maximize the probability of species persistence, but this may reduce the number of species that can be secured with a limited budget. Using a data set of 700 New Zealand species, we examine the trade-off between providing a high level of persistence for some species and a lower level of persistence for more species. We find that the target persistence level that delivers the highest conservation outcome is a function of the annual budget, such that lower budgets have lower optimal targets. However, it is never optimal to manage species below a 75% probability of persistence. We introduce a prioritization approach that maximizes biodiversity gains based on a flexible persistence target, and demonstrate how strategies with fixed high-persistence targets can be inefficient. We also illustrate the risks in spreading conservation resources too thinly by undertaking low levels of management on more species. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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33. When do we need multiple infectious disease models? Agreement between projection rank and magnitude in a multi-model setting.
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Wade-Malone K, Howerton E, Probert WJM, Runge MC, Viboud C, and Shea K
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- Humans, Epidemics statistics & numerical data, SARS-CoV-2, Models, Theoretical, Epidemiological Models, Public Health, Forecasting methods, Communicable Diseases epidemiology, COVID-19 epidemiology
- Abstract
Mathematical models are useful for public health planning and response to infectious disease threats. However, different models can provide differing results, which can hamper decision making if not synthesized appropriately. To address this challenge, multi-model hubs convene independent modeling groups to generate ensembles, known to provide more accurate predictions of future outcomes. Yet, these hubs are resource intensive, and how many models are sufficient in a hub is not known. Here, we compare the benefit of predictions from multiple models in different contexts: (1) decision settings that depend on predictions of quantitative outcomes (e.g., hospital capacity planning), where assessments of the benefits of multi-model ensembles have largely focused; and (2) decisions settings that require the ranking of alternative epidemic scenarios (e.g., comparing outcomes under multiple possible interventions and biological uncertainties). We develop a mathematical framework to mimic a multi-model prediction setting, and use this framework to quantify how frequently predictions from different models agree. We further explore multi-model agreement using real-world, empirical data from 14 rounds of U.S. COVID-19 Scenario Modeling Hub projections. Our results suggest that the value of multiple models could be different in different decision contexts, and if only a few models are available, focusing on the rank of alternative epidemic scenarios could be more robust than focusing on quantitative outcomes. Although additional exploration of the sufficient number of models for different contexts is still needed, our results indicate that it may be possible to identify decision contexts where it is robust to rely on fewer models, a finding that can inform the use of modeling resources during future public health crises., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
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- 2024
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34. Scenario design for infectious disease projections: Integrating concepts from decision analysis and experimental design.
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Runge MC, Shea K, Howerton E, Yan K, Hochheiser H, Rosenstrom E, Probert WJM, Borchering R, Marathe MV, Lewis B, Venkatramanan S, Truelove S, Lessler J, and Viboud C
- Subjects
- Humans, Forecasting, SARS-CoV-2, Communicable Diseases epidemiology, Pandemics prevention & control, Decision Making, Research Design, COVID-19 epidemiology, COVID-19 prevention & control, COVID-19 transmission, Decision Support Techniques
- Abstract
Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, situational awareness, horizon scanning, forecasting, and value of information) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings., Competing Interests: Declaration of Competing Interest MCR reports stock ownership in Becton Dickinson & Co., which manufactures medical equipment used in COVID-19 testing, vaccination, and treatment. JL has served as an expert witness on cases where the likely length of the pandemic was of issue. There are no other competing interests to declare., (Published by Elsevier B.V.)
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- 2024
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35. Scenario Design for Infectious Disease Projections: Integrating Concepts from Decision Analysis and Experimental Design.
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Runge MC, Shea K, Howerton E, Yan K, Hochheiser H, Rosenstrom E, Probert WJM, Borchering R, Marathe MV, Lewis B, Venkatramanan S, Truelove S, Lessler J, and Viboud C
- Abstract
Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, value of information, situational awareness, horizon scanning, and forecasting) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.
- Published
- 2023
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36. Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models.
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Hinch R, Panovska-Griffiths J, Probert WJM, Ferretti L, Wymant C, Di Lauro F, Baya N, Ghafari M, Abeler-Dörner L, and Fraser C
- Subjects
- Communicable Disease Control, Humans, Seasons, COVID-19 epidemiology, SARS-CoV-2 genetics
- Abstract
The SARS-CoV-2 epidemic has been extended by the evolution of more transmissible viral variants. In autumn 2020, the B.1.177 lineage became the dominant variant in England, before being replaced by the B.1.1.7 (Alpha) lineage in late 2020, with the sweep occurring at different times in each region. This period coincided with a large number of non-pharmaceutical interventions (e.g. lockdowns) to control the epidemic, making it difficult to estimate the relative transmissibility of variants. In this paper, we model the spatial spread of these variants in England using a meta-population agent-based model which correctly characterizes the regional variation in cases and distribution of variants. As a test of robustness, we additionally estimated the relative transmissibility of multiple variants using a statistical model based on the renewal equation, which simultaneously estimates the effective reproduction number R . Relative to earlier variants, the transmissibility of B.1.177 is estimated to have increased by 1.14 (1.12-1.16) and that of Alpha by 1.71 (1.65-1.77). The vaccination programme starting in December 2020 is also modelled. Counterfactual simulations demonstrate that the vaccination programme was essential for reopening in March 2021, and that if the January lockdown had started one month earlier, up to 30 k (24 k-38 k) deaths could have been prevented. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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- 2022
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37. Vote-processing rules for combining control recommendations from multiple models.
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Probert WJM, Nicol S, Ferrari MJ, Li SL, Shea K, Tildesley MJ, and Runge MC
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- Animals, Disease Outbreaks prevention & control, Models, Theoretical
- Abstract
Mathematical modelling is used during disease outbreaks to compare control interventions. Using multiple models, the best method to combine model recommendations is unclear. Existing methods weight model projections, then rank control interventions using the combined projections, presuming model outputs are directly comparable. However, the way each model represents the epidemiological system will vary. We apply electoral vote-processing rules to combine model-generated rankings of interventions. Combining rankings of interventions, instead of combining model projections, avoids assuming that projections are comparable as all comparisons of projections are made within each model. We investigate four rules: First-past-the-post, Alternative Vote (AV), Coombs Method and Borda Count. We investigate rule sensitivity by including models that favour only one action or including those that rank interventions randomly. We investigate two case studies: the 2014 Ebola outbreak in West Africa (37 compartmental models) and a hypothetical foot-and-mouth disease outbreak in UK (four individual-based models). The Coombs Method was least susceptible to adding models that favoured a single action, Borda Count and AV were most susceptible to adding models that ranked interventions randomly. Each rule chose the same intervention as when ranking interventions by mean projections, suggesting that combining rankings provides similar recommendations with fewer assumptions about model comparability. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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- 2022
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38. Anticipating future learning affects current control decisions: A comparison between passive and active adaptive management in an epidemiological setting.
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Atkins BD, Jewell CP, Runge MC, Ferrari MJ, Shea K, Probert WJM, and Tildesley MJ
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- Humans, Learning, Models, Theoretical, Uncertainty, Disease Outbreaks prevention & control, Epidemics
- Abstract
Infectious disease epidemics present a difficult task for policymakers, requiring the implementation of control strategies under significant time constraints and uncertainty. Mathematical models can be used to predict the outcome of control interventions, providing useful information to policymakers in the event of such an epidemic. However, these models suffer in the early stages of an outbreak from a lack of accurate, relevant information regarding the dynamics and spread of the disease and the efficacy of control. As such, recommendations provided by these models are often incorporated in an ad hoc fashion, as and when more reliable information becomes available. In this work, we show that such trial-and-error-type approaches to management, which do not formally take into account the resolution of uncertainty and how control actions affect this, can lead to sub-optimal management outcomes. We compare three approaches to managing a theoretical epidemic: a non-adaptive management (AM) approach that does not use real-time outbreak information to adapt control, a passive AM approach that incorporates real-time information if and when it becomes available, and an active AM approach that explicitly incorporates the future resolution of uncertainty through gathering real-time information into its initial recommendations. The structured framework of active AM encourages the specification of quantifiable objectives, models of system behaviour and possible control and monitoring actions, followed by an iterative learning and control phase that is able to employ complex control optimisations and resolve system uncertainty. The result is a management framework that is able to provide dynamic, long-term projections to help policymakers meet the objectives of management. We investigate in detail the effect of different methods of incorporating up-to-date outbreak information. We find that, even in a highly simplified system, the method of incorporating new data can lead to different results that may influence initial policy decisions, with an active AM approach to management providing better information that can lead to more desirable outcomes from an epidemic., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)
- Published
- 2020
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39. COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support.
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Shea K, Borchering RK, Probert WJM, Howerton E, Bogich TL, Li S, van Panhuis WG, Viboud C, Aguás R, Belov A, Bhargava SH, Cavany S, Chang JC, Chen C, Chen J, Chen S, Chen Y, Childs LM, Chow CC, Crooker I, Valle SYD, España G, Fairchild G, Gerkin RC, Germann TC, Gu Q, Guan X, Guo L, Hart GR, Hladish TJ, Hupert N, Janies D, Kerr CC, Klein DJ, Klein E, Lin G, Manore C, Meyers LA, Mittler J, Mu K, Núñez RC, Oidtman R, Pasco R, Piontti APY, Paul R, Pearson CAB, Perdomo DR, Perkins TA, Pierce K, Pillai AN, Rael RC, Rosenfeld K, Ross CW, Spencer JA, Stoltzfus AB, Toh KB, Vattikuti S, Vespignani A, Wang L, White L, Xu P, Yang Y, Yogurtcu ON, Zhang W, Zhao Y, Zou D, Ferrari M, Pannell D, Tildesley M, Seifarth J, Johnson E, Biggerstaff M, Johansson M, Slayton RB, Levander J, Stazer J, Salerno J, and Runge MC
- Abstract
Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.
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- 2020
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40. Decision-making for foot-and-mouth disease control: Objectives matter.
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Probert WJ, Shea K, Fonnesbeck CJ, Runge MC, Carpenter TE, Dürr S, Garner MG, Harvey N, Stevenson MA, Webb CT, Werkman M, Tildesley MJ, and Ferrari MJ
- Subjects
- Animals, Decision Support Techniques, Disease Outbreaks prevention & control, Foot-and-Mouth Disease prevention & control, Foot-and-Mouth Disease transmission
- Abstract
Formal decision-analytic methods can be used to frame disease control problems, the first step of which is to define a clear and specific objective. We demonstrate the imperative of framing clearly-defined management objectives in finding optimal control actions for control of disease outbreaks. We illustrate an analysis that can be applied rapidly at the start of an outbreak when there are multiple stakeholders involved with potentially multiple objectives, and when there are also multiple disease models upon which to compare control actions. The output of our analysis frames subsequent discourse between policy-makers, modellers and other stakeholders, by highlighting areas of discord among different management objectives and also among different models used in the analysis. We illustrate this approach in the context of a hypothetical foot-and-mouth disease (FMD) outbreak in Cumbria, UK using outputs from five rigorously-studied simulation models of FMD spread. We present both relative rankings and relative performance of controls within each model and across a range of objectives. Results illustrate how control actions change across both the base metric used to measure management success and across the statistic used to rank control actions according to said metric. This work represents a first step towards reconciling the extensive modelling work on disease control problems with frameworks for structured decision making., (Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.)
- Published
- 2016
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41. Effect of risk aversion on prioritizing conservation projects.
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Tulloch AI, Maloney RF, Joseph LN, Bennett JR, Di Fonzo MM, Probert WJ, O'Connor SM, Densem JP, and Possingham HP
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- Animals, Conservation of Natural Resources legislation & jurisprudence, Cost-Benefit Analysis, Invertebrates, New Zealand, Plants, Risk, Uncertainty, Vertebrates, Conservation of Natural Resources economics, Conservation of Natural Resources methods, Decision Making
- Abstract
Conservation outcomes are uncertain. Agencies making decisions about what threat mitigation actions to take to save which species frequently face the dilemma of whether to invest in actions with high probability of success and guaranteed benefits or to choose projects with a greater risk of failure that might provide higher benefits if they succeed. The answer to this dilemma lies in the decision maker's aversion to risk--their unwillingness to accept uncertain outcomes. Little guidance exists on how risk preferences affect conservation investment priorities. Using a prioritization approach based on cost effectiveness, we compared 2 approaches: a conservative probability threshold approach that excludes investment in projects with a risk of management failure greater than a fixed level, and a variance-discounting heuristic used in economics that explicitly accounts for risk tolerance and the probabilities of management success and failure. We applied both approaches to prioritizing projects for 700 of New Zealand's threatened species across 8303 management actions. Both decision makers' risk tolerance and our choice of approach to dealing with risk preferences drove the prioritization solution (i.e., the species selected for management). Use of a probability threshold minimized uncertainty, but more expensive projects were selected than with variance discounting, which maximized expected benefits by selecting the management of species with higher extinction risk and higher conservation value. Explicitly incorporating risk preferences within the decision making process reduced the number of species expected to be safe from extinction because lower risk tolerance resulted in more species being excluded from management, but the approach allowed decision makers to choose a level of acceptable risk that fit with their ability to accommodate failure. We argue for transparency in risk tolerance and recommend that decision makers accept risk in an adaptive management framework to maximize benefits and avoid potential extinctions due to inefficient allocation of limited resources., (© 2014 Society for Conservation Biology.)
- Published
- 2015
- Full Text
- View/download PDF
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