1
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Eze PU, Geard N, Baker CM, Campbell PT, Chades I. Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19. BMC Health Serv Res 2023; 23:485. [PMID: 37179300 PMCID: PMC10182758 DOI: 10.1186/s12913-023-09479-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 04/29/2023] [Indexed: 05/15/2023] Open
Abstract
BACKGROUND During the early stages of the COVID-19 pandemic, there was considerable uncertainty surrounding epidemiological and clinical aspects of SARS-CoV-2. Governments around the world, starting from varying levels of pandemic preparedness, needed to make decisions about how to respond to SARS-CoV-2 with only limited information about transmission rates, disease severity and the likely effectiveness of public health interventions. In the face of such uncertainties, formal approaches to quantifying the value of information can help decision makers to prioritise research efforts. METHODS In this study we use Value of Information (VoI) analysis to quantify the likely benefit associated with reducing three key uncertainties present in the early stages of the COVID-19 pandemic: the basic reproduction number ([Formula: see text]), case severity (CS), and the relative infectiousness of children compared to adults (CI). The specific decision problem we consider is the optimal level of investment in intensive care unit (ICU) beds. Our analysis incorporates mathematical models of disease transmission and clinical pathways in order to estimate ICU demand and disease outcomes across a range of scenarios. RESULTS We found that VoI analysis enabled us to estimate the relative benefit of resolving different uncertainties about epidemiological and clinical aspects of SARS-CoV-2. Given the initial beliefs of an expert, obtaining more information about case severity had the highest parameter value of information, followed by the basic reproduction number [Formula: see text]. Resolving uncertainty about the relative infectiousness of children did not affect the decision about the number of ICU beds to be purchased for any COVID-19 outbreak scenarios defined by these three parameters. CONCLUSION For the scenarios where the value of information was high enough to justify monitoring, if CS and [Formula: see text] are known, management actions will not change when we learn about child infectiousness. VoI is an important tool for understanding the importance of each disease factor during outbreak preparedness and can help to prioritise the allocation of resources for relevant information.
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Affiliation(s)
- Peter U Eze
- School of Computing and Information Systems, University of Melbourne, Victoria, Australia.
| | - Nicholas Geard
- School of Computing and Information Systems, University of Melbourne, Victoria, Australia
| | - Christopher M Baker
- School of Mathematics and Statistics, University of Melbourne, Victoria, Australia
- Melbourne Centre for Data Science, University of Melbourne, Victoria, Australia
- Centre of Excellence for Biosecurity Risk Analysis, University of Melbourne, Victoria, Australia
| | - Patricia T Campbell
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, University of Melbourne, Victoria, Australia
- Melbourne School of Population and Global Health, University of Melbourne, Victoria, Australia
| | - Iadine Chades
- CSIRO Land and Water Dutton Park, CSIRO, Brisbane, Australia
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2
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Flaig J, Houy N. Optimal Epidemic Control under Uncertainty: Tradeoffs between Information Collection and Other Actions. Med Decis Making 2023; 43:350-361. [PMID: 36843493 DOI: 10.1177/0272989x231158295] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2023]
Abstract
BACKGROUND Recent epidemics and measures taken to control them-through vaccination or other actions-have highlighted the role and importance of uncertainty in public health. There is generally a tradeoff between information collection and other uses of resources. Whether this tradeoff is solved explicitly or implicitly, the concept of value of information is central to inform policy makers in an uncertain environment. METHOD We use a deterministic SIR (susceptible, infectious, recovered) disease emergence and transmission model with vaccination that can be administered as 1 or 2 doses. The disease parameters and vaccine characteristics are uncertain. We study the tradeoffs between information acquisition and 2 other measures: bringing vaccination forward and acquiring more vaccine doses. To do this, we quantify the expected value of perfect information (EVPI) under different constraints faced by public health authorities (i.e., the time of the vaccination campaign implementation and the number of vaccine doses available). RESULTS We discuss the appropriateness of different responses under uncertainty. We show that, in some cases, vaccinating later or with less vaccine doses but more information about the epidemic, and the efficacy of control strategies may bring better results than vaccinating earlier or with more doses and less information, respectively. CONCLUSION In the present methodological article, we show in an abstract setting how clearly defining and treating the tradeoff between information acquisition and the relaxation of constraints can improve public health decision making. HIGHLIGHTS Uncertainties can seriously hinder epidemic control, but resolving them is costly. Thus, there are tradeoffs between information collection and alternative uses of resources.We use a generic SIR model with vaccination and a value-of-information framework to explore these tradeoffs.We show in which cases vaccinating later with more information about the epidemic and the efficacy of control measures may be better-or not-than vaccinating earlier with less information.We show in which cases vaccinating with fewer vaccine doses and more information about the epidemic and the efficacy of control measures may be better-or not-than vaccinating with more doses and less information.
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Affiliation(s)
- Julien Flaig
- Epidemiology and Modelling of Infectious Diseases (EPIMOD), Lyon, France
| | - Nicolas Houy
- University of Lyon, Lyon, France.,CNRS, GATE Lyon Saint-Etienne, France
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3
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Tao Y, Hite JL, Lafferty KD, Earn DJD, Bharti N. Transient disease dynamics across ecological scales. THEOR ECOL-NETH 2021; 14:625-640. [PMID: 34075317 PMCID: PMC8156581 DOI: 10.1007/s12080-021-00514-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 05/04/2021] [Indexed: 11/25/2022]
Abstract
Analyses of transient dynamics are critical to understanding infectious disease transmission and persistence. Identifying and predicting transients across scales, from within-host to community-level patterns, plays an important role in combating ongoing epidemics and mitigating the risk of future outbreaks. Moreover, greater emphases on non-asymptotic processes will enable timely evaluations of wildlife and human diseases and lead to improved surveillance efforts, preventive responses, and intervention strategies. Here, we explore the contributions of transient analyses in recent models spanning the fields of epidemiology, movement ecology, and parasitology. In addition to their roles in predicting epidemic patterns and endemic outbreaks, we explore transients in the contexts of pathogen transmission, resistance, and avoidance at various scales of the ecological hierarchy. Examples illustrate how (i) transient movement dynamics at the individual host level can modify opportunities for transmission events over time; (ii) within-host energetic processes often lead to transient dynamics in immunity, pathogen load, and transmission potential; (iii) transient connectivity between discrete populations in response to environmental factors and outbreak dynamics can affect disease spread across spatial networks; and (iv) increasing species richness in a community can provide transient protection to individuals against infection. Ultimately, we suggest that transient analyses offer deeper insights and raise new, interdisciplinary questions for disease research, consequently broadening the applications of dynamical models for outbreak preparedness and management. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s12080-021-00514-w.
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Affiliation(s)
- Yun Tao
- Intelligence Community Postdoctoral Research Fellowship Program, Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, CA 93106 USA
| | - Jessica L. Hite
- School of Veterinary Medicine, Department of Pathobiological Sciences, University of Wisconsin, Madison, WI 53706 USA
| | - Kevin D. Lafferty
- Western Ecological Research Center at UCSB Marine Science Institute, U.S. Geological Survey, CA 93106 Santa Barbara, USA
| | - David J. D. Earn
- Department of Mathematics and Statistics, McMaster University, Hamilton, ON L8S 4K1 Canada
| | - Nita Bharti
- Department of Biology Center for Infectious Disease Dynamics, Penn State University, University Park, PA 16802 USA
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4
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Tao Y, Probert WJM, Shea K, Runge MC, Lafferty K, Tildesley M, Ferrari M. Causes of delayed outbreak responses and their impacts on epidemic spread. J R Soc Interface 2021; 18:20200933. [PMID: 33653111 PMCID: PMC8086880 DOI: 10.1098/rsif.2020.0933] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Livestock diseases have devastating consequences economically, socially and politically across the globe. In certain systems, pathogens remain viable after host death, which enables residual transmissions from infected carcasses. Rapid culling and carcass disposal are well-established strategies for stamping out an outbreak and limiting its impact; however, wait-times for these procedures, i.e. response delays, are typically farm-specific and time-varying due to logistical constraints. Failing to incorporate variable response delays in epidemiological models may understate outbreak projections and mislead management decisions. We revisited the 2001 foot-and-mouth epidemic in the United Kingdom and sought to understand how misrepresented response delays can influence model predictions. Survival analysis identified farm size and control demand as key factors that impeded timely culling and disposal activities on individual farms. Using these factors in the context of an existing policy to predict local variation in response times significantly affected predictions at the national scale. Models that assumed fixed, timely responses grossly underestimated epidemic severity and its long-term consequences. As a result, this study demonstrates how general inclusion of response dynamics and recognition of partial controllability of interventions can help inform management priorities during epidemics of livestock diseases.
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Affiliation(s)
- Yun Tao
- Intelligence Community Postdoctoral Research Fellowship Program, Oak Ridge, TN, USA.,Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, CA, USA
| | - William J M Probert
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Katriona Shea
- Department of Biology, 208 Mueller Laboratory, Pennsylvania State University, University Park, PA, USA.,The Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, USA
| | - Michael C Runge
- US Geological Survey, Patuxent Wildlife Research Center, Laurel, MD, USA
| | - Kevin Lafferty
- US Geological Survey, Western Ecological Research Center at Marine Science Institute, University of California, Santa Barbara, CA, USA
| | - Michael Tildesley
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, West Midlands, UK
| | - Matthew Ferrari
- Department of Biology, 208 Mueller Laboratory, Pennsylvania State University, University Park, PA, USA.,The Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, USA
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5
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Anticipating future learning affects current control decisions: A comparison between passive and active adaptive management in an epidemiological setting. J Theor Biol 2020; 506:110380. [PMID: 32698028 PMCID: PMC7511697 DOI: 10.1016/j.jtbi.2020.110380] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 03/19/2020] [Accepted: 06/15/2020] [Indexed: 11/21/2022]
Abstract
Adaptive epidemic control. Using real-time outbreak information to improve epidemic control. Active Adaptive Management in an epidemiological setting. Analysing the interaction between control and monitoring during an epidemic.
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.
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6
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van Andel M, Tildesley MJ, Gates MC. Challenges and opportunities for using national animal datasets to support foot-and-mouth disease control. Transbound Emerg Dis 2020; 68:1800-1813. [PMID: 32986919 DOI: 10.1111/tbed.13858] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 09/20/2020] [Accepted: 09/21/2020] [Indexed: 11/29/2022]
Abstract
National level databases of animal numbers, locations and movements provide the essential foundations for disease preparedness, outbreak investigations and control activities. These activities are particularly important for managing and mitigating the risks of high-impact transboundary animal disease outbreaks such as foot-and-mouth disease (FMD), which can significantly affect international trade access and domestic food security. In countries where livestock production systems are heavily subsidized by the government, producers are often required to provide detailed animal movement and demographic data as a condition of business. In the remaining countries, it can be difficult to maintain these types of databases and impossible to estimate the extent of missing or inaccurate information due to the absence of gold standard datasets for comparison. Consequently, competent authorities are often required to make decisions about disease preparedness and control based on available data, which may result in suboptimal outcomes for their livestock industries. It is important to understand the limitations of poor data quality as well as the range of methods that have been developed to compensate in both disease-free and endemic situations. Using FMD as a case example, this review first discusses the different activities that competent authorities use farm-level animal population data for to support (1) preparedness activities in disease-free countries, (2) response activities during an acute outbreak in a disease-free country, and (3) eradication and control activities in an endemic country. We then discuss (4) data requirements needed to support epidemiological investigations, surveillance, and disease spread modelling both in disease-free and endemic countries.
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Affiliation(s)
- Mary van Andel
- Ministry for Primary Industries, Operations Branch, Diagnostic and Surveillance Services Directorate, Wallaceville, New Zealand
| | - Michael J Tildesley
- School of Life Sciences, Gibbet Hill Campus, The University of Warwick, Coventry, UK
| | - M Carolyn Gates
- School of Veterinary Science, Massey University, Palmerston North, New Zealand
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7
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Bernard RF, Evans J, Fuller NW, Reichard JD, Coleman JTH, Kocer CJ, Campbell Grant EH. Different management strategies are optimal for combating disease in East Texas cave versus culvert hibernating bat populations. CONSERVATION SCIENCE AND PRACTICE 2019. [DOI: 10.1111/csp2.106] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Riley F. Bernard
- Department of Ecosystem Science and Management Pennsylvania State University University Park Pennsylvania
- U.S. Geological Survey, Patuxent Wildlife Research Center, S. O. Conte Anadromous Fish Laboratory Turners Falls Massachusetts
| | - Jonah Evans
- Texas Parks and Wildlife Department Boerne Texas
| | - Nathan W. Fuller
- Department of Biological Sciences Texas Tech University Lubbock Texas
| | | | | | | | - Evan H. Campbell Grant
- U.S. Geological Survey, Patuxent Wildlife Research Center, S. O. Conte Anadromous Fish Laboratory Turners Falls Massachusetts
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8
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Probert WJM, Lakkur S, Fonnesbeck CJ, Shea K, Runge MC, Tildesley MJ, Ferrari MJ. Context matters: using reinforcement learning to develop human-readable, state-dependent outbreak response policies. Philos Trans R Soc Lond B Biol Sci 2019; 374:20180277. [PMID: 31104604 PMCID: PMC6558555 DOI: 10.1098/rstb.2018.0277] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/26/2019] [Indexed: 02/06/2023] Open
Abstract
The number of all possible epidemics of a given infectious disease that could occur on a given landscape is large for systems of real-world complexity. Furthermore, there is no guarantee that the control actions that are optimal, on average, over all possible epidemics are also best for each possible epidemic. Reinforcement learning (RL) and Monte Carlo control have been used to develop machine-readable context-dependent solutions for complex problems with many possible realizations ranging from video-games to the game of Go. RL could be a valuable tool to generate context-dependent policies for outbreak response, though translating the resulting policies into simple rules that can be read and interpreted by human decision-makers remains a challenge. Here we illustrate the application of RL to the development of context-dependent outbreak response policies to minimize outbreaks of foot-and-mouth disease. We show that control based on the resulting context-dependent policies, which adapt interventions to the specific outbreak, result in smaller outbreaks than static policies. We further illustrate two approaches for translating the complex machine-readable policies into simple heuristics that can be evaluated by human decision-makers. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.
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Affiliation(s)
- W. J. M. Probert
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7LF, UK
| | - S. Lakkur
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA
| | - C. J. Fonnesbeck
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA
| | - K. Shea
- Department of Biology, Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA 16802, USA
| | - M. C. Runge
- US Geological Survey, Patuxent Wildlife Research Center, Laurel, MD 20708, USA
| | - M. J. Tildesley
- Department of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
| | - M. J. Ferrari
- Department of Biology, Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA 16802, USA
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9
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Li SL, Ferrari MJ, Bjørnstad ON, Runge MC, Fonnesbeck CJ, Tildesley MJ, Pannell D, Shea K. Concurrent assessment of epidemiological and operational uncertainties for optimal outbreak control: Ebola as a case study. Proc Biol Sci 2019; 286:20190774. [PMID: 31213182 PMCID: PMC6599986 DOI: 10.1098/rspb.2019.0774] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Determining how best to manage an infectious disease outbreak may be hindered by both epidemiological uncertainty (i.e. about epidemiological processes) and operational uncertainty (i.e. about the effectiveness of candidate interventions). However, these two uncertainties are rarely addressed concurrently in epidemic studies. We present an approach to simultaneously address both sources of uncertainty, to elucidate which source most impedes decision-making. In the case of the 2014 West African Ebola outbreak, epidemiological uncertainty is represented by a large ensemble of published models. Operational uncertainty about three classes of interventions is assessed for a wide range of potential intervention effectiveness. We ranked each intervention by caseload reduction in each model, initially assuming an unlimited budget as a counterfactual. We then assessed the influence of three candidate cost functions relating intervention effectiveness and cost for different budget levels. The improvement in management outcomes to be gained by resolving uncertainty is generally high in this study; appropriate information gain could reduce expected caseload by more than 50%. The ranking of interventions is jointly determined by the underlying epidemiological process, the effectiveness of the interventions and the size of the budget. An epidemiologically effective intervention might not be optimal if its costs outweigh its epidemiological benefit. Under higher-budget conditions, resolution of epidemiological uncertainty is most valuable. When budgets are tight, however, operational and epidemiological uncertainty are equally important. Overall, our study demonstrates that significant reductions in caseload could result from a careful examination of both epidemiological and operational uncertainties within the same modelling structure. This approach can be applied to decision-making for the management of other diseases for which multiple models and multiple interventions are available.
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Affiliation(s)
- Shou-Li Li
- 1 Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University , University Park, PA , USA.,2 State Key Laboratory of Grassland Agro-ecosystems, and College of Pastoral, Agriculture Science and Technology, Lanzhou University , People's Republic of China
| | - Matthew J Ferrari
- 1 Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University , University Park, PA , USA
| | - Ottar N Bjørnstad
- 1 Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University , University Park, PA , USA
| | - Michael C Runge
- 3 US Geological Survey, Patuxent Wildlife Research Center , Laurel, MD , USA
| | | | - Michael J Tildesley
- 5 Systems Biology and Infectious Disease Epidemiology Research Centre, School of Life Sciences and Mathematics Institute, University of Warwick , Coventry CV4 7AL , UK
| | - David Pannell
- 6 School of Agriculture and Environment, The University of Western Australia (M087) , Crawley, WA 6009 , Australia
| | - Katriona Shea
- 1 Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University , University Park, PA , USA
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10
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Arzt J, Branan MA, Delgado AH, Yadav S, Moreno-Torres KI, Tildesley MJ, Stenfeldt C. Quantitative impacts of incubation phase transmission of foot-and-mouth disease virus. Sci Rep 2019; 9:2707. [PMID: 30804426 PMCID: PMC6389902 DOI: 10.1038/s41598-019-39029-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Accepted: 01/04/2019] [Indexed: 01/13/2023] Open
Abstract
The current investigation applied a Bayesian modeling approach to a unique experimental transmission study to estimate the occurrence of transmission of foot-and-mouth disease (FMD) during the incubation phase amongst group-housed pigs. The primary outcome was that transmission occurred approximately one day prior to development of visible signs of disease (posterior median 21 hours, 95% CI: 1.1-45.0). Updated disease state durations were incorporated into a simulation model to examine the importance of addressing preclinical transmission in the face of robust response measures. Simulation of FMD outbreaks in the US pig production sector demonstrated that including a preclinical infectious period of one day would result in a 40% increase in the median number of farms affected (166 additional farms and 664,912 pigs euthanized) compared to the scenario of no preclinical transmission, assuming suboptimal outbreak response. These findings emphasize the importance of considering transmission of FMD during the incubation phase in modeling and response planning.
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Affiliation(s)
- Jonathan Arzt
- Foreign Animal Disease Research Unit, Plum Island Animal Disease Center, Agricultural Research Service, United States Department of Agriculture, Greenport, NY, USA.
| | - Matthew A Branan
- Monitoring and Modeling, Center for Epidemiology and Animal Health, Animal and Plant Health Inspection Service, United States Department of Agriculture, Fort Collins, CO, USA
| | - Amy H Delgado
- Monitoring and Modeling, Center for Epidemiology and Animal Health, Animal and Plant Health Inspection Service, United States Department of Agriculture, Fort Collins, CO, USA
| | - Shankar Yadav
- Foreign Animal Disease Research Unit, Plum Island Animal Disease Center, Agricultural Research Service, United States Department of Agriculture, Greenport, NY, USA
- Monitoring and Modeling, Center for Epidemiology and Animal Health, Animal and Plant Health Inspection Service, United States Department of Agriculture, Fort Collins, CO, USA
- PIADC Research Participation Program, Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA
| | - Karla I Moreno-Torres
- Foreign Animal Disease Research Unit, Plum Island Animal Disease Center, Agricultural Research Service, United States Department of Agriculture, Greenport, NY, USA
- Monitoring and Modeling, Center for Epidemiology and Animal Health, Animal and Plant Health Inspection Service, United States Department of Agriculture, Fort Collins, CO, USA
- PIADC Research Participation Program, Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA
| | - Michael J Tildesley
- Zeeman Institute (SBIDER), School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, UK
| | - Carolina Stenfeldt
- Foreign Animal Disease Research Unit, Plum Island Animal Disease Center, Agricultural Research Service, United States Department of Agriculture, Greenport, NY, USA.
- Department of Veterinary Population Biology, University of Minnesota, St. Paul, MN, USA.
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11
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Porphyre T, Rich KM, Auty HK. Assessing the Economic Impact of Vaccine Availability When Controlling Foot and Mouth Disease Outbreaks. Front Vet Sci 2018; 5:47. [PMID: 29594161 PMCID: PMC5859371 DOI: 10.3389/fvets.2018.00047] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Accepted: 02/23/2018] [Indexed: 11/26/2022] Open
Abstract
Predictive models have been used extensively to assess the likely effectiveness of vaccination policies as part of control measures in the event of a foot and mouth disease (FMD) outbreak. However, the availability of vaccine stocks and the impact of vaccine availability on disease control strategies represent a key uncertainty when assessing potential control strategies. Using an epidemiological, spatially explicit, simulation model in combination with a direct cost calculator, we assessed how vaccine availability constraints may affect the economic benefit of a “vaccination-to-live” strategy during a FMD outbreak in Scotland, when implemented alongside culling of infected premises and dangerous contacts. We investigated the impact of vaccine stock size and restocking delays on epidemiological and economic outcomes. We also assessed delays in the initial decision to vaccinate, maximum daily vaccination capacity, and vaccine efficacy. For scenarios with conditions conducive to large outbreaks, all vaccination strategies perform better than the strategy where only culling is implemented. A stock of 200,000 doses, enough to vaccinate 12% of the Scottish cattle population, would be sufficient to maximize the relative benefits of vaccination, both epidemiologically and economically. However, this generates a wider variation in economic cost than if vaccination is not implemented, making outcomes harder to predict. The probability of direct costs exceeding £500 million is reduced when vaccination is used and is steadily reduced further as the size of initial vaccine stock increases. If only a suboptimal quantity of vaccine doses is initially available (100,000 doses), restocking delays of more than 2 weeks rapidly increase the cost of controlling outbreaks. Impacts of low vaccine availability or restocking delays are particularly aggravated by delays in the initial decision to vaccinate, or low vaccine efficacy. Our findings confirm that implementing an emergency vaccination-to-live strategy in addition to the conventional stamping out strategy is economically beneficial in scenarios with conditions conducive to large FMD outbreaks in Scotland. However, the size of the initial vaccine stock available at the start of the outbreak and the interplay with other factors, such as vaccine efficacy and delays in restocking or implementing vaccination, should be considered in making decisions about optimal control strategies for FMD outbreaks.
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Affiliation(s)
- Thibaud Porphyre
- Royal (Dick) School of Veterinary Studies, The Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
| | - Karl M Rich
- Epidemiology Research Unit, Scotland's Rural College, Inverness, United Kingdom.,East and Southeast Asia Regional Office, International Livestock Research Institute, Hanoi, Vietnam
| | - Harriet K Auty
- Epidemiology Research Unit, Scotland's Rural College, Inverness, United Kingdom
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12
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Thompson RN, Gilligan CA, Cunniffe NJ. Control fast or control smart: When should invading pathogens be controlled? PLoS Comput Biol 2018; 14:e1006014. [PMID: 29451878 PMCID: PMC5833286 DOI: 10.1371/journal.pcbi.1006014] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Revised: 03/01/2018] [Accepted: 02/04/2018] [Indexed: 12/20/2022] Open
Abstract
The intuitive response to an invading pathogen is to start disease management as rapidly as possible, since this would be expected to minimise the future impacts of disease. However, since more spread data become available as an outbreak unfolds, processes underpinning pathogen transmission can almost always be characterised more precisely later in epidemics. This allows the future progression of any outbreak to be forecast more accurately, and so enables control interventions to be targeted more precisely. There is also the chance that the outbreak might die out without any intervention whatsoever, making prophylactic control unnecessary. Optimal decision-making involves continuously balancing these potential benefits of waiting against the possible costs of further spread. We introduce a generic, extensible data-driven algorithm based on parameter estimation and outbreak simulation for making decisions in real-time concerning when and how to control an invading pathogen. The Control Smart Algorithm (CSA) resolves the trade-off between the competing advantages of controlling as soon as possible and controlling later when more information has become available. We show-using a generic mathematical model representing the transmission of a pathogen of agricultural animals or plants through a population of farms or fields-how the CSA allows the timing and level of deployment of vaccination or chemical control to be optimised. In particular, the algorithm outperforms simpler strategies such as intervening when the outbreak size reaches a pre-specified threshold, or controlling when the outbreak has persisted for a threshold length of time. This remains the case even if the simpler methods are fully optimised in advance. Our work highlights the potential benefits of giving careful consideration to the question of when to start disease management during emerging outbreaks, and provides a concrete framework to allow policy-makers to make this decision.
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Affiliation(s)
- Robin N. Thompson
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, United Kingdom
- Department of Zoology, University of Oxford, Oxford OX1 3PS, United Kingdom
- Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Oxford OX2 6GG, United Kingdom
- Christ Church, University of Oxford, Oxford OX1 1DP, United Kingdom
| | | | - Nik J. Cunniffe
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, United Kingdom
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Motta P, Handel IG, Rydevik G, Hamman SM, Ngwa VN, Tanya VN, Morgan KL, Bronsvoort BMD, Porphyre T. Drivers of Live Cattle Price in the Livestock Trading System of Central Cameroon. Front Vet Sci 2018; 4:244. [PMID: 29387687 PMCID: PMC5776083 DOI: 10.3389/fvets.2017.00244] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Accepted: 12/21/2017] [Indexed: 11/23/2022] Open
Abstract
Livestock production and trade are critical for the food security and welfare of rural households in sub-Saharan Africa. In Cameroon, animal trade consists mainly of live cattle commercialized through livestock markets. Identifying the factors contributing to cattle price formation is critical for designing effective policies for sustainable production and for increasing food availability. In this study, we evaluated the influence of a range of individual- and market-level factors on the price of cattle that were sold in all transactions (n = 118,017) recorded over a 12-month period from 31 livestock markets in the main cattle production area of the country. An information-theoretic approach using a generalized additive mixed-effect model was implemented to select the best explanatory model as well as evaluate the robustness of the identified drivers and the predictive ability of the model. The age and gender of the cattle traded were consistently found to be important drivers of the price (p < 0.01). Also, strong, but complex, relationships were found between cattle prices and both local human and bovine population densities. Finally, the model highlighted a positive association between the number of incoming trading connections of a livestock market and the price of the traded live cattle (p < 0.01). Although our analysis did not account for factors informing on specific phenotypic traits nor breed characteristics of cattle traded, nearly 50% of the observed variation in live cattle prices was explained by the final model. Ultimately, our model gives a large scale overview of drivers of cattle price formation in Cameroon and to our knowledge is the first study of this scale in Central Africa. Our findings represent an important milestone in designing efficient and sustainable animal health management programme in Cameroon and ensure livelihood sustainability for rural households.
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Affiliation(s)
- Paolo Motta
- Royal (Dick) School of Veterinary Studies, The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, United Kingdom.,The European Commission for the Control of Foot-and-Mouth Disease (EuFMD), Food and Agriculture Organization of the United Nations, Rome, Italy
| | - Ian G Handel
- Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, United Kingdom
| | - Gustaf Rydevik
- Royal (Dick) School of Veterinary Studies, The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, United Kingdom
| | - Saidou M Hamman
- Institute of Agricultural Research for Development, Regional Centre of Wakwa, Ngaoundere, Cameroon
| | - Victor Ngu Ngwa
- School of Veterinary Medicine and Sciences, University of Ngaoundere, Ngaoundere, Cameroon
| | | | - Kenton L Morgan
- School of Veterinary Science, Institute of Ageing and Chronic Disease, University of Liverpool, Neston, Wirral, United Kingdom
| | - Barend M deC Bronsvoort
- Royal (Dick) School of Veterinary Studies, The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, United Kingdom
| | - Thibaud Porphyre
- Royal (Dick) School of Veterinary Studies, The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, United Kingdom
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