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Ellis J, Brown E, Colenutt C, Schley D, Gubbins S. Inferring transmission routes for foot-and-mouth disease virus within a cattle herd using approximate Bayesian computation. Epidemics 2024; 46:100740. [PMID: 38232411 DOI: 10.1016/j.epidem.2024.100740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 12/06/2023] [Accepted: 01/03/2024] [Indexed: 01/19/2024] Open
Abstract
To control an outbreak of an infectious disease it is essential to understand the different routes of transmission and how they contribute to the overall spread of the pathogen. With this information, policy makers can choose the most efficient methods of detection and control during an outbreak. Here we assess the contributions of direct contact and environmental contamination to the transmission of foot-and-mouth disease virus (FMDV) in a cattle herd using an individual-based model that includes both routes. Model parameters are inferred using approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC) applied to data from transmission experiments and the 2007 epidemic in Great Britain. This demonstrates that the parameters derived from transmission experiments are applicable to outbreaks in the field, at least for closely related strains. Under the assumptions made in the model we show that environmental transmission likely contributes a majority of infections within a herd during an outbreak, although there is a lot of variation between simulated outbreaks. The accumulation of environmental contamination not only causes infections within a farm, but also has the potential to spread between farms via fomites. We also demonstrate the importance and effectiveness of rapid detection of infected farms in reducing transmission between farms, whether via direct contact or the environment.
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Affiliation(s)
- John Ellis
- The Pirbright Institute, Pirbright, Surrey, UK.
| | - Emma Brown
- The Pirbright Institute, Pirbright, Surrey, UK
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2
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Seibel RL, Meadows AJ, Mundt C, Tildesley M. Modeling target-density-based cull strategies to contain foot-and-mouth disease outbreaks. PeerJ 2024; 12:e16998. [PMID: 38436010 PMCID: PMC10909358 DOI: 10.7717/peerj.16998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 02/02/2024] [Indexed: 03/05/2024] Open
Abstract
Total ring depopulation is sometimes used as a management strategy for emerging infectious diseases in livestock, which raises ethical concerns regarding the potential slaughter of large numbers of healthy animals. We evaluated a farm-density-based ring culling strategy to control foot-and-mouth disease (FMD) in the United Kingdom (UK), which may allow for some farms within rings around infected premises (IPs) to escape depopulation. We simulated this reduced farm density, or "target density", strategy using a spatially-explicit, stochastic, state-transition algorithm. We modeled FMD spread in four counties in the UK that have different farm demographics, using 740,000 simulations in a full-factorial analysis of epidemic impact measures (i.e., culled animals, culled farms, and epidemic length) and cull strategy parameters (i.e., target farm density, daily farm cull capacity, and cull radius). All of the cull strategy parameters listed above were drivers of epidemic impact. Our simulated target density strategy was usually more effective at combatting FMD compared with traditional total ring depopulation when considering mean culled animals and culled farms and was especially effective when daily farm cull capacity was low. The differences in epidemic impact measures among the counties are likely driven by farm demography, especially differences in cattle and farm density. To prevent over-culling and the associated economic, organizational, ethical, and psychological impacts, the target density strategy may be worth considering in decision-making processes for future control of FMD and other diseases.
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Affiliation(s)
- Rachel L. Seibel
- Mathematics Institute, University of Warwick, Coventry, West Midlands, United Kingdom
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States
| | - Amanda J. Meadows
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States
- Ginkgo Bioworks, San Bruno, California, United States
| | - Christopher Mundt
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States
| | - Michael Tildesley
- Mathematics Institute, University of Warwick, Coventry, West Midlands, United Kingdom
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3
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Hill EM, Prosser NS, Brown PE, Ferguson E, Green MJ, Kaler J, Keeling MJ, Tildesley MJ. Incorporating heterogeneity in farmer disease control behaviour into a livestock disease transmission model. Prev Vet Med 2023; 219:106019. [PMID: 37699310 DOI: 10.1016/j.prevetmed.2023.106019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 08/07/2023] [Accepted: 08/29/2023] [Indexed: 09/14/2023]
Abstract
Human behaviour is critical to effective responses to livestock disease outbreaks, especially with respect to vaccination uptake. Traditionally, mathematical models used to inform this behaviour have not taken heterogeneity in farmer behaviour into account. We address this by exploring how heterogeneity in farmers vaccination behaviour can be incorporated to inform mathematical models. We developed and used a graphical user interface to elicit farmers (n = 60) vaccination decisions to an unfolding fast-spreading epidemic and linked this to their psychosocial and behavioural profiles. We identified, via cluster analysis, robust patterns of heterogeneity in vaccination behaviour. By incorporating these vaccination behavioural groupings into a mathematical model for a fast-spreading livestock infection, using computational simulation we explored how the inclusion of heterogeneity in farmer disease control behaviour may impact epidemiological and economic focused outcomes. When assuming homogeneity in farmer behaviour versus configurations informed by the psychosocial profile cluster estimates, the modelled scenarios revealed a disconnect in projected distributions and threshold statistics across outbreak size, outbreak duration and economic metrics.
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Affiliation(s)
- Edward M Hill
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom.
| | - Naomi S Prosser
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Leicestershire, United Kingdom
| | - Paul E Brown
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom
| | - Eamonn Ferguson
- School of Psychology, University Park, University of Nottingham, Nottingham, United Kingdom; National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, United Kingdom
| | - Martin J Green
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Leicestershire, United Kingdom
| | - Jasmeet Kaler
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Leicestershire, United Kingdom
| | - Matt J Keeling
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom
| | - Michael J Tildesley
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom
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4
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Gilbertson K, Brommesson P, Minter A, Hallman C, Miller RS, Portacci K, Sellman S, Tildesley MJ, Webb CT, Lindström T, Beck-Johnson LM. The Importance of Livestock Demography and Infrastructure in Driving Foot and Mouth Disease Dynamics. Life (Basel) 2022; 12:1604. [PMID: 36295038 PMCID: PMC9605081 DOI: 10.3390/life12101604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 09/25/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023] Open
Abstract
Transboundary animal diseases, such as foot and mouth disease (FMD) pose a significant and ongoing threat to global food security. Such diseases can produce large, spatially complex outbreaks. Mathematical models are often used to understand the spatio-temporal dynamics and create response plans for possible disease introductions. Model assumptions regarding transmission behavior of premises and movement patterns of livestock directly impact our understanding of the ecological drivers of outbreaks and how to best control them. Here, we investigate the impact that these assumptions have on model predictions of FMD outbreaks in the U.S. using models of livestock shipment networks and disease spread. We explore the impact of changing assumptions about premises transmission behavior, both by including within-herd dynamics, and by accounting for premises type and increasing the accuracy of shipment predictions. We find that the impact these assumptions have on outbreak predictions is less than the impact of the underlying livestock demography, but that they are important for investigating some response objectives, such as the impact on trade. These results suggest that demography is a key ecological driver of outbreaks and is critical for making robust predictions but that understanding management objectives is also important when making choices about model assumptions.
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Affiliation(s)
- Kendra Gilbertson
- Department of Biology, Colorado State University, 1878 Campus Delivery, Fort Collins, CO 80523, USA
| | - Peter Brommesson
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, 581 83 Linköping, Sweden
| | - Amanda Minter
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
| | - Clayton Hallman
- USDA APHIS Veterinary Services, Center for Epidemiology and Animal Health, Fort Collins, CO 80526, USA
| | - Ryan S. Miller
- USDA APHIS Veterinary Services, Center for Epidemiology and Animal Health, Fort Collins, CO 80526, USA
| | - Katie Portacci
- USDA APHIS Veterinary Services, Center for Epidemiology and Animal Health, Fort Collins, CO 80526, USA
| | - Stefan Sellman
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, 581 83 Linköping, Sweden
| | - Michael J. Tildesley
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
| | - Colleen T. Webb
- Department of Biology, Colorado State University, 1878 Campus Delivery, Fort Collins, CO 80523, USA
| | - Tom Lindström
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, 581 83 Linköping, Sweden
| | - Lindsay M. Beck-Johnson
- Department of Biology, Colorado State University, 1878 Campus Delivery, Fort Collins, CO 80523, USA
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5
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Chaulagain B, Contina JB, Mills K, Seibel RL, Mundt CC. Comparing the efficacy of control strategies for infectious disease outbreaks using field and simulation studies. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e2631. [PMID: 35403765 DOI: 10.1002/eap.2631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 02/03/2022] [Accepted: 02/09/2022] [Indexed: 06/14/2023]
Abstract
Diseases characterized by long distance inoculum dispersal (LDD) are among the fastest spreading epidemics in both natural and managed landscapes. Management of such epidemics is extremely challenging because of asymptomatic infection extending at large spatial scales and frequent escape from the newly established disease sources. We compared the efficacy of area- and timing-based disease management strategies in artificially initiated field epidemics of wheat stripe rust and complemented with simulations from an updated version of the spatially explicit model EPIMUL, using model parameters relevant to field epidemics. The model was further used to expand the number of epidemic mitigations beyond that feasible to incorporate in the field. The field experiment was conducted for 2 years in two locations having different climatic conditions. Culling and protection treatments were applied at different times after epidemic initiation and to different spatial extents surrounding the outbreaks. In each experiment, treatments were replicated four times in plots 33.5 m long and 1.52 m wide with a 0.76 × 0.76 m inoculated focus centered within each plot. Disease gradients were assessed along the center lines of the plots at 1.52 m intervals both upwind and downwind from the focus. Both field and simulation results indicated that control measures applied over the entire population were highly effective in suppressing the epidemics by more than 99% but may not always be logistically and economically feasible at large spatial scales. Comparison between the variable sized treatment areas and application timings suggested that implementing contiguous premises (CP) cull at 1 day after first sporulation in the outbreak focus reduced rust by 52% and 60% in Corvallis and Madras, respectively. However, altering the cull size did not significantly affect the disease epidemic development, which suggested that early timing had a greater influence in suppressing the epidemics than did increased area of application. However, sufficiently large, treated areas may compensate for a delay in application timing to some extent. Results from these replicated treatments may help to devise appropriate management strategies for other LDD pathogens.
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Affiliation(s)
- Bhim Chaulagain
- College of Agricultural Sciences, Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon, USA
| | - Jean Bertrand Contina
- College of Agricultural Sciences, Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon, USA
| | - Karasi Mills
- College of Agricultural Sciences, Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon, USA
| | - Rachel L Seibel
- College of Agricultural Sciences, Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon, USA
| | - Christopher C Mundt
- College of Agricultural Sciences, Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon, USA
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6
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Guyver-Fletcher G, Gorsich EE, Tildesley MJ. A model exploration of carrier and movement transmission as potential explanatory causes for the persistence of foot-and-mouth disease in endemic regions. Transbound Emerg Dis 2021; 69:2712-2726. [PMID: 34936219 DOI: 10.1111/tbed.14423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 11/26/2021] [Accepted: 12/11/2021] [Indexed: 11/30/2022]
Abstract
Foot-and-mouth disease (FMD) is a virulent and economically important disease of livestock, still endemic in many areas of Asia and sub-Saharan Africa. Transmission from persistently infected livestock, also known as carriers, has been proposed as a mechanism to support the persistence of FMD in endemic regions. However, whether carrier livestock can infect susceptible animals is controversial; recovered virus is infectious and there are claims of field transmission, but it remains undemonstrated experimentally. Alternate hypotheses for persistence include the movement of livestock within and between regions, and fomite contamination of the environment. Using a stochastic compartmental ordinary differential equation (ODE) model, we investigate the minimum rates of carrier transmission necessary to contribute to the maintenance of FMD in a region, and compare this to the alternate mechanism of persistence through cattle shipments. We find that carrier transmission can theoretically support persistence even at transmission rates much lower than the highest realistic rates previously proposed, and that the parameters with the most effect on the feasibility of carrier-mediated persistence are the average duration of both the carrier phase and natural immunity. However, shipment-mediated persistence remains a viable alternate mechanism for persistence without carrier transmission.
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Affiliation(s)
- Glen Guyver-Fletcher
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK.,School of Life Sciences, University of Warwick, Coventry, UK
| | - Erin E Gorsich
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK.,School of Life Sciences, University of Warwick, Coventry, UK
| | - Michael J Tildesley
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK.,School of Life Sciences, University of Warwick, Coventry, UK.,Mathematics Institute, University of Warwick, Coventry, UK
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7
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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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8
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Jordan A, Sadler RJ, Sawford K, van Andel M, Ward M, Cowled B. Mycoplasma bovis outbreak in New Zealand cattle: An assessment of transmission trends using surveillance data. Transbound Emerg Dis 2020; 68:3381-3395. [PMID: 33259697 DOI: 10.1111/tbed.13941] [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: 05/05/2020] [Revised: 10/23/2020] [Accepted: 11/26/2020] [Indexed: 01/15/2023]
Abstract
Mycoplasma bovis most likely infected New Zealand cattle in the latter half of 2015. Infection was detected in mid-2017 after which control activities were implemented. An official eradication programme commenced in mid-2018, which is ongoing. We examined farm-level tracing and surveillance data to describe the outbreak, analyse transmission trends and make inference on progress towards eradication. Results indicate that cattle movements were the primary means of spread. Although case farms were distributed throughout both islands of New Zealand, most animal movements off infected farms did not result in newly infected farms, indicating Mycoplasma bovis is not highly transmissible between farms. To describe and analyse outbreak trends, we undertook a standard descriptive outbreak investigation, including construction of an epidemic curve and calculation of estimated dissemination ratios. We then employed three empirical models-a non-linear growth model, time series model and branching process model based on time-varying effective reproduction numbers-to further analyse transmission trends and provide short-term forecasts of farm-level incidence. Our analyses suggest that Mycoplasma bovis transmission in New Zealand has declined and progress towards eradication has been made. Few incident cases were forecast for the period between 8 September and 17 December 2019. To date, no case farms with an estimated infection date assigned to this period have been detected; however, case detection is ongoing, and these results need to be interpreted cautiously considering model validation and other important contextual information on performance of the eradication programme, such as the time between infection, detection and implementation of movement controls on case farms.
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Affiliation(s)
- AshleyG Jordan
- Ausvet Pty Ltd, Canberra, ACT, Australia.,Australian Government Department of Agriculture, Canberra, Australia
| | | | - Kate Sawford
- Ministry for Primary Industries (New Zealand), Wellington, New Zealand.,Kate Sawford Epidemiological Consulting, Braidwood, NSW, Australia
| | - Mary van Andel
- Ministry for Primary Industries (New Zealand), Wellington, New Zealand
| | - Michael Ward
- Sydney School of Veterinary Science, The University of Sydney, Sydney, NSW, Australia
| | - BrendanD Cowled
- Ausvet Pty Ltd, Canberra, ACT, Australia.,Sydney School of Veterinary Science, The University of Sydney, Sydney, NSW, Australia
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9
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Zaheer MU, Salman MD, Steneroden KK, Magzamen SL, Weber SE, Case S, Rao S. Challenges to the Application of Spatially Explicit Stochastic Simulation Models for Foot-and-Mouth Disease Control in Endemic Settings: A Systematic Review. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:7841941. [PMID: 33294003 PMCID: PMC7700052 DOI: 10.1155/2020/7841941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 10/20/2020] [Accepted: 10/30/2020] [Indexed: 11/17/2022]
Abstract
Simulation modeling has become common for estimating the spread of highly contagious animal diseases. Several models have been developed to mimic the spread of foot-and-mouth disease (FMD) in specific regions or countries, conduct risk assessment, analyze outbreaks using historical data or hypothetical scenarios, assist in policy decisions during epidemics, formulate preparedness plans, and evaluate economic impacts. Majority of the available FMD simulation models were designed for and applied in disease-free countries, while there has been limited use of such models in FMD endemic countries. This paper's objective was to report the findings from a study conducted to review the existing published original research literature on spatially explicit stochastic simulation (SESS) models of FMD spread, focusing on assessing these models for their potential use in endemic settings. The goal was to identify the specific components of endemic FMD needed to adapt these SESS models for their potential application in FMD endemic settings. This systematic review followed the PRISMA guidelines, and three databases were searched, which resulted in 1176 citations. Eighty citations finally met the inclusion criteria and were included in the qualitative synthesis, identifying nine unique SESS models. These SESS models were assessed for their potential application in endemic settings. The assessed SESS models can be adapted for use in FMD endemic countries by modifying the underlying code to include multiple cocirculating serotypes, routine prophylactic vaccination (RPV), and livestock population dynamics to more realistically mimic the endemic characteristics of FMD. The application of SESS models in endemic settings will help evaluate strategies for FMD control, which will improve livestock health, provide economic gains for producers, help alleviate poverty and hunger, and will complement efforts to achieve the Sustainable Development Goals.
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Affiliation(s)
- Muhammad Usman Zaheer
- Animal Population Health Institute, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins CO 80523, USA
- FMD Project Office, Food and Agriculture Organization of the United Nations, ASI Premises, NARC Gate # 2, Park Road, Islamabad 44000, Pakistan
| | - Mo D. Salman
- Animal Population Health Institute, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins CO 80523, USA
| | - Kay K. Steneroden
- Animal Population Health Institute, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins CO 80523, USA
| | - Sheryl L. Magzamen
- Department of Environmental and Radiological Health Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins CO 80523, USA
| | - Stephen E. Weber
- Animal Population Health Institute, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins CO 80523, USA
| | - Shaun Case
- Department of Civil and Environmental Engineering, Walter Scott, Jr. College of Engineering, Colorado State University, Fort Collins CO 80521, USA
| | - Sangeeta Rao
- Animal Population Health Institute, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins CO 80523, USA
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10
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Sumption K, Knight-Jones TJD, McLaws M, Paton DJ. Parallels, differences and lessons: a comparison of the management of foot-and-mouth disease and COVID-19 using UK 2001/2020 as points of reference. Proc Biol Sci 2020; 287:20200906. [PMID: 33143581 PMCID: PMC7735262 DOI: 10.1098/rspb.2020.0906] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Foot-and-mouth disease (FMD) is an extremely infectious viral infection of cloven-hoofed animals which is highly challenging to control and can give rise to national animal health crises, especially if there is a lack of pre-existing immunity due to the emergence of new strains or following incursions into disease-free regions. The 2001 FMD epidemic in the UK was on a scale that initially overwhelmed the national veterinary services and was eventually controlled by livestock lockdown and slaughter on an unprecedented scale. In 2020, the rapid emergence of COVID-19 has led to a human pandemic unparalleled in living memory. The enormous logistics of multi-agency control efforts for COVID-19 are reminiscent of the 2001 FMD epidemic in the UK, as are the use of movement restrictions, not normally a feature of human disease control. The UK experience is internationally relevant as few countries have experienced national epidemic crises for both diseases. In this review, we reflect on the experiences and lessons learnt from UK and international responses to FMD and COVID-19 with respect to their management, including the challenge of preclinical viral transmission, threat awareness, early detection, different interpretations of scientific information, lockdown, biosecurity behaviour change, shortage of testing capacity and the choices for eradication versus living with infection. A major lesson is that the similarity of issues and critical resources needed to manage large-scale outbreaks demonstrates that there is benefit to a ‘One Health’ approach to preparedness, with potential for greater cooperation in planning and the consideration of shared critical resources.
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Affiliation(s)
- Keith Sumption
- European Commission for the control of Foot-and-Mouth Disease, via Terme di Caracalla, Rome, Italy
| | | | - Melissa McLaws
- European Commission for the control of Foot-and-Mouth Disease, via Terme di Caracalla, Rome, Italy
| | - David J Paton
- European Commission for the control of Foot-and-Mouth Disease, via Terme di Caracalla, Rome, Italy
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11
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Latent likelihood ratio tests for assessing spatial kernels in epidemic models. J Math Biol 2020; 81:853-873. [PMID: 32892255 PMCID: PMC7519007 DOI: 10.1007/s00285-020-01529-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 08/10/2020] [Indexed: 12/02/2022]
Abstract
One of the most important issues in the critical assessment of spatio-temporal stochastic models for epidemics is the selection of the transmission kernel used to represent the relationship between infectious challenge and spatial separation of infected and susceptible hosts. As the design of control strategies is often based on an assessment of the distance over which transmission can realistically occur and estimation of this distance is very sensitive to the choice of kernel function, it is important that models used to inform control strategies can be scrutinised in the light of observation in order to elicit possible evidence against the selected kernel function. While a range of approaches to model criticism is in existence, the field remains one in which the need for further research is recognised. In this paper, building on earlier contributions by the authors, we introduce a new approach to assessing the validity of spatial kernels—the latent likelihood ratio tests—which use likelihood-based discrepancy variables that can be used to compare the fit of competing models, and compare the capacity of this approach to detect model mis-specification with that of tests based on the use of infection-link residuals. We demonstrate that the new approach can be used to formulate tests with greater power than infection-link residuals to detect kernel mis-specification particularly when the degree of mis-specification is modest. This new tests avoid the use of a fully Bayesian approach which may introduce undesirable complications related to computational complexity and prior sensitivity.
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12
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Firestone SM, Hayama Y, Lau MSY, Yamamoto T, Nishi T, Bradhurst RA, Demirhan H, Stevenson MA, Tsutsui T. Transmission network reconstruction for foot-and-mouth disease outbreaks incorporating farm-level covariates. PLoS One 2020; 15:e0235660. [PMID: 32667952 PMCID: PMC7363093 DOI: 10.1371/journal.pone.0235660] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 06/22/2020] [Indexed: 11/19/2022] Open
Abstract
Transmission network modelling to infer ‘who infected whom’ in infectious disease outbreaks is a highly active area of research. Outbreaks of foot-and-mouth disease have been a key focus of transmission network models that integrate genomic and epidemiological data. The aim of this study was to extend Lau’s systematic Bayesian inference framework to incorporate additional parameters representing predominant species and numbers of animals held on a farm. Lau’s Bayesian Markov chain Monte Carlo algorithm was reformulated, verified and pseudo-validated on 100 simulated outbreaks populated with demographic data Japan and Australia. The modified model was then implemented on genomic and epidemiological data from the 2010 outbreak of foot-and-mouth disease in Japan, and outputs compared to those from the SCOTTI model implemented in BEAST2. The modified model achieved improvements in overall accuracy when tested on the simulated outbreaks. When implemented on the actual outbreak data from Japan, infected farms that held predominantly pigs were estimated to have five times the transmissibility of infected cattle farms and be 49% less susceptible. The farm-level incubation period was 1 day shorter than the latent period, the timing of the seeding of the outbreak in Japan was inferred, as were key linkages between clusters and features of farms involved in widespread dissemination of this outbreak. To improve accessibility the modified model has been implemented as the R package ‘BORIS’ for use in future outbreaks.
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Affiliation(s)
- Simon M. Firestone
- Melbourne Veterinary School, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, Victoria, Australia
- * E-mail:
| | - Yoko Hayama
- Viral Disease and Epidemiology Research Division, National Institute of Animal Health, National Agriculture Research Organization, Tsukuba, Ibaraki, Japan
| | - Max S. Y. Lau
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Takehisa Yamamoto
- Viral Disease and Epidemiology Research Division, National Institute of Animal Health, National Agriculture Research Organization, Tsukuba, Ibaraki, Japan
| | - Tatsuya Nishi
- Exotic Disease Research Station, National Institute of Animal Health, National Agriculture and Food Research Organization, Kodaira, Tokyo, Japan
| | - Richard A. Bradhurst
- Centre of Excellence for Biosecurity Risk Analysis, The University of Melbourne, Parkville, VIC, Australia
| | - Haydar Demirhan
- Mathematical Sciences Discipline, School of Science, RMIT University, Melbourne, VIC, Australia
| | - Mark A. Stevenson
- Melbourne Veterinary School, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, Victoria, Australia
| | - Toshiyuki Tsutsui
- Viral Disease and Epidemiology Research Division, National Institute of Animal Health, National Agriculture Research Organization, Tsukuba, Ibaraki, Japan
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13
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Tildesley M, Brand S, Brooks Pollock E, Bradbury N, Werkman M, Keeling M. The Role of Movement Restrictions in Limiting the Economic Impact of Livestock Infections. NATURE SUSTAINABILITY 2019; 2:834-840. [PMID: 31535037 PMCID: PMC6751075 DOI: 10.1038/s41893-019-0356-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 07/15/2019] [Indexed: 06/10/2023]
Abstract
Movements are essential for the economic success of the livestock industry. These movements however bring the risk of long-range spread of infection, potentially bringing infection to previously disease-free areas where subsequent localised transmission can be devastating. Mechanistic predictive models usually consider controls that minimize the number of livestock affected without considering other costs of an ongoing epidemic. However, it is more appropriate to consider the economic burden, as movement restrictions have major consequences for the economic revenue of farms. Using mechanistic models of foot-and-mouth disease (FMD), bluetongue virus (BTV) and bovine tuberculosis (bTB) in the UK, we contrast the economically optimal control strategies for these diseases. We show that for FMD, the optimal strategy is to ban movements in a small radius around infected farms; the balance between disease control and maintaining 'business as usual' varies between regions. For BTV and bTB, we find that the cost of any movement ban is more than the epidemiological benefits due to the low within-farm prevalence and slow rate of disease spread. This work suggests that movement controls need to be carefully matched to the epidemiological and economic consequences of the disease, and optimal movement bans are often far shorter than existing policy.
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Affiliation(s)
- M.J. Tildesley
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - S. Brand
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - E. Brooks Pollock
- Bristol Veterinary School, University of Bristol, Bristol, BS8 1TH, United Kingdom
| | - N.V. Bradbury
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - M. Werkman
- London Centre for Neglected Tropical Disease Research (LCNTDR), Department of Infectious Disease Epidemiology, St Mary’s Campus, Imperial College London, London, United Kingdom
| | - M.J Keeling
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom
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14
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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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15
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Unraveling R0: Considerations for Public Health Applications. Am J Public Health 2018; 108:S445-S454. [PMCID: PMC6291768 DOI: 10.2105/ajph.2013.301704r] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/23/2018] [Indexed: 09/29/2023]
Abstract
We assessed public health use of R 0, the basic reproduction number, which estimates the speed at which a disease is capable of spreading in a population. These estimates are of great public health interest, as evidenced during the 2009 influenza A (H1N1) virus pandemic. We reviewed methods commonly used to estimate R 0, examined their practical utility, and assessed how estimates of this epidemiological parameter can inform mitigation strategy decisions. In isolation, R 0 is a suboptimal gauge of infectious disease dynamics across populations; other disease parameters may provide more useful information. Nonetheless, estimation of R 0 for a particular population is useful for understanding transmission in the study population. Considered in the context of other epidemiologically important parameters, the value of R 0 may lie in better understanding an outbreak and in preparing a public health response.
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16
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Ridenhour B, Kowalik JM, Shay DK. El número reproductivo básico (R0): consideraciones para su aplicación en la salud póblica. Am J Public Health 2018. [DOI: 10.2105/ajph.2013.301704s] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Benjamin Ridenhour
- División de Gripe, Centros para el Control y la Prevención de Enfermedades, Atlanta, Georgia, Estados Unidos de América
| | - Jessica M. Kowalik
- División de Gripe, Centros para el Control y la Prevención de Enfermedades, Atlanta, Georgia, Estados Unidos de América
| | - David K. Shay
- División de Gripe, Centros para el Control y la Prevención de Enfermedades, Atlanta, Georgia, Estados Unidos de América
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17
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Wiratsudakul A, Sekiguchi S. The implementation of cattle market closure strategies to mitigate the foot-and-mouth disease epidemics: A contact modeling approach. Res Vet Sci 2018; 121:76-84. [PMID: 30359814 DOI: 10.1016/j.rvsc.2018.10.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Revised: 08/30/2018] [Accepted: 10/10/2018] [Indexed: 01/03/2023]
Abstract
Foot-and-mouth disease (FMD) is one of the most endemic diseases in livestock worldwide. The disease occurrence generally results in a huge economic impact. The virus may distribute across countries or even continents along the contact network of animal movements. The present study, therefore, aimed to explore a cattle movement network originated in Tak, a Thailand-Myanmar bordered province and to demonstrate how FMDV spread among the nodes of market, source and destination. Subsequently, we examined the effectiveness of market closure intervention. The market-market (M-M) network was constructed to highlight the inter-market connections and the FMDV was modeled to spread along the trade chain. Four market closure scenarios based on rapidness and duration of implementation were examined. Our results indicate that two of the three major markets located in the province were highly connected and a strongly connected component was identified. The intra-provincial animal movements, which were currently overlooked, should be moved into sights as most of the high-risk sources for FMD epidemics were recognized in a close proximity to the cattle markets. Simultaneously, remote destinations across the country were identified. The inter-provincial animal movement control must be strengthened once FMD outbreak is notified. Based on our simulations, closing markets with low inter-market connectivity may not prevent the spread of FMDV. A selective market closure strategy targeting highly connected markets together with cattle trader tracking system was an alternative approach. However, socio-economic consequences regarding this intervention must be considered.
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Affiliation(s)
- Anuwat Wiratsudakul
- Department of Clinical Sciences and Public Health, Faculty of Veterinary Science, Mahidol University, Nakhon Pathom, Thailand; The Monitoring and Surveillance Center for Zoonotic Diseases in Wildlife and Exotic Animals, Faculty of Veterinary Science, Mahidol University, Nakhon Pathom, Thailand.
| | - Satoshi Sekiguchi
- Department of Veterinary Science, Faculty of Agriculture, University of Miyazaki, Miyazaki, Japan; Center for Animal Disease Control, University of Miyazaki, Miyazaki, Japan
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18
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Hill EM, House T, Dhingra MS, Kalpravidh W, Morzaria S, Osmani MG, Brum E, Yamage M, Kalam MA, Prosser DJ, Takekawa JY, Xiao X, Gilbert M, Tildesley MJ. The impact of surveillance and control on highly pathogenic avian influenza outbreaks in poultry in Dhaka division, Bangladesh. PLoS Comput Biol 2018; 14:e1006439. [PMID: 30212472 PMCID: PMC6155559 DOI: 10.1371/journal.pcbi.1006439] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 09/25/2018] [Accepted: 08/16/2018] [Indexed: 11/19/2022] Open
Abstract
In Bangladesh, the poultry industry is an economically and socially important sector, but it is persistently threatened by the effects of H5N1 highly pathogenic avian influenza. Thus, identifying the optimal control policy in response to an emerging disease outbreak is a key challenge for policy-makers. To inform this aim, a common approach is to carry out simulation studies comparing plausible strategies, while accounting for known capacity restrictions. In this study we perform simulations of a previously developed H5N1 influenza transmission model framework, fitted to two separate historical outbreaks, to assess specific control objectives related to the burden or duration of H5N1 outbreaks among poultry farms in the Dhaka division of Bangladesh. In particular, we explore the optimal implementation of ring culling, ring vaccination and active surveillance measures when presuming disease transmission predominately occurs from premises-to-premises, versus a setting requiring the inclusion of external factors. Additionally, we determine the sensitivity of the management actions under consideration to differing levels of capacity constraints and outbreaks with disparate transmission dynamics. While we find that reactive culling and vaccination policies should pay close attention to these factors to ensure intervention targeting is optimised, across multiple settings the top performing control action amongst those under consideration were targeted proactive surveillance schemes. Our findings may advise the type of control measure, plus its intensity, that could potentially be applied in the event of a developing outbreak of H5N1 amongst originally H5N1 virus-free commercially-reared poultry in the Dhaka division of Bangladesh. Ongoing circulation of avian influenza H5N1 viruses in poultry pose a global public health risk and cause extensive damage to the livestock industry. One of several countries in South Asia gravely affected is Bangladesh, where the poultry industry is an economically and socially important sector. Identifying the optimal control response in anticipation of further outbreaks is therefore a key challenge for policy-makers. This study tested a series of culling, vaccination and active surveillance intervention actions, assessing specific control objectives related to the burden or duration of H5N1 outbreaks among commercial poultry farms in the Dhaka division. This assessment was achieved through performing computational simulations of a previously developed H5N1 influenza transmission mathematical model. The findings of this assessment indicate that the impact of reactive culling and vaccination control policies are dependent upon transmission characteristics, control objectives and availability of resources to enact the control action, whereas proactive surveillance schemes significantly outperform reactive surveillance procedures irrespective of these conditions.
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Affiliation(s)
- Edward M. Hill
- Zeeman Institute: Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, United Kingdom
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- * E-mail:
| | - Thomas House
- School of Mathematics, The University of Manchester, Manchester, United Kingdom
| | - Madhur S. Dhingra
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium
- Food and Agricultural Organization of the United Nations, Rome, Italy
| | - Wantanee Kalpravidh
- Food and Agricultural Organization of the United Nations Regional Office for Asia and the Pacific, Bangkok, Thailand
| | - Subhash Morzaria
- Food and Agricultural Organization of the United Nations, Rome, Italy
| | | | - Eric Brum
- Emergency Centre for Transboundary Animal Diseases (ECTAD), Food and Agriculture Organization of the United Nations, Dhaka, Bangladesh
| | - Mat Yamage
- Emergency Centre for Transboundary Animal Diseases (ECTAD), Food and Agriculture Organization of the United Nations, Dhaka, Bangladesh
| | - Md. A. Kalam
- Institute of Epidemiology, Disease Control & Research (IEDCR), Dhaka, Bangladesh
| | - Diann J. Prosser
- USGS Patuxent Wildlife Research Center, Laurel, Maryland, United States of America
| | - John Y. Takekawa
- U.S. Geological Survey, Western Ecological Research Center, San Francisco Bay Estuary Field Station, Vallejo, California, United States of America
| | - Xiangming Xiao
- Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, Oklahoma, United States of America
| | - Marius Gilbert
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium
- Fonds National de la Recherche Scientifique, Brussels, Belgium
| | - Michael J. Tildesley
- Zeeman Institute: Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, United Kingdom
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
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19
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Meadows AJ, Mundt CC, Keeling MJ, Tildesley MJ. Disentangling the influence of livestock vs. farm density on livestock disease epidemics. Ecosphere 2018. [DOI: 10.1002/ecs2.2294] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- Amanda J. Meadows
- Department of Botany and Plant Pathology; Oregon State University; Cordley Hall, 2701 SW Campus Way Corvallis Oregon 97331 USA
| | - Christopher C. Mundt
- Department of Botany and Plant Pathology; Oregon State University; Cordley Hall, 2701 SW Campus Way Corvallis Oregon 97331 USA
| | - Matt J. Keeling
- Department of Biological Sciences; University of Warwick; Gibbet Hill Road Coventry CV4 7AL UK
| | - Michael J. Tildesley
- Department of Biological Sciences; University of Warwick; Gibbet Hill Road Coventry CV4 7AL UK
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20
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Probert WJM, Jewell CP, Werkman M, Fonnesbeck CJ, Goto Y, Runge MC, Sekiguchi S, Shea K, Keeling MJ, Ferrari MJ, Tildesley MJ. Real-time decision-making during emergency disease outbreaks. PLoS Comput Biol 2018; 14:e1006202. [PMID: 30040815 PMCID: PMC6075790 DOI: 10.1371/journal.pcbi.1006202] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 08/03/2018] [Accepted: 05/15/2018] [Indexed: 01/18/2023] Open
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.
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Affiliation(s)
- William J. M. Probert
- Department of Life Sciences, University of Warwick, Coventry, United Kingdom
- Mathematics Institute, Zeeman Building, University of Warwick, Coventry, United Kingdom
| | - Chris P. Jewell
- CHICAS, Lancaster University, Bailrigg, Lancaster, United Kingdom
| | - Marleen Werkman
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, St Mary’s Campus, Imperial College London, London, United Kingdom
| | | | - Yoshitaka Goto
- Center for Animal Disease Control, University of Miyazaki, Miyazaki, Japan
- Department of Veterinary Sciences, Faculty of Agriculture, University of Miyazaki, Miyazaki, Japan
| | - Michael C. Runge
- US Geological Survey, Patuxent Wildlife Research Center, Laurel, Maryland, United States of America
| | - Satoshi Sekiguchi
- Center for Animal Disease Control, University of Miyazaki, Miyazaki, Japan
- Department of Veterinary Sciences, Faculty of Agriculture, University of Miyazaki, Miyazaki, Japan
| | - Katriona Shea
- Center for Infectious Disease Dynamics, Department of Biology, Eberly College of Science, The Pennsylvania State University, Pennsylvania, United States of America
- Department of Biology and Intercollege Graduate Degree Program in Ecology, 208 Mueller Laboratory, The Pennsylvania State University, Pennsylvania, United States of America
| | - Matt J. Keeling
- Department of Life Sciences, University of Warwick, Coventry, United Kingdom
- Mathematics Institute, Zeeman Building, University of Warwick, Coventry, United Kingdom
| | - Matthew J. Ferrari
- Center for Infectious Disease Dynamics, Department of Biology, Eberly College of Science, The Pennsylvania State University, Pennsylvania, United States of America
- Department of Biology and Intercollege Graduate Degree Program in Ecology, 208 Mueller Laboratory, The Pennsylvania State University, Pennsylvania, United States of America
| | - Michael J. Tildesley
- Department of Life Sciences, University of Warwick, Coventry, United Kingdom
- Mathematics Institute, Zeeman Building, University of Warwick, Coventry, United Kingdom
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21
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Sellman S, Tsao K, Tildesley MJ, Brommesson P, Webb CT, Wennergren U, Keeling MJ, Lindström T. Need for speed: An optimized gridding approach for spatially explicit disease simulations. PLoS Comput Biol 2018; 14:e1006086. [PMID: 29624574 PMCID: PMC5906030 DOI: 10.1371/journal.pcbi.1006086] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 04/18/2018] [Accepted: 03/12/2018] [Indexed: 11/21/2022] Open
Abstract
Numerical models for simulating outbreaks of infectious diseases are powerful tools for informing surveillance and control strategy decisions. However, large-scale spatially explicit models can be limited by the amount of computational resources they require, which poses a problem when multiple scenarios need to be explored to provide policy recommendations. We introduce an easily implemented method that can reduce computation time in a standard Susceptible-Exposed-Infectious-Removed (SEIR) model without introducing any further approximations or truncations. It is based on a hierarchical infection process that operates on entire groups of spatially related nodes (cells in a grid) in order to efficiently filter out large volumes of susceptible nodes that would otherwise have required expensive calculations. After the filtering of the cells, only a subset of the nodes that were originally at risk are then evaluated for actual infection. The increase in efficiency is sensitive to the exact configuration of the grid, and we describe a simple method to find an estimate of the optimal configuration of a given landscape as well as a method to partition the landscape into a grid configuration. To investigate its efficiency, we compare the introduced methods to other algorithms and evaluate computation time, focusing on simulated outbreaks of foot-and-mouth disease (FMD) on the farm population of the USA, the UK and Sweden, as well as on three randomly generated populations with varying degree of clustering. The introduced method provided up to 500 times faster calculations than pairwise computation, and consistently performed as well or better than other available methods. This enables large scale, spatially explicit simulations such as for the entire continental USA without sacrificing realism or predictive power. Numerical models for simulating the outbreak of infectious disease are powerful tools that can be used to inform policy decisions by simulating outbreaks and control actions. However, they rely on considerable computational power to explore all outcomes and scenarios of interest. Focusing on model types commonly used for livestock diseases, we here introduce novel algorithms for efficient computation, alongside techniques to optimize them based on simplifying assumptions. Through simulations of FMD outbreak in the US, the UK and Sweden, as well as in computer generated landscapes, we test how these methods perform under realistic conditions. We find that our optimization techniques works well, and when the introduced algorithms are implemented with these optimizations, computation time can be reduced by more than two orders of magnitude compared to pairwise calculations. We propose that the considered algorithms—which are straight forward to implement—will be useful for simulation of a wide range of diseases, and will promote the use of simulation models for policy recommendation.
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Affiliation(s)
- Stefan Sellman
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, Linköping, Sweden
- * E-mail:
| | - Kimberly Tsao
- Department of Biology, Colorado State University, Fort Collins, CO, United States of America
| | - Michael J. Tildesley
- Zeeman Institute (SBIDER), School of Life Sciences and Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UK
| | - Peter Brommesson
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, Linköping, Sweden
| | - Colleen T. Webb
- Department of Biology, Colorado State University, Fort Collins, CO, United States of America
| | - Uno Wennergren
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, Linköping, Sweden
| | - Matt J. Keeling
- Zeeman Institute (SBIDER), School of Life Sciences and Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UK
| | - Tom Lindström
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, Linköping, Sweden
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22
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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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23
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Keeling MJ, Datta S, Franklin DN, Flatman I, Wattam A, Brown M, Budge GE. Efficient use of sentinel sites: detection of invasive honeybee pests and diseases in the UK. J R Soc Interface 2018; 14:rsif.2016.0908. [PMID: 28446703 PMCID: PMC5414905 DOI: 10.1098/rsif.2016.0908] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Accepted: 04/03/2017] [Indexed: 11/13/2022] Open
Abstract
Sentinel sites, where problems can be identified early or investigated in detail, form an important part of planning for exotic disease outbreaks in humans, livestock and plants. Key questions are: how many sentinels are required, where should they be positioned and how effective are they at rapidly identifying new invasions? The sentinel apiary system for invasive honeybee pests and diseases illustrates the costs and benefits of such approaches. Here, we address these issues with two mathematical modelling approaches. The first approach is generic and uses probabilistic arguments to calculate the average number of affected sites when an outbreak is first detected, providing rapid and general insights that we have applied to a range of infectious diseases. The second approach uses a computationally intensive, stochastic, spatial model to simulate multiple outbreaks and to determine appropriate sentinel locations for UK apiaries. Both models quantify the anticipated increase in success of sentinel sites as their number increases and as non-sentinel sites become worse at detection; however, unexpectedly sentinels perform relatively better for faster growing outbreaks. Additionally, the spatial model allows us to quantify the substantial role that carefully positioned sentinels can play in the rapid detection of exotic invasions.
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Affiliation(s)
- Matt J Keeling
- Zeeman Institute: SBIDER, University of Warwick, Coventry CV4 7AL, UK .,Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK.,School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
| | - Samik Datta
- Zeeman Institute: SBIDER, University of Warwick, Coventry CV4 7AL, UK.,Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
| | - Daniel N Franklin
- Zeeman Institute: SBIDER, University of Warwick, Coventry CV4 7AL, UK.,School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
| | - Ivor Flatman
- Animal and Plant Health Agency, Sand Hutton, York YO41 1LZ, UK
| | - Andy Wattam
- Animal and Plant Health Agency, Sand Hutton, York YO41 1LZ, UK
| | - Mike Brown
- Animal and Plant Health Agency, Sand Hutton, York YO41 1LZ, UK
| | - Giles E Budge
- Fera, Sand Hutton, York YO41 1LZ, UK .,Institute for Agri-Food Research and Innovation, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
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25
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Dórea FC, Nöremark M, Widgren S, Frössling J, Boklund A, Halasa T, Ståhl K. Evaluation of Strategies to Control a Potential Outbreak of Foot-and-Mouth Disease in Sweden. Front Vet Sci 2017; 4:118. [PMID: 28791298 PMCID: PMC5523145 DOI: 10.3389/fvets.2017.00118] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 07/07/2017] [Indexed: 11/26/2022] Open
Abstract
To minimize the potential consequences of an introduction of foot-and-mouth disease (FMD) in Europe, European Union (EU) member states are required to present a contingency plan. This study used a simulation model to study potential outbreak scenarios in Sweden and evaluate the best control strategies. The model was informed by the Swedish livestock structure using herd information from cattle, pig, and small ruminant holdings in the country. The contact structure was based on animal movement data and studies investigating the movements between farms of veterinarians, service trucks, and other farm visitors. All scenarios of outbreak control included depopulation of detected herds, 3 km protection and 10 km surveillance zones, movement tracing, and 3 days national standstill. The effect of availability of surveillance resources, i.e., number of field veterinarians per day, and timeliness of enforcement of interventions, was assessed. With the estimated currently available resources, an FMD outbreak in Sweden is expected to be controlled (i.e., last infected herd detected) within 3 weeks of detection in any evaluated scenario. The density of farms in the area where the epidemic started would have little impact on the time to control the outbreak, but spread in high density areas would require more surveillance resources, compared to areas of lower farm density. The use of vaccination did not result in a reduction in the expected number of infected herds. Preemptive depopulation was able to reduce the number of infected herds in extreme scenarios designed to test a combination of worst-case conditions of virus introduction and spread, but at the cost of doubling the number of herds culled. This likely resulted from a combination of the small outbreaks predicted by the spread model, and the high efficacy of the basic control measures evaluated, under the conditions of the Swedish livestock industry, and considering the assumed control resources available. The results indicate that the duration and extent of FMD outbreaks could be kept limited in Sweden using the EU standard control strategy and a 3 days national standstill.
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Affiliation(s)
- Fernanda C Dórea
- Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), Uppsala, Sweden
| | - Maria Nöremark
- Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), Uppsala, Sweden
| | - Stefan Widgren
- Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), Uppsala, Sweden
| | - Jenny Frössling
- Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), Uppsala, Sweden
| | - Anette Boklund
- Department of Diagnostics and Scientific Advice, The National Veterinary Institute, Copenhagen, Denmark
| | - Tariq Halasa
- Department of Diagnostics and Scientific Advice, The National Veterinary Institute, Copenhagen, Denmark
| | - Karl Ståhl
- Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), Uppsala, Sweden
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Hill EM, House T, Dhingra MS, Kalpravidh W, Morzaria S, Osmani MG, Yamage M, Xiao X, Gilbert M, Tildesley MJ. Modelling H5N1 in Bangladesh across spatial scales: Model complexity and zoonotic transmission risk. Epidemics 2017; 20:37-55. [PMID: 28325494 DOI: 10.1016/j.epidem.2017.02.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Revised: 02/15/2017] [Accepted: 02/17/2017] [Indexed: 10/20/2022] Open
Abstract
Highly pathogenic avian influenza H5N1 remains a persistent public health threat, capable of causing infection in humans with a high mortality rate while simultaneously negatively impacting the livestock industry. A central question is to determine regions that are likely sources of newly emerging influenza strains with pandemic causing potential. A suitable candidate is Bangladesh, being one of the most densely populated countries in the world and having an intensifying farming system. It is therefore vital to establish the key factors, specific to Bangladesh, that enable both continued transmission within poultry and spillover across the human-animal interface. We apply a modelling framework to H5N1 epidemics in the Dhaka region of Bangladesh, occurring from 2007 onwards, that resulted in large outbreaks in the poultry sector and a limited number of confirmed human cases. This model consisted of separate poultry transmission and zoonotic transmission components. Utilising poultry farm spatial and population information a set of competing nested models of varying complexity were fitted to the observed case data, with parameter inference carried out using Bayesian methodology and goodness-of-fit verified by stochastic simulations. For the poultry transmission component, successfully identifying a model of minimal complexity, which enabled the accurate prediction of the size and spatial distribution of cases in H5N1 outbreaks, was found to be dependent on the administration level being analysed. A consistent outcome of non-optimal reporting of infected premises materialised in each poultry epidemic of interest, though across the outbreaks analysed there were substantial differences in the estimated transmission parameters. The zoonotic transmission component found the main contributor to spillover transmission of H5N1 in Bangladesh was found to differ from one poultry epidemic to another. We conclude by discussing possible explanations for these discrepancies in transmission behaviour between epidemics, such as changes in surveillance sensitivity and biosecurity practices.
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Affiliation(s)
- Edward M Hill
- Centre for Complexity Science, University of Warwick, Coventry, CV4 7AL, United Kingdom; Zeeman Institute: Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, CV4 7AL, United Kingdom.
| | - Thomas House
- School of Mathematics, The University of Manchester, Manchester, M13 9PL, United Kingdom
| | - Madhur S Dhingra
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, B-1050 Brussels, Belgium; Department of Animal Husbandry and Dairying, Government of Haryana, Panchkula, India
| | - Wantanee Kalpravidh
- Food and Agricultural Organization of the United Nations Regional Office for Asia and the Pacific, Bangkok, Thailand
| | - Subhash Morzaria
- Food and Agricultural Organization of the United Nations, Rome, Italy
| | | | - Mat Yamage
- Emergency Centre for Transboundary Animal Diseases (ECTAD), Food and Agriculture Organization of the United Nations, Dhaka, Bangladesh
| | - Xiangming Xiao
- Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, United States
| | - Marius Gilbert
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, B-1050 Brussels, Belgium; Fonds National de la Recherche Scientifique, B-1000 Brussels, Belgium
| | - Michael J Tildesley
- Zeeman Institute: Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, CV4 7AL, United Kingdom; School of Life Sciences, University of Warwick, Coventry, CV4 7AL, United Kingdom; Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom
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27
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Quantifying the Value of Perfect Information in Emergency Vaccination Campaigns. PLoS Comput Biol 2017; 13:e1005318. [PMID: 28207777 PMCID: PMC5312803 DOI: 10.1371/journal.pcbi.1005318] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Accepted: 12/19/2016] [Indexed: 11/22/2022] Open
Abstract
Foot-and-mouth disease outbreaks in non-endemic countries can lead to large economic costs and livestock losses but the use of vaccination has been contentious, partly due to uncertainty about emergency FMD vaccination. Value of information methods can be applied to disease outbreak problems such as FMD in order to investigate the performance improvement from resolving uncertainties. Here we calculate the expected value of resolving uncertainty about vaccine efficacy, time delay to immunity after vaccination and daily vaccination capacity for a hypothetical FMD outbreak in the UK. If it were possible to resolve all uncertainty prior to the introduction of control, we could expect savings of £55 million in outbreak cost, 221,900 livestock culled and 4.3 days of outbreak duration. All vaccination strategies were found to be preferable to a culling only strategy. However, the optimal vaccination radius was found to be highly dependent upon vaccination capacity for all management objectives. We calculate that by resolving the uncertainty surrounding vaccination capacity we would expect to return over 85% of the above savings, regardless of management objective. It may be possible to resolve uncertainty about daily vaccination capacity before an outbreak, and this would enable decision makers to select the optimal control action via careful contingency planning. In the UK during 2001 there was an outbreak of foot-and-mouth disease (FMD) which cost the economy an estimated £8 billion and led to the culling of approximately 7 million livestock. The main methods used to control the epidemic were movement bans and culling of infected and high-risk livestock. FMD vaccines were available but not used because of concerns about their effectiveness and how their use would affect the UK’s disease-free status. Using the Warwick FMD model, we ran simulations of FMD outbreaks in the UK including ring vaccination as a method of outbreak control with varying levels of vaccine efficacy, time delay between vaccination and conferral of immunity, and vaccination capacity. We applied value of information analysis to these results and found that the most important factor in determining the optimal vaccination strategy was knowledge of the vaccination capacity. In contrast, vaccine efficacy and delay between vaccination and immunity were relatively unimportant from a decision making perspective. This work could inform contingency planning that would lead to cost savings in the event of a future FMD outbreak and could also be applied to other infectious diseases.
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Abstract
Preparing for and responding to outbreaks of serious livestock infectious diseases are critical measures to safeguard animal health, public health, and food supply. Almost all of the current control strategies are empirical, and mass culling or “stamping out” is frequently the principal strategy for controlling epidemics. However, there are ethical, ecological, and economic reasons to consider less drastic control strategies. Here we use modeling to quantitatively study the efficacy of different control measures for viral outbreaks, where the infectiousness, transmissibility and death rate of animals commonly depends on their viral load. We develop a broad theoretical framework for exploring and understanding this heterogeneity. The model includes both direct transmission from infectious animals and indirect transmission from an environmental reservoir. We then incorporate a large variety of control measures, including vaccination, antivirals, isolation, environmental disinfection, and several forms of culling, which may result in fewer culled animals. We provide explicit formulae for the basic reproduction number, R0, for each intervention and for combinations. We evaluate the control methods for a realistic simulated outbreak of low pathogenic avian influenza on a mid-sized turkey farm. In this simulated outbreak, culling results in more total dead birds and dramatically more when culling all of the infected birds.
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29
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Webb CT, Ferrari M, Lindström T, Carpenter T, Dürr S, Garner G, Jewell C, Stevenson M, Ward MP, Werkman M, Backer J, Tildesley M. Ensemble modelling and structured decision-making to support Emergency Disease Management. Prev Vet Med 2017; 138:124-133. [PMID: 28237227 DOI: 10.1016/j.prevetmed.2017.01.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2016] [Accepted: 01/02/2017] [Indexed: 02/07/2023]
Abstract
Epidemiological models in animal health are commonly used as decision-support tools to understand the impact of various control actions on infection spread in susceptible populations. Different models contain different assumptions and parameterizations, and policy decisions might be improved by considering outputs from multiple models. However, a transparent decision-support framework to integrate outputs from multiple models is nascent in epidemiology. Ensemble modelling and structured decision-making integrate the outputs of multiple models, compare policy actions and support policy decision-making. We briefly review the epidemiological application of ensemble modelling and structured decision-making and illustrate the potential of these methods using foot and mouth disease (FMD) models. In case study one, we apply structured decision-making to compare five possible control actions across three FMD models and show which control actions and outbreak costs are robustly supported and which are impacted by model uncertainty. In case study two, we develop a methodology for weighting the outputs of different models and show how different weighting schemes may impact the choice of control action. Using these case studies, we broadly illustrate the potential of ensemble modelling and structured decision-making in epidemiology to provide better information for decision-making and outline necessary development of these methods for their further application.
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Affiliation(s)
- Colleen T Webb
- Department of Biology, Colorado State University, Fort Collins, CO, USA.
| | - Matthew Ferrari
- Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, USA
| | - Tom Lindström
- Department of Biology, Colorado State University, Fort Collins, CO, USA; IFM, Theory and Modelling, Linköpings Universitet, Linköping, Sweden
| | - Tim Carpenter
- EpiCentre, Massey University, Palmerston North, New Zealand
| | - Salome Dürr
- Veterinary Public Health Institute, Vetsuisse Faculty, University of Berne, Switzerland
| | - Graeme Garner
- Animal Health Policy Branch, Department of Agriculture, Canberra, Australia
| | - Chris Jewell
- Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand
| | - Mark Stevenson
- Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Michael P Ward
- Faculty of Veterinary Science, The University of Sydney, Camden, Australia
| | - Marleen Werkman
- Central Veterinary Institute part of Wageningen UR (CVI), Lelystad, The Netherlands
| | - Jantien Backer
- Central Veterinary Institute part of Wageningen UR (CVI), Lelystad, The Netherlands
| | - Michael Tildesley
- Warwick Infectious Disease Epidemiology Research (WIDER) Group, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, UK
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30
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Brommesson P, Wennergren U, Lindström T. Spatiotemporal Variation in Distance Dependent Animal Movement Contacts: One Size Doesn't Fit All. PLoS One 2016; 11:e0164008. [PMID: 27760155 PMCID: PMC5070834 DOI: 10.1371/journal.pone.0164008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Accepted: 09/19/2016] [Indexed: 11/18/2022] Open
Abstract
The structure of contacts that mediate transmission has a pronounced effect on the outbreak dynamics of infectious disease and simulation models are powerful tools to inform policy decisions. Most simulation models of livestock disease spread rely to some degree on predictions of animal movement between holdings. Typically, movements are more common between nearby farms than between those located far away from each other. Here, we assessed spatiotemporal variation in such distance dependence of animal movement contacts from an epidemiological perspective. We evaluated and compared nine statistical models, applied to Swedish movement data from 2008. The models differed in at what level (if at all), they accounted for regional and/or seasonal heterogeneities in the distance dependence of the contacts. Using a kernel approach to describe how probability of contacts between farms changes with distance, we developed a hierarchical Bayesian framework and estimated parameters by using Markov Chain Monte Carlo techniques. We evaluated models by three different approaches of model selection. First, we used Deviance Information Criterion to evaluate their performance relative to each other. Secondly, we estimated the log predictive posterior distribution, this was also used to evaluate their relative performance. Thirdly, we performed posterior predictive checks by simulating movements with each of the parameterized models and evaluated their ability to recapture relevant summary statistics. Independent of selection criteria, we found that accounting for regional heterogeneity improved model accuracy. We also found that accounting for seasonal heterogeneity was beneficial, in terms of model accuracy, according to two of three methods used for model selection. Our results have important implications for livestock disease spread models where movement is an important risk factor for between farm transmission. We argue that modelers should refrain from using methods to simulate animal movements that assume the same pattern across all regions and seasons without explicitly testing for spatiotemporal variation.
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Affiliation(s)
- Peter Brommesson
- Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Uno Wennergren
- Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Tom Lindström
- Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
- * E-mail:
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31
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Preserving privacy whilst maintaining robust epidemiological predictions. Epidemics 2016; 17:35-41. [PMID: 27792892 DOI: 10.1016/j.epidem.2016.10.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Revised: 10/10/2016] [Accepted: 10/12/2016] [Indexed: 11/21/2022] Open
Abstract
Mathematical models are invaluable tools for quantifying potential epidemics and devising optimal control strategies in case of an outbreak. State-of-the-art models increasingly require detailed individual farm-based and sensitive data, which may not be available due to either lack of capacity for data collection or privacy concerns. However, in many situations, aggregated data are available for use. In this study, we systematically investigate the accuracy of predictions made by mathematical models initialised with varying data aggregations, using the UK 2001 Foot-and-Mouth Disease Epidemic as a case study. We consider the scenario when the only data available are aggregated into spatial grid cells, and develop a metapopulation model where individual farms in a single subpopulation are assumed to behave uniformly and transmit randomly. We also adapt this standard metapopulation model to capture heterogeneity in farm size and composition, using farm census data. Our results show that homogeneous models based on aggregated data overestimate final epidemic size but can perform well for predicting spatial spread. Recognising heterogeneity in farm sizes improves predictions of the final epidemic size, identifying risk areas, determining the likelihood of epidemic take-off and identifying the optimal control strategy. In conclusion, in cases where individual farm-based data are not available, models can still generate meaningful predictions, although care must be taken in their interpretation and use.
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32
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Dawson PM, Werkman M, Brooks-Pollock E, Tildesley MJ. Epidemic predictions in an imperfect world: modelling disease spread with partial data. Proc Biol Sci 2016; 282:20150205. [PMID: 25948687 PMCID: PMC4455802 DOI: 10.1098/rspb.2015.0205] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
‘Big-data’ epidemic models are being increasingly used to influence government policy to help with control and eradication of infectious diseases. In the case of livestock, detailed movement records have been used to parametrize realistic transmission models. While livestock movement data are readily available in the UK and other countries in the EU, in many countries around the world, such detailed data are not available. By using a comprehensive database of the UK cattle trade network, we implement various sampling strategies to determine the quantity of network data required to give accurate epidemiological predictions. It is found that by targeting nodes with the highest number of movements, accurate predictions on the size and spatial spread of epidemics can be made. This work has implications for countries such as the USA, where access to data is limited, and developing countries that may lack the resources to collect a full dataset on livestock movements.
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Affiliation(s)
- Peter M Dawson
- Centre for Complexity Science, University of Warwick, Coventry CV4 7AL, UK
| | - Marleen Werkman
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington LE12 5RD, UK Central Veterinary Institute, Wageningen UR (CVI), PO Box 65, 8200 AB Lelystad, The Netherlands
| | - Ellen Brooks-Pollock
- School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Michael J Tildesley
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington LE12 5RD, UK Fogarty International Center, US National Institute of Health, Bethesda, MD 20892, USA
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33
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Pandit P, Hoch T, Ezanno P, Beaudeau F, Vergu E. Spread of Coxiella burnetii between dairy cattle herds in an enzootic region: modelling contributions of airborne transmission and trade. Vet Res 2016; 47:48. [PMID: 27048416 PMCID: PMC4822316 DOI: 10.1186/s13567-016-0330-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Accepted: 02/04/2016] [Indexed: 11/10/2022] Open
Abstract
Q fever, a worldwide zoonotic disease caused by Coxiella burnetii, is a looming concern for livestock and public health. Epidemiological features of inter-herd transmission of C. burnetii in cattle herds by wind and trade of cows are poorly understood. We present a novel dynamic spatial model describing the inter-herd regional spread of C. burnetii in dairy cattle herds, quantifying the ability of airborne transmission and animal trade in C. burnetii propagation in an enzootic region. Among all the new herd infections, 92% were attributed to airborne transmission and the rest to cattle trade. Infections acquired following airborne transmission were shown to cause relatively small and ephemeral intra-herd outbreaks. On the contrary, disease-free herds purchasing an infectious cow experienced significantly higher intra-herd prevalence. The results also indicated that, for short duration, both transmission routes were independent from each other without any synergistic effect. The model outputs applied to the Finistère department in western France showed satisfactory sensitivity (0.71) and specificity (0.80) in predicting herd infection statuses at the end of one year in a neighbourhood of 3 km around expected incident herds, when compared with data. The model developed here thus provides important insights into the spread of C. burnetii between dairy cattle herds and paves the way for implementation and assessment of control strategies.
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Affiliation(s)
- Pranav Pandit
- INRA, LUNAM Université, Oniris, UMR1300 BioEpAR, CS40706, 44307, Nantes, France.
| | - Thierry Hoch
- INRA, LUNAM Université, Oniris, UMR1300 BioEpAR, CS40706, 44307, Nantes, France
| | - Pauline Ezanno
- INRA, LUNAM Université, Oniris, UMR1300 BioEpAR, CS40706, 44307, Nantes, France
| | - François Beaudeau
- INRA, LUNAM Université, Oniris, UMR1300 BioEpAR, CS40706, 44307, Nantes, France
| | - Elisabeta Vergu
- MaIAGE, INRA, Université Paris-Saclay, 78350, Jouy-en-Josas, France
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Margevicius KJ, Generous N, Abeyta E, Althouse B, Burkom H, Castro L, Daughton A, Del Valle SY, Fairchild G, Hyman JM, Kiang R, Morse AP, Pancerella CM, Pullum L, Ramanathan A, Schlegelmilch J, Scott A, Taylor-McCabe KJ, Vespignani A, Deshpande A. The Biosurveillance Analytics Resource Directory (BARD): Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance. PLoS One 2016; 11:e0146600. [PMID: 26820405 PMCID: PMC4731202 DOI: 10.1371/journal.pone.0146600] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2015] [Accepted: 12/18/2015] [Indexed: 11/18/2022] Open
Abstract
Epidemiological modeling for infectious disease is important for disease management and its routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. A key need is a universal framework to facilitate model description and understanding of its features. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. The framework was developed through a consensus among a panel of subject matter experts. In this paper, we describe the framework, its application to model characterization, and the development of the Biosurveillance Analytics Resource Directory (BARD; http://brd.bsvgateway.org/brd/), to facilitate the rapid selection of operational models for specific infectious/communicable diseases. We offer this framework and associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models.
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Affiliation(s)
- Kristen J Margevicius
- Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Nicholas Generous
- Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Esteban Abeyta
- Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Ben Althouse
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
| | - Howard Burkom
- Johns Hopkins University-Applied Physics Laboratory, Laurel, Maryland, United States of America
| | - Lauren Castro
- Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Ashlynn Daughton
- Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Sara Y. Del Valle
- Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Geoffrey Fairchild
- Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - James M. Hyman
- Department of Mathematics, Tulane University, New Orleans, Louisiana, United States of America
| | - Richard Kiang
- National Aeronautics and Space Administration, Greenbelt, Maryland, United States of America
| | - Andrew P. Morse
- Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, Liverpool, United Kingdom
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, United Kingdom
| | - Carmen M. Pancerella
- Distributed Systems Research, Sandia National Laboratories, Livermore, California, United States of America
| | - Laura Pullum
- Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America
| | - Arvind Ramanathan
- Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America
| | - Jeffrey Schlegelmilch
- National Center for Disaster Preparedness, The Earth Institute—Columbia University, New York, New York, United States of America
| | - Aaron Scott
- USDA APHIS Veterinary Services, Science, Technology, and Analysis Services, Fort Collins, Colorado, United States of America
| | - Kirsten J Taylor-McCabe
- Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Alina Deshpande
- Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- * E-mail:
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35
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Zingg D, Häsler S, Schuepbach-Regula G, Schwermer H, Dürr S. Evidence for Emergency Vaccination Having Played a Crucial Role to Control the 1965/66 Foot-and-Mouth Disease Outbreak in Switzerland. Front Vet Sci 2015; 2:72. [PMID: 26697436 PMCID: PMC4677095 DOI: 10.3389/fvets.2015.00072] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 11/26/2015] [Indexed: 11/23/2022] Open
Abstract
Foot-and-mouth disease (FMD) is a highly contagious disease that caused several large outbreaks in Europe in the last century. The last important outbreak in Switzerland took place in 1965/66 and affected more than 900 premises and more than 50,000 animals were slaughtered. Large-scale emergency vaccination of the cattle and pig population has been applied to control the epidemic. In recent years, many studies have used infectious disease models to assess the impact of different disease control measures, including models developed for diseases exotic for the specific region of interest. Often, the absence of real outbreak data makes a validation of such models impossible. This study aimed to evaluate whether a spatial, stochastic simulation model (the Davis Animal Disease Simulation model) can predict the course of a Swiss FMD epidemic based on the available historic input data on population structure, contact rates, epidemiology of the virus, and quality of the vaccine. In addition, the potential outcome of the 1965/66 FMD epidemic without application of vaccination was investigated. Comparing the model outcomes to reality, only the largest 10% of the simulated outbreaks approximated the number of animals being culled. However, the simulation model highly overestimated the number of culled premises. While the outbreak duration could not be well reproduced by the model compared to the 1965/66 epidemic, it was able to accurately estimate the size of the area infected. Without application of vaccination, the model predicted a much higher mean number of culled animals than with vaccination, demonstrating that vaccination was likely crucial in disease control for the Swiss FMD outbreak in 1965/66. The study demonstrated the feasibility to analyze historical outbreak data with modern analytical tools. However, it also confirmed that predicted epidemics from a most carefully parameterized model cannot integrate all eventualities of a real epidemic. Therefore, decision makers need to be aware that infectious disease models are useful tools to support the decision-making process but their results are not equal valuable as real observations and should always be interpreted with caution.
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Affiliation(s)
- Dana Zingg
- Veterinary Public Health Institute, Vetsuisse Faculty, University of Bern , Bern , Switzerland
| | - Stephan Häsler
- Swiss Association for the History of Veterinary Medicine , Gasel , Switzerland
| | | | | | - Salome Dürr
- Veterinary Public Health Institute, Vetsuisse Faculty, University of Bern , Bern , Switzerland
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Probert WJM, Shea K, Fonnesbeck CJ, Runge MC, Carpenter TE, Dürr S, Garner MG, Harvey N, Stevenson MA, Webb CT, Werkman M, Tildesley MJ, Ferrari MJ. Decision-making for foot-and-mouth disease control: Objectives matter. Epidemics 2015; 15:10-9. [PMID: 27266845 DOI: 10.1016/j.epidem.2015.11.002] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2015] [Revised: 11/03/2015] [Accepted: 11/25/2015] [Indexed: 11/18/2022] Open
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.
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Affiliation(s)
- William J M Probert
- Center for Infectious Disease Dynamics, Department of Biology, Eberly College of Science, The Pennsylvania State University, University Park, PA, United States; Department of Biology and Intercollege Graduate Degree Program in Ecology, 208 Mueller Laboratory, The Pennsylvania State University, University Park, PA, United States; School of Veterinary Medicine and Science, University of Nottingham, Leicestershire LE12 5RD, United Kingdom.
| | - Katriona Shea
- Center for Infectious Disease Dynamics, Department of Biology, Eberly College of Science, The Pennsylvania State University, University Park, PA, United States; Department of Biology and Intercollege Graduate Degree Program in Ecology, 208 Mueller Laboratory, The Pennsylvania State University, University Park, PA, United States
| | | | - Michael C Runge
- US Geological Survey, Patuxent Wildlife Research Center, 12100 Beech Forest Rd, Laurel, MD, United States
| | - Tim E Carpenter
- EpiCentre, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand
| | - Salome Dürr
- Veterinary Public Health Institute, University of Bern, Bern, Switzerland
| | - M Graeme Garner
- Animal Health Policy Branch, Australian Government, Department of Agriculture, GPO Box 858, Canberra 2601, ACT, Australia
| | - Neil Harvey
- Department of Computing and Information Science, University of Guelph, Guelph, ON, Canada N1G 2W1
| | - Mark A Stevenson
- Faculty of Veterinary Science, University of Melbourne, Melbourne, VIC, Australia
| | - Colleen T Webb
- Department of Biology, Colorado State University, Fort Collins, CO, United States
| | - Marleen Werkman
- Central Veterinary Institute, Wageningen University and Research Centre, Houtribweg 39, 8221 RA Lelystad, The Netherlands; School of Veterinary Medicine and Science, University of Nottingham, Leicestershire LE12 5RD, United Kingdom
| | - Michael J Tildesley
- School of Veterinary Medicine and Science, University of Nottingham, Leicestershire LE12 5RD, United Kingdom
| | - Matthew J Ferrari
- Center for Infectious Disease Dynamics, Department of Biology, Eberly College of Science, The Pennsylvania State University, University Park, PA, United States
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Pomeroy LW, Bansal S, Tildesley M, Moreno-Torres KI, Moritz M, Xiao N, Carpenter TE, Garabed RB. Data-Driven Models of Foot-and-Mouth Disease Dynamics: A Review. Transbound Emerg Dis 2015; 64:716-728. [PMID: 26576514 DOI: 10.1111/tbed.12437] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Indexed: 11/28/2022]
Abstract
Foot-and-mouth disease virus (FMDV) threatens animal health and leads to considerable economic losses worldwide. Progress towards minimizing both veterinary and financial impact of the disease will be made with targeted disease control policies. To move towards targeted control, specific targets and detailed control strategies must be defined. One approach for identifying targets is to use mathematical and simulation models quantified with accurate and fine-scale data to design and evaluate alternative control policies. Nevertheless, published models of FMDV vary in modelling techniques and resolution of data incorporated. In order to determine which models and data sources contain enough detail to represent realistic control policy alternatives, we performed a systematic literature review of all FMDV dynamical models that use host data, disease data or both data types. For the purpose of evaluating modelling methodology, we classified models by control strategy represented, resolution of models and data, and location modelled. We found that modelling methodology has been well developed to the point where multiple methods are available to represent detailed and contact-specific transmission and targeted control. However, detailed host and disease data needed to quantify these models are only available from a few outbreaks. To address existing challenges in data collection, novel data sources should be considered and integrated into models of FMDV transmission and control. We suggest modelling multiple endemic areas to advance local control and global control and better understand FMDV transmission dynamics. With incorporation of additional data, models can assist with both the design of targeted control and identification of transmission drivers across geographic boundaries.
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Affiliation(s)
- L W Pomeroy
- Department of Veterinary Preventive Medicine, The Ohio State University, Columbus, OH, USA
| | - S Bansal
- Department of Biology, Georgetown University, Washington, DC, USA.,Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - M Tildesley
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.,School of Veterinary Medicine, University of Nottingham, Bonington, Leicestershire, UK
| | - K I Moreno-Torres
- Department of Veterinary Preventive Medicine, The Ohio State University, Columbus, OH, USA
| | - M Moritz
- Department of Anthropology, The Ohio State University, Columbus, OH, USA
| | - N Xiao
- Department of Geography, The Ohio State University, Columbus, OH, USA
| | - T E Carpenter
- Epicentre, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand
| | - R B Garabed
- Department of Veterinary Preventive Medicine, The Ohio State University, Columbus, OH, USA.,Public Health Preparedness for Infectious Disease Program, The Ohio State University, Columbus, OH, USA
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Pomeroy LW, Bjørnstad ON, Kim H, Jumbo SD, Abdoulkadiri S, Garabed R. Serotype-Specific Transmission and Waning Immunity of Endemic Foot-and-Mouth Disease Virus in Cameroon. PLoS One 2015; 10:e0136642. [PMID: 26327324 PMCID: PMC4556668 DOI: 10.1371/journal.pone.0136642] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Accepted: 08/06/2015] [Indexed: 11/19/2022] Open
Abstract
Foot-and-mouth disease virus (FMDV) causes morbidity and mortality in a range of animals and threatens local economies by acting as a barrier to international trade. The outbreak in the United Kingdom in 2001 that cost billions to control highlighted the risk that the pathogen poses to agriculture. In response, several mathematical models have been developed to parameterize and predict both transmission dynamics and optimal disease control. However, a lack of understanding of the multi-strain etiology prevents characterization of multi-strain dynamics. Here, we use data from FMDV serology in an endemic setting to probe strain-specific transmission and immunodynamics. Five serotypes of FMDV affect cattle in the Far North Region of Cameroon. We fit both catalytic and reverse catalytic models to serological data to estimate the force of infection and the rate of waning immunity, and to detect periods of sustained transmission. For serotypes SAT2, SAT3, and type A, a model assuming life-long immunity fit better. For serotypes SAT1 and type O, the better-fit model suggests that immunity may wane over time. Our analysis further indicates that type O has the greatest force of infection and the longest duration of immunity. Estimates for the force of infection were time-varying and indicated that serotypes SAT1 and O displayed endemic dynamics, serotype A displayed epidemic dynamics, and SAT2 and SAT3 did not sustain local chains of transmission. Since these results were obtained from the same population at the same time, they highlight important differences in transmission specific to each serotype. They also show that immunity wanes at rates specific to each serotype, which influences patterns of local persistence. Overall, this work shows that viral serotypes can differ significantly in their epidemiological and immunological characteristics. Patterns and processes that drive transmission in endemic settings must consider complex viral dynamics for accurate representation and interpretation.
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Affiliation(s)
- Laura W. Pomeroy
- Department of Veterinary Preventive Medicine, Ohio State University, Columbus, OH, United States of America
- * E-mail:
| | - Ottar N. Bjørnstad
- Department of Biology, Pennsylvania State University, University Park, PA, United States of America
- Department of Entomology, Pennsylvania State University, University Park, PA, United States of America
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Hyeyoung Kim
- Department of Geography, Ohio State University, Columbus, OH, United States of America
| | | | | | - Rebecca Garabed
- Department of Veterinary Preventive Medicine, Ohio State University, Columbus, OH, United States of America
- Public Health Preparedness for Infectious Disease Program, The Ohio State University, Columbus, OH, United States of America
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Lindström T, Tildesley M, Webb C. A Bayesian ensemble approach for epidemiological projections. PLoS Comput Biol 2015; 11:e1004187. [PMID: 25927892 PMCID: PMC4415763 DOI: 10.1371/journal.pcbi.1004187] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Accepted: 02/11/2015] [Indexed: 12/14/2022] Open
Abstract
Mathematical models are powerful tools for epidemiology and can be used to compare control actions. However, different models and model parameterizations may provide different prediction of outcomes. In other fields of research, ensemble modeling has been used to combine multiple projections. We explore the possibility of applying such methods to epidemiology by adapting Bayesian techniques developed for climate forecasting. We exemplify the implementation with single model ensembles based on different parameterizations of the Warwick model run for the 2001 United Kingdom foot and mouth disease outbreak and compare the efficacy of different control actions. This allows us to investigate the effect that discrepancy among projections based on different modeling assumptions has on the ensemble prediction. A sensitivity analysis showed that the choice of prior can have a pronounced effect on the posterior estimates of quantities of interest, in particular for ensembles with large discrepancy among projections. However, by using a hierarchical extension of the method we show that prior sensitivity can be circumvented. We further extend the method to include a priori beliefs about different modeling assumptions and demonstrate that the effect of this can have different consequences depending on the discrepancy among projections. We propose that the method is a promising analytical tool for ensemble modeling of disease outbreaks.
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Affiliation(s)
- Tom Lindström
- Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
- Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America
- US National Institute of Health, Bethesda, Maryland, United States of America
- University of Exeter, Exeter, United Kingdom
- * E-mail:
| | - Michael Tildesley
- US National Institute of Health, Bethesda, Maryland, United States of America
- School of Veterinary Medicine and Science, University of Nottingham, Leicestershire, United Kingdom
| | - Colleen Webb
- Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America
- US National Institute of Health, Bethesda, Maryland, United States of America
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Brand SPC, Tildesley MJ, Keeling MJ. Rapid simulation of spatial epidemics: a spectral method. J Theor Biol 2015; 370:121-34. [PMID: 25659478 DOI: 10.1016/j.jtbi.2015.01.027] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2014] [Revised: 01/15/2015] [Accepted: 01/26/2015] [Indexed: 10/24/2022]
Abstract
Spatial structure and hence the spatial position of host populations plays a vital role in the spread of infection. In the majority of situations, it is only possible to predict the spatial spread of infection using simulation models, which can be computationally demanding especially for large population sizes. Here we develop an approximation method that vastly reduces this computational burden. We assume that the transmission rates between individuals or sub-populations are determined by a spatial transmission kernel. This kernel is assumed to be isotropic, such that the transmission rate is simply a function of the distance between susceptible and infectious individuals; as such this provides the ideal mechanism for modelling localised transmission in a spatial environment. We show that the spatial force of infection acting on all susceptibles can be represented as a spatial convolution between the transmission kernel and a spatially extended 'image' of the infection state. This representation allows the rapid calculation of stochastic rates of infection using fast-Fourier transform (FFT) routines, which greatly improves the computational efficiency of spatial simulations. We demonstrate the efficiency and accuracy of this fast spectral rate recalculation (FSR) method with two examples: an idealised scenario simulating an SIR-type epidemic outbreak amongst N habitats distributed across a two-dimensional plane; the spread of infection between US cattle farms, illustrating that the FSR method makes continental-scale outbreak forecasting feasible with desktop processing power. The latter model demonstrates which areas of the US are at consistently high risk for cattle-infections, although predictions of epidemic size are highly dependent on assumptions about the tail of the transmission kernel.
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Affiliation(s)
- Samuel P C Brand
- Department of Life Sciences, University of Warwick, Gibbet Hill Rd, Coventry CV4 7AL, United Kingdom.
| | - Michael J Tildesley
- Mathematics Institute, University of Warwick, Gibbet Hill Rd, Coventry CV4 7AL, United Kingdom
| | - Matthew J Keeling
- Department of Life Sciences, University of Warwick, Gibbet Hill Rd, Coventry CV4 7AL, United Kingdom; Mathematics Institute, University of Warwick, Gibbet Hill Rd, Coventry CV4 7AL, United Kingdom
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41
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Niu S, Luo Y, Dietze MC, Keenan TF, Shi Z, Li J, III FSC. The role of data assimilation in predictive ecology. Ecosphere 2014. [DOI: 10.1890/es13-00273.1] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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Buhnerkempe MG, Tildesley MJ, Lindström T, Grear DA, Portacci K, Miller RS, Lombard JE, Werkman M, Keeling MJ, Wennergren U, Webb CT. The impact of movements and animal density on continental scale cattle disease outbreaks in the United States. PLoS One 2014; 9:e91724. [PMID: 24670977 PMCID: PMC3966763 DOI: 10.1371/journal.pone.0091724] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2013] [Accepted: 02/14/2014] [Indexed: 11/19/2022] Open
Abstract
Globalization has increased the potential for the introduction and spread of novel pathogens over large spatial scales necessitating continental-scale disease models to guide emergency preparedness. Livestock disease spread models, such as those for the 2001 foot-and-mouth disease (FMD) epidemic in the United Kingdom, represent some of the best case studies of large-scale disease spread. However, generalization of these models to explore disease outcomes in other systems, such as the United States's cattle industry, has been hampered by differences in system size and complexity and the absence of suitable livestock movement data. Here, a unique database of US cattle shipments allows estimation of synthetic movement networks that inform a near-continental scale disease model of a potential FMD-like (i.e., rapidly spreading) epidemic in US cattle. The largest epidemics may affect over one-third of the US and 120,000 cattle premises, but cattle movement restrictions from infected counties, as opposed to national movement moratoriums, are found to effectively contain outbreaks. Slow detection or weak compliance may necessitate more severe state-level bans for similar control. Such results highlight the role of large-scale disease models in emergency preparedness, particularly for systems lacking comprehensive movement and outbreak data, and the need to rapidly implement multi-scale contingency plans during a potential US outbreak.
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Affiliation(s)
- Michael G. Buhnerkempe
- Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America
- * E-mail:
| | - Michael J. Tildesley
- Center for Complexity Science, Mathematics Institute, University of Warwick, Coventry, United Kingdom
| | - Tom Lindström
- Department of Physics, Chemistry, and Biology, Linköping University, Linköping, Sweden
| | - Daniel A. Grear
- Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America
| | - Katie Portacci
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Centers for Epidemiology and Animal Health, Fort Collins, Colorado, United States of America
| | - Ryan S. Miller
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Centers for Epidemiology and Animal Health, Fort Collins, Colorado, United States of America
| | - Jason E. Lombard
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Centers for Epidemiology and Animal Health, Fort Collins, Colorado, United States of America
| | - Marleen Werkman
- Center for Complexity Science, Mathematics Institute, University of Warwick, Coventry, United Kingdom
| | - Matt J. Keeling
- Center for Complexity Science, Mathematics Institute, University of Warwick, Coventry, United Kingdom
| | - Uno Wennergren
- Department of Physics, Chemistry, and Biology, Linköping University, Linköping, Sweden
| | - Colleen T. Webb
- Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America
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Rock K, Brand S, Moir J, Keeling MJ. Dynamics of infectious diseases. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2014; 77:026602. [PMID: 24444713 DOI: 10.1088/0034-4885/77/2/026602] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Modern infectious disease epidemiology has a strong history of using mathematics both for prediction and to gain a deeper understanding. However the study of infectious diseases is a highly interdisciplinary subject requiring insights from multiple disciplines, in particular a biological knowledge of the pathogen, a statistical description of the available data and a mathematical framework for prediction. Here we begin with the basic building blocks of infectious disease epidemiology--the SIS and SIR type models--before considering the progress that has been made over the recent decades and the challenges that lie ahead. Throughout we focus on the understanding that can be developed from relatively simple models, although accurate prediction will inevitably require far greater complexity beyond the scope of this review. In particular, we focus on three critical aspects of infectious disease models that we feel fundamentally shape their dynamics: heterogeneously structured populations, stochasticity and spatial structure. Throughout we relate the mathematical models and their results to a variety of real-world problems.
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Affiliation(s)
- Kat Rock
- WIDER Centre, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UK. Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UK
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Ridenhour B, Kowalik JM, Shay DK. Unraveling R0: considerations for public health applications. Am J Public Health 2013; 104:e32-41. [PMID: 24328646 DOI: 10.2105/ajph.2013.301704] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
We assessed public health use of R0, the basic reproduction number, which estimates the speed at which a disease is capable of spreading in a population. These estimates are of great public health interest, as evidenced during the 2009 influenza A (H1N1) virus pandemic. We reviewed methods commonly used to estimate R0, examined their practical utility, and assessed how estimates of this epidemiological parameter can inform mitigation strategy decisions. In isolation, R0 is a suboptimal gauge of infectious disease dynamics across populations; other disease parameters may provide more useful information. Nonetheless, estimation of R0 for a particular population is useful for understanding transmission in the study population. Considered in the context of other epidemiologically important parameters, the value of R0 may lie in better understanding an outbreak and in preparing a public health response.
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Affiliation(s)
- Benjamin Ridenhour
- At the time of this study, Benjamin Ridenhour and Jessica M. Kowalik were with the Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN. David K. Shay was with the Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA
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Pepin KM, Spackman E, Brown JD, Pabilonia KL, Garber LP, Weaver JT, Kennedy DA, Patyk KA, Huyvaert KP, Miller RS, Franklin AB, Pedersen K, Bogich TL, Rohani P, Shriner SA, Webb CT, Riley S. Using quantitative disease dynamics as a tool for guiding response to avian influenza in poultry in the United States of America. Prev Vet Med 2013; 113:376-97. [PMID: 24462191 PMCID: PMC3945821 DOI: 10.1016/j.prevetmed.2013.11.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2013] [Revised: 11/22/2013] [Accepted: 11/24/2013] [Indexed: 02/02/2023]
Abstract
Wild birds are the primary source of genetic diversity for influenza A viruses that eventually emerge in poultry and humans. Much progress has been made in the descriptive ecology of avian influenza viruses (AIVs), but contributions are less evident from quantitative studies (e.g., those including disease dynamic models). Transmission between host species, individuals and flocks has not been measured with sufficient accuracy to allow robust quantitative evaluation of alternate control protocols. We focused on the United States of America (USA) as a case study for determining the state of our quantitative knowledge of potential AIV emergence processes from wild hosts to poultry. We identified priorities for quantitative research that would build on existing tools for responding to AIV in poultry and concluded that the following knowledge gaps can be addressed with current empirical data: (1) quantification of the spatio-temporal relationships between AIV prevalence in wild hosts and poultry populations, (2) understanding how the structure of different poultry sectors impacts within-flock transmission, (3) determining mechanisms and rates of between-farm spread, and (4) validating current policy-decision tools with data. The modeling studies we recommend will improve our mechanistic understanding of potential AIV transmission patterns in USA poultry, leading to improved measures of accuracy and reduced uncertainty when evaluating alternative control strategies.
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Affiliation(s)
- K M Pepin
- Department of Biology, Colorado State University, Fort Collins, CO, USA; Fogarty International Center, National Institute of Health, Bethesda, MD, USA.
| | - E Spackman
- Southeast Poultry Research Laboratory, Agricultural Research Service, United States Department of Agriculture, Athens, GA, USA.
| | - J D Brown
- Department of Population Health, College of Veterinary Medicine, University of Georgia, Athens, GA, USA.
| | - K L Pabilonia
- Department of Microbiology, Immunology and Pathology, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, USA.
| | - L P Garber
- Centers for Epidemiology and Animal Health, Veterinary Services, Animal and Plant Health Inspection Service, United States Department of Agriculture, Fort Collins, CO, USA.
| | - J T Weaver
- Centers for Epidemiology and Animal Health, Veterinary Services, Animal and Plant Health Inspection Service, United States Department of Agriculture, Fort Collins, CO, USA.
| | - D A Kennedy
- Fogarty International Center, National Institute of Health, Bethesda, MD, USA; Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, State College, PA, USA.
| | - K A Patyk
- Centers for Epidemiology and Animal Health, Veterinary Services, Animal and Plant Health Inspection Service, United States Department of Agriculture, Fort Collins, CO, USA.
| | - K P Huyvaert
- Warner College of Natural Resources, Colorado State University, Fort Collins, CO, USA.
| | - R S Miller
- Centers for Epidemiology and Animal Health, Veterinary Services, Animal and Plant Health Inspection Service, United States Department of Agriculture, Fort Collins, CO, USA.
| | - A B Franklin
- National Wildlife Research Center, Wildlife Services, Animal and Plant Health Inspection Service, United States Department of Agriculture, Fort Collins, CO, USA.
| | - K Pedersen
- National Wildlife Research Center, Wildlife Services, Animal and Plant Health Inspection Service, United States Department of Agriculture, Fort Collins, CO, USA.
| | - T L Bogich
- Fogarty International Center, National Institute of Health, Bethesda, MD, USA; Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
| | - P Rohani
- Fogarty International Center, National Institute of Health, Bethesda, MD, USA; Department of Ecology and Evolutionary Biology, Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI, USA.
| | - S A Shriner
- National Wildlife Research Center, Wildlife Services, Animal and Plant Health Inspection Service, United States Department of Agriculture, Fort Collins, CO, USA.
| | - C T Webb
- Department of Biology, Colorado State University, Fort Collins, CO, USA; Fogarty International Center, National Institute of Health, Bethesda, MD, USA.
| | - S Riley
- Fogarty International Center, National Institute of Health, Bethesda, MD, USA; MRC Centre for Outbreak Analysis and Disease Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, UK.
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Flood JS, Porphyre T, Tildesley MJ, Woolhouse MEJ. The performance of approximations of farm contiguity compared to contiguity defined using detailed geographical information in two sample areas in Scotland: implications for foot-and-mouth disease modelling. BMC Vet Res 2013; 9:198. [PMID: 24099627 PMCID: PMC4126065 DOI: 10.1186/1746-6148-9-198] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2013] [Accepted: 10/01/2013] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND When modelling infectious diseases, accurately capturing the pattern of dissemination through space is key to providing optimal recommendations for control. Mathematical models of disease spread in livestock, such as for foot-and-mouth disease (FMD), have done this by incorporating a transmission kernel which describes the decay in transmission rate with increasing Euclidean distance from an infected premises (IP). However, this assumes a homogenous landscape, and is based on the distance between point locations of farms. Indeed, underlying the spatial pattern of spread are the contact networks involved in transmission. Accordingly, area-weighted tessellation around farm point locations has been used to approximate field-contiguity and simulate the effect of contiguous premises (CP) culling for FMD. Here, geographic data were used to determine contiguity based on distance between premises' fields and presence of landscape features for two sample areas in Scotland. Sensitivity, positive predictive value, and the True Skill Statistic (TSS) were calculated to determine how point distance measures and area-weighted tessellation compared to the 'gold standard' of the map-based measures in identifying CPs. In addition, the mean degree and density of the different contact networks were calculated. RESULTS Utilising point distances <1 km and <5 km as a measure for contiguity resulted in poor discrimination between map-based CPs/non-CPs (TSS 0.279-0.344 and 0.385-0.400, respectively). Point distance <1 km missed a high proportion of map-based CPs; <5 km point distance picked up a high proportion of map-based non-CPs as CPs. Area-weighted tessellation performed best, with reasonable discrimination between map-based CPs/non-CPs (TSS 0.617-0.737) and comparable mean degree and density. Landscape features altered network properties considerably when taken into account. CONCLUSION The farming landscape is not homogeneous. Basing contiguity on geographic locations of field boundaries and including landscape features known to affect transmission into FMD models are likely to improve individual farm-level accuracy of spatial predictions in the event of future outbreaks. If a substantial proportion of FMD transmission events are by contiguous spread, and CPs should be assigned an elevated relative transmission rate, the shape of the kernel could be significantly altered since ability to discriminate between map-based CPs and non-CPs is different over different Euclidean distances.
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Affiliation(s)
- Jessica S Flood
- Epidemiology Group, Centre for Immunity, Infection and Evolution, School of Biological Sciences, University of Edinburgh, Kings Buildings, West Mains Road, Edinburgh EH9 3JT, UK.
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Porphyre T, Auty HK, Tildesley MJ, Gunn GJ, Woolhouse MEJ. Vaccination against foot-and-mouth disease: do initial conditions affect its benefit? PLoS One 2013; 8:e77616. [PMID: 24204895 PMCID: PMC3815046 DOI: 10.1371/journal.pone.0077616] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2013] [Accepted: 09/11/2013] [Indexed: 11/29/2022] Open
Abstract
When facing incursion of a major livestock infectious disease, the decision to implement a vaccination programme is made at the national level. To make this decision, governments must consider whether the benefits of vaccination are sufficient to outweigh potential additional costs, including further trade restrictions that may be imposed due to the implementation of vaccination. However, little consensus exists on the factors triggering its implementation on the field. This work explores the effect of several triggers in the implementation of a reactive vaccination-to-live policy when facing epidemics of foot-and-mouth disease. In particular, we tested whether changes in the location of the incursion and the delay of implementation would affect the epidemiological benefit of such a policy in the context of Scotland. To reach this goal, we used a spatial, premises-based model that has been extensively used to investigate the effectiveness of mitigation procedures in Great Britain. The results show that the decision to vaccinate, or not, is not straightforward and strongly depends on the underlying local structure of the population-at-risk. With regards to disease incursion preparedness, simply identifying areas of highest population density may not capture all complexities that may influence the spread of disease as well as the benefit of implementing vaccination. However, if a decision to vaccinate is made, we show that delaying its implementation in the field may markedly reduce its benefit. This work provides guidelines to support policy makers in their decision to implement, or not, a vaccination-to-live policy when facing epidemics of infectious livestock disease.
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Affiliation(s)
- Thibaud Porphyre
- Epidemiology Group, Centre for Immunity, Infection and Evolution, University of Edinburgh, Ashworth Laboratories, Edinburgh, United Kingdom
- * E-mail:
| | - Harriet K. Auty
- Epidemiology Research Unit, Scotland’s Rural College, Inverness, United Kingdom
| | - Michael J. Tildesley
- Centre for Complexity Science, Zeeman Building, University of Warwick, Coventry, United Kingdom
| | - George J. Gunn
- Epidemiology Research Unit, Scotland’s Rural College, Inverness, United Kingdom
| | - Mark E. J. Woolhouse
- Epidemiology Group, Centre for Immunity, Infection and Evolution, University of Edinburgh, Ashworth Laboratories, Edinburgh, United Kingdom
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Tildesley MJ, Smith G, Keeling MJ. Modeling the spread and control of foot-and-mouth disease in Pennsylvania following its discovery and options for control. Prev Vet Med 2011; 104:224-39. [PMID: 22169708 DOI: 10.1016/j.prevetmed.2011.11.007] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2010] [Revised: 11/11/2011] [Accepted: 11/12/2011] [Indexed: 01/13/2023]
Abstract
In this paper, we simulate outbreaks of foot-and-mouth disease in the Commonwealth of Pennsylvania, USA - after the introduction of a state-wide movement ban - as they might unfold in the presence of mitigation strategies. We have adapted a model previously used to investigate FMD control policies in the UK to examine the potential for disease spread given an infection seeded in each county in Pennsylvania. The results are highly dependent upon the county of introduction and the spatial scale of transmission. Should the transmission kernel be identical to that for the UK, the epidemic impact is limited to fewer than 20 premises, regardless of the county of introduction. However, for wider kernels where infection can spread further, outbreaks seeded in or near the county with highest density of premises and animals result in large epidemics (>150 premises). Ring culling and vaccination reduce epidemic size, with the optimal radius of the rings being dependent upon the county of introduction. Should the kernel width exceed a given county-dependent threshold, ring culling is unable to control the epidemic. We find that a vaccinate-to-live policy is generally preferred to ring culling (in terms of reducing the overall number of premises culled), indicating that well-targeted control can dramatically reduce the risk of large scale outbreaks of foot-and-mouth disease occurring in Pennsylvania.
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Affiliation(s)
- Michael J Tildesley
- Centre for Complexity Science, Zeeman Building, University of Warwick, Coventry, CV4 7AL, UK.
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Woolhouse M. How to make predictions about future infectious disease risks. Philos Trans R Soc Lond B Biol Sci 2011; 366:2045-54. [PMID: 21624924 PMCID: PMC3130384 DOI: 10.1098/rstb.2010.0387] [Citation(s) in RCA: 92] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Formal, quantitative approaches are now widely used to make predictions about the likelihood of an infectious disease outbreak, how the disease will spread, and how to control it. Several well-established methodologies are available, including risk factor analysis, risk modelling and dynamic modelling. Even so, predictive modelling is very much the ‘art of the possible’, which tends to drive research effort towards some areas and away from others which may be at least as important. Building on the undoubted success of quantitative modelling of the epidemiology and control of human and animal diseases such as AIDS, influenza, foot-and-mouth disease and BSE, attention needs to be paid to developing a more holistic framework that captures the role of the underlying drivers of disease risks, from demography and behaviour to land use and climate change. At the same time, there is still considerable room for improvement in how quantitative analyses and their outputs are communicated to policy makers and other stakeholders. A starting point would be generally accepted guidelines for ‘good practice’ for the development and the use of predictive models.
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Affiliation(s)
- Mark Woolhouse
- Centre for Infectious Diseases, University of Edinburgh, Ashworth Laboratories, Kings Buildings, West Mains Road, Edinburgh EH9 3JT, UK.
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Zhang XS, Chase-Topping ME, McKendrick IJ, Savill NJ, Woolhouse ME. Spread of E. coli O157 infection among Scottish cattle farms: stochastic models and model selection. Epidemics 2011; 2:11-20. [PMID: 20640032 PMCID: PMC2890141 DOI: 10.1016/j.epidem.2010.02.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2009] [Revised: 01/29/2010] [Accepted: 02/02/2010] [Indexed: 12/14/2022] Open
Abstract
Identifying risk factors for the presence of Escherichia coli O157 infection on cattle farms is important for understanding the epidemiology of this zoonotic infection in its main reservoir and for informing the design of interventions to reduce the public health risk. Here, we use data from a large-scale field study carried out in Scotland to fit both "SIS"-type dynamical models and statistical risk factor models. By comparing the fit (assessed using maximum likelihood) of different dynamical models we are able to identify the most parsimonious model (using the AIC statistic) and compare it with the model suggested by risk factor analysis. Both approaches identify 2 key risk factors: the movement of cattle onto the farm and the number of cattle on the farm. There was no evidence for a role of other livestock species or seasonality, or of significant risk of local spread. However, the most parsimonious dynamical model does predict that farms can infect other farms through routes other than cattle movement, and that there is a nonlinear relationship between the force of infection and the number of infected farms. An important prediction from the most parsimonious model is that although only approximately 20% farms may harbour E. coli O157 infection at any given time approximately 80% may harbour infection at some point during the course of a year.
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Affiliation(s)
- Xu-Sheng Zhang
- Centre for Infectious Diseases, University of Edinburgh, Kings Buildings, West Mains Road, Edinburgh EH9 3JT, UK
- Corresponding author. Present address: Statistics, Modelling and Bioinformatics Department, Health Protection Agency, Centre for Infections, London, NW9 5EQ, UK.
| | - Margo E. Chase-Topping
- Centre for Infectious Diseases, University of Edinburgh, Kings Buildings, West Mains Road, Edinburgh EH9 3JT, UK
| | - Iain J. McKendrick
- Biomathematics and Statistics Scotland (BioSS), James Clerk Maxwell Building, Kings Buildings, Edinburgh, EH9 3JZ, UK
| | - Nicholas J. Savill
- Centre for Infectious Diseases, University of Edinburgh, Kings Buildings, West Mains Road, Edinburgh EH9 3JT, UK
| | - Mark E.J. Woolhouse
- Centre for Infectious Diseases, University of Edinburgh, Kings Buildings, West Mains Road, Edinburgh EH9 3JT, UK
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