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Beck-Johnson LM, Gorsich EE, Hallman C, Tildesley MJ, Miller RS, Webb CT. An exploration of within-herd dynamics of a transboundary livestock disease: A foot and mouth disease case study. Epidemics 2023; 42:100668. [PMID: 36696830 DOI: 10.1016/j.epidem.2023.100668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 12/20/2022] [Accepted: 01/09/2023] [Indexed: 01/19/2023] Open
Abstract
Transboundary livestock diseases are a high priority for policy makers because of the serious economic burdens associated with infection. In order to make well informed preparedness and response plans, policy makers often utilize mathematical models to understand possible outcomes of different control strategies and outbreak scenarios. Many of these models focus on the transmission between herds and the overall trajectory of the outbreak. While the course of infection within herds has not been the focus of the majority of models, a thorough understanding of within-herd dynamics can provide valuable insight into a disease system by providing information on herd-level biological properties of the infection, which can be used to inform decision making in both endemic and outbreak settings and to inform larger between-herd models. In this study, we develop three stochastic simulation models to study within-herd foot and mouth disease dynamics and the implications of different empirical data-based assumptions about the timing of the onset of infectiousness and clinical signs. We also study the influence of herd size and the proportion of the herd that is initially infected on the outcome of the infection. We find that increasing herd size increases the duration of infectiousness and that the size of the herd plays a more significant role in determining this duration than the number of initially infected cattle in that herd. We also find that the assumptions made regarding the onset of infectiousness and clinical signs, which are based on contradictory empirical findings, can result in the predictions about when infection would be detectable differing by several days. Therefore, the disease progression used to characterize the course of infection in a single bovine host could have significant implications for determining when herds can be detected and subsequently controlled; the timing of which could influence the overall predicted trajectory of outbreaks.
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Affiliation(s)
| | - Erin E Gorsich
- Department of Biology, Colorado State University, United States of America
| | - Clayton Hallman
- USDA APHIS Veterinary Services, Center for Epidemiology and Animal Health, United States of America
| | - Michael J Tildesley
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), School of Life Sciences and Mathematics Institute, University of Warwick, United Kingdom
| | - Ryan S Miller
- USDA APHIS Veterinary Services, Center for Epidemiology and Animal Health, United States of America
| | - Colleen T Webb
- Department of Biology, Colorado State University, United States of America
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Tsao K, Sellman S, Beck-Johnson LM, Murrieta DJ, Hallman C, Lindström T, Miller RS, Portacci K, Tildesley MJ, Webb CT. Effects of regional differences and demography in modelling foot-and-mouth disease in cattle at the national scale. Interface Focus 2019; 10:20190054. [PMID: 31897292 DOI: 10.1098/rsfs.2019.0054] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/01/2019] [Indexed: 12/12/2022] Open
Abstract
Foot-and-mouth disease (FMD) is a fast-spreading viral infection that can produce large and costly outbreaks in livestock populations. Transmission occurs at multiple spatial scales, as can the actions used to control outbreaks. The US cattle industry is spatially expansive, with heterogeneous distributions of animals and infrastructure. We have developed a model that incorporates the effects of scale for both disease transmission and control actions, applied here in simulating FMD outbreaks in US cattle. We simulated infection initiating in each of the 3049 counties in the contiguous US, 100 times per county. When initial infection was located in specific regions, large outbreaks were more likely to occur, driven by infrastructure and other demographic attributes such as premises clustering and number of cattle on premises. Sensitivity analyses suggest these attributes had more impact on outbreak metrics than the ranges of estimated disease parameter values. Additionally, although shipping accounted for a small percentage of overall transmission, areas receiving the most animal shipments tended to have other attributes that increase the probability of large outbreaks. The importance of including spatial and demographic heterogeneity in modelling outbreak trajectories and control actions is illustrated by specific regions consistently producing larger outbreaks than others.
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Affiliation(s)
- Kimberly Tsao
- Department of Biology, Colorado State University, Fort Collins, CO 80523-1878, USA
| | - Stefan Sellman
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, Linköping, Sweden.,The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, UK
| | | | - Deedra J Murrieta
- Department of Biology, Colorado State University, Fort Collins, CO 80523-1878, USA
| | - Clayton Hallman
- Department of Biology, Colorado State University, Fort Collins, CO 80523-1878, USA
| | - Tom Lindström
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, Linköping, Sweden
| | - Ryan S Miller
- USDA APHIS Veterinary Services, Center for Epidemiology and Animal Health, Fort Collins, CO, USA
| | - Katie Portacci
- USDA APHIS Veterinary Services, Center for Epidemiology and Animal Health, Fort Collins, CO, USA
| | - 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, UK
| | - Colleen T Webb
- Department of Biology, Colorado State University, Fort Collins, CO 80523-1878, USA
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Greischar MA, Beck-Johnson LM, Mideo N. Partitioning the influence of ecology across scales on parasite evolution. Evolution 2019; 73:2175-2188. [PMID: 31495911 DOI: 10.1111/evo.13840] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 08/31/2019] [Indexed: 11/30/2022]
Abstract
Vector-borne parasites must succeed at three scales to persist: they must proliferate within a host, establish in vectors, and transmit back to hosts. Ecology outside the host undergoes dramatic seasonal and human-induced changes, but predicting parasite evolutionary responses requires integrating their success across scales. We develop a novel, data-driven model to titrate the evolutionary impact of ecology at multiple scales on human malaria parasites. We investigate how parasites invest in transmission versus proliferation, a life-history trait that influences disease severity and spread. We find that transmission investment controls the pattern of host infectiousness over the course of infection: a trade-off emerges between early and late infectiousness, and the optimal resolution of that trade-off depends on ecology outside the host. An expanding epidemic favors rapid proliferation, and can overwhelm the evolutionary influence of host recovery rates and mosquito population dynamics. If transmission investment and recovery rate are positively correlated, then ecology outside the host imposes potent selection for aggressive parasite proliferation at the expense of transmission. Any association between transmission investment and recovery represents a key unknown, one that is likely to influence whether the evolutionary consequences of interventions are beneficial or costly for human health.
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Affiliation(s)
- Megan A Greischar
- Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, Ontario, M5S 3B2, Canada
| | | | - Nicole Mideo
- Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, Ontario, M5S 3B2, Canada
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Beck-Johnson LM, Nelson WA, Paaijmans KP, Read AF, Thomas MB, Bjørnstad ON. The importance of temperature fluctuations in understanding mosquito population dynamics and malaria risk. R Soc Open Sci 2017; 4:160969. [PMID: 28405386 PMCID: PMC5383843 DOI: 10.1098/rsos.160969] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2016] [Accepted: 02/06/2017] [Indexed: 05/16/2023]
Abstract
Temperature is a key environmental driver of Anopheles mosquito population dynamics; understanding its central role is important for these malaria vectors. Mosquito population responses to temperature fluctuations, though important across the life history, are poorly understood at a population level. We used stage-structured, temperature-dependent delay-differential equations to conduct a detailed exploration of the impacts of diurnal and annual temperature fluctuations on mosquito population dynamics. The model allows exploration of temperature-driven temporal changes in adult age structure, giving insights into the population's capacity to vector malaria parasites. Because of temperature-dependent shifts in age structure, the abundance of potentially infectious mosquitoes varies temporally, and does not necessarily mirror the dynamics of the total adult population. In addition to conducting the first comprehensive theoretical exploration of fluctuating temperatures on mosquito population dynamics, we analysed observed temperatures at four locations in Africa covering a range of environmental conditions. We found both temperature and precipitation are needed to explain the observed malaria season in these locations, enhancing our understanding of the drivers of malaria seasonality and how temporal disease risk may shift in response to temperature changes. This approach, tracking both mosquito abundance and age structure, may be a powerful tool for understanding current and future malaria risk.
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Affiliation(s)
- Lindsay M. Beck-Johnson
- Department of Biology, Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
- Author for correspondence: Lindsay M. Beck-Johnson e-mail:
| | - William A. Nelson
- Department of Biology, Queen’s University, Kingston, Ontario, Canada
| | - Krijn P. Paaijmans
- ISGlobal, Barcelona Ctr. Int. Health Res. (CRESIB), Hospital Clinic, Universitat de Barcelona, Barcelona, Spain
| | - Andrew F. Read
- Department of Biology, Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
- Department of Entomology, Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Matthew B. Thomas
- Department of Entomology, Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Ottar N. Bjørnstad
- Department of Biology, Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
- Department of Entomology, Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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Beck-Johnson LM, Nelson WA, Paaijmans KP, Read AF, Thomas MB, Bjørnstad ON. The effect of temperature on Anopheles mosquito population dynamics and the potential for malaria transmission. PLoS One 2013; 8:e79276. [PMID: 24244467 PMCID: PMC3828393 DOI: 10.1371/journal.pone.0079276] [Citation(s) in RCA: 163] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2013] [Accepted: 09/19/2013] [Indexed: 11/18/2022] Open
Abstract
The parasites that cause malaria depend on Anopheles mosquitoes for transmission; because of this, mosquito population dynamics are a key determinant of malaria risk. Development and survival rates of both the Anopheles mosquitoes and the Plasmodium parasites that cause malaria depend on temperature, making this a potential driver of mosquito population dynamics and malaria transmission. We developed a temperature-dependent, stage-structured delayed differential equation model to better understand how climate determines risk. Including the full mosquito life cycle in the model reveals that the mosquito population abundance is more sensitive to temperature than previously thought because it is strongly influenced by the dynamics of the juvenile mosquito stages whose vital rates are also temperature-dependent. Additionally, the model predicts a peak in abundance of mosquitoes old enough to vector malaria at more accurate temperatures than previous models. Our results point to the importance of incorporating detailed vector biology into models for predicting the risk for vector borne diseases.
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Affiliation(s)
- Lindsay M. Beck-Johnson
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- * E-mail:
| | - William A. Nelson
- Department of Biology, Queen's University, Kingston, Ontario, Canada
| | - Krijn P. Paaijmans
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Entomology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Andrew F. Read
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Entomology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Matthew B. Thomas
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Entomology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Ottar N. Bjørnstad
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Entomology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
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