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Severns PM, Mundt CC. Delays in Epidemic Outbreak Control Cost Disproportionately Large Treatment Footprints to Offset. Pathogens 2022; 11:pathogens11040393. [PMID: 35456068 PMCID: PMC9030382 DOI: 10.3390/pathogens11040393] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/21/2022] [Accepted: 03/22/2022] [Indexed: 12/10/2022] Open
Abstract
Epidemic outbreak control often involves a spatially explicit treatment area (quarantine, inoculation, ring cull) that covers the outbreak area and adjacent regions where hosts are thought to be latently infected. Emphasis on space however neglects the influence of treatment timing on outbreak control. We conducted field and in silico experiments with wheat stripe rust (WSR), a long-distance dispersed plant disease, to understand interactions between treatment timing and area interact to suppress an outbreak. Full-factorial field experiments with three different ring culls (outbreak area only to a 25-fold increase in treatment area) at three different disease control timings (1.125, 1.25, and 1.5 latent periods after initial disease expression) indicated that earlier treatment timing had a conspicuously greater suppressive effect than the area treated. Disease spread computer simulations over a broad range of influential epidemic parameter values (R0, outbreak disease prevalence, epidemic duration) suggested that potentially unrealistically large increases in treatment area would be required to compensate for even small delays in treatment timing. Although disease surveillance programs are costly, our results suggest that treatments early in an epidemic disease outbreak require smaller areas to be effective, which may ultimately compensate for the upfront costs of proactive disease surveillance programs.
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Affiliation(s)
- Paul M. Severns
- Department of Plant Pathology, University of Georgia, Athens, GA 30602, USA
- Correspondence:
| | - Christopher C. Mundt
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA;
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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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Deng P, de Vargas Roditi L, van Ditmarsch D, Xavier JB. The ecological basis of morphogenesis: branching patterns in swarming colonies of bacteria. NEW JOURNAL OF PHYSICS 2014; 16:015006-15006. [PMID: 24587694 PMCID: PMC3935381 DOI: 10.1088/1367-2630/16/1/015006] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Understanding how large-scale shapes in tissues, organs and bacterial colonies emerge from local interactions among cells and how these shapes remain stable over time are two fundamental problems in biology. Here we investigate branching morphogenesis in an experimental model system, swarming colonies of the bacterium Pseudomonas aeruginosa. We combine experiments and computer simulation to show that a simple ecological model of population dispersal can describe the emergence of branching patterns. In our system, morphogenesis depends on two counteracting processes that act on different length-scales: (1) colony expansion, which increases the likelihood of colonizing a patch at a close distance and (2) colony repulsion, which decreases the colonization likelihood over a longer distance. The two processes are included in a kernel based mathematical model using an integro-differential approach borrowed from ecological theory. Computer simulations show that the model can indeed reproduce branching, but only for a narrow range of parameter values, suggesting that P. aeruginosa has a fine-tuned physiology for branching. Simulations further show that hyperswarming, a process where highly dispersive mutants reproducibly arise within the colony and disrupt branching patterns, can be interpreted as a change in the spatial kernel.
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Affiliation(s)
- Pan Deng
- Program in Computational Biology, Memorial Sloan-Kettering Cancer Center, New York NY, USA
| | - Laura de Vargas Roditi
- Program in Computational Biology, Memorial Sloan-Kettering Cancer Center, New York NY, USA
| | - Dave van Ditmarsch
- Program in Computational Biology, Memorial Sloan-Kettering Cancer Center, New York NY, USA
| | - Joao B. Xavier
- Program in Computational Biology, Memorial Sloan-Kettering Cancer Center, New York NY, USA
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Lindström T, Grear DA, Buhnerkempe M, Webb CT, Miller RS, Portacci K, Wennergren U. A bayesian approach for modeling cattle movements in the United States: scaling up a partially observed network. PLoS One 2013; 8:e53432. [PMID: 23308223 PMCID: PMC3537632 DOI: 10.1371/journal.pone.0053432] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2012] [Accepted: 11/28/2012] [Indexed: 11/26/2022] Open
Abstract
Networks are rarely completely observed and prediction of unobserved edges is an important problem, especially in disease spread modeling where networks are used to represent the pattern of contacts. We focus on a partially observed cattle movement network in the U.S. and present a method for scaling up to a full network based on Bayesian inference, with the aim of informing epidemic disease spread models in the United States. The observed network is a 10% state stratified sample of Interstate Certificates of Veterinary Inspection that are required for interstate movement; describing approximately 20,000 movements from 47 of the contiguous states, with origins and destinations aggregated at the county level. We address how to scale up the 10% sample and predict unobserved intrastate movements based on observed movement distances. Edge prediction based on a distance kernel is not straightforward because the probability of movement does not always decline monotonically with distance due to underlying industry infrastructure. Hence, we propose a spatially explicit model where the probability of movement depends on distance, number of premises per county and historical imports of animals. Our model performs well in recapturing overall metrics of the observed network at the node level (U.S. counties), including degree centrality and betweenness; and performs better compared to randomized networks. Kernel generated movement networks also recapture observed global network metrics, including network size, transitivity, reciprocity, and assortativity better than randomized networks. In addition, predicted movements are similar to observed when aggregated at the state level (a broader geographic level relevant for policy) and are concentrated around states where key infrastructures, such as feedlots, are common. We conclude that the method generally performs well in predicting both coarse geographical patterns and network structure and is a promising method to generate full networks that incorporate the uncertainty of sampled and unobserved contacts.
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Affiliation(s)
- Tom Lindström
- Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
- School of Biological Sciences, University of Sydney, Sydney, New South Wales, Australia
| | - Daniel A. Grear
- Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America
| | - Michael Buhnerkempe
- Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America
| | - Colleen T. Webb
- Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America
| | - Ryan S. Miller
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Center for Epidemiology and Animal Health, Fort Collins, Colorado, United States of America
| | - Katie Portacci
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Center for Epidemiology and Animal Health, Fort Collins, Colorado, United States of America
| | - Uno Wennergren
- Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
- * E-mail:
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Guttal V, Bartumeus F, Hartvigsen G, Nevai AL. Retention time variability as a mechanism for animal mediated long-distance dispersal. PLoS One 2011; 6:e28447. [PMID: 22194837 PMCID: PMC3237446 DOI: 10.1371/journal.pone.0028447] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2011] [Accepted: 11/08/2011] [Indexed: 11/19/2022] Open
Abstract
Long-distance dispersal (LDD) events, although rare for most plant species, can strongly influence population and community dynamics. Animals function as a key biotic vector of seeds and thus, a mechanistic and quantitative understanding of how individual animal behaviors scale to dispersal patterns at different spatial scales is a question of critical importance from both basic and applied perspectives. Using a diffusion-theory based analytical approach for a wide range of animal movement and seed transportation patterns, we show that the scale (a measure of local dispersal) of the seed dispersal kernel increases with the organisms' rate of movement and mean seed retention time. We reveal that variations in seed retention time is a key determinant of various measures of LDD such as kurtosis (or shape) of the kernel, thinkness of tails and the absolute number of seeds falling beyond a threshold distance. Using empirical data sets of frugivores, we illustrate the importance of variability in retention times for predicting the key disperser species that influence LDD. Our study makes testable predictions linking animal movement behaviors and gut retention times to dispersal patterns and, more generally, highlights the potential importance of animal behavioral variability for the LDD of seeds.
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Affiliation(s)
- Vishwesha Guttal
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America.
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Lindström T, Lewerin SS, Wennergren U. Influence on disease spread dynamics of herd characteristics in a structured livestock industry. J R Soc Interface 2011; 9:1287-94. [PMID: 22112656 PMCID: PMC3350725 DOI: 10.1098/rsif.2011.0625] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Studies of between-herd contacts may provide important insight to disease transmission dynamics. By comparing the result from models with different levels of detail in the description of animal movement, we studied how factors influence the final epidemic size as well as the dynamic behaviour of an outbreak. We investigated the effect of contact heterogeneity of pig herds in Sweden due to herd size, between-herd distance and production type. Our comparative study suggests that the production-type structure is the most influential factor. Hence, our results imply that production type is the most important factor to obtain valid data for and include when modelling and analysing this system. The study also revealed that all included factors reduce the final epidemic size and also have yet more diverse effects on initial rate of disease spread. This implies that a large set of factors ought to be included to assess relevant predictions when modelling disease spread between herds. Furthermore, our results show that a more detailed model changes predictions regarding the variability in the outbreak dynamics and conclude that this is an important factor to consider in risk assessment.
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Affiliation(s)
- Tom Lindström
- IFM Theory and Modelling, Linköping University, Linköping 581 83 Sweden
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Vinatier F, Lescourret F, Duyck PF, Martin O, Senoussi R, Tixier P. Should I stay or should I go? A habitat-dependent dispersal kernel improves prediction of movement. PLoS One 2011; 6:e21115. [PMID: 21765890 PMCID: PMC3134457 DOI: 10.1371/journal.pone.0021115] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2011] [Accepted: 05/19/2011] [Indexed: 11/26/2022] Open
Abstract
The analysis of animal movement within different landscapes may increase our understanding of how landscape features affect the perceptual range of animals. Perceptual range is linked to movement probability of an animal via a dispersal kernel, the latter being generally considered as spatially invariant but could be spatially affected. We hypothesize that spatial plasticity of an animal's dispersal kernel could greatly modify its distribution in time and space. After radio tracking the movements of walking insects (Cosmopolites sordidus) in banana plantations, we considered the movements of individuals as states of a Markov chain whose transition probabilities depended on the habitat characteristics of current and target locations. Combining a likelihood procedure and pattern-oriented modelling, we tested the hypothesis that dispersal kernel depended on habitat features. Our results were consistent with the concept that animal dispersal kernel depends on habitat features. Recognizing the plasticity of animal movement probabilities will provide insight into landscape-level ecological processes.
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Lindström T, Sisson SA, Lewerin SS, Wennergren U. Bayesian analysis of animal movements related to factors at herd and between herd levels: Implications for disease spread modeling. Prev Vet Med 2010; 98:230-42. [PMID: 21176982 DOI: 10.1016/j.prevetmed.2010.11.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2010] [Revised: 11/04/2010] [Accepted: 11/07/2010] [Indexed: 11/26/2022]
Abstract
A method to assess the influence of between herd distances, production types and herd sizes on patterns of between herd contacts is presented. It was applied on pig movement data from a central database of the Swedish Board of Agriculture. To determine the influence of these factors on the contact between holdings we used a Bayesian model and Markov chain Monte Carlo (MCMC) methods to estimate the posterior distribution of model parameters. The analysis showed that the contact pattern via animal movements is highly heterogeneous and influenced by all three factors, production type, herd size, and distance between holdings. Most production types showed a positive relationship between maximum capacity and the probability of both incoming and outgoing movements. In agreement with previous studies, holdings also differed in both the number of contacts as well as with what holding types contact occurred with. Also, the scale and shape of distance dependence in contact probability was shown to differ depending on the production types of holdings.To demonstrate how the methodology may be used for risk assessment, disease transmissions via animal movements were simulated with the model used for analysis of contacts, and parameterized by the analyzed posterior distribution. A Generalized Linear Model showed that herds with production types Sow pool center, Multiplying herd and Nucleus herd have higher risk of generating a large number of new infections. Multiplying herds are also expected to generate many long distance transmissions, while transmissions generated by Sow pool centers are confined to more local areas. We argue that the methodology presented may be a useful tool for improvement of risk assessment based on data found in central databases.
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Affiliation(s)
- Tom Lindström
- IFM Theory and Modelling, Linköping University, 581 83 Linköping, Sweden
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Lindström T, Håkansson N, Wennergren U. The shape of the spatial kernel and its implications for biological invasions in patchy environments. Proc Biol Sci 2010; 278:1564-71. [PMID: 21047854 DOI: 10.1098/rspb.2010.1902] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Ecological and epidemiological invasions occur in a spatial context. We investigated how these processes correlate to the distance dependence of spread or dispersal between spatial entities such as habitat patches or epidemiological units. Distance dependence is described by a spatial kernel, characterized by its shape (kurtosis) and width (variance). We also developed a novel method to analyse and generate point-pattern landscapes based on spectral representation. This involves two measures: continuity, which is related to autocorrelation and contrast, which refers to variation in patch density. We also analysed some empirical data where our results are expected to have implications, namely distributions of trees (Quercus and Ulmus) and farms in Sweden. Through a simulation study, we found that kernel shape was not important for predicting the invasion speed in randomly distributed patches. However, the shape may be essential when the distribution of patches deviates from randomness, particularly when the contrast is high. We conclude that the speed of invasions depends on the spatial context and the effect of the spatial kernel is intertwined with the spatial structure. This implies substantial demands on the empirical data, because it requires knowledge of shape and width of the spatial kernel, and spatial structure.
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Affiliation(s)
- Tom Lindström
- IFM Theory and Modelling, Linköping University, 581 83 Linköping, Sweden
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Kesler DC, Walters JR, Kappes JJ. Social influences on dispersal and the fat-tailed dispersal distribution in red-cockaded woodpeckers. Behav Ecol 2010. [DOI: 10.1093/beheco/arq158] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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Mercader RJ, Siegert NW, Liebhold AM, McCullough DG. Influence of foraging behavior and host spatial distribution on the localized spread of the emerald ash borer,
Agrilus planipennis. POPUL ECOL 2010. [DOI: 10.1007/s10144-010-0233-6] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Rodrigo J. Mercader
- Department of EntomologyMichigan State University243 Natural Sciences Building48824East LansingMIUSA
| | - Nathan W. Siegert
- Department of EntomologyMichigan State University243 Natural Sciences Building48824East LansingMIUSA
| | - Andrew M. Liebhold
- Northern Research StationUSDA Forest Service180 Canfield Street26505MorgantownWVUSA
| | - Deborah G. McCullough
- Department of EntomologyMichigan State University243 Natural Sciences Building48824East LansingMIUSA
- Department of ForestryMichigan State University243 Natural Sciences Building48824East LansingMIUSA
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Lindström T, Sisson SA, Nöremark M, Jonsson A, Wennergren U. Estimation of distance related probability of animal movements between holdings and implications for disease spread modeling. Prev Vet Med 2009; 91:85-94. [DOI: 10.1016/j.prevetmed.2009.05.022] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2008] [Revised: 05/13/2009] [Accepted: 05/16/2009] [Indexed: 11/27/2022]
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Hawkes C. Linking movement behaviour, dispersal and population processes: is individual variation a key? J Anim Ecol 2009; 78:894-906. [DOI: 10.1111/j.1365-2656.2009.01534.x] [Citation(s) in RCA: 119] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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