Relevant bibliographies by topics / Metapopulation; Epidemics

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Metapopulation; Epidemics'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Metapopulation; Epidemics"

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Dearlove, Bethany, and Daniel J. Wilson. "Coalescent inference for infectious disease: meta-analysis of hepatitis C." Philosophical Transactions of the Royal Society B: Biological Sciences 368, no. 1614 (March 19, 2013): 20120314. http://dx.doi.org/10.1098/rstb.2012.0314.

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Genetic analysis of pathogen genomes is a powerful approach to investigating the population dynamics and epidemic history of infectious diseases. However, the theoretical underpinnings of the most widely used, coalescent methods have been questioned, casting doubt on their interpretation. The aim of this study is to develop robust population genetic inference for compartmental models in epidemiology. Using a general approach based on the theory of metapopulations, we derive coalescent models under susceptible–infectious (SI), susceptible–infectious–susceptible (SIS) and susceptible–infectious–recovered (SIR) dynamics. We show that exponential and logistic growth models are equivalent to SI and SIS models, respectively, when co-infection is negligible. Implementing SI, SIS and SIR models in BEAST, we conduct a meta-analysis of hepatitis C epidemics, and show that we can directly estimate the basic reproductive number ( R 0 ) and prevalence under SIR dynamics. We find that differences in genetic diversity between epidemics can be explained by differences in underlying epidemiology (age of the epidemic and local population density) and viral subtype. Model comparison reveals SIR dynamics in three globally restricted epidemics, but most are better fit by the simpler SI dynamics. In summary, metapopulation models provide a general and practical framework for integrating epidemiology and population genetics for the purposes of joint inference.
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Lloyd, Alun L., and Vincent A. A. Jansen. "Spatiotemporal dynamics of epidemics: synchrony in metapopulation models." Mathematical Biosciences 188, no. 1-2 (March 2004): 1–16. http://dx.doi.org/10.1016/j.mbs.2003.09.003.

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Watts, D. J., R. Muhamad, D. C. Medina, and P. S. Dodds. "Multiscale, resurgent epidemics in a hierarchical metapopulation model." Proceedings of the National Academy of Sciences 102, no. 32 (July 29, 2005): 11157–62. http://dx.doi.org/10.1073/pnas.0501226102.

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Wang, Jian-Bo, and Xiang Li. "Uncovering Spatial Invasion on Metapopulation Networks with SIR Epidemics." IEEE Transactions on Network Science and Engineering 6, no. 4 (October 1, 2019): 788–800. http://dx.doi.org/10.1109/tnse.2018.2873609.

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Ball, Frank, Tom Britton, Thomas House, Valerie Isham, Denis Mollison, Lorenzo Pellis, and Gianpaolo Scalia Tomba. "Seven challenges for metapopulation models of epidemics, including households models." Epidemics 10 (March 2015): 63–67. http://dx.doi.org/10.1016/j.epidem.2014.08.001.

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Nagatani, Takashi, Genki Ichinose, and Kei-ichi Tainaka. "Epidemics of random walkers in metapopulation model for complete, cycle, and star graphs." Journal of Theoretical Biology 450 (August 2018): 66–75. http://dx.doi.org/10.1016/j.jtbi.2018.04.029.

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Wiratsudakul, Anuwat, Parinya Suparit, and Charin Modchang. "Dynamics of Zika virus outbreaks: an overview of mathematical modeling approaches." PeerJ 6 (March 22, 2018): e4526. http://dx.doi.org/10.7717/peerj.4526.

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BackgroundThe Zika virus was first discovered in 1947. It was neglected until a major outbreak occurred on Yap Island, Micronesia, in 2007. Teratogenic effects resulting in microcephaly in newborn infants is the greatest public health threat. In 2016, the Zika virus epidemic was declared as a Public Health Emergency of International Concern (PHEIC). Consequently, mathematical models were constructed to explicitly elucidate related transmission dynamics.Survey MethodologyIn this review article, two steps of journal article searching were performed. First, we attempted to identify mathematical models previously applied to the study of vector-borne diseases using the search terms “dynamics,” “mathematical model,” “modeling,” and “vector-borne” together with the names of vector-borne diseases including chikungunya, dengue, malaria, West Nile, and Zika. Then the identified types of model were further investigated. Second, we narrowed down our survey to focus on only Zika virus research. The terms we searched for were “compartmental,” “spatial,” “metapopulation,” “network,” “individual-based,” “agent-based” AND “Zika.” All relevant studies were included regardless of the year of publication. We have collected research articles that were published before August 2017 based on our search criteria. In this publication survey, we explored the Google Scholar and PubMed databases.ResultsWe found five basic model architectures previously applied to vector-borne virus studies, particularly in Zika virus simulations. These include compartmental, spatial, metapopulation, network, and individual-based models. We found that Zika models carried out for early epidemics were mostly fit into compartmental structures and were less complicated compared to the more recent ones. Simple models are still commonly used for the timely assessment of epidemics. Nevertheless, due to the availability of large-scale real-world data and computational power, recently there has been growing interest in more complex modeling frameworks.DiscussionMathematical models are employed to explore and predict how an infectious disease spreads in the real world, evaluate the disease importation risk, and assess the effectiveness of intervention strategies. As the trends in modeling of infectious diseases have been shifting towards data-driven approaches, simple and complex models should be exploited differently. Simple models can be produced in a timely fashion to provide an estimation of the possible impacts. In contrast, complex models integrating real-world data require more time to develop but are far more realistic. The preparation of complicated modeling frameworks prior to the outbreaks is recommended, including the case of future Zika epidemic preparation.
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Li, Zhengyan, Huichun Li, Xue Zhang, and Chengli Zhao. "Estimation of Human Mobility Patterns for Forecasting the Early Spread of Disease." Healthcare 9, no. 9 (September 16, 2021): 1224. http://dx.doi.org/10.3390/healthcare9091224.

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Human mobility data are indispensable in modeling large-scale epidemics, especially in predicting the spatial spread of diseases and in evaluating spatial heterogeneity intervention strategies. However, statistical data that can accurately describe large-scale population migration are often difficult to obtain. We propose an algorithm model based on the network science approach, which estimates the travel flow data in mainland China by transforming location big data and airline operation data into network structure information. In addition, we established a simplified deterministic SEIR (Susceptible-Exposed-Infectious-Recovered)-metapopulation model to verify the effectiveness of the estimated travel flow data in the study of predicting epidemic spread. The results show that individual travel distance in mainland China is mainly within 100 km. There is far more travel between prefectures within the same province than across provinces. The epidemic spatial spread model incorporating estimated travel data accurately predicts the spread of COVID-19 in mainland China. The results suggest that there are far more travelers than usual during the Spring Festival in mainland China, and the number of travelers from Wuhan mainly determines the number of confirmed cases of COVID-19 in each prefecture.
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Wu, Charles, Catherine Wu, and Kun Chan Wu. "Response to the Coronavirus Disease-2019 Pandemic: Lessons Learned from the Taiwan Model." Asian Social Science 16, no. 10 (September 24, 2020): 16. http://dx.doi.org/10.5539/ass.v16n10p16.

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or coronavirus disease-2019 (COVID-19), emerged in December 2019 in Wuhan, China and has since then spurred a global pandemic (Lai et al., 2020). Taiwan and China, separated only by 130 km across the Taiwan Strait, have frequent cross-strait interactions with each other; millions of people travel to and from between the two countries (Wang & Lin, 2020). Considering these facts, Lauren Gardner, an associate professor at the Johns Hopkins University, even predicted that Taiwan will have the second highest number of COVID-19 cases among the world using a metapopulation model (Gardner et al., 2020). However, with a population of 23.7 million people, Taiwan leads one of the least COVID-19 cases worldwide. With the help of technology, swift reactions, advanced deployment of resources, and complete transparency, the Taiwan model has made its success. By analyzing the actions taken and how they functioned in Taiwan in preventing a nationwide epidemic, other countries may benefit in understanding how to design better models for the prevention of future epidemics and pandemics.
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Lieberthal, Brandon, and Allison M. Gardner. "Connectivity, reproduction number, and mobility interact to determine communities’ epidemiological superspreader potential in a metapopulation network." PLOS Computational Biology 17, no. 3 (March 18, 2021): e1008674. http://dx.doi.org/10.1371/journal.pcbi.1008674.

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Disease epidemic outbreaks on human metapopulation networks are often driven by a small number of superspreader nodes, which are primarily responsible for spreading the disease throughout the network. Superspreader nodes typically are characterized either by their locations within the network, by their degree of connectivity and centrality, or by their habitat suitability for the disease, described by their reproduction number (R). Here we introduce a model that considers simultaneously the effects of network properties and R on superspreaders, as opposed to previous research which considered each factor separately. This type of model is applicable to diseases for which habitat suitability varies by climate or land cover, and for direct transmitted diseases for which population density and mitigation practices influences R. We present analytical models that quantify the superspreader capacity of a population node by two measures: probability-dependent superspreader capacity, the expected number of neighboring nodes to which the node in consideration will randomly spread the disease per epidemic generation, and time-dependent superspreader capacity, the rate at which the node spreads the disease to each of its neighbors. We validate our analytical models with a Monte Carlo analysis of repeated stochastic Susceptible-Infected-Recovered (SIR) simulations on randomly generated human population networks, and we use a random forest statistical model to relate superspreader risk to connectivity, R, centrality, clustering, and diffusion. We demonstrate that either degree of connectivity or R above a certain threshold are sufficient conditions for a node to have a moderate superspreader risk factor, but both are necessary for a node to have a high-risk factor. The statistical model presented in this article can be used to predict the location of superspreader events in future epidemics, and to predict the effectiveness of mitigation strategies that seek to reduce the value of R, alter host movements, or both.
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Dissertations / Theses on the topic "Metapopulation; Epidemics"

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Teissier, Yoann. "Metapopulation dynamics of dengue epidemics in French Polynesia." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCB008.

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La dengue circule en Polynésie française sur un mode épidémique depuis plus de 35 ans. Néanmoins, en dépit de la taille relativement faible de la population de Polynésie française, la circulation de la dengue peut persister à de faibles niveaux pendant de nombreuses années. L’objectif de ce travail de thèse est de déterminer si l'épidémiologie de la dengue dans le système insulaire de la Polynésie française répond aux critères d’un contexte de métapopulation. Après avoir constitué une base de données regroupant les cas de dengue répertoriés sur les 35 dernières années, nous avons réalisé des analyses épidémiologiques descriptives et statistiques. Celles-ci ont révélé des disparités spatio-temporelles distinctes pour l’incidence de la dengue des archipels et des îles, mais la structure de l'épidémie globale à l’échelle de la Polynésie française pour un même sérotype ne semble pas être affectée. Les analyses de la métapopulation ont révélé l'incidence asynchrone de la dengue dans un grand nombre d’îles. Celle-ci s’observe plus particulièrement par la différence de dynamique de l’incidence entre les îles plus peuplées et celles ayant une population plus faible. La taille critique de la communauté nécessaire à la persistance de la dengue n’est même pas atteinte par la plus grande île de Polynésie Française, Tahiti. Ce résultat suggère que la dengue peut uniquement persister grâce à sa propagation d’île en île. L'incorporation de la connectivité des îles à travers des modèles de migration humaine dans un modèle mathématique a produit une dynamique de la dengue davantage en adéquation avec les données observées, que les tentatives de modélisation traitant la population dans son ensemble. Le modèle de la métapopulation a été capable de simuler la même dynamique que les cas de dengue observés pour l'épidémie et la transmission endémique qui a suivi pour la période de 2001 à 2008. Des analyses complémentaires sur la différenciation de l'incidence de la maladie et de l'infection seront probablement instructives pour affiner le modèle de métapopulation de l'épidémiologie de la dengue en Polynésie française
Dengue has been epidemic in French Polynesia for the past 35 years. Despite the relatively small population size in French Polynesia, dengue does not disappear and can persist at low levels for many years. In light of the large number of islands comprising French Polynesia, this thesis addresses the extent to which a metapopulation context may be the most appropriate to describe the epidemiology and persistence of dengue in this case. After compiling a database of dengue cases over the last 35 years, we used a number of descriptive and statistical epidemiological analyses that revealed distinct spatio-temporal disparity in dengue incidence for archipelago and islands. But the global structure of the epidemics of the same serotype were not affected. Metapopulation analyses revealed asynchronous dengue incidence among many of the islands and most notably larger islands lagged behind the smaller islands. The critical community size, which determines dengue persistence, was found to exceed even the largest island of Tahiti, suggesting that dengue can only exist by island-hopping. Incorporation of island connectedness through patterns of human migration into a mathematical model enabled a much better fit to the observed data than treating the population as a whole. The metapopulation model was able to capture to some extent the epidemic and low level transmission dynamics observed for the period of 2001-2008. Further analyses on differentiating incidence of disease and infection will likely prove informative for the metapopulation model of dengue epidemiology in French Polynesia
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Lloyd, Alun Lewis. "Mathematical models for spatial heterogeneity in population dynamics and epidemiology." Thesis, University of Oxford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337603.

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Schumm, Phillip Raymond Brooke. "Characterizing epidemics in metapopulation cattle systems through analytic models and estimation methods for data-driven model inputs." Diss., Kansas State University, 2013. http://hdl.handle.net/2097/16897.

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Doctor of Philosophy
Department of Electrical and Computer Engineering
Caterina Maria Scoglio
We have analytically discovered the existence of two global epidemic invasion thresholds in a directed meta-population network model of the United States cattle industry. The first threshold describes the outbreak of disease first within the core of the livestock system while the second threshold describes the invasion of the epidemic into a second class of locations where the disease would pose a risk for contamination of meat production. Both thresholds have been verified through extensive numerical simulations. We have further derived the relationship between the pair of thresholds and discovered a unique dependence on the network topology through the fractional compositions and the in-degree distributions of the transit and sink nodes. We then addressed a major challenge for epidemiologists and their efforts to model disease outbreaks in cattle. There is a critical shortfall in the availability of large-scale livestock movement data for the United States. We meet this challenge by developing a method to estimate cattle movement parameters from publicly available data. Across 10 Central States of the US, we formulated a large, convex optimization problem to predict the cattle movement parameters which, having minimal assumptions, provide the best fit to the US Department of Agriculture's Census database and follow constraints defined by scientists and cattle experts. Our estimated parameters can produce distributions of cattle shipments by head which compare well with shipment distributions also provided by the US Department of Agriculture. This dissertation concludes with a brief incorporation of the analytic models and the parameter estimation. We approximated the critical movement rates defined by the global invasion thresholds and compared them with the average estimated cattle movement rates to find a significant opportunity for epidemics to spread through US cattle populations.
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Sallah, Kankoe. "Diffusion spatio-temporelle des épidémies : approche comparée des modélisations mathématiques et biostatistiques, cibles d'intervention et mobilité humaine." Thesis, Aix-Marseille, 2017. http://www.theses.fr/2017AIXM0607.

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Dans la première partie de cette thèse, nous avons mis en place un métamodèle de transmission du paludisme basé sur la modélisation compartimentale susceptible-infecté-résistant (SIR) et prenant en compte les flux de mobilité humaine entre différents villages du Centre Sénégal. Les stratégies d’intervention géographiquement ciblées, s’étaient avérées efficaces pour réduire l’incidence du paludisme aussi bien dans les zones d’intervention qu’à l’extérieur de ces zones. Cependant, des actions combinées ciblant à la fois le vecteur et l’hôte, coordonnées à large échelle sont nécessaires dans les régions et pays visant l’élimination du paludisme à court/moyen terme.Dans la deuxième partie nous avons évalué différentes méthodes d’estimation de la mobilité humaine en l’absence de données individuelles. Ces méthodes incluaient la traçabilité spatio-temporelle des téléphones mobiles ainsi que les modèles mathématiques de gravité et de radiation. Le transport de l’agent pathogène dans l’espace géographique, par la mobilité d’un sujet infecté est un déterminant majeur de la vitesse de propagation d’une épidémie. Nous avons introduit le modèle d’impédance qui minimise l’erreur quadratique moyen sur les estimations de mobilité, en particulier dans les contextes où les ensembles de population sont caractérisés par leurs tailles hétérogènes.Nous avons enfin élargi le cadre des hypothèses sous-jacentes à la calibration des modèles de gravité de la mobilité humaine. L’hypothèse d’une distribution avec excès de zéros a fourni un meilleur ajustement et une meilleure prédictibilité, comparée aux hypothèses classiques n’assumant pas un excès de zéros : Poisson, Quasipoisson
In the first part of this thesis, we have developed a malaria transmission metamodel based on the susceptible-infected-resistant compartmental modeling framework (SIR) and taking into consideration human mobility flows between different villages in the Center of Senegal. Geographically targeted intervention strategies had been shown to be effective in reducing the incidence of malaria both within and outside of intervention areas. However, combined interventions targeting both vector and host, coordinated on a large scale are needed in regions and countries aiming to achieve malaria elimination in the short/medium term.In the second part we have evaluated different methods of estimating human mobility in the absence of real data. These methods included spatio-temporal traceability of mobile phones, mathematical models of gravity and radiation. The transport of the pathogen through the geographical space via the mobility of an infected subject is a major determinant of the spread of an epidemic. We introduced the impedance model that minimized the mean square error on mobility estimates, especially in contexts where population sets are characterized by their heterogeneous sizes.Finally, we have expanded the framework of assumptions underlying the calibration of the gravity models of human mobility. The hypothesis of a zero inflated distribution provided a better fit and a better predictability, compared to the classical approach not assuming an excess of zeros: Poisson, Quasipoisson
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"Epidemic Dynamics of Metapopulation Models." Doctoral diss., 2014. http://hdl.handle.net/2286/R.I.21041.

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abstract: Mathematical modeling of infectious diseases can help public health officials to make decisions related to the mitigation of epidemic outbreaks. However, over or under estimations of the morbidity of any infectious disease can be problematic. Therefore, public health officials can always make use of better models to study the potential implication of their decisions and strategies prior to their implementation. Previous work focuses on the mechanisms underlying the different epidemic waves observed in Mexico during the novel swine origin influenza H1N1 pandemic of 2009 and showed extensions of classical models in epidemiology by adding temporal variations in different parameters that are likely to change during the time course of an epidemic, such as, the influence of media, social distancing, school closures, and how vaccination policies may affect different aspects of the dynamics of an epidemic. This current work further examines the influence of different factors considering the randomness of events by adding stochastic processes to meta-population models. I present three different approaches to compare different stochastic methods by considering discrete and continuous time. For the continuous time stochastic modeling approach I consider the continuous-time Markov chain process using forward Kolmogorov equations, for the discrete time stochastic modeling I consider stochastic differential equations using Wiener's increment and Poisson point increments, and also I consider the discrete-time Markov chain process. These first two stochastic modeling approaches will be presented in a one city and two city epidemic models using, as a base, our deterministic model. The last one will be discussed briefly on a one city SIS and SIR-type model.
Dissertation/Thesis
Ph.D. Applied Mathematics for the Life and Social Sciences 2014
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Zivković, Gojović Marija. "Structured influenza model for metapopulation /." 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR29635.

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Thesis (M.Sc.)--York University, 2006. Graduate Programme in Science.
Typescript. Includes bibliographical references (leaves 62-65). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR29635
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Book chapters on the topic "Metapopulation; Epidemics"

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Avinyó, Albert, Marta Pellicer, Jordi Ripoll, and Joan Saldaña. "Density-Dependent Diffusion and Epidemics on Heterogeneous Metapopulations." In Trends in Mathematics, 143–48. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22129-8_25.

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Conference papers on the topic "Metapopulation; Epidemics"

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Wang, Jian-Bo, Cong Li, and Xiang Li. "Predicting spatial transmission at the early stage of epidemics on a networked metapopulation." In 2016 12th IEEE International Conference on Control and Automation (ICCA). IEEE, 2016. http://dx.doi.org/10.1109/icca.2016.7505262.

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Wang, Jian-Bo, Xiang Li, and Lin Wang. "Inferring spatial transmission of epidemics in networked metapopulations." In 2015 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2015. http://dx.doi.org/10.1109/iscas.2015.7168781.

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COLIZZA, V., F. GARGIULO, J. J. RAMASCO, A. BARRAT, and A. VESPIGNANI. "NETWORK STRUCTURE AND EPIDEMIC WAVES IN METAPOPULATION MODELS." In International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789814271820_0005.

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Preciado, Victor M., and Michael Zargham. "Traffic optimization to control epidemic outbreaks in metapopulation models." In 2013 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2013. http://dx.doi.org/10.1109/globalsip.2013.6737024.

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Wang, Jingyuan, Xiaojian Wang, and Junjie Wu. "Inferring Metapopulation Propagation Network for Intra-city Epidemic Control and Prevention." In KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3219819.3219865.

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