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      Transmission dynamics of re-emerging rabies in domestic dogs of rural China

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          Abstract

          Despite ongoing efforts to control transmission, rabies prevention remains a challenge in many developing countries, especially in rural areas of China where re-emerging rabies is under-reported due to a lack of sustained animal surveillance. By taking advantage of detailed genomic and epidemiological data for the re-emerging rabies outbreak in Yunnan Province, China, collected between 1999 and 2015, we reconstruct the demographic and dispersal history of domestic dog rabies virus (RABV) as well as the dynamics of dog-to-dog and dog-to-human transmission. Phylogeographic analyses reveal a lower diffusion coefficient than previously estimated for dog RABV dissemination in northern Africa. Furthermore, epidemiological analyses reveal transmission rates between dogs, as well as between dogs and humans, lower than estimates for Africa. Finally, we show that reconstructed epidemic history of RABV among dogs and the dynamics of rabid dogs are consistent with the recorded human rabies cases. This work illustrates the benefits of combining phylogeographic and epidemic modelling approaches for uncovering the spatiotemporal dynamics of zoonotic diseases, with both approaches providing estimates of key epidemiological parameters.

          Author summary

          Although dogs are known to be the primary reservoir and vector of human rabies in African and Asian countries, the spatial epidemiology of rabies virus (RABV) spread in developing regions is still unclear. Using 17 years of genomic and epidemiological data, we reconstruct the recent dispersal history of RABV in domestic dogs in Yunnan, a rural province of China, and estimate RABV transmission rate between dogs and from dogs to humans. Using a phylogeographic approach, we also evaluated the potential impact of several environmental factors on the mode and tempo of virus lineage dispersal. Our findings have implications for rabies prevention and control in Asian countries.

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          A methodology for performing global uncertainty and sensitivity analysis in systems biology.

          Accuracy of results from mathematical and computer models of biological systems is often complicated by the presence of uncertainties in experimental data that are used to estimate parameter values. Current mathematical modeling approaches typically use either single-parameter or local sensitivity analyses. However, these methods do not accurately assess uncertainty and sensitivity in the system as, by default, they hold all other parameters fixed at baseline values. Using techniques described within we demonstrate how a multi-dimensional parameter space can be studied globally so all uncertainties can be identified. Further, uncertainty and sensitivity analysis techniques can help to identify and ultimately control uncertainties. In this work we develop methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models. We compare two specific types of global sensitivity analysis indexes that have proven to be among the most robust and efficient. Through familiar and new examples of mathematical and computer models, we provide a complete methodology for performing these analyses, in both deterministic and stochastic settings, and propose novel techniques to handle problems encountered during these types of analyses.
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            Re-evaluating the burden of rabies in Africa and Asia.

            To quantify the public health and economic burden of endemic canine rabies in Africa and Asia. Data from these regions were applied to a set of linked epidemiological and economic models. The human population at risk from endemic canine rabies was predicted using data on dog density, and human rabies deaths were estimated using a series of probability steps to determine the likelihood of clinical rabies developing in a person after being bitten by a dog suspected of having rabies. Model outputs on mortality and morbidity associated with rabies were used to calculate an improved disability-adjusted life year (DALY) score for the disease. The total societal cost incurred by the disease is presented. Human mortality from endemic canine rabies was estimated to be 55 000 deaths per year (90% confidence interval (CI) = 24 000-93 000). Deaths due to rabies are responsible for 1.74 million DALYs lost each year (90% CI = 0.75-2.93). An additional 0.04 million DALYs are lost through morbidity and mortality following side-effects of nerve-tissue vaccines. The estimated annual cost of rabies is USD 583.5 million (90% CI = USD 540.1-626.3 million). Patient-borne costs for post-exposure treatment form the bulk of expenditure, accounting for nearly half the total costs of rabies. Rabies remains an important yet neglected disease in Africa and Asia. Disparities in the affordability and accessibility of post-exposure treatment and risks of exposure to rabid dogs result in a skewed distribution of the disease burden across society, with the major impact falling on those living in poor rural communities, in particular children.
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              Improving Bayesian population dynamics inference: a coalescent-based model for multiple loci.

              Effective population size is fundamental in population genetics and characterizes genetic diversity. To infer past population dynamics from molecular sequence data, coalescent-based models have been developed for Bayesian nonparametric estimation of effective population size over time. Among the most successful is a Gaussian Markov random field (GMRF) model for a single gene locus. Here, we present a generalization of the GMRF model that allows for the analysis of multilocus sequence data. Using simulated data, we demonstrate the improved performance of our method to recover true population trajectories and the time to the most recent common ancestor (TMRCA). We analyze a multilocus alignment of HIV-1 CRF02_AG gene sequences sampled from Cameroon. Our results are consistent with HIV prevalence data and uncover some aspects of the population history that go undetected in Bayesian parametric estimation. Finally, we recover an older and more reconcilable TMRCA for a classic ancient DNA data set.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Investigation
                Role: Formal analysisRole: Funding acquisitionRole: MethodologyRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysis
                Role: Formal analysis
                Role: Formal analysis
                Role: Data curation
                Role: Data curation
                Role: Data curation
                Role: Data curation
                Role: Data curation
                Role: Formal analysisRole: Funding acquisitionRole: Writing – original draftRole: Writing – review & editing
                Role: Funding acquisitionRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: ResourcesRole: Supervision
                Role: Formal analysisRole: Funding acquisitionRole: MethodologyRole: SoftwareRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Pathog
                PLoS Pathog
                plos
                plospath
                PLoS Pathogens
                Public Library of Science (San Francisco, CA USA )
                1553-7366
                1553-7374
                6 December 2018
                December 2018
                : 14
                : 12
                : e1007392
                Affiliations
                [1 ] State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
                [2 ] Yunnan Institute of Endemic Diseases Control and Prevention, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Dali, China
                [3 ] KU Leuven, Department of Microbiology and Immunology, Rega Institute, Laboratory of Evolutionary and Computational Virology, Leuven, Belgium
                [4 ] Institut de Biologie de l’École Normale Supérieure UMR 8197, Eco-Evolutionary Mathematics, École Normale Supérieure, France
                [5 ] Unité Mixte Internationnale 209, Mathematical and Computational Modeling of Complex Systems, Institut de Recherche pour le Développement et Université Pierre et Marie Curie, Bondy, France
                [6 ] School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, Texas, United States of America
                [7 ] Department of Zoology, University of Oxford, Oxford, United Kingdom
                [8 ] Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Blindern, Oslo, Norway
                [9 ] Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
                [10 ] Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
                Thomas Jefferson University, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-4466-0858
                http://orcid.org/0000-0001-6547-5283
                http://orcid.org/0000-0002-7972-361X
                http://orcid.org/0000-0001-7818-1081
                http://orcid.org/0000-0001-9558-1052
                Article
                PPATHOGENS-D-18-01239
                10.1371/journal.ppat.1007392
                6283347
                30521641
                8f41e6a0-48dc-4573-8106-6b94cf661572
                © 2018 Tian et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 June 2018
                : 8 October 2018
                Page count
                Figures: 4, Tables: 3, Pages: 18
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81673234, 41476161
                Award Recipient :
                Funded by: Bijzonder Onderzoeksfonds KU Leuven
                Award ID: OT/14/115
                Funded by: VIROGENESIS
                Award ID: 634650
                Funded by: Fundamental Research Funds for the Central Universities
                Award Recipient :
                Funded by: National Key Research and Development Program of China
                Award ID: 2016YFA0600104
                Award Recipient :
                Funded by: European Union’s Seventh Framework Programme
                Award ID: 614725-PATHPHYLODYN
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003130, Fonds Wetenschappelijk Onderzoek;
                Award ID: FWO
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003130, Fonds Wetenschappelijk Onderzoek;
                Award ID: FWO
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100002661, Fonds De La Recherche Scientifique - FNRS;
                Award ID: FNRS
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100007601, Horizon 2020;
                Award ID: 725422-ReservoirDOCS
                Funding for this study was provided by the National Natural Science Foundation of China (81673234, 41476161), http://www.nsfc.gov.cn/; the Bijzonder Onderzoeksfonds KU Leuven (BOF) (No. OT/14/115), https://www.kuleuven.be/onderzoek/ondersteuning/if; the VIROGENESIS project (receiving funding from the European Union’s Horizon 2020 research and innovation program under grant agreement number 634650), https://www.kuleuven.be/english/research/EU/p/horizon2020/sc/sc1/Virogenesis; Fundamental Research Funds for the Central Universities, http://www.moe.gov.cn/; National Key Research and Development Program of China (2016YFA0600104), http://www.most.gov.cn/; Beijing Natural Science Foundation (Beijing Science Foundation for Distinguished Young Scholars), http://kw.beijing.gov.cn/jjb/; the China Association for Science and Technology Youth Talent Lift Project, http://www.cast.org.cn/; European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/European Research Council grant agreement number 614725-PATHPHYLODYN and under the European Union's Horizon 2020 research and innovation programme, grant agreement no. 725422-ReservoirDOCS, https://ec.europa.eu/programmes/horizon2020/; BV and SD were funded by a postdoctoral fellowship from the Fonds Wetenschappelijk Onderzoek (FWO, Belgium), http://www.fwo.be/ (G066215N, G0D5117N and G0B9317N); SD is also supported by the Fonds National de la Recherche Scientifique (FNRS, Belgium). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Dogs
                Medicine and Health Sciences
                Tropical Diseases
                Neglected Tropical Diseases
                Rabies
                Medicine and Health Sciences
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                Rabies
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                Rabies
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                Custom metadata
                All relevant data are within the paper and its Supporting Information files. All sequences generated in this study are available in GenBank under accession numbers listed in S1 Table.

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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