{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T22:54:08Z","timestamp":1791327248645,"version":"4.3.1"},"reference-count":49,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2017,1,31]],"date-time":"2017-01-31T00:00:00Z","timestamp":1485820800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["Ka 1082\/29\u20101"],"award-info":[{"award-number":["Ka 1082\/29\u20101"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004938","name":"Deutsches Primatenzentrum","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004938","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Animal Ecology"],"published-print":{"date-parts":[[2017,5]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>\n<jats:list>\n\n<jats:list-item><jats:p>Social networks provide an established tool to implement heterogeneous contact structures in epidemiological models. Dynamic temporal changes in contact structure and ranging behaviour of wildlife may impact disease dynamics. A consensus has yet to emerge, however, concerning the conditions in which network dynamics impact model outcomes, as compared to static approximations that average contact rates over longer time periods. Furthermore, as many pathogens can be transmitted both environmentally and via close contact, it is important to investigate the relative influence of both transmission routes in real\u2010world populations.<\/jats:p><\/jats:list-item>\n\n<jats:list-item><jats:p>Here, we use empirically derived networks from a population of wild primates, Verreaux's sifakas (<jats:italic>Propithecus verreauxi<\/jats:italic>), and simulated networks to investigate pathogen spread in dynamic vs. static social networks.<\/jats:p><\/jats:list-item>\n\n<jats:list-item><jats:p>First, we constructed a susceptible\u2010exposed\u2010infected\u2010recovered model of <jats:italic>Cryptosporidium<\/jats:italic> spread in wild Verreaux's sifakas. We incorporated social and environmental transmission routes and parameterized the model for two different climatic seasons. Second, we used simulated networks and greater variation in epidemiological parameters to investigate the conditions in which dynamic networks produce larger outbreak sizes than static networks.<\/jats:p><\/jats:list-item>\n\n<jats:list-item><jats:p>We found that average outbreak size of <jats:italic>Cryptosporidium<\/jats:italic> infections in sifakas was larger when the disease was introduced in the dry season than in the wet season, driven by an increase in home range overlap towards the end of the dry season. Regardless of season, dynamic networks always produced larger average outbreak sizes than static networks. Larger outbreaks in dynamic models based on simulated networks occurred especially when the probability of transmission and recovery were low. Variation in tie strength in the dynamic networks also had a major impact on outbreak size, while network modularity had a weaker influence than epidemiological parameters that determine transmission and recovery.<\/jats:p><\/jats:list-item>\n\n<jats:list-item><jats:p>Our study adds to emerging evidence that dynamic networks can change predictions of disease dynamics, especially if the disease shows low transmissibility and a long infectious period, and when environmental conditions lead to enhanced between\u2010group contact after an infectious agent has been introduced.<\/jats:p><\/jats:list-item>\n<\/jats:list>\n<\/jats:p>","DOI":"10.1111\/1365-2656.12617","type":"journal-article","created":{"date-parts":[[2016,12,15]],"date-time":"2016-12-15T15:52:44Z","timestamp":1481817164000},"page":"419-433","source":"Crossref","is-referenced-by-count":33,"title":["Dynamic vs. static social networks in models of parasite transmission: predicting <i><scp>C<\/scp>ryptosporidium<\/i> spread in wild lemurs"],"prefix":"10.1111","volume":"86","author":[{"given":"Andrea","family":"Springer","sequence":"first","affiliation":[{"name":"Behavioral Ecology and Sociobiology Unit German Primate Center G\u00f6ttingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter M.","family":"Kappeler","sequence":"additional","affiliation":[{"name":"Behavioral Ecology and Sociobiology Unit German Primate Center G\u00f6ttingen Germany"},{"name":"Department of Sociobiology and Anthropology University of G\u00f6ttingen G\u00f6ttingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles L.","family":"Nunn","sequence":"additional","affiliation":[{"name":"Department of Evolutionary Anthropology Duke University Durham NC USA"},{"name":"Duke Global Health Institute Duke University Durham NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2017,1,31]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1079\/9780851994215.0000"},{"key":"e_1_2_8_3_1","unstructured":"Barton K.(2016)MuMIn: Multi\u2010Model Inference. 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