<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Magiorkinis, G</style></author><author><style face="normal" font="default" size="100%">Karamitros, T.</style></author><author><style face="normal" font="default" size="100%">Vasylyeva, T. I.</style></author><author><style face="normal" font="default" size="100%">Williams, L. D.</style></author><author><style face="normal" font="default" size="100%">Mbisa, J. L.</style></author><author><style face="normal" font="default" size="100%">Hatzakis, A</style></author><author><style face="normal" font="default" size="100%">Paraskevis, D</style></author><author><style face="normal" font="default" size="100%">Friedman, S. R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Innovative Study Design to Assess the Community Effect of Interventions to Mitigate HIV Epidemics Using Transmission-Chain Phylodynamics</style></title><secondary-title><style face="normal" font="default" size="100%">Am J EpidemiolAm J EpidemiolAm J Epidemiol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">American journal of epidemiology</style></alt-title><short-title><style face="normal" font="default" size="100%">American journal of epidemiologyAmerican journal of epidemiology</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">*Adaptation, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">*Research Design</style></keyword><keyword><style  face="normal" font="default" size="100%">Epidemics</style></keyword><keyword><style  face="normal" font="default" size="100%">Global Health</style></keyword><keyword><style  face="normal" font="default" size="100%">Hemagglutinin Glycoproteins, Influenza Virus/immunology</style></keyword><keyword><style  face="normal" font="default" size="100%">HIV Infections/epidemiology/*prevention &amp; control/*transmission</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Statistical</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword><keyword><style  face="normal" font="default" size="100%">Public Health Surveillance/*methods</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Dec 1</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">12</style></number><edition><style face="normal" font="default" size="100%">2018/08/14</style></edition><volume><style face="normal" font="default" size="100%">187</style></volume><pages><style face="normal" font="default" size="100%">2615-2622</style></pages><isbn><style face="normal" font="default" size="100%">0002-9262 (Print)0002-9262</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Given globalization and other social phenomena, controlling the spread of infectious diseases has become an imperative public health priority. A plethora of interventions that in theory can mitigate the spread of pathogens have been proposed and applied. Evaluating the effectiveness of such interventions is costly and in many circumstances unrealistic. Most important, the community effect (i.e., the ability of the intervention to minimize the spread of the pathogen from people who received the intervention to other community members) can rarely be evaluated. Here we propose a study design that can build and evaluate evidence in support of the community effect of an intervention. The approach exploits molecular evolutionary dynamics of pathogens in order to track new infections as having arisen from either a control or an intervention group. It enables us to evaluate whether an intervention reduces the number and length of new transmission chains in comparison with a control condition, and thus lets us estimate the relative decrease in new infections in the community due to the intervention. We provide as an example one working scenario of a way the approach can be applied with a simulation study and associated power calculations.</style></abstract><accession-num><style face="normal" font="default" size="100%">30101288</style></accession-num><notes><style face="normal" font="default" size="100%">1476-6256Magiorkinis, GkikasKaramitros, TimokratisVasylyeva, Tetyana IWilliams, Leslie DMbisa, Jean LHatzakis, AngelosParaskevis, DimitriosFriedman, Samuel RDP1 DA034989/DA/NIDA NIH HHS/United StatesJournal ArticleResearch Support, N.I.H., ExtramuralAm J Epidemiol. 2018 Dec 1;187(12):2615-2622. doi: 10.1093/aje/kwy160.</style></notes><custom2><style face="normal" font="default" size="100%">PMC6269241</style></custom2><auth-address><style face="normal" font="default" size="100%">Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Athens, Greece.Hellenic Pasteur Institute, Athens, Greece.Department of Zoology, University of Oxford, Oxford, United Kingdom.National Development and Research Institutes, New York, New York.Virus Reference Department, Public Health England, London, United Kingdom.</style></auth-address><remote-database-provider><style face="normal" font="default" size="100%">NLM</style></remote-database-provider></record></records></xml>