Tuesday 3rd December 2019 05:30-07:00 Welcome reception and poster session 1 Wednesday 4th December 2019 08:30-08:40 Welcome and opening remarks by session chair 08:40-09:10 [INV01] Changing Dynamics of the US Drug Overdose Epidemic Donald (Don) S. Burke, University of Pittsburgh, USA 09:10-09:50 [INV02] From dependent happenings to causal inference with interference Betz Halloran, University of Washington, USA and Fred Hutchinson Cancer Research Center, USA 09:50-10:30 [INV03] How to get a good travel history from a malaria parasite Bryan Greenhouse, University of California San Francisco, USA 10:30-11:00 Refreshment break 11:00-12:40 Session 1 Vaccination 1 Session 2 Dynamics Session 3 Phylodynamics 1 11:00-11:20 [O1.1] Successes and failures [O2.1] The influence of birth [O3.1] Phylodynamic of the live-attenuated rate and meteorological inference of transmission influenza vaccine: can we indices on the temporal pathways for pathogens with do better? patterns of rotavirus infection low genetic diversity using L. Matrajt*1, E. Halloran1,2, R. in Dhaka, Bangladesh the Kolmogorov Forward Antia3 E.O. Asare*1, M.A. Al- Equations 1Fred Hutchinson Cancer Mamun1, M. Sarmin2, A.S.G. G. Rossi*1, J. Crispell2, S.J. Research Center, Faruque2, T. Ahmed2, V.E. Lycett1, D. Balaz1, R.J. USA, 2University of Pitzer1 Delahay3, R.R. Kao1 Washington, USA, 3Emory 1Yale School of Public Health, 1University of Edinburgh, University, USA USA, 2International Centre for UK, 2University College Diarrhoeal Disease Research, Dublin, Ireland, 3APHA, UK Bangladesh (ICDDR, B), Bangladesh 11:20-11:40 [O1.2] Direct and indirect [O2.2] Environmental and [O3.2] Deep learning from effects of immunising school- Demographic Drivers of phylogenies to understand age children against Respiratory Syncytial Virus the dynamics of epidemics influenza: evidence from a (RSV) Transmission in the US J. Voznica*1,2, A. Zhukova1, T. three year pilot programme K. Sun*1, C. Viboud1, Z. Dot1, K. Ocaña1, F. Lemoine1, in England Karaca2 M. Moslonka-Lefebvre1, O. E. van Leeuwen*1, R.G. 1Fogarty International Gascuel1 Pebody1, P. Klepac3, S. Riley2, Center, National Institutes of 1Unité Bioinformatique M. Baguelin2 Health, Bethesda, MD, Evolutive, Institut Pasteur, 1Public Health England, USA, 2Agency for Healthcare C3BI – USR 3756 IP & CNRS, UK, 2Imperial College Research and Quality, Paris, France, 2Université Paris London, UK, 3London School Rockville, MD, USA Descartes, Sorbonne Paris for Hygiene & Tropical Cité, Paris, France Medicine, UK 11:40-12:00 [O1.3] Potential public health [O2.3] Evidence synthesis [O3.3] Statistical inference of benefits from reduced delay revealing the transmission epidemiological parameters: in the production of dynamics of Respiratory what is the value of virus pandemic influenza vaccine Syncytial Virus (RSV) and phylogenies? K. Ainslie*, D. Haw, J. Hay, C. impact of vaccination F. Giardina*1,2, T. Britton1 Walters, A. Yan, S. Riley M. van Boven*1, A. Teirlinck1, 1Stockholm Univeristy, Dept Inf Dis Epi, Imperial R. Reeves2, M. Hooiveld1, A. Sweden, 2Erasmus MC, The College, UK Meijer1, W. van der Hoek1 Netherlands 1National Institute for Public Health, The Netherlands, 2University of Edinburgh, UK 12:00-12:20 [O1.4] Drivers of uneven [O2.4] Dynamics of cholera [O3.4] High-resolution gene-specific evolutionary outbreaks in sub-Saharan mapping of respiratory rates shaping microbial Africa pathogens in the Seattle genomes in highly Q. Zheng*, J. Kaminsky, H. Metro area vaccinated populations McKay, A. Azman, J. Lessler M. Famulare*1, T. Bedford2, A. Bento*1,2, A. King3, R. Johns Hopkins Bloomberg M. Boeckh2, H.Y. Chu3, J.A. Taujale2, T. Bosch4, C. Schot4, School of Public Health, Englund4, B.R. Lutz3, D.A. P. Rohani2 Baltimore, USA Nickerson3, M. Rieder3, L.M. 1Indiana University, Starita3, M. Thompson3 et al USA, 2University of Georgia, 1Institute for Disease USA, 3University of Michigan, Modeling, USA, 2University of USA, 4RIVM, The Netherlands Washington, USA, 3Fred Hutchinson Cancer Research Center, USA, 4Seattle Children’s Hospital, USA, 5Blaze Clinical, USA, 6Microsoft, USA, 7Kaiser Permanente Research Institute, USA 12:20-12:40 [O1.5] Comparison of rubella [O2.5] Unraveling the [O3.5] Estimating transmission models and results: The seasonal epidemiology of bottleneck sizes from viral effects of small variations in pneumococcus variants unique to recipient methods, assumptions, and M. Domenech de Celles1,2, H. hosts data Arduin1,2, D. Lévy-Bruhl4, S. J. Harris*1, K. Johnson2, K. S. Truelove1, A.K. Winter*1, T. Georges4, C. Souty3,5, D. Koelle1 Papadopolous2, C.J.E. Guillemot1,2, L. Watier1,3, L. 1Emory University, USA, 2New Metcalf3,4, J. Lessler1, E. Opatowski*1,2 York University, USA Vynnycky2,5 1Université de Versailles Saint 1Johns Hopkins University, Quentin, France, 2Institut USA, 2Public Health England, Pasteur, France, 3Inserm, UK, 3Princeton University, France, 4Santé Publique USA, 4University of Oxford, France, France, 5Sorbonne UK, 5London School of Université, France Hygiene & Tropical Medicine, UK 12:40-13:40 Lunch 12:40-13:40 Author workshop 13:40-15:40 Session 4 Vaccination 2 Session 5 Forecasting Session 6 HIV 13:40-14:00 [O4.1] Estimating sample size [O5.1] FluSight: Six seasons of [O6.1] Bayesian synthesis of for future chikungunya forecasting influenza in the behavioural survey data: vaccine trials based on United States, 2013-14 to estimating the potential projections of epidemic size 2018-19 impact of pre-exposure that account for pre-existing M. Biggerstaff*, F.S. Dahlgren, prophylaxis on the HIV immunity C.S. Lutz, M.A. Johansson, C. epidemic Q. Tran*1, J. Soda1, A. Siraj1, S. Reed M. Irvine1 Moore1, H. Clapham2,3, A. Centers for Disease Control 1University of British Columbia, Perkins1 and Prevention, USA Canada, 2British Columbia 1University of Notre Dame, Centre for Disease Control, USA, 2Oxford University Canada Clinical Research Unit, Viet Nam, 3University of Oxford, UK 14:00-14:20 [O4.2] Evaluating [O5.2] Accuracy of multi- [O6.2] Natural selection prophylactic Ebola model ensemble influenza favoring more transmissible vaccination in the presence forecasts in the US: Results HIV detected in U.S. of reactive ring vaccination from the 2017/2018 and molecular transmission C.A.B. Pearson*1, T.J. 2018/2019 seasons network Hladish2, W.J. Edmunds1, R.M. N.G. Reich*1, C.J. J.O. Wertheim*1, A.M. Oster2, Eggo1 McGowan2, T.K. Yamana3, W.M. Switzer2, C. Zhang2, N. 1London School of Hygiene & E.L. Ray4, D. Osthus5, S. Panneer2, E. Campbell2, N. Tropical Medicine, Kandula3, L.C. Brooks6, G.C. Saduvala3, J.A. Johnson2, W. UK, 2University of Florida, USA Gibson1, N. Wattanachit1, M. Heneine2 Biggerstaff2 et al 1University of California San 1University of Massachusetts Diego, USA, 2Centers for Amherst, USA, 2Centers for Disease Control and Disease Control and Prevention, USA, 3ICF Prevention, USA, 3Columbia International, USA University, USA, 4Mount Holyoke College, USA, 5Los Alamos National Laboratory, USA, 6Carnegie Mellon University, USA 14:20-14:40 [O4.3] Predicting evolution [O5.3] Integrative forecasting [O6.3] Using phylogenies to using frequency-dependent of seasonal influenza A/H3N2 detect fitness differences and selection in bacterial evolution by genotype and selection among HIV-1 populations phenotype strains of different viral load T. Azarian*1,2, P.P. Martinez2, J. Huddleston*1,2, R. Neher3,4, L. Zhao*, L. Ferretti, C. L.R. Grant3, J. Corander4,5, C. T. Bedford1 Fraser et al Fraser7, N.J. Croucher8, L.L. 1Fred Hutchinson Cancer University of Oxford, UK Hammitt3, K.L. O’Brien9, M. Research Center, Lipsitch2, W.P. Hanage2 et al USA, 2University of 1University of Central Florida, Washington, USA, 3University USA, 2Harvard University, of Basel, Switzerland, 4SIB USA, 3Johns Hopkins Swiss Institute of Bloomberg School of Public Bioinformatics, Switzerland Health, USA, 4University of Helsinki, Finland, 5University of Oslo, Norway, 6Wellcome Trust Genome Campus, UK, 7University of Oxford, UK, 8Imperial College London, UK, 9World Health Organization, Switzerland 14:40-15:00 [O4.4] How should the [O5.4] Real-time forecasting [O6.4] Quantifying the dynamics of EBV transmission of epidemic trajectories contribution of different-aged inform a vaccine target using computational men and women to onwards product profile and future dynamic ensembles transmission of HIV-1 in vaccination strategy? G. Chowell*1,2, R. Luo1, K. generalised epidemics in L. Goscé*1, J.R. Winter1,2, G.S. Sun2, K. Roosa1, A. Tariq1, C. sub-Saharan Africa: a Taylor3, J.E.A. Lewis1,4, H.R. Viboud2 modelling and phylogenetics Stagg1,5 1Georgia State University approach from the HPTN071 1University College London, School of Public Health, (PopART) trial UK, 2King's college London, USA, 2Fogarty International M. Hall*1, W. Probert1, X. Xi2, UK, 3University of Birmingham, Center, National Institutes of R. Sauter1, T. Golubchik1, D. UK, 4Imperial College, Health, USA Bonsall1, L. Abeler-Dörner1, M. UK, 5The University of Pickles1, A. Cori2, J. Edinburgh, UK Bwalya3 et al 1University of Oxford, UK, 2Imperial College London, UK, 3Zambart, Zambia, 4London School of Hygiene and Tropical Medicine, UK, 5Stellenbosch University, South Africa, 6Fred Hutchinson Cancer Research Center, USA, 7Johns Hopkins University, USA, 8African Health Research Institute, South Africa 15:00-15:20 [O4.5] The impact of [O5.5] A look into the future - [O6.5] Linking phylogenetic geographic targeting of oral using digital disease topology with HIV cholera vaccination in sub- surveillance tools for near transmission history Saharan Africa: a modelling real-time epidemic C.J. Villabona-Arenas*1, M. study forecasting Hall2, S.G. Gaffney3, K.A. E.C. Lee*1, A.S. Azman1, J. S. Bhatia*1, B. Lassmann2, E. Lythgoe2, S. Hue1, K.E. Kaminsky1, S.M. Moore2, H.S. Cohn3, M. Kraemer4, M. Atkins1,4 McKay1, J. Lessler1 Herringer5, J. Brownstein3, L. 1Centre for Mathematical 1Johns Hopkins Bloomberg Madoff2, A. Cori1, P. modelling of infectious School of Public Health, Nouvellet6 diseases, London School of USA, 2University of Notre 1Imperial College London, Hygiene & Tropical Medicine, Dame, USA UK, 2ProMED, International UK, 2Big Data Institute, Society for Infectious University of Oxford, UK, 3Yale Diseases, USA, 3Boston School of Public Health, Yale Children's Hospital, University, USA, 4Centre for USA, 4University of Oxford, Global Health, Usher Institute UK, 5healthsites.io, for Population Health UK, 6University of Sussex, UK Sciences and Informatics, The University of Edinburgh, UK 15:20-15:40 [O4.6] Prediction of post- [O5.6] Adaptively stacked [O6.6] Why does age at vaccine Streptococcus ensembles for influenza infection correlate with set pneumoniae lineage forecasting with incomplete point viral load among frequencies based on data persons with HIV? potential accessory genes T. McAndrew*, N. Reich S. Goodreau*, S. Stansfield, J. under selection University of Massachusetts Murphy, N. Abernethy, G. P.P. Martinez*1, S. Palace1, T. Amherst, USA Gottlieb, M. Reid, J. Mittler, J. Azarian1,2, W. Hanage1, M. Herbeck Lipsitch1 University of Washington, USA 1Harvard, USA, 2University of Central Florida, USA 15:40-16:10 Refreshment break 16:10-17:50 Session 7 Machine learning Session 8 Transmission Session 9 Phylodynamics 2 16:10-16:30 [O7.1] Estimating the relative [O8.1] Modelling interhuman [O9.1] Evidence of probability of direct transmission dynamics during Environmental Persistence- transmission between pneumonic plague driven Evolution of Vibrio infectious disease patients outbreaks in Madagascar choleraein Aquatic S.V. Leavitt*1, R.S. Lee2, P. B. Nikolay*1, V. Reservoirs. Sebastiani1, C.R. Horsburgh Andrianaivoarimanana2, H. C. Mavian*, T.K. Paisie, M.T. Jr.1, H.E. Jenkins1, L.F. White1 Razafimandimby3, S. Alam, S. Nembrini, M.N. 1Boston University, Rahelinirina2, P. Piola2, S. Cash, E. Nelson, A. Ali, J.G. USA, 2Harvard T.H. Chan Andrianalimanana4, V. Morris Jr, M. Salemi School of Public Health, USA Richard2, C. Rogier2, M. University of Florida, USA Ratsitorahina2, M. Rajerison2 et al 1Institut Pasteur, France, 2Institut Pasteur de Madagascar, Madagascar, 3Ministry of Public Health, Madagascar, 4Central Laboratory of Plague, Madagascar 16:30-16:50 [O7.2] Developing machine [O8.2] Using cross-sectional [O9.2] Selective pressure of learning algorithms using serology to infer leptospirosis American mosquito vectors natural language processing transmission dynamics in Fiji on P. falciparum genes to extract infectious disease E. Rees*1, C. Lau2, E. Togami3, M.S. Tagliamonte*, M. Salemi, data from open-source data J. Edmunds1, R. Lowe1, A. J.B. Dame A. Verster1, E.E. Rees2, F. Liu3, Kucharski1 University of Florida, USA J. Knox2, G. Penn3, V. Ng*2 1London School of Hygiene & 1Health Canada, Tropical Medicine, Canada, 2Public Health UK, 2University of Agency of Canada, Queensland, Australia, 3Yale Canada, 3University of School of Public Health, USA Toronto, Canada 16:50-17:10 [O7.3] Super-ensemble [O8.3] Probabilistic [O9.3] Phylodynamic seasonal influenza reconstruction of measles approaches for investigating forecasting with machine transmission clusters in the the dynamics of Ebola Virus learning augmented United States from 2003-2017 Disease in Sierra Leone. mechanistic models using routine case reports. V. Hill*, A. Rambaut X. Xiong*1, Q. Zhang1, F. Lu2, A. Robert*, A.J. Kucharski, S. University of Edinburgh, UK M. Santillana2,3, A. Funk Vespignani1,4 London School of Hygiene 1NORTHEASTERN UNIVERSITY, and Tropical Medicine, UK USA, 2Boston Children's Hospital, USA, 3Harvard Medical School, USA, 4ISI Foundation, Italy 17:10-17:30 [O7.4] Feasibility of [O8.4] Global and country- [O9.4] West Nile virus spread incorporating contextual specific rubella R0 estimation in North America: what can data in reconstructing HIV using serology and Bayesian we learn from genomic transmission network: a methods data? machine learning approach S. Truelove*1, A.K. Winter1, S. Dellicour*1,2, S. Lequime1, B. S. Mazrouee*, S. Little, J. C.J.E. Metcalf2,3, J. Lessler1 Vrancken1, M.S. Bastide1, K. Wertheim 1Johns Hopkins University, Gangavarapu3, M.I. Nelson4, University of California - San USA, 2Princeton University, N. Grubaugh5,3, K.G. Diego, USA USA, 3University of Oxford, UK Andersen3,6, O.G. Pybus7, P. Lemey1 1University of Brussels (ULB), Belgium, 2KU Leuven - University of Leuven, Belgium, 3Scripps Research Institute, USA, 4National Institutes of Health, USA, 5Yale School of Public Health, USA, 6Scripps Research Translational Institute, USA, 7University of Oxford, UK 17:30-17:50 [O7.5] Machine learning for [O8.5] Waning and boosting [O9.5] Real-time genomic vector representation of GB of immunity of pertussis epidemiology and data farms within cattle under natural infection and sharing to guide MERS-CoV movement network to vaccination policy decision making and predict future herd-level bTB L.M. Childs*1, Z. Feng2, J. control actions and BVD breakdowns Glasser3, J. Heffernan4, J. Li5, M.D. Van Kerkhove*1, R.L. K. Stanski*, S. Lycett, T. G. Rost6 Grant1, D.M. Aanensen2,3 Porphyre, B.M. Bronsvoort 1Virginia Tech, USA, 2Purdue 1World Health Organization, The Roslin Institute, The University, USA, 3Centers for Switzerland, 2University of University of Edinburgh, UK Disease Control and Oxford, UK, 3Wellcome Prevention, USA, 4York Genome Campus, UK University, Canada, 5California State University, Northridge, USA, 6University of Szeged, Hungar 17:50-19:20 Poster session 2 Thursday 5th December 2019 08:30-09:10 [INV04] TBD Justin Lessler, Johns Hopkins University, USA 09:10-09:50 [INV05] The new fight against an old foe: Making the case for typhoid conjugate vaccine introduction Virginia Pitzer, Yale School of Public Health, USA 09:50-10:30 [INV06] Modeling and decision-making in crises: some lessons from recent case studies Rebecca Grais, Epicentre MSF, France 10:30-11:00 Refreshment break 11:00-12:40 Session 10 Non-vacc. interv. Session 11 Vector-borne Session 12 Interaction 1 infec. 11:00-11:20 [O10.1] The ‘breakpoint’ of [O11.1] Focal transmission [O12.1] Measurability of the soil-transmitted helminthiasis and the role of human epidemic reproduction with infected human mobility in the 2017 number and generation time migration chikungunya outbreak in in data-driven contact R.J. Hardwick*, C. Vegvari, Lazio, Italy networks J.E. Truscott, R.M. Anderson G. Guzzetta*1, F. Vairo2, A. M. Ajelli*1,2, Q-H. Liu3,1, A. Imperial College London, UK Mammone2, S. Lanini2, P. Aleta4, S. Merler1, Y. Poletti1, M. Manica4, R. Moreno4,5, A. Vespignani1,5 Rosa'3,4, B. Caputo5, A. 1Northeastern University, Solimini5, A. Della Torre5, P. USA, 2Bruno Kessler Scognamiglio1, A. Zumla1, G. Foundation, Italy, 3University Ippolito1, S. Merler1 et al of Electronic Science and 1Fondazione Bruno Kessler, Technology of China, Italy, 2National Institute for China, 4University of Infectious Diseases Lazzaro Zaragoza, Spain, 5ISI Spallanzani, Italy, 3University Foundation, Italy of Trento, Italy, 4Fondazione Edmund Mach, Italy, 5Sapienza University of Rome, Italy, 6University College London, UK 11:20-11:40 [O10.2] Changes in historical [O11.2] Joint epidemic and [O12.2] Model-based typhoid transmission across geostatistical modelling indicators of nodes criticality 16 U.S. cities, 1889-1931: approach for vector-borne to design and assess Quantifying the impact of disease outbreaks targeted strategies for investments in water and L. Koeppel*, C.P. Jewell, P.J. epidemic control on a cattle sewer infrastructures Neal trade network M.T. Phillips*1, K.A. Owers1, B.T. Lancaster University, UK P. Montagnon1,2, F. Grenfell2, V.E. Pitzer1 Deslandes1, V. Bansaye2, E. 1Yale School of Public Health, Vergu*1 USA, 2Princeton University, 1INRA, France, 2Ecole USA Polytechnique, France 11:40-12:00 [O10.3] Measuring the effect [O11.3] Burden is in the eye [O12.3] Metapopulation of reactive school closure in of the beholder: yellow fever models of infectious disease altering the network of social burden estimates are highly dynamics are sensitive to interactions and reducing sensitive to choices about underlying host movement the spread of influenza data interpretation models M. Litvinova*1,2, Q-H. Liu3,1, T.A. Perkins*, J.H. Huber, Q. D. Citron*1, C. Guerra2, J. E.S. Kulikov4, M. Ajelli1,5 Tran, R.J. Oidtman, M.K. Henry1, S. Wu3, H. Sánchez3, 1Northeastern University, Walters, A.S. Siraj, S.M. Moore D. Smith1 USA, 2ISI Foundation, University of Notre Dame, 1University of Washington, Italy, 3University of Electronic USA USA, 2Medical Care Science and Technology of Development International, China, China, 4Siberian State USA, 3University of California Medical University, Berkeley, USA Russia, 5Bruno Kessler Foundation, Ital 12:00-12:20 [O10.4] Predicting the impact [O11.4] Leveraging multiple [O12.4] Reorganization of of public health interventions data types to estimate the nurse scheduling reduces the for preventing antibiotic- true size of the Zika epidemic risk of healthcare associated resistant infections in the Americas infections N.G. Davies*1, C. Chae1, S. S. Moore*1, R. Oidtman1, K.J. E. Valdano2, C. Poletto1, P-Y. Flasche1, M. Jit1,2, K.E. Atkins1,3 Soda1, A. Siraj1, R. Reiner2, C. Boelle1, V. Colizza*1 1London School of Hygiene Barker3, T.A. Perkins1 1INSERM, Sorbonne Université, and Tropical Medicine, 1University of Notre Dame, Institut Pierre Louis UK, 2Public Health England, USA, 2University of d’Epidémiologie et de Santé UK, 3University of Edinburgh, Washington, USA, 3University Publique (IPLESP), UK of California, Davis, USA France, 2Center for Biomedical Modeling, The Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, 760 Westwood Plaza, University of California Los Angeles, USA 12:20-12:40 [O10.5] Optimization of the [O11.5] A chain-binomial [O12.5] Host contact effects of school closures on model accounting for dynamics shapes richness mitigating influenza transmission risk factors with and dominance of pathogen epidemics in Hong Kong application to Zika and strains S.T. Ali*, E.H.Y. Lau, V.J. Fang, Chikungunya transmission in C. Poletto*1, F. Pinotti1, E. G.M. Leung, B.J. Cowling households in French Guiana Fleury2, D. Guillemot3, P-Y. The University of Hong Kong, A. Cousien*1, D. Rousset2, S. Boëlle1 Hong Kong Cauchemez1, C. Flamand2 1INSERM, Sorbonne Université, 1Institut Pasteur, Paris, France, 2INRIA, France, 2Institut Pasteur, France, 3INSERM, Institut Cayenne, French Guiana Pasteur, Université Paris- Saclay, France 12:40-13:40 Lunch 13:40-15:40 Session 13 Statistics Session 14 Malaria Session 15 Interaction 2 13:40-14:00 [O13.1] Serosolver: an open [O14.1] Parameterising [O15.1] On the source tool to infer individual-based malaria generalisability of within epidemiological and models to diverse data using household contact networks immunological dynamics Gaussian Process Stacked and impact on epidemic from serological data Generalization spread J.A. Hay*1, A. Minter2, K. T. Reiker*1,2, E. Cameron3, S. P.N. Krivitsky1, P. Coletti*2, N. Ainslie1, J. Lessler3, A. Filippi4, M. Penny1,2 Hens2,3 Kucharski2, S. Riley1 1Swiss Tropical and Public 1University of Wollongong, 1Imperial College London, Health Institute, Australia, 2Hasselt University, UK, 2London School of Switzerland, 2University of Belgium, 3University of Hygiene and Tropical Basel, Switzerland, 3Big Data Antwerp, Belgium Medicine, UK, 3Johns Hopkins Institute, University of Oxford, Bloomberg School of Public UK, 4Imperial College, UK Health, USA 14:00-14:20 [O13.2] Novel approaches for [O14.2] Breaking highly [O15.2] Household contact the reconstruction of endemic transmission in studies for tuberculosis: epidemics history from malaria: a novel potential for population serological surveys epidemiological threshold inferences N. Hozé, H. Salje*, S. related to parasite antigenic L.F. White*, T. Li, H.E. Jenkins, Cauchemez diversification C.R. Horsburgh Institut Pasteur, France Q. He*, M. Pascual Boston University, USA University of Chicago, USA 14:20-14:40 [O13.3] Feature selection for [O14.3] Modelling the [O15.3] A household- dynamic epidemiological population dynamics of structured approach to models Plasmodium falciparum endemic infections E. Glennon*1, B. Han2, O. gametocytes in humans J. Hilton*, M.J. Keeling Restif1 during malaria infection University of Warwick, UK 1University of Cambridge, J.M. McCaw*1,2, P. Cao1, K.A. UK, 2Cary Institute of Collins3, S.G. Zaloumis1, T. Ecosystem Studies, USA Wattanakul4,5, J. Tarning4,5, J.A. Simpson1, J.S. McCarthy6 1The University of Melbourne, Australia, 2Peter Doherty Institute for Infection and Immunity, Australia, 3Radboud University Medical Center, The Netherlands, 4Mahidol University, Thailand, 5University of Oxford, UK, 6QIMR Berghofer Medical Research Institute, Australia 14:40-15:00 [O13.4] Statistical methods [O14.4] Modelling the effect [O15.4] Fine-scale family for calibrating a dynamic of hypothesised interactions structure shapes influenza transmission model to between falciparum and transmission risk in setting-specific historical vivax malaria on prevalence households: insights from malaria trends R.I. Hickson*1, R. Aguas2,4, A. primary schools in M. Winkel*1,2, M. Runge1,2, E. Devine3, J.M. McCaw1, P. Matsumoto city, 2014/15 Pothin1,2, T. Smith1,2 Ngor4,5, D.J. Price1, L. White2,4 A. Endo*1, M. Uchida2, A.J. 1Swiss Tropical and Public 1The University of Melbourne, Kucharski1, S. Funk1 Health Institute, Australia, 2Oxford University, 1London School of Hygiene & Switzerland, 2University of UK, 3Menzies School of Health Tropical Medicine, Basel, Switzerland, 3Clinton Research, Australia, 4Mahidol UK, 2Graduate School of Health Access Initiative, USA Oxford Tropical Medicine Research Unit, Medicine, Gunma University, Thailand, 5National Center Japan for Parasitology, Entomology and Malaria Control, Cambodia 15:00-15:20 [O13.5] Integrating [O14.5] Tracking progress [O15.5] Disentangling social geostatistical maps and towards malaria in contagion and media drivers transmission models using elimination in China: in the emergence of health adaptive multiple estimates of malaria threats awareness importance sampling reproduction numbers and P. Bosetti*1, P. Poletti2, C. R. Retkute*1, P. Touloupou1, their spatio-temporal Consonni3, B. Lepri2, D. Lazer4, D. Hollingsworth2, S.E.F. variation S. Merler2, A. Vespignani4 Spencer1 I. Routledge*1, S. Lai2, K.E. 1Institut Pasteur, 1University of Warwick, Battle3, A.C. Ghani1, M. France, 2Fondazione Bruno UK, 2University of Oxford, UK Gomez Rodriguez4, K.B. Kessler, Italy, 3University of Gustafson5, S. Mishra1, J.L. Trento, Italy, 4Northeastern Proctor6, Z. Li7, S. Bhatt1 et al University, USA 1Imperial College London, UK, 2University of Southampton, UK, 3University of Oxford, UK, 4Max Planck Institute for Software Systems, Germany, 5United States Department of the Navy, USA, 6Institute of Disease Modelling, USA, 7Chinese Centers for Disease Control and Prevention, China 15:20-15:40 [O13.6] Paths to valid [O14.6] Inference [O15.6] How contact patterns inferences through theory of Plasmodium change in an ageing and data falciparum transmission population J. Koopman networks in the presence of J.A. Backer*1, J. Van de University of Michigan, USA superinfection by genetically Kassteele1, J. Wallinga1,2 distinct parasites 1National Institute for Public A. Lerch*1, M. Hsiang2, J. Health and the Environment, Huber1, R. Nielsen3, B. The Netherlands, 2Leiden Greenhouse4, A. Perkins1 University Medical Center, 1University of Notre Dame, The Netherlands USA, 2University of Texas Southwestern Medical Center, USA, 3University of California Berkeley, USA, 4University of California San Francisco, USA 15:40-16:10 Refreshment break 16:10-17:30 Session 16 Surveillance Session 17 Within-host dyn. Session 18 Phylodynamics 3 16:10-16:30 [O16.1] Designing a typhoid [O17.1] A macroparasite [O18.1] Relating whole environmental surveillance within-host framework genome sequencing of study: a simulation model for accommodating spatial methicillin-resistant optimum sampling site structure can recapitulate staphylococcus allocation key aspects of influenza A aureus isolates to Y-K. Wang*1, C.L. Moe1, S. infection dynamics transmission dynamics and Dutta2, A. Wadhwa1, S. M. Gallagher*1, R. Ke2, C. efficacy of control Kanungo2, W. Mairinger1, Y- Brooke3, K. Koelle1 interventions C. Zhao3, Y. Jiang3, P.F.M. 1Emory University, USA, 2Los S. Blumberg*1,2, T. Porco1, B. Teunis1 Alamos National Labs, Shopsin2, M. Phillips2 1Emory University, USA, 3University of Illinois 1University of California, San USA, 2National Institute of Urbana-Champaign, USA Francisco, USA, 2New York Cholera and Enteric University, USA Diseases, India, 3Georgia State University, USA 16:30-16:50 [O16.2] The typo challenge: a [O17.2] Modelling the impact [O18.2] Bayesian citizen science approach to of immunity on artemisinin reconstruction of nosocomial improve data quality during sensitive and resistant outbreaks using whole outbreaks using a Bayesian Plasmodium falciparum genome sequences and inferential framework infections patient ward data A. Cori*1, M. Baguelin1,2 S.G. Zaloumis*1, I. Harris1, P. F. Campbell*1, A. Cori1, N. 1Imperial College London, Cao1, S. Dini1, J.M. McCaw1, Ferguson1, T. Jombart2,1 UK, 2London School of J. Tarning2,3, E.A. Ashley2,3, J- 1Imperial College London, Hygiene and Tropical A. Chan4, K. O’Flaherty4, E. UK, 2London School of Medicine, UK Takashima5 et al Hygiene and Tropical 1University of Melbourne, Medicine, UK Australia, 2Mahidol University, Thailand, 3University of Oxford, UK, 4Burnet Institute, Australia, 5Ehime University, Japan, 6University of Maryland School of Medicine, USA, 7National Institutes of Health, USA, 8National Center for Parasitology, Entomology and Malaria Control, Cambodia, 9Hospital for Tropical Diseases, Viet Nam, 10Department of Medical Research, Myanmar, 11Mahosot Hospital, People's Democratic Republic of Lao, 12University of Health Sciences, People's Democratic Republic of Lao, 13Malaria Research Group and Dev Care Foundation, Bangladesh, 14Monash University, Australia 16:50-17:10 [O16.3] Optimizing [O17.3] Ensemble modeling [O18.3] Inferring respiratory virus surveillance highlights importance of environmental transmission networks using uncertainty understanding parasite-host using phylodynamics: A propagation behavior in preclinical case-study using simulated S. Pei*1, X. Teng2, P. Lewis3, J. antimalarial drug evolution of an enteric Shaman1 development pathogen 1Columbia University, L. Burgert*1,2, M. Rottmann1,2, D. Dawson*, D. Rasmussen, X. USA, 2University of Pittsburgh, S. Wittlin1,2, N. Gobeau1,3, A. Peng, C. Lanzas USA, 3Armed Forces Health Krause1,4, J. Dingemanse1,4, North Carolina State Surveillance Branch, USA J.J. Möhrle1,3, M.A. Penny1,2 University, USA 1Swiss Tropical and Publich Health Institute, Switzerland, 2University of Basel, Switzerland, 3Medicines for Malaria Venture, Switzerland, 4Idorsia Pharmaceuticals Ltd, Switzerland 17:10-15:30 [O16.4] Reconstructing [O17.4 Quantifying influenza [O18.4] Quantitative Mayaro virus circulation in viral dynamics in different difference between intra- French Guiana shows cell types to understand how host HCV populations from frequent spillovers receptor binding switching persons with recently N. Hozé*1, H. Salje1, D. facilitates human adaptation established and persistent Rousset2, C. Fritzell2, J. A.W.C. Yan*, J. Zhou, W.S. infections Vanhomwegen1, S. Bailly2, M. Barclay, S. Riley P. Icer1, J. Lara2, A. Najm1, A. Enfissi2, J-C. Imperial College London, UK Zelikovsky1, Y. Khudyakov2, P. Manuguerra1, C. Flamand2 et Skums*1 al 1Georgia State University, 1Institut Pasteur, USA, 2Centers for Disease France, 2Institut Pasteur, Control and Prevention, USA French Guiana 17:30-19:00 Poster session 3 19:00-22:00 Conference dinner (ticket holders only) Friday 6th December 2019 08:30-09:10 [INV07] Geospatial data integration to map population distributions, demographics and dynamics for disease control Andy Tatem, University of Southampton, UK 09:10-09:50 [INV08] Sex differences in immune function: probing ultimate drivers, and exploring consequences Jessica Metcalf, Princeton University, USA 09:50-10:20 Refreshment break 10:20-12:00 Session 19 Interaction 3 Session 20 Dengue Session 21 Influenza evolution 10:20-10:40 [O19.1] Characterizing [O20.1] Reconstructing four [O21.1] Impact of influenza human mobility patterns in decades of Dengue antigenic evolution on low-income settings as a transmission in Bangkok, disease dynamics in the function of trip distance and Thailand United States urbanicity L. Wang*1, A. Huang2,4, R. A. Perofsky*1, J. Huddleston2, H. Meredith*1, J. Guiles1, C. Jarman3, S. Fernandez4, S. N. Trovão1, M. Nelson1, T. Buckee2, A. Tatem3,4, J. Cauchemez1, D. Cummings2, Bedford2, C. Viboud1 Metcalf5, A. Wesolowski1 H. Salje1,5 1Fogarty International 1Department of 1Institut Pasteur, UMR2000, Center, National Institutes of Epidemiology, Johns Hopkins CNRS, France, 2University of Health, USA, 2Fred Hutchinson Bloomberg School of Public Florida, USA, 3Walter Reed Cancer Research Center, Health, USA, 2Center for Army Institute of Research, USA Communicable Disease USA, 4Armed Forces Dynamics and Department Research Institute of Medical of Epidemiology, Harvard TH Sciences, Thailand, 5Johns Chan School of Public Hopkins Bloomberg School of Health, Boston, Public Health, USA USA, 3Worldpop, School of Geography and Environmental Science, University of Southampton, UK, 4Flowminder Foundation, Sweden, 5Department of Ecology and Evolutionary Biology and Woodrow Wilson School of Public and International Affairs, Princeton University, USA 10:40-11:00 [O19.2] Measuring human [O20.2] Modeling the effects [O21.2] Evolutionary and interactions and determining of vaccination on dengue epidemiological dynamics of optimal vaccination virulence evolution influenza viruses in Hong strategies using mobile E. Mainou*1,2, K. Koelle1 Kong during 1998-2018 phones and citizen science 1Emory University, USA, 2Penn W. Yang*1, E.H.Y. Lau2, B.J. S. Kissler*1,2, P. Klepac3, M. State University, USA Cowling2 Tang1, A. Conlan1, J. Gog1 1Columbia University, 1University of Cambridge, USA, 2WHO Collaborating UK, 2Harvard T.H. Chan Centre for Infectious Disease School of Public Health, Epidemiology and Control, USA, 3London School of School of Public Health, Li Ka Hygiene and Tropical Shing Faculty of Medicine, Medicine, UK the University of Hong Kong, Hong Kong 11:00-11:20 [O19.3] Mobile phone data, [O20.3] Using longitudinal [O21.3] High-resolution human mobility networks, serology and surveillance phylogenetic tracking of and epidemic risk in data to quantify dengue city-level spread of seasonal megacities transmission and control influenza T.S. Brown*1,4, K. Engø- during two recent outbreaks T. Bedford*2, H.Y. Chu1, M. Monsen2, M. Kiang3, C. in Fiji Boeckh2, J.A. Englund3, M. Buckee4 A. Henderson*1, M. Kama2, V- Famulare4, B.R. Lutz1, D.A. 1Massachusetts General M. Cao-Lormeau3, M. Aubry3, Nickerson1, M. Rieder1, L.M. Hospital, USA, 2Telenor C. Lau4, P. Matadigo2, J. Starita1, M. Thompson1 et al Group, Norway, 3Stanford Edmunds1, A. Kucharski1 1University of Washington, University, USA, 4Harvard 1London School of Hygiene & Seattle WA, USA, 2Fred University, USA Tropical Medicine, UK, 2The Hutchinson Cancer Research Centre of Communicable Center, Seattle WA, Diseases, Fiji, 3Institut Louis USA, 3Seattle Children’s Malardé, French Hospital, Seattle WA, Polynesia, 4Australian USA, 4Institute for Disease National University, Australia Modeling, Bellevue WA, USA 11:20-11:40 [O19.4] When a megacity [O20.4] Designing effective [O21.4] Reconstructing the goes on holiday: the impact control of dengue with antigenic evolution of of population mobility on the combined interventions influenza A viruses in multiple spread of local epidemics T.J. Hladish*1, C.A.B. hosts A. Mahmud1,2, I. Kabir3, K. Pearson2,3, K.B. Toh1, D.P. N.S. Trovao*1,2, J. Cherry3, S. Engø-Monsen4, C. Buckee*2 Rojas1, P. Manrique-Saide4, Khan2, T. Bedford4, P. Lemey5, 1University of California, G.M. Vazquez-Prokopec5, M.I. Nelson2 Berkeley, USA, 2Harvard T. H. M.E. Halloran6,7, I.M. Longini1 1Department of Chan School of Public 1University of Florida, Microbiology, Icahn School Health, USA, 3National USA, 2London School of of Medicine at Mount Sinai, Institute of Preventive and Hygiene & Tropical Medicine, USA, 2Division of International Social Medicine, UK, 3Stellenbosch University, Epidemiology and Bangladesh, 4Telenor South Africa, 4Universidad Population Studies, Fogarty Research, Norway Autónoma de Yucatán, International Center, Mexico, 5Emory University, National Institutes of Health, USA, 6Fred Hutchinson USA, 3National Center for Cancer Research Center, Biotechnology Information, USA, 7University of National Library of Medicine, Washington, USA National Institutes of Health, USA, 4Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, USA, 5Department of Microbiology and Immunology, Rega Institute, University of Leuven, Belgium 11:40-12:00 [O19.5] Social networks with [O20.5] Genomic tracing of [O21.5] Identifying age strong spatial embedding dengue household spread in cohort effects in seasonal generate non-standard Kamphaeng Phet, Thailand: influenza epidemic dynamics driven Implications for surveillance D. Vera Cruz*1, K. Koelle1 by higher-order clustering and control 1Duke University, USA, 2Emory D.J. Haw*, R. Pung, S. Riley I. Maljkovic Berry*1, M. University, USA Imperial College London, UK Melendrez1, S. Pollett1, C. Klungthong2, A. Nisalak2, M. Panciera1, B. Thaisomboonsuk2, T. Li1, S. Thomas3, T. Endy3 et al 1Walter Reed Army Institute of Research, USA, 2Armed Forces Research Institute of Medical Sciences, Thailand, 3Upstate Medical University of New York, USA 12:00-12:20 Refreshment break 12:20-13:00 [INV09] Ecological and Analytical Approaches to Predicting and Preventing Viral Emergence Kevin Olival, EcoHealth Alliance, USA 13:00-13:20 Conference closing