Modeling and Predicting Human Infectious Diseases

The spreading of infectious diseases has dramatically shaped our history and society. The quest to understand and prevent their spreading dates more than two centuries. Over the years, advances in Medicine, Biology, Mathematics, Physics, Network Science, Computer Science, and Technology in general contributed to the development of modern epidemiology. In this chapter, we present a summary of different mathematical and computational approaches aimed at describing, modeling, and forecasting the diffusion of viruses. We start from the basic concepts and models in an unstructured population and gradually increase the realism by adding the effects of realistic contact structures within a population as well as the effects of human mobility coupling different subpopulations. Building on these concepts we present two realistic data-driven epidemiological models able to forecast the spreading of infectious diseases at different geographical granularities. We conclude by introducing some recent developments in diseases modeling rooted in the big-data revolution.

[1]  Brian F. Havel,et al.  International Air Transport Association , 2010 .

[2]  Jari Saramäki,et al.  Temporal Networks , 2011, Encyclopedia of Social Network Analysis and Mining.

[3]  E. Nsoesie,et al.  Guess Who’s Not Coming to Dinner? Evaluating Online Restaurant Reservations for Disease Surveillance , 2014, Journal of medical Internet research.

[4]  Dennis L. Chao,et al.  FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model , 2010, PLoS Comput. Biol..

[5]  Marcel Salathé,et al.  Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious Disease Dynamics and Control , 2011, PLoS Comput. Biol..

[6]  R. Pastor-Satorras,et al.  Activity driven modeling of time varying networks , 2012, Scientific Reports.

[7]  Lada A. Adamic,et al.  Computational Social Science , 2009, Science.

[8]  John S. Brownstein,et al.  Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in the United States in Near Real-Time , 2014, PLoS Comput. Biol..

[9]  Alessandro Vespignani,et al.  influenza A(H1N1): a Monte Carlo likelihood analysis based on , 2009 .

[10]  David M. Pennock,et al.  Predicting consumer behavior with Web search , 2010, Proceedings of the National Academy of Sciences.

[11]  D. Lazer,et al.  The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.

[12]  Caroline O. Buckee,et al.  Digital Epidemiology , 2012, PLoS Comput. Biol..

[13]  M. D. McKay,et al.  Creating synthetic baseline populations , 1996 .

[14]  Alberto Maria Segre,et al.  The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic , 2011, PloS one.

[15]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[16]  M Elizabeth Halloran,et al.  Planning for the control of pandemic influenza A (H1N1) in Los Angeles County and the United States. , 2011, American journal of epidemiology.

[17]  César A. Hidalgo,et al.  Scale-free networks , 2008, Scholarpedia.

[18]  Reuven Cohen,et al.  Efficient immunization strategies for computer networks and populations. , 2002, Physical review letters.

[19]  Alessandro Vespignani,et al.  Time varying networks and the weakness of strong ties , 2013, Scientific Reports.

[20]  Stefano Merler,et al.  Determinants of the Spatiotemporal Dynamics of the 2009 H1N1 Pandemic in Europe: Implications for Real-Time Modelling , 2011, PLoS Comput. Biol..

[21]  Gail E. Potter,et al.  The Transmissibility and Control of Pandemic Influenza A (H1N1) Virus , 2009, Science.

[22]  C. Macken,et al.  Mitigation strategies for pandemic influenza in the United States. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[23]  E. Lyons,et al.  Pandemic Potential of a Strain of Influenza A (H1N1): Early Findings , 2009, Science.

[24]  M. Morris,et al.  Telling tails explain the discrepancy in sexual partner reports , 1993, Nature.

[25]  Aron Culotta,et al.  Towards detecting influenza epidemics by analyzing Twitter messages , 2010, SOMA '10.

[26]  Nicola Perra,et al.  The Scaling of Human Contacts in Reaction-Diffusion Processes on Heterogeneous Metapopulation Networks , 2014 .

[27]  J. Shaman,et al.  Forecasting seasonal outbreaks of influenza , 2012, Proceedings of the National Academy of Sciences.

[28]  Alessandro Vespignani.  Modelling dynamical processes in complex socio-technical systems , 2011, Nature Physics.

[29]  Alessandro Vespignani,et al.  Modeling Dynamical Processes in Complex Socio-technical Systems , 2015 .

[30]  Alessandro Vespignani,et al.  Dynamical Processes on Complex Networks , 2008 .

[31]  Alessandro Vespignani,et al.  Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case study , 2007, BMC medicine.

[32]  Andy Blackburn,et al.  Google Flu trends , 2008 .

[33]  Michael J. Paul,et al.  National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic , 2013, PloS one.

[34]  Petter Holme,et al.  Simulated Epidemics in an Empirical Spatiotemporal Network of 50,185 Sexual Contacts , 2010, PLoS Comput. Biol..

[35]  Norman T. J. Bailey,et al.  The Mathematical Theory of Infectious Diseases , 1975 .

[36]  Mark Dredze,et al.  You Are What You Tweet: Analyzing Twitter for Public Health , 2011, ICWSM.

[37]  M. Keeling,et al.  Modeling Infectious Diseases in Humans and Animals , 2007 .

[38]  N. Britton.  Reaction-diffusion equations and their applications to biology. , 1989 .

[39]  Reuven Cohen,et al.  Complex Networks: Structure, Robustness and Function , 2010 .

[40]  Rumi Chunara,et al.  Public health for the people: participatory infectious disease surveillance in the digital age , 2014, Emerging Themes in Epidemiology.

[41]  A. Vespignani.  Predicting the Behavior of Techno-Social Systems , 2009, Science.

[42]  Ciro Cattuto,et al.  What's in a crowd? Analysis of face-to-face behavioral networks , 2010, Journal of theoretical biology.

[43]  Alessandro Vespignani,et al.  The role of the airline transportation network in the prediction and predictability of global epidemics , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[44]  Alina Deshpande,et al.  Detecting epidemics using Wikipedia article views: A demonstration of feasibility with language as location proxy , 2014, ArXiv.

[45]  Albert-László Barabási,et al.  Scale-free networks , 2008, Scholarpedia.

[46]  Míriam Antón-Rodríguez,et al.  A content analysis of chronic diseases social groups on Facebook and Twitter. , 2012, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[47]  Alessandro Vespignani,et al.  Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations. , 2007, Journal of theoretical biology.

[48]  J. Brownstein,et al.  Digital disease detection--harnessing the Web for public health surveillance. , 2009, The New England journal of medicine.

[49]  Alessandro Vespignani,et al.  Assessing the International Spreading Risk Associated with the 2014 West African Ebola Outbreak , 2014, PLoS currents.

[50]  R. Pastor-Satorras,et al.  Epidemic spreading in correlated complex networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[51]  Alessandro Vespignani,et al.  Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm , 2012, BMC Medicine.

[52]  L. Sattenspiel,et al.  Population structure and the spread of disease. , 1987, Human biology.

[53]  M. Newman.  Spread of epidemic disease on networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[54]  Alessandro Vespignani,et al.  Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models , 2010, BMC infectious diseases.

[55]  Alberto M. Segre,et al.  Using Twitter to Estimate H1N1 Influenza Activity , 2010 .

[56]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[57]  Zbigniew Smoreda,et al.  The scaling of human interactions with city size , 2012, Journal of The Royal Society Interface.

[58]  Alessandro Vespignani,et al.  Human mobility and the worldwide impact of intentional localized highly pathogenic virus release , 2013, Scientific Reports.

[59]  R. Levins.  Some Demographic and Genetic Consequences of Environmental Heterogeneity for Biological Control , 1969 .

[60]  K. Dietz,et al.  A structured epidemic model incorporating geographic mobility among regions. , 1995, Mathematical biosciences.

[61]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[62]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[63]  Piet Van Mieghem,et al.  Epidemic processes in complex networks , 2014, ArXiv.

[64]  Emily H. Chan,et al.  Global capacity for emerging infectious disease detection , 2010, Proceedings of the National Academy of Sciences.

[65]  Manuel Cebrián,et al.  Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks , 2012, PloS one.

[66]  Alessandro Vespignani,et al.  Activity driven modeling of dynamic networks , 2012, ArXiv.

[67]  Naren Ramakrishnan,et al.  Satellite Imagery Analysis: What Can Hospital Parking Lots Tell Us about a Disease Outbreak? , 2014, Computer.

[68]  Alessandro Vespignani,et al.  Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model , 2010, J. Comput. Sci..

[69]  Marián Boguñá,et al.  Correlations in weighted networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[70]  Alessandro Vespignani,et al.  Modeling human mobility responses to the large-scale spreading of infectious diseases , 2011, Scientific reports.

[71]  Andrea Baronchelli,et al.  Epidemic Spreading in Non-Markovian Time-Varying Networks , 2014, ArXiv.

[72]  Alessandro Vespignani,et al.  Multiscale mobility networks and the spatial spreading of infectious diseases , 2009, Proceedings of the National Academy of Sciences.

[73]  Marcel Salathé,et al.  Validating models for disease detection using twitter , 2013, WWW.

[74]  Claudio Castellano,et al.  Thresholds for epidemic spreading in networks , 2010, Physical review letters.

[75]  Cécile Viboud,et al.  Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza: A Comparative Epidemiological Study at Three Geographic Scales , 2013, PLoS Comput. Biol..

[76]  A Vespignani,et al.  Web‐based participatory surveillance of infectious diseases: the Influenzanet participatory surveillance experience , 2013, Clinical Microbiology and Infection.

[77]  M. E. Williams,et al.  TRANSIMS: TRANSPORTATION ANALYSIS AND SIMULATION SYSTEM , 1995 .

[78]  Albert-László Barabási,et al.  Linked - how everything is connected to everything else and what it means for business, science, and everyday life , 2003 .