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      Conjunction of factors triggering waves of seasonal influenza

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          Abstract

          Using several longitudinal datasets describing putative factors affecting influenza incidence and clinical data on the disease and health status of over 150 million human subjects observed over a decade, we investigated the source and the mechanistic triggers of influenza epidemics. We conclude that the initiation of a pan-continental influenza wave emerges from the simultaneous realization of a complex set of conditions. The strongest predictor groups are as follows, ranked by importance: (1) the host population’s socio- and ethno-demographic properties; (2) weather variables pertaining to specific humidity, temperature, and solar radiation; (3) the virus’ antigenic drift over time; (4) the host population’€™s land-based travel habits, and; (5) recent spatio-temporal dynamics, as reflected in the influenza wave auto-correlation. The models we infer are demonstrably predictive (area under the Receiver Operating Characteristic curve 80%) when tested with out-of-sample data, opening the door to the potential formulation of new population-level intervention and mitigation policies.

          eLife digest

          Influenza – or ‘the flu’ – is a contagious disease which sweeps across the globe like clockwork, claiming tens of thousands of lives. This is known as ‘seasonal flu’.

          Many scientists have tried to identify the factors that spark these yearly outbreaks. Some past studies have found that seasonal flu occurs when air that is normally humid turns dry, suggesting weather patterns play an important part. Other research has shown that air travel contributes to the flu spreading across the world. However, these studies typically focus on just one or two factors on their own. It is still not clear how exactly these factors combine to drive outbreaks, and then sustain the wave of infection.

          To address this, Chattopadhyay et al. analyze the medical histories of 150 million American people over a decade, combining this information with large datasets about the different factors that trigger flu outbreaks. This includes detailed data about air travel and weather patterns, as well as census data that describe features of the population. Patterns of movement are also examined, for example by processing billions of Twitter messages “tagged” with a location. Chattopadhyay et al. used all of these datasets to model outbreaks of the flu in the United States, and see which factors play the biggest role.

          It turns out that yearly outbreaks of seasonal flu are a result of a combination of elements. Some factors interact to help trigger the start of the wave, like humid weather in a highly populated area with nearby airports. Other factors, such how people move, encourage the spread of the infection. Finally, certain features of the population, for example how closely knitted a community is, make specific areas of the country more susceptible to the arrival of the disease. Overall, some of the most important elements of the model relate to the characteristics of the populations, the weather, the type of virus, and the number of short-distance journeys (rather than air travel).

          Understanding how and why outbreaks occur can help policy-makers design strategies that reduce the spread and impact of seasonal flu, which could potentially save thousands of lives. Ultimately, the model developed by Chattopadhyay et al. could be used to test whether these policies would work before they are implemented in the real world.

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          Most cited references66

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          Emergence and pandemic potential of swine-origin H1N1 influenza virus.

          Influenza viruses cause annual epidemics and occasional pandemics that have claimed the lives of millions. The emergence of new strains will continue to pose challenges to public health and the scientific communities. A prime example is the recent emergence of swine-origin H1N1 viruses that have transmitted to and spread among humans, resulting in outbreaks internationally. Efforts to control these outbreaks and real-time monitoring of the evolution of this virus should provide us with invaluable information to direct infectious disease control programmes and to improve understanding of the factors that determine viral pathogenicity and/or transmissibility.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                27 February 2018
                2018
                : 7
                : e30756
                Affiliations
                [1 ]deptInstitute of Genomics and Systems Biology University of Chicago ChicagoUnited States
                [2 ]deptDepartment of Medicine University of Chicago ChicagoUnited States
                [3 ]deptInformation and Data Science Group Microsoft Research RedmondUnited States
                [4 ]deptComputation Institute University of Chicago ChicagoUnited States
                [5 ]deptDepartment of Environmental Health Sciences, Mailman School of Public Health Columbia University New YorkUnited States
                [6 ]deptDepartments of Human Genetics University of Chicago ChicagoUnited States
                [7]London School of Hygiene & Tropical Medicine, and Public Health England United Kingdom
                [8]London School of Hygiene & Tropical Medicine, and Public Health England United Kingdom
                Author information
                http://orcid.org/0000-0001-8339-8162
                http://orcid.org/0000-0001-5429-468X
                http://orcid.org/0000-0001-6959-7405
                Article
                30756
                10.7554/eLife.30756
                5864297
                29485041
                d20f2ea8-9b9f-4ab6-bd6f-5266adc2a108
                © 2018, Chattopadhyay et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 26 July 2017
                : 13 February 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: U01HL108634-01
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01GM100467
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: U01GM110748
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: 1P50MH094267
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006502, Defense Sciences Office, DARPA;
                Award ID: W911NF1410333
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01HL122712
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Epidemiology and Global Health
                Custom metadata
                Three complementary computational approaches reveal a set of putative causes of initiation of pan-USA waves of influenza.

                Life sciences
                poisson regression,granger causality,matching analysis,us influenza epidemic,virus,human
                Life sciences
                poisson regression, granger causality, matching analysis, us influenza epidemic, virus, human

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