Conjunction of factors triggering waves of seasonal influenza
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2018-02-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/30756 |
| _version_ | 1811200781130399744 |
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| author | Ishanu Chattopadhyay Emre Kiciman Joshua W Elliott Jeffrey L Shaman Andrey Rzhetsky |
| author_facet | Ishanu Chattopadhyay Emre Kiciman Joshua W Elliott Jeffrey L Shaman Andrey Rzhetsky |
| author_sort | Ishanu Chattopadhyay |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T02:09:26Z |
| format | Article |
| id | doaj.art-c042974d58844427908d2ca5ecc4cb3b |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-12T02:09:26Z |
| publishDate | 2018-02-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-c042974d58844427908d2ca5ecc4cb3b2022-12-22T03:52:27ZengeLife Sciences Publications LtdeLife2050-084X2018-02-01710.7554/eLife.30756Conjunction of factors triggering waves of seasonal influenzaIshanu Chattopadhyay0https://orcid.org/0000-0001-8339-8162Emre Kiciman1https://orcid.org/0000-0001-5429-468XJoshua W Elliott2Jeffrey L Shaman3Andrey Rzhetsky4https://orcid.org/0000-0001-6959-7405Institute of Genomics and Systems Biology, University of Chicago, Chicago, United States; Department of Medicine, University of Chicago, Chicago, United StatesInformation and Data Science Group, Microsoft Research, Redmond, United StatesComputation Institute, University of Chicago, Chicago, United StatesDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, United StatesInstitute of Genomics and Systems Biology, University of Chicago, Chicago, United States; Department of Medicine, University of Chicago, Chicago, United States; Computation Institute, University of Chicago, Chicago, United States; Departments of Human Genetics, University of Chicago, Chicago, United StatesUsing 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.https://elifesciences.org/articles/30756poisson regressionGranger causalitymatching analysisUS influenza epidemic |
| spellingShingle | Ishanu Chattopadhyay Emre Kiciman Joshua W Elliott Jeffrey L Shaman Andrey Rzhetsky Conjunction of factors triggering waves of seasonal influenza eLife poisson regression Granger causality matching analysis US influenza epidemic |
| title | Conjunction of factors triggering waves of seasonal influenza |
| title_full | Conjunction of factors triggering waves of seasonal influenza |
| title_fullStr | Conjunction of factors triggering waves of seasonal influenza |
| title_full_unstemmed | Conjunction of factors triggering waves of seasonal influenza |
| title_short | Conjunction of factors triggering waves of seasonal influenza |
| title_sort | conjunction of factors triggering waves of seasonal influenza |
| topic | poisson regression Granger causality matching analysis US influenza epidemic |
| url | https://elifesciences.org/articles/30756 |
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