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Conjunction of factors triggering waves of seasonal influenza.

Ishanu Chattopadhyay | Emre Kiciman | Joshua W Elliott | Jeffrey L Shaman | Andrey Rzhetsky
eLife | 2018

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.

Pubmed ID: 29485041

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None found

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Associated grants

  • Agency: NHLBI NIH HHS, United States
    Id: R01 HL122712
  • Agency: NIMH NIH HHS, United States
    Id: 1P50MH094267
  • Agency: NHLBI NIH HHS, United States
    Id: U01HL108634-01
  • Agency: NIMH NIH HHS, United States
    Id: P50 MH094267
  • Agency: NHLBI NIH HHS, United States
    Id: R01HL122712
  • Agency: NHLBI NIH HHS, United States
    Id: U01 HL108634
  • Agency: NIGMS NIH HHS, United States
    Id: U01GM110748
  • Agency: NIGMS NIH HHS, United States
    Id: U01 GM110748
  • Agency: NIGMS NIH HHS, United States
    Id: R01 GM100467
  • Agency: NIGMS NIH HHS, United States
    Id: R01GM100467
  • Agency: NIEHS NIH HHS, United States
    Id: P30 ES009089

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PRISM (tool)

RRID:SCR_005375

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.

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