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Forecasting national and regional influenza-like illness for the USA.

ABSTRACT: Health planners use forecasts of key metrics associated with influenza-like illness (ILI); near-term weekly incidence, week of season onset, week of peak, and intensity of peak. Here, we describe our participation in a weekly prospective ILI forecasting challenge for the United States for the 2016-17 season and subsequent evaluation of our performance. We implemented a metapopulation model framework with 32 model variants. Variants differed from each other in their assumptions about: the force-of-infection (FOI); use of uninformative priors; the use of discounted historical data for not-yet-observed time points; and the treatment of regions as either independent or coupled. Individual model variants were chosen subjectively as the basis for our weekly forecasts; however, a subset of coupled models were only available part way through the season. Most frequently, during the 2016-17 season, we chose; FOI variants with both school vacations and humidity terms; uninformative priors; the inclusion of discounted historical data for not-yet-observed time points; and coupled regions (when available). Our near-term weekly forecasts substantially over-estimated incidence early in the season when coupled models were not available. However, our forecast accuracy improved in absolute terms and relative to other teams once coupled solutions were available. In retrospective analysis, we found that the 2016-17 season was not typical: on average, coupled models performed better when fit without historically augmented data. Also, we tested a simple ensemble model for the 2016-17 season and found that it underperformed our subjective choice for all forecast targets. In this study, we were able to improve accuracy during a prospective forecasting exercise by coupling dynamics between regions. Although reduction of forecast subjectivity should be a long-term goal, some degree of human intervention is likely to improve forecast accuracy in the medium-term in parallel with the systematic consideration of more sophisticated ensemble approaches.

SUBMITTER: Ben-Nun M

PROVIDER: S-EPMC6557527 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Forecasting national and regional influenza-like illness for the USA.

Ben-Nun Michal M Riley Pete P Turtle James J Bacon David P DP Riley Steven S

PLoS computational biology 20190523 5

Health planners use forecasts of key metrics associated with influenza-like illness (ILI); near-term weekly incidence, week of season onset, week of peak, and intensity of peak. Here, we describe our participation in a weekly prospective ILI forecasting challenge for the United States for the 2016-17 season and subsequent evaluation of our performance. We implemented a metapopulation model framework with 32 model variants. Variants differed from each other in their assumptions about: the force-o ...[more]

PMID: 31120881

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Project description:Acute respiratory viruses often result in significant morbidity and mortality. The potential impact of human respiratory coronavirus (CoV) infections was underestimated until the severe acute respiratory syndrome (SARS-CoV) outbreak in 2003, which showed that new, highly pathogenic coronaviruses could be introduced to humans, highlighting the importance of monitoring the circulating coronaviruses. The use of sensitive molecular methods has contributed to the differential diagnosis of viruses circulating in humans. Our study aim was to investigate the molecular epidemiology of human CoV strains circulating in Arkansas, their genetic variability and their association with reported influenza-like symptoms. We analyzed 200 nasal swab samples, collected by the Arkansas Department of Health in 2010, for influenza diagnosis. All samples were from patients showing acute respiratory symptoms while testing negative for influenza. Samples were pre-screened, using a quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) multiprobe for coronavirus, and subjected to confirmatory pancoronavirus and/or strain-specific reverse transcriptase (RT)-PCR followed by sequence analysis. Seventy-nine samples (39.5%) were positive by qRT-PCR and 35 samples (17.5%) were confirmed by conventional RT-PCR. Twenty-three of the confirmed samples (59%) were sequenced. The most frequent strain detected was HCoV-OC43-like followed by NL63-like; only one sample was positive for HCoV-229E and one for HCoV-HKU1. Feline-like CoV strains were detected in three samples, representing possible evidence of interspecies transmission or a new human strain. Seventeen percent of the coronavirus positive samples were also positive for other respiratory viruses, such as Respiratory Syncytial Virus (RSV), Parainfluenza 2 and 3, and Rhinovirus. Thus, HCoV-OC43, NL63, HKU1 and new feline-like strains were circulating in Arkansas in 2010. HCoV was the sole respiratory virus detected in 16% of the patients who showed acute respiratory symptoms with negative diagnoses for influenza virus.

Dataset Information

Forecasting national and regional influenza-like illness for the USA.

Publications

Forecasting national and regional influenza-like illness for the USA.

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