tetano
Editor, Senior Moderator
PLoS Comput Biol
. 2020 Nov 2;16(11):e1008180.
doi: 10.1371/journal.pcbi.1008180. Online ahead of print.
Tracking and predicting U.S. influenza activity with a real-time surveillance network
Sequoia I Leuba[SUP] 1 [/SUP], Reza Yaesoubi[SUP] 2 [/SUP], Marina Antillon[SUP] 3 4 [/SUP], Ted Cohen[SUP] 1 [/SUP], Christoph Zimmer[SUP] 1 [/SUP]
Affiliations
Abstract
Each year in the United States, influenza causes illness in 9.2 to 35.6 million individuals and is responsible for 12,000 to 56,000 deaths. The U.S. Centers for Disease Control and Prevention (CDC) tracks influenza activity through a national surveillance network. These data are only available after a delay of 1 to 2 weeks, and thus influenza epidemiologists and transmission modelers have explored the use of other data sources to produce more timely estimates and predictions of influenza activity. We evaluated whether data collected from a national commercial network of influenza diagnostic machines could produce valid estimates of the current burden and help to predict influenza trends in the United States. Quidel Corporation provided us with de-identified influenza test results transmitted in real-time from a national network of influenza test machines called the Influenza Test System (ITS). We used this ITS dataset to estimate and predict influenza-like illness (ILI) activity in the United States over the 2015-2016 and 2016-2017 influenza seasons. First, we developed linear logistic models on national and regional geographic scales that accurately estimated two CDC influenza metrics: the proportion of influenza test results that are positive and the proportion of physician visits that are ILI-related. We then used our estimated ILI-related proportion of physician visits in transmission models to produce improved predictions of influenza trends in the United States at both the regional and national scale. These findings suggest that ITS can be leveraged to improve "nowcasts" and short-term forecasts of U.S. influenza activity.
. 2020 Nov 2;16(11):e1008180.
doi: 10.1371/journal.pcbi.1008180. Online ahead of print.
Tracking and predicting U.S. influenza activity with a real-time surveillance network
Sequoia I Leuba[SUP] 1 [/SUP], Reza Yaesoubi[SUP] 2 [/SUP], Marina Antillon[SUP] 3 4 [/SUP], Ted Cohen[SUP] 1 [/SUP], Christoph Zimmer[SUP] 1 [/SUP]
Affiliations
- PMID: 33137088
- DOI: 10.1371/journal.pcbi.1008180
Abstract
Each year in the United States, influenza causes illness in 9.2 to 35.6 million individuals and is responsible for 12,000 to 56,000 deaths. The U.S. Centers for Disease Control and Prevention (CDC) tracks influenza activity through a national surveillance network. These data are only available after a delay of 1 to 2 weeks, and thus influenza epidemiologists and transmission modelers have explored the use of other data sources to produce more timely estimates and predictions of influenza activity. We evaluated whether data collected from a national commercial network of influenza diagnostic machines could produce valid estimates of the current burden and help to predict influenza trends in the United States. Quidel Corporation provided us with de-identified influenza test results transmitted in real-time from a national network of influenza test machines called the Influenza Test System (ITS). We used this ITS dataset to estimate and predict influenza-like illness (ILI) activity in the United States over the 2015-2016 and 2016-2017 influenza seasons. First, we developed linear logistic models on national and regional geographic scales that accurately estimated two CDC influenza metrics: the proportion of influenza test results that are positive and the proportion of physician visits that are ILI-related. We then used our estimated ILI-related proportion of physician visits in transmission models to produce improved predictions of influenza trends in the United States at both the regional and national scale. These findings suggest that ITS can be leveraged to improve "nowcasts" and short-term forecasts of U.S. influenza activity.