2020
DOI: 10.1371/journal.pcbi.1008180
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Tracking and predicting U.S. influenza activity with a real-time surveillance network

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… Show more

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Cited by 13 publications
(8 citation statements)
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References 26 publications
(59 reference statements)
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“…Currently, there are some risks to human health from preventing in uenza, like some non-pharmaceutical interventions that might be effective early control strategies such as the use of masks, hand washing and other hygiene measures or closing schools (MacIntyre et al 2009). Yet as evidenced by the trends in in uenza incidence in countries such as Europe, the United States and Japan (Leuba et al 2020, Sakai et al 2004), the situation remains grim and worldwide preventive measures have little impact on the trend of in uenza outbreaks. As there are currently no models that can effectively predict in uenza outbreaks, it is possible to prevent or at least reduce in uenza morbidity and associated costs by monitoring in uenza morbidity indicators daily and predicting in uenza virus outbreaks in advance if environmental factors that predict outbreaks can be identi ed and modelled.…”
Section: Introductionmentioning
confidence: 99%
“…Currently, there are some risks to human health from preventing in uenza, like some non-pharmaceutical interventions that might be effective early control strategies such as the use of masks, hand washing and other hygiene measures or closing schools (MacIntyre et al 2009). Yet as evidenced by the trends in in uenza incidence in countries such as Europe, the United States and Japan (Leuba et al 2020, Sakai et al 2004), the situation remains grim and worldwide preventive measures have little impact on the trend of in uenza outbreaks. As there are currently no models that can effectively predict in uenza outbreaks, it is possible to prevent or at least reduce in uenza morbidity and associated costs by monitoring in uenza morbidity indicators daily and predicting in uenza virus outbreaks in advance if environmental factors that predict outbreaks can be identi ed and modelled.…”
Section: Introductionmentioning
confidence: 99%
“…Unlike existing data sources, the dataset used here provides access to spatial resolution as precise as zip code. The Quidel point of care dataset has been used for one other study [ 33 ], which focused on U.S. regional level nowcasting and forecasting. Point-of-care diagnostic results are becoming increasingly available to researchers.…”
Section: Introductionmentioning
confidence: 99%
“…The failure of GFT was, however, success in disguise, as it stimulated the research community to develop novel ways to integrate proxy data that have considerably improved on the initial results. In particular, several research efforts were devoted to the exploration and combination of additional novel data streams-such as Twitter, hospital records, Wikipedia searches, anonymous influenza test or syndromic records, to name a few-in their predictive system of seasonal influenza [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Similar data streams have been explored for other epidemics like Dengue [32], Zika [33], hand-foot-mouth diseases [34], Ebola, plague, and yellow fever [35].…”
Section: Introductionmentioning
confidence: 99%