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 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.

WITHDRAWN: Lagging effects and prediction of pollutants and their interaction modifiers on influenza in northeastern China

“…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.…”

Accurate influenza forecasts using type-specific incidence data for small geographic units

“…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.…”

Predicting seasonal influenza using supermarket retail records

“…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].…”