Influenza forecasting for the French regions by using EHR, web and climatic data sources with an ensemble approach ARGONet

Abstract

Effective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza activity information that lags real-time by 1 to 3 weeks. This temporal data gap introduces uncertainty that prevents public health officials from having a timely perspective on the population-level disease activity. Here, we present a machine-learning modeling approach that produces real-time estimates and short-term forecasts of influenza activity for the 12 continental regions of France by leveraging multiple disparate data sources that include, Google search activity, real-time and local weather information, flu-related Twitter micro-blogs, electronic health records data, and historical disease activity synchronicities across regions. Our results show that all data sources contribute to improving influenza surveillance and that machine-learning ensembles that combine all data sources lead to accurate and timely predictions.

Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models

“…Previous methods, such as the ARGONet model [ 11 , 45 ], have been shown to make accurate real-time prediction at the state level in the United States for seasonal infectious diseases such as influenza. In addition, Chinazzi et al [ 16 ] showed that it was possible to estimate the evolution of an emerging outbreak using a mechanistic model.…”

A nowcasting framework for correcting for reporting delays in malaria surveillance

“…We generated ensemble model estimates, consistent with the first ARGONet procedure described in Poirier et al 35 by assigning the point estimate for a given month to that of the DIM, NM, and NM2, depending on which model yielded the lowest rRMSE for the prior three months [24,35]. We assigned the mean across models as the point estimate for the first three months of observations.…”

“…To provide decision-makers with a prospective uncertainty quantification (error bars) of our estimates, we constructed 95% confidence intervals by considering the error (RMSE) of model predictions for the prior 24 months, starting with January 2009 (two years after the first out-of-sample prediction). Subsequently, following an approach outlined in Poirier et al [ 35 ] citing work by Yang et al [ 36 ] to capture changing uncertainty at different points in time, we generated the lower and upper limits of the confidence intervals by subtracting or adding the RMSE associated to a moving window of the last 24 errors, respectively, to the upcoming point estimate [ 35 , 36 ]. This confidence interval construction is well-justified as we found that the RMSEs broadly reflected the standard deviation (STD) of the distribution of residuals (predictions—eventually reported or “true” case counts) for each time point ( S7 Fig ) [ 35 , 36 ], and thus, assuming that our residuals are Gaussian distributed, 95% of residuals should be captured within the mean (the point estimate) plus and minus the STD of residuals.…”

“…Histogram + Gaussian density curve for region 4 DIM (rmse = 122.38 and sd = 122.00). (follows the format provided in Poirier et al [ 35 ].…”