Forecasting influenza incidence as an ordinal variable using machine learning

Many mechanisms contribute to the variation in the incidence of influenza disease, such as strain evolution, the waning of immunity and changes in social mixing. Although machine learning methods have been developed for forecasting, these methods are used less commonly in influenza forecasts than statistical and mechanistic models. In this study, we applied a relatively new machine learning method, Extreme Gradient Boosting (XGBoost), to ordinal country-level influenza disease data. We developed a machine learning forecasting framework by adopting the XGBoost algorithm and training it with surveillance data for over 30 countries between 2010 and 2018 from the World Health Organisation’s FluID platform. We then used the model to predict incidence 1- to 4-week ahead. We evaluated the performance of XGBoost forecast models by comparing them with a null model and a historical average model using mean-zero error (MZE) and macro-averaged mean absolute error (mMAE). The XGBoost models were consistently more accurate than the null and historical models for all forecast time horizons. For 1-week ahead predictions across test sets, the mMAE of the XGBoost model with an extending training window was reduced by 78% on average compared to the null model. Although the mMAE increased with longer prediction horizons, XGBoost models showed a 62% reduction in mMAE compared to the null model for 4-week ahead predictions. Our results highlight the potential utility of machine learning methods in forecasting infectious disease incidence when that incidence is defined as an ordinal variable. In particular, the XGBoost model can be easily extended to include more features, thus capturing complex patterns and improving forecast accuracy. Given that many natural extreme phenomena, such as floods and earthquakes, are often described on an ordinal scale when informing planning and response, these results motivate further investigation of using similar scales for communicating risk from infectious diseases. Author Summary Accurate and timely influenza forecasting is essential to help policymakers improve influenza preparedness and responses to potential outbreaks and allocate medical resources effectively. Here, we present a machine learning framework based on Extreme Gradient Boosting (XBoost) for forecast influenza activity. We used publicly available weekly influenza-like illness (ILI) incidence data in 32 countries. The predictive performance of the machine learning framework was evaluated using several accuracy metrics and compared with baseline models. XGBoost model was shown to be the most accurate prediction approach, and its accuracy remained stable with increasing prediction time horizons. Our results suggest that the machine learning framework for forecasting ILI has the potential to be adopted as a valuable public health tool globally in the future. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The authors acknowledge funding from the MRC Centre for Global Infectious Disease Analysis (MR/R015600/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement, and also part of the EDCTP2 programme supported by the European Union. S.R. acknowledges the support from Wellcome Trust Investigator Award (UK, 200861/Z/16/Z). KOK acknowledges funding from HMRF (INF-CUHK-1). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced are available online at