Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness

• Published in: PLoS computational biology 2020-10, Vol.16 (10), p.e1008301
• Main Authors: Pei, Sen, Shaman, Jeffrey
• Format: Article
• Language: English
• Subjects: Adenoviruses; Bias; Causes of; Computational Biology - methods; Coronaviruses; Disease transmission; Environmental health; Etiology; Forecasting; Forecasts and trends; Health aspects; Health sciences; Historical account; Humans; Humidity; Illnesses; Influenza; Influenza A; Influenza, Human - epidemiology; Markov chains; Medical laboratories; Models, Statistical; Monte Carlo method; Monte Carlo simulation; Parainfluenza; Pathogenic microorganisms; Pathogens; Physicians; Predictions; Public health; Regions; Respiratory diseases; Respiratory syncytial virus; Respiratory tract diseases; Respiratory Tract Infections - epidemiology; Seasonal variations; Surveillance; Time series; Virus Diseases - epidemiology; Viruses
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1008301

Influenza-like illness (ILI) is a commonly measured syndromic signal representative of a range of acute respiratory infections. Reliable forecasts of ILI can support better preparation for patient surges in healthcare systems. Although ILI is an amalgamation of multiple pathogens with variable seasonal phasing and attack rates, most existing process-based forecasting systems treat ILI as a single infectious agent. Here, using ILI records and virologic surveillance data, we show that ILI signal can be disaggregated into distinct viral components. We generate separate predictions for six contributing pathogens (influenza A/H1, A/H3, B, respiratory syncytial virus, and human parainfluenza virus types 1-2 and 3), and develop a method to forecast ILI by aggregating these predictions. The relative contribution of each pathogen to the total ILI signal is estimated using a Markov Chain Monte Carlo (MCMC) method upon forecast aggregation. We find highly variable overall contributions from influenza type A viruses across seasons, but relatively stable contributions for the other pathogens. Using historical data from 1997 to 2014 at US national and regional levels, the proposed forecasting system generates improved predictions of both seasonal and near-term targets relative to a baseline method that simulates ILI as a single pathogen. The hierarchical forecasting system can generate predictions for each viral component, as well as infer and predict their contributions to ILI, which may additionally help physicians determine the etiological causes of ILI in clinical settings.