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

Sen Pei and Jeffrey Shaman

PLOS Computational Biology, 2020, vol. 16, issue 10, 1-19

Abstract: 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.Author summary: Influenza-like illness (ILI) is a widely used medical diagnosis of possible infection with influenza or another acute respiratory illness. Accurate forecasting of ILI can support better planning of interventions against respiratory infections, as well as early preparation for patient surges in healthcare facilities during periods of peak incidence. Although ILI is an amalgamation of multiple pathogens with variable seasonal phasing and contributions to incidence, to our knowledge, all existing process-based forecasting systems treat ILI as a single infectious agent. This leads to model misspecification that compromises forecast precision. In this study, we address this issue by forecasting ILI as the aggregation of predictions for individual contributing respiratory viruses. Using ILI records and virologic surveillance data, we show that ILI signal can be disaggregated into distinct viral components and develop a method to forecast ILI by aggregating predictions for six pathogens. We find highly variable overall contributions from influenza type A viruses across seasons, but relatively stable contributions from other pathogens. In retrospective forecasts, the proposed multi-pathogen forecasting system generates substantially more accurate predictions of both seasonal and near-term targets relative to a baseline method that simulates ILI as a single pathogen.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1008301

DOI: 10.1371/journal.pcbi.1008301

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