Joint COVID-19 and influenza-like illness forecasts in the United States using internet search information

• Published in: Communications medicine Vol. 3; no. 1; p. 39
• Main Authors: Ma, Simin, Ning, Shaoyang, Yang, Shihao
• Format: Journal Article
• Language: English
• Published: England Springer Nature B.V 24.03.2023; Nature Publishing Group UK; Nature Portfolio
• Subjects: Coronaviruses; COVID-19 vaccines; Datasets; Disease control; Disease transmission; Epidemics; Forecasting; Generalized linear models; Health surveillance; Hospitalization; Illnesses; Immunization; Influenza; Internet; Pandemics; Patient admissions; Queries; Time series; Trends; New York; Florida; United States > US; New York City New York
• ISSN: 2730-664X; 2730-664X
• DOI: 10.1038/s43856-023-00272-2

As the prolonged COVID-19 pandemic continues, severe seasonal Influenza (flu) may happen alongside COVID-19. This could cause a "twindemic", in which there are additional burdens on health care resources and public safety compared to those occurring in the presence of a single infection. Amidst the raising trend of co-infections of the two diseases, forecasting both Influenza-like Illness (ILI) outbreaks and COVID-19 waves in a reliable and timely manner becomes more urgent than ever. Accurate and real-time joint prediction of the twindemic aids public health organizations and policymakers in adequate preparation and decision making. However, in the current pandemic, existing ILI and COVID-19 forecasting models face shortcomings under complex inter-disease dynamics, particularly due to the similarities in symptoms and healthcare-seeking patterns of the two diseases. Inspired by the interconnection between ILI and COVID-19 activities, we combine related internet search and bi-disease time series information for the U.S. national level and state level forecasts. Our proposed ARGOX-Joint-Ensemble adopts a new ensemble framework that integrates ILI and COVID-19 disease forecasting models to pool the information between the two diseases and provide joint multi-resolution and multi-target predictions. Through a winner-takes-all ensemble fashion, our framework is able to adaptively select the most predictive COVID-19 or ILI signals. In the retrospective evaluation, our model steadily outperforms alternative benchmark methods, and remains competitive with other publicly available models in both point estimates and probabilistic predictions (including intervals). The success of our approach illustrates that pooling information between the ILI and COVID-19 leads to improved forecasting models than individual models for either of the disease.