Predicting COVID-19 incidence in French hospitals using human contact network analytics. (October 2021)

• Record Type: Journal Article
• Authors: Selinger, Christian; Choisy, Marc; Alizon, Samuel
• Is Part Of: International journal of infectious diseases. Volume 111(2021)
• Page Start: 100
• Page End: 107
• Publication Date: 2021-10
• Subjects: time series -- human mobility -- networks -- infectious disease; Communicable diseases -- Periodicals; Communicable Diseases -- Periodicals; Communicable diseases; Periodicals; Electronic journals; 616.9
• DOI: 10.1016/j.ijid.2021.08.029
• Languages: English
• ISSNs: 1201-9712

Abstract

Highlights: We produce novel human contact network analytics for the COVID-19 pandemic. We use these analytics for time series models of French hospital incidence. National-level predictions are greatly improved by adding these novel analytics. Subnational analyses reveal spatial correlations of incidence. Abstract: Background COVID-19 was first detected in Wuhan, China, in 2019 and spread worldwide within a few weeks. The COVID-19 epidemic started to gain traction in France in March 2020. Subnational hospital admissions and deaths were then recorded daily and served as the main policy indicators. Concurrently, mobile phone positioning data have been curated to determine the frequency of users being colocalized within a given distance. Contrarily to individual tracking data, these can be a proxy for human contact networks between subnational administrative units. Methods Motivated by numerous studies correlating human mobility data and disease incidence, we developed predictive time series models of hospital incidence between July 2020 and April 2021. We added human contact network analytics, such as clustering coefficients, contact network strength, null links or curvature, as regressors. Findings We found that predictions can be improved substantially (by more than 50 % ) at both the national level and the subnational level for up to 2 weeks. Our subnational analysis also revealed the importance of spatial structure, as incidence in colocalized administrative units improved predictions. This original application of network analytics from colocalization data to epidemic spread opens new perspectives for epidemic forecasting and public health.