ArticleInternational journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases2021
Predicting COVID-19 incidence in French hospitals using human contact network analytics.
Article in International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Mobile phone data analyses for public health research: a scoping review.Frontiers in public health · 2025Article
- Methods for measuring short-term geographical mobility used in infectious disease research: a scoping review protocol.BMJ open · 2023Article
- Data-driven multiscale dynamical framework to control a pandemic evolution with non-pharmaceutical interventions.PloS one · 2023Article
- A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning.Computers in biology and medicine · 2022Article
- Epidemiological and clinical insights from SARS-CoV-2 RT-PCR crossing threshold values, France, January to November 2020.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2022Article
- Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China.International journal of environmental research and public health · 2022Article
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3 authors.
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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.
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