Dataset Information

Predicting COVID-19 incidence in French hospitals using human contact network analytics.

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.

SUBMITTER: Selinger C

PROVIDER: S-EPMC8364404 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Predicting COVID-19 incidence in French hospitals using human contact network analytics.

Selinger Christian C Choisy Marc M Alizon Samuel S

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases 20210814

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 ...[more]

PMID: 34403783

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Project description:Background The Coronavirus Disease 2019 pandemic provided a natural experiment to study the effect of social distancing on the risk of developing Hirschsprung's Associated Enterocolitis (HAEC). Methods Using the Pediatric Health Information System (PHIS), a retrospective cohort study of children (<18 years) with Hirschsprung's Disease (HSCR) across 47 United States children's hospitals was performed. The primary outcome was HAEC admissions per 10,000 patient-days. The exposure (COVID-19) was defined as April 2020–December 2021. The unexposed (historical control) period was April 2018–December 2019. Secondary outcomes included sepsis, bowel perforation, intensive care unit (ICU) admission, mortality, and length of stay. Results Overall, we included 5707 patients with HSCR during the study period. There were 984 and 834 HAEC admissions during the pre-pandemic and pandemic periods, respectively (2.6 vs. 1.9 HAEC admissions per 10,000 patient-days, incident rate ratio [95% confidence interval]: 0.74 [0.67, 0.81], p < 0.001). Compared to pre-pandemic, those with HAEC during the pandemic were younger (median [IQR]: 566 [162, 1430] days pandemic vs. 746 [259, 1609] days pre-pandemic, p < 0.001) and more likely to live in the lowest quartile of median household income zip codes (24% pandemic vs. 19% pre-pandemic, p = 0.02). There were no significant differences in rates of sepsis (6.1% pandemic vs. 6.1% pre-pandemic, p > 0.9), bowel perforation (1.3% pandemic vs. 1.2% pre-pandemic, p = 0.8), ICU admissions (9.6% pandemic vs. 12% pre-pandemic, p = 0.2), mortality (0.5% pandemic vs. 0.6% pre-pandemic, p = 0.8), or length of stay (median [interquartile range]: 4 [(Pastor et al., 2009; Gosain and Brinkman, 2015) 2,112,11 days pandemic vs. 5 [(Pastor et al., 2009; Tang et al., 2020) 2,102,10 days pre-pandemic, p = 0.4). Conclusions The COVID-19 pandemic was associated with significantly decreased incidence of HAEC admissions across US children's hospitals. Possible etiologies such as social distancing should be explored. Level of evidence II.

Dataset Information

Predicting COVID-19 incidence in French hospitals using human contact network analytics.

Publications

Predicting COVID-19 incidence in French hospitals using human contact network analytics.

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