ArticleScience advances2022
Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- The impact of public health and social measures on social contact patterns and transmission of COVID-19 in Hong Kong.BMC medicine · 2026Article
- Assessing the usefulness of digital contact tracing using real-world contact data.Scientific reports · 2026Article
- Premature termination of unemployment benefits increased COVID-19 transmission and deaths in the USA.Nature human behaviour · 2026Article
- Global approaches to infectious disease surveillance and modeling.Nature medicine · 2026Review
- Explaining COVID-19 dynamics through user activity data from digital platforms with Yandex's self-isolation index as a case study.Scientific reports · 2025Article
- Characterising non-household contact patterns relevant to respiratory transmission in the USA: analysis of a cross-sectional survey.The Lancet. Digital health · 2025Article
- Impacts of human mobility on the citywide transmission dynamics of 18 respiratory viruses in pre- and post-COVID-19 pandemic years.Nature communications · 2024Article
- Students can encourage their grandparents to vaccinate.Nature aging · 2024Article
- Does behavior mediate the effect of weather on SARS-CoV-2 transmission? Evidence from cell-phone data.medRxiv : the preprint server for health sciences · 2024Article
- Does behavior mediate the effect of weather on SARS-CoV-2 transmission? evidence from cell-phone data.PloS one · 2024Article
- Statistical inference for complete and incomplete mobility trajectories under the flight-pause model.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Heterogeneous changes in mobility in response to the SARS-CoV-2 Omicron BA.2 outbreak in Shanghai.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- The limits of human mobility traces to predict the spread of COVID-19: A transfer entropy approach.PNAS nexus · 2023Article
- Association of close-range contact patterns with SARS-CoV-2: a household transmission study.eLife · 2023Article
- Patterns of Infectious Disease Associated With Injection Drug Use in Massachusetts.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Article
- Night-time population consistently explains the transmission dynamics of coronavirus disease 2019 in three megacities in Japan.Frontiers in public health · 2023Article
- Article
- Implementing a provisional overarching intervention for COVID-19 monitoring and control in the Brazil-Colombia-Peru frontier.Frontiers in public health · 2023Article
- Direct indicators of social distancing effectiveness in COVID-19 outbreak stages: a correlational analysis of case contacts and population mobility in Korea.Epidemiology and health · 2023Article
- Close Contacts, Infected Cases, and the Trends of SARS-CoV-2 Omicron Epidemic in Shenzhen, China.Healthcare (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We quantified interpersonal contact at the population level using mobile device geolocation data. We computed the frequency of contact (within 6 feet) between people in Connecticut during February 2020 to January 2021 and aggregated counts of contact events by area of residence. When incorporated into a SEIR-type model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns. Contact in Connecticut explains the initial wave of infections during March to April, the drop in cases during June to August, local outbreaks during August to September, broad statewide resurgence during September to December, and decline in January 2021. The transmission model fits COVID-19 transmission dynamics better using the contact rate than other mobility metrics. Contact rate data can help guide social distancing and testing resource allocation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.