{"id":17621,"date":"2022-01-31T11:58:57","date_gmt":"2022-01-31T11:58:57","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=17621"},"modified":"2022-01-31T11:58:57","modified_gmt":"2022-01-31T11:58:57","slug":"predicting-covid-19-outbreaks-mobile-device-data","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/predicting-covid-19-outbreaks-mobile-device-data\/17621\/","title":{"rendered":"Predicting COVID-19 outbreaks using mobile device data\u00a0\u00a0"},"content":{"rendered":"

Researchers at Yale School of Public Health accurately forecast COVID-19 outbreaks in Connecticut municipalities by utilising mobile device data.<\/span>\u00a0<\/span><\/h2>\n

In order to conduct their novel study, the researchers used anonymous location information from mobile devices. The method employed by the team could prove useful in helping health officials predict local COVID-19 outbreaks, thus enabling them to allocate testing resources more efficiently.<\/span>\u00a0<\/span><\/p>\n

The study was led by data scientists and epidemiologists from the Yale School of Public Health<\/a>, the Connecticut Department of Public Health, the US Centers for Disease Control and Prevention and Whitespace Ltd., a spatial data analytics firm.<\/span>\u00a0<\/span><\/p>\n

The study\u2019s result\u2019s have been published in <\/span>Science Advances<\/span><\/i>.<\/span>\u00a0<\/span><\/p>\n

Achieving accurate results<\/span>\u00a0<\/span><\/h3>\n

The key to the researchers\u2019 results was the accuracy with which they were able to detect incidents of high frequency close personal contact \u2013 within a radius of six feet \u2013 in Connecticut, down to the municipal level.\u00a0<\/span>\u00a0<\/span><\/p>\n

\u201cClose contact between people is the primary route for transmission of SARS-CoV-2<\/a>, the virus that causes COVID-19,\u201d explained the study\u2019s lead author Forrest Crawford, an associate professor of biostatistics at the Yale School of Public Health and an associate professor of ecology and evolutionary biology, management, statistics and data science at Yale.<\/span>\u00a0<\/span><\/p>\n

Measuring close contact<\/span>\u00a0<\/span><\/h3>\n

\u201cWe measured close interpersonal contact within a six-foot radius everywhere in Connecticut using mobile device geolocation data over the course of an entire year,\u201d Crawford said. \u201cThis effort gave Connecticut epidemiologists and policymakers insight to people\u2019s social distancing behaviour state<\/span> wide.\u201d<\/span>\u00a0<\/span><\/p>\n

Previous studies have utilised \u2018mobility metrics\u2019 as proxy measures for social distancing behaviour and possible COVID-19 transmission. However, this analysis has limitations.<\/span>\u00a0<\/span><\/p>\n

\u201cMobility metrics often measure distance travelled or time spent away from a location, such as your home,\u201d Crawford added. \u201cBut we all know it\u2019s possible to move around a lot and still not get very close to other people. So<\/span>,<\/span> mobility metrics are not a great proxy for transmission risk. We feel close contact predicts infections and local outbreaks better.\u201d<\/span>\u00a0<\/span><\/p>\n

The team\u2019s results are based on a review of Connecticut mobile device geolocation data from February 2020 to January 2021. All of the data was anonymised and aggregated, and they did not collect any personally identifiable information.<\/span>\u00a0<\/span><\/p>\n

Algorithm predicts COVID-19<\/span>\u00a0<\/span><\/h3>\n

A novel algorithm calculated the probability of close contact events across the state \u2013 occurrences where mobile devices were within six feet of each other \u2013 based on geolocation data. This data was then integrated into a standard COVID-19 transmission model to forecast COVID-19 case levels not only across Connecticut, but in individual Connecticut towns, census tracts, and census block groups.<\/span>\u00a0<\/span><\/p>\n

The researchers claim to have accurately projected an initial wave of Connecticut COVID-19 cases from March to April 2020, as well as a drop in state-wide cases during June to August and localised outbreaks in certain Connecticut towns in August and September.<\/span>\u00a0<\/span><\/p>\n

At present<\/span>, health officials often depend on general surveillance data including the number of confirmed cases, hospitalisations and deaths to trace the spread of COVID-19. But that process can lag actual disease transmission<\/span>s<\/span> by days and weeks. Analysing close personal contact rates is much faster, the researchers explained.<\/span>\u00a0<\/span><\/p>\n

\u201cThe contact rate we developed in this study can reveal high-contact conditions likely to spawn local outbreaks and areas where residents are at high transmission risk days or weeks before the resulting cases are detected through testing, traditional case investigations and contact tracing,\u201d Crawford concluded.<\/span>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Researchers at Yale School of Public Health accurately forecast COVID-19 outbreaks in Connecticut municipalities by utilising mobile device data.\u00a0 In order to conduct their novel study, the researchers used anonymous location information from mobile devices. The method employed by the team could prove useful in helping health officials predict local COVID-19 outbreaks, thus enabling them […]<\/p>\n","protected":false},"author":13,"featured_media":17622,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[830],"tags":[24128,16871,885],"acf":[],"yoast_head":"\nPredicting COVID-19 outbreaks using mobile device data\u00a0\u00a0<\/title>\n<meta name=\"description\" content=\"Researchers at Yale School of Public Health accurately forecast COVID-19 outbreaks in Connecticut municipalities using mobile device data.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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