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10.1371/journal.pone.0249726 http://scihub22266oqcxt.onion/10.1371/journal.pone.0249726 33857208!8049304!33857208     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33857208&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2021 ; 16 (4): e0249726 Nephropedia Template TP {{tp|p=33857208|t=2021. Analysis of mobility data to build contact networks for COVID-19 |pdf=|usr=}}{{33857208}} gab.com Text Analysis of mobility data to build contact networks for COVID-19 JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33857208 #FOAvip As social distancing policies and recommendations went into effect in response to COVID-19, people made rapid changes to the places they visit. These changes are clearly seen in mobility data, which records foot traffic using location trackers in cell phones. While mobility data is often used to extract the number of customers that visit a particular business or business type, it is the frequency and duration of concurrent occupancy at those sites that governs transmission. Understanding the way people interact at different locations can help target policies and inform contact tracing and prevention strategies. This paper outlines methods to extract interactions from mobility data and build networks that can be used in epidemiological models. Several measures of interaction are extracted: interactions between people, the cumulative interactions for a single person, and cumulative interactions that occur at particular businesses. Network metrics are computed to identify structural trends which show clear changes based on the timing of stay-at-home orders. Measures of interaction and structural trends in the resulting networks can be used to better understand potential spreading events, the percent of interactions that can be classified as close contacts, and the impact of policy choices to control transmission. Twit Text FOAVip Analysis of mobility data to build contact networks for COVID-19 ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33857208 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33857208 #FOAvip English Wikipedia <ref>{{Cite pmid|33857208}}</ref> Analysis of mobility data to build contact networks for COVID-19 #MMPMID33857208 Klise K; Beyeler W; Finley P; Makvandi M PLoS One 2021[]; 16 (4): e0249726 PMID33857208show ga As social distancing policies and recommendations went into effect in response to COVID-19, people made rapid changes to the places they visit. These changes are clearly seen in mobility data, which records foot traffic using location trackers in cell phones. While mobility data is often used to extract the number of customers that visit a particular business or business type, it is the frequency and duration of concurrent occupancy at those sites that governs transmission. Understanding the way people interact at different locations can help target policies and inform contact tracing and prevention strategies. This paper outlines methods to extract interactions from mobility data and build networks that can be used in epidemiological models. Several measures of interaction are extracted: interactions between people, the cumulative interactions for a single person, and cumulative interactions that occur at particular businesses. Network metrics are computed to identify structural trends which show clear changes based on the timing of stay-at-home orders. Measures of interaction and structural trends in the resulting networks can be used to better understand potential spreading events, the percent of interactions that can be classified as close contacts, and the impact of policy choices to control transmission. |*Cell Phone[MESH] |*Contact Tracing[MESH] |Algorithms[MESH] |COVID-19/*epidemiology[MESH] |Humans[MESH] |Physical Distancing[MESH]Analysis of mobility data to build contact networks for COVID-19 (epmc).pdf DeepDyve Pubget Overpricing