Health Data Science / 2022 / Article / Tab 7

Review Article

Mobile Sensing in the COVID-19 Era: A Review

Table 7

Study characteristics of epidemiological study literature.

AuthorData typePopulation scaleSensing durationDescription

Hao et al. [63]GPS/CDRsCityWeeksAssessing intracity mobility for understanding virus spread using LBS data
Li et al. [64]GPS/CDRsCommunityMonthsAssessing the association between community mobility using Google Mobility Index
Kephart et al. [65]GPS/CDRsCityMonthsAnalyzing subcity population mobility and COVID incidence using LBS data
Martin et al. [66]GPS/CDRsCityMonthsEvaluating social distancing strategies using detailed movement data in cities
Rader et al. [67]GPS/CDRsCityMonthsPredict epidemiology with data on climate, population, mobility, and outbreak responses
Gondauri and Batiashvili [68]GPS/CDRsCityMonthsStudying time-delayed impacts of pedestrians, traffic, and transit traffic on virus spreading
Cintia et al. [69]GPS/CDRsCityMonthsDiscovering the relationship between mobility flows and net reproduction using LBS data
Zhou et al. [70]GPS/CDRsCityMonthsBuilding a transmission model for COVID-19 using mobile sighting data
Showalter et al. [71]GPS/CDRsCityMonthsUnderstanding human mobility differences between tribal/nontribal and rural/urban
Gao et al. [72]GPS/CDRsCountyMonthsUnderstanding the mobility pattern changes with mobile data at county level
Xiong et al. [73]GPS/CDRsCountyMonthsComputing the origin-destination travel and infections using LBS data
Kishore et al. [74]GPS/CDRsCountyMonthsCapturing the contact patterns of COVID-19 transmission from aggregated LBS data
Pan et al. [75]GPS/CDRsCountyMonthsConstructing social distancing index by using LBS data to study location mobility
Sehra et al. [76]GPS/CDRsCountyMonthsUnderstanding the differences in human activity between workplace and residence
Vinceti et al. [77]GPS/CDRsStateMonthsRelating mobile phone data to measure mobility restriction with the number of cases
Gao et al. [78]GPS/CDRsStateMonthsHuman mobility patterns changed during stay-at-home orders and reduced the cases
Unwin et al. [79]GPS/CDRsStateMonthsUsing mobility changes to capture the impact of NPIs on the transmission of COVID
Kraemer et al. [80]GPS/CDRsNationalMonthsQuantifying control measures and their impact on human mobility from open data
Tian et al. [81]GPS/CDRsNationalMonthsInvestigating COVID-19 spread with human movement and public intervention data
Pepe et al. [82]GPS/CDRsNationalMonthsAggregating mobility to monitor lockdown’s impact on the epidemic trajectory
Garcia-Cremades et al. [83]GPS/CDRsNationalMonthsCreating the decision support system for early prediction of the COVID-19 evolution
Kang et al. [84]GPS/CDRsNationalMonthsMonitor epidemic spreading using mobile phone visit data from SafeGraph
Chang et al. [85]GPS/CDRsNationalMonthsLinking the associations between mobility complexity and infection risks
Sirkeci and Yucesahin [86]GPS/CDRsInternationalMonthsPredicting the COVID-19 spread based on migrant stock and travel data
Chatterjee et al. [87]GPS/CDRsInternationalMonthsUsing LSTM models to forecast the new cases and caused death
Chinazzi et al. [88]GPS/CDRsInternationalMonthsUsing the disease transmission model to project the impact of travel limitations
Liu et al. [89]GPS/CDRsInternationalMonthsLeveraging mobility data from Baidu and Google to analyze flow intensities