Cell phone mobility data and manifold learning: Insights into population behavior during the COVID-19 pandemic

Levin, Roman; Chao, Dennis L.; Wenger, E. A.; Proctor, Joshua L.

doi:10.1101/2020.10.31.20223776

Cited by 8 publications

(8 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A correlation between changes in the amount of time spent at home, the workplace, and shopping, and rates of infection has been shown [8][9][10]. It has been shown that compliance rates for staying at home are not uniform across different regions [11]. Stagnation of economic activity with an increase in the number of infections has been observed based on night-time illumination and power consumption [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Visualizing Social and Behavior Change due to the Outbreak of COVID-19 Using Mobile Phone Location Data

2021

View full text Add to dashboard Cite

We visualize the rates of stay-home for residents by region using the difference between day-time and night-time populations to detect residential areas, and then observing the numbers of people leaving residential areas. There are issues with measuring stay-home rates by observing numbers of people visiting downtown areas, such as central urban shopping centers and major train stations. The first is that we cannot eliminate the possibility that people will avoid areas being observed and go to other areas. The second is that for people visiting downtown areas, we cannot know where they reside. These issues can be resolved if we quantify the degree of stay-home using the number of people leaving residential areas. There are significant differences in stay-home levels by region throughout Japan. By this visualization, residents of each region can see whether their level of stay-home is adequate or not, and this can provide incentive toward compliance suited to the residents of the region.

show abstract

Section: Introductionmentioning

confidence: 99%

Visualizing Social and Behavior Change due to the Outbreak of COVID-19 Using Mobile Phone Location Data

2021

View full text Add to dashboard Cite

show abstract

“…During the final stages of processing, the hb_shrink function was used to perform hierarchical Bayesian shrinkage on the county-to-CBG level to improve the reliability of the estimates. SafeGraph's stay-at-home index has been used in many studies in order to analyze the impact of mobility patterns and physical distancing after the implementation of shelter-in-place policies in the USA [35][36][37]40].…”

Section: Methodsmentioning

confidence: 99%

“…t = T t−1 + t , where t = ( 1t , … , Kt ) t ∼ N(0, 2 Q W, S −1 )1 For an excellent tutorial on the methods used in this paper, see Lee, D (40)…”

mentioning

confidence: 99%

Variability in Opioid-Related Drug Overdoses, Social Distancing, and Area-Level Deprivation during the COVID-19 Pandemic: a Bayesian Spatiotemporal Analysis

et al. 2022

View full text Add to dashboard Cite

Monitoring the spatial and temporal course of opioid-related drug overdose mortality is a key public health determinant. Despite previous studies exploring the evolution of drug-related fatalities following the stay-at-home mandates during the COVID-19 pandemic, little is known about the spatiotemporal dynamics that mitigation efforts had on overdose deaths. The purpose of this study was to describe the spatial and temporal dynamics of overdose death relative risk using a 4-week interval over a span of 5 months following the implementation of the COVID-19 lockdown in the city of Chicago, IL. A Bayesian space–time model was used to produce posterior risk estimates and exceedance probabilities of opioid-related overdose deaths controlling for measures of area-level deprivation and stay-at-home mandates. We found that area-level temporal risk and inequalities in drug overdose mortality increased significantly in the initial months of the pandemic. We further found that a change in the area-level deprivation from the first to the fourth quintile increased the relative risk of a drug overdose risk by 44.5%. The social distancing index measuring the proportion of persons who stayed at home in each census block group was not associated with drug overdose mortality. We conclude by highlighting the importance of contextualizing the spatial and temporal risk in overdose mortality for implementing effective and safe harm reduction strategies during a global pandemic. Supplementary Information The online version contains supplementary material available at 10.1007/s11524-022-00675-x.

show abstract

“…For example, citywide smart card travel data in Sydney, Australia, has been utilised to cluster passenger types along multiple mobility dimensions and develop intervention strategies for disease spread (Shoghri et al, 2020). Similarly, manifold learning techniques have been applied to cell-phone mobility data in the United States during the Covid-19 pandemic, distinguishing mobility trends in multiple geographic regions and demographics (Levin et al, 2020). Wisesty and Mengko (2021) leverage dimensionality reduction techniques to cluster and analyse highly dimensional genome sequence data of Covid-19.…”

Section: Introductionmentioning

confidence: 99%

On the intrinsic dimensionality of Covid-19 data: a global perspective

Varghese¹,

Santos–Fernández²,

Denti³

et al. 2022

Preprint

View full text Add to dashboard Cite

This paper aims to develop a global perspective of the complexity of the relationship between the standardised per-capita growth rate of Covid-19 cases, deaths, and the OxCGRT Covid-19 Stringency Index, a measure describing a country's stringency of lockdown policies. To achieve our goal, we use a heterogeneous intrinsic dimension estimator implemented as a Bayesian mixture model, called Hidalgo. We identify that the Covid-19 dataset may project onto two low-dimensional manifolds without significant information loss. The low dimensionality suggests strong dependency among the standardised growth rates of cases and deaths per capita and the OxCGRT Covid-19 Stringency Index for a country over 2020-2021. Given the low dimensional structure, it may be feasible to model observable Covid-19 dynamics with few parameters. Importantly, we identify spatial autocorrelation in the intrinsic dimension distribution worldwide. Moreover, we highlight that high-income countries are more likely to lie on low-dimensional manifolds, likely arising from aging populations, comorbidities, and increased per capita mortality burden from Covid-19. Finally, we temporally stratify the dataset to examine the intrinsic dimension at a more granular level throughout the Covid-19 pandemic.

show abstract

Cell phone mobility data and manifold learning: Insights into population behavior during the COVID-19 pandemic

Cited by 8 publications

References 28 publications

Visualizing Social and Behavior Change due to the Outbreak of COVID-19 Using Mobile Phone Location Data

Visualizing Social and Behavior Change due to the Outbreak of COVID-19 Using Mobile Phone Location Data

Variability in Opioid-Related Drug Overdoses, Social Distancing, and Area-Level Deprivation during the COVID-19 Pandemic: a Bayesian Spatiotemporal Analysis

On the intrinsic dimensionality of Covid-19 data: a global perspective

Contact Info

Product

Resources

About