Mobile phone location data reveal the effect and geographic variation of social distancing on the spread of the COVID-19 epidemic
Song Gao, Jinmeng Rao, Yuhao Kang, Yunlei Liang, Jake Kruse, Doerte Doepfer, Ajay K. Sethi, Juan Francisco Mandujano Reyes, Jonathan Patz, Brian S. Yandell
The emergence of SARS-CoV-2 and the coronavirus infectious disease (COVID-19) has become a pandemic. Social (physical) distancing is a key non-pharmacologic control measure to reduce the transmission rate of SARS-COV-2, but high-level adherence is needed. Using daily travel distance and stay-at-home time derived from large-scale anonymous mobile phone location data provided by Descartes Labs and SafeGraph, we quantify the degree to which social distancing mandates have been followed in the U.S. and its effect on growth of COVID-19 cases. The correlation between the COVID-19 growth rate and travel distance decay rate and dwell time at home change rate was -0.586 (95% CI: -0.742 ~ -0.370) and 0.526 (95% CI: 0.293 ~ 0.700), respectively. Increases in state-specific doubling time of total cases ranged from 1.04 ~ 6.86 days to 3.66 ~ 30.29 days after social distancing orders were put in place, consistent with mechanistic epidemic prediction models. Social distancing mandates reduce the spread of COVID-19 when they are followed.
Mobile phone datasets allow for the analysis of human behavior on an unprecedented scale. The social network, temporal dynamics and mobile behavior of mobile phone users have often been analyzed independently from each other using mobile phone datasets. In this article, we explore the connections between various features of human behavior extracted from a large mobile phone dataset. Our observations are based on the analysis of communication data of 100000 anonymized and randomly chosen individuals in a dataset of communications in Portugal. We show that clustering and principal component anal…
To contain the Coronavirus disease (COVID-19) pandemic, one of the non-pharmacological epidemic control measures in response to the COVID-19 outbreak is reducing the transmission rate of SARS-COV-2 in the population through (physical) social distancing. An interactive web-based mapping platform that provides timely quantitative information on how people in different counties and states reacted to the social distancing guidelines was developed with the support of the National Science Foundation (NSF). It integrates geographic information systems (GIS) and daily updated human mobility statistica…
In this paper, we review some advances made recently in the study of mobile phone datasets. This area of research has emerged a decade ago, with the increasing availability of large-scale anonymized datasets, and has grown into a stand-alone topic. We will survey the contributions made so far on the social networks that can be constructed with such data, the study of personal mobility, geographical partitioning, urban planning, and help towards development as well as security and privacy issues.
The enormous amount of recently available mobile phone data is providing unprecedented direct measurements of human behavior. Early recognition and prediction of behavioral patterns are of great importance in many societal applications like urban planning, transportation optimization, and health-care. Understanding the relationships between human behaviors and location's context is an emerging interest for understanding human-environmental dynamics. Growing availability of Web 2.0, i.e. the increasing amount of websites with mainly user created content and social platforms opens up an opportun…
Recent re-opening policies in the US, following a period of social distancing measures, introduced a significant increase in daily COVID-19 infections, calling for a roll-back or substantial revisiting of these policies in many states. The situation is suggestive of difficulties modeling the impact of partial distancing/re-opening policies on future epidemic spread for purposes of choosing safe alternatives. More specifically, one needs to understand the impact of manipulating the availability of social interaction venues (e.g., schools, workplaces, and retail establishments) on virus spread. …
Epidemic outbreaks are an important healthcare challenge, especially in developing countries where they represent one of the major causes of mortality. Approaches that can rapidly target subpopulations for surveillance and control are critical for enhancing containment processes during epidemics. Using a real-world dataset from Ivory Coast, this work presents an attempt to unveil the socio-geographical heterogeneity of disease transmission dynamics. By employing a spatially explicit meta-population epidemic model derived from mobile phone Call Detail Records (CDRs), we investigate how the diff…
Understanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the epidemics. In this report, we use mobile phone data to infer the movements of people between Italian provinces and municipalities, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the …
In the past decade, large scale mobile phone data have become available for the study of human movement patterns. These data hold an immense promise for understanding human behavior on a vast scale, and with a precision and accuracy never before possible with censuses, surveys or other existing data collection techniques. There is already a significant body of literature that has made key inroads into understanding human mobility using this exciting new data source, and there have been several different measures of mobility used. However, existing mobile phone based mobility measures are incon…
This study provides the first confirmation that individual employment status can be predicted from standard mobile phone network logs externally validated with household survey data. Individual welfare and households vulnerability to shocks are intimately connected to employment status and professions of household breadwinners. At a societal level unemployment is an important indicator of the performance of an economy. By deriving a broad set of novel mobile phone network indicators reflecting users financial, social and mobility patterns we show how machine learning models can be used to pred…
Use of mobile phone sensing data to estimate residence and mobility times in urban patches during the COVID-19 epidemic: The case of the 2020 outbreak in Hermosillo, Mexico
In this study, Call Detail Records (CDRs) from downtown Budapest were analysed, focusing on a large-scale event in August 2014. The attendees of the main event of the Hungarian State Foundation Day have been analysed based on their Socioeconomic Status (SES). This paper proposes an approach to estimating SES by the price and age of the subscribers' phones, obtained by fusing a mobile phone property database with the CDRs. We have found some tendencies between the attendees based on the location, from where they watched the fireworks. However, the results do not show significant differences in …
People's daily activities in the urban environment are complex and vary by individuals. Existing studies using mobile phone data revealed distinct and recurrent transitional activity patterns, known as mobility motifs, in people's daily lives. However, the limitation in using only a few inferred activity types hinders our ability to examine general patterns in detail. We proposed a mobility network approach with geographic context-aware Twitter data to investigate granular daily activity patterns in the urban environment. We first utilized publicly accessible geo-located tweets to track the mo…
The present study provides the first evidence that illiteracy can be reliably predicted from standard mobile phone logs. By deriving a broad set of mobile phone indicators reflecting users financial, social and mobility patterns we show how supervised machine learning can be used to predict individual illiteracy in an Asian developing country, externally validated against a large-scale survey. On average the model performs 10 times better than random guessing with a 70% accuracy. Further we show how individual illiteracy can be aggregated and mapped geographically at cell tower resolution. Geo…
Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analyzing millions of anonymous mobile phone users' visits to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are compu…
Understanding how people move within a geographic area, e.g. a city, a country or the whole world, is fundamental in several applications, from predicting the spatio-temporal evolution of an epidemics to inferring migration patterns. Mobile phone records provide an excellent proxy of human mobility, showing that movements exhibit a high level of memory. However, the precise role of memory in widely adopted proxies of mobility, as mobile phone records, is unknown. Here we use 560 millions of call detail records from Senegal to show that standard Markovian approaches, including higher-order ones…
The last two decades have seen a tremendous surge in research on social networks and their implications. The studies includes inferring social relationships, which in turn have been used for target advertising, recommendations, search customization etc. However, the offline experiences of human, the conversations with people and face-to-face interactions that govern our lives interactions have received lesser attention. We introduce DAIICT Spatio-Temporal Network (DSSN), a spatiotemporal dataset of 0.7 million data points of continuous location data logged at an interval of every 2 minutes by …
Controlling the COVID-19 pandemic is an urgent global challenge. The rapid geographic spread of SARS-CoV-2 directly reflects the social structure. Before effective vaccines and treatments are widely available, we have to rely on alternative, non-pharmaceutical interventions, including frequency testing, contact tracing, social distancing, mask wearing, and hand-washing, as public health practises to slow down the spread of the disease. However frequent testing is the key in the absence of any alternative. We propose a network approach to determine the optimal low resources setting oriented poo…
A range of early studies have been conducted to illustrate human mobility patterns using different tracking data, such as dollar notes, cell phones and taxicabs. Here, we explore human mobility patterns based on massive tracking data of US flights. Both topological and geometric properties are examined in detail. We found that topological properties, such as traffic volume (between airports) and degree of connectivity (of individual airports), including both in- and outdegrees, follow a power law distribution but not a geometric property like travel lengths. The travel lengths exhibit an expon…
Standard epidemiological models for COVID-19 employ variants of compartment (SIR) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 U.S cities, we conclude that heterogeneity in population distribution can have large impacts o…
In this paper, we present a novel approach to predict crime in a geographic space from multiple data sources, in particular mobile phone and demographic data. The main contribution of the proposed approach lies in using aggregated and anonymized human behavioral data derived from mobile network activity to tackle the crime prediction problem. While previous research efforts have used either background historical knowledge or offenders' profiling, our findings support the hypothesis that aggregated human behavioral data captured from the mobile network infrastructure, in combination with basic …
We outline a contact-tracing strategy based on proximity sensing using mobile devices. We discuss what an ideal system should look like and what it can do. We show that, when adopted sufficiently broadly, such a contact-tracing strategy can bring COVID-19 under complete control, end the need of social distancing, and return the society to full normalcy. We also review some of the challenges faced by the current generation of proximity-sensing technologies, including Bluetooth Low Energy used by phones, and consider both interim and longer-term solutions. Our main contribution is that we reason…
With about 300 million affected people, major depressive disorder (MDD) is one of the most common diseases worldwide. During the COVID-19 pandemic, the number of cases increased even further, by 28%. Many factors may be correlated with MDD, including the excessive use of social media apps. In this paper, we investigated the relationship between the use of social media and communication apps and depressive symptoms during the COVID-19 pandemic. The pandemic and social distancing like lockdowns probably changed smartphone usage times and usage patterns. While previous studies have shown an assoc…
Humans are social beings. It is therefore not surprising that the social distancing and movement restrictions associated with the Covid-19 pandemic had severe consequences for the well-being of large sections of the population, leading to increased loneliness and related mental diseases. Many people found emotional shelter in online multiplayer games, which have already proven to be great social incubators. While the positive effect games had on individuals has become evident, we are still unaware of how and if games fostered the fundamental need for connectedness that people sought. In other …
The pervasive use of new mobile devices has allowed a better characterization in space and time of human concentrations and mobility in general. Besides its theoretical interest, describing mobility is of great importance for a number of practical applications ranging from the forecast of disease spreading to the design of new spaces in urban environments. While classical data sources, such as surveys or census, have a limited level of geographical resolution (e.g., districts, municipalities, counties are typically used) or are restricted to generic workdays or weekends, the data coming from m…
In most developing countries, a huge proportion of the national food basket is supported by small subsistence agricultural systems. A major challenge to these systems is disease and pest attacks which have a devastating effect on the smallholder farmers that depend on these systems for their livelihoods. A key component of any proposed solution is a good disease surveillance network. However, current surveillance efforts are unable to provide sufficient data for monitoring such phenomena over a vast geographic area efficiently and effectively due to limited resources, both human and financial.…
Optimizing the Deployment of Electric Vehicle Charging Stations Using Pervasive Mobility Data
Massachusetts Institute of Technology（麻省理工学院）；Senseable City Lab, Massachusetts Institute of Technology（麻省理工学院感知城市实验室）；Department of Urban Studies and Planning, MIT（麻省理工学院城市研究与规划系）；Istituto di Informatica e Telematica del CNR（意大利国家研究委员会信息与电信研究所）
In 2020, countries affected by the COVID-19 pandemic implemented various non-pharmaceutical interventions to contrast the spread of the virus and its impact on their healthcare systems and economies. Using Italian data at different geographic scales, we investigate the relationship between human mobility, which subsumes many facets of the population's response to the changing situation, and the spread of COVID-19. Leveraging mobile phone data from February through September 2020, we find a striking relationship between the decrease in mobility flows and the net reproduction number. We find tha…
Dalhousie University（达尔豪斯大学）；University of Saskatchewan（萨斯喀彻温大学）
The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioural change and policy initiatives, such as physical distancing, have been implemented to control the spread of the coronavirus. Social media data can reveal public perceptions toward how governments and health agencies across the globe are handling the pandemic, as well as the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of …
