scholarly journals Twitter reveals human mobility dynamics during the COVID-19 pandemic

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241957 ◽  
Author(s):  
Xiao Huang ◽  
Zhenlong Li ◽  
Yuqin Jiang ◽  
Xiaoming Li ◽  
Dwayne Porter
Keyword(s):  
Time Window ◽  
Human Mobility ◽  
Global Scale ◽  
Mitigation Measures ◽  
Mobility Patterns ◽  
Protection Measures ◽  
Social Challenges ◽  
Rapid Spread ◽  
Collaborative Efforts ◽  
State Scale

The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission. In this study, we analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographic regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amenable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people’s travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states.

scholarly journals Use of Twitter social media activity as a proxy for human mobility to predict the spatiotemporal spread of COVID-19 at global scale

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Donal Bisanzio ◽  
Moritz U.G. Kraemer ◽  
Isaac I. Bogoch ◽  
Thomas Brewer ◽  
John S Brownstein ◽  
...  
Keyword(s):  
Social Media ◽  
Human Mobility ◽  
Mainland China ◽  
Vulnerability Index ◽  
Global Scale ◽  
Study Cohort ◽  
Mobility Patterns ◽  
Social Media Data ◽  
Current Outbreak ◽  
Media Data

As of February 27, 2020, 82,294 confirmed cases of coronavirus disease (COVID-19) have been reported since December 2019, including 2,804 deaths, with cases reported throughout China, as well as in 45 international locations outside of mainland China. We predict the spatiotemporal spread of reported COVID- 19 cases at the global level during the first few weeks of the current outbreak by analyzing openly available geolocated Twitter social media data. Human mobility patterns were estimated by analyzing geolocated 2013–2015 Twitter data from users who had: i) tweeted at least twice on consecutive days from Wuhan, China, between November 1, 2013, and January 28, 2014, and November 1, 2014, and January 28, 2015; and ii) left Wuhan following their second tweet during the time period under investigation. Publicly available COVID-19 case data were used to investigate the correlation among cases reported during the current outbreak, locations visited by the study cohort of Twitter users, and airports with scheduled flights from Wuhan. Infectious Disease Vulnerability Index (IDVI) data were obtained to identify the capacity of countries receiving travellers from Wuhan to respond to COVID-19. Our study cohort comprised 161 users. Of these users, 133 (82.6%) posted tweets from 157 Chinese cities (1,344 tweets) during the 30 days after leaving Wuhan following their second tweet, with a median of 2 (IQR= 1–3) locations visited and a mean distance of 601 km (IQR= 295.2–834.7 km) traveled. Of our user cohort, 60 (37.2%) traveled abroad to 119 locations in 28 countries. Of the 82 COVID-19 cases reported outside China as of January 30, 2020, 54 cases had known geolocation coordinates and 74.1% (40 cases) were reported less than 15 km (median = 7.4 km, IQR= 2.9–285.5 km) from a location visited by at least one of our study cohort’s users. Countries visited by the cohort’s users and which have cases reported by January 30, 2020, had a median IDVI equal to 0.74. We show that social media data can be used to predict the spatiotemporal spread of infectious diseases such as COVID-19. Based on our analyses, we anticipate cases to be reported in Saudi Arabia and Indonesia; additionally, countries with a moderate to low IDVI (i.e. ≤0.7) such as Indonesia, Pakistan, and Turkey should be on high alert and develop COVID- 19 response plans as soon as permitting.

scholarly journals Public Transport GPS Probe and Rail Gate Data for Assessing the Pattern of Human Mobility in the Bangkok Metropolitan Region, Thailand

2021 ◽  
Vol 13 (4) ◽  
pp. 2178
Author(s):  
Songkorn Siangsuebchart ◽  
Sarawut Ninsawat ◽  
Apichon Witayangkurn ◽  
Surachet Pravinvongvuth
Keyword(s):  
Performance Assessment ◽  
Public Transport ◽  
Human Mobility ◽  
Open Data ◽  
Capital City ◽  
Metropolitan Region ◽  
Traffic Information ◽  
Mobility Patterns ◽  
Transport Planning ◽  
Java Program

Bangkok, the capital city of Thailand, is one of the most developed and expansive cities. Due to the ongoing development and expansion of Bangkok, urbanization has continued to expand into adjacent provinces, creating the Bangkok Metropolitan Region (BMR). Continuous monitoring of human mobility in BMR aids in public transport planning and design, and efficient performance assessment. The purpose of this study is to design and develop a process to derive human mobility patterns from the real movement of people who use both fixed-route and non-fixed-route public transport modes, including taxis, vans, and electric rail. Taxi GPS open data were collected by the Intelligent Traffic Information Center Foundation (iTIC) from all GPS-equipped taxis of one operator in BMR. GPS probe data of all operating GPS-equipped vans were collected by the Ministry of Transport’s Department of Land Transport for daily speed and driving behavior monitoring. Finally, the ridership data of all electric rail lines were collected from smartcards by the Automated Fare Collection (AFC). None of the previous works on human mobility extraction from multi-sourced big data have used van data; therefore, it is a challenge to use this data with other sources in the study of human mobility. Each public transport mode has traveling characteristics unique to its passengers and, therefore, specific analytical tools. Firstly, the taxi trip extraction process was developed using Hadoop Hive to process a large quantity of data spanning a one-month period to derive the origin and destination (OD) of each trip. Secondly, for van data, a Java program was used to construct the ODs of van trips. Thirdly, another Java program was used to create the ODs of the electric rail lines. All OD locations of these three modes were aggregated into transportation analysis zones (TAZ). The major taxi trip destinations were found to be international airports and provincial bus terminals. The significant trip destinations of vans were provincial bus terminals in Bangkok, electric rail stations, and the industrial estates in other provinces of BMR. In contrast, electric rail destinations were electric rail line interchange stations, the central business district (CBD), and commercial office areas. Therefore, these significant destinations of taxis and vans should be considered in electric rail planning to reduce the air pollution from gasoline vehicles (taxis and vans). Using the designed procedures, the up-to-date dataset of public transport can be processed to derive a time series of human mobility as an input into continuous and sustainable public transport planning and performance assessment. Based on the results of the study, the procedures can benefit other cities in Thailand and other countries.

scholarly journals A Conflict between Traditional Flood Measures and Maintaining River Ecosystems? A Case Study Based upon the River Lærdal, Norway

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1884
Author(s):  
Ana Juárez ◽  
Knut Alfredsen ◽  
Morten Stickler ◽  
Ana Adeva-Bustos ◽  
Rodrigo Suárez ◽  
...  
Keyword(s):  
Remote Sensing Data ◽  
Freshwater Ecosystems ◽  
River Channel ◽  
Flood Mitigation ◽  
Mitigation Measures ◽  
Flood Events ◽  
Protection Measures ◽  
River Ecosystems ◽  
Hydraulic Models

Floods are among the most damaging of natural disasters, and flood events are expected to increase in magnitude and frequency with the effects of climate change and changes in land use. As a consequence, much focus has been placed on the engineering of structural flood mitigation measures in rivers. Traditional flood protection measures, such as levees and dredging of the river channel, threaten floodplains and river ecosystems, but during the last decade, sustainable reconciliation of freshwater ecosystems has increased. However, we still find many areas where these traditional measures are proposed, and it is challenging to find tools for evaluation of different measures and quantification of the possible impacts. In this paper, we focus on the river Lærdal in Norway to (i) present the dilemma between traditional flood measures and maintaining river ecosystems and (ii) quantify the efficiency and impact of different solutions based on 2D hydraulic models, remote sensing data, economics, and landscape metrics. Our results show that flood measures may be in serious conflict with environmental protection and legislation to preserve biodiversity and key nature types.

scholarly journals Understanding senior's daily mobility patterns in California using human mobility motifs

2021 ◽  
Vol 94 ◽  
pp. 103117
Author(s):  
Rongxiang Su ◽  
Jingyi Xiao ◽  
Elizabeth C. McBride ◽  
Konstadinos G. Goulias
Keyword(s):  
Human Mobility ◽  
Mobility Patterns ◽  
Daily Mobility

scholarly journals Importation of Ticks on Companion Animals and the Risk of Spread of Tick-Borne Diseases to Non-Endemic Regions in Europe

Animals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Alicja Buczek ◽  
Weronika Buczek
Keyword(s):  
Tick Species ◽  
Potential Role ◽  
Human Mobility ◽  
Companion Animals ◽  
Rhipicephalus Sanguineus ◽  
European Countries ◽  
Rapid Spread ◽  
Risk Of Exposure ◽  
Geographic Regions

Increased human mobility elevates the risk of exposure of companion animals travelling with their owners or imported from other regions to tick attacks. In this study, we highlight the potential role of dogs and cats taken for tourist trips or imported animals in the spread of ticks and tick-borne pathogens. The Rhipicephalus sanguineus tick, which is a vector of numerous pathogens causing diseases in animals and humans, is imported most frequently from endemic areas to many European countries. Additionally, alien tick species with high epizootic and epidemiological importance can be imported on dogs from other continents. Companion animals play an even greater role in the spread of autochthonous tick species and transmission of tick pathogens to other animals and humans. Although the veterinary and medical effects of the parasitism of ticks carried by companion animals travelling with owners or imported animals are poorly assessed, these animals seem to play a role in the rapid spread of tick-borne diseases. Development of strategies for protection of the health of companion animals in different geographic regions should take into account the potential emergence of unknown animal tick-borne diseases that can be transmitted by imported ticks.

scholarly journals A novel geo-hierarchical population mobility model for spatial spreading of resurgent epidemics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexandru Topîrceanu ◽  
Radu-Emil Precup
Keyword(s):  
Computational Models ◽  
Human Mobility ◽  
Discrete Event ◽  
Mobility Model ◽  
Hierarchical Organization ◽  
Population Mobility ◽  
Mobility Patterns ◽  
Fine Grained ◽  
Epidemic Size ◽  
Control Of Epidemics

AbstractComputational models for large, resurgent epidemics are recognized as a crucial tool for predicting the spread of infectious diseases. It is widely agreed, that such models can be augmented with realistic multiscale population models and by incorporating human mobility patterns. Nevertheless, a large proportion of recent studies, aimed at better understanding global epidemics, like influenza, measles, H1N1, SARS, and COVID-19, underestimate the role of heterogeneous mixing in populations, characterized by strong social structures and geography. Motivated by the reduced tractability of studies employing homogeneous mixing, which make conclusions hard to deduce, we propose a new, very fine-grained model incorporating the spatial distribution of population into geographical settlements, with a hierarchical organization down to the level of households (inside which we assume homogeneous mixing). In addition, population is organized heterogeneously outside households, and we model the movement of individuals using travel distance and frequency parameters for inter- and intra-settlement movement. Discrete event simulation, employing an adapted SIR model with relapse, reproduces important qualitative characteristics of real epidemics, like high variation in size and temporal heterogeneity (e.g., waves), that are challenging to reproduce and to quantify with existing measures. Our results pinpoint an important aspect, that epidemic size is more sensitive to the increase in distance of travel, rather that the frequency of travel. Finally, we discuss implications for the control of epidemics by integrating human mobility restrictions, as well as progressive vaccination of individuals.

scholarly journals Reproducing scientists’ mobility: a data-driven model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giacomo Vaccario ◽  
Luca Verginer ◽  
Frank Schweitzer
Keyword(s):  
Decision Rules ◽  
Migration Policy ◽  
Large Data ◽  
Global Scale ◽  
Global Network ◽  
Data Sets ◽  
Mobility Patterns ◽  
Agent Based ◽  
Hiring Process ◽  
Geographical Distances

AbstractHigh skill labour is an important factor underpinning the competitive advantage of modern economies. Therefore, attracting and retaining scientists has become a major concern for migration policy. In this work, we study the migration of scientists on a global scale, by combining two large data sets covering the publications of 3.5 million scientists over 60 years. We analyse their geographical distances moved for a new affiliation and their age when moving, this way reconstructing their geographical “career paths”. These paths are used to derive the world network of scientists’ mobility between cities and to analyse its topological properties. We further develop and calibrate an agent-based model, such that it reproduces the empirical findings both at the level of scientists and of the global network. Our model takes into account that the academic hiring process is largely demand-driven and demonstrates that the probability of scientists to relocate decreases both with age and with distance. Our results allow interpreting the model assumptions as micro-based decision rules that can explain the observed mobility patterns of scientists.

scholarly journals The Impact of Policy Measures on Human Mobility, COVID-19 Cases, and Mortality in the US: A Spatiotemporal Perspective

2021 ◽  
Vol 18 (3) ◽  
pp. 996
Author(s):  
Yun Li ◽  
Moming Li ◽  
Megan Rice ◽  
Haoyuan Zhang ◽  
Dexuan Sha ◽  
...  
Keyword(s):  
Growth Rate ◽  
Human Mobility ◽  
Public Information ◽  
Key Factors ◽  
Information Campaigns ◽  
Rapid Spread ◽  
The Us ◽  
Policy Measures ◽  
The Impact ◽  
At Home

Social distancing policies have been regarded as effective in containing the rapid spread of COVID-19. However, there is a limited understanding of policy effectiveness from a spatiotemporal perspective. This study integrates geographical, demographical, and other key factors into a regression-based event study framework, to assess the effectiveness of seven major policies on human mobility and COVID-19 case growth rates, with a spatiotemporal emphasis. Our results demonstrate that stay-at-home orders, workplace closures, and public information campaigns were effective in decreasing the confirmed case growth rate. For stay-at-home orders and workplace closures, these changes were associated with significant decreases (p < 0.05) in mobility. Public information campaigns did not see these same mobility trends, but the growth rate still decreased significantly in all analysis periods (p < 0.01). Stay-at-home orders and international/national travel controls had limited mitigation effects on the death case growth rate (p < 0.1). The relationships between policies, mobility, and epidemiological metrics allowed us to evaluate the effectiveness of each policy and gave us insight into the spatiotemporal patterns and mechanisms by which these measures work. Our analysis will provide policymakers with better knowledge regarding the effectiveness of measures in space–time disaggregation.

scholarly journals Mobility Patterns in Different Age Groups in Japan during the COVID-19 Pandemic: a Small Area Time Series Analysis through March 2021

2021 ◽  
Author(s):  
Shuhei Nomura ◽  
Yuta Tanoue ◽  
Daisuke Yoneoka ◽  
Stuart Gilmour ◽  
Takayuki Kawashima ◽  
...  
Keyword(s):  
Care Delivery ◽  
Human Mobility ◽  
Age Groups ◽  
Virus Transmission ◽  
Initial Response ◽  
Health Care Delivery System ◽  
Population Characteristics ◽  
Mobility Patterns ◽  
Mobility Data ◽  
The Status

AbstractIn the COVID-19 era, movement restrictions are crucial to slow virus transmission and have been implemented in most parts of the world, including Japan. To find new insights on human mobility and movement restrictions encouraged (but not forced) by the emergency declaration in Japan, we analyzed mobility data at 35 major stations and downtown areas in Japan—each defined as an area overlaid by several 125-meter grids—from September 1, 2019 to March 19, 2021. Data on the total number of unique individuals per hour passing through each area were obtained from Yahoo Japan Corporation (i.e., more than 13,500 data points for each area). We examined the temporal trend in the ratio of the rolling seven-day daily average of the total population to a baseline on January 16, 2020, by ten-year age groups in five time frames. We demonstrated that the degree and trend of mobility decline after the declaration of a state of emergency varies across age groups and even at the subregional level. We demonstrated that monitoring dynamic geographic and temporal mobility information stratified by detailed population characteristics can help guide not only exit strategies from an ongoing emergency declaration, but also initial response strategies before the next possible resurgence. Combining such detailed data with data on vaccination coverage and COVID-19 incidence (including the status of the health care delivery system) can help governments and local authorities develop community-specific mobility restriction policies. This could include strengthening incentives to stay home and raising awareness of cognitive errors that weaken people's resolve to refrain from nonessential movement.

scholarly journals Interstates of Infection: Preliminary Investigations of Human Mobility Patterns in the COVID‐19 Pandemic

2021 ◽  
Vol 37 (2) ◽  
pp. 266-271
Author(s):  
Jacob M. Souch ◽  
Jeralynn S. Cossman ◽  
Mark D. Hayward
Keyword(s):  
Human Mobility ◽  
Mobility Patterns
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