scholarly journals Large-scale assessment of human mobility during COVID-19 outbreak

2020 ◽  
Author(s):  
Lucas Queiroz ◽  
Lucas Queiroz ◽  
José Luciano Melo ◽  
Gabriel Barboza ◽  
Alysson H. Urbanski ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Cell Phone ◽  
Large Scale ◽  
Human Mobility ◽  
Population Mobility ◽  
Social Distancing ◽  
Public Health Systems ◽  
Large Scale Assessment ◽  
Strong Control

Social distancing is an important measure to prevent the collapse of public health systems during the COVID-19 pandemic. While some countries have managed to impose a strong control for social distancing, countries like Brazil largely depend on the population’s cooperation. Therefore, it is very necessary to monitor human mobility to detect whether social distancing policies are being implemented and to adjust them in places where the population is not adhering to these. By using cell phone data of millions of people, we were able to assess the population mobility in Brazil’s biggest city, São Paulo. Our analysis revealed the reduction in the circulation of people in most neighborhoods after social distancing policies began. We also showed the dispersion of people by tracking the visits of people to the GRU airport and the visit locations of the same people after they left the airport. Over the course of a few days, it was possible to detect over 70,000 visits across Brazil, with distances greater than 2,000 km from the GRU airport. We hope that data when collected in real time can be useful to stem the progress of the COVID-19 epidemic, or at least to help “flatten the curve”.

scholarly journals How public perceptions of social distancing evolved over a critical time period: communication lessons learnt from the American state of Wisconsin

2020 ◽  
Vol 19 (05) ◽  
pp. A11
Author(s):  
Kaiping Chen ◽  
Luye Bao ◽  
Anqi Shao ◽  
Pauline Ho ◽  
Shiyu Yang ◽  
...  
Keyword(s):  
Public Health ◽  
Computational Methods ◽  
Large Scale ◽  
Critical Time ◽  
Effective Communication ◽  
Public Perceptions ◽  
Social Distancing ◽  
Time Period ◽  
Large Scale Survey ◽  
Main Barrier

Understanding how individuals perceive the barriers and benefits of precautionary actions is key for effective communication about public health crises, such as the COVID-19 outbreak. This study used innovative computational methods to analyze 30,000 open-ended responses from a large-scale survey to track how Wisconsin (U.S.A.) residents' perceptions of the benefits of and barriers to performing social distancing evolved over a critical time period (March 19th to April 1st, 2020). Initially, the main barrier was practical related, however, individuals later perceived more multifaceted barriers to social distancing. Communication about COVID-19 should be dynamic and evolve to address people's experiences and needs overtime.

scholarly journals Research on Collective Human Mobility in Shanghai Based on Cell Phone Data

2020 ◽  
Vol 9 (1) ◽  
pp. 44-62
Author(s):  
Xiyuan Ren ◽  
De Wang
Keyword(s):  
Temporal Variation ◽  
High Frequency ◽  
Cell Phone ◽  
Large Scale ◽  
Human Mobility ◽  
Indicator System ◽  
Data Sources ◽  
Census Tracts ◽  
Aggregate Distribution ◽  
Human Movements

The high-frequency mobility of a massive population has caused an enormous influence on the urban internal structure, which is unable to be described by traditional data sources. While recent advances in location-based technologies provides new opportunities for researchers to understand daily human movements and the structure as a whole. The article aims to explore human spatial movements and their aggregate distribution in Shanghai using large-scale cell phone data. The trajectory of each individual is extracted from cell phone data after data cleansing. Then, an indicator system which includes mobility intensity, mobility stability, influential range, and temporal variation is developed to describe collective human mobility features in census tracts scale. Finally, spatial elements are extracted using the indicator system and the structure of human mobility in Shanghai is discussed.

scholarly journals The U.S. COVID-19 Trends and Impact Survey, 2020-2021: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing and vaccination

2021 ◽  
Author(s):  
Joshua A Salomon ◽  
Alex Reinhart ◽  
Alyssa Bilinski ◽  
Eu Jing Chua ◽  
Wichida La Motte-Kerr ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Large Scale ◽  
Survey Methods ◽  
Health Priorities ◽  
Surveillance Systems ◽  
Cross Sectional ◽  
Essential Information ◽  
Public Health Reporting ◽  
The U.S

The U.S. COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, Internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey -- over 20 million responses in its first year of operation -- allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

scholarly journals A sequential test to compare the real-time fatality rates of a disease among multiple groups with an application to COVID-19 data

2021 ◽  
pp. 096228022110619
Author(s):  
Yuanke Qu ◽  
Chun Yin Lee ◽  
KF Lam
Keyword(s):  
Public Health ◽  
Real Time ◽  
Disease Severity ◽  
Null Hypothesis ◽  
Large Scale ◽  
Mainland China ◽  
Cumulative Number ◽  
Finite Sample ◽  
Test Statistic ◽  
Multiple Groups

Infectious diseases, such as the ongoing COVID-19 pandemic, pose a significant threat to public health globally. Fatality rate serves as a key indicator for the effectiveness of potential treatments or interventions. With limited time and understanding of novel emerging epidemics, comparisons of the fatality rates in real-time among different groups, say, divided by treatment, age, or area, have an important role to play in informing public health strategies. We propose a statistical test for the null hypothesis of equal real-time fatality rates across multiple groups during an ongoing epidemic. An elegant property of the proposed test statistic is that it converges to a Brownian motion under the null hypothesis, which allows one to develop a sequential testing approach for rejecting the null hypothesis at the earliest possible time when statistical evidence accumulates. This property is particularly important as scientists and clinicians are competing with time to identify possible treatments or effective interventions to combat the emerging epidemic. The method is widely applicable as it only requires the cumulative number of confirmed cases, deaths, and recoveries. A large-scale simulation study shows that the finite-sample performance of the proposed test is highly satisfactory. The proposed test is applied to compare the difference in disease severity among Wuhan, Hubei province (exclude Wuhan) and mainland China (exclude Hubei) from February to March 2020. The result suggests that the disease severity is potentially associated with the health care resource availability during the early phase of the COVID-19 pandemic in mainland China.

scholarly journals The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination

2021 ◽  
Vol 118 (51) ◽  
pp. e2111454118 ◽  
Author(s):  
Joshua A. Salomon ◽  
Alex Reinhart ◽  
Alyssa Bilinski ◽  
Eu Jing Chua ◽  
Wichada La Motte-Kerr ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Large Scale ◽  
Survey Methods ◽  
Health Priorities ◽  
Surveillance Systems ◽  
Cross Sectional ◽  
Essential Information ◽  
Public Health Reporting ◽  
The Us

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey—over 20 million responses in its first year of operation—allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

scholarly journals Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing

2020 ◽  
Author(s):  
Kathy Leung ◽  
Joseph T Wu ◽  
Gabriel M Leung
Keyword(s):  
Real Time ◽  
Disease Transmission ◽  
Human Mobility ◽  
Population Mobility ◽  
Mobility Data ◽  
Physical Mixing ◽  
Real Time Tracking ◽  
Case Data ◽  
Transmission Models ◽  
New Framework

AbstractDigital proxies of human mobility and physical mixing have been used to monitor viral transmissibility and effectiveness of social distancing interventions in the ongoing COVID-19 pandemic. We developed a new framework that parameterizes disease transmission models with age-specific digital mobility data. By fitting the model to case data in Hong Kong, we were able to accurately track the local effective reproduction number of COVID-19 in near real time (i.e. no longer constrained by the delay of around 9 days between infection and reporting of cases) which is essential for quick assessment of the effectiveness of interventions on reducing transmissibility. Our findings showed that accurate nowcast and forecast of COVID-19 epidemics can be obtained by integrating valid digital proxies of physical mixing into conventional epidemic models.

scholarly journals Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yixuan Pan ◽  
Aref Darzi ◽  
Aliakbar Kabiri ◽  
Guangchen Zhao ◽  
Weiyu Luo ◽  
...  
Keyword(s):  
United States ◽  
Real Time ◽  
Human Mobility ◽  
The United States ◽  
Control Measures ◽  
Social Distancing ◽  
First Case ◽  
Mobility Behaviour ◽  
Novel Coronavirus ◽  
Behaviour Changes

AbstractSince the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including in the United States, as a major community mitigation strategy. However, our understanding remains limited in how people would react to such control measures, as well as how people would resume their normal behaviours when those orders were relaxed. We utilize an integrated dataset of real-time mobile device location data involving 100 million devices in the contiguous United States (plus Alaska and Hawaii) from February 2, 2020 to May 30, 2020. Built upon the common human mobility metrics, we construct a Social Distancing Index (SDI) to evaluate people’s mobility pattern changes along with the spread of COVID-19 at different geographic levels. We find that both government orders and local outbreak severity significantly contribute to the strength of social distancing. As people tend to practice less social distancing immediately after they observe a sign of local mitigation, we identify several states and counties with higher risks of continuous community transmission and a second outbreak. Our proposed index could help policymakers and researchers monitor people’s real-time mobility behaviours, understand the influence of government orders, and evaluate the risk of local outbreaks.

scholarly journals Adequacy of Existing Surveillance Systems to Monitor Racism, Social Stigma and COVID Inequities: A Detailed Assessment and Recommendations

2021 ◽  
Vol 18 (24) ◽  
pp. 13099
Author(s):  
Chandra L. Ford ◽  
Bita Amani ◽  
Nina T. Harawa ◽  
Randall Akee ◽  
Gilbert C. Gee ◽  
...  
Keyword(s):  
Public Health ◽  
Critical Race Theory ◽  
Real Time ◽  
Health Systems ◽  
Social Stigma ◽  
Evaluation Criteria ◽  
Surveillance Systems ◽  
Related Factors ◽  
Public Health Systems ◽  
Detailed Assessment

The populations impacted most by COVID are also impacted by racism and related social stigma; however, traditional surveillance tools may not capture the intersectionality of these relationships. We conducted a detailed assessment of diverse surveillance systems and databases to identify characteristics, constraints and best practices that might inform the development of a novel COVID surveillance system that achieves these aims. We used subject area expertise, an expert panel and CDC guidance to generate an initial list of N > 50 existing surveillance systems as of 29 October 2020, and systematically excluded those not advancing the project aims. This yielded a final reduced group (n = 10) of COVID surveillance systems (n = 3), other public health systems (4) and systems tracking racism and/or social stigma (n = 3, which we evaluated by using CDC evaluation criteria and Critical Race Theory. Overall, the most important contribution of COVID-19 surveillance systems is their real-time (e.g., daily) or near-real-time (e.g., weekly) reporting; however, they are severely constrained by the lack of complete data on race/ethnicity, making it difficult to monitor racial/ethnic inequities. Other public health systems have validated measures of psychosocial and behavioral factors and some racism or stigma-related factors but lack the timeliness needed in a pandemic. Systems that monitor racism report historical data on, for instance, hate crimes, but do not capture current patterns, and it is unclear how representativeness the findings are. Though existing surveillance systems offer important strengths for monitoring health conditions or racism and related stigma, new surveillance strategies are needed to monitor their intersecting relationships more rigorously.

scholarly journals Social-distancing Fatigue: Evidence from Real-time Crowd-sourced Traffic Data

2021 ◽  
Author(s):  
Jenni A. Shearston ◽  
Micaela E. Martinez ◽  
Yanelli Nunez ◽  
Markus Hilpert
Keyword(s):  
Time Series ◽  
Real Time ◽  
Traffic Congestion ◽  
Human Mobility ◽  
Additive Models ◽  
School Closure ◽  
High Temporal Resolution ◽  
Social Distancing ◽  
The Impact ◽  
At Home

ABSTRACTIntroductionTo mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and results in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies.MethodsWe used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions.ResultsMobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels.ConclusionUsing crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.GRAPHICAL ABSTRACT

scholarly journals Social Distancing is Effective at Mitigating COVID-19 Transmission in the United States

2020 ◽  
Author(s):  
Hamada S. Badr ◽  
Hongru Du ◽  
Max Marshall ◽  
Ensheng Dong ◽  
Marietta Squire ◽  
...  
Keyword(s):  
United States ◽  
Large Scale ◽  
Complex Dynamics ◽  
Case Reports ◽  
Human Mobility ◽  
Local Level ◽  
The United States ◽  
Mobile Phone Data ◽  
Variable Intensity ◽  
Social Distancing

AbstractCOVID-19 is present in every state and over 90 percent of all counties in the United States. Decentralized government efforts to reduce spread, combined with the complex dynamics of human mobility and the variable intensity of local outbreaks makes assessing the effect of large-scale social distancing on COVID-19 transmission in the U.S.a challenge. We generate a novel metric to represent social distancing behavior derived from mobile phone data and examine its relationship with COVID-19 case reports at the county level. Our analysis reveals that social distancing is strongly correlated with decreased COVID-19 case growth rates for the 25 most affected counties in the United States, with a lag period consistent with the incubation time of SARS-CoV-2. We also demonstrate evidence that social distancing was already under way in many U.S. counties before state or local-level policies were implemented. This study strongly supports social distancing as an effective way to mitigate COVID-19 transmission in the United States.

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