scholarly journals Use of open mobile mapping tool to assess human mobility traceability in rural offline populations with contrasting malaria dynamics

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6298 ◽  
Author(s):  
Gabriel Carrasco-Escobar ◽  
Marcia C. Castro ◽  
Jose Luis Barboza ◽  
Jorge Ruiz-Cabrejos ◽  
Alejandro Llanos-Cuentas ◽  
...  
Keyword(s):  
Malaria Transmission ◽  
Human Mobility ◽  
Human Movement ◽  
Mobile Mapping ◽  
Mobility Spectrum ◽  
Mobility Data ◽  
Mapping Tool ◽  
Commercial Logging ◽  
Tree Coverage ◽  
Transmission Studies

Infectious disease dynamics are affected by human mobility more powerfully than previously thought, and thus reliable traceability data are essential. In rural riverine settings, lack of infrastructure and dense tree coverage deter the implementation of cutting-edge technology to collect human mobility data. To overcome this challenge, this study proposed the use of a novel open mobile mapping tool, GeoODK. This study consists of a purposive sampling of 33 participants in six villages with contrasting patterns of malaria transmission that demonstrates a feasible approach to map human mobility. The self-reported traceability data allowed the construction of the first human mobility framework in rural riverine villages in the Peruvian Amazon. The mobility spectrum in these areas resulted in travel profiles ranging from 2 hours to 19 days; and distances between 10 to 167 km. Most Importantly, occupational-related mobility profiles with the highest displacements (in terms of time and distance) were observed in commercial, logging, and hunting activities. These data are consistent with malaria transmission studies in the area that show villages in watersheds with higher human movement are concurrently those with greater malaria risk. The approach we describe represents a potential tool to gather critical information that can facilitate malaria control activities.

scholarly journals The mobilize center: an NIH big data to knowledge center to advance human movement research and improve mobility

2015 ◽  
Vol 22 (6) ◽  
pp. 1120-1125 ◽  
Author(s):  
Joy P Ku ◽  
Jennifer L Hicks ◽  
Trevor Hastie ◽  
Jure Leskovec ◽  
Christopher Ré ◽  
...  
Keyword(s):  
Data Science ◽  
Human Mobility ◽  
Wearable Sensors ◽  
Human Movement ◽  
Science Research ◽  
Science Methods ◽  
Social Modeling ◽  
Mobility Data ◽  
Using Data ◽  
And Training

Abstract Regular physical activity helps prevent heart disease, stroke, diabetes, and other chronic diseases, yet a broad range of conditions impair mobility at great personal and societal cost. Vast amounts of data characterizing human movement are available from research labs, clinics, and millions of smartphones and wearable sensors, but integration and analysis of this large quantity of mobility data are extremely challenging. The authors have established the Mobilize Center ( http://mobilize.stanford.edu ) to harness these data to improve human mobility and help lay the foundation for using data science methods in biomedicine. The Center is organized around 4 data science research cores: biomechanical modeling, statistical learning, behavioral and social modeling, and integrative modeling. Important biomedical applications, such as osteoarthritis and weight management, will focus the development of new data science methods. By developing these new approaches, sharing data and validated software tools, and training thousands of researchers, the Mobilize Center will transform human movement research.

scholarly journals Identifying Malaria Transmission Foci for Elimination Using Human Mobility Data

2016 ◽  
Vol 12 (4) ◽  
pp. e1004846 ◽  
Author(s):  
Nick W. Ruktanonchai ◽  
Patrick DeLeenheer ◽  
Andrew J. Tatem ◽  
Victor A. Alegana ◽  
T. Trevor Caughlin ◽  
...  
Keyword(s):  
Malaria Transmission ◽  
Human Mobility ◽  
Mobility Data ◽  
Transmission Foci

scholarly journals The impact of human mobility data scales and processing on movement predictability

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kamil Smolak ◽  
Katarzyna Siła-Nowicka ◽  
Jean-Charles Delvenne ◽  
Michał Wierzbiński ◽  
Witold Rohm
Keyword(s):  
Prediction Models ◽  
Human Mobility ◽  
Reference Value ◽  
Human Movement ◽  
Temporal Data ◽  
Movement Trajectories ◽  
Mobility Data ◽  
Wide Range ◽  
Spatio Temporal ◽  
The Impact

AbstractPredictability of human movement is a theoretical upper bound for the accuracy of movement prediction models, which serves as a reference value showing how regular a dataset is and to what extent mobility can be predicted. Over the years, the predictability of various human mobility datasets was found to vary when estimated for differently processed datasets. Although attempts at the explanation of this variability have been made, the extent of these experiments was limited. In this study, we use high-precision movement trajectories of individuals to analyse how the way we represent the movement impacts its predictability and thus, the outcomes of analyses made on these data. We adopt a number of methods used in the last 11 years of research on human mobility and apply them to a wide range of spatio-temporal data scales, thoroughly analysing changes in predictability and produced data. We find that spatio-temporal resolution and data processing methods have a large impact on the predictability as well as geometrical and numerical properties of human mobility data, and we present their nonlinear dependencies.

scholarly journals Forecasting the spatial transmission of influenza in the United States

2018 ◽  
Vol 115 (11) ◽  
pp. 2752-2757 ◽  
Author(s):  
Sen Pei ◽  
Sasikiran Kandula ◽  
Wan Yang ◽  
Jeffrey Shaman
Keyword(s):  
Human Mobility ◽  
State Level ◽  
Human Movement ◽  
The United States ◽  
Spatial Spread ◽  
Influenza Outbreak ◽  
Mobility Data ◽  
Real Time Information ◽  
Time Information ◽  
Spatial Transmission

Recurrent outbreaks of seasonal and pandemic influenza create a need for forecasts of the geographic spread of this pathogen. Although it is well established that the spatial progression of infection is largely attributable to human mobility, difficulty obtaining real-time information on human movement has limited its incorporation into existing infectious disease forecasting techniques. In this study, we develop and validate an ensemble forecast system for predicting the spatiotemporal spread of influenza that uses readily accessible human mobility data and a metapopulation model. In retrospective state-level forecasts for 35 US states, the system accurately predicts local influenza outbreak onset,—i.e., spatial spread, defined as the week that local incidence increases above a baseline threshold—up to 6 wk in advance of this event. In addition, the metapopulation prediction system forecasts influenza outbreak onset, peak timing, and peak intensity more accurately than isolated location-specific forecasts. The proposed framework could be applied to emergent respiratory viruses and, with appropriate modifications, other infectious diseases.

scholarly journals Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics

10.2196/24432 ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. e24432
Author(s):  
Zhenlong Li ◽  
Xiaoming Li ◽  
Dwayne Porter ◽  
Jiajia Zhang ◽  
Yuqin Jiang ◽  
...  
Keyword(s):  
Public Health ◽  
Big Data ◽  
Infectious Diseases ◽  
Spatial Scales ◽  
Human Mobility ◽  
Human Movement ◽  
Data Sources ◽  
Mobile Phone Data ◽  
Spatial Spread ◽  
Mobility Data

Background Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). Objective Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). Methods We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. Results This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. Conclusions Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. International Registered Report Identifier (IRRID) DERR1-10.2196/24432

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