ArticleJMIR research protocols2020
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.
Article in JMIR research protocols, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 46 citations in OpenAlex.
- The Impacts of HIV-Related Service Interruptions During the COVID-19 Pandemic: Protocol of a Mixed Methodology Longitudinal Study.AIDS and behavior · 2024Article
- Nonrepresentativeness of Human Mobility Data and its Impact on Modeling Dynamics of the COVID-19 Pandemic: Systematic Evaluation.JMIR formative research · 2024Article
- Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.PloS one · 2024Article
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- A new method for spatio-temporal transmission prediction of COVID-19.Chaos, solitons, and fractals · 2023Article
- A data-driven framework to assess population dynamics during novel coronavirus outbreaks: A case study on Xiamen Island, China.PloS one · 2023Article
- Using geographic information systems to link population estimates to wastewater surveillance data in New York State, USA.PLOS global public health · 2023Article
- Research on emergency management of global public health emergencies driven by digital technology: A bibliometric analysis.Frontiers in public health · 2022Article
- A high-resolution temporal and geospatial content analysis of Twitter posts related to the COVID-19 pandemic.Journal of computational social science · 2022Article
- Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review.Frontiers in public health · 2022Review
- Spatiotemporal patterns of the COVID-19 control measures impact on industrial production in Wuhan using time-series earth observation data.Sustainable cities and society · 2021Article
- Detection of Spatiotemporal Clusters of COVID-19-Associated Symptoms and Prevention Using a Participatory Surveillance App: Protocol for the @choum Study.JMIR research protocols · 2021Article
- A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020.Transactions in GIS : TG · 2021Review
- Measuring global multi-scale place connectivity using geotagged social media data.Scientific reports · 2021Article
- Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis.Journal of medical Internet research · 2021Article
- Spatial-temporal relationship between population mobility and COVID-19 outbreaks in South Carolina: A time series forecasting analysis.medRxiv : the preprint server for health sciences · 2021Article
- ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility.PloS one · 2021Article
- Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
backgroundHuman 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).
objectiveBig 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).
methodsWe 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.
resultsThis project was funded as of May 2020. We have started the data collection, processing, and analysis for the project.
conclusionsResearch 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.