ArticleJournal of medical Internet research2021
Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study.Journal of medical Internet research · 2024Article
- Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights.JMIR public health and surveillance · 2024Article
- Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review.JMIR public health and surveillance · 2024Article
- Residential Segregation and County-Level COVID-19 Booster Coverage in the Deep South: Surveillance Report and Ecological Study.JMIR public health and surveillance · 2023Article
- Spatio-temporal model to investigate COVID-19 spread accounting for the mobility amongst municipalities.Spatial and spatio-temporal epidemiology · 2023Article
- Revealing geographic transmission pattern of COVID-19 using neighborhood-level simulation with human mobility data and SEIR model: A case study of South Carolina.International journal of applied earth observation and geoinformation : ITC journal · 2023Article
- Was it really different? COVID-19-pandemic period in long-term recreation monitoring - A case study from Polish forests.Journal of outdoor recreation and tourism · 2023Article
- A data-driven framework to assess population dynamics during novel coronavirus outbreaks: A case study on Xiamen Island, China.PloS one · 2023Article
- Using geospatial social media data for infectious disease studies: a systematic review.International journal of digital earth · 2023Article
- Article
- Crowdsourced Perceptions of Human Behavior to Improve Computational Forecasts of US National Incident Cases of COVID-19: Survey Study.JMIR public health and surveillance · 2022Article
- Analysis of Residential Segregation and Racial and Ethnic Disparities in Severe Maternal Morbidity Before and During the COVID-19 Pandemic.JAMA network open · 2022Article
- Social media mining under the COVID-19 context: Progress, challenges, and opportunities.International journal of applied earth observation and geoinformation : ITC journal · 2022Article
- Revealing geographic transmission pattern of COVID-19 using neighborhood-level simulation with human mobility data and SEIR model: A Case Study of South Carolina.medRxiv : the preprint server for health sciences · 2022Article
- A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning.Computers in biology and medicine · 2022Article
- Population Mobility and Aging Accelerate the Transmission of Coronavirus Disease 2019 in the Deep South: A County-Level Longitudinal Analysis.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022Article
- A linear dynamical perspective on epidemiology: interplay between early COVID-19 outbreak and human mobility.Nonlinear dynamics · 2022Article
- Spatiotemporal distribution of COVID-19 during the first 7 months of the epidemic in Vietnam.BMC infectious diseases · 2021Article
- Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence.JMIR public health and surveillance · 2021Article
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7 authors at 1 institution in 1 country.
Funding
Abstract
backgroundPopulation mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases.
objectiveThe aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina.
methodsThis longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting.
resultsPopulation mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%.
conclusionsUsing Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.
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