Optimizing county-level infectious respiratory disease forecasts: a pandemic case study integrating social media-based physical and social connectivity networks
Forecasting infectious respiratory diseases is crucial for effective prevention and intervention strategies. However, existing time series forecasting models that incorporate human mobility data have faced challenges in making localized predictions on a large scale across the country due to data cos...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2436486 |
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| author | Fengrui Jing Zhenlong Li Shan Qiao M. Naser Lessani Huan Ning Wenjun Ma Jinjing Hu Pan Yang Xiaoming Li |
| author_facet | Fengrui Jing Zhenlong Li Shan Qiao M. Naser Lessani Huan Ning Wenjun Ma Jinjing Hu Pan Yang Xiaoming Li |
| author_sort | Fengrui Jing |
| collection | DOAJ |
| description | Forecasting infectious respiratory diseases is crucial for effective prevention and intervention strategies. However, existing time series forecasting models that incorporate human mobility data have faced challenges in making localized predictions on a large scale across the country due to data costs and constraints. Using the COVID-19 pandemic as a case study, this research explores whether integrating social media-based place and social connectivity networks can improve predictions of disease transmission at the county level across various regions. Place connectivity networks, derived from Twitter users and tweets, and social connectivity networks, based on Facebook interactions, were used to map spatial and social linkages between locations. These networks were integrated into weekly COVID-19 incidence data across 2,927 U.S. counties using Long Short-Term Memory (LSTM) models. The combined connectivity-weighted model significantly enhanced prediction accuracy, reducing Mean Absolute Percentage Error (MAPE) by 49.38% across 96.62% of the counties, with the greatest improvements observed in urban and Northeastern counties. The results demonstrate that combining connectivity networks enhances prediction accuracy, offering a scalable and sustainable solution for localized disease forecasting on a large scale across diverse geographic areas using publicly accessible social media data. |
| format | Article |
| id | doaj-art-35b4e691b135405082c70aecb3b49688 |
| institution | OA Journals |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-35b4e691b135405082c70aecb3b496882025-08-20T02:30:48ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552024-12-0117110.1080/17538947.2024.2436486Optimizing county-level infectious respiratory disease forecasts: a pandemic case study integrating social media-based physical and social connectivity networksFengrui Jing0Zhenlong Li1Shan Qiao2M. Naser Lessani3Huan Ning4Wenjun Ma5Jinjing Hu6Pan Yang7Xiaoming Li8Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, People’s Republic of ChinaGeoinformation and Big Data Research Lab, Department of Geography, The Pennsylvania State University, University Park, PA, USABig Data Health Science Center, University of South Carolina, Columbia, SC, USAGeoinformation and Big Data Research Lab, Department of Geography, The Pennsylvania State University, University Park, PA, USAGeoinformation and Big Data Research Lab, Department of Geography, The Pennsylvania State University, University Park, PA, USADepartment of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, People’s Republic of ChinaSchool of Geography and Planning, Sun Yat-sen University, Guangzhou, People's Republic of ChinaDepartment of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, People’s Republic of ChinaBig Data Health Science Center, University of South Carolina, Columbia, SC, USAForecasting infectious respiratory diseases is crucial for effective prevention and intervention strategies. However, existing time series forecasting models that incorporate human mobility data have faced challenges in making localized predictions on a large scale across the country due to data costs and constraints. Using the COVID-19 pandemic as a case study, this research explores whether integrating social media-based place and social connectivity networks can improve predictions of disease transmission at the county level across various regions. Place connectivity networks, derived from Twitter users and tweets, and social connectivity networks, based on Facebook interactions, were used to map spatial and social linkages between locations. These networks were integrated into weekly COVID-19 incidence data across 2,927 U.S. counties using Long Short-Term Memory (LSTM) models. The combined connectivity-weighted model significantly enhanced prediction accuracy, reducing Mean Absolute Percentage Error (MAPE) by 49.38% across 96.62% of the counties, with the greatest improvements observed in urban and Northeastern counties. The results demonstrate that combining connectivity networks enhances prediction accuracy, offering a scalable and sustainable solution for localized disease forecasting on a large scale across diverse geographic areas using publicly accessible social media data.https://www.tandfonline.com/doi/10.1080/17538947.2024.2436486Infectious respiratory disease transmissionplace and social connectivity networksgeospatial social media datatime series forecasting |
| spellingShingle | Fengrui Jing Zhenlong Li Shan Qiao M. Naser Lessani Huan Ning Wenjun Ma Jinjing Hu Pan Yang Xiaoming Li Optimizing county-level infectious respiratory disease forecasts: a pandemic case study integrating social media-based physical and social connectivity networks International Journal of Digital Earth Infectious respiratory disease transmission place and social connectivity networks geospatial social media data time series forecasting |
| title | Optimizing county-level infectious respiratory disease forecasts: a pandemic case study integrating social media-based physical and social connectivity networks |
| title_full | Optimizing county-level infectious respiratory disease forecasts: a pandemic case study integrating social media-based physical and social connectivity networks |
| title_fullStr | Optimizing county-level infectious respiratory disease forecasts: a pandemic case study integrating social media-based physical and social connectivity networks |
| title_full_unstemmed | Optimizing county-level infectious respiratory disease forecasts: a pandemic case study integrating social media-based physical and social connectivity networks |
| title_short | Optimizing county-level infectious respiratory disease forecasts: a pandemic case study integrating social media-based physical and social connectivity networks |
| title_sort | optimizing county level infectious respiratory disease forecasts a pandemic case study integrating social media based physical and social connectivity networks |
| topic | Infectious respiratory disease transmission place and social connectivity networks geospatial social media data time series forecasting |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2024.2436486 |
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