Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis
Currently, the outbreak of COVID-19 is rapidly spreading especially in Wuhan city, and threatens 14 million people in central China. In the present study we applied the Moran index, a strong statistical tool, to the spatial panel to show that COVID-19 infection is spatially dependent and mainly spr...
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| Format: | Article |
| Language: | English |
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The Journal of Infection in Developing Countries
2020-03-01
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| Series: | Journal of Infection in Developing Countries |
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| Online Access: | https://jidc.org/index.php/journal/article/view/12585 |
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| author | Rui Huang Miao Liu Yongmei Ding |
| author_facet | Rui Huang Miao Liu Yongmei Ding |
| author_sort | Rui Huang |
| collection | DOAJ |
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Currently, the outbreak of COVID-19 is rapidly spreading especially in Wuhan city, and threatens 14 million people in central China. In the present study we applied the Moran index, a strong statistical tool, to the spatial panel to show that COVID-19 infection is spatially dependent and mainly spread from Hubei Province in Central China to neighbouring areas. Logistic model was employed according to the trend of available data, which shows the difference between Hubei Province and outside of it. We also calculated the reproduction number R0 for the range of [2.23, 2.51] via SEIR model. The measures to reduce or prevent the virus spread should be implemented, and we expect our data-driven modeling analysis providing some insights to identify and prepare for the future virus control.
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| format | Article |
| id | doaj-art-fa3d00bfa52e49c19e73b00c91a3dc6a |
| institution | DOAJ |
| issn | 1972-2680 |
| language | English |
| publishDate | 2020-03-01 |
| publisher | The Journal of Infection in Developing Countries |
| record_format | Article |
| series | Journal of Infection in Developing Countries |
| spelling | doaj-art-fa3d00bfa52e49c19e73b00c91a3dc6a2025-08-20T02:57:18ZengThe Journal of Infection in Developing CountriesJournal of Infection in Developing Countries1972-26802020-03-01140310.3855/jidc.12585Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysisRui Huang0Miao Liu1Yongmei Ding2Department of Mathematics and Statistics, College of Science, Wuhan University of Science and Technology, Wuhan, Hubei Province, ChinaDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School Boston, MA, United StatesDepartment of Mathematics and Statistics, College of Science, Wuhan University of Science and Technology, Wuhan, Hubei Province, China Currently, the outbreak of COVID-19 is rapidly spreading especially in Wuhan city, and threatens 14 million people in central China. In the present study we applied the Moran index, a strong statistical tool, to the spatial panel to show that COVID-19 infection is spatially dependent and mainly spread from Hubei Province in Central China to neighbouring areas. Logistic model was employed according to the trend of available data, which shows the difference between Hubei Province and outside of it. We also calculated the reproduction number R0 for the range of [2.23, 2.51] via SEIR model. The measures to reduce or prevent the virus spread should be implemented, and we expect our data-driven modeling analysis providing some insights to identify and prepare for the future virus control. https://jidc.org/index.php/journal/article/view/12585COVID-19Spatial-temporal distributionLogistic modelSEIR |
| spellingShingle | Rui Huang Miao Liu Yongmei Ding Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis Journal of Infection in Developing Countries COVID-19 Spatial-temporal distribution Logistic model SEIR |
| title | Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis |
| title_full | Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis |
| title_fullStr | Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis |
| title_full_unstemmed | Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis |
| title_short | Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis |
| title_sort | spatial temporal distribution of covid 19 in china and its prediction a data driven modeling analysis |
| topic | COVID-19 Spatial-temporal distribution Logistic model SEIR |
| url | https://jidc.org/index.php/journal/article/view/12585 |
| work_keys_str_mv | AT ruihuang spatialtemporaldistributionofcovid19inchinaanditspredictionadatadrivenmodelinganalysis AT miaoliu spatialtemporaldistributionofcovid19inchinaanditspredictionadatadrivenmodelinganalysis AT yongmeiding spatialtemporaldistributionofcovid19inchinaanditspredictionadatadrivenmodelinganalysis |