Integration of a Kalman filter in the geographically weighted regression for modeling the transmission of hand, foot and mouth disease
Abstract Background Hand, foot and mouth disease (HFMD) is a common infectious disease whose mechanism of transmission continues to remain a puzzle for researchers. The measurement and prediction of the HFMD incidence can be combined to improve the estimation accuracy, and provide a novel perspectiv...
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| Language: | English |
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BMC
2020-04-01
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| Series: | BMC Public Health |
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| Online Access: | http://link.springer.com/article/10.1186/s12889-020-08607-7 |
| _version_ | 1819228167242514432 |
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| author | Bisong Hu Wenqing Qiu Chengdong Xu Jinfeng Wang |
| author_facet | Bisong Hu Wenqing Qiu Chengdong Xu Jinfeng Wang |
| author_sort | Bisong Hu |
| collection | DOAJ |
| description | Abstract Background Hand, foot and mouth disease (HFMD) is a common infectious disease whose mechanism of transmission continues to remain a puzzle for researchers. The measurement and prediction of the HFMD incidence can be combined to improve the estimation accuracy, and provide a novel perspective to explore the spatiotemporal patterns and determinant factors of an HFMD epidemic. Methods In this study, we collected weekly HFMD incidence reports for a total of 138 districts in Shandong province, China, from May 2008 to March 2009. A Kalman filter was integrated with geographically weighted regression (GWR) to estimate the HFMD incidence. Spatiotemporal variation characteristics were explored and potential risk regions were identified, along with quantitatively evaluating the influence of meteorological and socioeconomic factors on the HFMD incidence. Results The results showed that the average error covariance of the estimated HFMD incidence by district was reduced from 0.3841 to 0.1846 compared to the measured incidence, indicating an overall improvement of over 50% in error reduction. Furthermore, three specific categories of potential risk regions of HFMD epidemics in Shandong were identified by the filter processing, with manifest filtering oscillations in the initial, local and long-term periods, respectively. Amongst meteorological and socioeconomic factors, the temperature and number of hospital beds per capita, respectively, were recognized as the dominant determinants that influence HFMD incidence variation. Conclusions The estimation accuracy of the HFMD incidence can be significantly improved by integrating a Kalman filter with GWR and the integration is effective for exploring spatiotemporal patterns and determinants of an HFMD epidemic. Our findings could help establish more accurate HFMD prevention and control strategies in Shandong. The present study demonstrates a novel approach to exploring spatiotemporal patterns and determinant factors of HFMD epidemics, and it can be easily extended to other regions and other infectious diseases similar to HFMD. |
| first_indexed | 2024-12-23T10:52:58Z |
| format | Article |
| id | doaj.art-07d76ed886d54a46bbcaf1456ad6f4ee |
| institution | Directory Open Access Journal |
| issn | 1471-2458 |
| language | English |
| last_indexed | 2024-12-23T10:52:58Z |
| publishDate | 2020-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj.art-07d76ed886d54a46bbcaf1456ad6f4ee2022-12-21T17:49:51ZengBMCBMC Public Health1471-24582020-04-0120111510.1186/s12889-020-08607-7Integration of a Kalman filter in the geographically weighted regression for modeling the transmission of hand, foot and mouth diseaseBisong Hu0Wenqing Qiu1Chengdong Xu2Jinfeng Wang3School of Geography and Environment, Jiangxi Normal UniversitySchool of Geography and Environment, Jiangxi Normal UniversityState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesAbstract Background Hand, foot and mouth disease (HFMD) is a common infectious disease whose mechanism of transmission continues to remain a puzzle for researchers. The measurement and prediction of the HFMD incidence can be combined to improve the estimation accuracy, and provide a novel perspective to explore the spatiotemporal patterns and determinant factors of an HFMD epidemic. Methods In this study, we collected weekly HFMD incidence reports for a total of 138 districts in Shandong province, China, from May 2008 to March 2009. A Kalman filter was integrated with geographically weighted regression (GWR) to estimate the HFMD incidence. Spatiotemporal variation characteristics were explored and potential risk regions were identified, along with quantitatively evaluating the influence of meteorological and socioeconomic factors on the HFMD incidence. Results The results showed that the average error covariance of the estimated HFMD incidence by district was reduced from 0.3841 to 0.1846 compared to the measured incidence, indicating an overall improvement of over 50% in error reduction. Furthermore, three specific categories of potential risk regions of HFMD epidemics in Shandong were identified by the filter processing, with manifest filtering oscillations in the initial, local and long-term periods, respectively. Amongst meteorological and socioeconomic factors, the temperature and number of hospital beds per capita, respectively, were recognized as the dominant determinants that influence HFMD incidence variation. Conclusions The estimation accuracy of the HFMD incidence can be significantly improved by integrating a Kalman filter with GWR and the integration is effective for exploring spatiotemporal patterns and determinants of an HFMD epidemic. Our findings could help establish more accurate HFMD prevention and control strategies in Shandong. The present study demonstrates a novel approach to exploring spatiotemporal patterns and determinant factors of HFMD epidemics, and it can be easily extended to other regions and other infectious diseases similar to HFMD.http://link.springer.com/article/10.1186/s12889-020-08607-7HandFoot and mouth diseaseKalman filterGeographically weighted regressionSpatiotemporal patternDeterminant factors |
| spellingShingle | Bisong Hu Wenqing Qiu Chengdong Xu Jinfeng Wang Integration of a Kalman filter in the geographically weighted regression for modeling the transmission of hand, foot and mouth disease BMC Public Health Hand Foot and mouth disease Kalman filter Geographically weighted regression Spatiotemporal pattern Determinant factors |
| title | Integration of a Kalman filter in the geographically weighted regression for modeling the transmission of hand, foot and mouth disease |
| title_full | Integration of a Kalman filter in the geographically weighted regression for modeling the transmission of hand, foot and mouth disease |
| title_fullStr | Integration of a Kalman filter in the geographically weighted regression for modeling the transmission of hand, foot and mouth disease |
| title_full_unstemmed | Integration of a Kalman filter in the geographically weighted regression for modeling the transmission of hand, foot and mouth disease |
| title_short | Integration of a Kalman filter in the geographically weighted regression for modeling the transmission of hand, foot and mouth disease |
| title_sort | integration of a kalman filter in the geographically weighted regression for modeling the transmission of hand foot and mouth disease |
| topic | Hand Foot and mouth disease Kalman filter Geographically weighted regression Spatiotemporal pattern Determinant factors |
| url | http://link.springer.com/article/10.1186/s12889-020-08607-7 |
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