Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability
Hand, foot, and mouth disease (HFMD) is a significant public health issue in China and an accurate prediction of epidemic can improve the effectiveness of HFMD control. This study aims to develop a weather-based forecasting model for HFMD using the information on climatic variables and HFMD surveill...
Gespeichert in:
| Veröffentlicht in: | International journal of biometeorology 2018-04, Vol.62 (4), p.565-574 |
|---|---|
| Hauptverfasser: | , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 574 |
|---|---|
| container_issue | 4 |
| container_start_page | 565 |
| container_title | International journal of biometeorology |
| container_volume | 62 |
| creator | Liu, Sijun Chen, Jiaping Wang, Jianming Wu, Zhuchao Wu, Weihua Xu, Zhiwei Hu, Wenbiao Xu, Fei Tong, Shilu Shen, Hongbing |
| description | Hand, foot, and mouth disease (HFMD) is a significant public health issue in China and an accurate prediction of epidemic can improve the effectiveness of HFMD control. This study aims to develop a weather-based forecasting model for HFMD using the information on climatic variables and HFMD surveillance in Nanjing, China. Daily data on HFMD cases and meteorological variables between 2010 and 2015 were acquired from the Nanjing Center for Disease Control and Prevention, and China Meteorological Data Sharing Service System, respectively. A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed and validated by dividing HFMD infection data into two datasets: the data from 2010 to 2013 were used to construct a model and those from 2014 to 2015 were used to validate it. Moreover, we used weekly prediction for the data between 1 January 2014 and 31 December 2015 and leave-1-week-out prediction was used to validate the performance of model prediction. SARIMA (2,0,0)
52
associated with the average temperature at lag of 1 week appeared to be the best model (
R
2
= 0.936, BIC = 8.465), which also showed non-significant autocorrelations in the residuals of the model. In the validation of the constructed model, the predicted values matched the observed values reasonably well between 2014 and 2015. There was a high agreement rate between the predicted values and the observed values (sensitivity 80%, specificity 96.63%). This study suggests that the SARIMA model with average temperature could be used as an important tool for early detection and prediction of HFMD outbreaks in Nanjing, China. |
| doi_str_mv | 10.1007/s00484-017-1465-3 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1958536456</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2694748470</sourcerecordid><originalsourceid>FETCH-LOGICAL-c466t-89ec419810a32c89d48e13db6f47cffab0c1e863f4ddfbbbf02b30529d4fead53</originalsourceid><addsrcrecordid>eNp9kcFqFTEUhoMo9rb6AG4k4MbFjZ5kMpmMu3KxKhR1oeuQTE56c52Z1GRGKfjwptyqIOgqgfP9_wn5CHnC4QUH6F4WAKklA94xLlXLmntkw2UjGBetvE82AAJYx4U-IaelHKBmtOoekhPRg1agxYb8-JjRx2GJ8xVd9kjTuriM9gtNge7t7Lc0pLRsab3SqQ731MeCtiCNM31v50MNbuluH2f7ilq6xAlZwRyxVNzjSF1lPU0z_Y62Lsj0m83RujjG5eYReRDsWPDx3XlGPl-8_rR7yy4_vHm3O79kg1RqYbrHQfJec7CNGHTvpUbeeKeC7IYQrIOBo1ZNkN4H51wA4RpoRQUDWt82Z-T5sfc6p68rlsVMsQw4jnbGtBbD-1a3jZKtquizv9BDWvNcX2eE6mVX_7uD_1LAe6F0L_tK8SM15FRKxmCuc5xsvjEczK1AcxRoqkBzK9A0NfP0rnl1E_rfiV_GKiCOQKmj-Qrzn9X_bv0JEmCldg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2019268949</pqid></control><display><type>article</type><title>Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability</title><source>SpringerLink Journals</source><creator>Liu, Sijun ; Chen, Jiaping ; Wang, Jianming ; Wu, Zhuchao ; Wu, Weihua ; Xu, Zhiwei ; Hu, Wenbiao ; Xu, Fei ; Tong, Shilu ; Shen, Hongbing</creator><creatorcontrib>Liu, Sijun ; Chen, Jiaping ; Wang, Jianming ; Wu, Zhuchao ; Wu, Weihua ; Xu, Zhiwei ; Hu, Wenbiao ; Xu, Fei ; Tong, Shilu ; Shen, Hongbing</creatorcontrib><description>Hand, foot, and mouth disease (HFMD) is a significant public health issue in China and an accurate prediction of epidemic can improve the effectiveness of HFMD control. This study aims to develop a weather-based forecasting model for HFMD using the information on climatic variables and HFMD surveillance in Nanjing, China. Daily data on HFMD cases and meteorological variables between 2010 and 2015 were acquired from the Nanjing Center for Disease Control and Prevention, and China Meteorological Data Sharing Service System, respectively. A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed and validated by dividing HFMD infection data into two datasets: the data from 2010 to 2013 were used to construct a model and those from 2014 to 2015 were used to validate it. Moreover, we used weekly prediction for the data between 1 January 2014 and 31 December 2015 and leave-1-week-out prediction was used to validate the performance of model prediction. SARIMA (2,0,0)
52
associated with the average temperature at lag of 1 week appeared to be the best model (
R
2
= 0.936, BIC = 8.465), which also showed non-significant autocorrelations in the residuals of the model. In the validation of the constructed model, the predicted values matched the observed values reasonably well between 2014 and 2015. There was a high agreement rate between the predicted values and the observed values (sensitivity 80%, specificity 96.63%). This study suggests that the SARIMA model with average temperature could be used as an important tool for early detection and prediction of HFMD outbreaks in Nanjing, China.</description><identifier>ISSN: 0020-7128</identifier><identifier>EISSN: 1432-1254</identifier><identifier>DOI: 10.1007/s00484-017-1465-3</identifier><identifier>PMID: 29086082</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Animal Physiology ; Biological and Medical Physics ; Biophysics ; Climate ; Climate change ; Data retrieval ; Disease control ; Disease prevention ; Earth and Environmental Science ; Environment ; Environmental Health ; Epidemics ; Hand-foot-and-mouth disease ; Health surveillance ; Infections ; Infectious diseases ; Mathematical models ; Meteorological data ; Meteorology ; Modelling ; Original Paper ; Outbreaks ; Plant Physiology ; Predictions ; Public health ; Rain ; Tropical diseases ; Weather forecasting</subject><ispartof>International journal of biometeorology, 2018-04, Vol.62 (4), p.565-574</ispartof><rights>ISB 2017</rights><rights>International Journal of Biometeorology is a copyright of Springer, (2017). All Rights Reserved.</rights><rights>ISB 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c466t-89ec419810a32c89d48e13db6f47cffab0c1e863f4ddfbbbf02b30529d4fead53</citedby><cites>FETCH-LOGICAL-c466t-89ec419810a32c89d48e13db6f47cffab0c1e863f4ddfbbbf02b30529d4fead53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00484-017-1465-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00484-017-1465-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29086082$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Sijun</creatorcontrib><creatorcontrib>Chen, Jiaping</creatorcontrib><creatorcontrib>Wang, Jianming</creatorcontrib><creatorcontrib>Wu, Zhuchao</creatorcontrib><creatorcontrib>Wu, Weihua</creatorcontrib><creatorcontrib>Xu, Zhiwei</creatorcontrib><creatorcontrib>Hu, Wenbiao</creatorcontrib><creatorcontrib>Xu, Fei</creatorcontrib><creatorcontrib>Tong, Shilu</creatorcontrib><creatorcontrib>Shen, Hongbing</creatorcontrib><title>Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability</title><title>International journal of biometeorology</title><addtitle>Int J Biometeorol</addtitle><addtitle>Int J Biometeorol</addtitle><description>Hand, foot, and mouth disease (HFMD) is a significant public health issue in China and an accurate prediction of epidemic can improve the effectiveness of HFMD control. This study aims to develop a weather-based forecasting model for HFMD using the information on climatic variables and HFMD surveillance in Nanjing, China. Daily data on HFMD cases and meteorological variables between 2010 and 2015 were acquired from the Nanjing Center for Disease Control and Prevention, and China Meteorological Data Sharing Service System, respectively. A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed and validated by dividing HFMD infection data into two datasets: the data from 2010 to 2013 were used to construct a model and those from 2014 to 2015 were used to validate it. Moreover, we used weekly prediction for the data between 1 January 2014 and 31 December 2015 and leave-1-week-out prediction was used to validate the performance of model prediction. SARIMA (2,0,0)
52
associated with the average temperature at lag of 1 week appeared to be the best model (
R
2
= 0.936, BIC = 8.465), which also showed non-significant autocorrelations in the residuals of the model. In the validation of the constructed model, the predicted values matched the observed values reasonably well between 2014 and 2015. There was a high agreement rate between the predicted values and the observed values (sensitivity 80%, specificity 96.63%). This study suggests that the SARIMA model with average temperature could be used as an important tool for early detection and prediction of HFMD outbreaks in Nanjing, China.</description><subject>Animal Physiology</subject><subject>Biological and Medical Physics</subject><subject>Biophysics</subject><subject>Climate</subject><subject>Climate change</subject><subject>Data retrieval</subject><subject>Disease control</subject><subject>Disease prevention</subject><subject>Earth and Environmental Science</subject><subject>Environment</subject><subject>Environmental Health</subject><subject>Epidemics</subject><subject>Hand-foot-and-mouth disease</subject><subject>Health surveillance</subject><subject>Infections</subject><subject>Infectious diseases</subject><subject>Mathematical models</subject><subject>Meteorological data</subject><subject>Meteorology</subject><subject>Modelling</subject><subject>Original Paper</subject><subject>Outbreaks</subject><subject>Plant Physiology</subject><subject>Predictions</subject><subject>Public health</subject><subject>Rain</subject><subject>Tropical diseases</subject><subject>Weather forecasting</subject><issn>0020-7128</issn><issn>1432-1254</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kcFqFTEUhoMo9rb6AG4k4MbFjZ5kMpmMu3KxKhR1oeuQTE56c52Z1GRGKfjwptyqIOgqgfP9_wn5CHnC4QUH6F4WAKklA94xLlXLmntkw2UjGBetvE82AAJYx4U-IaelHKBmtOoekhPRg1agxYb8-JjRx2GJ8xVd9kjTuriM9gtNge7t7Lc0pLRsab3SqQ731MeCtiCNM31v50MNbuluH2f7ilq6xAlZwRyxVNzjSF1lPU0z_Y62Lsj0m83RujjG5eYReRDsWPDx3XlGPl-8_rR7yy4_vHm3O79kg1RqYbrHQfJec7CNGHTvpUbeeKeC7IYQrIOBo1ZNkN4H51wA4RpoRQUDWt82Z-T5sfc6p68rlsVMsQw4jnbGtBbD-1a3jZKtquizv9BDWvNcX2eE6mVX_7uD_1LAe6F0L_tK8SM15FRKxmCuc5xsvjEczK1AcxRoqkBzK9A0NfP0rnl1E_rfiV_GKiCOQKmj-Qrzn9X_bv0JEmCldg</recordid><startdate>20180401</startdate><enddate>20180401</enddate><creator>Liu, Sijun</creator><creator>Chen, Jiaping</creator><creator>Wang, Jianming</creator><creator>Wu, Zhuchao</creator><creator>Wu, Weihua</creator><creator>Xu, Zhiwei</creator><creator>Hu, Wenbiao</creator><creator>Xu, Fei</creator><creator>Tong, Shilu</creator><creator>Shen, Hongbing</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QH</scope><scope>7TG</scope><scope>7UA</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88F</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KL.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M1Q</scope><scope>M2P</scope><scope>M7P</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>20180401</creationdate><title>Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability</title><author>Liu, Sijun ; Chen, Jiaping ; Wang, Jianming ; Wu, Zhuchao ; Wu, Weihua ; Xu, Zhiwei ; Hu, Wenbiao ; Xu, Fei ; Tong, Shilu ; Shen, Hongbing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c466t-89ec419810a32c89d48e13db6f47cffab0c1e863f4ddfbbbf02b30529d4fead53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Animal Physiology</topic><topic>Biological and Medical Physics</topic><topic>Biophysics</topic><topic>Climate</topic><topic>Climate change</topic><topic>Data retrieval</topic><topic>Disease control</topic><topic>Disease prevention</topic><topic>Earth and Environmental Science</topic><topic>Environment</topic><topic>Environmental Health</topic><topic>Epidemics</topic><topic>Hand-foot-and-mouth disease</topic><topic>Health surveillance</topic><topic>Infections</topic><topic>Infectious diseases</topic><topic>Mathematical models</topic><topic>Meteorological data</topic><topic>Meteorology</topic><topic>Modelling</topic><topic>Original Paper</topic><topic>Outbreaks</topic><topic>Plant Physiology</topic><topic>Predictions</topic><topic>Public health</topic><topic>Rain</topic><topic>Tropical diseases</topic><topic>Weather forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Sijun</creatorcontrib><creatorcontrib>Chen, Jiaping</creatorcontrib><creatorcontrib>Wang, Jianming</creatorcontrib><creatorcontrib>Wu, Zhuchao</creatorcontrib><creatorcontrib>Wu, Weihua</creatorcontrib><creatorcontrib>Xu, Zhiwei</creatorcontrib><creatorcontrib>Hu, Wenbiao</creatorcontrib><creatorcontrib>Xu, Fei</creatorcontrib><creatorcontrib>Tong, Shilu</creatorcontrib><creatorcontrib>Shen, Hongbing</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Military Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Military Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>International journal of biometeorology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Sijun</au><au>Chen, Jiaping</au><au>Wang, Jianming</au><au>Wu, Zhuchao</au><au>Wu, Weihua</au><au>Xu, Zhiwei</au><au>Hu, Wenbiao</au><au>Xu, Fei</au><au>Tong, Shilu</au><au>Shen, Hongbing</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability</atitle><jtitle>International journal of biometeorology</jtitle><stitle>Int J Biometeorol</stitle><addtitle>Int J Biometeorol</addtitle><date>2018-04-01</date><risdate>2018</risdate><volume>62</volume><issue>4</issue><spage>565</spage><epage>574</epage><pages>565-574</pages><issn>0020-7128</issn><eissn>1432-1254</eissn><abstract>Hand, foot, and mouth disease (HFMD) is a significant public health issue in China and an accurate prediction of epidemic can improve the effectiveness of HFMD control. This study aims to develop a weather-based forecasting model for HFMD using the information on climatic variables and HFMD surveillance in Nanjing, China. Daily data on HFMD cases and meteorological variables between 2010 and 2015 were acquired from the Nanjing Center for Disease Control and Prevention, and China Meteorological Data Sharing Service System, respectively. A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed and validated by dividing HFMD infection data into two datasets: the data from 2010 to 2013 were used to construct a model and those from 2014 to 2015 were used to validate it. Moreover, we used weekly prediction for the data between 1 January 2014 and 31 December 2015 and leave-1-week-out prediction was used to validate the performance of model prediction. SARIMA (2,0,0)
52
associated with the average temperature at lag of 1 week appeared to be the best model (
R
2
= 0.936, BIC = 8.465), which also showed non-significant autocorrelations in the residuals of the model. In the validation of the constructed model, the predicted values matched the observed values reasonably well between 2014 and 2015. There was a high agreement rate between the predicted values and the observed values (sensitivity 80%, specificity 96.63%). This study suggests that the SARIMA model with average temperature could be used as an important tool for early detection and prediction of HFMD outbreaks in Nanjing, China.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>29086082</pmid><doi>10.1007/s00484-017-1465-3</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0020-7128 |
| ispartof | International journal of biometeorology, 2018-04, Vol.62 (4), p.565-574 |
| issn | 0020-7128 1432-1254 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1958536456 |
| source | SpringerLink Journals |
| subjects | Animal Physiology Biological and Medical Physics Biophysics Climate Climate change Data retrieval Disease control Disease prevention Earth and Environmental Science Environment Environmental Health Epidemics Hand-foot-and-mouth disease Health surveillance Infections Infectious diseases Mathematical models Meteorological data Meteorology Modelling Original Paper Outbreaks Plant Physiology Predictions Public health Rain Tropical diseases Weather forecasting |
| title | Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T05%3A04%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Predicting%20the%20outbreak%20of%20hand,%20foot,%20and%20mouth%20disease%20in%20Nanjing,%20China:%20a%20time-series%20model%20based%20on%20weather%20variability&rft.jtitle=International%20journal%20of%20biometeorology&rft.au=Liu,%20Sijun&rft.date=2018-04-01&rft.volume=62&rft.issue=4&rft.spage=565&rft.epage=574&rft.pages=565-574&rft.issn=0020-7128&rft.eissn=1432-1254&rft_id=info:doi/10.1007/s00484-017-1465-3&rft_dat=%3Cproquest_cross%3E2694748470%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2019268949&rft_id=info:pmid/29086082&rfr_iscdi=true |