{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T06:09:39Z","timestamp":1789193379904,"version":"build-2803163510"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Hemorrhagic fever with renal syndrome (HFRS) is seriously endemic in China with 70%~90% of the notified cases worldwide and showing an epidemic tendency of upturn in recent years. Early detection for its future epidemic trends plays a pivotal role in combating this threat. In this scenario, our study investigates the suitability for application in analyzing and forecasting the epidemic tendencies based on the monthly HFRS morbidity data from 2005 through 2019 using the nonlinear model-based self-exciting threshold autoregressive (SETAR) and logistic smooth transition autoregressive (LSTAR) methods. The experimental results manifested that the SETAR and LSTAR approaches presented smaller values among the performance measures in both two forecasting subsamples, when compared with the most extensively used seasonal autoregressive integrated moving average (SARIMA) method, and the former slightly outperformed the latter. Descriptive statistics showed an epidemic tendency of downturn with average annual percent change (AAPC) of \u22125.640% in overall HFRS, however, an upward trend with an AAPC\u2009=\u20091.213% was observed since 2016 and according to the forecasts using the SETAR, it would seemingly experience an outbreak of HFRS in China in December 2019. Remarkably, there were dual-peak patterns in HFRS incidence with a strong one occurring in November until January of the following year, additionally, a weak one in May and June annually. Therefore, the SETAR and LSTAR approaches may be a potential useful tool in analyzing the temporal behaviors of HFRS in China.<\/jats:p>","DOI":"10.1038\/s41598-020-66758-4","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T10:02:44Z","timestamp":1592215364000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Time series analysis of temporal trends in hemorrhagic fever with renal syndrome morbidity rate in China from 2005 to 2019"],"prefix":"10.1038","volume":"10","author":[{"given":"Yongbin","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunjie","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weidong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingchao","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihui","family":"Gui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanqiao","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,6,15]]},"reference":[{"key":"66758_CR1","doi-asserted-by":"publisher","first-page":"e0003530","DOI":"10.1371\/journal.pntd.0003530","volume":"9","author":"HY Tian","year":"2015","unstructured":"Tian, H. Y. et al. Changes in rodent abundance and weather conditions potentially drive hemorrhagic fever with renal syndrome outbreaks in Xi\u2019an, China, 2005-2012. PLoS Neglected Tropical Diseases 9, e0003530, https:\/\/doi.org\/10.1371\/journal.pntd.0003530 (2015).","journal-title":"PLoS Neglected Tropical Diseases"},{"key":"66758_CR2","doi-asserted-by":"publisher","first-page":"1850030","DOI":"10.1142\/s1793524518500304","volume":"11","author":"L Li","year":"2018","unstructured":"Li, L. et al. Hemorrhagic fever with renal syndrome in China: Mechanisms on two distinct annual peaks and control measures. International Journal of Biomathematics 11, 1850030, https:\/\/doi.org\/10.1142\/s1793524518500304 (2018).","journal-title":"International Journal of Biomathematics"},{"key":"66758_CR3","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2334-11-218","volume":"11","author":"Q Liu","year":"2011","unstructured":"Liu, Q., Liu, X., Jiang, B. & Yang, W. Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model. BMC Infectious Diseases 11, 218, https:\/\/doi.org\/10.1186\/1471-2334-11-218 (2011).","journal-title":"BMC Infectious Diseases"},{"key":"66758_CR4","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.4269\/ajtmh.12-0473","volume":"89","author":"CP Li","year":"2013","unstructured":"Li, C. P. et al. Association between hemorrhagic fever with renal syndrome epidemic and climate factors in Heilongjiang Province, China. American Journal of Tropical Medicine and Hygiene 89, 1006\u20131012, https:\/\/doi.org\/10.4269\/ajtmh.12-0473 (2013).","journal-title":"American Journal of Tropical Medicine and Hygiene"},{"key":"66758_CR5","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.1017\/s0950268818002030","volume":"146","author":"L Sun","year":"2018","unstructured":"Sun, L. & Zou, L. X. Spatiotemporal analysis and forecasting model of hemorrhagic fever with renal syndrome in mainland China. Epidemiology and Infection 146, 1680\u20131688, https:\/\/doi.org\/10.1017\/s0950268818002030 (2018).","journal-title":"Epidemiology and Infection"},{"key":"66758_CR6","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.3201\/eid1309.061481","volume":"13","author":"L Yan","year":"2007","unstructured":"Yan, L. et al. Landscape elements and Hantaan virus-related hemorrhagic fever with renal syndrome, People\u2019s Republic of China. Emerging Infectious Diseases 13, 1301\u20131306, https:\/\/doi.org\/10.3201\/eid1309.061481 (2007).","journal-title":"Emerging Infectious Diseases"},{"key":"66758_CR7","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2334-11-331","volume":"11","author":"X Liu","year":"2011","unstructured":"Liu, X., Jiang, B., Gu, W. & Liu, Q. Temporal trend and climate factors of hemorrhagic fever with renal syndrome epidemic in Shenyang City, China. BMC Infectious Diseases 11, 331, https:\/\/doi.org\/10.1186\/1471-2334-11-331 (2011).","journal-title":"BMC Infectious Diseases"},{"key":"66758_CR8","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2334-14-384","volume":"14","author":"S Zhang","year":"2014","unstructured":"Zhang, S. et al. Epidemic characteristics of hemorrhagic fever with renal syndrome in China, 2006-2012. BMC Infectious Diseases 14, 384, https:\/\/doi.org\/10.1186\/1471-2334-14-384 (2014).","journal-title":"BMC Infectious Diseases"},{"key":"66758_CR9","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-18819-4","volume":"8","author":"W Liang","year":"2018","unstructured":"Liang, W. et al. Mapping the epidemic changes and risks of hemorrhagic fever with renal syndrome in Shaanxi Province, China, 2005-2016. Scientific Reports 8, 749, https:\/\/doi.org\/10.1038\/s41598-017-18819-4 (2018).","journal-title":"Scientific Reports"},{"key":"66758_CR10","doi-asserted-by":"publisher","first-page":"e789","DOI":"10.1371\/journal.pntd.0000789","volume":"4","author":"LQ Fang","year":"2010","unstructured":"Fang, L. Q. et al. Spatiotemporal trends and climatic factors of hemorrhagic fever with renal syndrome epidemic in Shandong Province, China. PLoS Neglected Tropical Diseases 4, e789, https:\/\/doi.org\/10.1371\/journal.pntd.0000789 (2010).","journal-title":"PLoS Neglected Tropical Diseases"},{"key":"66758_CR11","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1038\/emi.2017.92","volume":"6","author":"F Jiang","year":"2017","unstructured":"Jiang, F. et al. Meteorological factors affect the epidemiology of hemorrhagic fever with renal syndrome via altering the breeding and hantavirus-carrying states of rodents and mites: a 9 years\u2019 longitudinal study. Emerging Microbes & Infections 6, e104, https:\/\/doi.org\/10.1038\/emi.2017.92 (2017).","journal-title":"Emerging Microbes & Infections"},{"key":"66758_CR12","doi-asserted-by":"publisher","first-page":"e0006604","DOI":"10.1371\/journal.pntd.0006604","volume":"12","author":"Y Wei","year":"2018","unstructured":"Wei, Y. et al. Meteorological factors and risk of hemorrhagic fever with renal syndrome in Guangzhou, southern China, 2006-2015. PLoS Neglected Tropical Diseases 12, e0006604, https:\/\/doi.org\/10.1371\/journal.pntd.0006604 (2018).","journal-title":"PLoS Neglected Tropical Diseases"},{"key":"66758_CR13","doi-asserted-by":"publisher","first-page":"e3344","DOI":"10.1371\/journal.pntd.0003344","volume":"8","author":"WY Zhang","year":"2014","unstructured":"Zhang, W. Y. et al. Spatiotemporal transmission dynamics of hemorrhagic fever with renal syndrome in China, 2005-2012. PLoS Neglected Tropical Diseases 8, e3344, https:\/\/doi.org\/10.1371\/journal.pntd.0003344 (2014).","journal-title":"PLoS Neglected Tropical Diseases"},{"key":"66758_CR14","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s00484-019-01796-w","volume":"64","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y. et al. Using big data to predict pertussis infections in Jinan city, China: a time series analysis. International Journal of Biometeorology 64, 95\u2013104, https:\/\/doi.org\/10.1007\/s00484-019-01796-w (2019).","journal-title":"International Journal of Biometeorology"},{"key":"66758_CR15","doi-asserted-by":"publisher","first-page":"e024409","DOI":"10.1136\/bmjopen-2018-024409","volume":"9","author":"Y Wang","year":"2019","unstructured":"Wang, Y. et al. Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model. BMJ Open 9, e024409, https:\/\/doi.org\/10.1136\/bmjopen-2018-024409 (2019).","journal-title":"BMJ Open"},{"key":"66758_CR16","doi-asserted-by":"publisher","unstructured":"Cong, J., Ren, M., Xie, S. & Wang, P. Predicting Seasonal Influenza Based on SARIMA Model, in Mainland China from 2005 to 2018. International Journal of Environmental Research and Public Health 16, https:\/\/doi.org\/10.3390\/ijerph16234760 (2019).","DOI":"10.3390\/ijerph16234760"},{"key":"66758_CR17","doi-asserted-by":"publisher","first-page":"e0149401","DOI":"10.1371\/journal.pone.0149401","volume":"11","author":"X Zhang","year":"2016","unstructured":"Zhang, X. et al. Time Series Modelling of Syphilis Incidence in China from 2005 to 2012. PloS One 11, e0149401, https:\/\/doi.org\/10.1371\/journal.pone.0149401 (2016).","journal-title":"PloS One"},{"key":"66758_CR18","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/S0305-0548(99)00123-9","volume":"28","author":"GP Zhang","year":"2001","unstructured":"Zhang, G. P., Patuwo, B. E. & Hu, M. Y. A simulation study of artificial neural networks for nonlinear time-series forecasting. Computers & Operations Research 28, 381\u2013396 (2001).","journal-title":"Computers & Operations Research"},{"key":"66758_CR19","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-019-4028-x","volume":"19","author":"W Wu","year":"2019","unstructured":"Wu, W., An, S. Y., Guan, P., Huang, D. S. & Zhou, B. S. Time series analysis of human brucellosis in mainland China by using Elman and Jordan recurrent neural networks. BMC Infectious Diseases 19, 414, https:\/\/doi.org\/10.1186\/s12879-019-4028-x (2019).","journal-title":"BMC Infectious Diseases"},{"key":"66758_CR20","doi-asserted-by":"publisher","first-page":"e88075","DOI":"10.1371\/journal.pone.0088075","volume":"9","author":"X Zhang","year":"2014","unstructured":"Zhang, X., Zhang, T., Young, A. A. & Li, X. Applications and comparisons of four time series models in epidemiological surveillance data. PloS One 9, e88075, https:\/\/doi.org\/10.1371\/journal.pone.0088075 (2014).","journal-title":"PloS One"},{"key":"66758_CR21","doi-asserted-by":"publisher","first-page":"e0116832","DOI":"10.1371\/journal.pone.0116832","volume":"10","author":"YL Zheng","year":"2015","unstructured":"Zheng, Y. L., Zhang, L. P., Zhang, X. L., Wang, K. & Zheng, Y. J. Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China. PloS One 10, e0116832, https:\/\/doi.org\/10.1371\/journal.pone.0116832 (2015).","journal-title":"PloS One"},{"key":"66758_CR22","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-33165-9","volume":"8","author":"Y Wang","year":"2018","unstructured":"Wang, Y. et al. Temporal trends analysis of human brucellosis incidence in mainland China from 2004 to 2018. Scientific Reports 8, 15901, https:\/\/doi.org\/10.1038\/s41598-018-33165-9 (2018).","journal-title":"Scientific Reports"},{"key":"66758_CR23","unstructured":"National Health Commission of the people\u2019s Republic of China. Diagnostic criteria for epidemic hemorrhagic fever, http:\/\/www.nhc.gov.cn\/wjw\/s9491\/200802\/39043.shtml. Accessed on 2020 May 5."},{"key":"66758_CR24","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.2147\/idr.s190418","volume":"12","author":"Z Li","year":"2019","unstructured":"Li, Z. et al. Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population. Infection and Drug Resistance 12, 1011\u20131020, https:\/\/doi.org\/10.2147\/idr.s190418 (2019).","journal-title":"Infection and Drug Resistance"},{"key":"66758_CR25","unstructured":"Eviews Help: Discrete Threshold Regression, http:\/\/www.eviews.com\/help\/helpintro.html#page\/content\/tar-Discrete_Threshold_Regression.html. Accessed on 2019 December 14."},{"key":"66758_CR26","doi-asserted-by":"crossref","unstructured":"Feng, H. & Liu, J. A SETAR model for Canadian GDP: non-linearities and forecast comparisons. Applied Economics 35, 1957\u20131964.","DOI":"10.1080\/0003684032000160674"},{"key":"66758_CR27","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Rodr\u00edguez, J. V., Torra, S. & Andrada-F\u00e9lix, J. STAR and ANN models: forecasting performance on the Spanish \u201cIbex-35\u201d stock index. Journal of Empirical Finance 12, 1\u201339.","DOI":"10.1016\/j.jempfin.2004.03.001"},{"key":"66758_CR28","doi-asserted-by":"crossref","unstructured":"Boero. The performance of SETAR models: a regime conditional evaluation of point, interval and density forecasts. International Journal of Forecasting 20, 305\u2013320.","DOI":"10.1016\/j.ijforecast.2003.09.011"},{"key":"66758_CR29","unstructured":"tsDyn: Nonlinear Time Series Models with Regime Switching, http:\/\/cran.rapporter.net\/web\/packages\/tsDyn\/index.html. Accessed on 2019 December 14."},{"key":"66758_CR30","doi-asserted-by":"publisher","unstructured":"Gutierrez, A. N. Detecting Nonlinear Dynamics Using BDS Test and Surrogate Data in Financial Time Series. Journal of Mathematics and System Science 9, https:\/\/doi.org\/10.17265\/2159-5291\/2019.02.002 (2019).","DOI":"10.17265\/2159-5291\/2019.02.002"},{"key":"66758_CR31","doi-asserted-by":"publisher","first-page":"1438","DOI":"10.1016\/j.energy.2009.04.026","volume":"34","author":"HT Pao","year":"2009","unstructured":"Pao, H. T. Forecasting energy consumption in Taiwan using hybrid nonlinear models. Energy 34, 1438\u20131446, https:\/\/doi.org\/10.1016\/j.energy.2009.04.026 (2009).","journal-title":"Energy"},{"key":"66758_CR32","doi-asserted-by":"publisher","first-page":"2311","DOI":"10.2147\/idr.s207809","volume":"12","author":"Q Liu","year":"2019","unstructured":"Liu, Q. et al. Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses. Infection and Drug Resistance 12, 2311\u20132322, https:\/\/doi.org\/10.2147\/idr.s207809 (2019).","journal-title":"Infection and Drug Resistance"},{"key":"66758_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-52044-5","volume":"9","author":"T Fu","year":"2019","unstructured":"Fu, T. et al. Development and comparison of forecast models of hand-foot-mouth disease with meteorological factors. Scientific Reports 9, 15691, https:\/\/doi.org\/10.1038\/s41598-019-52044-5 (2019).","journal-title":"Scientific Reports"},{"key":"66758_CR34","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1098\/rsif.2007.1292","volume":"5","author":"S Cauchemez","year":"2008","unstructured":"Cauchemez, S. & Ferguson, N. M. Likelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London. Journal of the Royal Society Interface 5, 885\u2013897, https:\/\/doi.org\/10.1098\/rsif.2007.1292 (2008).","journal-title":"Journal of the Royal Society Interface"},{"key":"66758_CR35","doi-asserted-by":"publisher","first-page":"e0185528","DOI":"10.1371\/journal.pone.0185528","volume":"12","author":"AD Becker","year":"2017","unstructured":"Becker, A. D. & Grenfell, B. T. tsiR: An R package for time-series Susceptible-Infected-Recovered models of epidemics. PloS One 12, e0185528, https:\/\/doi.org\/10.1371\/journal.pone.0185528 (2017).","journal-title":"PloS One"},{"key":"66758_CR36","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/s0140-6736(20)30260-9","volume":"395","author":"JT Wu","year":"2020","unstructured":"Wu, J. T., Leung, K. & Leung, G. M. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet 395, 689\u2013697, https:\/\/doi.org\/10.1016\/s0140-6736(20)30260-9 (2020).","journal-title":"Lancet"},{"key":"66758_CR37","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.2147\/idr.s169820","volume":"11","author":"YH Cheng","year":"2018","unstructured":"Cheng, Y. H. et al. Assessing health burden risk and control effect on dengue fever infection in the southern region of Taiwan. Infection and Drug Resistance 11, 1423\u20131435, https:\/\/doi.org\/10.2147\/idr.s169820 (2018).","journal-title":"Infection and Drug Resistance"},{"key":"66758_CR38","doi-asserted-by":"publisher","first-page":"20150468","DOI":"10.1098\/rsif.2015.0468","volume":"12","author":"MU Kraemer","year":"2015","unstructured":"Kraemer, M. U. et al. Big city, small world: density, contact rates, and transmission of dengue across Pakistan. Journal of the Royal Society Interface 12, 20150468, https:\/\/doi.org\/10.1098\/rsif.2015.0468 (2015).","journal-title":"Journal of the Royal Society Interface"},{"key":"66758_CR39","doi-asserted-by":"publisher","first-page":"e1002136","DOI":"10.1371\/journal.pcbi.1002136","volume":"7","author":"DA Rasmussen","year":"2011","unstructured":"Rasmussen, D. A., Ratmann, O. & Koelle, K. Inference for nonlinear epidemiological models using genealogies and time series. PLoS Computational Biology 7, e1002136, https:\/\/doi.org\/10.1371\/journal.pcbi.1002136 (2011).","journal-title":"PLoS Computational Biology"},{"key":"66758_CR40","doi-asserted-by":"publisher","DOI":"10.1038\/srep07838","volume":"5","author":"Y Xiao","year":"2015","unstructured":"Xiao, Y., Tang, S. & Wu, J. Media impact switching surface during an infectious disease outbreak. Scientific Reports 5, 7838, https:\/\/doi.org\/10.1038\/srep07838 (2015).","journal-title":"Scientific Reports"},{"key":"66758_CR41","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/j.amc.2014.08.028","volume":"246","author":"G-Q Sun","year":"2014","unstructured":"Sun, G.-Q. & Zhang, Z.-K. Global stability for a sheep brucellosis model with immigration. Applied Mathematics and Computation 246, 336\u2013345, https:\/\/doi.org\/10.1016\/j.amc.2014.08.028 (2014).","journal-title":"Applied Mathematics and Computation"},{"key":"66758_CR42","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-017-2506-6","volume":"17","author":"YP Joshi","year":"2017","unstructured":"Joshi, Y. P., Kim, E. H. & Cheong, H. K. The influence of climatic factors on the development of hemorrhagic fever with renal syndrome and leptospirosis during the peak season in Korea: an ecologic study. BMC Infectious Diseases 17, 406, https:\/\/doi.org\/10.1186\/s12879-017-2506-6 (2017).","journal-title":"BMC Infectious Diseases"},{"key":"66758_CR43","doi-asserted-by":"publisher","first-page":"1249","DOI":"10.1016\/j.scitotenv.2018.04.407","volume":"636","author":"J Xiang","year":"2018","unstructured":"Xiang, J. et al. Impact of meteorological factors on hemorrhagic fever with renal syndrome in 19 cities in China, 2005-2014. Science of the Total Environment 636, 1249\u20131256, https:\/\/doi.org\/10.1016\/j.scitotenv.2018.04.407 (2018).","journal-title":"Science of the Total Environment"},{"key":"66758_CR44","doi-asserted-by":"publisher","first-page":"e92700","DOI":"10.1371\/journal.pone.0092700","volume":"9","author":"YH Zhang","year":"2014","unstructured":"Zhang, Y. H. et al. The epidemic characteristics and changing trend of hemorrhagic fever with renal syndrome in Hubei Province, China. PloS One 9, e92700, https:\/\/doi.org\/10.1371\/journal.pone.0092700 (2014).","journal-title":"PloS One"},{"key":"66758_CR45","doi-asserted-by":"publisher","first-page":"e2260","DOI":"10.1371\/journal.pntd.0002260","volume":"7","author":"H Xiao","year":"2013","unstructured":"Xiao, H. et al. Atmospheric moisture variability and transmission of hemorrhagic fever with renal syndrome in Changsha City, Mainland China, 1991-2010. PLoS Neglected Tropical Diseases 7, e2260, https:\/\/doi.org\/10.1371\/journal.pntd.0002260 (2013).","journal-title":"PLoS Neglected Tropical Diseases"},{"key":"66758_CR46","doi-asserted-by":"publisher","first-page":"274","DOI":"10.3201\/eid2202.151516","volume":"22","author":"L Wang","year":"2016","unstructured":"Wang, L. et al. Hemorrhagic Fever with Renal Syndrome, Zibo City, China, 2006-2014. Emerging Infectious Diseases 22, 274\u2013276, doi:10.3201eid\/2202.151516 (2016).","journal-title":"Emerging Infectious Diseases"},{"key":"66758_CR47","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-28610-8","volume":"8","author":"H Wu","year":"2018","unstructured":"Wu, H. et al. Spatial-temporal characteristics and the epidemiology of haemorrhagic fever with renal syndrome from 2007 to 2016 in Zhejiang Province, China. Scientific Reports 8, 10244, https:\/\/doi.org\/10.1038\/s41598-018-28610-8 (2018).","journal-title":"Scientific Reports"},{"key":"66758_CR48","doi-asserted-by":"publisher","first-page":"427","DOI":"10.7589\/0090-3558-47.2.427","volume":"47","author":"J Ryou","year":"2011","unstructured":"Ryou, J. et al. Prevalence of hantavirus infection in wild rodents from five provinces in Korea, 2007. Journal of Wildlife Diseases 47, 427\u2013432, https:\/\/doi.org\/10.7589\/0090-3558-47.2.427 (2011).","journal-title":"Journal of Wildlife Diseases"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-66758-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-66758-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-66758-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T12:39:51Z","timestamp":1670330391000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-66758-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,15]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["66758"],"URL":"https:\/\/doi.org\/10.1038\/s41598-020-66758-4","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,15]]},"assertion":[{"value":"14 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"9609"}}