{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T08:24:23Z","timestamp":1787214263366,"version":"build-2736575974"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Hemorrhagic fever with renal syndrome (HFRS) is a serious zoonotic infectious disease. To characterize the spatiotemporal epidemiological patterns of HFRS in the Jiaodong Peninsula from 2019 to 2025 and to explore the influence of meteorological factors on its spatiotemporal heterogeneity, the present study conducted relevant analyses with the aim of providing evidence for the development of scientific and effective HFRS prevention and control measures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>Data on HFRS were collected through the Chinese Center for Disease Control and Prevention Information System. Descriptive epidemiology, Joinpoint regression analysis, spatial autocorrelation analysis, spatiotemporal clustering analysis and spatiotemporal geographically weighted regression model analysis were employed to examine the incidence of HFRS in the Jiaodong Peninsula region from 2019 to 2025.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result<\/jats:title>\n                    <jats:p>\n                      From 2019 to 2025, a total of 1,492 cases of HFRS were reported in the Jiaodong Peninsula region, with an average annual incidence rate of 1.073 per 100,000 people. The peak incidence period occurs annually from October to January of the following year. The age distribution is concentrated among those aged 45\u201374, with a male-to-female ratio of 2.58:1, and the occupational distribution is dominated by farmers. Overall, the spatial distribution pattern shows higher rates in the north and west compared to the south and east. Analysis using a join-point regression model indicates that the incidence rate showed an upward trend from 2019 to 2021, with an annual percentage change (APC) of 40.70% (95% CI: \u221215.72 to 134.88%,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.103); however, this upward trend was not statistically significant. The incidence rate showed a downward trend from 2021 to 2025, with an APC of \u221213.60% (95% CI: \u221225.55 to 0.26%,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.052); this downward trend was marginally significant. Analysis of global spatial autocorrelation revealed that, with the exception of 2019 (Moran\u2019s I\u202f=\u202f0.205,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.052) and 2022 (Moran\u2019s I\u202f=\u202f0.189,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.059), the incidence of HFRS showed spatial clustering in all other years (all with\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.05). Among these, 2021 had the highest agglomeration index (Moran\u2019s I\u202f=\u202f0.438). Analysis of local spatial autocorrelation shows that \u201chigh-high\u201d clusters are primarily concentrated in the central and western parts of Yantai (around Laiyang, Haiyang, and Laizhou) and in the northern part of Qingdao (around Laixi). Spatio-temporal scanning analysis identified a total of three clusters, all concentrated in the fall and winter of 2021. The largest cluster spanned seven counties (cities and districts), primarily distributed across the inland and coastal areas of central and western Yantai (Laizhou, Zhaoyuan, Longkou, Laiyang, and Qixia) and northern Qingdao (Pingdu and Laixi). The Core Cluster scanning radius was 80.54\u202fkm. The high-incidence period was from November 1, 2021, to December 31, 2021. The Relative risk (RR)\u202f=\u202f13.56, Log-likelihood ratio ((LLR)\u202f=\u202f194.16,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.01). GTWR model results demonstrated significant spatiotemporal heterogeneity of meteorological impacts on HFRS. Temperature showed an overall positive effect with a spatial pattern of west-strong and east-weak, south-high and north-low. Precipitation was positively associated with HFRS in eastern study areas but negatively in western areas, while relative humidity showed the opposite spatial pattern. Total solar radiation and wind speed exerted consistent negative inhibitory effects across the whole region, with wind speed presenting the strongest suppression. Atmospheric pressure mainly played a negative role, with only weak positive effects in partial southern Qingdao.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>From 2019 to 2025, HFRS in the Jiaodong Peninsula region exhibited characteristics of seasonal prevalence, spatiotemporal clustering, and spatiotemporal heterogeneity. Health education should be strengthened among key populations, such as middle-aged and older male farmers, and prevention and control measures should be intensified in high-risk areas, including central-western Yantai and northern Qingdao. Future prevention and control strategies should incorporate region-specific climatic characteristics to formulate targeted interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1868358","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T07:49:10Z","timestamp":1787212150000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Epidemiological characteristics and spatiotemporal heterogeneity of hemorrhagic fever with renal syndrome in the Jiaodong Peninsula region, China, 2019\u20132025"],"prefix":"10.3389","volume":"14","author":[{"given":"Xiaofang","family":"Guo","sequence":"first","affiliation":[{"name":"College of Public Health, Shandong Second Medical University","place":["Weifang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ti","family":"Liu","sequence":"additional","affiliation":[{"name":"Shandong Center for Disease Control and Prevention","place":["Jinan, China"]},{"name":"Shandong Provincial Key Laboratory of Intelligent Monitoring, Early Warning, Prevention and Control for Infectious Diseases","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruixiao","family":"Li","sequence":"additional","affiliation":[{"name":"Shandong Center for Disease Control and Prevention","place":["Jinan, China"]},{"name":"Shandong Provincial Key Laboratory of Intelligent Monitoring, Early Warning, Prevention and Control for Infectious Diseases","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueying","family":"Tian","sequence":"additional","affiliation":[{"name":"Shandong Center for Disease Control and Prevention","place":["Jinan, China"]},{"name":"Shandong Provincial Key Laboratory of Intelligent Monitoring, Early Warning, Prevention and Control for Infectious Diseases","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shandong Center for Disease Control and Prevention","place":["Jinan, China"]},{"name":"Shandong Provincial Key Laboratory of Intelligent Monitoring, Early Warning, Prevention and Control for Infectious Diseases","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaotong","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Public Health, Shandong Second Medical University","place":["Weifang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Duan","sequence":"additional","affiliation":[{"name":"Shandong Center for Disease Control and Prevention","place":["Jinan, China"]},{"name":"Shandong Provincial Key Laboratory of Intelligent Monitoring, Early Warning, Prevention and Control for Infectious Diseases","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renpeng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Public Health, Shandong Second Medical University","place":["Weifang, China"]},{"name":"Shandong Mental Health Center, Shandong University","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zengqiang","family":"Kou","sequence":"additional","affiliation":[{"name":"College of Public Health, Shandong Second Medical University","place":["Weifang, China"]},{"name":"Shandong Center for Disease Control and Prevention","place":["Jinan, China"]},{"name":"Shandong Provincial Key Laboratory of Intelligent Monitoring, Early Warning, Prevention and Control for Infectious Diseases","place":["Jinan, 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