Evidence map›Paper›PMID 41734223›Full record

ArticlePLoS neglected tropical diseases2026

Spatiotemporal pattern of hemorrhagic fever with renal syndrome and driving factors in Shandong Province of China, 2018-2024.

Qing Duan, Yinlong Li, Suying Guo, Shiyi Huang, Yan Li, Yuwei Zhang, Ruixiao Li, Shan Lv, Shizhu Li, Jing Xu and 2 more

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Qing DuanNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Chinese Center for Tropical Diseases Research, NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China.ORCID https://orcid.org/0000-0002-0213-2715
Yinlong LiNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Chinese Center for Tropical Diseases Research, NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China.
Suying GuoNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Chinese Center for Tropical Diseases Research, NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China.
Shiyi HuangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Chinese Center for Tropical Diseases Research, NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China.
Yan LiShandong Provincial Center for Disease Control and Prevention, Jinan, China.
Yuwei ZhangShandong Provincial Center for Disease Control and Prevention, Jinan, China.
Ruixiao LiShandong Provincial Center for Disease Control and Prevention, Jinan, China.
Shan LvNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Chinese Center for Tropical Diseases Research, NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China.
Shizhu LiNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Chinese Center for Tropical Diseases Research, NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China.
Jing XuNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Chinese Center for Tropical Diseases Research, NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China.ORCID https://orcid.org/0009-0002-0258-951X
Zengqiang KouShandong Provincial Center for Disease Control and Prevention, Jinan, China.
Ti LiuShandong Provincial Center for Disease Control and Prevention, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHemorrhagic fever with renal syndrome (HFRS) is a widespread zoonotic disease transmitted by rodents, posing a serious public health threat in People's Republic of China. Due to the higher incidence of HFRS occurred in Shandong Province, this study aims to understand the spatiotemporal pattern of HFRS, identify the driving factors and predict potential high-risk areas in Shandong Province, to provide guidance for public health policy making.

methodsCase information on HFRS occurred in Shandong Province from 2018 to 2024 was collected from the China Information System for Disease Control and Prevention (CISDCP). Incidence rate of HFRS was calculated monthly and annually to explore its preliminary distribution trend. Spatiotemporal scanning analysis was used to determine the temporal and spatial clustering characteristics of HFRS cases. The maximum entropy (MaxEnt) model was employed to explore the major factors influencing HFRS and predict high-risk areas of HFRS in Shandong Province.

resultsFrom 2018 to 2024, a total of 4,837 cases of HFRS were reported in Shandong Province, with the incidence rate showing a fluctuating downward trend. The peak incidence period occurred annually from October to December. Spatiotemporal scanning analysis showed the first cluster involved 29 counties across 5 prefecture-level cities in eastern Shandong Province, spanning October to November 2018. The second cluster involved 16 counties across 6 prefecture-level cities in central Shandong Province, spanning November to December 2021. The third cluster area involved 4 counties across 2 prefecture-level cities in southwestern Shandong Province, spanning March to April 2018. The fourth cluster area was located in Shanghe County, north of Jinan City, spanning November to December 2021. When optimizing the MaxEnt model, the optimal performance was achieved with the feature class (FC) set to linear, quadratic, hinge, product, and threshold (LQHPT) and the regularization multiplier (RM) set to 0.2. Yearly average air temperature, normalized difference vegetation index, yearly average relative humidity and yearly average sunshine duration were identified as the main factors influencing the occurrence of HFRS. The risk prediction map showed that high-risk areas for HFRS were primarily concentrated in the eastern and central regions of Shandong Province, covering an area of 26682.92 square kilometers, accounting for 16.90% of the province's total area.

conclusionHFRS in Shandong Province exhibited obvious spatiotemporal patterns and was influenced by multiple factors, including temperature, vegetation, humidity and sunshine. These findings highlight the need for health authorities to integrate environmental and socio-economic considerations into the design of strategy or countermeasures against HFRS, particularly during high-incidence season and in high-risk areas.

Indexed as

Hemorrhagic Fever with Renal SyndromeAnimalsChinaHumansIncidenceRisk FactorsSeasonsSpatio-Temporal Analysis

Identifiers

PMID41734223
PMCPMC12948312

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