Evidence map›Paper›PMID 40032911›Full record

ArticleScientific reports2025

Spatial interpolation and spatiotemporal scanning analysis of human brucellosis in mainland China from 2012 to 2018.

Yuan Zhao, Dongfeng Pan, Yanfang Zhang, Lixu Ma, Hong Li, Jingjing Li, Shanghong Liu, Peifeng Liang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

8 authors.

Yuan ZhaoPublic Health Center, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, 750004, China.
Dongfeng PanDepartment of Emergency, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, 750004, China.
Yanfang ZhangPublic Health Center, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, 750004, China.
Lixu MaPublic Health Center, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, 750004, China.
Hong LiPublic Health Center, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, 750004, China.
Jingjing LiPublic Health Center, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, 750004, China.
Shanghong LiuPublic Health Center, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, 750004, China.
Peifeng LiangPublic Health Center, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, 750004, China. doctor_pf@126.com.

Funding

Basic research project of Northwest University for Nationalities 31920180088Ningxia Provincial Natural Science Foundation NO. NZ17187
6 · The paper itself

Abstract

Despite the considerable efforts made to address the issue of brucellosis worldwide, its prevalence in dairy products continues to be difficult to estimate and represents a significant public health concern globally. The aim of the present study was to gain a better understanding of the epidemiology of this disease in mainland China. We set out to investigate the annual spatial distribution and potential hotspots of the disease. Data on the incidence rate of brucellosis from 2012 to 2018 was obtained from the China Disease Control and Prevention Information System (CDCIS). ArcGIS 10.6 software was employed to perform kriging interpolation analysis and to create a predictive distribution map for brucellosis. Additionally, SaTScan software was utilized to conduct spatial-temporal scanning analysis to identify potential spatial-temporal changes in the incidence rate of brucellosis in China. There is a seasonal trend in the incidence of brucellosis in China, with higher rates observed during the warm season, particularly peaking in May. The results of the exploratory analysis of kriging data indicate that the average incidence map, generated using the second-order Gaussian semi-variance model with log-kriging interpolation, demonstrates the highest accuracy. Spatial and temporal clustering analyses reveal a primary clustering area centered in Heilongjiang, along with three secondary clustering areas located in Tibet, Shanxi, and Hubei. Additionally, the predictive distribution map for brucellosis in China, along with the analysis of the scanning statistic, indicates that the high-incidence area is situated in the northwest region of mainland China, although there is a noticeable trend of shifting towards the south. There are distinct spatial patterns of brucellosis in China. In high-incidence areas, it is essential to allocate additional resources for prevention and control to effectively contain the spread of brucellosis epidemics. In low-incidence areas, it is vital to promptly identify favorable factors that can help mitigate the occurrence of brucellosis.

Indexed as

BrucellosisChinaCluster AnalysisHumansIncidenceSeasonsSpatio-Temporal AnalysisChinaDynamic seriesHuman brucellosisKriging interpolationSpatial-temporal scanning

Identifiers

PMID40032911
PMCPMC11876684

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.