Geospatial Health最新文献_第2页

Mapping livestock systems, bovine and caprine diseases in Mayo-Kebbi Ouest Province, Chad. 绘制乍得西部梅奥-凯比省的牲畜系统、牛和山羊疾病地图。

IF 1 4区医学

Geospatial Health Pub Date : 2025-02-03 DOI: 10.4081/gh.2025.1365

Kella Douzouné, Joseph Oloukoi, Ismaila Toko Imorou, Toure Gorgui Ba, Derrick Chefor Ymele Demeveng

{"title":"Mapping livestock systems, bovine and caprine diseases in Mayo-Kebbi Ouest Province, Chad.","authors":"Kella Douzouné, Joseph Oloukoi, Ismaila Toko Imorou, Toure Gorgui Ba, Derrick Chefor Ymele Demeveng","doi":"10.4081/gh.2025.1365","DOIUrl":"10.4081/gh.2025.1365","url":null,"abstract":"This study aimed to compile an inventory of the main diseases affecting these species in Mayo-Kebbi Ouest Province in Chad. A survey was conducted between 6 May and 7 August 2024 using a cascade data collection method identifying 310 farmers and 19 veterinarians with an average of 10 to 12 years of experience in advising and supporting livestock practices The data collected included socio-professional characteristics of participants, livestock practices, and geospatial information. These data were managed in Excel and analysed with R. The analysis involved descriptive and inferential statistical techniques including binary logistic regression resulting in maps illustrating disease hotspots and livestock systems. Thematic maps, tables and charts with a 5% significance threshold visualised risk areas and associated livestock practices. The results show a predominance of male farmers (91.9%) from 20 different ethnic groups. The livestock systems identified include data on farming divided into extensive (14.8%), mixed (0.3%) and semi-intensive farming (84.8%). On average, farms have 41 cattle and 25 goats. Animal diseases were found to cause 29.5% reduction in herd productivity. Transhumance (p=0.000356) and animal disease incidence (p=0.03) were observed as significant risk factors associated with the abandonment of livestock farming. The main diseases recorded in cattle include contagious bovine pleuropneumonia (11.3%), bovine tuberculosis (2.5%), foot-and-mouth disease (45.0%), bluetongue (1.7%) and disease with symptoms reminiscent of rinderpest (2.5%). For goats, notable diseases include brucellosis (3.8%), lumpy skin disease (19.2%), goat plague (7.9%) and Rift Valley fever (6.3%). These findings confirm the importance of a geospatial epidemiological surveillance tool for monitoring animal diseases in this region.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143124265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial association of socioeconomic and health service factors with antibiotic self-medication in Thailand. 泰国社会经济和卫生服务因素与抗生素自我用药的空间关联

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-27 DOI: 10.4081/gh.2025.1329

Worrayot Darasawang, Wongsa Laohasiriwong, Kittipong Sornlorm, Warangkana Sungsitthisawad, Roshan Kumar Mahato

{"title":"Spatial association of socioeconomic and health service factors with antibiotic self-medication in Thailand.","authors":"Worrayot Darasawang, Wongsa Laohasiriwong, Kittipong Sornlorm, Warangkana Sungsitthisawad, Roshan Kumar Mahato","doi":"10.4081/gh.2025.1329","DOIUrl":"10.4081/gh.2025.1329","url":null,"abstract":"Antibiotic Self-Medication (ASM) is a major contributing factor to Antimicrobial Resistance (AMR) that can lead to both mortality and long-term hospitalizations. High provincial ASM proportions associated with mortality due to AMR have been observed in Thailand but there is a lack of studies on geographic factors contributing to ASM. The present study aimed to quantify the distribution of ASM in Thailand and its correlated factors. Socioeconomic and health services factors were included in the spatial analysis. Moran's I was performed to identify global autocorrelation with the significance level set at p=0.05 and spatial regression were applied to identify the factors associated with ASM, the proportion of which is predominant in the north-eastern, central and eastern regions with Phitsanulok Province reporting the highest proportion of Thailand's 77 provinces. Autocorrelation between Night-Time Light (NTL) and the proportion of ASM was observed to be statistically significant at p=0.030. The Spatial Lag Model (SLM) and the Spatial Error Model (SEM) were used with the latter providing both the lowest R2 and Akaike Information Criterion (AIC). It was demonstrated that the proportion of alcohol consumption significantly increased the proportion of ASM. The annual number of outpatient department visits and the average NTL decreased the proportion of ASM by 1.5% and 0.4%, respectively. Average monthly household expenditures also decreased the ASM proportion. Policies to control alcohol consumption while promoting healthcare visits are essential strategies to mitigate the burden of AMR in Thailand.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Socio-spatial vulnerability index of type 2 diabetes mellitus in Mexico in 2020. 2020年墨西哥2型糖尿病社会空间脆弱性指数

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-27 DOI: 10.4081/gh.2025.1348

Enríque Ibarra-Zapata, Darío Gaytán-Hernández, Yolanda Terán-Figueroa, Verónica Gallegos-García, Carmen Del Pilar Suárez-Rodríguez, Sergio Zarazúa Guzmán, Omar Parra Rodríguez

{"title":"Socio-spatial vulnerability index of type 2 diabetes mellitus in Mexico in 2020.","authors":"Enríque Ibarra-Zapata, Darío Gaytán-Hernández, Yolanda Terán-Figueroa, Verónica Gallegos-García, Carmen Del Pilar Suárez-Rodríguez, Sergio Zarazúa Guzmán, Omar Parra Rodríguez","doi":"10.4081/gh.2025.1348","DOIUrl":"10.4081/gh.2025.1348","url":null,"abstract":"This study aimed to estimate a socio-spatial vulnerability index for type 2 diabetes mellitus (T2DM) at the municipal level in Mexico for 2020. It incorporated factors such as poverty, social backwardness, marginalization index, and human development index. This retrospective ecological study analyzed 317,011 incident cases of T2DM in 2020. Utilizing multi-criteria decision analysis, weighted values were assigned to each vulnerability criterion. A multiple linear regression model was developed, complemented by cluster and outlier analyses using Moran I's and the high-low clustering method. A clustered spatial autocorrelation of high values was found across 17.65% of Mexico, which was statistically significant (p < 0.001). Conversely, 37.78% of the territory showed a pattern of low values without significant evidence of groupings. The analysis revealed 117 nodes of very high vulnerability forming six focal areas, 172 nodes with high vulnerability across five areas, 168 nodes with medium vulnerability in two areas, 112 nodes with low vulnerability across 16 areas, and 152 nodes with very low vulnerability in 24 focal areas. This method proves to be robust and offers a technical-scientific basis for guiding T2DM prevention strategies and actions using a spatial/epidemiological approach. It is recommended that future strategies take into account factors such as poverty, social backwardness, marginalization index, and human development index to be effective.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Risk discrepancies in COVID-19-related community environments based on spatiotemporal monitoring. 基于时空监测的covid -19相关社区环境风险差异分析

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-04-28 DOI: 10.4081/gh.2025.1286

Jihong Zhang, Guohua Yin, Qiuhua Zhang, Juan Fang, Duo Jiang, Chao Yang, Na Sun

{"title":"Risk discrepancies in COVID-19-related community environments based on spatiotemporal monitoring.","authors":"Jihong Zhang, Guohua Yin, Qiuhua Zhang, Juan Fang, Duo Jiang, Chao Yang, Na Sun","doi":"10.4081/gh.2025.1286","DOIUrl":"https://doi.org/10.4081/gh.2025.1286","url":null,"abstract":"The geo-inequality of COVID-19 risk has attracted a great deal of research attention. In this study, the spatial correlation between community environment and the incidence of COVID-19 cases in 30 Chinese cities is discussed. The spread of the disease is analyzed based on timing and spatial monitoring at the km2-grid level, with the use of publicly available data relating to housing prices, Gross Deomestic Product (GDP), medical facilities, consumer sites, public green spaces, and industrial sites. The results indicate substantial geographical variations in the distribution of COVID-19 communities in all 30 cities. Significant global bivariate spatial dependence was observed between the disease and housing prices (Moran's I =0.099, p<0.01, z=488.6), medical facilities (Moran's I = 0.349, p<0.01, z=1675.0), consumer sites (Moran's I =0.369, p<0.01, z=1843.4), green space (Moran's I =0.205, p<0.01, z=1037.8), and industrial sites (Moran's I =0.234, p<0.01, z=1178.6). The risk of COVID-19 under the influence of GDP is further examined for cities with per capita GDPs from high to low ranging from 1.69 to 4.62 (1.69~3.74~4.62, 95% CI). These findings provide greater detail on the interplay between the infectious disease and community environments.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144007918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sentiment analysis using a lexicon-based approach in Lisbon, Portugal. 在葡萄牙里斯本使用基于词典的方法进行情感分析。

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-04-24 DOI: 10.4081/gh.2025.1344

Iuria Betco, Ana Isabel Ribeiro, David S Vale, Luis Encalada-Abarca, Cláudia M Viana, Jorge Rocha

引用次数: 0

Environmental and geographical factors influence malaria transmission in KwaZulu-Natal province, South Africa. 环境和地理因素影响南非夸祖鲁-纳塔尔省的疟疾传播。

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-06-09 DOI: 10.4081/gh.2025.1370

Osadolor Ebhuoma, Michael Gebreslasie, Oswaldo Villena, Ali Arab

{"title":"Environmental and geographical factors influence malaria transmission in KwaZulu-Natal province, South Africa.","authors":"Osadolor Ebhuoma, Michael Gebreslasie, Oswaldo Villena, Ali Arab","doi":"10.4081/gh.2025.1370","DOIUrl":"https://doi.org/10.4081/gh.2025.1370","url":null,"abstract":"The malaria burden remains largely concentrated in sub- Saharan Africa. South Africa, a country within this region, has made significant progress toward malaria elimination. However, malaria continues to be endemic in three of its nine provinces: Limpopo, Mpumalanga, and KwaZulu-Natal (KZN), which are located in the northern part of the country and share borders with Botswana, Zimbabwe, and Mozambique. This study focuses on KZN, where district municipalities report monthly malaria cases ranging from zero to 8,981. Fitting Bayesian zero-inflated models in the INLA R package, we assessed the effects of various climate and environmental variables on malaria prevalence and spatio-temporal transmission dynamics from 2005-2014. Specifically, we analyzed precipitation, day and night land surface temperature, the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI) and elevation data for KZN local municipalities. Our findings indicate that the best model was the Zero- Inflated Negative Binomial (ZINB) and that at 95% Bayesian Credible Interval (CI), NDVI (0.74; CI (0.95, 3.87) is significantly related to malaria transmission in KZN, with the north-eastern part of the province exhibiting the highest risk of malaria transmission. Additionally, our model captured the reduction of malaria from 2005 to 2010 and the following resurgence. The modelling approach employed in this study represents a valuable tool for understanding and monitoring the influence of climate and environmental variables on the spatial heterogeneity of malaria. Also, this study reveals the need to strengthen the already existing crossborder collaborations to fortify KZN's malaria elimination goals.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The future of spatial epidemiology in the AI era: enhancing machine learning approaches with explicit spatial structure. 人工智能时代空间流行病学的未来：增强具有明确空间结构的机器学习方法。

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-06-04 DOI: 10.4081/gh.2025.1386

Nima Kianfar, Benn Sartorius, Colleen L Lau, Robert Bergquist, Behzad Kiani

{"title":"The future of spatial epidemiology in the AI era: enhancing machine learning approaches with explicit spatial structure.","authors":"Nima Kianfar, Benn Sartorius, Colleen L Lau, Robert Bergquist, Behzad Kiani","doi":"10.4081/gh.2025.1386","DOIUrl":"https://doi.org/10.4081/gh.2025.1386","url":null,"abstract":"Spatial epidemiology, defined as the study of spatial patterns in disease burdens or health outcomes, aims to estimate disease risk or incidence by identifying geographical risk factors and populations at risk (Morrison et al., 2024). Research in spatial epidemiology relies on both conventional approaches and Machine- Learning (ML) algorithms to explore geographic patterns of diseases and identify influential factors (Pfeiffer & Stevens, 2015). Traditional spatial techniques, including spatial autocorrelation using global Moran's I, Geary's C (Amgalan et al., 2022), and Ripley's K Function (Kan et al., 2022), Local Indicators of Spatial Association (LISA) (Sansuk et al., 2023), hotspot analysis by Getis-Ord Gi* (Lun et al., 2022), spatial lag models (Rey & Franklin, 2022), and Geographically Weighted Regression (GWR) (Kiani et al., 2024) are designed to explicitly incorporate the spatial structure of data into spatial modelling, often referred to as spatially aware models (Reich et al., 2021). Beyond these models, several other spatially aware approaches that have been widely applied in epidemiological studies include but are not limited to Bayesian spatial models that account for spatial uncertainty in disease mapping, such as Bayesian Hierarchical models, Conditional Autoregressive (CAR), and Besage, York, and Mollie' (BYM) models (Louzada et al., 2021). Bayesian methods are statistically rigorous techniques that assume neighboring regions share similar values. Kulldorff's Spatial Scan Statistic is another traditional spatial technique that uses a moving circular window to extract significant disease clusters (Tango, 2021). Moreover, geostatistical models such as Kriging and Inverse Distance Weighting (IDW) allow for continuous spatial interpolation of health data (Nayak et al., 2021). [...].","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dengue risk-mapping in an Amazonian locality in Colombia based on regression and multi-criteria analysis. 基于回归和多标准分析的哥伦比亚亚马逊地区登革热风险制图

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-06-04 DOI: 10.4081/gh.2025.1292

Maria Camila Lesmes, Alvaro Ávila-Díaz, Erika Santamaría, Carlos Andrés Morales, Horacio Cadena, Patricia Fuya, Nicolas Frutos, Ximena Porcasi, Catalina Marceló-Díaz

{"title":"Dengue risk-mapping in an Amazonian locality in Colombia based on regression and multi-criteria analysis.","authors":"Maria Camila Lesmes, Alvaro Ávila-Díaz, Erika Santamaría, Carlos Andrés Morales, Horacio Cadena, Patricia Fuya, Nicolas Frutos, Ximena Porcasi, Catalina Marceló-Díaz","doi":"10.4081/gh.2025.1292","DOIUrl":"10.4081/gh.2025.1292","url":null,"abstract":"The potential of dengue infection is of prime public health concern in tropical and subtropical countries. In Colombia, the management of this disease is based mainly on epidemiological monitoring and vector control. This study, covering the period 2015-2022, adds to this approach by investigating a tool that identifies dengue risk zones considering its environmental and sociodemographic determinants. For this purpose, an analytical, comparative, ecological study was carried out in three stages: i) selection of indicators associated with the occurrence of dengue through hierarchical analysis; ii) execution of a spatial-based Ordinary Least Squares (OLS) regression technique; and iii) multi-criteria analysis of the risk data obtained. Consequently, two optimal models, one for the rainy season (R2=0.5761; AIC=366.3929) and the other for the dry season (R2=0.8560; AIC=440.7557) were obtained for the Dengue Incidence Rate (DIR) during the study period mainly based on socio-demographic and environmental variables. A dengue risk map was generated, showing the impact on three neighbourhoods in the municipality of Piamonte in the Cauca Department covering both seasons. In conclusion, the dengue risk map made it possible to identify highrisk areas and also to identify the determinants of disease occurrence, which can contribute to improving disease management in tropical and subtropical regions.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial multilevel modelling male partners' influence on women's modern contraceptive use: a study in Angola and Zambia. 男性伴侣对女性现代避孕药具使用影响的空间多层次建模：安哥拉和赞比亚的研究。

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-05-12 DOI: 10.4081/gh.2025.1340

Kabeya Clement Mulamba

{"title":"Spatial multilevel modelling male partners' influence on women's modern contraceptive use: a study in Angola and Zambia.","authors":"Kabeya Clement Mulamba","doi":"10.4081/gh.2025.1340","DOIUrl":"https://doi.org/10.4081/gh.2025.1340","url":null,"abstract":"The main objective of this paper was to model the relationship between married women's contraceptive use and the influence of their male partners. The study took place in Angola and Zambia, which stems from the fact that these countries ratified the Maputo Protocol that emphasises promotion of reproductive health among women. Most previous studies investigating women's progress towards the realisation of what is advocated in this protocol have overlooked the role of the male partners. Hence, it has become imperative to reduce this gap in the literature. This paper discusses the application of spatial multilevel modelling, which incorporates two levels of information based on the nature of the data available. This approach acknowledges the hypothesis that contraceptive use is a social phenomenon occurring within the geographical space and is therefore susceptible to autocorrelation. Findings confirm that the level of influence of male partners' exertion on women's contraceptive use is dependent on the situation in the country where it takes place as shown by various study variables analysed. The results indicate that socioeconomic and education factors play a major role, a phenomenon that calls for tailor-made reproductive health policies considering these aspects.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact of climate change on dengue fever: a bibliometric analysis. 气候变化对登革热的影响：文献计量学分析。

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-02-19 DOI: 10.4081/gh.2025.1301

Mai Liu, Yin Zhang

{"title":"Impact of climate change on dengue fever: a bibliometric analysis.","authors":"Mai Liu, Yin Zhang","doi":"10.4081/gh.2025.1301","DOIUrl":"10.4081/gh.2025.1301","url":null,"abstract":"Dengue is the most widespread and fastest-growing vectorborne disease worldwide. We employed bibliometric analysis to provide an overview of research on the impact of climate change on dengue fever focusing on both global and Southeast Asian regions. Using the Web of Science Core Collection (WoSCC) database, we reviewed studies on the impact of climate change on dengue fever between 1974 and 2022 taking into account study locations and international collaboration. The VOS viewer software (https://www.vosviewer.com/) and the Bibliometrix R package (https://www.bibliometrix.org/) were used to visualise country networks and keywords. We collected 2,055 relevant articles published globally between 1974 and 2022 on the impact of climate change on dengue fever, 449 of which published in Southeast Asia. Peaking in 2021, the overall number of publications showed a strong increase in the period 2000-2022. The United States had the highest number of publications (n=558) followed by China (261) and Brazil (228). Among the Southeast Asian countries, Thailand had most publications (n=123). Global and Southeast Asian concerns about the impact of climate change on dengue fever are essentially the same. They all emphasise the relationship between temperature and other climatic conditions on the one hand and the transmission of Aedes aegypti on the other. A significant positive correlation exists between the number of national publications and socioeconomic index and between international collaboration and scientific productivity in the field. Our study demonstrates the current state of research on the impact of climate change on dengue and provides a comparative analysis of the Southeast Asian region. Publication output in Southeast Asia lags behind that of major countries worldwide, and various strategies should be implemented to improve international collaboration, such as increasing the number of international collaborative projects and providing academic resources and research platforms for researchers.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0