Geospatial Health最新文献

{"title":"The spatial distribution of interleukin-4 (IL-4) reference values in China based on a back propagation (BP) neural network.","authors":"Zhao Rong Huang,&nbsp;Miao Ge,&nbsp;Xin Rui Pang,&nbsp;Pu Song,&nbsp;Congxia Wang","doi":"10.4081/gh.2023.1197","DOIUrl":"https://doi.org/10.4081/gh.2023.1197","url":null,"abstract":"<p><p>This study aimed to investigate the geospatial distribution of normal reference values of Interleukin 4 (IL-4) in healthy Chinese adults and to provide a basis for the development of standard references. IL-4 values of 5,221 healthy adults from 64 cities in China were collected and analyzed for a potential correlation with 24 topographical, climatic and soil factors. Seven of these factors were extracted and used to build a back propagation (BP) neural network model that was used to predict IL-4 reference values in healthy individuals from 2,317 observation sites nationwide. The predicted values were tested for normality and geographic distribution by analytic Kriging interpolation to map the geographic distribution of IL-4 reference values in healthy Chinese subjects. The results showed that IL-4 values generally decreased and then increased from the South to the North. We concluded that the BP neural network model applies to this approach, where certain geographical factors determine levels of various biochemical and immunological standards in healthy adults in regions with different topography, climate and soil indices.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10608374","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}

{"title":"Province clustering based on the percentage of communicable disease using the BCBimax biclustering algorithm.","authors":"Muhammad Nur Aidi,&nbsp;Cynthia Wulandari,&nbsp;Sachnaz Desta Oktarina,&nbsp;Taufiqur Rakhim Aditra,&nbsp;Fitrah Ernawati,&nbsp;Efriwati Efriwati,&nbsp;Nunung Nurjanah,&nbsp;Rika Rachmawati,&nbsp;Elisa Diana Julianti,&nbsp;Dian Sundari,&nbsp;Fifi Retiaty,&nbsp;Aya Yuriestia Arifin,&nbsp;Rita Marleta Dewi,&nbsp;Nazarina Nazaruddin,&nbsp;Salimar Salimar,&nbsp;Noviati Fuada,&nbsp;Yekti Widodo,&nbsp;Budi Setyawati,&nbsp;Nuzuliyati Nurhidayati,&nbsp;Sudikno Sudikno,&nbsp;Irlina Raswanti Irawan,&nbsp;Widoretno Widoretno","doi":"10.4081/gh.2023.1202","DOIUrl":"https://doi.org/10.4081/gh.2023.1202","url":null,"abstract":"<p><p>Indonesia needs to lower its high infectious disease rate. This requires reliable data and following their temporal changes across provinces. We investigated the benefits of surveying the epidemiological situation with the imax biclustering algorithm using secondary data from a recent national scale survey of main infectious diseases from the National Basic Health Research (Riskesdas) covering 34 provinces in Indonesia. Hierarchical and k-means clustering can only handle one data source, but BCBimax biclustering can cluster rows and columns in a data matrix. Several experiments determined the best row and column threshold values, which is crucial for a useful result. The percentages of Indonesia's seven most common infectious diseases (ARI, pneumonia, diarrhoea, tuberculosis (TB), hepatitis, malaria, and filariasis) were ordered by province to form groups without considering proximity because clusters are usually far apart. ARI, pneumonia, and diarrhoea were divided into toddler and adult infections, making 10 target diseases instead of seven. The set of biclusters formed based on the presence and level of these diseases included 7 diseases with moderate to high disease levels, 5 diseases (formed by 2 clusters), 3 diseases, 2 diseases, and a final order that only included adult diarrhoea. In 6 of 8 clusters, diarrhea was the most prevalent infectious disease in Indonesia, making its eradication a priority. Direct person-to-person infections like ARI, pneumonia, TB, and diarrhoea were found in 4-6 of 8 clusters. These diseases are more common and spread faster than vector-borne diseases like malaria and filariasis, making them more important.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10578726","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}

{"title":"Spatial association and modelling of under-5 mortality in Thailand, 2020.","authors":"Suparerk Suerungruang,&nbsp;Kittipong Sornlorm,&nbsp;Wongsa Laohasiriwong,&nbsp;Roshan Kumar Mahato","doi":"10.4081/gh.2023.1220","DOIUrl":"https://doi.org/10.4081/gh.2023.1220","url":null,"abstract":"<p><p>Under-5 mortality rate (U5MR) is a key indicator of child health and overall development. In Thailand, despite significant steps made in child health, disparities in U5MR persist across different provinces. We examined various socio-economic variables, health service availability and environmental factors impacting U5MR in Thailand to model their influences through spatial analysis. Global and Local Moran's I statistics for spatial autocorrelation of U5MR and its related factors were used on secondary data from the Ministry of Public Health, National Centers for Environmental Information, National Statistical Office, and the Office of the National Economic and Social Development Council in Thailand. The relationships between U5MR and these factors were modelled using ordinary least squares (OLS) estimation, spatial lag model (SLM) and spatial error model (SEM). There were significant spatial disparities in U5MR across Thailand. Factors such as low birth weight, unemployment rate, and proportion of land use for agricultural purposes exhibited significant positive spatial autocorrelation, directly influencing U5MR, while average years of education, community organizations, number of beds for inpatients per 1,000 population, and exclusive breastfeeding practices acted as protective factors against U5MR (R2 of SEM = 0.588).The findings underscore the need for comprehensive, multi-sectoral strategies to address the U5MR disparities in Thailand. Policy interventions should consider improving socioeconomic conditions, healthcare quality, health accessibility, and environmental health in high U5M areas. Overall, this study provides valuable insights into the spatial distribution of U5MR and its associated factors, which highlights the need for tailored and localized health policies and interventions.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10211287","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}

{"title":"Correction. <i>Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020)</i>.","authors":"The Publisher","doi":"10.4081/gh.2023.1233","DOIUrl":"https://doi.org/10.4081/gh.2023.1233","url":null,"abstract":"<p><p>In the Article titled \"Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020).\" published May 5th, 2021, in Vol. 16(1) of Geospatial Health, an author's name was misspelled. The seventh author's name should be \"Alamgir\".   Reference: Ullah S, Mohd Nor NH, Daud H, Zainuddin N, Gandapur MS J, Ali I, Khalil A, 2021. Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020). Geospatial Health, 16:961. https://doi.org/10.4081/gh.2021.961.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9916658","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}

{"title":"Correction. <i>Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach.</i>.","authors":"The Publisher","doi":"10.4081/gh.2023.1232","DOIUrl":"https://doi.org/10.4081/gh.2023.1232","url":null,"abstract":"<p><p>In the Article titled \"Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach.\" published November 6th, 2017, in Vol 12(2) of Geospatial Health, an author's name was misspelled. The fifth author's name should be \"Alamgir\". Reference: Ullah S, Daud H, Dass SC, Khan HN, Khalil A, 2017. Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach. Geospatial Health, 12:567. https://doi.org/10.4081/gh.2017.567.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9916653","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}

{"title":"Geospatial analysis in the United States reveals the changing roles of temperature on COVID-19 transmission.","authors":"Ruiwen Xiong,&nbsp;Xiaolong Li","doi":"10.4081/gh.2023.1213","DOIUrl":"https://doi.org/10.4081/gh.2023.1213","url":null,"abstract":"<p><p>Environmental factors are known to affect outbreak patterns of infectious disease, but their impacts on the spread of COVID-19 along with the evolution of this relationship over time intervals and in different regions are unclear. This study utilized 3 years of data on COVID-19 cases in the continental United States from 2020 to 2022 and the corresponding weather data. We used regression analysis to investigate weather impacts on COVID-19 spread in the mainland United States and estimate the changes of these impacts over space and time. Temperature exhibited a significant and moderately strong negative correlation for most of the US while relative humidity and precipitation experienced mixed relationships. By regressing temperature factors with the spreading rate of waves, we found temperature change can explain over 20% of the spatial-temporal variation in the COVID-19 spreading, with a significant and negative response between temperature change and spreading rate. The pandemic in the continental United States during 2020-2022 was characterized by seven waves, with different transmission rates and wave peaks concentrated in seven time periods. When repeating the analysis for waves in the seven periods and nine climate zones, we found temperature impacts evolve over time and space, possibly due to virus mutation, changes in population susceptibility, social behavior, and control measures. Temperature impacts became weaker in 6 of 9 climate zones from the beginning of the epidemic to the end of 2022, suggesting that COVID-19 has increasingly adapted to wider weather conditions.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9885913","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}

{"title":"Where to place emergency ambulance vehicles: use of a capacitated maximum covering location model with real call data.","authors":"Soheil Hashtarkhani,&nbsp;Stephen A Matthews,&nbsp;Ping Yin,&nbsp;Alireza Mohammadi,&nbsp;Shahab MohammadEbrahimi,&nbsp;Mahmood Tara,&nbsp;Behzad Kiani","doi":"10.4081/gh.2023.1198","DOIUrl":"https://doi.org/10.4081/gh.2023.1198","url":null,"abstract":"<p><p>This study integrates geographical information systems (GIS) with a mathematical optimization technique to enhance emergency medical services (EMS) coverage in a county in the northeast of Iran. EMS demand locations were determined through one-year EMS call data analysis. We formulated a maximal covering location problem (MCLP) as a mixed-integer linear programming model with a capacity threshold for vehicles using the CPLEX optimizer, an optimization software package from IBM. To ensure applicability to the EMS setting, we incorporated a constraint that maintains an acceptable level of service for all EMS calls. Specifically, we implemented two scenarios: a relocation model for existing ambulances and an allocation model for new ambulances, both using a list of candidate locations. The relocation model increased the proportion of calls within the 5-minute coverage standard from 69% to 75%. With the allocation model, we found that the coverage proportion could rise to 84% of total calls by adding ten vehicles and eight new stations. The incorporation of GIS techniques into optimization modelling holds promise for the efficient management of scarce healthcare resources, particularly in situations where time is of the essence.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9885916","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}

{"title":"Association of socioeconomic indicators with COVID-19 mortality in Brazil: a population-based ecological study.","authors":"João Batista Cavalcante Filho, Marco Aurélio de Oliveira Góes, Damião da Conceição Araújo, Marcus Valerius da Silva Peixoto, Marco Antônio Prado Nunes","doi":"10.4081/gh.2023.1206","DOIUrl":"10.4081/gh.2023.1206","url":null,"abstract":"<p><p>The article presents an analysis of the spatial distribution of mortality from COVID-19 and its association with socioeconomic indicators in the north-eastern region of Brazil - an area particularly vulnerable with regard to these indicators. This populationbased ecology study was carried out at the municipal level in the years 2020 and 2021, with analyses performed by spatial autocorrelation, multiple linear regression and spatial autoregressive models. The results showed that mortality from COVID-19 in this part of Brazil was higher in the most populous cities with better socioeconomic indicators. Factors such as the onset of the COVID-19 pandemic in large cities, the agglomerations existing within them, the pressure to maintain economic activities and mistakes in the management of the pandemic by the Brazilian federal Government were part of the complex scenario related to the spread of COVID-19 in the country and this study was undertaken in an attempt to understand this situation. Analysing the different scenarios is essential to face the challenges posed by the pandemic to the world's health systems.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 2","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9878255","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}

{"title":"Spatiotemporal distribution and geostatistically interpolated mapping of the melioidosis risk in an endemic zone in Thailand.","authors":"Jaruwan Wongbutdee,&nbsp;Jutharat Jittimanee,&nbsp;Wacharapong Saengnill","doi":"10.4081/gh.2023.1189","DOIUrl":"https://doi.org/10.4081/gh.2023.1189","url":null,"abstract":"<p><p>Melioidosis, a bacterial, infectious disease contracted from contaminated soil or water, is a public health problem identified in tropical regions and endemic several regions of Thailand. Surveillance and prevention are important for determining its distribution patterns and mapping its risk, which have been analysed in the present study. Case reports in Thailand were collected from 1 January 2016 to 31 December 2020. Spatial autocorrelation was analyzed using Moran's I and univariate local Moran's I. Spatial point data of melioidosis incidence were calculated, with riskmapping interpolation performed by Kriging. It was highest in 2016, at 32.37 cases per 100,000 people, and lowest in 2020, at 10.83 cases per 100,000 people. General observations revealed that its incidence decreased slightly from 2016 to 2018 and drastically in 2019 and 2020. The Moran's I values for melioidosis incidence exhibited a random spatial pattern in 2016 and clustered distribution from 2017 to 2020. The risk and variance maps show interval values. These findings may contribute to the monitoring and surveillance of melioidosis outbreaks.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10178155","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}

{"title":"Impacts of sample ratio and size on the performance of random forest model to predict the potential distribution of snail habitats.","authors":"Yuanhua Liu,&nbsp;Jun Zhang,&nbsp;Michael P Ward,&nbsp;Wei Tu,&nbsp;Lili Yu,&nbsp;Jin Shi,&nbsp;Yi Hu,&nbsp;Fenghua Gao,&nbsp;Zhiguo Cao,&nbsp;Zhijie Zhang","doi":"10.4081/gh.2023.1151","DOIUrl":"https://doi.org/10.4081/gh.2023.1151","url":null,"abstract":"<p><p>Few studies have considered the impacts of sample size and sample ratio of presence and absence points on the results of random forest (RF) testing. We applied this technique for the prediction of the spatial distribution of snail habitats based on a total of 15,000 sample points (5,000 presence samples and 10,000 control points). RF models were built using seven different sample ratios (1:1, 1:2, 1:3, 1:4, 2:1, 3:1, and 4:1) and the optimal ratio was identified via the Area Under the Curve (AUC) statistic. The impact of sample size was compared by RF models under the optimal ratio and the optimal sample size. When the sample size was small, the sampling ratios of 1:1, 1:2 and 1:3 were significantly better than the sample ratios of 4:1 and 3:1 at all four levels of sample sizes (p<0.01) and there was no significant difference among the ratios of 1:1, 1:2 and 1:3 (p>0.05). The sample ratio of 1:2 appeared to be optimal for a relatively large sample size with the lowest quartile deviation. In addition, increasing the sample size produced a higher AUC and a smaller slope and the most suitable sample size found in this study was 2400 (AUC=0.96). This study provides a feasible idea to select an appropriate sample size and sample ratio for ecological niche modelling (ENM) and also provides a scientific basis for the selection of samples to accurately identify and predict snail habitat distributions.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9753693","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}