Geospatial Health最新文献

During the COVID-19 pandemic, a system was established in China that required testing of all residents for COVID-19. It consisted of sampling stations, laboratories capable of carrying out DNA investigations and vehicles carrying out immediate transfer of all samples from the former to the latter. Using Beilin District, Xi'an City, Shaanxi Province, China as example, we designed a genetic algorithm based on a two-stage location coverage model for the location of the sampling stations with regard to existing residencies as well as the transfer between the sampling stations and the laboratories. The aim was to estimate the minimum transportation costs between these units. In the first stage, the model considered demands for testing in residential areas, with the objective of minimizing the costs related to travel between residencies and sampling stations. In the second stage, this approach was extended to cover the location of the laboratories doing the DNAinvestigation, with the aim of minimizing the transportation costs between them and the sampling stations as well as the estimating the number of laboratories needed. Solutions were based on sampling stations and laboratories existing in 2022, with the results visualized by geographic information systems (GIS). The results show that the genetic algorithm designed in this paper had a better solution speed than the Gurobi algorithm. The convergence was better and the larger the network size, the more efficient the genetic algorithm solution time.

Cardiovascular Disease (CVD) is currently the major challenge to people's health and the world's top cause of death. In Tanzania, deaths due to CVD account for about 13% of the total deaths caused by the non-communicable diseases. This study examined the spatio-temporal clustering of CVDs from 2010 to 2019 in Tanzania for retrospective spatio-temporal analysis using the Bernoulli probability model on data sampled from four selected hospitals. Spatial scan statistics was performed to identify CVD clusters and the effect of covariates on the CVD incidences was examined using multiple logistic regression. It was found that there was a comparatively high risk of CVD during 2011-2015 followed by a decline during 2015-2019. The spatio-temporal analysis detected two high-risk disease clusters in the coastal and lake zones from 2012 to 2016 (p<0.001), with similar results produced by purely spatial analysis. The multiple logistic model showed that sex, age, blood pressure, body mass index (BMI), alcohol intake and smoking were significant predictors of CVD incidence.

The purpose of this study was to determine whether there were any TB clusters in Aceh Province, Indonesia and their temporal distribution during the period of 2019-2021. A spatial geo-reference was conducted to 290 sub-districts coordinates by geocoding each sub-district's offices. By using SaTScan TM v9.4.4, a retrospective space-time scan statistics analysis based on population data and annual TB incidence was carried out. To determine the regions at high risk of TB, data from 1 January 2019 to 31 December 2021 were evaluated using the Poisson model. The likelihood ratio (LLR) value was utilized to locate the TB clusters based on a total of 999 permutations were performed. A Moran's I analysis (using GeoDa) was chosen for a study of both local and global spatial autocorrelation. The threshold for significance was fixed at 0.05. At the sub-district level, the spatial distribution of TB in Aceh Province from 2019-2021 showed 19 clusters (three most likely and 16 secondary ones), and there was a spatial autocorrelation of TB. The findings can be used to provide thorough knowledge on the spatial pattern of TB occurrence, which is important for designing effective TB interventions.

Spatial cluster analyses of health events are useful for enabling targeted interventions. Spatial scan statistic is the stateof- the-art method for this kind of analysis and the Poisson Generalized Linear Model (GLM) approach to the spatial scan statistic can be used for count data for spatial cluster detection with covariate adjustment. However, its use for modelling is limited due to data over-dispersion. A Generalized Linear Mixed Model (GLMM) has recently been proposed for modelling this kind of over-dispersion by incorporating random effects to model area-specific intrinsic variation not explained by other covariates in the model. However, these random effects may exhibit a geographical correlation, which may lead to a potential spatial cluster being undetected. To handle the over-dispersion in the count data, this study aimed to evaluate the performance of a negative binomial- GLM in spatial scan statistic on real-world data of low birth weights in Khyber-Pakhtunkhwa Province, Pakistan, 2019. The results were compared with the Poisson-GLM and GLMM, showing that the negative binomial-GLM is an ideal choice for spatial scan statistic in the presence of over-dispersed data. With a covariate (maternal anaemia) adjustment, the negative binomial-GLMbased spatial scan statistic detected one significant cluster covering Dir lower district. Without the covariate adjustment, it detected two clusters, each covering one district. The district of Peshawar was seen as the most likely cluster and Battagram as the secondary cluster. However, none of the clusters were detected by GLMM spatial scan statistic, which might be due to the spatial correlation of the random effects in GLMM.

While more and more health-related data is being produced and published every day, few of it is being prepared in a way that would be beneficial for daily use outside the scientific realm. Interactive visualizations that can slice and condense enormous amounts of multi-dimensional data into easy-to-digest portions are a promising tool that has been under-utilized for health-related topics. Here we present two case studies for how interactive maps can be utilized to make raw health data accessible to different target audiences: i) the European Notifiable Diseases Interactive Geovisualization (ENDIG) which aims to communicate the implementation status of disease surveillance systems across the European Union to public health experts and decision makers, and ii) the Zoonotic Infection Risk in Twente-Achterhoek Map (ZIRTA map), which aims to communicate information about zoonotic diseases and their regional occurrence to general practitioners and other healthcare providers tasked with diagnosing infectious diseases on a daily basis. With these two examples, we demonstrate that relatively straight-forward interactive visualization approaches that are already widely used elsewhere can be of benefit for the realm of public health.

Malaria is the leading cause of morbidity among children under five years of age and pregnant women in Côte d'Ivoire. We assessed the geographical distribution of its risk in all climatic zones of the country based on the Fifth Assessment Report (AR5) of the United Nations Intergovernmental Panel on Climate Change (IPCC) approach to climate risk analysis. This methodology considers three main driving components affecting the risk: Hazard, exposure and vulnerability. Considering the malaria impact chain, various variables were identified for each of the risk factors and for each variable, a measurable indicator was identified. These indicators were then standardized, weighted through a participatory approach based on expert judgement and finally aggregated to calculate current and future risk. With regard to the four climatic zones in the country: Attieen (sub-equatorial regime) in the South, Baouleen (humid tropical) in the centre, Sudanese or equatorial (tropical transition regime) in the North and the mountainous (humid) in the West. Malaria risk among pregnant women and children under 5 was found to be higher in the mountainous and the Baouleen climate, with the hazard highest in the mountainous climate and Exposure very high in the Attieen climate. The most vulnerable districts were those in Baouleen, Attieen and the mountainous climates. By 2050, the IPCC representative concentration pathway (RCP) 4.5 and 8.5 scenarios predict an increase in risk in almost all climatic zones, compared to current levels, with the former considering a moderate scenario, with an emissions peak around 2040 followed by a decline and RCP 8.5 giving the highest baseline emissions scenario, in which emissions continue to rise. It is expected that the AR5 approach to climate risk analysis will be increasingly used in climate risk assessment studies so that it can be better assessed at a variety of scales.

During the period 2013-2023, 917 cases of rabies among animals were registered in the Republic of Kazakhstan. Out of these, the number of cases in farm animals amounted to 515, in wild animals to 50 and in pets to 352. Data on rabies cases were obtained from the Committee for Veterinary Control and Supervision of Kazakhstan, as well as during expeditionary trips. This research was carried out to demonstrate the use of modern information and communication technologies, geospatial analysis technologies in particular, to identify and visualize spatio-temporal patterns of rabies emergence among different animal species in Kazakhstan. We also aimed to predict an expected number of cases next year based on time series analysis. Applying the 'space-time cube' technique to a time series representingcases from the three categories of animals at the district-level demonstrated a decreasing trend of incidence in most of the country over the study period. We estimated the expected number of rabies cases for 2024 using a random forest model based on the space-time cube in Arc-GIS. This type of model imposes only a few assumptions on the data and is useful when dealing with time series including complicated trends. The forecast showed that in most districts of Kazakhstan, a total of no more than one case of rabies should beexpected, with the exception of certain areas in the North and the East of the country, where the number of cases could reach three. The results of this research may be useful to the veterinary service in mapping the current epidemiological situation and in planning targeted vaccination campaigns among different categories of animals.

In June 2022, an exotic pest of the European honeybee (Apis mellifera), the varroa mite (Varroa destructor), was detected in surveillance hives at the Port of Newcastle, New South Wales (NSW). Previously, Australia remained the only continent free of the varroa mite. In September 2023, the National Management Group decided to shift the focus of the response from eradication to management. It is estimated that the establishment of varroa mite in Australia could lead to more than $70 million in losses each year due to greatly reduced pollination services. Currently, there are no reported studies on the epidemiology of varroa mite in NSW because it is such a recent outbreak, and there is little knowledge of the factors associated with the presence of V. destructor in the Australian context. We sourced publicly available varroa mite outbreak reports from June 22 to December 19, 2022, to determine if urbanization, land use, and distance from the incursion site are associated with the detection of varroa mite infestation in European honeybee colonies in NSW. The outcome investigated was epidemic day, relative to the first detected premises (June 22, 2022). The study population was comprised of 107 premises, which were declared varroa-infested. The median epidemic day was day 37 (July 29, 2022), and a bimodal distribution was observed from the epidemic curve, which was reflective of an intermittent source pattern of spread. We found that premises were detected to be infected with varroa mite earlier in urban areas [median epidemic day 25 (July 17, 2022)] compared to rural areas [median epidemic day 37.5 (July 29, 2022)]. Infected premises located in areas without cropping, forests, and irrigation were detected earlier in the outbreak [median epidemic days 23.5 (July 15, 2022), 30 (July 22, 2022), and 15 (July 7, 2022), respectively] compared to areas with cropping, forests, and irrigation [median epidemic days 50 (August 11, 2022), 43 (August 4, 2022), and 47 (August 8, 2022), respectively]. We also found that distance from the incursion site was not significantly correlated with epidemic day. Urbanization and land use are potential factors for the recent spread of varroa mite in European honeybee colonies in NSW. This knowledge is essential to managing the current varroa mite outbreak and preventing future mass varroa mite spread events.

Mpox is an emerging, infectious disease that has caused outbreaks in at least 91 countries from May to August 2022. We assessed the link between international air travel patterns and Mpox transmission risk, and the relationship between the translocation of Mpox and human mobility dynamics after travel restrictions due to the COVID-19 pandemic had been lifted. Our three novel observations were that: i) more people traveled internationally after the removal of travel restrictions in the summer of 2022 compared to pre-pandemic levels; ii) countries with a high concentration of global air travel have the most recorded Mpox cases; and iii) Mpox transmission includes a number of previously nonendemic regions. These results suggest that international airports should be a primary location for monitoring the risk of emerging communicable diseases. Findings highlight the need for global collaboration concerning proactive measures emphasizing realtime surveillance.

Individuals migrating with chronic diseases often face substantial health risks, and their patterns of healthcare-seeking behavior are commonly influenced by mobility. However, to our knowledge, no research has used spatial statistics to verify this phenomenon. Utilizing data from the China Migrant Dynamic Survey of 2017, we conducted a geostatistical analysis to identify clusters of chronic disease patients among China's internal migrants. Geographically weighted regressions were utilized to examine the driving factors behind the reasons why treatment was not sought by 711 individuals among a population sample of 9272 migrant people with chronic diseases. The results indicate that there is a spatial correlation in the clustering of internal migrants with chronic diseases in China. The prevalence is highly clustered in Zhejiang and Xinjiang in north-eastern China. Hotspots were found in the northeast (Jilin and Liaoning), the north (Hebei, Beijing, and Tianjin), and the east (Shandong) and also spread into surrounding provinces. The factors that affect the migrants with no treatment were found to be the number of hospital beds per thousand population, the per capita disposable income of medical care, and the number of participants receiving health education per 1000 Chinese population. To rectify this situation, the local government should "adapt measures to local conditions." Popularizing health education and coordinating the deployment of high-quality medical facilities and medical workers are effective measures to encourage migrants to seek reasonable medical treatment.