Access to urban green spaces and use of social services and institutional long-term care among older people in Malmö, Sweden: a longitudinal register study.

BACKGROUND: Finding ways to prolong independence in daily life among older people would be beneficial for both individuals and society. Urban green spaces have been found to improve health, but only a few studies have evaluated the association between urban green spaces and independence in daily life. The aim of this study was to assess the long-term effect of urban green spaces on independence in daily life, using social services and support, mobility aids, and relocation to institutional long-term care as proxies, among community dwelling people 65 + years. METHODS: We identified 40 357 people 65 + years living in the city of Malmö, Sweden in 2010. Using geographical information systems (GIS), we determined the amount of urban green spaces (total, public, and quiet) within 300 m of each person's residence. All three measures were categorized based on their respective percentiles, so that the first quartile represented the 25% with the least access and the fourth quartile the 25% with the most access. In 2015 and 2019, we assessed the outcomes minor assistance (non-personal support), major assistance (personal support), and relocation into institutional long-term care. These three outcome measures were used as proxies for independence in daily life. The effect of amount of urban green spaces in 2010 on the three outcomes in 2015 and 2019, respectively, was assessed by pairwise comparing the three highest quartiles to the lowest. RESULTS: Compared to the lowest quartile, those in the highest quartile of quiet green spaces in 2010 were less likely to receive minor assistance in both 2015 and 2019. Besides this, there were no indications that any of the measures of urban green space affected independence in daily life at the five- and nine-year follow-up, respectively. CONCLUSION: Although urban green spaces are known to have positive impact on health, physical activity, and social cohesion among older people, we found no effect of total, public, or quiet green spaces on independence in daily life. This could possibly be a result of the choice of measures of urban green spaces, including spatial and temporal aspects, an inability to capture important qualitative aspects of the green spaces, or the proxy measures used to assess independence in daily life.

Predicted distribution of Metaparasitylenchus hypothenemi (Tylenchida: Allantonematidae), parasite of the coffee berry borer.

Metaparasitylenchus hypothenemi is an endoparasitic nematode of the coffee berry borer Hypothenemus hampei. The nematode has only been recorded across a limited geographical range in coffee-growing areas of southeastern Mexico. Because of its confined geographical distribution, the effect of altitude, temperature, and mean annual precipitation on M. hypothenemi's presence/absence in the Soconusco region of Mexico was investigated. The geographical distribution of this parasite was predicted based on current data, using geographical information systems (GIS), the MaxEnt algorithm, and historical data to improve the prediction accuracy for other Neotropical regions. In Soconusco, the presence of this parasite is directly related to annual precipitation, especially in the areas with the highest annual rainfall (4000 - 4700 mm/year). Four species distribution models were generated for the Neotropical region with environmental variables for sites with parasite presence data, predicting a range of possible distribution with a high probability of occurrence in southeastern Mexico and southwestern Guatemala and a low probability in areas of Central and South America. Characterization of the abiotic habitat conditions suitable for M. hypothenemi development allows us to predict its distribution in the Neotropics and contributes to our understanding of its ecological relationship with environmental variables.

Geographic variation in 5-year mortality following HIV diagnosis: implications for clinical interventions.

Characterizing spatial distribution of HIV outcomes is vital for targeting interventions to areas most at risk. We performed spatial analysis to identify geographic clusters and factors associated with mortality in KwaZulu-Natal, South Africa. We utilized Sizanani trial (NCT01188941) data, which enrolled participants August 2010-January 2013 and obtained vital status at 5.8 (IQR 5.0-6.4) years of follow-up. We mapped geocoded addresses to 2011 Census-defined small area layer (SAL) centroids, used Kulldorff's spatial scan statistic to identify mortality clusters, and compared socio-demographic factors for SALs within and outside mortality clusters. We assigned 1,143 participants living with HIV (260 [23%] of whom died during follow-up) to 677 SALs. One lower mortality cluster (n = 90, RR = 0.23, p = 0.022) was identified near a hospital outside Durban. SALs in the cluster were younger (24y vs 25y, p < 0.001); had fewer bedrooms/household (3 vs 4, p < 0.001); had more females (52% vs 51%, p = 0.013) and residents with no schooling past age 20 (4% vs 3%, p < 0.001) or no education at all (4% vs 3%, p < 0.001); had fewer residents with income >3,200 ZAR/month (5% vs 9%, p < 0.001); and had reduced access to piped water (p < 0.001), refuse disposal (p < 0.001), and toilets (p < 0.001). Targeted interventions may improve outcomes in areas with similar characteristics.

Impacts of seasonal flooding on geographical access to maternal healthcare in the Barotse Floodplain, Zambia.

BACKGROUND: Seasonal floods pose a commonly-recognised barrier to women's access to maternal services, resulting in increased morbidity and mortality. Despite their importance, previous GIS models of healthcare access have not adequately accounted for floods. This study developed new methodologies for incorporating flood depths, velocities, and extents produced with a flood model into network- and raster-based health access models. The methodologies were applied to the Barotse Floodplain to assess flood impact on women's walking access to maternal services and vehicular emergency referrals for a monthly basis between October 2017 and October 2018. METHODS: Information on health facilities were acquired from the Ministry of Health. Population density data on women of reproductive age were obtained from the High Resolution Settlement Layer. Roads were a fusion of OpenStreetMap and data manually delineated from satellite imagery. Monthly information on floodwater depth and velocity were obtained from a flood model for 13-months. Referral driving times between delivery sites and EmOC were calculated with network analysis. Walking times to the nearest maternal services were calculated using a cost-distance algorithm. RESULTS: The changing distribution of floodwaters impacted the ability of women to reach maternal services. At the peak of the dry season (October 2017), 55%, 19%, and 24% of women had walking access within 2-hrs to their nearest delivery site, EmOC location, and maternity waiting shelter (MWS) respectively. By the flood peak, this dropped to 29%, 14%, and 16%. Complete inaccessibility became stark with 65%, 76%, and 74% unable to access any delivery site, EmOC, and MWS respectively. The percentage of women that could be referred by vehicle to EmOC from a delivery site within an hour also declined from 65% in October 2017 to 23% in March 2018. CONCLUSIONS: Flooding greatly impacted health access, with impacts varying monthly as the floodwave progressed. Additional validation and application to other regions is still needed, however our first results suggest the use of a hydrodynamic model permits a more detailed representation of floodwater impact and there is great potential for generating predictive models which will be necessary to consider climate change impacts on future health access.

An Open-Source Platform for GIS Data Management and Analytics.

Precision agriculture has emerged as a promising approach to improve crop productivity and reduce the environmental impact. However, effective decision making in precision agriculture relies on accurate and timely data acquisition, management, and analysis. The collection of multisource and heterogeneous data for soil characteristics estimation is a critical component of precision agriculture, as it provides insights into key factors, such as soil nutrient levels, moisture content, and texture. To address these challenges, this work proposes a software platform that facilitates the collection, visualization, management, and analysis of soil data. The platform is designed to handle data from various sources, including proximity, airborne, and spaceborne data, to enable precision agriculture. The proposed software allows for the integration of new data, including data that can be collected directly on-board the acquisition device, and it also allows for the incorporation of custom predictive systems for soil digital mapping. The usability experiments conducted on the proposed software platform demonstrate that it is easy to use and effective. Overall, this work highlights the importance of decision support systems in the field of precision agriculture and the potential benefits of using such systems for soil data management and analysis.

Remote Sensing Provides a Rapid Epidemiological Context for the Control of African Swine Fever in Germany.

Transboundary disease control, as for African swine fever (ASF), requires rapid understanding of the locally relevant potential risk factors. Here, we show how satellite remote sensing can be applied to the field of animal disease control by providing an epidemiological context for the implementation of measures against the occurrence of ASF in Germany. We find that remotely sensed observations are of the greatest value at a lower jurisdictional level, particularly in support of wild boar carcass search efforts.

Urban life: a model of people and places.

We introduce the Urban Life agent-based simulation used by the Ground Truth program to capture the innate needs of a human-like population and explore how such needs shape social constructs such as friendship and wealth. Urban Life is a spatially explicit model to explore how urban form impacts agents' daily patterns of life. By meeting up at places agents form social networks, which in turn affect the places the agents visit. In our model, location and co-location affect all levels of decision making as agents prefer to visit nearby places. Co-location is necessary (but not sufficient) to connect agents in the social network. The Urban Life model was used in the Ground Truth program as a virtual world testbed to produce data in a setting in which the underlying ground truth was explicitly known. Data was provided to research teams to test and validate Human Domain research methods to an extent previously impossible. This paper summarizes our Urban Life model's design and simulation along with a description of how it was used to test the ability of Human Domain research teams to predict future states and to prescribe changes to the simulation to achieve desired outcomes in our simulated world.

Geospatial analysis of cesarean section in Iran (2016-2020): exploring clustered patterns and measuring spatial interactions of available health services.

BACKGROUND: The lives of babies and mothers are at risk due to the uneven distribution of healthcare facilities required for emergency cesarean sections (CS). However, CS without medical indications might cause complications for mothers and babies, which is a global health problem. Identifying spatiotemporal variations of CS rates in each geographical area could provide helpful information to understand the status of using CS services. METHODS: This cross-sectional study explored spatiotemporal patterns of CS in northeast Iran from 2016 to 2020. Space-time scan statistics and spatial interaction analysis were conducted using geographical information systems to visualize and explore patterns of CS services. RESULTS: The temporal analysis identified 2017 and 2018 as the statistically significant high clustered times in terms of CS rate. Five purely spatial clusters were identified that were distributed heterogeneously in the study region and included 14 counties. The spatiotemporal analysis identified four clusters that included 13 counties as high-rate areas in different periods. According to spatial interaction analysis, there was a solid spatial concentration of hospital facilities in the political center of the study area. Moreover, a high degree of inequity was observed in spatial accessibility to CS hospitals in the study area. CONCLUSIONS: CS Spatiotemporal clusters in the study area reveal that CS use in different counties among women of childbearing age is significantly different in terms of location and time. This difference might be studied in future research to identify any overutilization of CS or lack of appropriate CS in clustered counties, as both put women at risk. Hospital capacity and distance from population centers to hospitals might play an essential role in CS rate variations and spatial interactions among people and CS facilities. As a result, some healthcare strategies, e.g., building new hospitals and empowering the existing local hospitals to perform CS in areas out of service, might be developed to decline spatial inequity.

A GIS-aided cellular automata system for monitoring and estimating graph-based spread of epidemics.

In this study, we introduce an application of a Cellular Automata (CA)-based system for monitoring and estimating the spread of epidemics in real world, considering the example of a Greek city. The proposed system combines cellular structure and graph representation to approach the connections among the area's parts more realistically. The original design of the model is attributed to a classical SIR (Susceptible-Infected-Recovered) mathematical model. Aiming to upgrade the application's effectiveness, we have enriched the model with parameters that advances its functionality to become self-adjusting and more efficient of approaching real situations. Thus, disease-related parameters have been introduced, while human interventions such as vaccination have been represented in algorithmic manner. The model incorporates actual geographical data (GIS, geographic information system) to upgrade its response. A methodology that allows the representation of any area with given population distribution and geographical data in a graph associated with the corresponding CA model for epidemic simulation has been developed. To validate the efficient operation of the proposed model and methodology of data display, the city of Eleftheroupoli, in Eastern Macedonia-Thrace, Greece, was selected as a testing platform (Eleftheroupoli, Kavala). Tests have been performed at both macroscopic and microscopic levels, and the results confirmed the successful operation of the system and verified the correctness of the proposed methodology.

Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing.

The demand for cloud computing has drastically increased recently, but this paradigm has several issues due to its inherent complications, such as non-reliability, latency, lesser mobility support, and location-aware services. Fog computing can resolve these issues to some extent, yet it is still in its infancy. Despite several existing works, these works lack fault-tolerant fog computing, which necessitates further research. Fault tolerance enables the performing and provisioning of services despite failures and maintains anti-fragility and resiliency. Fog computing is highly diverse in terms of failures as compared to cloud computing and requires wide research and investigation. From this perspective, this study primarily focuses on the provision of uninterrupted services through fog computing. A framework has been designed to provide uninterrupted services while maintaining resiliency. The geographical information system (GIS) services have been deployed as a test bed which requires high computation, requires intensive resources in terms of CPU and memory, and requires low latency. Keeping different types of failures at different levels and their impacts on service failure and greater response time in mind, the framework was made anti-fragile and resilient at different levels. Experimental results indicate that during service interruption, the user state remains unaffected.

Assessing catchment scale water quality of agri-food systems and the scope for reducing unintended consequences using spatial life cycle assessment (LCA).

Life cycle assessment is a multidisciplinary framework usually deployed to appraise the sustainability of various product or service supply-chains. Over recent decades, its use in the agri-food sector has risen sharply, and alongside this, a wide range of methodological advances have been generated. Spatial-life cycle assessment, defined in the current document as the interpretation of life cycle assessment results within a geographical nature, has not gone unexplored entirely, yet its rise as a sub-method of life cycle assessment has been rather slow relative to other avenues of research (e.g., including the nutritional sciences within life cycle assessment). With this relative methodological stagnation as a motivating factor, our paper combines a process-based model, the Catchment Systems Model, with various life cycle impact assessments (ReCiPe, Centre for Environmental Studies and Environmental Product Declaration) to propose a simple, yet effective, approach for visualising the technically feasible efficacy of various on-farm intervention strategies. As water quality was the primary focus of this study, interventions reducing acidification and eutrophication potentials of both arable and livestock farm types in the Southeast of England were considered. The study site is an area with a marked range of agricultural practices in terms of intensity. All impacts to acidification potential and eutrophication potential are reported using a functional unit of 1 ha. Percentage changes relative to baseline farm types, i.e., those without any interventions, arising from various mitigation strategies, are mapped using geographical information systems. This approach demonstrates visually how a spatially-orientated life cycle assessment could provide regional-specific information for farmers and policymakers to guide the restoration of certain waterbodies. A combination of multiple mitigation strategies was found to generate the greatest reductions in pollutant losses to water, but in terms of individual interventions, optimising farm-based machinery (acidification potential) and fertiliser application strategies (eutrophication potential) were found to have notable benefits.

Modeling wildfire risk in western Iran based on the integration of AHP and GIS.

This study aimed at delineating the wildfire risk zones in a fire-prone region located in a rarely addressed area of western Iran (Paveh city) by assessing the potential of factors such as NDVI, topographic factors (elevation, slope, and aspect), land cover, and evaporation in explaining the fire occurrence probability. Analytic hierarchy process (AHP) and geographical information system (GIS) methods were used synergistically to integrate the mentioned factors into analysis, following an informed categorization of each factor based on the information on previous fire occurrence. In the AHP process, elevation and evaporation data were considered to be the most critical factors. It was found that the predicted wildfire risk areas were in agreement with past fire events by the use of the methodology proposed by this study. Accordingly, the study's final wildfire risk map indicated that approximately 64.7% of the study area is located in the high- and very high-risk zones. Land-use planners and decision-makers may use the developed map to setup and implement fire prevention strategies and enhance or develop the fire-surveillance logistics and infrastructure, including but not limited to the positions of fire watchtowers, fire lines, and fire sensors, with the aim to minimize potential fire impacts.

Investigation of land cover (LC)/land use (LU) change affecting forest and seminatural ecosystems in Istanbul (Turkey) metropolitan area between 1990 and 2018.

This study was conducted to examine the land cover (LC)/land use (LU) change affecting forest and seminatural ecosystems and the spatio-temporal development of urban expansion between 1990 and 2018 in the city of Istanbul, where urbanization is the most intense in Turkey. For this purpose, using Corine Land Cover (1990, 2000, 2006, 2012, and 2018) dataset, the land cover of the area was determined in 5 different classes (artificial surface, agriculture, forest, water bodies, water), maps were produced, and tabular data were created. The changes in LC/LU between 1990 and 2018 were determined according to the Puyravaud land cover change rate and hot spot analysis methods. According to our findings, we determined that urbanization in Istanbul expanded the most in the east-west direction, and the agricultural and forest areas gradually decreased by 3.02% and 6.66% respectively; urban areas increased at the same rate of 9.69%. It is predicted that this change will continue increasing until 2030 when the forecasting method is applied in the field. It has been determined that the most important reasons for this situation are local government policies, population growth, and economic development initiatives applied in the area. As a result, it has emerged that measures should be taken based on sustainability and naturalness approaches to design urban development plans and to protect natural areas on a large scale, in order to limit possible LC/LU conversion from natural structure to urbanization in the area.

Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models.

The main aim of this study was to predict current and future flood susceptibility under three climate change scenarios of RCP2.6 (i.e., optimistic), RCP4.5 (i.e., business as usual), and RCP8.5 (i.e., pessimistic) employing four machine learning models, including Gradient Boosting Machine (GBM), Random Forest (RF), Multilayer Perceptron Neural Network (MLP-NN), and Naïve Bayes (NB). The study was conducted for two watersheds in Canada, namely Lower Nicola River, BC and Loup, QC. Three statistical metrics were used to validate the models: Receiver Operating Characteristic Curve, Figure of Merit, and F1-score. Findings indicated that the RF model had the highest accuracy in providing the flood susceptibility maps (FSMs). Moreover, the provided FSMs indicated that flooding is more likely to occur in the Lower Nicola River watershed than the Loup watershed. Following the RCP4.5 scenario, the area percentages of the flood susceptibility classes in the Loup watershed in 2050 and 2080 have changed by the following percentages from the year 2020 and 2050, respectively: Very Low = -1.68%, Low = -5.82%, Moderate = +6.19%, High = +0.71%, and Very High = +0.6% and Very Low = -1.61%, Low = +2.98%, Moderate = -3.49%, High = +1.29%, and Very High = +0.83%. Likewise, in the Lower Nicola River watershed, the changes between the years 2020 and 2050 and between the years 2050 and 2080 were: Very Low = -0.38%, Low = -0.81%, Moderate = -0.95%, High = +1.72%, and Very High = +0.42% and Very Low = -1.31%, Low = -1.35%, Moderate = -1.81%, High = +2.37%, and Very High = +2.1%, respectively. The impact of climate changes on future flood-prone places revealed that the regions designated as highly and very highly susceptible to flooding, grow in the forecasts for both watersheds. The main contribution of this study lies in the novel insights it provides concerning the flood susceptibility of watersheds in British Columbia and Quebec over time and under various climate change scenarios.

Using gravity model to make store closing decisions: A data driven approach.

Many studies propose methods for finding the best location for new stores and facilities, but few studies address the store closing problem. As a result of the recent COVID-19 pandemic, many companies have been facing financial issues. In this situation, one of the most common solutions to prevent loss is to downsize by closing one or more chain stores. Such decisions are usually made based on single-store performance; therefore, the under-performing stores are subject to closures. This study first proposes a multiplicative variation of the well-known Huff gravity model and introduces a new attractiveness factor to the model. Then a forward-backward approach is used to train the model and predict customer response and revenue loss after the hypothetical closure of a particular store from a chain. In this research the department stores in New York City are studied using large-scale spatial, mobility, and spending datasets. The case study results suggest that the stores recommended being closed under the proposed model may not always match the single store performance, and emphasizes the fact that the performance of a chain is a result of interaction among the stores rather than a simple sum of their performance considered as isolated and independent units. The proposed approach provides managers and decision-makers with new insights into store closing decisions and will likely reduce revenue loss due to store closures.

Epidemiology and Spatial Emergence of Anaplasmosis, New York, USA, 2010‒2018.

Human granulocytic anaplasmosis, a tickborne disease caused by the bacterium Anaplasma phagocytophilum, was first identified during 1994 and is now an emerging public health threat in the United States. New York state (NYS) has experienced a recent increase in the incidence of anaplasmosis. We analyzed human case surveillance and tick surveillance data collected by the NYS Department of Health for spatiotemporal patterns of disease emergence. We describe the epidemiology and growing incidence of anaplasmosis cases reported during 2010-2018. Spatial analysis showed an expanding hot spot of anaplasmosis in the Capital Region, where incidence increased >8-fold. The prevalence of A. phagocytophilum increased greatly within tick populations in the Capital Region over the same period, and entomologic risk factors were correlated with disease incidence at a local level. These results indicate that anaplasmosis is rapidly emerging in a geographically focused area of NYS, likely driven by localized changes in exposure risk.

Association between heavy metals and colon cancer: an ecological study based on geographical information systems in North-Eastern Iran.

BACKGROUND: Colorectal cancer has increased in Middle Eastern countries and exposure to environmental pollutants such as heavy metals has been implicated. However, data linking them to this disease are generally lacking. This study aimed to explore the spatial pattern of age-standardized incidence rate (ASR) of colon cancer and its potential association with the exposure level of the amount of heavy metals existing in rice produced in north-eastern Iran. METHODS: Cancer data were drawn from the Iranian population-based cancer registry of Golestan Province, north-eastern Iran. Samples of 69 rice milling factories were analysed for the concentration levels of cadmium, nickel, cobalt, copper, selenium, lead and zinc. The inverse distance weighting (IDW) algorithm was used to interpolate the concentration of this kind of heavy metals on the surface of the study area. Exploratory regression analysis was conducted to build ordinary least squares (OLS) models including every possible combination of the candidate explanatory variables and chose the most useful ones to show the association between heavy metals and the ASR of colon cancer. RESULTS: The highest concentrations of heavy metals were found in the central part of the province and particularly counties with higher amount of cobalt were shown to be associated with higher ASR of men with colon cancer. In contrast, selenium concentrations were higher in areas with lower ASR of colon cancer in men. A significant regression equation for men with colon cancer was found (F(4,137) = 38.304, P < .000) with an adjusted R2 of 0.77. The predicted ASR of men colon cancer was - 58.36 with the coefficients for cobalt = 120.33; cadmium = 80.60; selenium = - 6.07; nickel = - 3.09; and zinc = - 0.41. The association of copper and lead with colon cancer in men was not significant. We did not find a significant outcome for colon cancer in women. CONCLUSION: Increased amounts of heavy metals in consumed rice may impact colon cancer incidence, both positively and negatively. While there were indications of an association between high cobalt concentrations and an increased risk for colon cancer, we found that high selenium concentrations might instead decrease the risk. Further investigations are needed to clarify if there are ecological or other reasons for these discrepancies. Regular monitoring of the amount of heavy metals in consumed rice is recommended.

Impact of Rural Hospital Closures on Health-Care Access.

BACKGROUND: Access to health care is an important issue, particularly in remote areas. Since 2010, 106 rural hospital have closed in the United States, potentially limiting geographic access to health care. The aim of this study was to evaluate the impact of these hospital closures on the proportion of the population who can reach a secondary care facility, by road, within 15, 30, 45, or 60 min. METHODS: Geographical information system analysis, using population data obtained from the 2010 U.S. Census Bureau and hospital data between 2010 and 2019 from the Center for Medicare and Medicaid Services, created 15-, 30-, 45-, and 60-min drive time isochrones (areas from which a central location can be reached within a set time). RESULTS: Rural hospital closures resulted in 0%-0.97% of the population no longer being able to access a hospital within 15 min. The most marked changes were in the East South Central (0.97%, 178,478 residents) and West South Central (0.54%, 197,660 residents) divisions. Lesser degrees of change were noted for longer drive times. The changes were more marked when the rural population was analyzed exclusively. CONCLUSIONS: Recent closures of rural hospitals in the United States have impacted population access to hospital care, although the extent varies. There are regions, such as the Southern and Southeastern United States, which demonstrate greater and potentially more concerning losses in population coverage, probably because of the greater number of closures. Future work should evaluate clinical implications of hospital closures and loss of population coverage.

Evaluation of zoonotic platyhelminthe infections identified in slaughtered livestock in Iran, 2015-2019.

BACKGROUND: Platyhelminth infections of livestock can result in considerable economic losses. This study aims to evaluate the spatial frequency of cystic echinococcosis (CE), dicrocoeliasis, and fascioliasis in livestock slaughtered in Iran during the years 2015-2019 and estimate direct costs associated with organ condemnation due to these parasites. METHODS: Abattoir data from 413 abattoirs representing all 31 Iranian provinces were collected from the Iran Veterinary Organization. Infection prevalence was calculated per year at the province level. The Local Moran's I statistic was performed to evaluate spatial autocorrelation of animals positive at slaughter for the years 2015-2019. Direct costs associated with condemned livers were calculated for each parasitic condition, with costs associated with condemned lungs also included for CE. RESULTS: Overall prevalence values for the study timeframe were as follows: sheep and goat fascioliasis 1.56% (95% CI: 1.56-1.56%), cattle fascioliasis 3.86% (95% CI: 3.85-3.88%), sheep and goat dicrocoeliasis 4.63% (95% CI: 4.62-4.63%), cattle dicrocoeliasis 3.08% (95% CI: 3.07-3.09%), sheep and goat CE 5.32% (95% CI: 5.32-5.33%), and cattle CE 7.26% (95% CI: 7.24-7.28%). Northwest Iran had the highest prevalence of CE and fascioliasis. High infection areas for Dicrocoelium spp. included the provinces of Zanjan, Gilan, Qazvin, and Tehran, which are located in northern Iran. Direct economic losses for sheep and goat fascioliasis, dicrocoeliasis, and CE for the study period were US$13,842,759, US$41,771,377, and US$22,801,054, respectively. Direct economic losses for cattle fascioliasis, dicrocoeliasis, and CE for the study period were US$1,989,200, US$1,668,986, and US$2,656,568, respectively. CONCLUSION: Our findings provide valuable data for future monitoring of these important parasitic diseases in Iranian livestock. Disease control strategies are required to reduce the economic and public health impact of these platyhelminths.

Development of a Web GIS for small-scale detection and analysis of COVID-19 (SARS-CoV-2) cases based on volunteered geographic information for the city of Cologne, Germany, in July/August 2020.

BACKGROUND: Various applications have been developed worldwide to contain and to combat the coronavirus disease-19 (COVID-19) pandemic. In this context, spatial information is always of great significance. The aim of this study is to describe the development of a Web GIS based on open source products for the collection and analysis of COVID-19 cases and its feasibility in terms of technical implementation and data protection. METHODS: With the help of this Web GIS, data on this issue were collected voluntarily from the Cologne area. Using house perimeters as a data basis, it was possible to check, in conjunction with the Official Topographic Cartographic Information System object type catalog, whether buildings with certain functions, for example residential building with trade and services, have been visited more frequently by infected persons than other types of buildings. In this context, data protection and ethical and legal issues were considered. RESULTS: The results of this study show that the development of a Web GIS for the generation and evaluation of volunteered geographic information (VGI) with the help of open source software is possible. Furthermore, there are numerous data protection and ethical and legal aspects to consider, which not only affect VGI per se but also affect IT security. CONCLUSIONS: From a data protection perspective, more attention needs to be paid to the intervention and post-processing of data. In addition, official data must always be used as a reference for the actual spatial consideration of the number of infections. However, VGI provides added value at a small-scale level, so that valid information can also be reliably derived in the context of health issues. The creation of guidelines for the consideration of data protection, ethical aspects, and legal requirements in the context of VGI-based applications must also be considered. Trial registration The article does not report the results of a health care intervention for human participants.