Fuzzy Entropy-Based Spatial Hotspot Reliability

Cluster techniques are used in hotspot spatial analysis to detect hotspots as areas on the map; an extension of the Fuzzy C-means that the clustering algorithm has been applied to locate hotspots on the map as circular areas; it represents a good trade-off between the accuracy in the detection of the hotspot shape and the computational complexity. However, this method does not measure the reliability of the detected hotspots and therefore does not allow us to evaluate how reliable the identification of a hotspot of a circular area corresponding to the detected cluster is; a measure of the reliability of hotspots is crucial for the decision maker to assess the need for action on the area circumscribed by the hotspots. We propose a method based on the use of De Luca and Termini’s Fuzzy Entropy that uses this extension of the Fuzzy C-means algorithm and measures the reliability of detected hotspots. We test our method in a disease analysis problem in which hotspots corresponding to areas where most oto-laryngo-pharyngeal patients reside, within a geographical area constituted by the province of Naples, Italy, are detected as circular areas. The results show a dependency between the reliability and fluctuation of the values of the degrees of belonging to the hotspots

Spatio-temporal distribution of malaria in Betong, Sarawak, Malaysia: a five years study

The emergence of malaria has become one of the major public health problems in Betong, Sarawak, Malaysia. The number of reported malaria cases are increasing continuously in recent years. The aim of this study was to analyse the spatio-temporal pattern based on the yearly malaria surveillance data. Descriptive analysis was done to investigate the malaria incidence by time, person and place. Further analysis was done by mapping all malaria cases reported from year 2013 to 2017 by using ArcGIS software. Distribution of malaria cases were mapped in term of crude incidence. The average nearest neighbour was used to determine the distance analysis between malaria cases while Kernel density was applied to detect spatial pattern of locality for malaria hotspots. Distribution of malaria cases was clustered and random based on distance analysis. Based on spatio-temporal analysis pattern, malaria cases were identified as clusters in Betong and Spaoh subdistricts. It was observed that high risk occurrence of malaria cases were reported in the months of July to October each year. All the socio-demographic variables were associated with the malaria infection. After adjusting the relationship of all potential predictors at P<0.05, potential predictors such as gender, ethnicity (excluding the Malays) and occupation had significant association with the malaria infection. Spatial mapping could be beneficial to visualize the distribution of malaria cases for public health prevention

Space-Time Kernel Density Estimation for Real-Time Interactive Visual Analytics

We present a GPU-based implementation of the Space-Time Kernel Density Estimation (STKDE) that provides massive speed up in analyzing spatial- temporal data. In our work we are able to achieve sub- second performance for data sizes transferable over the Internet in realistic time. We have integrated this into web-based visual interactive analytics tools for analyzing spatial-temporal data. The resulting inte- grated visual analytics (VA) system permits new anal- yses of spatial-temporal data from a variety of sources. Novel, interlinked interface elements permit efficient, meaningful analyses

EpiScanGIS: an online geographic surveillance system for meningococcal disease

<p>Abstract</p> <p>Background</p> <p>Surveillance of infectious diseases increasingly relies on Geographic Information Systems (GIS). The integration of pathogen fine typing data in dynamic systems and visualization of spatio-temporal clusters are a technical challenge for system development.</p> <p>Results</p> <p>An online geographic information system (EpiScanGIS) based on open source components has been launched in Germany in May 2006 for real time provision of meningococcal typing data in conjunction with demographic information (age, incidence, population density). Spatio-temporal clusters of disease detected by computer assisted cluster analysis (SaTScan™) are visualized on maps. EpiScanGIS enables dynamic generation of animated maps. The system is based on open source components; its architecture is open for other infectious agents and geographic regions. EpiScanGIS is available at <url>http://www.episcangis.org</url>, and currently has 80 registered users, mostly from the public health service in Germany. At present more than 2,900 cases of invasive meningococcal disease are stored in the database (data as of June 3, 2008).</p> <p>Conclusion</p> <p>EpiScanGIS exemplifies GIS applications and early-warning systems in laboratory surveillance of infectious diseases.</p

Integrating the landscape epidemiology and genetics of RNA viruses: rabies in domestic dogs as a model

Landscape epidemiology and landscape genetics combine advances in molecular techniques, spatial analyses and epidemiological models to generate a more real-world understanding of infectious disease dynamics and provide powerful new tools for the study of RNA viruses. Using dog rabies as a model we have identified how key questions regarding viral spread and persistence can be addressed using a combination of these techniques. In contrast to wildlife rabies, investigations into the landscape epidemiology of domestic dog rabies requires more detailed assessment of the role of humans in disease spread, including the incorporation of anthropogenic landscape features, human movements and socio-cultural factors into spatial models. In particular, identifying and quantifying the influence of anthropogenic features on pathogen spread and measuring the permeability of dispersal barriers are important considerations for planning control strategies, and may differ according to cultural, social and geographical variation across countries or continents. Challenges for dog rabies research include the development of metapopulation models and transmission networks using genetic information to uncover potential source/sink dynamics and identify the main routes of viral dissemination. Information generated from a landscape genetics approach will facilitate spatially strategic control programmes that accommodate for heterogeneities in the landscape and therefore utilise resources in the most cost-effective way. This can include the efficient placement of vaccine barriers, surveillance points and adaptive management for large-scale control programmes

Fuzzy-Based Spatiotemporal Hot Spot Intensity and Propagation—An Application in Crime Analysis

Cluster-based hot spot detection is applied in many disciplines to analyze the locations, concentrations, and evolution over time for a phenomenon occurring in an area of study. The hot spots consist of areas within which the phenomenon is most present; by detecting and monitoring the presence of hot spots in different time steps, it is possible to study their evolution over time. One of the most prominent problems in hot spot analysis occurs when measuring the intensity of a phenomenon in terms of the presence and impact on an area of study and evaluating its evolution over time. In this research, we propose a hot spot analysis method based on a fuzzy cluster hot spot detection algorithm, which allows us to measure the incidence of hot spots in the area of study. We analyze its variation over time, and in order to evaluate its reliability we use a well-known fuzzy entropy measure that was recently applied to measure the reliability of hot spots by executing fuzzy clustering algorithms. We apply this method in crime analysis of the urban area of the City of London, using a dataset of criminal events that have occurred since 2011, published by the City of London Police. The obtained results show a decrease in the frequency of all types of criminal events over the entire area of study in recent years

A DECISION SUPPORT SYSTEM FOR THE SPATIAL CONTROL OF INVASIVE BIOAGENTS

A Decision Support System (DSS) is developed and applied to the spatial control of invasive bioagents, exemplified in this study by the resident Canada goose species (Branta Canadensis) in the Anacostia River system of the District of Columbia. The DSS incorporates a model of goose movement that responds to resource distribution; a twocompartment Expert System (ES) that identifies the causes of goose congregation in hotspots (Diagnosis ES) and prescribes strategies for goose population control (Prescription ES); and a Geographic Information System (GIS) that stores, analyzes, and displays geographic data. The DSS runs on an HP xw8600 64-bit Workstation running Window XP Operating System. The mathematical model developed in this study simulates goose-resource dynamics using partial differential equations - solved numerically using the Finite Element Method (FEM). MATLAB software (v. 7.1) performed all simulations. ArcGIS software (v. 9.3) produced by Environmental Systems Research Institute (ESRI) was used to store and manipulate georeferenced data for mapping, image processing, data management, and hotspot analysis. The rule-based Expert Systems (ES) were implemented within the GIS via ModelBuilder, a modular and intuitive Graphical User Interface (GUI) of ArcGIS software. The Diagnosis ES was developed in three steps. The first step was to acquire knowledge about goose biology through a literature search and discussions with human experts. The second step was to formalize the knowledge acquired in step 1 in the form of logical sentences (IF-THEN statements) representing the goose invasion diagnosis rules. Finally, in the third step, the rules were translated into decision trees. The Prescription ES was developed by following the same steps as in the development of the Diagnosis ES, the major difference being that, in this case, knowledge was acquired relative to goose control strategies rather than overpopulation causes; and additionally, knowledge was formalized based on the Diagnosis and on other local factors. Results of the DSS application indicate that high accessibility to food and water resources is the most likely cause of the congregation of geese in the critical areas identified by the model. Other causes include high accessibility to breeding and nesting habitats, and supplementary, artificial food provided by people in urban areas. The DSS prescribed the application of chemical repellents at feeding sites as a goose control strategy (GCS) to reduce the quality of the food resources consumed by resident Canada geese, and therefore the densities of geese in the infested locations. Two other prescribed GCSs are egg destruction and harvest of breeding adult geese, both of which have direct impacts on the goose populations by reducing their densities at hotspots or slowing down their increase. Enclosing small wetlands with fencing and banning the feeding of geese in urban areas are other GCSs recommended by the ES. Model simulations predicted that these strategies would reduce goose densities at hotspots by over 90%. It is suggested that further research is needed to investigate the use of similar systems for the management of other invasive bioagents in ecologically similar environments

Earth observation in support of malaria control and epidemiology: MALAREO monitoring approaches

Malaria affects about half of the world's population, with the vast majority of cases occuring in Africa. National malaria control programmes aim to reduce the burden of malaria and its negative, socioeconomic effects by using various control strategies (e.g. vector control, environmental management and case tracking). Vector control is the most effective transmission prevention strategy, while environmental factors are the key parameters affecting transmission. Geographic information systems (GIS), earth observation (EO) and spatial modelling are increasingly being recognised as valuable tools for effective management and malaria vector control. Issues previously inhibiting the use of EO in epidemiology and malaria control such as poor satellite sensor performance, high costs and long turnaround times, have since been resolved through modern technology. The core goal of this study was to develop and implement the capabilities of EO data for national malaria control programmes in South Africa, Swaziland and Mozambique. High-and very high resolution (HR and VHR) land cover and wetland maps were generated for the identification of potential vector habitats and human activities, as well as geoinformation on distance to wetlands for malaria risk modelling, population density maps, habitat foci maps and VHR household maps. These products were further used for modelling malaria incidence and the analysis of environmental factors that favour vector breeding. Geoproducts were also transferred to the staff of national malaria control programmes in seven African countries to demonstrate how EO data and GIS can support vector control strategy planning and monitoring. The transferred EO products support better epidemiological understanding of environmental factors related to malaria transmission, and allow for spatio-temporal targeting of malaria control interventions, thereby improving the cost-effectiveness of interventions

Integration of multiple geospatial applications and intelligence for responding to COVID-19 in Ghana

Objective: We describe the use of integrated geospatial applications for the provision of access to timely and accurate data on samples, visualisation of Spatio-temporal patterns of cases and effective communication between field sample collectors, testing laboratories, Regional Health directors and Government Decision Makers.Design: This study describes how an integrated geospatial platform based on case location and intelligence was developed and used for effective COVID-19 response during the initial stages of COVID-19 in Ghana.Data Source: Collector for ArcGIS, ArcGIS Survey123Main outcome measure: successful development and deployment of integrated geospatial applications and analytics.&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Results: The Collector for ArcGIS app was customised to collect COVID-19 positive cases location information. Survey 123 was introduced as a COVID-19 contact tracing application to digitise the case-based forms and provide real-time results from the laboratories to GHS and other stakeholders. The laboratory backend allowed the testing laboratories access to specific information about each patient (sample) collected by the fieldworkers. The regional supervisors’ backend web application provided accessing test results for confidentiality and timely communication of results.Conclusion: Geospatial platforms were successfully established in Ghana to provide timely results to Regional Health Directors and Government decision-makers. This helped to improve the timeliness of response and contact tracing at the district level