How can you track infectious diseases in public spaces with GIS data sources?

1 GIS data sources for disease tracking

GIS data sources for disease tracking can include both spatial and non-spatial data. Spatial data is related to the location and shape of features on the earth's surface, while non-spatial data consists of the attributes or characteristics of those features. Different types of GIS data sources can be used to track infectious diseases in public spaces, such as remote sensing data collected by satellites, drones, or aircraft; survey data gathered by field workers, health workers, or volunteers; and administrative data from governments, agencies, or organizations. Remote sensing data can help detect changes in land use, vegetation, temperature, or precipitation that may affect disease transmission or vulnerability. Survey data can identify cases, contacts, or risk factors of diseases and map their distribution and density. Administrative data can provide official records of disease incidence, mortality, testing, vaccination, or treatment to analyze trends and patterns.

2 GIS data standards for disease tracking

GIS data standards are essential for ensuring quality, consistency, and interoperability of GIS data. They cover aspects such as data formats, metadata, projections, accuracy, completeness, and security. GIS data standards are important for disease tracking as they help to integrate and harmonize data from different sources and scales, share and exchange data with other users and systems, validate and verify data quality and reliability, as well as comply with ethical and legal requirements for data protection and privacy. Examples of GIS data standards for disease tracking include the Health Level Seven (HL7) standard which helps transfer and receive data from health information systems; the International Organization for Standardization (ISO) 19115 standard which helps document and describe the source, content, quality, and condition of GIS data; and the World Health Organization (WHO) GIS Data Model which helps store and manage GIS data in a consistent and logical way.

3 GIS data analysis for disease tracking

GIS data analysis is the process of applying statistical or spatial methods to GIS data to extract meaningful information and insights. By using GIS data analysis, you can track infectious diseases in public spaces by describing and summarizing their distribution and characteristics, exploring and testing the relationships between them and their determinants, predicting and forecasting their future trends and scenarios, and evaluating the effectiveness of interventions and policies. Examples of GIS data analysis methods for disease tracking include spatial statistics (e.g. spatial autocorrelation, spatial regression, or spatial interpolation), spatial modeling (e.g. spatial diffusion, spatial interaction, or spatial network models), and spatial visualization (e.g. maps, charts, or dashboards).

4 GIS data challenges for disease tracking

GIS data challenges are the issues and difficulties encountered when using GIS data for disease tracking. These can arise from the data itself, the data sources, the data standards, or the data analysis, which can affect the quality, accuracy, timeliness, or usability of your GIS data. Examples of GIS data challenges for disease tracking include data gaps, which are missing or incomplete data that can limit coverage, resolution, or representation of diseases and their determinants; data errors, which are incorrect or inaccurate data that can distort measurement, estimation, or interpretation; and data bias, which are systematic or intentional data that can skew analysis, inference, or conclusion of diseases and their determinants. Data gaps may result from insufficient or uneven data collection, transmission, or storage; errors can stem from human or technical mistakes; and bias may be caused by political or ethical influences.

5 GIS data solutions for disease tracking

GIS data solutions are the strategies and actions you can take to overcome or mitigate GIS data challenges for disease tracking. These solutions can involve improving or modifying the data itself, the data sources, the data standards, or the data analysis in order to enhance the quality, accuracy, timeliness, or usability of your GIS data. Examples of GIS data solutions for disease tracking include data integration, which is combining and harmonizing data from different sources and scales to fill gaps and increase completeness; data validation, which is checking and correcting errors to improve accuracy and reliability; and data transparency, which is disclosing and documenting sources, methods, and assumptions to reduce bias and increase trustworthiness.