Call for Papers

Epidemiological Insights from Geospatial Data: A Statistical Exploration

Submission deadline: Wednesday, 30 July 2025

Spatial epidemiology is the study and analysis of disease variations based on demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors. In epidemiology, statistics are primarily used to draw inferences about a population of interest when available data is limited to samples. Thematic maps that show the severity of a disease or its vector are produced with the aid of GIS. It can establish buffer zones around particular features and then calculate the number of cases that fall inside the buffer by combining this data with statistics on illness incidence. Analyzing epidemiological data aims to control illnesses and other health issues by identifying the distribution and factors of health-related occurrences. Geospatial analytics is a type of computational analysis that uses spatial, location, and geographic information to extract structured data that may be utilized for particular industries and applications. It also increasingly uses high-resolution imaging, computer vision, and other forms of AI. The three main methods used in epidemiology are experimental, analytical, and descriptive. While any of the three can be utilized to look into the occurrence of illness, descriptive epidemiology is the most frequently employed approach. Thus, mapping, research, and analysis of the data of pandemics and epidemics concerning location and time might be done using remote sensing and GIS.

Disease monitoring and disease prevention also use GPS and integrated remote sensing, both of which fall under the GIS purview. Finding factors associated with illness occurrence is the primary goal of epidemiology. By identifying these variables, both risk and causation factors, a logical foundation for prevention may be established. For example, the Cost of Auto Insurance, The state of the weather, traffic patterns, and other risk factors all impact risk. With the use of geospatial data analysis, insurers can investigate and evaluate regionally unique elements, as well as make necessary adjustments. The field employs several techniques and algorithms to extract knowledge from diverse geospatial data sources, including spatially interpolating, spatial regression analysis, spatial clustering, and spatial autocorrelation analysis. The two categories of epidemiologic activity commonly recognized are descriptive and analytical, and both rely on particular types of research.

Finding patterns and trends in data is the goal of statistical analysis, which involves data gathering and interpretation. It belongs to the data analytics component. Statistics can be applied to the design of surveys and studies, statistical modeling, and the collection of research interpretations. Targeting and implementing evidence-based control actions to safeguard public health and safety depend heavily on epidemiologic data. When conducting a field epidemiological investigation to determine the root cause of a pressing public health issue that needs to be addressed right away, statistics are more crucial than ever; determining which populations are most vulnerable to the health issue being studied gives decision-makers, the public, the media, and other interested parties up to date information regarding ongoing investigations and backs choices to start or change preventative and control actions.

Contributions are invited from a range of disciplines and perspectives, including but not restricted to:

• Investigative Data Analysis Tasks for Epidemiology in a Geovisualization Setting;
• Utilizing dynamic display and cluster embedding to examine spatiotemporal epidemiology data;
• A multi-method approach to geospatial study of developing infectious diseases at the county level;
• An analytical tool designed for ease of use that may be used to extract and visualize spatial variables from epidemiological data series;
• Measurement inaccuracy in environmental epidemiology brought on by spatial misalignment;
• An improved geographic information system design and usability for investigating multivariate health statistics;
• Problems in community biology and ecosystem science on a mathematical and computational level;
• An overview of the theories and methods of spatial analysis as they relate to risk modeling, containment, and mapping of infectious illnesses and other ailments;
• The value of dynamic agent models for comprehending that location affects health;
• Interactive visual analytics for controlling epidemics using human mobility constraints around the city;
• A cooperative study employing cutting-edge spatial data mining techniques to investigate the relationship between pollution mixes and unfavorable birth outcomes;
• Investigating environmental and health inequities in underprivileged neighborhoods through exploratory geographical data analysis.

Guest Editors:

Rolando Quintero National Polytechnic Institute, Mexico

Miguel Torres-Ruiz National Polytechnic Institute, Mexico

Rustam Asnawi Yogyakarta State University, Indonesia

Keywords: Geospatial; Epidemiological; Geovisualization; Geographical data analysis; GIS; Statistical Data.

Submission Guidelines/Instructions:

Please refer to the Author Guidelines to prepare your manuscript. When submitting your manuscript, please answer the question "Is this submission for a special issue?" by selecting the special issue title from the drop-down list.

Information about the Geoscience Data Journal:

CATEGORY

GEOSCIENCES, MULTIDISCIPLINARY

JCR YEAR: 2023

JIF RANK: 64/254

JIF QUARTILE: Q2

JIF PERCENTILE: 75.0

CATEGORY

METEOROLOGY & ATMOSPHERIC SCIENCES

JCR YEAR: 2023

JIF RANK: 43/110

JIF QUARTILE: Q2

JIF PERCENTILE: 61.4