Topic Editors

Department of Geography, Harokopio University, 70 El. Venizelou Str., 17671 Kallithea, Greece
Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece

Spatial Epidemiology and GeoInformatics

Abstract submission deadline
closed (30 June 2023)
Manuscript submission deadline
30 June 2024
Viewed by
15576

Topic Information

Dear Colleagues,

Under the broad area of Spatial Epidemiology that includes the description and analysis of geographic variation in health outcomes, this Topic collection will address the association of multiple contextual factors (including environmental, demographic, socio- economic, psychological, lifestyle and behavioral factors, e.g., dietary habits, smoking, physical activity, etc.), with a variety of health outcomes, including both NCD and communicable factors (e.g., COVID-19). Spatial epidemiology and geoinformatics are two interdisciplinary research approaches that have been increasingly used in recent years to address public health challenges. Spatial epidemiology studies the distribution of disease across space, while geoinformatics focuses on the use of geospatial data to identify geographic patterns in health outcomes. These approaches offer researchers unique opportunities to gain insights into how environmental factors influence the spread of disease and how specific interventions can be targeted to specific regions or populations. Despite their potential, there are a number of challenges associated with these research methods. These include, among others, the availability and quality of data, as well as the techniques and tools. This Topic collection aims to provide a wide overview of how to approach spatial problems in epidemiology, with a specific focus on novel methodologies, e.g., those deriving from spatial analysis, spatiotemporal modelling, statistical and probability theory and practice, artificial intelligence/machine learning techniques and software presentation, as well as public health applications. Papers that focus on spatial health and covariate data, visualization and spatial exploration, quantification of spatial patterns, detection of spatial heterogeneities and statistical, analytical methods for spatial prediction are welcome.

Prof. Dr. Christos Chalkias
Dr. Demosthenes Panagiotakos
Topic Editors

Keywords

  • spatial epidemiology
  • geoinformatics
  • health geography
  • GIS modeling
  • public health
  • global health
  • chronic disease
  • infectious disease
  • COVID-19
  • social medicine

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Healthcare
healthcare
2.8 2.7 2013 19.5 Days CHF 2700 Submit
International Journal of Environmental Research and Public Health
ijerph
- 5.4 2004 29.6 Days CHF 2500 Submit
ISPRS International Journal of Geo-Information
ijgi
3.4 6.2 2012 35.5 Days CHF 1700 Submit

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Published Papers (10 papers)

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0 pages, 3076 KiB  
Review
A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology
by Yufeng Wang, Xue Chen and Feng Xue
ISPRS Int. J. Geo-Inf. 2024, 13(3), 97; https://doi.org/10.3390/ijgi13030097 - 18 Mar 2024
Viewed by 1668
Abstract
Spatial epidemiology investigates the patterns and determinants of health outcomes over both space and time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity to incorporate spatial and temporal dependencies, uncertainties, and intricate interactions. However, the complexity of modelling [...] Read more.
Spatial epidemiology investigates the patterns and determinants of health outcomes over both space and time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity to incorporate spatial and temporal dependencies, uncertainties, and intricate interactions. However, the complexity of modelling and computations associated with Bayesian spatiotemporal models vary across different diseases. Presently, there is a limited comprehensive overview of Bayesian spatiotemporal models and their applications in epidemiology. This article aims to address this gap through a thorough review. The review commences by delving into the historical development of Bayesian spatiotemporal models concerning disease mapping, prediction, and regression analysis. Subsequently, the article compares these models in terms of spatiotemporal data distribution, general spatiotemporal data models, environmental covariates, parameter estimation methods, and model fitting standards. Following this, essential preparatory processes are outlined, encompassing data acquisition, data preprocessing, and available statistical software. The article further categorizes and summarizes the application of Bayesian spatiotemporal models in spatial epidemiology. Lastly, a critical examination of the advantages and disadvantages of these models, along with considerations for their application, is provided. This comprehensive review aims to enhance comprehension of the dynamic spatiotemporal distribution and prediction of epidemics. By facilitating effective disease scrutiny, especially in the context of the global COVID-19 pandemic, the review holds significant academic merit and practical value. It also aims to contribute to the development of improved ecological and epidemiological prevention and control strategies. Full article
(This article belongs to the Topic Spatial Epidemiology and GeoInformatics)
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17 pages, 4946 KiB  
Article
Bayesian Shared Component Spatial Modeling for Assessing the Shared and Age Group-Specific Mental Health Disorder Risk of Young and Old Age Groups: A Case Study of Toronto Neighborhoods, Canada
by Abu Yousuf Md Abdullah and Jane Law
ISPRS Int. J. Geo-Inf. 2024, 13(3), 75; https://doi.org/10.3390/ijgi13030075 - 28 Feb 2024
Viewed by 1181
Abstract
Mental health disorder risks of young and old age groups hold considerable importance for understanding present and future risk burdens. However, assessing mental health risks is significantly constrained by the influence of shared and age group-specific spatial processes and risk factors. Therefore, this [...] Read more.
Mental health disorder risks of young and old age groups hold considerable importance for understanding present and future risk burdens. However, assessing mental health risks is significantly constrained by the influence of shared and age group-specific spatial processes and risk factors. Therefore, this study employed Bayesian shared component spatial modeling (BSCSM) to analyze mental health disorder data obtained from young (20–44 years) and old (65+ years) age groups in Toronto. BSCSM was employed to model the shared and age group-specific disorder risk and to identify hotspot areas. The unmeasured covariates, overdispersion, and latent spatial processes were adjusted using spatial and non-spatial random effect terms. The findings from BSCSM were finally compared with non-shared component modeling approaches. The results suggest that over 60% of variations in mental health disorder risk for both age groups could be explained by the shared component. The high-risk neighborhoods were mainly localized in southern and north-central Toronto for the young and old age groups. Deviance information criterion values suggested that models from BSCSM outperformed non-BSCSM models. BSCSM risk maps were also better at identifying high-risk areas. This work demonstrated that both shared and age group-specific risks are essential for assessing mental health disorder risk and devising targeted interventions. Full article
(This article belongs to the Topic Spatial Epidemiology and GeoInformatics)
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17 pages, 4581 KiB  
Article
Measuring the Potential and Realized (or Revealed) Spatial Access from Places of Residence and Work to Food Outlets in Rural Communities of Québec, Canada
by Eric Robitaille, Gabrielle Durette, Marianne Dubé, Olivier Arbour and Marie-Claude Paquette
ISPRS Int. J. Geo-Inf. 2024, 13(2), 43; https://doi.org/10.3390/ijgi13020043 - 1 Feb 2024
Viewed by 1421
Abstract
This study aims to bridge the gap between the potential and realized spatial access to food outlets in rural areas of Québec, Canada. By assessing both aspects, this research aims to provide a comprehensive understanding of the challenges faced by rural communities in [...] Read more.
This study aims to bridge the gap between the potential and realized spatial access to food outlets in rural areas of Québec, Canada. By assessing both aspects, this research aims to provide a comprehensive understanding of the challenges faced by rural communities in accessing food resources and the effectiveness of existing interventions in addressing these challenges. A mixed methods approach was adopted to collect and analyze data, combining GIS-based spatial analysis with community-based surveys. The spatial analysis allowed for the quantification of the potential access metrics, while the community surveys provided valuable information on travel behaviors, preferences, and barriers experienced by residents when accessing food outlets. The results of the distance measurement calculations showed that for both the potential and realized distance measurements, convenience stores are more easily accessible than grocery stores and supermarkets. Thus, workers seem to have a strategy for minimizing the impact of long distances by combining work and grocery shopping. These results are measured for the realized accessibility to grocery stores and supermarkets and the principal retailer used. Finally, the results of the analyses show that there is a socio-economic gradient in the potential geographical accessibility from home to the food outlets. The importance of developing and strengthening the local food environment to make it favourable to healthy eating and supportive of food security is discussed. Full article
(This article belongs to the Topic Spatial Epidemiology and GeoInformatics)
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20 pages, 12497 KiB  
Article
Spatial Survival Model for COVID-19 in México
by Eduardo Pérez-Castro, María Guzmán-Martínez, Flaviano Godínez-Jaimes, Ramón Reyes-Carreto, Cruz Vargas-de-León and Alejandro Iván Aguirre-Salado
Healthcare 2024, 12(3), 306; https://doi.org/10.3390/healthcare12030306 - 24 Jan 2024
Viewed by 1119
Abstract
A spatial survival analysis was performed to identify some of the factors that influence the survival of patients with COVID-19 in the states of Guerrero, México, and Chihuahua. The data that we analyzed correspond to the period from 28 February 2020 to 24 [...] Read more.
A spatial survival analysis was performed to identify some of the factors that influence the survival of patients with COVID-19 in the states of Guerrero, México, and Chihuahua. The data that we analyzed correspond to the period from 28 February 2020 to 24 November 2021. A Cox proportional hazards frailty model and a Cox proportional hazards model were fitted. For both models, the estimation of the parameters was carried out using the Bayesian approach. According to the DIC, WAIC, and LPML criteria, the spatial model was better. The analysis showed that the spatial effect influences the survival times of patients with COVID-19. The spatial survival analysis also revealed that age, gender, and the presence of comorbidities, which vary between states, and the development of pneumonia increase the risk of death from COVID-19. Full article
(This article belongs to the Topic Spatial Epidemiology and GeoInformatics)
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0 pages, 2292 KiB  
Article
Quantitative Study on American COVID-19 Epidemic Predictions and Scenario Simulations
by Jingtao Sun, Jin Qi, Zhen Yan, Yadong Li, Jie Liang and Sensen Wu
ISPRS Int. J. Geo-Inf. 2024, 13(1), 31; https://doi.org/10.3390/ijgi13010031 - 18 Jan 2024
Viewed by 1876
Abstract
The COVID-19 pandemic has had a profound impact on people’s lives, making accurate prediction of epidemic trends a central focus in COVID-19 research. This study innovatively utilizes a spatiotemporal heterogeneity analysis (GTNNWR) model to predict COVID-19 deaths, simulate pandemic prevention scenarios, and quantitatively [...] Read more.
The COVID-19 pandemic has had a profound impact on people’s lives, making accurate prediction of epidemic trends a central focus in COVID-19 research. This study innovatively utilizes a spatiotemporal heterogeneity analysis (GTNNWR) model to predict COVID-19 deaths, simulate pandemic prevention scenarios, and quantitatively assess their preventive effects. The results show that the GTNNWR model exhibits superior predictive capacity to the conventional infectious disease dynamics model (SEIR model), which is approximately 9% higher, and reflects the spatial and temporal heterogeneity well. In scenario simulations, this study established five scenarios for epidemic prevention measures, and the results indicate that masks are the most influential single preventive measure, reducing deaths by 5.38%, followed by vaccination at 3.59%, and social distancing mandates at 2.69%. However, implementing single stringent preventive measures does not guarantee effectiveness across all states and months, such as California in January 2025, Florida in August 2024, and March–April 2024 in the continental U.S. On the other hand, the combined implementation of preventive measures proves 5 to-10-fold more effective than any single stringent measure, reducing deaths by 27.2%. The deaths under combined implementation measures never exceed that of standard preventive measures in any month. The research found that the combined implementation of measures in mask wearing, vaccination, and social distancing during winter can reduce the deaths by approximately 45%, which is approximately 1.5–3-fold higher than in the other seasons. This study provides valuable insights for COVID-19 epidemic prevention and control in America. Full article
(This article belongs to the Topic Spatial Epidemiology and GeoInformatics)
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19 pages, 7724 KiB  
Article
High-Temporal-Resolution Prediction of Malaria Transmission Risk in South Kivu, Democratic Republic of the Congo, Based on Multi-Criteria Evaluation Using Geospatial Data
by Ryunosuke Komura and Masayuki Matsuoka
ISPRS Int. J. Geo-Inf. 2023, 12(12), 489; https://doi.org/10.3390/ijgi12120489 - 6 Dec 2023
Viewed by 1487
Abstract
Malaria is a major public health concern, and accurate mapping of malaria risk is essential to effectively managing the disease. However, current models are unable to predict malaria risk with high temporal and spatial resolution. This study describes a climate-based model that can [...] Read more.
Malaria is a major public health concern, and accurate mapping of malaria risk is essential to effectively managing the disease. However, current models are unable to predict malaria risk with high temporal and spatial resolution. This study describes a climate-based model that can predict malaria risk in South Kivu, Democratic Republic of the Congo, daily at a resolution of 2 km using meteorological (relative humidity, precipitation, wind speed, and temperature) and elevation data. We used the multi-criteria evaluation technique to develop the model. For the weighting of factors, we employed the analytical hierarchy process and linear regression techniques to compare expert knowledge-driven and mathematical methods. Using climate data from the prior 2 weeks, the model successfully mapped regions with high malaria case numbers, enabling accurate prediction of high-risk regions. This research may contribute to the development of a sustainable malaria risk forecasting system, which has been a longstanding challenge. Overall, this study provides insights into model development to predict malaria risk with high temporal and spatial resolution, supporting malaria control and management efforts. Full article
(This article belongs to the Topic Spatial Epidemiology and GeoInformatics)
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16 pages, 4209 KiB  
Article
A Fine-Grained Simulation Study on the Incidence Rate of Dysentery in Chongqing, China
by Jian Hao and Jingwei Shen
ISPRS Int. J. Geo-Inf. 2023, 12(11), 459; https://doi.org/10.3390/ijgi12110459 - 9 Nov 2023
Viewed by 1445
Abstract
Dysentery is still a serious global public health problem. In Chongqing, China, there were 37,140 reported cases of dysentery from 2015 to 2021. However, previous research has relied on statistical data of dysentery incidence rate data based on administrative regions, while grained scale [...] Read more.
Dysentery is still a serious global public health problem. In Chongqing, China, there were 37,140 reported cases of dysentery from 2015 to 2021. However, previous research has relied on statistical data of dysentery incidence rate data based on administrative regions, while grained scale products are lacking. Thus, an initialized gradient-boosted decision trees (IGBDT) hybrid machine learning model was constructed to fill this gap in grained scale products. Socioeconomic factors, meteorological factors, topographic factors, and air quality factors were used as inputs of the IGBDT to map the statistical dysentery incidence rate data of Chongqing, China, from 2015 to 2021 on the grid scale. Then, dysentery incidence rate grained scale products (1 km) were generated. The products were evaluated using the total incidence of Chongqing and its districts, with resulting R2 values of 0.7369 and 0.5439, indicating the suitable prediction performance of the model. The importance and correlation of factors related to the dysentery incidence rate were investigated. The results showed that socioeconomic factors had the main impact (43.32%) on the dysentery incidence rate, followed by meteorological factors (33.47%). The Nighttime light, normalized difference vegetation index, and maximum temperature showed negative correlations, while the population, minimum and mean temperature, precipitation, and relative humidity showed positive correlations. The impacts of topographic factors and air quality factors were relatively weak. Full article
(This article belongs to the Topic Spatial Epidemiology and GeoInformatics)
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15 pages, 1565 KiB  
Article
Geospatial Overlap of Undernutrition and Tuberculosis in Ethiopia
by Fasil Wagnew, Kefyalew Addis Alene, Matthew Kelly and Darren Gray
Int. J. Environ. Res. Public Health 2023, 20(21), 7000; https://doi.org/10.3390/ijerph20217000 - 31 Oct 2023
Cited by 2 | Viewed by 1369
Abstract
Undernutrition is a key driver of the global tuberculosis (TB) epidemic, yet there is limited understanding regarding the spatial overlap of both diseases. This study aimed to determine the geographical co-distribution and socio-climatic factors of undernutrition and TB in Ethiopia. Data on undernutrition [...] Read more.
Undernutrition is a key driver of the global tuberculosis (TB) epidemic, yet there is limited understanding regarding the spatial overlap of both diseases. This study aimed to determine the geographical co-distribution and socio-climatic factors of undernutrition and TB in Ethiopia. Data on undernutrition were found from the Ethiopian Demographic and Health Survey (EDHS). Data on TB were obtained from the Ethiopia national TB prevalence survey. We applied a geostatistical model using a Bayesian framework to predict the prevalence of undernutrition and TB. Spatial overlap of undernutrition and TB prevalence was detected in the Afar and Somali regions. Population density was associated with the spatial distribution of TB [β: 0.008; 95% CrI: 0.001, 0.014], wasting [β: −0.017; 95% CrI: −0.032, −0.004], underweight [β: −0.02; 95% CrI: −0.031, −0.011], stunting [β: −0.012; 95% CrI: −0.017, −0.006], and adult undernutrition [β: −0.007; 95% CrI: −0.01, −0.005]. Distance to a health facility was associated with the spatial distribution of stunting [β: 0.269; 95% CrI: 0.08, 0.46] and adult undernutrition [β: 0.176; 95% CrI: 0.044, 0.308]. Healthcare access and demographic factors were associated with the spatial distribution of TB and undernutrition. Therefore, geographically targeted service integration may be more effective than nationwide service integration. Full article
(This article belongs to the Topic Spatial Epidemiology and GeoInformatics)
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30 pages, 68036 KiB  
Article
Investigating the Spatiotemporal Relationship between the Built Environment and COVID-19 Transmission
by Hao Huang, Haochen Shi, Mirna Zordan, Siu Ming Lo and Jin Yeu Tsou
ISPRS Int. J. Geo-Inf. 2023, 12(10), 390; https://doi.org/10.3390/ijgi12100390 - 27 Sep 2023
Cited by 1 | Viewed by 1517
Abstract
Earlier studies have examined various factors that may contribute to the contagion rate of COVID-19, such as urban climatic and socioeconomic characteristics. However, there is a lack of studies at the township level detailing the spatiotemporal settings of built environment attributes, especially in [...] Read more.
Earlier studies have examined various factors that may contribute to the contagion rate of COVID-19, such as urban climatic and socioeconomic characteristics. However, there is a lack of studies at the township level detailing the spatiotemporal settings of built environment attributes, especially in the context of lockdown as a response to the global Omicron outbreak. In this study, we extended the existing literature by relating the initial-stage Omicron pandemic conditions with more comprehensive measures of the built environment, including density, diversity, design, distance to transit, and destination accessibility. The variations from the confirmed clusters of COVID-19 and asymptomatic infected cases before, during, and after the lockdown throughout the Omicron outbreak were identified geographically using GIS methods in 218 township-level divisions across Shanghai during the lockdown period. We also compared the regression results of the ordinary least-squares regression, geographically weighted regression, and geographically and temporally weighted regression. Our results show that (1) among all the built environment variables, metro line length, walking accessibility, hotel and inn density, and population exhibited positive significance in influencing pandemic prevalence; (2) spatial and temporal variations were evident in the association between accessibility, mobility, density-related built environment variables, and COVID-19 transmission across three phases: pre-lockdown, during lockdown, and post-lockdown. This study highlights the importance of targeted public health interventions in densely populated areas with high demand for public transit. It emphasizes the significance of transportation network layout and walking accessibility in controlling the spread of infectious diseases in specific urban contexts. By considering these factors, policymakers and stakeholders can foster urban resilience and effectively mitigate the impact of outbreaks, aligning with the objectives of the 2030 UN Sustainable Development Goals. Full article
(This article belongs to the Topic Spatial Epidemiology and GeoInformatics)
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17 pages, 3935 KiB  
Article
Comparison of Soft Indicator and Poisson Kriging for the Noise-Filtering and Downscaling of Areal Data: Application to Daily COVID-19 Incidence Rates
by Pierre Goovaerts, Thomas Hermans, Peter F. Goossens and Ellen Van De Vijver
ISPRS Int. J. Geo-Inf. 2023, 12(8), 328; https://doi.org/10.3390/ijgi12080328 - 5 Aug 2023
Viewed by 1066
Abstract
This paper addresses two common challenges in analyzing spatial epidemiological data, specifically disease incidence rates recorded over small areas: filtering noise caused by small local population sizes and deriving estimates at different spatial scales. Geostatistical techniques, including Poisson kriging (PK), have been used [...] Read more.
This paper addresses two common challenges in analyzing spatial epidemiological data, specifically disease incidence rates recorded over small areas: filtering noise caused by small local population sizes and deriving estimates at different spatial scales. Geostatistical techniques, including Poisson kriging (PK), have been used to address these issues by accounting for spatial correlation patterns and neighboring observations in smoothing and changing spatial support. However, PK has a limitation in that it can generate unrealistic rates that are either negative or greater than 100%. To overcome this limitation, an alternative method that relies on soft indicator kriging (IK) is presented. The performance of this method is compared to PK using daily COVID-19 incidence rates recorded in 2020–2021 for each of the 581 municipalities in Belgium. Both approaches are used to derive noise-filtered incidence rates for four different dates of the pandemic at the municipality level and at the nodes of a 1 km spacing grid covering the country. The IK approach has several attractive features: (1) the lack of negative kriging estimates, (2) the smaller smoothing effect, and (3) the better agreement with observed municipality-level rates after aggregation, in particular when the original rate was zero. Full article
(This article belongs to the Topic Spatial Epidemiology and GeoInformatics)
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