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Special Issue "Vector-Borne Diseases and Geospatial Modeling"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Global Health".

Deadline for manuscript submissions: 31 May 2023 | Viewed by 441

Special Issue Editors

Dr. Kinley Wangdi
E-Mail Website
Guest Editor
Department of Global Health, National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT 2601, Australia
Interests: infectious diseases; spatial; modelling; mapping; Bayesian; vector-borne diseases; health systems
Special Issues, Collections and Topics in MDPI journals
Dr. Apiporn Thinkhamrop Suwannatrai
E-Mail Website
Guest Editor
Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
Interests: parasitology, tropical diseases; health GIS; spatial; modelling, Bayesian; ecology; epidemiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vector-borne diseases (VBDs) account for up to 17% of all infectious diseases in low- and middle-income countries of the world. Complex environmental, climatic, and social factors drive the distributions of VBDs. These factors can impact the VBDs by making transmission longer or more intense. For example, transmissions to newer regions can be caused by conducive climatic and environmental conditions. Furthermore, frequent epidemics of VBDs are driven by increased cross-border travel and trade, changing agricultural practices, and individual behavior. The increasing rates of these VBDs have major social, economic, and developmental impacts in resource-constrained settings, affecting the ability of people to work and contribute to their families’ income, preventing children from attending school, and posing significant medical costs. Together these factors contribute to rising health inequities and hinder socioeconomic development.

Spatial modeling increases our understanding of environmental and climatic drivers of VBDs. Therefore, geospatial modeling can help determine high-risk areas of VBDs and thus enhance interventions and control through surveillance. In addition, advanced spatial analysis and modeling can help policymakers identify high-risk (hot spots) areas and design geographically targeted measures for better resource allocations and disease control. The Special Issue “Vector-Borne Diseases and Geospatial Healthcare” welcomes high-quality original research articles and review articles in the broad subject area of VBDs and geospatial health.

Dr. Kinley Wangdi
Dr. Apiporn Thinkhamrop Suwannatrai
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • vector-borne diseases
  • spatial modeling
  • mapping
  • epidemiology
  • ecology
  • surveillance
  • control
  • interventions

Published Papers (1 paper)

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Distribution and Risk Factors of Malaria in the Greater Accra Region in Ghana
Int. J. Environ. Res. Public Health 2022, 19(19), 12006; https://doi.org/10.3390/ijerph191912006 - 22 Sep 2022
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Malaria remains a serious public health challenge in Ghana including the Greater Accra Region. This study aimed to quantify the spatial, temporal and spatio-temporal patterns of malaria in the Greater Accra Region to inform targeted allocation of health resources. Malaria cases data from [...] Read more.
Malaria remains a serious public health challenge in Ghana including the Greater Accra Region. This study aimed to quantify the spatial, temporal and spatio-temporal patterns of malaria in the Greater Accra Region to inform targeted allocation of health resources. Malaria cases data from 2015 to 2019 were obtained from the Ghanaian District Health Information and Management System and aggregated at a district and monthly level. Spatial analysis was conducted using the Global Moran’s I, Getis-Ord Gi*, and local indicators of spatial autocorrelation. Kulldorff’s space–time scan statistics were used to investigate space–time clustering. A negative binomial regression was used to find correlations between climatic factors and sociodemographic characteristics and the incidence of malaria. A total of 1,105,370 malaria cases were reported between 2015 and 2019. Significant seasonal variation was observed, with June and July being the peak months of reported malaria cases. The hotspots districts were Kpone-Katamanso Municipal District, Ashaiman Municipal Districts, Tema Municipal District, and La-Nkwantanang-Madina Municipal District. While La-Nkwantanang-Madina Municipal District was high-high cluster. The Spatio-temporal clusters occurred between February 2015 and July 2017 in the districts of Ningo-Prampram, Shai-Osudoku, Ashaiman Municipal, and Kpone-Katamanso Municipal with a radius of 26.63 km and an relative risk of 4.66 (p < 0.001). Malaria cases were positively associated with monthly rainfall (adjusted odds ratio [AOR] = 1.01; 95% confidence interval [CI] = 1.005, 1.016) and the previous month’s cases (AOR = 1.064; 95% CI 1.062, 1.065) and negatively correlated with minimum temperature (AOR = 0.86, 95% CI = 0.823, 0.899) and population density (AOR = 0.996, 95% CI = 0.994, 0.998). Malaria control and prevention should be strengthened in hotspot districts in the appropriate months to improve program effectiveness. Full article
(This article belongs to the Special Issue Vector-Borne Diseases and Geospatial Modeling)
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