Zhenlong Li is co-guest editing a Special Issue entitled “Spatial Analytics for COVID-19 Studies” for the International Journal of Environmental Research and Public Health (ISSN 1660-4601, IF 3.390, http://www.mdpi.com/journal/ijerph).
IJERPH is an open access journal indexed by SCI, SSCI, Scopus, and PubMed. According to Web of Science, IJERPH ranks 118/274 (Q2) in “Environmental Sciences” (SCIE), 68/203 (Q2) in “Public, Environmental, and Occupational Health” (SCIE), and 41/176 (Q1) in “Public, Environmental, and Occupational Health” (SSCI). The median processing time for submissions is less than 45 days, which includes a free English editing service after acceptance of the paper. The article processing charge (APC) is CHF 2300 (Swiss Francs) per accepted paper.
Dear Colleagues,
Coronavirus disease 2019 (COVID-19) is a global threat that has led to many health, economic, and social challenges. The spread of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that caused the COVID-19 pandemic is inherently a spatial process. Therefore, geospatial data, algorithms, models, tools, and platforms play an irreplaceable role in providing situational awareness that benefits decision making. The notable advances in Geographical Information Sciences (GIScience) have encouraged the incorporation of spatial analytics into various epidemiological studies over the past decade.
In this Special Issue, we focus on the development and application of advanced spatial analytics towards understanding the transmission and impacts of COVID-19. We invite contributions that address this general topic from a broad spectrum of data sources (public health, economics, socio-demographics, social media, mobile phone data, transportation records, surveys, etc.) and via a variety of spatial analytics including (but not limited to) spatial statistics, agent-based simulation, digital contact tracing, case forecasting, disease transmission modeling, geo-aware analysis, spatiotemporal prediction, intelligent algorithms (i.e., machine learning and deep learning), and big data analytics. We also welcome studies that produce, design, and develop shareable COVID-19 modeling-related data, online visualization/analytical platforms, and reusable analytical tools, packages, and models.
Dr. Tao Hu
Dr. Zhenlong Li
Dr. Xiao Huang
Guest Editors
More information can be found on the Special Issue website: https://www.mdpi.com/journal/ijerph/special_issues/Spatial_COVID_19
