ArcGIS Applications in COVID-19 Spatial Epidemiology: A Comprehensive Systematic Review

Year : 2025 | Volume : 16 | Issue : 02 | Page : 17 25
    By

    Husam H. Abdulmughni,

  • Ratnadeep R. Deshmukh,

  1. Professor, Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Chhatrapati Sambhaji Nagar, Maharashtra, India
  2. Faculty, Department of Information Technology, Faculty of Computer and Information Technology, Sana’a University, Sana’a, Yemen

Abstract

Addressing complicated community health concerns frequently necessitates the establishment of health practices. Professionals who study community health using information technology require a thorough framework. Health care professionals and authorities have had the ability to comprehend health-related geographical data and make timely judgments in different situations. In the field of epidemic disease prevention, including a viral spread model into a GIS is a popular issue. As a result, a GIS as well as other geographical technologies should be used to integrate pandemic models. Zoometric diseases account for a significant portion of outbreaks, making it difficult for public health organizations to detect and contain outbreaks. Taking advantage of new approaches and information sources has become critical in a global community. In the last 10 years, the number of geographical decision support papers has increased, making data collection, analysis, and judgment easier. The purpose of this work is to discover what made geographic planners so valuable in assisting public health officials in managing epidemics of zoometric illness. Long-term exposure to polluted water may cause health problems such as cancer. This research included a review of research journals on the geospatial and geometrical aspects of a geographical component of a 2020, corona virus disease (COVID-19) pandemic. The following disease map categories can be applied to various aspects of this study: spatiotemporal research, healthcare and social geography, environmental factors, data mining, and web based mapping. Understanding COVID- 19’s spatiotemporal dynamics is critical for its prevention because it helps determine the pandemic’s scope and impacts, as well as decision-making, planning, and public reaction. 

Keywords: Assessment of community health, numerical-spatial problem solving analytical processing in real time, geographical information systems (GIS), nosocomial infection

[This article belongs to Journal of Remote Sensing & GIS ]

How to cite this article:
Husam H. Abdulmughni, Ratnadeep R. Deshmukh. ArcGIS Applications in COVID-19 Spatial Epidemiology: A Comprehensive Systematic Review. Journal of Remote Sensing & GIS. 2025; 16(02):17-25.
How to cite this URL:
Husam H. Abdulmughni, Ratnadeep R. Deshmukh. ArcGIS Applications in COVID-19 Spatial Epidemiology: A Comprehensive Systematic Review. Journal of Remote Sensing & GIS. 2025; 16(02):17-25. Available from: https://journals.stmjournals.com/jorsg/article=2025/view=223073


References

  1. Abulibdeh A. Spatiotemporal analysis of water-electricity consumption in the context of the COVID-19 pandemic across six socioeconomic sectors in Doha City, Qatar. Appl Energy. 2021 Dec 15; 304: 117864.
  2. Ahasan R, Hossain MM. Leveraging GIS and spatial analysis for informed decision-making in COVID-19 pandemic. Health Policy Technol. 2020 Dec 9; 10(1): 7–9.
  3. Akbari M, Jarge MR, Madani SH. Assessment of decreasing of groundwater-table using geographic information system (GIS) (case study: Mashhad plain aquifer). Journal of Water and Soil Conservation. 2009; 16(4): 63–78.
  4. Almagrabi AO, Abdullah S, Shams M, Al-Otaibi YD, Ashraf S. A new approach to q-linear Diophantine fuzzy emergency decision support system for COVID19. J Ambient Intell Humaniz Comput. 2022 Apr 1; 13(4): 1687–1713.
  5.  Aubry N, Guyonnet R, Lima R. Spatiotemporal analysis of complex signals: theory and applications. J Stat Phys. 1991 Aug; 64: 683–739.
  6.  Gross B, Zheng Z, Liu S, Chen X, Sela A, Li J, Li D, Havlin S. Spatio-temporal Propagation of COVID-19 Pandemics. Europhysics Letters (EPL). 2020; 131(5): 58003.
  7. Baker DW. Trust in health care in the time of COVID-19. JAMA. 2020 Dec 15; 324(23): 2373–5.
  8. Baran E, AlZoubi D. Human-centered design as a frame for transition to remote teaching during the COVID-19 pandemic. Journal of Technology and Teacher Education (JTATE). 2020; 28(2): 365–72.
  9.  Pandemic C. Tracking the global outbreak. BBC News. 2020.
  10.  Bermudi PM, Lorenz C, de Aguiar BS, Failla MA, Barrozo LV, Chiaravalloti-Neto F. Spatiotemporal dynamic of COVID-19 mortality in the city of São Paulo, Brazil: shifting the high risk from the best to the worst socio-economic conditions. arXiv preprint arXiv:2008.02322. 2020 Aug 5.
  11. Betriana F, Tanioka T, Locsin RD, Malini H, Lenggogeni DP. Are Indonesian nurses ready for healthcare robots during the COVID-19 pandemic? Belitung Nurs J. 2020; 6(3): 63–6.
  12.  Briz-Redón Á, Serrano-Aroca Á. The effect of climate on the spread of the COVID-19 pandemic: A review of findings, and statistical and modelling techniques. Prog Phys Geogr: Earth and Environment. 2020 Oct; 44(5): 591–604.
  13.  Brunet D, Murray MM, Michel CM. Spatiotemporal analysis of multichannel EEG: CARTOOL. Comput Intell Neurosci. 2011; 2011(1): 813870.
  14.  Sánchez-Sánchez JA, Chuc VM, Canché EA, Uscanga FJ. Vulnerability assessing contagion risk of Covid-19 using geographic information systems and multi-criteria decision analysis: Case study Chetumal, México. In GIS LATAM: First Conference, GIS LATAM 2020, Mexico City, Mexico, September 28–30, 2020, Proceedings 1. Cham: Springer International Publishing; 2020; 1–17.
  15.  Centers for Disease Control and Prevention. (2020). When vaccine is limited, who gets vaccinated first. [Online]. URL https://www.cdc.gov/coronavirus/2019-ncov/vaccines/recommendations.html.
  16.  Chen HH, Mullett SJ, Stewart AF. Vgl-4, a novel member of the vestigial-like family of α -adrenergic activation of gene expression in cardiac myocytes. J Biol Chem. 2004 Jul 16; 279(29): 30800–6.
  17. Cos OD, Castillo V, Cantarero D. Facing a second wave from a regional view: Spatial patterns of COVID-19 as a key determinant for public health and geoprevention plans. Int J Environ Res Public Health. 2020 Nov; 17(22): 8468.
  18.  Held L, Meyer S. Forecasting based on surveillance data. In: Handbook of infectious disease data analysis. Florida, United States: Chapman and Hall/CRC; 2019 Nov 7. p. 509–527.
  19.  Curti BD, Kovacsovics-Bankowski M, Morris N, Walker E, Chisholm L, Floyd K, Walker J, Gonzalez I, Meeuwsen T, Fox BA, Moudgil T. OX40 is a potent immune-stimulating target in late stage cancer patients. Cancer Res. 2013 Dec 15; 73(24): 7189–98.
  20.  De Ridder D, Sandoval J, Vuilleumier N, Stringhini S, Spechbach H, Joost S, Kaiser L, Guessous I. Geospatial digital monitoring of COVID-19 cases at high spatiotemporal resolution. Lancet Digit Health. 2020 Aug 1; 2(8): e393–4.
  21.  Deo S, Manurkar S, Krishnan S, Franz C. COVID-19 vaccine: development, access and distribution in the Indian context. ORF Issue Brief no. 378. 2020 Jul 10; 16.
  22. Shahparvaria S, Hassanizadehb B, Mohammadic A, Kianid B, Hung K, Laua PC, et al. A decision support system for prioritised COVID-19 two-dosage vaccination allocation and distribution. Transp Res E Logist Transp Rev. 2022 Mar;159:102598.
  23.  Docherty AB, Harrison EM, Green CA, Hardwick HE, Pius R, Norman L, Holden KA, Read JM, Dondelinger F, Carson G, Merson L. Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. BMJ. 2020 May 22; 369: m1985.
  24. Eisler MC, Magona JW, Jonsson NN, Revie CW. A low cost decision support tool for the diagnosis of endemic bovine infectious diseases in the mixed crop–livestock production system of sub Saharan Africa. Epidemiol Infect. 2007 Jan; 135(1): 67–75.
  25. Pollock JR, Anderson ST, Moore ML, Smith JF. The Food and Drug Administration. In: Translational Orthopedics. ScienceDirect; 2024 Jan 1; 375–9.
  26.  Flottorp S, Oxman AD, Håvelsrud K, Treweek S, Herrin J. Cluster randomised controlled trial of tailored interventions to improve the management of urinary tract infections in women and sore throat. BMJ. 2002 Aug 17; 325(7360): 367.
  27. 27. ‐ ‐ ‐ à 0 0 GIS. 2021 Oct; 25(5): 2191–239.
  28.  Gangwar HS, Ray PC. Geographic information system-based analysis of COVID-19 cases in India during pre-lockdown, lockdown, and unlock phases. Int J Infect Dis. 2021 Apr 1; 105: 424–35.
  29.  Gatto M, Bertuzzo E, Mari L, Miccoli S, Carraro L, Casagrandi R, Rinaldo A. Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures. Proc Natl Acad Sci. 2020 May 12; 117(19): 10484–91.
  30. Ghosh P, Cartone A. A Spatio-temporal analysis of COVID-19 outbreak in Italy. Reg Sci Policy Pract. 2020; 12(6): 1047–1062.
  31. Graff G, Jezierski J. Minimizing the number of periodic points for smooth maps. Non-simply connected case. Topol Its Appl. 2011 Feb 15; 158(3): 276–90.
  32. Guha P. Spatiotemporal Analysis of COVID-19 Pandemic and Predictive Models based on Artificial Intelligence for different States of India. J Inst Eng (India): B. 2021; 102(6): 1265–1274.
  33.  Huang H, Wang Y, Wang Z, Liang Z, Qu S, Ma S, Mao G, Liu X. Epidemic features and control of 2019 novel coronavirus pneumonia in Wenzhou, China (3/3/2020). Available at SSRN. 2020 Mar 3.
  34.  Huang BC, Tseng YJ, Ho TW, Lin HC, Chen YC, Lai F. A healthcare-associated surgical site infection surveillance and decision support system. In The 7th 2014 IEEE Biomedical Engineering International Conference. 2014 Nov 26; 1–5.
  35. Huang X, Zhang R, Li X, Dadashova B, Zhu L, Zhang K, et al. Health-based geographic information systems for mapping and risk modeling of infectious diseases and COVID-19 to support spatial decision-making. Adv Exp Med Biol. 2022;1368:167-188.
  36.  Kamel Boulos MN, Geraghty EM. Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. Int J Health Geogr. 2020 Dec; 19(1): 1–2.
  37. Kar A, Motoyama Y, Carrel AL, Miller HJ, Le HT. Impact of COVID-19 on food shopping: A spatiotemporal analysis of changes in travel to supermarket and grocery stores. Mobility changes and community impacts of COVID-19. Annual Meeting of Transportation Research Board (TRB). 2021.
  38. Karakaya K. Design considerations in emergency remote teaching during the COVID-19 pandemic: A human-centered approach. Educ Technol Res Dev. 2021 Feb; 69(1): 295–9.
  39.  Kee Sae Yoon, Lee Jin Soo, Cheong Hee Jin, Chun Byung Chul, Song Joon Young, Choi Won Suk, Jo Yu Mi, Seo Yoo Bin, Kim Woo Joo. Influenza vaccine coverage rates and perceptions on vaccination in South Korea. J Infect. 2007 Sep; 55(3): 273–81.
  40.  Lak A, Sharifi A, Badr S, Zali A, Maher A, Mostafavi E, Khalili D. Spatio-temporal patterns of the COVID-19 pandemic, and place-based influential factors at the neighborhood scale in Tehran. Sustain Cities Soc. 2021 Sep 1; 72: 103034.
  41. Lee MM, Johnson AD, Yelick KA, Chayes JT. The Road for Recovery: Aligning COVID-19 efforts and building a more resilient future. IEEE Data Eng Bull. 2020 Jun;43(2):133–140.
  42.  Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable ai: A review of machine learning interpretability methods. Entropy. 2020 Dec 25; 23(1): 18.
  43. Liyanaage S, De Silva MA, Chandraratne DV. Sri Lanka Science and technology indicators: part II-Diffusion and assimilation of S and T knowhow in the Agricultural Sector of Sri Lanka. Report. 1988 Dec.
  44. Ltifi H, Trabelsi G, Ayed MB, Alimi AM. Dynamic decision support system based on bayesian networks application to fight against the nosocomial infections. arXiv preprint arXiv:1211.2126. 2012 Nov 9.
  45.  Lucas RE. Adaptation and the set-point model of subjective well-being: Does happiness change after major life events? Curr Dir Psychol Sci. 2007 Apr; 16(2): 75–9.
  46.  Mansilla HR, Solano GA, Lapitan MC. desside: A clinical decision-support tool for surgical site infection prediction. In 2020 IEEE International Conference on Artificial Intelligence in Information and Communication (ICAIIC). 2020 Feb 19; 367–372.
  47. McCormick JL, Clark TA, Shea CM, Hess DR, Lindenauer PK, Hill NS, Allen CE, Farmer MS, Hughes AM, Steingrub JS, Stefan MS. Exploring the patient experience with noninvasive ventilation: a human-centered design analysis to inform planning for better tolerance. Chronic Obstr Pulm Dis: Journal of the COPD Foundation. 2022 Jan 7; 9(1): 80–94.
  48. Phillips LD, Bana e Costa CA. Transparent prioritisation, budgeting and resource allocation with multi-criteria decision analysis and decision conferencing. Ann Oper Res. 2007 Oct;154(1):51–68.
  49. Read M, Gear T, Minkes L, Irving A. Using a group decision support system to make investment prioritisation decisions. In 2013 Proceedings of PICMET’13: Technology Management in the IT Driven Services (PICMET). IEEE; 2013 Jul 28. pp. 375–381.
  50.  Medlock J, Galvani AP. Optimizing influenza vaccine distribution. Science. 2009 Sep 25; 325(5948): 1705–8.
Regular Issue Subscription Review Article
Volume 16
Issue 02
Received 14/02/2025
Accepted 02/05/2025
Published 10/07/2025
Publication Time 146 Days
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