A conceptional model integrating geographic information systems (GIS) and social media data for disease exposure assessment

Submitted: 27 December 2023
Accepted: 28 February 2024
Published: 28 March 2024
Abstract Views: 1481
PDF: 380
HTML: 56
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

Although previous studies have acknowledged the potential of geographic information systems (GIS) and social media data (SMD) in assessment of exposure to various environmental risks, none has presented a simple, effective and user-friendly tool. This study introduces a conceptual model that integrates individual mobility patterns extracted from social media, with the geographic footprints of infectious diseases and other environmental agents utilizing GIS. The efficacy of the model was independently evaluated for selected case studies involving lead in the ground; particulate matter in the air; and an infectious, viral disease (COVID- 19). A graphical user interface (GUI) was developed as the final output of this study. Overall, the evaluation of the model demonstrated feasibility in successfully extracting individual mobility patterns, identifying potential exposure sites and quantifying the frequency and magnitude of exposure. Importantly, the novelty of the developed model lies not merely in its efficiency in integrating GIS and SMD for exposure assessment, but also in considering the practical requirements of health practitioners. Although the conceptual model, developed together with its associated GUI, presents a promising and practical approach to assessment of the exposure to environmental risks discussed here, its applicability, versatility and efficacy extends beyond the case studies presented in this study.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Aiello AE, Renson A, Zivich PN, 2019. Social media- and internet-based disease surveillance for public health. Annu Rev Public Health 41:101-18. DOI: https://doi.org/10.1146/annurev-publhealth-040119-094402
Ajayakumar J, Curtis AJ, Curtis J, 2019. Addressing the data guardian and geospatial scientist collaborator dilemma: how to share health records for spatial analysis while maintaining patient confidentiality. Int J Health Geogr 18:30. DOI: https://doi.org/10.1186/s12942-019-0194-8
Al-Kindi SG, Brook RD, Biswal S, Rajagopalan S, 2020. Environmental determinants of cardiovascular disease: lessons learned from air pollution. Nat Rev Cardiol 17:656–72. DOI: https://doi.org/10.1038/s41569-020-0371-2
Allam Z, Jones DS, 2020. On the Coronavirus (Covid-19) outbreak and the smart city network: universal data sharing standards coupled with Artificial Intelligence (Ai) to benefit urban health monitoring and management. Healthcare (Basel) 8:46. DOI: https://doi.org/10.3390/healthcare8010046
Allington D, Duffy B, Wessely S, Dhavan N, Rubin J, 2021. Health-protective behaviour, social media usage and conspiracy belief during the COVID-19 public health emergency. Psychol Med 51:1763–69. DOI: https://doi.org/10.1017/S003329172000224X
Alonso SG, de la Torre Díez I, Rodrigues JJPC, Hamrioui S, López-Coronado M, 2017. A Systematic Review of Techniques and Sources of Big Data in the Healthcare Sector. J Med Syst 41:183. DOI: https://doi.org/10.1007/s10916-017-0832-2
Angelo KM, Kozarsky PE, Ryan ET, Chen LH, Sotir MJ, 2017. What proportion of international travellers acquire a travel-related illness? A review of the literature. J Travel Med 24:10.1093/jtm/tax046. DOI: https://doi.org/10.1093/jtm/tax046
Amer S, & Bergquist R. 2021. Transport geography: Implications for public health. Geospatial Health 16:1009 DOI: https://doi.org/10.4081/gh.2021.1009
Assi MA, Hezmee MNM, Haron AW, Sabri MYM, Rajion MA, 2016. The detrimental effects of lead on human and animal health. Vet World 9:660–71. DOI: https://doi.org/10.14202/vetworld.2016.660-671
Basch CH, Hillyer GC, Jaime C, 2022. COVID-19 on TikTok: harnessing an emerging social media platform to convey important public health messages. Int J Adolesc Med Health 34:367–69. DOI: https://doi.org/10.1515/ijamh-2020-0111
Bisanzio D, Kraemer MUG, Bogoch II, Brewer T, Brownstein JS, Reithinger R, 2020. Use of twitter social media activity as a proxy for human mobility to predict the spatiotemporal spread of COVID-19 at global scale. Geospatial Health 15:882 DOI: https://doi.org/10.4081/gh.2020.882
Briggs D, 2005. The Role of GIS: coping with space (and time) in air pollution exposure assessment. J Toxicol Environ Heal Part A 68:1243–61. DOI: https://doi.org/10.1080/15287390590936094
Charkiewicz AE, Backstrand JR, 2020. Lead toxicity and pollution in Poland. Int J Environ Res Public Health 17:4385. DOI: https://doi.org/10.3390/ijerph17124385
Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, Pastore Y Piontti A, Mu K, Rossi L, Sun K, Viboud C, Xiong X, Yu H, Halloran ME, Longini IM Jr, Vespignani A, 2020. The effect of travel restrictions on the spread of the 2019 novel Coronavirus (COVID-19) Outbreak. Science 368:395–400. DOI: https://doi.org/10.1126/science.aba9757
Cleckner H, Allen TR, 2014. Dasymetric Mapping and Spatial Modeling of Mosquito Vector Exposure, Chesapeake, Virginia, USA. ISPRS Int J Geo-Information 3:891–913. DOI: https://doi.org/10.3390/ijgi3030891
De Cola L, 2002. Spatial forecasting of disease risk and uncertainty. Cartogr Geogr Inf Sci 29:363–80. DOI: https://doi.org/10.1559/152304002782008413
De Guzman C, 2022. Asia has kept COVID-19 at bay for 2 years. Omicron could change that. Retrieved September 29, 2022. Available from: https://time.com/6139851/asia-omicron-covid-surge/
DeBord DG, Carreón T, Lentz TJ, Middendorf PJ, Hoover MD, Schulte PA, 2016. Use of the ‘exposome’ in the practice of epidemiology: a primer on -omic technologies. Am J Epidemiol 184:302–14. DOI: https://doi.org/10.1093/aje/kwv325
Dummer T, 2008. Health geography: supporting public health policy and planning. C Can Med Assoc J 178:1177–80. DOI: https://doi.org/10.1503/cmaj.071783
Edward R, Wilson M, Kain K, 2002. Illness after international travel. N Engl J Med 347:1984. DOI: https://doi.org/10.1056/NEJM200212123472419
Fauci AS, Lane HC, Redfield RR, 2020. Covid-19 — navigating the uncharted. N Engl J Med 382:1268–69. DOI: https://doi.org/10.1056/NEJMe2002387
Fujita H, 2017. Information extraction and visualization from Twitter considering spatial structure. Cartogr Int J Geogr Inf Geovisualization 52:178–93. DOI: https://doi.org/10.3138/cart.52.2.3875
Fung IC, Duke CH, Finch KC, Snook KR, Tseng PL, Hernandez AC, Gambhir M, Fu KW, Tse ZTH, 2016. Ebola virus disease and social media: a systematic review. Am J Infect Control 44:1660–71. DOI: https://doi.org/10.1016/j.ajic.2016.05.011
Gardner LM, Bóta A, Gangavarapu K, Kraemer MUG, Grubaugh ND, 2018. Inferring the Risk Factors behind the Geographical Spread and Transmission of Zika in the Americas. PLoS Negl Trop Dis 12:e0006194. DOI: https://doi.org/10.1371/journal.pntd.0006194
Gautam S, 2020. The influence of COVID-19 on air quality in India: a boon or inutile. Bull Environ Contam Toxicol 104:724–26. DOI: https://doi.org/10.1007/s00128-020-02877-y
Gulnerman AG, Karaman H, Pekaslan D, Bilgi S, 2020. Citizens’ spatial footprint on Twitter—anomaly, trend and bias investigation in Istanbul. ISPRS Int J Geoinf 9:222. DOI: https://doi.org/10.3390/ijgi9040222
Gunasekeran DV, Chew A, Chandrasekar EK, Rajendram P, Kandarpa V, Rajendram M, Chia A, Smith H, Leong CK, 2022. The impact and applications of social media platforms for public health responses before and during the COVID-19 pandemic: systematic literature review. J Med Internet Res 24:e33680. DOI: https://doi.org/10.2196/33680
Hao J, Wang L, 2005. Improving urban air quality in China: Beijing case study. J Air Waste Manage Assoc 55:1298–305. DOI: https://doi.org/10.1080/10473289.2005.10464726
Heldman AB, Schindelar J, Weaver JB, 2013. Social media engagement and public health communication: implications for public health organizations being truly ‘social.’ Public Health Rev 35:1–18. DOI: https://doi.org/10.1007/BF03391698
Janke K, 2014. Air pollution, avoidance behaviour and children’s respiratory health: evidence from England. J Health Econ 38:23–42. DOI: https://doi.org/10.1016/j.jhealeco.2014.07.002
Jing F, Li Z, Qiao S, Zhang J, Olatosi B, Li X, 2023. Using geospatial social media data for infectious disease studies: a systematic review. Int J Digit Earth 16:130–57. DOI: https://doi.org/10.1080/17538947.2022.2161652
Jurdak R, Zhao K, Liu J, AbouJaoude M, Cameron M, Newth D, 2015. Understanding human mobility from Twitter. PLoS One 10:e0131469. DOI: https://doi.org/10.1371/journal.pone.0131469
Kamel Boulos MN, Geraghty EM, 2020. 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 19:8. DOI: https://doi.org/10.1186/s12942-020-00202-8
Katoto PDMC, Brand AS, Bakan B, Obadia PM, Kuhangana C, Kayembe-Kitenge T, Kitenge JP, Nkulu CBL, Vanoirbeek J, Nawrot TS, Hoet P, Nemery B, 2021. Acute and chronic exposure to air pollution in relation with incidence, prevalence, severity and mortality of COVID-19: a rapid systematic review. Environ Heal 20:41. DOI: https://doi.org/10.1186/s12940-021-00714-1
Kelly FJ, Fussell JC, 2015. Air pollution and public health: emerging hazards and improved understanding of risk. Environ Geochem Health 37:631–49. DOI: https://doi.org/10.1007/s10653-015-9720-1
Kozel TR, Burnham-Marusich AR, 2017. Crossm diseases: past, present, and future. J Clin Microbiol 55:2313–20. DOI: https://doi.org/10.1128/JCM.00476-17
Krutikov M, Manson J, 2016. Chikungunya virus infection: an update on joint manifestations and management. Rambam Maimonides Med J 7:e0033. DOI: https://doi.org/10.5041/RMMJ.10260
Kwan M, 2018. Human mobility, spatiotemporal context, and environmental health: recent advances in approaches and methods. Int J Environ Res Public Health 15(308).
Lanphear BP, Rauch S, Auinger P, Allen RW, Hornung RW, 2018. Low-level lead exposure and mortality in US adults: a population-based cohort study. Lancet Public Heal 3:e177–84. DOI: https://doi.org/10.1016/S2468-2667(18)30025-2
Leta S, Beyene TJ, De Clercq EM, Amenu K, Kraemer MUG, Revie CW, 2018. global risk mapping for major diseases transmitted by Aedes Aegypti and Aedes Albopictus. Int J Infect Dis 67:25–35. DOI: https://doi.org/10.1016/j.ijid.2017.11.026
Li D, Wang S, Li D, 2015. Spatial data mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 308 pp.
Liao Y, Yeh S, 2018. Predictability in human mobility based on geographical-boundary-free and long-time social media data. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, IEEE 2068-2073 pp. DOI: https://doi.org/10.1109/ITSC.2018.8569770
Lin CH, Wen TH, 2022. How spatial epidemiology helps understand infectious human disease transmission. Tropical Medicine and Infectious Disease 7:164. DOI: https://doi.org/10.3390/tropicalmed7080164
Liu Z, Zhou X, Shi W, Zhang A, 2018. Towards detecting social events by mining geographical patterns with VGI data. ISPRS Int J Geoinf 7:481. DOI: https://doi.org/10.3390/ijgi7120481
Luz S, Masoodian M, 2022. Exploring environmental and geographical factors influencing the spread of infectious diseases with interactive maps. Sustainability 14:9990. DOI: https://doi.org/10.3390/su14169990
Matsuyama A, Yasuda Y, Yasutake A, Xiaojie L, Pin J, Li L, Mei L, Yumin A, Liya Q, 2006. etailed pollution map of an area highly contaminated by mercury containing wastewater from an organic chemical factory in People's Republic of China. Bull Environ Contam Toxicol 77:82–87. DOI: https://doi.org/10.1007/s00128-006-1035-6
Mbunge E, Akinnuwesi B, Fashoto SG, Metfula AS, Mashwama P, 2021. A critical review of emerging technologies for tackling COVID-19 pandemic. Hum Behav Emerg Technol 3:25–39. DOI: https://doi.org/10.1002/hbe2.237
Middleton SE, Kordopatis-Zilos G, Papadopoulos S, Kompatsiaris Y, 2018. Location extraction from social media. ACM Trans Inf Syst 36:1–27. DOI: https://doi.org/10.1145/3202662
Musa GJ, Chiang PH, Sylk T, Bavley R, Keating W, Lakew B, Tsou HC, Hoven CW, 2013. Use of GIS mapping as a public health tool - from cholera to cancer. Heal Serv Insights 6:111–16. DOI: https://doi.org/10.4137/HSI.S10471
Okami S, Kohtake N, 2016. Fine-scale mapping by spatial risk distribution modeling for regional malaria endemicity and its implications under the low-to-moderate transmission setting in western cambodia. PLoS One 11:e0158737. DOI: https://doi.org/10.1371/journal.pone.0158737
Otsuki S, Nishiura H, 2016. Reduced risk of importing ebola virus disease because of travel restrictions in 2014: a retrospective epidemiological modeling study. PLoS One 11:e0163418. DOI: https://doi.org/10.1371/journal.pone.0163418
Panteras G, Wise S, Lu X, Croitoru A, Crooks A, Stefanidis A, 2015. Triangulating social multimedia content for event localization using Flickr and Twitter. Trans GIS 19:694–715. DOI: https://doi.org/10.1111/tgis.12122
Photis Y, 2016. Disease and health care geographies: mapping trends and patterns in a GIS. Heal Sci J 10:1–8.
SCENIHR, SCHER, and SCCS. 2011. Toxicity and Assessment of Chemical Mixtures. European Union; pp. 1–50.
Schlagenhauf P, Weld L, Goorhuis A, Gautret A, Weber R, von Sonnenburg F, Lopez-Vélez R, Jensenius M, Cramer J, Field V, Odolini S, Gkrania-Klotsas E, Chappuis F, Malvy D, van Genderen PJJ, Mockenhaupt F, Jauréguiberry S, Smith C, Beeching NJ, Ursing J, Rapp C, Parola P, Grobusch MP, EuroTravNet. 2015. Travel-Associated Infection Presenting in Europe (2008-12): An Analysis of EuroTravNet Longitudinal, Surveillance Data, and Evaluation of the Effect of the Pre-Travel Consultation. Lancet Infect Dis 15:55–64. DOI: https://doi.org/10.1016/S1473-3099(14)71000-X
Scholz J, Jeznik J, 2020. Evaluating geo-tagged twitter data to analyze tourist flows in Styria, Austria. ISPRS Int J Geo-Information 9:doi: 10.3390/ijgi9110681. DOI: https://doi.org/10.3390/ijgi9110681
Şengül AT, Bilgin Büyükkarabacak Y, Durgun Yetim T, Pirzirenli MG, Çelik B, Başoǧlu A, 2013. Early diagnosis saves lives in esophageal perforations. Turkish J Med Sci 43;939945. DOI: https://doi.org/10.3906/sag-1210-45
Singh G, Singh V, Wang ZX, Voisin G, Lefebvre F, Navenot JM, Evans B, Verma M, Anderson DW, Schneider JS, 2018. Effects of developmental lead exposure on the hippocampal methylome: influences of sex and timing and level of exposure. Toxicol Lett 290:63–72. DOI: https://doi.org/10.1016/j.toxlet.2018.03.021
Singh PK, Nandi S, Ghafoor KZ, Ghosh U, Rawat DB, 2021. Preventing COVID-19 spread using information and communication technology. IEEE Consum Electron Mag 10:18–27. DOI: https://doi.org/10.1109/MCE.2020.3047608
Sinnenberg L, Buttenheim AM, Padrez K, Mancheno C, Ungar L, Merchant RM, 2017. Twitter as a tool for health research: a systematic review. Am J Public Health 107:e1–8. DOI: https://doi.org/10.2105/AJPH.2016.303512
Sloan L, Morgan J, 2015. Who tweets with their location? understanding the relationship between demographic characteristics and the use of geoservices and geotagging on Twitter. PLoS One 10:e0142209. DOI: https://doi.org/10.1371/journal.pone.0142209
Smith AC, Thomas E, Snoswell CL, Haydon H, Mehrotra A, Clemensen J, Caffery LJ, 2020. Telehealth for global emergencies: implications for coronavirus disease 2019 (COVID-19). J Telemed Telecare 26:309–13. DOI: https://doi.org/10.1177/1357633X20916567
Thomala L, 2023. China: most popular social media platforms 2022. Statista Inc. Retrieved February 19, 2024. Available from: https://www.statista.com/statistics/250546/leading-social-network-sites-in-china/
Tompkins AM, McCreesh N, 2016. Migration statistics relevant for malaria transmission in Senegal derived from mobile phone data and used in an agent-based migration model. Geospatial Health 11:408 DOI: https://doi.org/10.4081/gh.2016.408
Trajer A, 2021. Aedes Aegypti in the mediterranean container ports at the time of climate change: a time bomb on the mosquito vector map of Europe. Heliyon 7:e07981. DOI: https://doi.org/10.1016/j.heliyon.2021.e07981
Wallace E, 2023. Lead exposure risk in your neighborhood | PolicyMap. Retrieved May 25, 2023. Available from: https://www.policymap.com/blog/lead-exposure-risk-in-your-neighborhood
WHO, World Health Organization, 2021. Contact tracing in the context of COVID-19. WHO guidelines: contact tracing in the context of COVID-19 2019 (May, 10):1–7. DOI: https://doi.org/10.15557/PiMR.2020.0005
WHO, World Health Organization, 2022. Lead poisoning fact sheet. 4:230. Available from: https://www.who.int/news-room/fact-sheets/detail/lead-poisoning-and-health#:~:text=At high levels of exposure,intellectual disability and behavioural disorders
Wu L, Zhi Y, Sui Z, Liu Y, 2014. Intra-urban human mobility and activity transition: evidence from social media check-in data. PLoS One 9:e97010. DOI: https://doi.org/10.1371/journal.pone.0097010
Xu P, Dredze M, Broniatowski DA, 2020. The twitter social mobility index: measuring social distancing practices with geolocated tweets. J Med Internet Res 22:e21499. DOI: https://doi.org/10.2196/21499
Yasobant S, Vora KS, Hughes C, Upadhyay A, Mavalankar DV, 2015. Geovisualization: A Newer GIS Technology for Implementation Research in Health, J Geogr Inf Syst 07:20–28. DOI: https://doi.org/10.4236/jgis.2015.71002
Ye X, Li S, Yang X, Qin C, 2016. Use of social media for the detection and analysis of infectious diseases in China. ISPRS Int J Geoinf 5:156. DOI: https://doi.org/10.3390/ijgi5090156
Yousefinaghani S, Dara R, Poljak Z, Bernardo TM, Sharif S, 2019. The assessment of Twitter’s potential for outbreak detection: avian influenza case study. Sci Rep 9:18147. DOI: https://doi.org/10.1038/s41598-019-54388-4
Zachlod C, Samuel O, Ochsner A, Werthmüller S, 2022. Analytics of social media data – state of characteristics and application. J Bus Res 144:1064–76. DOI: https://doi.org/10.1016/j.jbusres.2022.02.016
Zohar M, 2021. Geolocating Tweets via spatial inspection of information inferred from Tweet Meta-Fields. Int J Appl Earth Obs Geoinf 105:102593. DOI: https://doi.org/10.1016/j.jag.2021.102593

How to Cite

Enoe , J., Sutherland, M., Davis, D., Ramlal, B., Griffith-Charles , C., Bhola, K. H., & Asefa, E. M. (2024). A conceptional model integrating geographic information systems (GIS) and social media data for disease exposure assessment. Geospatial Health, 19(1). https://doi.org/10.4081/gh.2024.1264