The ECO4CO project among retrospective data, improving of algorithms, predictive hypothesis and future perspectives to tackle health emergencies

Authors

  • Cristiano Pesaresi Dipartimento di Lettere e Culture Moderne, Sapienza University of Rome, Rome, Italy
  • Sofiane Atek Dipartimento di Ingegneria Meccanica e Aerospaziale, Sapienza University of Rome, Rome, Italy
  • Corrado De Vito Dipartimento di Sanità Pubblica e Malattie Infettive, Sapienza University of Rome, Rome, Italy
  • Vincenzo Cardinale Dipartimento di Scienze e Biotecnologie Medico-Chirurgiche, Sapienza University of Rome, Umberto I Policlinico of Rome, Rome, Italy
  • Filippo Bianchini Telespazio S.p.A, Rome, Italy
  • Simone Novelli Dipartimento di Ingegneria Meccanica e Aerospaziale, Sapienza University of Rome, Rome, Italy
  • Marco Eugeni Dipartimento di Ingegneria Meccanica e Aerospaziale, Sapienza University of Rome, Rome, Italy
  • Massimo Mecella Dipartimento di Ingegneria Informatica, Automatica e Gestionale Antonio Ruberti, Sapienza University of Rome, Rome, Italy
  • Antonello Rescio Telespazio S.p.A, Rome, Italy
  • Luca Petronzio Telespazio S.p.A, Rome, Italy
  • Aldo Vincenzi Telespazio S.p.A, Rome, Italy
  • Pasquale Pistillo e-GEOS, Rome, Italy
  • Gianfranco Giusto e-GEOS, Rome, Italy
  • Giorgio Pasquali e-GEOS, Rome, Italy
  • Domenico Alvaro Sapienza Information-Based Technology InnovaTion Center for Health (STITCH), Sapienza University of Rome, Rome, Italy
  • Paolo Gaudenzi Dipartimento di Ingegneria Meccanica e Aerospaziale, Sapienza University of Rome, Rome, Italy
  • Marco Mancini Dipartimento di Lettere e Culture Moderne, Sapienza University of Rome, Rome, Italy
  • Paolo Villari Dipartimento di Sanità Pubblica e Malattie Infettive, Sapienza University of Rome, Rome, Italy

Abstract

International literature has evidenced how GIS, geotechnologies and Artificial Intelligence (AI) can provide very useful supports and indications to tackle COVID-19 and sanitary emergencies. This paper starts by giving some synthetic geographical considerations regarding the consequences of the COVID-19 pandemic in different fields of daily life and then provides a literary review concerning the actual state of the art and inputs for future perspectives using GIS, geotechnologies and AI to tackle infectious diseases and global emergencies, starting from the numerous studies conducted in the context of COVID-19. In fact, the current pandemic has also been an event capable of providing notable new indications for the profitable use of GIS applications, simulation and geolocalisation models, AI and satellite images. The paper then focuses the attention on the ECO4CO – Earth Cognitive System 4 Covid-19 project, co-funded by the European Space Agency (ESA) under its Business Applications programme and essentially aimed at advancing innovative proposals, combining geotechnological know-how and interdisciplinary approaches in the healthcare, epidemiological, engineering and geographical fields, through the use of geoinformatics models and algorithms and satellite resources, for ad hoc data elaboration and mapping. In particular, the ECO4CO service concept and service pillars are presented and the work is focused on Logistic Planning, a crucial service used to assist clinical teams and healthcare authorities in the organization of activities useful to tackle sanitary emergencies. Specific applied examples are shown to underline how it is possible to pass from retrospective data to predictive hypothesis through conceptual models based on interdisciplinary synergies and refining and educating algorithms.

References

Ahasan R., Alam M.S., Chakraborty T. and Hossain M.M., “Applications of GIS and geospatial analyses in COVID-19 research: A systematic review”, F1000Research, 9, 1379, 2022.

Akinwumiju A.S., Oluwafemi O., Mohammed Y.D. and Mobolaji J.W., “Geospatial evaluation of COVID-19 mortality: Influence of socio-economic status and underlying health conditions in contiguous USA”, Applied Geography, 141, 102671, 2022, pp. 13.

Aliyu A.A., “Public health ethics and the COVID-19 pandemic”, Annals of African medicine, 20, 3, 2021, pp. 157-163.

Alsharif W. and Qurashi A., “Effectiveness of COVID-19 diagnosis and management tools: A review”, published online 2020 (Radiography, 27, 2021, pp. 682-687).

Atek S., Pesaresi C., Eugeni M., De Vito C., Cardinale V., Mecella M., Rescio A., Petronzio L., Vincenzi A., Pistillo P., Bianchini F., Giusto G., Pasquali G. and Gaudenzi P., “A Geospatial Artificial Intelligence and Satellitebased Earth Observation Cognitive System in Response to COVID-19 Emergency”, Acta Astronautica, 2022, pp. 18.

Atek S., Pesaresi C., Eugeni M., Gaudenzi P., De Vito C., Cardinale V., Mecella M., Rescio A., Petronzio L., Vincenzi A., Pistillo P. and Vora A., “An Earth Observation Cognitive System in Response to Sars-Covid-19 Emergency”, Proceedings of the International Astronautical Congress – IAC (Dubai, United Arab Emirates, 25-29 October 2021), 2021, pp. 1-11.

Azevedo L., Pereira M.J., Ribeiro M.C. and Soares A., “Geostatistical COVID-19 infection risk maps for Portugal”, International Journal of Health Geographics, 19, 25, 2020, pp. 8.

Barbieri D., Giuliani E., Del Prete A., Losi A., Villani M. and Barbieri A., “How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic”, International Journal of Environmental Research and Public Health, 18, 7648, 2021, pp. 10.

Brunialti T., Dai Prà E. and Gabellieri N., “Malattie infettive e cartografia per l’analisi e il monitoraggio: il progetto di mappatura del COVID-19 in Trentino”, Bollettino della Associazione Italiana di Cartografia, 170, Special Issue, 2020, pp. 19-36.

Carballada A.M. and Balsa-Barreiro J., “Geospatial Analysis and Mapping Strategies for Fine-Grained and Detailed COVID-19 Data with GIS”, ISPRS International Journal of Geo-Information, 10, 602, 2021, pp. 27.

Casti E., Adobati F. and Negri I (Eds.), Mapping the Epidemic. A Systemic Geography of COVID-19 in Italy, Elsevier, 2021.

Chee M.L., Ong M.E.H., Siddiqui F.J., Zhang Z., Lim S.L., Ho A.F.W. and Liu N., “Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review”, International Journal of Environmental Research and Public Health, 18, 4749, 2021, pp. 15.

Chen C., Jyan H., Chien S., Jen H., Hsu C., Lee P., Lee C., Yang Y., Chen M., Chen L., Chen H. and Chan C., “Containing COVID- 19 Among 627,386 Persons in Contact With the Diamond Princess Cruise Ship Passengers Who Disembarked in Taiwan: Big Data Analytics”, Journal of Medical

Chen G., “A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation”, ArXiv, abs/1610.02583, 2016.

Dangermond J., De Vito C. and Pesaresi C., “Using GIS in the Time of the COVID-19 Crisis, casting a glance at the future. A joint discussion”, J-READING (Journal of Research and Didactics in Geography), 1, 9, 2020, pp. 195-205.

Grekousis G., Wang R. and Liu Y., “Mapping the geodemographics of racial, economic, health, and COVID-19 deaths inequalities in the conterminous US”, Applied Geography, 135, 102558, 2021, pp. 10.

Husnayain A., Fuad A. and Su E.C.-Y., “Applications of Google Search Trends for risk communication in infectious disease management: A case study of the COVID-19 outbreak in Taiwan”, International Journal of Infectious Diseases, 95, 2020, pp. 221-223.

Iyanda A.E., Boakye K.A., Lu Y. and Oppong J.R., “Racial/Ethnic Heterogeneity and Rural-Urban Disparity of COVID-19 Case Fatality Ratio in the USA: a Negative Binomial and GIS-Based Analysis”, Journal of Racial and Ethnic Health Disparities, 2021, pp. 14.

Kamel Boulos M.N. and Geraghty E.M., “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”, International Journal of Health Geographics, 19, 8, 2020.

Kanga S., Meraj G., Sudhanshu, Farooq M., Nathawat M.S. and Singh S.K., “Analyzing the Risk to COVID-19 Infection using Remote Sensing and GIS”, Risk Analysis, 41, 5, 2021, pp. 801-813.

Kim S.J. and Bostwick W., “Social Vulnerability and Racial Inequality in COVID-19 Deaths in Chicago”, Health Education & Behavior, 2020, pp. 1-5.

Lessler J., Azman A.S., McKay H.S. and Moore S.M., “What is a Hotspot Anyway?”, The American journal of tropical medicine and hygiene, 96, 6, 2017, pp. 1270- 1273.

Lyu T., Hair N., Yell N., Li Z., Qiao S., Liang C. and Li X., “Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina”, International Journal of Environmental Research and Public Health, 18, 9673, 2021, pp. 17.

Malik Y.S., Sircar S., Bhat S., Ansari M.I., Pande T., Kumar P., Mathapati B., Balasubramanian G., Kaushik R., Natesan S., Ezzikouri S., El Zowalaty M.E. and Dhama K., “How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future”, Reviews in medical virology, 31, 5, 2021, pp. 1-11.

Murgante B., Borruso G., Balletto G., Castiglia P. and Dettori M., “Why Italy First? Health, Geographical and Planning Aspects of the COVID-19 Outbreak”, Sustainability, 12, 5064, 2020, pp. 44.

Naudé W., Artificial Intelligence against COVID-19: An Early Review, Bonn, IZA (Institute of Labor Economics) DP N. 13110, 2020, pp. 14.

Pesaresi C., “A geographical and crosscutting look at the COVID-19 pandemic in an international framework. Introduction”, JREADING – Journal of Research and Didactics in Geography, 2, 9, 2020, pp. 13-19.

Pesaresi C., Riscopriamo la geografia. La pandemia minuto per minuto, Touring Club Italiano, 2022, http://www.touringmagazine. it/articolo/5556/riscopriamo-la-geografia-lapandemia- minuto-minuto.

Pesaresi C., Pavia D. and De Vito C., “Three geotechnological proposals to tackle health emergencies and the monitoring of infectious diseases. Inputs from the COVID- 19 pandemic for future preparedness”, Bollettino della Associazione Italiana di Cartografia, 170, Special Issue, 2020, pp. 58-75.

Pesaresi C., Pavia D., De Vito C., Barbara A., Cerabona V. and Di Rosa E., “Dynamic space-time diffusion simulator in a GIS environment to tackle the covid-19 emergency. Testing a geotechnological application in Rome”, Geographia Technica, 16, Special Issue, 2021, pp. 82-99.

Podda C. and Scanu G., “Trattamento spaziale dei dati pandemici: la cartografia del COVID- 19”, Bollettino della Associazione Italiana di Cartografia, 170, Special Issue, 2020, pp. 37-57.

Schmidt F., Dröge-Rothaar A. and Rienow A., “Development of a Web GIS for smallscale detection and analysis of COVID-19 (SARS-CoV-2) cases based on volunteered geographic information for the city of Cologne, Germany, in July/August 2020”, International Journal of Health Geographics, 20, 40, 2021, pp. 24.

Shi F., Wang J., Shi J., Wu Z., Wang Q., Tang Z., He K., Shi Y. and Shen D., “Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID- 19”, IEEE Reviews in Biomedical Engineering, 14, 2021, pp. 4-15.

Syrowatka A., Kuznetsova M., Alsubai A., Beckman A.L., Bain P.A., Craig K.J.T., Hu J., Jackson G.P., Rhee K. and Bates D.W., “Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases”, npj Digital Medicine, 4, 96, 2021, pp. 1-14.

Vaishya R., Javaid M., Khan I. and Haleem A., “Artificial intelligence (AI) applications for COVID‐19 pandemic”, Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14, 4, 2020, pp. 337‐339.

Wang S., Ding S. and Xiong, L., “A New System for Surveillance and Digital Contact Tracing for COVID-19: Spatiotemporal Reporting Over Network and GPS”, JMIR mHealth and uHealth, 8, 6, e19457, 2020.

Werner P.A., Kęsik-Brodacka M., Nowak K., Olszewski R., Kaleta M. and Liebers D.T., “Modeling the Spatial and Temporal Spread of COVID-19 in Poland Based on a Spatial Interaction Model”, ISPRS International Journal of Geo-Information, 11, 195, 2022, pp. 21.

Wu C., Zhou M., Liu P. and Yang M., “Analyzing COVID-19 using multisource data: An integrated approach of visualization, spatial regression, and machine learning”, GeoHealth, 5, 2021, pp. 14.

Zhang S., “Challenges in KNN Classification”, IEEE Transactions on Knowledge and Data Engineering, 2021.

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Published

2022-06-23

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