A Geospatial Artificial Intelligence and satellite-based earth observation cognitive system in response to COVID-19

Abstract

The pandemic emergency caused by the spread of COVID-19 has stressed the importance of promptly identifying new epidemic clusters and patterns, to ensure the implementation of local risk containment measures and provide the needed healthcare to the population. In this framework, artificial intelligence, GIS, geospatial analysis and space assets can play a crucial role. Social media analytics can be used to trigger Earth Observation (EO) satellite acquisitions over potential new areas of human aggregation. Similarly, EO satellites can be used jointly with social media analytics to systematically monitor well-known areas of aggregation (green urban areas, public markets, etc.). The information that can be obtained from the Earth Cognitive System 4 COVID-19 (ECO4CO) are both predictive, aiming to identify possible new clusters of outbreaks, and at the same time supervisorial, by monitoring infrastructures (i.e. traffic jams, parking lots) or specific categories (i.e. teenagers, doctors, teachers, etc.). In this perspective, the technologies described in this paper will allow us to detect critical areas where individuals can be involved in risky aggregation clusters. The ECO4CO data lake will be integrated with ad hoc data obtained by health care structures to understand trends and dynamics, to assess criticalities with respect to medical response and supplies, and to test possibilities useful to tackle potential future emergencies. The System will also provide geographical information on the spread of the infection which will allow an appropriate context-specific public health response to the epidemic. This project has been co-funded by the European Space Agency under its Business Applications programme.

Keywords: Business intelligence; COVID-19; Decision support system; Earth observation; Geospatial artificial intelligence.

Fig. 1

ECO4CO service concept.

Fig. 2

The ECO4CO high level system architecture.

Fig. 3

The ECO4CO services.

Fig. 4

The Dynamic Site Monitoring service concept.

Fig. 5

The Logistic Planning service concept.

Fig. 6

Forecast of weekly new positives vs. actual weekly positive for Lazio region.

Fig. 7

Non-Critical hospitalized patients in Lazio region: forecast vs. actual values.

Fig. 8

Predicted Intensive Care patients vs. real values for Lazio region.

Fig. 9

Comparison between emergency rooms attendance forecasts and actual values in Policlinico Casilino Hospital, Rome during March 2021.

Fig. 10

Comparison between forecasts and real stocks purchases. The X-axis reports the considered month, year, the medicine and the area (injectable anaesthetics refer to injectable general anaesthetics).

Fig. 11

Cluster Area Identification warnings.

Fig. 12

Car parking detection.

Fig. 13

Logistic Planning warnings (Emergency rooms attendance increase).

Fig. 14

Cluster Area Identification warnings.

Fig. 15

Logistic Planning warnings (colour code switch). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)