The Sapienza University of Rome network of automated external defibrillators: a prototype webMap developed to speed access to community defibrillators and increase survival from out-of-hospital cardiac arrest

OBJECTIVE

In Italy, only around 10% of people who experience out-of-hospital cardiac arrest (OHCA) survive. A large portion of OHCA events in public settings are characterized by an initial shockable rhythm, which requires prompt defibrillation. We aimed to create a system to quickly locate nearby public access automated external defibrillators (AEDs) on the campus of Sapienza University of Rome, the largest public university in Europe.

MATERIALS AND METHODS

We developed the AED webMap through a 6-step process involving the: 1) collection of information and geographical coordinates for each AED from the university management system; 2) development of a new geolocation database; 3) integration of information contained in the new database with data provided by university departments; 4) geolocation of AEDs in the Google MyMaps environment; 5) graphic representation of all AEDs on digital map templates using specific symbols, with pop-ups containing additional information for each AED; and 6) publication of the webMap on the university website.

RESULTS

The AED webMap was published on the university website (https://www.uniroma1.it/it/pagina/defibrillatori-sapienza-in-rete) and facilitates prompt identification of nearby AEDs by providing: 1) detailed AED geolocalization with interactive pop-up information for each AED, including whether the AED is located internally or externally; 2) the option to use different base maps (e.g., digital street map); 3) calculation and display of the route to reach the chosen AED; and 4) the possibility to migrate towards multiple platforms.

CONCLUSIONS

The webMap can help bystanders quickly identify, locate, and reach nearby AEDs present on the campus of the largest public university in Europe, a measure that could help speed defibrillation and maximize the life-saving potential of AEDs in the event of OHCA.

A predictive decision support system for coronavirus disease 2019 response management and medical logistic planning

Objective

Coronavirus disease 2019 demonstrated the inconsistencies in adequately responding to biological threats on a global scale due to a lack of powerful tools for assessing various factors in the formation of the epidemic situation and its forecasting. Decision support systems have a role in overcoming the challenges in health monitoring systems in light of current or future epidemic outbreaks. This paper focuses on some applied examples of logistic planning, a key service of the Earth Cognitive System for Coronavirus Disease 2019 project, here presented, evidencing the added value of artificial intelligence algorithms towards predictive hypotheses in tackling health emergencies.

Methods

Earth Cognitive System for Coronavirus Disease 2019 is a decision support system designed to support healthcare institutions in monitoring, management and forecasting activities through artificial intelligence, social media analytics, geospatial analysis and satellite imaging. The monitoring, management and prediction of medical equipment logistic needs rely on machine learning to predict the regional risk classification colour codes, the emergency rooms attendances, and the forecast of regional medical supplies, synergically enhancing geospatial and temporal dimensions.

Results

The overall performance of the regional risk colour code classifier yielded a high value of the macro-average F1-score (0.82) and an accuracy of 85%. The prediction of the emergency rooms attendances for the Lazio region yielded a very low root mean square error (<11 patients) and a high positive correlation with the actual values for the major hospitals of the Lazio region which admit about 90% of the region's patients. The prediction of the medicinal purchases for the regions of Lazio and Piemonte has yielded a low root mean squared percentage error of 16%.

Conclusions

Accurate forecasting of the evolution of new cases and drug utilisation enables the resulting excess demand throughout the supply chain to be managed more effectively. Forecasting during a pandemic becomes essential for effective government decision-making, managing supply chain resources, and for informing tough policy decisions.

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

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.

A dynamic GIS space-time diffusion model to tackle COVID-19 emergency

Background

Surveillance and containment of the spread of COVID-19 requires the use of advanced geographic information science and technology (GIS&T) to map the spread and eventually to guide interventions. A dynamic space-time diffusion model in a GIS environment was developed and succesfully tested in Rome, Italy.

Methods

Information on cases of SARS-CoV-2 infection confirmed by molecular diagnostics from Feb 25 to Sep 26 2020 (collected by a large Local Health Unit of Rome, Italy) was used to test a GIS simulator model able to monitor the spatial diffusion and temporal evolution of the spread of the disease. Data included information on: sex, date and place of birth, healthcare facility of hospitalization, date of notification, start date and end date of isolation, date of recovery (both clinical and laboratory confirmed), residence address.

Results

Globally, 3,056 cases were geocoded and analysed. The spatio-temporal analysis of the first 45 days since 25 Feb 2020 shows that the spread of COVID-19 was very fast (1,230 cases recorded on 11 Apr) and spatially widespread. Number of cases was highest in the city centre with clusters, thickets and axes in different sub-municipal areas. A slowdown occurred the following month, confirming the positive effect of the lockdown. This effect continued until 11 Jun with a small increase in the number of cases (+10.9%). The period up to 26 Sep is paradigmatic of the second wave, with a continuous increase in cases that spread from the city centre to the suburbs.

Conclusions

Using geocoding process and a detailed GIS mapping it is possible to identify streets, buildings and census sections where the number of cases is high and tends to increase rapidly and, at the same time, it is possible to distinguish clusters and axes that should be kept immediately under special observation as potential pools of super-diffusion. Development of its use in near-real time could bring significant advantages in controlling the spread of COVID-19.

Key messages

The use of GIS technology is fundamental for mapping the spread of COVID-19.

A greater effort should be made by institutions to increase the digitisation of health data and the possibility of using them for both research and surveillance purposes.