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.

Biotransformation of ethinylestradiol by microalgae

The capability of biotransformation of 11 microalgae strains was tested on ethinylestradiol (EE). Seven strains were ineffective whilst Selenastrum capricornutum, Scenedesmus quadricauda, Scenedesmus vacuolatus and Ankistrodesmus braunii biotransformed the substrate. EE was converted by S. capricornutum in three products (ethinylestradiol glucoside, 3-beta-D-glucopyranosyl-2-hydroxyethinylestradiol, and 3-beta-D-glucopyranosyl-6beta-hydroxyethinyl estradiol) in 40%, 5%, and 5% yields, respectively. S. quadricauda transformed EE into 17alpha-ethinyl-1,4-estradien-10,17beta-diol-3-one (12%) and A. braunii transformed EE into 6-alpha-hydroxy-ethinylestradiol (25%). It is noteworthy that EE is converted in 92% yield in ethinylestradiol glucoside by S. capricornutum when using optimal algal density conditions.