A systems analysis of applications of earth orbital space technology to selected cases in water management and agriculture. Volume 1 - Technical summary

Systems analysis of agricultural and water management information systems utilizing satellite borne multispectral band scanner, radar, and television equipmen

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, geo- spatial 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 fore- cast 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 hos- pitals 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

Could Cultures Determine the Course of Epidemics and Explain Waves of COVID-19?

Coronavirus Disease (COVID-19), caused by the SARS-CoV-2 virus, is an infectious disease that quickly became a pandemic spreading with different patterns in each country. Travel bans, lockdowns, social distancing, and non-essential business closures caused significant economic disruptions and stalled growth worldwide in the pandemic’s first year. In almost every country, public health officials forced and/or encouraged Nonpharmaceutical Interventions (NPIs) such as contact tracing, social distancing, masks, and quarantine. Human behavioral decision-making regarding social isolation significantly impedes global success in containing the pandemic. This thesis focuses on human behaviors and cultures related to the decision-making of social isolation during the pandemic. Within a COVID-19 disease transmission model, we created a conceptual and deterministic model of human behavior and cultures. This study emphasizes the importance of human behavior in successful disease control strategies. Additionally, we introduce a back engineering approach to determine whether cultures are explained by the courses of COVID-19 epidemics. We used a deep learning technique based on a convolutional neural network (CNN) to predict cultures from COVID-19 courses. In this system, CNN is used for deep feature extraction with ordinary convolution and with residual blocks. Also, a novel concept is introduced that converts tabular data into an image using matrix transformation and image processing validated by identifying some well-known function. Despite having a small and novel data set, we have achieved an 80-95% accuracy, depending on the cultural measures