Hybrid model for early identification post-Covid-19 sequelae.

Artificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation.

Hybrid model for early identification post-Covid-19 sequelae

Artificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation.

Heterogeneous impacts of mobility restrictions on air quality in the State of Sao Paulo during the COVID-19 pandemic.

Air quality in the State of Sao Paulo was evaluated during the first general State plan of mobility restrictions due to the COVID-19 pandemic (24th March to May 31, 2020). Nitrogen dioxide (NO2), ozone (O3), particulate matter PM10 and PM2.5 and sulphur dioxide (SO2) concentrations were assessed in cities of the Sao Paulo State with a monitoring station and compared to historical data. Linear regression models were built to investigate the relationship between the isolation of the population - determined using mobile phone monitoring data - and the concentration of each pollutant during the studied period. Although the reduction of pollutants such as NO2, SO2 and PM2.5 is very clear, the economic and climatic characteristics of each region were decisive in the general behaviour of O3 and PM10. It was not possible to establish a correlation between the pollutants and the isolation index, partly due to the lack of data, partly due to the compliance of the population to those measurements, which was variable over time. Another important limitation factor was the absence of data related to the pollutants of interest in many of the stations. However, the isolation measures carried out in the State opened the opportunity to individually assess the air quality measurements in each of the stations, enabling an understanding that will allow in the future the design of air quality policies together with local sanitary policies.

Calculating a Drop in Carbon Emissions in the Strait of Gibraltar (Spain) from Domestic Shipping Traffic Caused by the COVID-19 Crisis

As a consequence of the COVID-19 pandemic, the Spanish government declared a State of Emergency, and domestic passenger ship traffic was restricted in Spanish ports. This manuscript presents scenarios of emissions from domestic shipping traffic in the seas of the Strait of Gibraltar (Spain) over three months of the COVID-19 pandemic. Emissions were estimated for only 90 days of the pandemic, and two scenarios were studied: emissions while vessels were berthed at the Algeciras Port and emissions as a consequence of the interruption of passenger ship transportation in the Strait of Gibraltar. To this end, the authors’own model was used, which has near zero uncertainties. This model was used for the first time in this study and takes into account both meteorological and sea condition parameters, as well as the efficiency of the propulsion system. The manuscript concentrates on the emissions of greenhouse gases (GHGs), nitrogen oxides (NOx), sulphur oxides (SOx), carbon dioxide (CO2), and particulate matter (PM) from six Ro-Pax ships that ceased to operate. The main finding is that as a consequence of the pandemic, reductions of up to 12% were found in the Strait of Gibraltar in all the pollutants and GHGs when taking into account all international traffic, while the decrease in emissions from domestic traffic only reached 51%.