ABSTRACT
The air transport sector is characterized by big volumes of passengers transported globaly. These transports, interconnecting geographically diverse localities, turn this modal into a potencial disseminator of infectious diseases in general. With the Covid-19 pandemic, governments and airports all over the world have implemented restrictive measures on airport operations, in a attempt to mitigate this dissemination potencial, resulting in negative impacts on the operating results of these airports. This work aims to analyze the impact of the Covid-19 pandemic on the efficiency values of 17 Brazilian airports, and also the influence of different factors on these efficiencies. The methodology took place in two stages, with the application of Data Envelopment Analysis (DEA), to obtain the efficiency scores, followed by tobit regression to identify and analyze the influence exerted by different factors on these efficiencies. Data from 2010 to 2020 were used. The results showed that there was no significant reduction in efficiency scores of these airports, due to the Covid-19 pandemic. The models obtained with the tobit regression showed a positive influence of GDP per capita on airport efficiency values, and expressed the statistical insignificance of the influence of privatization on these values.
ABSTRACT
Right after the Chinese example in conducting COVID-19 epidemic originated in Wuhan, the readiness to detect and respond by health authorities to local (sometimes global) epidemics has become central lately. Within the idea of health 4.0, information about the individual is essential in supporting public community health policies. This paper presents a proposal for an epidemiological surveillance system applied to arboviruses. Data mining techniques and Machine Learning (ML) are used to design mathematical models for detecting epidemics enhanced by Aedes Aegypti (vector for dengue, chikungunaya, yellow fever and zica). Based on data, it is proposed an adaptive manner to reach better stability on results. A Prove of Concept (PoC) is presented for dengue epidemics detection, a common endemic disease in the semiarid region of Brazil. © 2021 IEEE.