RESUMEN
In the COVID-19 pandemic, access to data on the disease has become strategic for controlling public health measures. Faced with the health emergency, a large volume of data needed to be minimally organized and made available in a quick and automated way, composing the open government data. After two years of a pandemic and in order to present an overview of the publication of open data by the federal government of Brazil, on COVID-19, this study sought to evaluate the open government data made available through the Application Programming Interface (API). The methodology involved the identification of datasets on COVID-19 in Brazil, in Application Programming Interface, until April 2022, the analysis of the documentation and the evaluation using the DGABr metric. The evaluation considered the five perspectives of the metric that measures fundamental elements about the open government data, essential for interoperability and consequently reuse of the data and was based on the published documentation. As results, the open government data on COVID-19, made available in Application Programming Interface, presented a good score in the metric, reaching level 4. This result indicates that the use of APIs was an important and agile technological resource for the organization and availability of open government data, promoting its reuse. However, it is important to highlight that this availability to society was late, it needs constant improvements, mainly in technical issues such as the connection of data with other sources, and that the effective reuse actions were limited to data visualization panels on COVID-19.
RESUMEN
COVID-19 is one of the pandemic diseases that has hit the world including Sri Lanka. He has a virus that became the target of bids to stop its spread. Including the implementation of health protocols, to provide information about the spread of the virus emergency response, detection services for suspicious persons infected with the virus, and programs to contain the spread of the virus ensuring that the whole of Sri Lanka gets vaccinated. Here, the research focuses on the minimal spread of the face mask in the office e nvironment a n i dentification system that uses a deep learning model that prioritizes object recognition for the identification o f e mployees w ho w ear a f ace m ask and detects social distancing and crowd gathering, if any if there is a violation, it will inform via a voice notification. L oss o f Smell after the next component. One person can use one disposable card to check the smell of sniffing. E ach d isposable c ard has QR codes, and all QR codes are encrypted by adding data. The user scans the QR code on their ticket and then scratches off and smelled the smelling area and selected the corresponding scent on the disposable card. Employee company attendance is a proposed automated attendance system using facial recognition. Because it requires minimal human influence a nd o ffers a high level of accuracy and marking employee attendance and employee body temperature measurement, facial recognition will appear to be a practical option. This system aims to provide a high level of protection. Automated Attendance systems that detect and recognize are safe, fast, and time-consuming savings. This technique can also be used to identify an unknown person. © 2022 IEEE.