COVID-19

This dataset has the following data about the COVID-19 pandemic in the State of Maranhão, Brazil:

• Number of daily cases
• Number of daily deaths

In addition, this dataset also contains data from Google Trends on some subjects related to the pandemic, related to searches carried out in the State of Maranhão.

The data follows a timeline that begins on March 20, 2020, the date of the first case of COVID-19 in the State of Maranhão, until July 9, 2020.

We present GeoCoV19, a large-scale Twitter dataset related to the ongoing COVID-19 pandemic. The dataset has been collected over a period of 90 days from February 1 to May 1, 2020 and consists of more than 524 million multilingual tweets. As the geolocation information is essential for many tasks such as disease tracking and surveillance, we employed a gazetteer-based approach to extract toponyms from user location and tweet content to derive their geolocation information using the Nominatim (Open Street Maps) data at different geolocation granularity levels. In terms of geographical coverage, the dataset spans over 218 countries and 47K cities in the world. The tweets in the dataset are from more than 43 million Twitter users, including around 209K verified accounts. These users posted tweets in 62 different languages.

This dataset is very vast and contains Bengali tweets related to COVID-19. There are 36117 unique tweet-ids in the whole dataset that ranges from December 2019 till May 2020 . The keywords that have been used to crawl the tweets are 'corona',  ,  'covid ' , 'sarscov2 ',  'covid19', 'coronavirus '.  For getting the other 33 fields of data drop a mail at "avishekgarain@gmail.com". Code snippet is given in Documentation file. Sharing Twitter data other than Tweet ids publicly violates Twitter regulation policies.    

Purpose is to describe the dynamics of the COVID19 pandemics accounting for the mitigation measures, for the introduction or removal of the quarantine, and for the effect of vaccination when and if introduced.

Methods include the derivation of the Pandemic Equation describing the mitigation measures via the evolution of the growth time constant in the Pandemic Equation resulting in an asymmetric pandemic curve with a steeper rise than a decrease.