Cita 1
Research · Big Data · Social Media
Coronavirus
The 2019-2020 coronavirus outbreak, officially known as COVID-19, was caused by the SARS-CoV-2 virus. The outbreak started in December 2019 in Wuhan, China, when several cases of pneumonia of unknown origin were reported.
This project approaches the topic from the perspective of Big Data and social media, using Twitter as a textual source to study temporal correlations between the evolution of the virus and social perception.
The implementation can be found at 1938.com.es/en/coronavirus-tweets-csv-tweepy .
Study context
Twitter is useful for capturing immediate public reactions because it concentrates short, timely messages that can be collected and analyzed through the platform API.
That makes it a practical source for studying trends, public concerns and changes in perception over time.
This page provides a short introductory overview of the virus, its origin and some key concepts before moving into data analysis.
Why are they called coronaviruses?
Because under an electron microscope they show an outer structure that resembles a crown.
Was this the first coronavirus we knew?
No. Several other coronaviruses were already known in humans, including HCoV-229E, HCoV-OC43, HCoV-NL63, HKU1, SARS-CoV and MERS-CoV.
The most severe earlier outbreaks were SARS-CoV and MERS-CoV, both of which caused serious mortality but were eventually contained.
How does it spread?
Viruses cannot reproduce on their own. They need to enter living cells and use their biological machinery to multiply.
Those outer spikes are precisely what the virus uses to enter human cells.
Do we know its genetic material?
Yes. RNA from infected patients was sequenced and shows roughly 30,000 letters encoding the viral genome.
Coronavirus and bats
The viral genome is highly similar to known coronaviruses found in bats.
Even so, it is still not fully certain whether the virus passed directly from bats or through another host animal first.
How can spread be reconstructed?
As the virus spreads, its RNA accumulates small mutations. Comparing genomes from different patients makes it possible to partially reconstruct transmission paths.
How to read this page today
This article preserves the original context of an early COVID-19 data project, so some public-health facts reflect the moment when the work was written. Its current value is not medical guidance, but a practical example of how a fast-moving event can be framed as a data-analysis problem.
When using social data for this kind of study, the most important limitation is representativeness: tweets do not describe the whole population, API access can change over time and highly active accounts can distort the apparent level of concern. A useful analysis should therefore combine social signals with official sources, dates, geographic context and clear assumptions.
The related implementation page explains the collection step, while this page gives the background needed to interpret the dataset without confusing online conversation with epidemiological reality.
References
Bibliography
Sources and materials used in this section of the project.
Cita 2
Coronavirus
AutorPor Francisco R. Villatoro, el 24 febrero, 2020
PublicaciónLa Ciencia de la mula Francis
References
Bibliography
Sources and materials used in this section of the project.
Cita 1
El asunto del coronavirus
Cita 2
Coronavirus
AutorPor Francisco R. Villatoro, el 24 febrero, 2020
PublicaciónLa Ciencia de la mula Francis