Open AccessA Missing Data on Covid-19 Forecasts
Author(s) -
Raúl Isea
Publication year - 2021
Publication title -
international journal of coronaviruses
Language(s) - English
Resource type - Journals
ISSN - 2692-1537
DOI - 10.14302/issn.2692-1537.ijcv-21-3918
Subject(s) - covid-19 , python (programming language) , granger causality , causality (physics) , econometrics , panama , geography , computer science , statistics , economics , mathematics , virology , medicine , infectious disease (medical specialty) , outbreak , programming language , physics , disease , pathology , quantum mechanics
Mathematical and computational studies of Covid-19 have underestimated the influence that other countries have on their daily records. To visualize this, a Granger causality analysis was implemented in Python to determine if the cases registered in Brazil, Chile, Colombia, Ecuador, Panama, Paraguay, Peru and the USA have any effect on Venezuela, and between all of them. Finally, this paper highlights the need to incorporate causality analysis employing only the cases of Covid-19 to improve mid and long term forecasts.