Embedded Files
Disclaimer: The views expressed on this website are my own and do not necessarily reflect those of the Banco de España or the Eurosystem.
On this page, you will find data, codes and material related to the Covid-19 pandemic.
Covid-19 Vaccine Efficacy Calculator
Covid-19 Vaccine Efficacy Calculator
The media often report the outcome depending on the vaccination status without reporting the vaccine coverage, which may be misleading for some people. For example, seeing that two thirds of the hospitalized patients are vaccinated may lead an uninformed person to declare that the Covid-19 vaccines have not demonstrated efficacy. However, if there are more than two thirds of vaccinated individuals in the general population, this is evidence of a certain efficacy level of the vaccine.
The following vaccine efficacy calculator bridges that gap and allows to predict the share of non-vaccinated and vaccinated individuals in a particular group (infected, hospitalized, deceased) according to two parameters: vaccine coverage and vaccine efficacy. Conversely, it is also possible to determine the efficacy of the vaccine from the share of infected indivduals who are vaccinated and the vaccine coverage.
The reader should be aware that the vaccine efficacy calculator only produces gross results (i. e. not stratified by relevant grouping such as age or any other risk factor), which may lead to erroneous conclusions in presence of the Simpson's paradox. Caution is therefore advised in interpreting the results.
For more information on the Simpson's paradox and its implication on Covid-19 data, please see this excellent piece by Jeffrey Morris.
Cost-Benefit analysis of air travel restrictions during the Covid-19 pandemic
Cost-Benefit analysis of air travel restrictions during the Covid-19 pandemic
Based on an appraisal of the contribution of air travel in the spatial diffusion of Covid-19 across the globe, I present a ballpark estimate of the cost-effectiveness associated with air travel restrictions at the height of the epidemic (mid-March to mid-April). In Gonne and Hubert (2020) we argue that the conclusions of a short-run cost-benefit analysis of travel restrictions highly depend on a handful of parameter values.
We derive from a Spatial Durbin-Watson econometric model that, on average, 8-9% of cases recorded domestically can be attributed to air traffic.
On the cost side, we estimate that a 4-week shutdown of the aviation sector represents an economic loss of USD 37 billion in OECD members and countries that host the 50 largest airports.
On the benefit side, a 4-week freeze in global air traffic means that 13,715 deaths could be avoided. If we take the monetary value of a human life suggested by the OECD of USD 3 million, the benefits amount to USD 44 billion.
Though a sudden stop of the flow of air passengers for a month would pass this back-of-the-envelope risk analysis, there is considerable uncertainty regarding the values of the parameters underlying the conclusion.
I provide a simulation tool to illustrate the last point.
How to use the simulation tool (right or below): Drag the sliders to change the parameters of the simulation. The simulation is based on Gonne and Hubert (2020), available here. You can also find the VoxEU column here.
(Note: JavaScript must be enabled in your browser)
Importation of data from the Johns Hopkins repository on GitHub.
Importation of data from the Johns Hopkins repository on GitHub.
Data: the file can be downloaded here. (last updated: 19/09/2021)
Code: the MATLAB code to generate a panel from the Johns Hopkins GitHub repository can be downloaded here.
Other resources
Other resources
Alessia Pacagnini is compiling various pieces of research on her website that gather the current state of knowledge of the Coronavirus epidemic from an economics point of view. You can access other resources at her website.
Page updated
Google Sites
Report abuse