Pandemics: New tools for prediction and measuring economic effects – Center for AI and Data Analytics for Science, Technology, Business, and Society (HEC-IP Paris) webinar
There is a huge uncertainty about how the virus disseminates, how long it will take to stop the current pandemic, and what are its effects on the economy. Uncertainty can be reduced by using better mathematical models and more data for predicting the evolution of a pandemic. Moreover, granular economic data can be used to better understand and predict the impact of social distancing and lockdowns on infection rates and employment
Join us to hear the views of Stephan Clémençon (IP Paris)/Viet-Chi Tran (Université Gustave Eiffel) and Jean-Noël Barrot (HEC Paris) on these questions.
Talk by Stephan Clemencon (IP Paris)/Viet-Chi Tran (Univ. Gustave Eiffel):Mathematical Epidemiology of Infectious Diseases in the Big Data Era – Towards More Predictive/Explanatory Models
For decades, mathematicians have developed tools for understanding the dynamics of transmissible infectious diseases. Many of the concepts introduced, such as the ‘R0 coefficient’, the basic reproduction number, have passed into common language. In epidemiology, as in many other domains, the trend today is towards more and more observations, at an ever finer level of granularity. Expectations are high, it is hoped that epidemic models will permit to determine optimal control strategies, assess the role played by certain factors (e.g. genetic traits, environmental conditions) and will be used for prediction purposes. In this webinar, we propose a brief tour of stochastic concepts and tools for mathematical epidemiology (probabilistic modeling and analysis, statistical inference, simulation), show how they can be put into concrete form through the description of real examples and try to explain how the epidemic datasets now available may lead to new mathematical elaborations and techniques.
Talk by Jean-Noël Barrot (HEC Paris): Costs and benefits of closing businesses in a pandemic
Typical government responses to pandemics involve social distancing measures implemented to curb disease propagation. We evaluate the impact of state-mandated business closures in the context of the Covid-19 crisis in the US. Using state-level variations in the set of sectors defined as non-essential and forced to shut down, and geographic variations in industry composition, we estimate the effects of business closure decisions on firms’ market value, and on infection and death rates. We find that a 10 percentage point increase in the share of restricted labor is associated with a drop by 3 percentage points in April 2020 employment, a 1.87% drop in firms’ market value, and 0.15 and 0.011 percentage points lower Covid-19 infection and death rates, respectively.