Drawing policy suggestions to fight Covid-19 from hardly reliable data. A machine-learning contribution on lockdowns analysis
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- Giovanni Dosi & Lucrezia Fanti & Maria Enrica Virgillito, 2020. "Unequal societies in usual times, unjust societies in pandemic ones," LEM Papers Series 2020/14, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- Domenico Depalo, 2021.
"True COVID-19 mortality rates from administrative data,"
Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 253-274, January.
- Depalo, Domenico, 2020. "True Covid-19 mortality rates from administrative data," GLO Discussion Paper Series 630, Global Labor Organization (GLO).
- Bonacini, Luca & Gallo, Giovanni & Scicchitano, Sergio, 2020. "All that glitters is not gold. Effects of working from home on income inequality at the time of COVID-19," GLO Discussion Paper Series 541, Global Labor Organization (GLO).
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More about this item
Keywords
Covid-19; coronavirus; lockdown; feedback control; mitigation strategies;All these keywords.
JEL classification:
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
- I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2020-05-18 (Big Data)
- NEP-CMP-2020-05-18 (Computational Economics)
- NEP-EUR-2020-05-18 (Microeconomic European Issues)
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