Publication: A Bayesian policy learning model of COVID-19 non-pharmaceutical interventions
A Bayesian policy learning model of COVID-19 non-pharmaceutical interventions
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Mamatzakis, E., Ongena, S., Patel, P. C., & Tsionas, M. (2024). A Bayesian policy learning model of COVID-19 non-pharmaceutical interventions. Applied Economics, 56(25), 2990–3010. https://doi.org/10.1080/00036846.2023.2203462
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This article examines the impact of non-pharmaceutical interventions on the initial exponential growth of the infected population and the final exponential decay of the infected population. We employ a Bayesian dynamic model to test whether there is learning, a random walk pattern, or another type of learning with evolving epidemiological data over time across 168 countries and 41,706 country-date observations. Although we show that Bayesian learning is not taking place, most policy measures appear to assert some effect. In particular
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- Mamatzakis, Emmanuel
- Patel, Pankaj C
- Tsionas, Mike
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Citations
Mamatzakis, E., Ongena, S., Patel, P. C., & Tsionas, M. (2024). A Bayesian policy learning model of COVID-19 non-pharmaceutical interventions. Applied Economics, 56(25), 2990–3010. https://doi.org/10.1080/00036846.2023.2203462
