Cargando…
How to go viral: A COVID-19 model with endogenously time-varying parameters
We estimate a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
North-Holland Pub. Co.]
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7833926/ https://www.ncbi.nlm.nih.gov/pubmed/33519026 http://dx.doi.org/10.1016/j.jeconom.2021.01.001 |
_version_ | 1783642171419656192 |
---|---|
author | Ho, Paul Lubik, Thomas A. Matthes, Christian |
author_facet | Ho, Paul Lubik, Thomas A. Matthes, Christian |
author_sort | Ho, Paul |
collection | PubMed |
description | We estimate a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. Our Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point increase in the death rate among reported cases. The model forecasts perform well, even relative to models from epidemiology and statistics. |
format | Online Article Text |
id | pubmed-7833926 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | North-Holland Pub. Co.] |
record_format | MEDLINE/PubMed |
spelling | pubmed-78339262021-01-26 How to go viral: A COVID-19 model with endogenously time-varying parameters Ho, Paul Lubik, Thomas A. Matthes, Christian J Econom Article We estimate a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. Our Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point increase in the death rate among reported cases. The model forecasts perform well, even relative to models from epidemiology and statistics. North-Holland Pub. Co.] 2023-01 2021-01-15 /pmc/articles/PMC7833926/ /pubmed/33519026 http://dx.doi.org/10.1016/j.jeconom.2021.01.001 Text en Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Ho, Paul Lubik, Thomas A. Matthes, Christian How to go viral: A COVID-19 model with endogenously time-varying parameters |
title | How to go viral: A COVID-19 model with endogenously time-varying parameters |
title_full | How to go viral: A COVID-19 model with endogenously time-varying parameters |
title_fullStr | How to go viral: A COVID-19 model with endogenously time-varying parameters |
title_full_unstemmed | How to go viral: A COVID-19 model with endogenously time-varying parameters |
title_short | How to go viral: A COVID-19 model with endogenously time-varying parameters |
title_sort | how to go viral: a covid-19 model with endogenously time-varying parameters |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7833926/ https://www.ncbi.nlm.nih.gov/pubmed/33519026 http://dx.doi.org/10.1016/j.jeconom.2021.01.001 |
work_keys_str_mv | AT hopaul howtogoviralacovid19modelwithendogenouslytimevaryingparameters AT lubikthomasa howtogoviralacovid19modelwithendogenouslytimevaryingparameters AT mattheschristian howtogoviralacovid19modelwithendogenouslytimevaryingparameters |