Policy evaluation using model over-fitting: the Nordic case

Armando Tapia, Silvestre L. González (), Jose R. Vergara, Mariano Villafuerte and Luis V. Montiel ()
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Armando Tapia: Universidad Nacional Auntónoma de México - UNAM, Ciudad Universitaria, CDMX
Silvestre L. González: Universidad Nacional Auntónoma de México - UNAM, Ciudad Universitaria, CDMX
Jose R. Vergara: Universidad Nacional Auntónoma de México - UNAM, Ciudad Universitaria, CDMX
Mariano Villafuerte: Universidad Nacional Auntónoma de México - UNAM, Ciudad Universitaria, CDMX
Luis V. Montiel: Universidad Nacional Auntónoma de México - UNAM, Ciudad Universitaria, CDMX

Computational Statistics, 2025, vol. 40, issue 6, No 6, 2955-2980

Abstract: Abstract The interest of this article is to better understand the effects of different public policy alternatives to handle the COVID-19 pandemic. In this work we use the susceptible, infected, recovered (SIR) model to find which of these policies have an actual impact on the dynamic of the spread. Starting with raw data on the number of deceased people in a country, we over-fit our SIR model to find the times $$t_i$$ t i at which the main parameters, the number of daily contacts and the probability of contagion, require adjustments. For each $$t_i$$ t i , we go to historic records to find policies and social events that could explain these changes. This approach helps to evaluate events through the eyes of the popular epidemiological SIR model, and to find insights that are hard to recognize in a standard econometric model.

Keywords: COVID-19; Simulation; SIR-model; Public-policy (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-023-01348-2

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