A causal framework to determine the effectiveness of dynamic quarantine policy to mitigate COVID-19

Abstract

Since the start of the pandemic caused by the novel coronavirus, COVID-19, more than 106 million people have been infected and global deaths have surpassed 2.4 million. In Chile, the government restricted the activities and movement of people, organizations, and companies, under the concept of dynamic quarantine across municipalities for a predefined period of time. Chile is an interesting context to study because reports to have a higher quantity of infections per million people as well as a higher number of polymerise chain reaction (PCR) tests per million people. The higher testing rate means that Chile has good measurement of the contagious compared to other countries. Further, the heterogeneity of the social, economic, and demographic variables collected of each Chilean municipality provides a robust set of control data to better explain the contagious rate for each city. In this paper, we propose a framework to determine the effectiveness of the dynamic quarantine policy by analyzing different causal models (meta-learners and causal forest) including a time series pattern related to effective reproductive number. Additionally, we test the ability of the proposed framework to understand and explain the spread over benchmark traditional models and to interpret the Shapley Additive Explanations (SHAP) plots. The conclusions derived from the proposed framework provide important scientific information for government policymakers in disease control strategies, not only to analyze COVID-19 but to have a better model to determine social interventions for future outbreaks.

Social sentiment segregation: Evidence from Twitter and Google Trends in Chile during the COVID-19 dynamic quarantine strategy

“…For the rest of the cohorts in columns (1) to (5), the estimated coefficients are statistically insignificant, with much smaller R 2 coefficients. For the (−1, +1) window in columns (7) to (12), our point estimators are similar to the ones we obtained for the (−1, 0) window. Still, they all turn out to be statistically insignificant.…”

“…Similarly to the phenomenon observed for the abnormal returns of the stock market, we obtain a nearly monotonically decreasing magnitude of market sentiment responses to government announcements as we move from the wealthiest municipalities in column (7) to the whole population in column (12). For instance, for the richest 4.68% of the population in column (7), the point estimate of the change in population variable reaches a value of −4.4, two and half times bigger than the coefficient obtained for the total population in column (12), though it is only significant at the 15% level using bootstrapped errors. In column (11), when we consider the municipalities from the first wealth quintile that comprises 22.37% of the country's population, the point estimate drops to −2.77, but remains both economically and statistically significant at the 10% level, with an R 2 close to 0.65.…”

“…In columns (1) to ( 6) we present results for the (−1, 0) event window. In columns (7) to (12) we report the results for the (−1, +1) window. It should be noted that the cumulative abnormal return of the IGPA index, included as a control in all specifications, is a generated regressors.…”

“…The Chilean health authorities implemented a sanitary strategy known as a dynamic quarantine or strategic quarantine to cope with the COVID-19 pandemic. Under this system, lockdowns are implemented every week in some municipalities and lifted from others, according to the health authorities’ assessment of their epidemiological situation [ 7 ]. The decisions are made considering different factors, including the number of new cases in a given municipality, the size of its elderly population, and the access of its inhabitants to health care.…”

An explainable and efficient deep learning framework for video anomaly detection

“…Zhou et al explain the model prediction of factors affecting injury severity by using SHAP [ 26 ]. Kristjanpoller et al interpret the model prediction of evaluating the quarantine policy for COVID-19 by using SHAP plots [ 27 ]. The method proposed by Antwarg et al [ 28 ] is the first work to use SHAP to explain autoencoder for anomaly detection.…”