TY - GEN
T1 - Data-Driven Estimation of Effectiveness of COVID-19 Non-pharmaceutical Intervention Policies
AU - Mahajan, Yash
AU - Islam, Sheikh Rabiul
AU - Amin, Mohammad Ruhul
AU - Karmaker Santu, Shubhra Kanti
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Non-pharmaceutical Interventions (NPIs), such as Stay-at-Home, and Face-Mask-Mandate, are essential components of the public health response to contain an outbreak like COVID-19. However, it is very challenging to quantify the individual or joint effectiveness of NPIs and their impact on people from different racial and ethnic groups or communities in general. Therefore, in this paper, we study the following two research questions: 1) How can we quantitatively estimate the effectiveness of different NPI policies pertaining to the COVID-19 pandemic?; and 2) Do these policies have considerably different effects on communities from different races and ethnicity? To answer these questions, we model the impact of an NPI as a joint function of stringency and effectiveness over a duration of time. Consequently, we propose a novel stringency function that can provide an estimate of how strictly an NPI was implemented on a particular day. Next, we applied two popular tree-based discriminative classifiers, considering the change in daily COVID cases and death counts as binary target variables, while using stringency values of different policies as independent features. Finally, we interpreted the learned feature weights as the effectiveness of COVID-19 NPIs. Our experimental results suggest that, at the country level, restaurant closures and stay-at-home policies were most effective in restricting the COVID-19 confirmed cases and death cases respectively; and overall, restaurant closing was most effective in hold-down of COVID-19 cases at individual community levels such as Asian, White, Black, AIAN and, NHPI. Additionally, we also performed a comparative analysis between race-specific effectiveness and country-level effectiveness to see whether different communities were impacted differently. Our findings suggest that the different policies impacted communities (race and ethnicity) differently.
AB - Non-pharmaceutical Interventions (NPIs), such as Stay-at-Home, and Face-Mask-Mandate, are essential components of the public health response to contain an outbreak like COVID-19. However, it is very challenging to quantify the individual or joint effectiveness of NPIs and their impact on people from different racial and ethnic groups or communities in general. Therefore, in this paper, we study the following two research questions: 1) How can we quantitatively estimate the effectiveness of different NPI policies pertaining to the COVID-19 pandemic?; and 2) Do these policies have considerably different effects on communities from different races and ethnicity? To answer these questions, we model the impact of an NPI as a joint function of stringency and effectiveness over a duration of time. Consequently, we propose a novel stringency function that can provide an estimate of how strictly an NPI was implemented on a particular day. Next, we applied two popular tree-based discriminative classifiers, considering the change in daily COVID cases and death counts as binary target variables, while using stringency values of different policies as independent features. Finally, we interpreted the learned feature weights as the effectiveness of COVID-19 NPIs. Our experimental results suggest that, at the country level, restaurant closures and stay-at-home policies were most effective in restricting the COVID-19 confirmed cases and death cases respectively; and overall, restaurant closing was most effective in hold-down of COVID-19 cases at individual community levels such as Asian, White, Black, AIAN and, NHPI. Additionally, we also performed a comparative analysis between race-specific effectiveness and country-level effectiveness to see whether different communities were impacted differently. Our findings suggest that the different policies impacted communities (race and ethnicity) differently.
KW - COVID-19
KW - Communities
KW - Non-Pharmaceutical Interventions (NPIs)
KW - Policy Stringency
UR - https://www.scopus.com/pages/publications/85147976587
UR - https://www.scopus.com/pages/publications/85147976587#tab=citedBy
U2 - 10.1109/BigData55660.2022.10020822
DO - 10.1109/BigData55660.2022.10020822
M3 - Conference contribution
AN - SCOPUS:85147976587
T3 - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
SP - 5312
EP - 5321
BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
A2 - Tsumoto, Shusaku
A2 - Ohsawa, Yukio
A2 - Chen, Lei
A2 - Van den Poel, Dirk
A2 - Hu, Xiaohua
A2 - Motomura, Yoichi
A2 - Takagi, Takuya
A2 - Wu, Lingfei
A2 - Xie, Ying
A2 - Abe, Akihiro
A2 - Raghavan, Vijay
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Big Data, Big Data 2022
Y2 - 17 December 2022 through 20 December 2022
ER -