AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19

Abstract

The Coronavirus disease 2019 (COVID-19) global pandemic has transformed almost every facet of human society throughout the world. Against an emerging, highly transmissible disease, governments worldwide have implemented non-pharmaceutical interventions (NPIs) to slow the spread of the virus. Examples of such interventions include community actions, such as school closures or restrictions on mass gatherings, individual actions including mask wearing and self-quarantine, and environmental actions such as cleaning public facilities. We present the Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC), a comprehensive dataset consisting of over 6,000 NPIs implemented worldwide since the start of the pandemic. WNTRAC covers NPIs implemented across 261 countries and territories, and classifies NPIs into a taxonomy of 16 NPI types. NPIs are automatically extracted daily from Wikipedia articles using natural language processing techniques and then manually validated to ensure accuracy and veracity. We hope that the dataset will prove valuable for policymakers, public health leaders, and researchers in modeling and analysis efforts to control the spread of COVID-19.

Predictive and Causal Analysis of No-Shows for Medical Exams During COVID-19: A Case Study of Breast Imaging in a Nationwide Israeli Health Organization

“…Governments implemented a wide variety of nonpharmaceutical interventions (NPIs) aimed at controlling the disease spread and enforcing social isolation. Confinement and school closure as well as entertainment and cultural sector closure were the predominant NPIs taken by governments in 2020 1 . Accordingly, many radiology departments decreased the number of elective imaging examinations to minimize the spread of infection and free up much needed medical resources and staff 2–4 .…”

Inferring Global-Scale Temporal Latent Topics From News Reports to Predict Public Health Interventions for COVID-19

“…Second, this study is conducted using two data sets consisting of COVID-19-related news reports. We intend to study the generalizability of EpiTopics by exploring other sources of data which may cover a broader range of countries, NPIs, tasks and documents [5,16]. For instance, the proposed method could be integrated into existing automated surveillance systems such as Global Public Health Intelligence Network (GPHIN) [17], which monitors general media news potentially related to public health, instead of being applied exclusively to COVID-19-related news.…”

“…Our proposed approach is novel both in terms of the public health focus and the machine learning methods. Other researchers have applied supervised learning to online media [3] and Wikipedia articles [5] to identify COVID-19 NPI, but the 'black box' nature of the models has made it difficult to interpret results and model parameters. Our approach also has less demand for labelled data as it exploits large-scale unlabeled data via unsupervised learning and transfer learning.…”

Cross-National Measures of the Intensity of COVID-19 Public Health Policies

“…Since the outbreak of the pandemic, a growing number of research projects have tried to capture the diverse ways that governments have implemented policies to slow the spread of COVID-19. Some of these projects focus on a single type of policy (UNDP and UN Women COVID-19 Global Gender Response Tracker 2021; Elgin, Basbug, and Yalaman 2020) or a particular region of the world (Naqvi 2021;Adolph et al 2021), whereas others aim at collecting data at world-wide scale across a range of indicators (COVID-19 Government Measures Dataset 2020; Porcher 2020; Grundy, Quinn, and Todowede 2021; Suryanarayanan et al 2021). Of this latter set, the datasets with the widest coverage and most detailed indicators include CoronaNet, OxCGRT, the Complexity Science Hub COVID-19 Control Strategies List (CCCSL) Desvars-Larrive et al 2020) and Health Intervention Tracking for COVID-19 (HIT-COVID) (Zheng et al 2020).…”