ABSTRACT
One key task in the early fight against the COVID-19 pandemic was to plan non-pharmaceutical interventions to reduce the spread of the infection while limiting the burden on the society and economy. With more data on the pandemic being generated, it became possible to model both the infection trends and intervention costs, transforming the creation of an intervention plan into a computational optimization problem. This paper proposes a framework developed to help policy-makers plan the best combination of non-pharmaceutical interventions and to change them over time. We developed a hybrid machine-learning epidemiological model to forecast the infection trends, aggregated the socio-economic costs from the literature and expert knowledge, and used a multi-objective optimization algorithm to find and evaluate various intervention plans. The framework is modular and easily adjustable to a real-world situation, it is trained and tested on data collected from almost all countries in the world, and its proposed intervention plans generally outperform those used in real life in terms of both the number of infections and intervention costs.
ABSTRACT
One key task in the early fight against the COVID-19 pandemic was to plan non-pharmaceutical interventions to reduce the spread of the infection while limiting the burden on the society and economy. With more data on the pandemic being generated, it became possible to model both the infection trends and intervention costs, transforming the creation of an intervention plan into a computational optimization problem. This paper proposes a framework developed to help policy-makers plan the best combination of non-pharmaceutical interventions and to change them over time. We developed a hybrid machine-learning epidemiological model to forecast the infection trends, aggregated the socio-economic costs from the literature and expert knowledge, and used a multi-objective optimization algorithm to find and evaluate various intervention plans. The framework is modular and easily adjustable to a real-world situation, it is trained and tested on data collected from almost all countries in the world, and its proposed intervention plans generally outperform those used in real life in terms of both the number of infections and intervention costs.
Subject(s)
Artificial Intelligence , COVID-19 , Humans , COVID-19/epidemiology , COVID-19/prevention & control , Pandemics , Algorithms , Machine LearningABSTRACT
In this review, we examine 34 studies based on experimental data that estimate and compare the effective ness of 12 non-pharmaceutical government interventions against COVID-19 based on cases, deaths, and/or transmission rates to assess their overall effectiveness. The studies reviewed are based on daily country level data and cover four to 200 countries and regions worldwide with varying time intervals, spanning the period between December 2019 and August 2021. We found that the overall most effective interventions are restrictions on gatherings, workplace closing, public information campaigns, and school closing, while the least effective are close public transport, contact tracing, and testing policy.