Publication

<i>What If...?</i> Pandemic Policy-Decision-Support to Guide a Cost-Benefit-Optimised, Country-Specific Response

2021

Abstract

Background: After 18 months of responding to the COVID-19 pandemic, there is still no agreement on the optimal combination of mitigation strategies. The efficacy and collateral damage of pandemic policies are dependent on constantly evolving viral epidemiology as well as the volatile distribution of socioeconomic and cultural factors. This study proposes a data-driven approach to quantify the efficacy of the type, duration, and stringency of COVID-19 mitigation policies in terms of transmission control and economic loss, personalised to individual countries.

Methods: We present What If...?, a deep learning pandemic-policy-decision-support algorithm simulating pandemic scenarios to guide and evaluate policy impact in real time. It leverages a uniquely diverse live global data-stream of socioeconomic, demographic, climatic, and epidemic trends on over a year of data (04/2020—06/2021) from 116 countries. The economic damage of the policies is also evaluated on the 29 countries for which data is available. The efficacy and economic damage estimates are derived from two hybrid (recurrent + feed forward) neural networks that infer respectively the daily R-value (RE) and unemployment rate (UER). Reinforcement learning then pits these models against each other to find the optimal policies minimising both RE and UER.

Findings: The models made high accuracy predictions of RE (average mean squared error [aMSE] 0·043 with a 95% confidence interval [CI95] of [0·042, 0·044] over 116 countries) and UER (aMSE 4·473 CI95 [2·619, 6·326]% over 29 countries). Comparing policy strategies of our reinforcement learning agent to those that were truly implemented, the agent outperformed policymakers across all 29 countries over a range of randomly selected time periods (predicted average RE reduction of 0·250 (versus 0·025) and 1·595% (versus 0·057%) for UER).

Interpretation: These results show that deep learning has the potential to guide evidence-based understanding and implementation of public health policies.

Official source

https://infoscience.epfl.ch/entities/publication/d117c4a3-c7de-4a2a-aaa7-6c82b95892d1

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2021

Abstract

Interpretation: These results show that deep learning has the potential to guide evidence-based understanding and implementation of public health policies.

Official source

https://infoscience.epfl.ch/entities/publication/d117c4a3-c7de-4a2a-aaa7-6c82b95892d1

About this result

Related concepts (32)

Deep reinforcement learning

Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs (e.g.

Reinforcement learning

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.

Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

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<i>What If...?</i> Pandemic Policy-Decision-Support to Guide a Cost-Benefit-Optimised, Country-Specific Response

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