Predicting and interpreting COVID-19 transmission rates from the ensemble of government policies

  1. C. K. Sruthi
  2. Malay Ranjan Biswal
  3. Brijesh Saraswat
  4. Himanshu Joshi
  5. Meher K. Prakash

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Abstract

Several questions resonate as the governments relax their COVID-19 mitigation policies - is it too early to relax them, were the policies as effective as they could have been. Answering these questions about the past or crafting newer policy decisions in the future requires a quantification of how policy choices affect the spread of the infection. Policy landscape as well as the infection trajectories from different states and countries diverged so fast that comparing and learning from them has not been easy. In this work, we standardize and pool together the ensemble of lockdown and graded re-opening policies adopted by the 50 states of USA in any given week between 9th March and 9th August. Using artificial intelligence (AI) on this pooled data, we build a predictive model ( , ) for the weekly-averaged transmission rate of infections. Predictability conceptually raises the possibility of an evidence-based or data-driven mitigation policy-making by evaluating the relative merits of the different policy scenarios. Probing the predictions with interpretable AI highlights how factors such as the closing of bars or the use of masks influence transmission, effects which have been hard to decouple from the ensemble of policy instrument combinations. While acknowledging the limitations of our predictions as well as of the infection testing, we ask the theoretical question if the observed transmission rates in the states were as efficient as they could have been under various levels of restrictions, and if the mitigation policies of the states are ‘overdesigned’. The model can be further refined with a more detailed inclusion of geographies and policy compliances, as well as expanded as newer policies emerge.

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    SciScore for 10.1101/2020.08.27.20179853: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

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    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The Python implementation of SHAP (https://github.com/slundberg/shap) was used for our interpretable AI analyses as well as for generating figures.
    Python
    suggested: (IPython, RRID:SCR_001658)

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  2. Version published to 10.1101/2020.08.27.20179853 on medRxiv