An Investigation of COVID-19 Spreading Factors with Explainable AI Techniques

TL;DR

This study uses explainable AI techniques to evaluate the effectiveness of various COVID-19 public health measures across 18 countries, identifying city lockdowns, contact tracing, and face masks as key interventions.

Contribution

It introduces the application of SHAP and ECPI explainable AI methods to assess COVID-19 measures' effectiveness and identifies the most impactful interventions during early pandemic stages.

Findings

City lockdowns and contact tracing are most effective measures.

Face masks significantly reduce transmission when $R_t$<1.

Warm temperatures aid in decreasing COVID-19 spread.

Abstract

Since COVID-19 was first identified in December 2019, various public health interventions have been implemented across the world. As different measures are implemented at different countries at different times, we conduct an assessment of the relative effectiveness of the measures implemented in 18 countries and regions using data from 22/01/2020 to 02/04/2020. We compute the top one and two measures that are most effective for the countries and regions studied during the period. Two Explainable AI techniques, SHAP and ECPI, are used in our study; such that we construct (machine learning) models for predicting the instantaneous reproduction number ($R_t$) and use the models as surrogates to the real world and inputs that the greatest influence to our models are seen as measures that are most effective. Across-the-board, city lockdown and contact tracing are the two most effective measures. For ensuring $R_t<1$, public wearing face masks is also important. Mass testing alone is not the most effective measure although when paired with other measures, it can be effective. Warm temperature helps for reducing the transmission.