Contact Tracing has been used to identify people who were in close proximity to those infected with SARS-Cov2 coronavirus. A number of digital contract tracing applications have been introduced to facilitate or complement physical contact tracing. However, there are a number of privacy issues in the implementation of contract tracing applications, which make people reluctant to install or update their infection status on these applications. In this concept paper, we present ideas from Graph Neural Networks and explainability, that could improve trust in these applications, and encourage adoption by people.

Efforts in contact tracing to try to control the spread of the Covid-19 virus had been going on before Google and Apple in early April announced their partnership on contact tracing technology. However, the two tech giants have proposed a way to share data while keeping user privacy central to the design. Recent news out of Singapore may point the way to how this is likely to go, pointing in the direction of the surveillance state. The pursuit of effective contact tracing embodies a confluence of issues around AI and surveillance, data privacy and public safety, and the roles of government and industry. Most contact tracing apps installed on smartphones use Bluetooth radio technology to record when other phones with the same app are detected nearby When a user shows symptoms or tests positive for Covid-19, alerts can be sent to all those in proximity over the previous week or two, along with suggestions for how to respond.

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.

We are acutely aware of the travails caused by the COVID-19 Pandemic. Enough has already been written and read. The untold suffering and misery, the massive economic impact and effect on human lives is multitudinous. We wake up each morning and pray that the new day brings lesser pain than the one gone by. Pictures of migrants trudging back to the hinterland, with babies in their arms, moves the bravest of us.