AI & Big-Data can help tackle Covid-19’s spread
By: Dimitris Vayenas
Tags: Covid 19, AI, Big Data, Health

Adapting social distancing policies via the monitoring of crucial home and public behaviour data can help contain the Covid-19 offensive.

Let’s take a closer look in this, widely circulated, chart regarding the progression of the infections:

The Infection Trajectory as depicted by the reported cases vs. the days since case count passed 500 (22/3). An interactive - regularly updated version of this chart is also available at the Visual Capitalist website

It appears that its spread -when the case count passes 500- is strikingly similar, regardless of the size or the geographical density of the “sample”.

The chart was provided to me by David Gorelick, Professor of Psychiatry at the School of Medicine of the University of Maryland with whom we happened to discuss the capability of predicting electoral outcomes (as discussed in a series of articles that started here, whereas in one of the articles it was mentioned how data analysis can be used to draw meaningful conclusions that are insensitive to polling sample size and density).

I reverted to him with a message that I feel worthwhile sharing here (in a revised/enhanced and edited for publication form) regarding social distancing policies that are crucial in pacing the spread of the pandemic as the resilience of the Health Care Systems is tested; As this brilliant simulator displays, Health Systems collapse when the rate of concurrent cases exceeds their capacity, thus patients -who otherwise could have been saved in ICUs- are dying due to lack of resources:

“As we were discussing how I am going about in predicting elections, here is a way to approach this curve in order to get meaningful data that may shape “social distancing” policies:

1. Obviously, the initial cases had to do with travel in China or other in countries that do business with China (e.g. Italy’s fashion industry and Germany’s car parts complex as well as the interplay between the Italian and German cases).

2. For sure, it would have been beneficial to consider together with the bare numbers, the professions of the patients. How many of them are in the medical domain (doctors, nurses and caregivers exposed to the initial patients). How many of them are related to these doctors, nurses, caregivers (and the “delay”/rate of contagion).

3. How many of them were in public transport (air crew, drivers etc.), delivery boys, supermarket/shop employees (and the cases related to them).

4. Investigate their hobbies (Church/Social Club goers, Sport fans who attended matches etc) and perform an analysis among the cases of all those who are church goers, football fans etc.

5. Check the mobility of each patient vs. the overall mobility index of their respective segments (Professions, Hobbies etc.). Mobility data are readily available by Google, Facebook and Apple (and in many countries by the Network Operators).

6. Examine the available space to them (e.g. get metrics of the their home, which floor they live in, if they use public transport, if they work in a desk-job etc.)

7. As the volume of the data to be analysed may become hard to handle, one may issue rules based on the profile of the “candidate”. For example, there are suggestions that the time of exposure to the virus should be over 15 minutes for a higher probability of infection (I am sure the epidemiologists have more accurate data about it) that can be used accordingly to filter “irrelevant” data.

This knowledge is already available in some countries that are ahead of others may help us make informed decisions about which services to block (e.g. it may be the case that the public transport should shutdown) as well as go to predictive patterns in clusters about the behaviour of the “virus”.

If I were an American or British official I would have asked the Chinese, Italians etc. to share with me all this info — if not make all this info available to all by all — as well as take a note of the measures and their impact, and adapt our own accordingly”.

Such a meta-analysis may also help us cross-check and validate the official data provided by countries not known for their transparency. For example the South Korean “first 500” cases may be different than any other country’s “first 500” cases in this chart, given the fact that the preventative testing of the Koreans far surpasses the testing in the other countries; thus subsequent correlations and normalisations ought to be considered.

The medical authorities should get related personal details (other than address, age & sex) of each patient and the authorization to access their mobile phone’s data in order to investigate the case’s movements for a thorough forensic analysis that may help save lives.

If this virus is relatively mild, others in the future may not be so, and we have to be ready for them too; consider this as a preparatory step for similar and potentially more deadly diseases that may follow. Sadly, we do not have the luxury to philosophise about the usage of our Personal and Privacy data when Life is at risk.

Edit: less than three weeks later, Apple and Google announced their collaboration in order to enable interoperable contact tracing in their devices.