‘Over the past few months the world has experienced a series of Covid-19 outbreaks that have generally followed the same pathway: an initial phase with few infections and limited response, followed by a take-off of the famous epidemic curve accompanied by a country-wide lockdown to flatten the curve. Then, once the curve peaks, governments have to address what President Trump has called “the biggest decision” of his life: when and how to manage de-confinement.
Throughout the pandemic, great emphasis has been placed on the sharing (or lack of it) of critical information across countries — in particular from China — about the spread of the disease. By contrast, relatively little has been said about how Covid-19 could have been better managed by leveraging the advanced data technologies that have transformed businesses over the past 20 years. In this article we discuss one way that governments could leverage those technologies in managing a future pandemic — and perhaps even the closing phases of the current one.
The Power of Personalized Prediction
An alternative approach for policy makers to consider adding in their mix for battling Covid-19 is based on the technology of personalized prediction, which has transformed many industries over the last 20 years. Using machine learning and artificial intelligence (AI) technology, data-driven firms (from “Big Tech” to financial services, travel, insurance, retail, and media) make personalized recommendations for what to buy, and practice personalized pricing, risk, credit, and the like using the data that they have amassed about their customers.
In a recent HBR article, for example, Ming Zeng, Alibaba’s former chief strategy officer, described how Ant Financial, his company’s small business lending operation, can assess loan applicants in real time by analyzing their transaction and communications data on Alibaba’s e-commerce platforms. Meanwhile, companies like Netflix evaluate consumers’ past choices and characteristics to make predictions about what they’ll watch next.
The same approach could work for pandemics — and even the future of Covid-19. Using multiple sources of data, machine-learning models would be trained to measure an individual’s clinical risk of suffering severe outcomes (if infected with Covid): what is the probability they will need intensive care, for which there are limited resources? How likely is it that they will die? The data could include individuals’ basic medical histories (for Covid-19, the severity of the symptoms seems to increase with age and with the presence of co-morbidities such as diabetes or hypertension) as well as other data, such as household composition. For example, a young, healthy individual (who might otherwise be classified as “low risk”) could be classified as “high risk” if he or she lives with old or infirm people who would likely need intensive care should they get infected.’
Read more: Harvard: Using AI For Personalized Predictive Quarantine