Since the last few months, the world is experiencing COVID-19 outbreaks that generally follow a similar pathway: an initial phase with few infections and limited response, followed by a take-off of the epidemic curve along with a national lockdown to flatten the curve. Amidst all this, governments across the world are burdened by the question as to 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. However, 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. Now it’s time for governments to leverage those technologies in managing this pandemic.
Utilising Power of Personalised Prediction Model
A personalized approach has multiple benefits. It may help build herd immunity with lower mortality and fast. It would also allow better and fairer resource allocation, for example of scarce medical equipment (such as test kits, protective masks, and hospital beds) or other resources.
Delineating De-confinement Strategies
Challenge of Privacy
Implementing the technological innovations, however, will require policy changes. Existing policies covering data privacy and cybersecurity, and their respective and differing interpretations across countries, will largely prohibit this kind of personalized pandemic management approach. This is largely because current policies do not differentiate between the input data (used to train a model), the prediction models themselves, and the “output data” (predictions from the trained model). When a policy, implicitly or explicitly, prohibits data sharing or requires data to be stored on servers within a country, it covers anything that can be legally interpreted as data, including models and their parameters. Therefore, it is pertinent that policymakers consider distinguishing the sharing of models and the sharing of data.
Conclusion
The need of the hour is that the national governments agree on a protocol for determining when data could be shared. For example, a declaration by the WHO or UN that a particular outbreak qualified as a pandemic could serve as a trigger to suspend normal privacy laws to allow the sharing of anonymized data. In fact, during times like these, many people might be willing to exceptionally and temporarily provide their data, through appropriate and secure channels, for training models that can guide policy decisions with major life and economic consequences. If this materialises, there is a great chance that modern data science and AI could mitigate the fallout from this pandemic and prepare us for limiting the impact of the next pandemic in future.