Data is integral to the digital revolution of the 21st Century. There has never in human history been such an explosion of information. This can be attributed to the rise of smartphones, sensors, and connected vehicles and appliances, among other digital artefacts. Much of what we touch and work with now automatically generates data. But the real reason why we’re seeing this increase is the growing utility of data analytics and automated responses to analytic decisions.
Given the disruption caused by the Covid pandemic to economies and lives, it has become more urgent for the governments to comprehend the dynamic usage of data during the pandemic era.
India has been a major contributor to the worldwide generated data, but sophisticated and efficient data management seems missing to effectively tackle the health crisis. Underreporting of infection cases, deaths and supply chain blocks have been rampant in India, hampering the fight against the pandemic. The integration and analysis of multiple heterogeneous datatypes are crucial for getting a holistic picture and help India to control and manage public health.
Data should be used to track the virus flow and forecast hotspots on a real-time basis. Predictive data science models are key to predicting where the disease might spread. This data can be pulled into a National Surveillance Dashboard to give a real-time feed of the virus trails, improving forecasting accuracy and enabling officials to make data-driven decisions. Data can be harnessed to learn more about who might be most at risk. Covid doesn’t impact everyone in the same way. Due to a variety of factors, the disease can range from extremely mild to fatal. Government databases can be useful in targeting the most vulnerable groups in future trials and vaccination rollouts.
In the battle against Covid-19, free foodgrain distribution and cash handouts are important tools for the government. The government should leverage Aadhaar in the delivery of public subsidies with speed and efficiency.
Part of the challenge in evidence-based decision-making pertains to the standardisation of data collection and the seamless integration of data analytics pipelines for outbreak analytics. The use of data standards instils consistency, reduces errors and enables transparency.
Fixing the glitches
Next, we need to fix delays in data collection and access. At present, due to time lags in confirming and reporting cases, even regions that report hospitalisation data are unable to present the real picture on the health system.
Even with error-free reporting, there are delays in the interpretation of data and timely implementation of remedial measures. For instance, it took seven days for the country to realise that it had an oxygen crisis and another 25 days to fix it.
South Korea, Taiwan, and Singapore used a combination of mobile-based tracking and surveillance-camera footage in combination with significantly higher levels of testing to facilitate contact tracing and virus containment. The US used just mobile tracking data to predict infection hotspots.
Another tool that has been helpful for private citizens, government policy-makers and healthcare professionals are dashboards from entities such as the WHO that provide real-time data.
Adopting such technology and data science to manage the healthcare needs efficiently requires multiple privacy trade-offs, which requires thoughtful legislation at least in the short term. This calls for an infrastructure with built-in safeguards to ensure data encryption and privacy.
Even with all the safeguards in place, the focal point in data management lies in the trust factor and the genuineness of the data.
The writer is a leading consultant & columnist working on Market Entry, Innovation & Public Policy