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The Power of Data Analytics during Covid-19 Pandemic

Data Analytics is an important tool in fighting the covid-19 pandemic. All these advanced technologies are being employed to help and make doctors and governments more efficient and better equipped to fight this pandemic covid-19. Coronavirus bringing terms like data sets, modeling, predictive analytics to the forefront, there's a spike in data analytics interest. Data Analytics gathers momentum, it is creating a great career opportunity for IT professionals with data analytics skills. With companies scurrying around for data analytics professionals, it is an apt time to gather the necessary skills to land on one of the hottest jobs today.

Technology has always been a strategic instrument in providing ways to improve people’s lives and bring optimization and magnification for businesses. Covid-19 pandemic has triggered an unprecedented demand for digital health solutions. Countries are utilizing data analytics to accurately obviate and control future coronavirus cases. Data collection and analysis are availing authorities make better decisions for aversion work.

Data Analytics has emerged as a leading technology to stop the spread of novel coronavirus. Here is how data analytics fight against covid-19.

Tamil Nadu to set up a Data Analytics Centre to fight COVID-19

The Data Analytics Centre, will avail capture data trends from across all districts of Tamil Nadu in authentic time and exhibit them live to apprise the Government’s future decisions on the degree of replication needed for each district and also for graded relaxation of the current lockdown to resume economic activities.

Contact Tracking

The Indian government has launched the Arogya Setu app, which is providing valuable data for an integrated coronavirus control strategy. This mobile app harnesses 21st-century technology that can accelerate the notification process while maintaining ethics to slow the rate of contagion.  

South Korea, Israel, Singapore are using contact tracing platforms to locate, test, and isolate potential carriers of the novel coronavirus.

With social network analytics, contact tracing becomes much easier. With available data, "epidemic detectives" can begin building networks of contacts. The more data, the better the chances of identifying points of vulnerability. Then, using data visualization techniques, health officials can begin identifying common contact points and building a robust, reliable picture of an infected person’s social interactions.

Big Data Analytics and Successes in Taiwan

The national health insurance database of Taiwan with data from its immigration and customs database. By centralizing the data in this way, when faced with coronavirus, they were able to get authentic-time alerts regarding who might be infected predicated on symptoms and peregrinate history. In additament to this, they had QR code scanning and online reporting of peregrinate and health symptoms that availed them relegate traveler’s infection risks and a toll-free hotline for denizens to report suspicious symptoms. 


Ensuring demand planning stability
Demand forecasts for everything are topsy-turvy at present – from the hospital, patient flows to goods and services. What can data and analytics teams do? 
Improving existing forecasts of ongoing or expected COVID-19 disruptions with a broad range of forecasting methods – including data patterns as the virus moves across regions – may help to improve short- and midterm forecasts.

At a recent Institute for Business Forecasting  virtual town hall, SAS forecasting expert Mike Gilliland noted some of the key points made by his fellow forecasters:

  • Macro forecasting is hard right now, but micro forecasting (down to the product level) is even harder.
  • There are many interventions going on beyond what is normal, such as government stimulus and falling oil prices, all adding to the uncertainty and complexity.
  • Distinguish the passive work of observing and collecting data from the proactive work of driving demand away from shortages and toward substitute (available) products.
  • Try to use additional streams of external data, not just your own internal data.

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