Recent advancements in data analytics in healthcare have given rise to pandemic analytics. Pandemic analysis and modeling focus on the interpretation of the outbreak of pandemics. The spread of novel coronavirus has sparked mankind’s desire for innovating data-sets to visualize and pave way for a new branch of analytics, which is known as pandemic analytics
Across the globe, coronavirus pandemic has hit millions of lives with thousands of deaths globally. The threat of coronavirus is increasing every day new cases are emerging. However, developed countries that are affected by coronavirus are now leveraging pandemic analytics to combat the novel coronavirus.
According to our analytics experts, AI and pandemic analytics are going to play a significant role in combating and curbing the spread of coronavirus.
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How is pandemic analytics helping healthcare researchers develop a healthcare plan?
In the early 19th century, London witnessed the widespread outbreak of cholera. A group of epidemiologists came up with data patterns that revealed that cholera cases were mostly found around water sources. This was the first time when doctors and researchers leveraged data to design healthcare strategies. With this available data, they were able to quantify the risk related to cholera and design a clever response strategy. Times have changed now- data analytics in healthcare has evolved along with the emergence of artificial intelligence and machine learning. In many healthcare organizations across the world, medical researchers are now using data-driven predictive modeling to forecast the spread of pandemics. Few healthcare researchers are even using pandemic analysis and modeling that are capable of offering predictive insights based on collected data of outbreak.
Similarly, the outbreak of coronavirus has given rise to widescale usage of pandemic analysis and modeling and improved the scope of data analytics for healthcare.
Pandemic analytics to track the spread of the outbreak: By analyzing real-time data, numerous healthcare industry is now extracting valuable insights to make better well-informed decisions.
Pandemic analytics to curate response to the outbreak of coronavirus: Using a mathematical approach is not enough to tackle a widespread pandemic. But creating a multi-dimensional analytics model using contextual variables is the key to curate a response to this outbreak of coronavirus.
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How can pandemic analytics help in diagnosing and treating the outbreak of coronavirus?
Devising healthcare solutions is one of the most significant challenges that the world is facing. This includes the challenges of collecting, analyzing, and interpreting data on a standardized scale. Artificial intelligence can be deployed in such situations to help in diagnosing coronavirus, and it can bring down hours of diagnosis into just five minutes. Pandemic analytics clubbed with artificial intelligence and machine learning can also help in decreasing the workloads of healthcare workers who are working on medical reports.
Data analytics in healthcare is now helping medical researchers to reduce drug development time. Drug discovery and development which took months earlier now takes a few weeks. The whole world, still in urgent need of an antidote for novel coronavirus, is looking forward to the most exceptional synchronization of pandemic analytics and the human brain in the healthcare sector.
What are the challenges involved in leveraging pandemic analytics?
As the world is eagerly waiting to put a full stop to the impact and spread of novel coronavirus. One must remember in such cases that pandemic analytics is a tool which a product of the human mind. Mankind is still unsure about what is coming up next, but with the right mix of pandemic analysis and modeling, humans can surely analyze and derive insights on minimizing the impact of outbreaks like coronavirus.
There are a few critical challenges involved in the implementation of a pandemic analytics-driven predictive model. The main problem is to make sure that the data you have is complete, accurate, and formatted correctly to use in multiple scenarios. The second challenge lies in data storage and the costs involved. Data in healthcare is a crucial asset; hence data security in such cases is extremely important, and healthcare organizations must work on securing their data first.