How Technology is Predicting and Preventing Diseases

Data science and predictive analytics have enhanced the diagnosis, treatment, and forecasting of disease by using abundant data pools.

How fascinating does understanding the future sound? We have always done guesswork on the future and tried to foresee it, as we want it to be. But what if we can know the future and prepare ourselves to deal with it? That is exactly what data science and predictive analytics offer.

Data science and predictive analytics are redefining industries with their ability to foresee future business outcomes and prevent risks. Healthcare is an important beneficiary of these technologies since they contribute towards disease forecasting, diagnosis, and treatment. In the scenario of Covid-19, data scientists effectively contributed to predict the urge and dips of the pandemic. According to an article by Health IT Analytics, a team from Colorado State University could predict the potential hotspots for Covid-19 transmission with cellular wireless network data and this helped regions manage the risk. Trusted organizations like WHO and Centres for Disease Control and Prevention (CDC) would provide databases on the disease spread, which is analyzed to produce insights on the pandemic's journey.

Forecasting diseases can provide insights on the magnitude and the pathway of the spread and this helps in preparing health resources, creating awareness, and containing the spread. The Epidemic Prediction Initiative (EPI) by CDC is an example of organizations using data science to predict the trajectory and impact of diseases. The EPI collaborates with different research teams to assemble short-term and long-term forecasts, analyze them, and provide all the data on their website. During The pandemic, CDC partnered with the University of Massachusetts Amherst to create Covid-19 Forecast Hub, which serves as a central repository of forecasts from various research teams.

Healthcare can use predictive analytics to prevent common diseases through a data-driven approach. Predictive models use existing data, analyze it, and interpret it to establish correlations and to provide accurate predictions. Data science also helps in understanding a person's health status and provides care to prevent future risks. The Spatiotemporal Epidemiological Modeler or STEM, developed by IBM helps scientists to predict the trajectory of infectious diseases by providing computational models and tools.

Data science can change and impact how diseases are diagnosed and treated, thus helping in the prevention of diseases in the future.

A report in CIO mentions that advances in forecasting technologies like Deep Neural Networks, specifically innovations around Recurrent Neural Networks are increasingly being utilized and are effective compared to traditional statistical techniques.

Data is today's digital currency where the near future will see an abundance in the data-generation. Tracking devices such as wearables will also witness a surge soon, which makes it possible to detect and diagnose diseases and health conditions before it crosses the risk zone. Genetic mapping is another advancement, which will prevent diseases before it occurs through identifying and analyzing genetic mutations and disorders. Cutting-edge technologies like AI, big data, electronic health records, and IoT can be leveraged to understand diseases and risks to diagnose and treat them before they become fatal.