What Can Data Science Do to Help Prevent Pandemics in the Future?

We are currently confronting a worldwide emergency. From a public health point of view, to fight an epidemic, authorities must take various actions, for example, creating effective awareness, setting guidelines for health experts, targeting contamination clusters, limiting population developments, and allocating scarce resources. 

Quick and accurate data analytics that can pinpoint outbreaks and anticipate movement is critical to fighting irresistible infections. Historical methodologies, such as investigator reports and hospital records, are dependable yet moderate at best at forecasting. There is a growing belief that more current methodologies, including cell-phone tracking and data mining of search engines, and social media, can help give a quicker, progressively refined, picture of where sicknesses are unfurling and where they may spread next.

Data science can play an important role in breaking down the large-scale testing of individuals by connecting these outcomes with the anonymized health attributes of hospitalized patients. This would enable us to comprehend key risk factors and better protect individuals who are at the highest risk of infection. The more information there is, the more precise these predictions could be.

Power of Prediction

Innovation of prediction has changed numerous enterprises over the last 20 years. Organizations like BlueDot and Metabiota utilize a scope of natural language processing (NLP) algorithms to screen news outlets and official healthcare reports in various languages around the globe. Their predictive devices can likewise draw on air-travel information to survey the risk that transit hubs may see contaminated individuals either showing up or leaving.

Utilizing different sources of big data, machine learning models could be trained to quantify a person’s clinical risk of developing severe disease if they contract a serious infection such as COVID-19: what is the likelihood that they would require specialised care, for which the assets are limited? How likely are they to succumb to the illness? Such data could incorporate people’s fundamental medical histories. 

The outcomes are sensibly accurate. For instance, Metabiota’s most recent public report on February 25 anticipated that on 3 March, there would be a total of 1,27,000 COVID-19 cases around the world. This number was overshot by around 30,000, yet Mark Gallivan, the then Director of Data Science of the company, said that this was still within the room of error. It additionally recorded the nations that are most likely to report new cases, which included China, Italy, Iran and the United States of America. 

Google’s DeepMind AI system is being used to distinguish the attributes of the virus, which may help understand how it functions. This data would prove to be helpful in determining what medications to seek. Others have incorporated the technology developed by the UK-based bioinformatics startup BenevolentAI, which is using artificial intelligence to find promising existing treatments for different diseases, which could be effective in treating COVID-19. 

China’s usage of SenseTime’s facial recognition technology and temperature detection software to detect individuals who may have a fever and may be bound to have the infection has helped as well. A similar innovation powers the ‘smart helmets’ that are utilized by the authorities in the Sichuan territory to detect individuals with fever. 

The Chinese government has additionally built a monitoring system called Health Code that employs big data to identify and assess the risk of every individual depending on their travel history, the amount of time they have spent in infection hotspots, and potential exposure to individuals with the virus. Residents are assigned a color code (red, yellow, or green), which they can obtain by means of the mainstream applications WeChat or AliPay to show whether they should be isolated or permitted to go out in the public.

Unlike medical tests that are scarce, costly, and are often delivered with delays, this clinical-data-driven digital personalization approach can be applied rapidly and is quite easy to scale. It would permit better and more attractive asset allocation in case of rare medical equipment, for example, test units, protective masks, and hospital beds.

It could empower us with the correct models and enable more secure de-quarantining at a much faster rate than allowed by the current test-track-segregated best practices for COVID-19, under which anybody infected and their contacts would remain in confinement, regardless of whether they are in general safe or are showing symptoms of severe disease.

Mining for Data

The human mobility information and telecom data that was made use of during the Ebola outbreak in West Africa and has additionally been investigated by the UNICEF Innovation Lab, Flowminder, and other organizations. The underlying primary objective is to comprehend human mobility trends with respect to lockdown measures and assess the danger of disease progressing in a particular region. 

On ground, by using an application of  EPI Info Viral Hemorrhagic Fever this Disease can be Control, an open-source program which identifies those exposed to virus and builds a huge database of patient data that incorporates the name, gender, age, location, medical history & numerous other identifiers. 

In the application of big data analytics, the Swedish firm name Flowminder utilized 2013 phone records in Senegal to overlay past outbreaks of infection on traffic patterns to foresee the movement and growth of Ebola within the nation. While a significant part of the response to Ebola is established in physical infrastructure & operations, it is clear that the response is augmented by the ability to leverage data. 

A second encouraging road is the data mining of social media and search engine activity, which can rapidly show where an outbreak is occurring. However, data from social sharing and search engine queries could be misleading and ought not to be trusted exclusively. Rather, healthcare organizations are consolidating information from these sources with traditional medical data sets and using medical ability when dissecting trends. Daniel Bausch, the Director of the UK Public Health Rapid Support Team, sees incredible potential in the data sets gathered from social media.

Recently, the Big Data Laboratory at Nizhny Novgorod Development Strategy Project Office developed a mathematical model to predict the spread of COVID-19. The model used the information on most nations and districts that have published COVID-19 insights, including 297 regions of the world and 21 territories in Italy. 

The team continually observed Russian and global research on COVID-19. This implies that they could gather the information for the model from everywhere in the world, both aggregated by nation, and distributed by region and smaller territories. The analysis incorporated a few dozen urban cities, in order to distinguish the ones that have epidemiological parameters that are closest to their own (policies, population size and density). The model so developed helps in forecasting the pandemic with an accuracy of 2.5%.

One approach to adopt is to set up independent ethical committees or data trusts. Their job would be to create data governance mechanisms to discover the harmony between contending public interests while ensuring individual security. 

Also read: Productive Things To Do in Lockdown

Conclusion

Now, as we develop new advances that are expected to collect, disseminate, and utilize information to help in the battle against any pandemic, we also need to ensure they respect ethical best practices. Indeed, even amid an emergency, we need to follow data security guidelines and guarantee that the information is being exploited ethically.

Getting pioneers in governments, businesses and medical services to trust these tools would fundamentally change how rapidly we respond to disease outbreaks.

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