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The Role of Big Data During COVID-19 Pandemic

Published 2020-06-15 07:14:28

 

Throughout history, mankind’s existence has been plighted with disease outbreaks that have changed the course of humanity. From the 1918 flu pandemic that infected 500 million people to the Ebola outbreak that swept West Africa, governments across all seven continents have grappled with the challenges of managing mass population infections and their aftermath. The insurgence of the COVID-19 pandemic has been no different. Since its first reported case in the Chinese city of Wuhan, COVID-19 exponentially increased in infection rates, exposing the vulnerabilities of health care systems and their infrastructure globally.

Nonetheless, containment strategies across the globe have had success in mitigating further infections. A study published on the 6th of March in Science by scientists in Italy, China and the United States found that lockdown in Wuhan delayed disease spread to other cities in China by roughly four days. Furthermore, another study found that cities that suspended public transport, closed entertainment venues, and banned public gatherings before their first COVID-19 case had 37% fewer cases than cities that did not implement such measures.[i]

A large contributing factor in flattening the COVID-19 curve has been the use of big data. Innovations in data management, modelling, and artificial intelligence have allowed leaders to utilise big data analytics and technologies to reduce the impact of the viral pandemic whilst improving real-time forecasting techniques and decision-making to guide quicker responses.

Forecasting and data-driven modelling have played a pivotal role in the response against COVID-19. Entities such as John Hopkins have utilised the power of big data to provide citizens, healthcare professionals and governments with dashboards that pull data from multiple sources. A cross-disciplinary team of John Hopkins University researchers, in partnership with the Johns Hopkins Applied Physics Laboratory, the Whiting School of Engineering, and the Bloomberg School of Public Health, developed a COVID-19 dashboard that is accessed more than a billion times a day.[ii]

Data visualisation through tools such the John Hopkins dashboard have been key in helping the public understand how and where the pandemic is spreading. Mapping the spread of the virus graphically has also made it easy to identify trends and patterns essential to effective responses. One such application of visualisation has come from a team of Southern Illinois University researchers, who developed a visualisation tool that utilises GPS information to show users the locations of known COVID-19 cases. 

Additionally, models from big data have supported the prediction of the pandemics potential reach. Lockdown protocols initiated by governments around the world have leveraged the information supplied by big data infection models, as they provide insights into the number of cases, infection rates, and how rapidly the virus is affecting certain communities.

While contact tracing has become a controversial topic due to privacy laws, China’s government has proven how effective the method can be in containing the spread of COVID-19. According to the World Health Organization (WHO), contact tracing is the process of identifying, assessing, and managing people who have been exposed to a disease to prevent onward transmission.[iii] In China, public health surveillance has been carried out through security cameras that track citizens movements. This kind of surveillance helped authorities in detecting crimes, people who disobeyed quarantine, and potential COVID-19 positive cases via thermal scanners in train stations.

In the event authorities detected elevated temperatures, these people would have to undergo testing. If results came back positive, passengers who may have been exposed were notified and alerted to quarantine immediately until testing confirmed status.  This was achieved as the public transport system in China requires citizens to log their names and government issued ID’s when travelling. According to a study published in The Lancet Infectious Diseases, patients identified through symptom-based surveillance were identified and isolated, on average, 4.6 days after symptom onset. Contact tracing reduced this time to 2.7 days. [iv]

Most cities across East-Asia have also implemented QR code scanning to contain a resurgence of the virus as economies re-open. The system operates using a colour-based health code whereby citizens provide their personal information including their travel history, and whether they have had encounters with any confirmed or suspected COVID-19 patients in the past 14 days. They are required to log any symptoms after which the system verifies the information through authorities.

If a person is assigned a green code, they are healthy and safe to travel, if they are assigned orange, they are required to quarantine for 7 days, and if they are assigned red, they are required to go into government quarantine or self-quarantine for 14-days. According to the South China Morning Post, more than 100 Chinese cities are now using the app.

The health codes also serve as trackers of people’s movement as citizens are required to scan every time they enter public spaces such as malls, public transportation, and office parks. In the event a person is diagnosed COVID-19 positive, authorities can use the data to identify the source of the patient and alert people who may have been exposed in that area. Although contact tracing faces criticisms, many expert’s and health specialists consider its application to be important as industries start trading globally.

Several organisations have also started utilising big data to accelerate COVID-19 drug treatment discovery. In early April, pharmaceutical companies GlaxoSmithKline (GSK) and VIR Biotechnology partnered to advance coronavirus treatment development using artificial intelligence and CRISPR. Google’s Deep Mind AI system is currently being used to identify characteristics of the virus that may help to understand how it functions. Furthermore, In December 2019, Toronto-based startup BlueDot utilised AI to detect the cluster of pneumonia cases in Wuhan before the CDC and WHO issued statements of a potential viral outbreak. The algorithm analysed data ranging from airline ticket data, government notice boards to news reports.

At MIT, Professor Dimitri Bertismas together with a group of researchers, used big data to develop the COVIDanalytics Platform. Harnessing the power of machine learning, the system forecasts patient outcomes based on progression and characteristics. The software then recommends treatment options such as hospitalisation or the movement of a patient to intensive care.

AI tech has also been effectively deployed in hospitals such as the Tampa General Hospital in Florida. The system makes use of advanced facial recognition to scan visitors for fever upon visitation. If a visitor is suspected of being a potential carrier of the virus, they are recommended for testing. The information collected from the software is used to provide the facility managers with data that helps them to manage hospital resources such as the number of respirators available, beds available, and the amount of staff on duty equipped to administer COVID-related care.

The large pools of data collected about the coronavirus hold valuable data that could help make decisions about how we deal with future outbreaks. AI can also assist in building models that will predict when and where the next pandemic is likely to occur. Much like the Zika virus and Ebola, COVID-19 originated in animals which is why many researchers predict that further outbreaks are likely to occur as humanity develops infrastructure in more ecosystems.

The COVID-19 pandemic has ushered in an opportunity for health care systems across the globe to critically re-evaluate their infrastructure and responses to future outbreaks. Accelerating the adoption of innovative technologies, trade-offs between personal privacy and public safety in the midst of a pandemic, and the availability of resource provisions in the event of economic shutdown are just some of the factors that will have to be addressed post-COVID19. While the future holds much uncertainty, at the core of all these decisions is data that will help in developing rapid and effective global responses to other contagions.

Read more about how Qode Health Solutions has developed solutions that help flatten the COVID-19 curve: Qode COVID-19 Solutions

 


[i] Chinazzi, M. et al. Science https://doi.org/10.1126/science.aba9757 (2020). Accessed 17 June 2020.

[ii] April 9, Hub staff report / Published, and 2020. “Johns Hopkins Adds New Data Visualization Tools alongside COVID-19 Tracking Map.” The Hub, 9 Apr. 2020, hub.jhu.edu/2020/04/09/data-visualization-covid19-map/. Accessed 17 June 2020.

[iii] WHO. “Coronavirus Disease (COVID-19) - Events as They Happen.” Www.Who.Int, 11 June 2020, www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen. Accessed 17 June 2020.

[iv] Bi, Qifang, et al. “Epidemiology and Transmission of COVID-19 in 391 Cases and 1286 of Their Close Contacts in Shenzhen, China: A Retrospective Cohort Study.” The Lancet Infectious Diseases, vol. 0, no. 0, 27 Apr. 2020, www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30287-5/fulltext, 10.1016/S1473-3099(20)30287-5.

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