How Big Data Can Be Used to Predict and Prevent the Next Pandemic

The pandemic led to increased data collection. In some countries in Asia, surges of infections were only successfully controlled because of the extensive data collected from ordinary people.

Data Collection During COVID-19

South Korea, one of the first to be hit by COVID-19, fought several waves of infections through extensive testing and contact tracing. To prevent patients from violating quarantine protocol, authorities instructed them to download a smartphone app that will connect them to health care workers for treatment and track their location through GPS. But, even before they tested positive, there is already a surveillance system in place that collects massive amounts of data, from electronic transactions to mobile phone location logs, to determine who was within the vicinity of a person who has tested positive for COVID-19.

Singapore, which also successfully kept the illness under control, was able to do so by implementing strict measures, especially to outsiders coming in. Travelers who want to enter the country were made to quarantine for two weeks and wear an electronic monitoring device that will ensure that they did not go out.

Other countries did not enforce measures as strictly, but there still was increased collection of data in 2020. People were sharing their location history for contact tracing, and they shared their health — both physical and mental. There were office buildings, shopping malls, and, of course, airports and other transportation hubs that used thermal imaging solutions to monitor the symptoms of those who enter the premises.

There are tons of data being collected everywhere, which informs policy creation and decisions that influence the trajectory of the pandemic. The trove of data collected during the pandemic could also be used to predict how disease can spread and be controlled in preparation for the next global public health crisis.

Big Data: How It Will Predict and Avert the Next Pandemic

Now that vaccination efforts are underway in most countries, the question in everyone’s mind is when the next pandemic will happen. Experts have warned about the emergence of a highly contagious and deadly disease that can wipe away huge swaths of the global population. Although COVID-19 has claimed the lives of 3.5 million people, experts fear that a still unknown illness that could cause more deaths around the world is still coming. COVID-19 was a practice to avoid more tragedies in the future.

One failing that experts saw in the past year was the inability to set up a centralized data access. So, while there was a wealth of data being collected, there was no way to utilize it properly. Early into the pandemic, the symptoms identified were cough, fever, sore throat, and difficulty breathing. Then, a while later, more symptoms were reported, such as rashes and discoloration of toes and feet. Later on, doctors found out that patients were dying of silent hypoxia, a condition in which the patient suffers low blood-oxygen levels without other external signs of distress. Then, loss of smell and taste was also included in the lengthening list of symptoms associated with COVID-19.

A central database could have identified all the symptoms that appear alongside a positive test. It would also help identify where hotspots are emerging around the world. Moreover, researchers could have had access to this information and use it to identify effective treatments, especially for patients with underlying conditions and a history of diseases.

Moreover, with the use of machine learning, data can be utilized to monitor all the moving parts that can lead to the emergence of disease and the occurrence of a pandemic. One example is an application used to determine the speed at which the virus was spreading, where it is spreading, and who is at risk. All these are determined based on the appearance of symptoms.

AI also parsed through data from all over the world to identify and flag the rise of unusual pneumonia cases in Wuhan, China, on Dec. 30, 2019.

In addition, big data and machine learning can be applied to pinpoint areas in the world where disease can emerge and epidemics can take place. These models will use geographic data that include human, animal, and environmental factors. Simulations can also be used to predict outcomes and identify solutions to slow and prevent epidemics.

The pandemic has generated so much data from across the world. It presents an opportunity for experts of public health and other sectors to use and study with the aim of preventing the next pandemic. This way, industries and the population don’t have to be caught off-guard by another deadly disease.

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