While the world was celebrating the start of a new decade, a pandemic was lurking in the dark. Within months of the new year, the world found itself battling a contagious, deadly virus. Within just three months, 550,000 have been infected worldwide, with 24,073 deaths (as of, 03/27/2020). The numbers are rising exponentially, doubling every three days.
There is a mutual global consensus that ground figures are far more than reported figures – unreported cases include asymptomatic cases and mild cases that go unnoticed, making it practically impossible to get perfect information. The virus is new. It’s difficult to contain. There is no vaccine. Social distancing is the only preventive measure we have, but it’s taking a significant toll on our economy.
We are in the fog of a pandemic war – and the only weapon we have at our disposal is Data.
What Does it Mean to be in the Fog of a Pandemic War?
Simply put, it’s the flawed statistics and the answers we don’t have, making it difficult to rapidly come up with a solution that can save lives and the economy. The COVID-19 is a new strain of the SARS virus family, but more deadly and contagious with an R 2 (number of people infected by one person) infection rate.
As governments across the world are scrambling to contain the virus, there is little information on its behavior.
- Will it subside in the summers?
- Does it affect only the old?
- Can one easily recover from it?
- Most importantly, how long will this last?
- Can our economy survive longer periods of a shutdown?
The estimated cost of the lockdown could cost the economy $2.7 Trillion [1] and that would just be the beginning of it. Across the world, countries are reporting a major plummet to their economies. And we still don’t know how long this will last. And this is the battle we’re fighting.
As Derek Thompson explains in The Atlantic:
“What we’re experiencing now is the fog of pandemic. The officials tracking COVID-19 are swimming in statistics: infection rates, case-fatality ratios, economic data. But in these early stages of the fight against the coronavirus, these figures each have their own particular limitations.”
Yes, we have limitations on our data.
Yes, the numbers are not perfect.
Yes, the statistics are flawed.
But, this is just one side of the coin. The other side? Data is saving our lives.
How’s Data Helping Us When We Don’t Even Know Real Numbers?
When we’re talking about data, we’re not just talking about basic quantitative, binary units of number of infections, deaths or recoveries. We’re talking big data, AI, predictive analysis, behavioral and trend analysis.
When the world was afflicted with the H1N1 flu, commonly known as the Spanish Flu in 1918, it was not equipped with the ability to study the spread and impact of the virus, much less develop a vaccine for it.
We’re living in a better time. We’re not ravaged by war. We have the technology to control and curb this pandemic. We have AI to scan through medical databases and come up with the most viable vaccine options.
Here’s what we’ve achieved so far with data.
Using Data to Flattening the Curve:
For the uninitiated, flattening the curve is a statistical term that refers to slowing the spread of the virus.
The COVID-19 virus spreads exponentially instead of linearly; meaning 1 person can cause 2 more to get infected. Linearly would be 1 person spreading to 1 person only.
Here’s an overview of how rapidly the virus spread across the world, doubling exponentially.
The problem with exponential spread is not just the number of infections, but the eventual burden on the healthcare system. Sadly, despite advancements in technology, some of the world’s most developed countries, including the US and the EU have a shortage of healthcare workers.
As the number of infections rises, mild or severe, so will the number of people admitted or isolated in hospitals. It doesn’t end there. More infected people means higher risks for hospital staff. Front-line workers are currently the highest-risk group for the COVID-19 infection as they attend to patients. Add to that a gross shortage in medical equipment such as masks and PPE, you have a crisis that isn’t “flattening.”
Flattening the curve simply means to control the spread to an extent that hospitals are not overburdened to the extent that drastic decisions have to be made – in the case of Italy where doctors had to decide who to let die and who to save. This can be achieved only through social distancing.
Arindam Basu, Associate Professor of Epidemiology and Environmental Health at the University of Canterbury, in New Zealand, defines social distancing as:
“a way of creating a barrier of physical distance between two or more people so that transmission of virus can be prevented or halted”
If we practice social distancing, for instance, where we avoid large gatherings and keep a significant distance between ourselves and others’ bodies, we can reduce the transmission rate and cause the decline of the spread. Consequentially, the healthcare system will have enough time to attend to those who direly need medical attention.
Data obtained from hospitals in China was used to assess the virus behavior, the outbreak, the transmission dynamics, disease progression, and severity. Based on this data, the Chinese government implemented a sound disease containment strategy that resulted in a significant decline of daily new cases. On the first day, there were 2478 reported cases. Two weeks later, the country reported 409 new confirmed cases. [2]
This data was instrumental in helping countries across the world to apply similar social distancing and lockdown measures, thus enabling the flattening of the curve and buying hospitals much needed time in helping patients.
Using Location Data to Manage the Crisis
Unlike the previous era, today, countries can monitor, trace and control the outbreak through location and cellular data.
In Israel for example, citizens who are believed to have been exposed to the virus were sent alerts to either get a test or to self-quarantine.
In Taiwan, cellular data was used to identify people who implemented social distancing and who did not. In a tweet, a Taiwanese student in quarantine describes how the police were at his door within 45 minutes as his cellphone battery died out.
Similar cellular and location data tracking initiatives have been applied by countries across the world to monitor and trace suspected carriers. In Hong Kong, location-tracking wristbands are given to those put under quarantine. In Singapore, the government uses text messages to contact people, who must click on a link to prove they are at home.
Countries not implementing tracing or monitoring initiatives are finding it hard to keep their citizens indoor – after all, a two or four-week quarantine or social isolation seems almost impossible for people to practice.
The Use of AI to Create a Vaccine
The vaccine for the Spanish flu was not developed until 20 years later and by the time it was developed, the flu had already subsided, after killing 500-million people, or one-third of the world’s population. [4]
With AI though, experts are hoping for the production of a vaccine soon.
According to Professor Andrew Hopkins as reported by the BBC, AI can be used to:
- Accelerate the development of anti-bodies and vaccines for the COVID-19 virus
- Scan existing drugs and discover any that could be repurposed (this will involve significant data matching across and within databases)
- Design a drug that can be used to fight current and future outbreaks. [4]
Although this sounds optimistic, we must understand that it will not be as fast as we hope. Experts around the world agree that it will take anywhere from 18 to 24 months to develop the vaccine. Even then, it will take some time for the vaccine to be available globally. The trials, the manufacturing limitations, the safety testing will take time. AI can just be used to get the process running.
Using Big Data Analytics to Map Disease Spread
China’s response to the pandemic is the strongest in the world. Making use of AI, Big Data, mass surveillance systems, location, and cellular data, the country has initiated a stringent monitoring system that allows authorities to track people’s movements and ensure they do not break quarantine rules.
Of course, this begs the question of privacy, but keeping this aspect of the discussion aside (since it’s political and irrelevant in this context), big data has been instrumental in helping startups and authorities predict the possible spread of the virus, record and implement preventive measures (such as handwashing, six-feet distance, etc) and identify which regions of the world are the most vulnerable to the outbreak.
Researchers, tech and medical startups, governments across the world are working to alleviate this global health crisis through the collection of data which is then used to track potential carriers, identify shortages in hospitals and obtain information on the virus’s behavior like never before. Technology empowered scientists to acquire the genome sequence of the virus within days of the infection – a process that would typically take months.
But Data and Technology is Not Enough
Data and technology have empowered us with the knowledge we need to mitigate the situation and handle the crisis, but it’s not enough.
Real world problems cannot be ignored.
Most countries hit hardest by the virus including the US are not prepared to process or handle the crisis. Second, the process that worked in China may not have its way in the US and EU, especially with data privacy concerns. Moreover, data collection is just part of the process. Companies and authorities involved in the process need resources (manual or automated) to process that data and translate it into practical solutions. There is a significant amount of data integration, data sorting and data matching with the most challenging task is making use of only data that is relevant.
It’s noteworthy to mention that Asian countries such as South Korean, Singapore, Taiwan, and China did not just practice social distancing – they consistently tested, tracked and maintained a system that allowed them to control the spread from within.
Singapore, for example, had only 0.3% of mortality rate – the best in the world. Their strategy involved the cooperation of different government departments using technology to create a centralized action center. The country’s Ministry of Transportation acted as a single point of contact for all requests for medical supplies, whereas the Ministry of Communication created information for public consumption, focusing on TV, social media ads, billboards etc. Their education system shifted online too, making it possible for children to continue with their studies.
People were allowed to go about their daily lives. No lockdowns were imposed. This was made possible because the country practiced a stringent trace and test program where the relatives of those who were tested positive were tested too. Singapore practiced the WHO guidelines of rigorous testing and monitoring to curb the spread.[6]
There is plenty of evidence now that a pandemic can only be countered if swift action is taken and technology is used aggressively as an enabler of that action. Statistics and data alone won’t give us much.
Keeping in mind the fog we’re in, we don’t need perfect data to counter the enemy. What we need is intelligence from available data to create strategic action plans and find our way through the fog.
The goal right to prevent spread and flatten the curve. We must do everything we can in our power to achieve this. Slow action will result in the losing of precious lives and an economic depression never seen before.
If your startup is using data to help the government beat this disease, get in touch with us to see how we can help with data integration and data matching across and within databases. Together, we can beat this.
