A cautionary tale on politicising data

Arunav Konwar

As I look at the COVID-19 dashboard by the Johns Hopkins University while I write this on 9th July 2020, I see over 3 Million cases of COVID-19 in the USA, more than 1.72 Million cases in Brazil, and over 700K+ cases in India being reported, rounding up the top 3 countries. Needless to say, the virus has gone out of hand and 2020 marks the end of almost a decade long global economic growth post the 2008 financial crisis. Northern Italy, with some of the best healthcare systems in the world, was overwhelmed by the sheer number of patients at the height of the pandemic. Similar stories in New York City, Boston, and many major cities around the world. It’s July now and with the curve failing to disappear or even flatten I couldn’t help but wonder, how did we end up here? Could we have done things differently?

What binds all of these above stories together is a common thread of policy and enforcement failures at multiple levels within governments around the globe. An amalgamation of unpreparedness, indecisiveness, bad governance, and turning a blind eye to data, and politics itself. It’s a cautionary tale of how vulnerable modern economies are and how things can spiral out of hand, and that too very fast.

The first possible hints of coronavirus were reported to the World Health Organization (WHO) as pneumonia of unknown cause in Wuhan, China on December 31, 2019. The time it took for most countries around the world to notice and act on it astonishes almost anyone now looking back at the unfolding events. The U.S is a prime example of failing to heed to early warnings. The country and its leaders refused (and still refuses) to lockdown and officials failed to enforce the guidelines laid down by the WHO, even after repeated warnings from experts. Unfortunately, this is still the case in the U.S, Brazil, and many more countries. Politicians with their own agenda have been seen misrepresenting data and calling for re-opening the economy. Similarly, the U.K government failed to lockdown the country on time and let massive events such as the widely popular English Premier League keep on continuing. These massive events leading up to the eventual lockdown have been traced back to as avenues for the spread of the virus in the country.

Although some experts say that the Indian government had the foresight of locking down early, a mistake it and many other countries made is reopening the economy at a time when the infection curve was still on the rise. The pressure to reopen the economy had been high since the day India went into a full lockdown on March 25, and this pressure likely interfered with the government’s re-opening decisions. Global consultant McKinsey’s report from April said India’s economy must be “managed alongside persistent infection risks”. The reopenings could have been done in a more data-driven manner which optimized for both the recovery of the economy and the well-being of the people.

This finally brings us to the case of Taiwan, an island country in the South China sea with a population of around 23 Million, which has become a case study on how to tackle a pandemic like this. Their success is in no part an accident. They have been at the forefront of adopting the latest technologies that can inform their leaders and policymakers, and help pave a path towards drafting effective policies to tackle such major outbreaks. Taiwan leveraged its national health insurance database and integrated it with its immigration and customs database to begin the creation of big data for analytics. This combined with online user reporting systems and timely SMSs to people with different categories of risk (categorized by data analytics) created an effective system to both inform and keep their citizens safe.

Establishing such a framework to tap into existing data is not only sensible but also an insurance policy against similar future situations going out of control. This calls for the establishment of permanent data-driven departments within government agencies to deal with similar future outbreaks. In recent times, travel, fueled by globalization, urbanization, and climate change has accelerated the potential for new outbreaks. According to the WHO, there are 7,000 signals of potential outbreaks every month. This in itself should be a compelling reason for the adoption of big data analysis in policy and governance to make sense of the massive amount of data in our everyday lives. A combination of machines distilling the data and humans interpreting it and enforcing actionable items provides us with the best line of defense against such future events.

Taiwan established the National Health Command Center (NHCC) in 2004 following their experience with the deadly SARS outbreak. Due to this existing infrastructure and the foresight to look at data to understand the situation, the Taiwanese officials were able to start responding as early as January 5, 2020, by boarding flights coming from Wuhan and taking temperature readings of people.

While the application of such data-driven decision-making frameworks can be different for each country, the need to implement them while taking care of security and privacy concerns is of utmost importance. Finally, we need to remember that behind every statistic is a human life associated with it. In the end, it’s as much a social problem as it is a data problem.

And for leaders worldwide, this global fallout of COVID-19 can be a reminder for them that problems like these transcend politics and can only be solved through listening to data and experts.

The results are clear, the governments that made decisions based on data were more likely to bring pandemics under control. No matter how harsh lockdowns and other social distancing and sanitization measures seem, doubling down on our fight against COVID-19 seems to be our best bet to beat it. And this needs to be enforced by the government even when under the intense pressure and opposition of certain sections of the society and especially the political opposition.

Finding the optimal balance between reopening the economy vs keeping the infection rate low needs to be informed by data and not through emotions, and certainly not through politics.

References

COVID-19 Map - Johns Hopkins Coronavirus Resource Center (https://coronavirus.jhu.edu/map.html)

McKinsey_coronavirus’ impact on India (https://www.mckinsey.com/featured-insights/india/getting-ahead-of-coronavirus-saving-lives-and-livelihoods-in-india)

COVID-19 and global epidemics are becoming more frequent. This is why (https://www.weforum.org/agenda/2020/03/coronavirus-global-epidemics-health-pandemic-covid-19/)

The global economy is woefully unprepared for biological threats. This is what we need to do (https://www.weforum.org/agenda/2019/03/our-economy-is-woefully-underprepared-for-biological-threats/)

NHCC - Taiwan Centers for Disease Control (https://www.cdc.gov.tw/En/Category/MPage/gL7-bARtHyNdrDq882pJ9Q)