On January 30th, 2019, the WHO declared a global health emergency based on scientific evidence of a rapidly spreading coronavirus from the SARS family (Sars-CoV-2, and the disease Covid-19). The evidence the WHO used to declare this emergency came almost entirely from Chinese data.
The Chinese data demonstrated an alarming spread rate in January, as shown in Figure 1. Without the Chinese data, there would have been little cause for alarm as all other countries combined had barely 90 known cases in that period, and not a single death.
In early January, the Chinese government took measures to block news and data1 related to the virus; however, Chinese scientists still managed to follow open science practices (updated news on this here) including sharing partial-gene sequence data with the world. This allowed the WHO to take appropriate measures and enabled scientists in Germany to develop tests to identify the novel coronavirus. The German team shared publicly their methods on the WHO website on January 13th. Technology and global communications have evolved to the point where governments can slow but not stop the free flow of information.
Sharing all data and findings is the best form of science, but not always practiced. The Open Science Movement has the goal of changing this. If everyone in the world has equal access to the theory, methods, data and results of all other scientific research, quality and efficiency increases exponentially. This is evidenced in the open science practices behind the global fight against Covid-19 that saved and will save lives, potentially millions of them.
Figure 2 is a simulation predicting how many people would die of the virus in any given country depending on when governments follow WHO recommended operating procedures, as in: issue stay-at-home orders, engage in widespread testing and quarantine both individuals with the virus and those they were in contact with. ‘Day 0’ in Figure 2 is the moment when there are at least 3 symptomatic cases per million people, usually about 2 months after the first case in a country but much faster if several cases arrive at once.
The reader should keep in mind that Figure 2 is a simplified simulation. The reality of the situation is extremely complex. In particular, governments do not go from normal operations to full lockdown of society in one day, this usually proceeds in stages. Nonetheless, this simulation comes from the best known predictive epidemiology models and helps demonstrate how even one day of indecision can cost thousands of lives.
In response to this outbreak in China and rapid appearance of Covid-19 globally, South Korea followed the WHO’s standard emergency operating procedures. Meaning: Test everyone possible, isolate all cases, restrict travel and gatherings, close non-essential businesses. The virus was contained and only 200 people died. Of course, previous virus outbreaks heightened their preparedness level. Germany was also well prepared given its rapid development of tests, and because they learned from the experience of Italy as Europe’s ‘ground zero’.
Roughly speaking, Italy crossed the ‘Day 0’ threshold in Figure 2 around February 15th. It was the least prepared as a country because it was the first in Europe, and a place where people from around the globe flock as tourists if not football fans. Thus, Italy’s case is not a story of major government failure, also given that there were reasons to be suspicious of the Chinese case.
The ‘Day 0’ threshold came around March 2nd in Germany, and ‘Day 0’ was around March 8th in New York at least on paper. But New York only started testing people with success around March 1st because the CDC’s own test kits released in early February failed. This left plenty of time, in ‘pandemic terms’, to source the accurate tests being deployed in the rest of the world since January. This did not happen in New York or the US as a whole. Thus, New York was caught completely unprepared but not because the virus was a surprise arrival.
When combined with the data from China, South Korea and several other countries, the WHO upgraded the global emergency to a global pandemic on March 12th. New York had issued a state of emergency but only gave stay at home orders as of March 20th. It was not until a week later that most schools were closed and police authorized to enforce these orders (the blue arrow around ‘Day 31’ in Figure 2). Despite massive open science efforts channeled through the WHO, New York and much of the US simply failed to heed obvious scientific evidence and predictions. This is even more shocking because Seattle, not New York, was ‘ground zero’ in the US. Washington State as a whole implemented early and successful emergency measures.
Reviewing the failures of countries, states or cities to immediately take drastic emergency measures before or on January 30th (global emergency) or March 12th (pandemic) is not the subject of this blog post. The world had access to all the same data and knowledge of how to test for the virus thanks to open science practices, the WHO and several partner organizations and websites.
The message I want to convey is that open science is not enough. Its limits are found in governments. In many countries, science has little place in government decision making. This is perhaps understandable in a dysfunctional authoritarian regime where nearly all political decisions are made to maintain and concentrate power. This is certainly a reason that the worst horrors of the virus are yet to come in sub-Saharan Africa and Central Asia. But it is shocking in democracies where there are throngs of scientists and agencies tasked with monitoring and advising the government on what to do to protect its people.
The United States had ample information that Covid-19 was in the US and spreading rapidly, how to design effective tests, and exactly what to do to reduce the spread of the virus and its death toll months before any major actions were taken – the same information Washington State used to stem the spread. But this scientific information, shared at a scale and speed not seen before in world history, was simply not enough.
The Open Science Movement has ethical principles underlying its open access, data, methods and sharing recommendations. It is not just that open science practices make science more reliable and effective; they promote social justice, or scientific justice if you will. When every scientist in the world can access all the information that every other scientist in the world has, there is scientific equality. While rich universities boycott Elsevier, poorer universities cannot even afford a subscription. Thus, open access would bring a global North-South and a endowed v. not-endowed university equality to the world. But it can’t bring justice to those who are potential virus victims.
In the case of the Covid-19 pandemic, open science looked to undermine the bickering and buffonery of governments at first, but it could only ring the doorbell. Some governments simply refused to open the door, to take action. This begs the question if the Open Science Movement needs to adopt principles of political action that extend beyond policies promoting transparency and reproducibility. Does the Open Science Movement need to push governments to adopt administrative, if not constitutional procedures that make governments accountable to scientists in a natural disaster or emergency like a hurricane or pandemic?
I say yes from an ethical stand point. But its not so simple. As soon as we start pushing things like procedural reform, deep-pocketed special interests get involved and it gets ugly. As scientists we are not likely suited to mudslinging and political maneuvering. Not to mention that the more time we spend on lobbying, the less time we have for science. There are some of us with the ability to lead the Movement and influence governments, but most of us are ill-equipped to combat the powers-that-be behind politics.
That brings up the end game question: Is it enough to give the right answers to governments even if they ignore them? Have we done our duty as scientists if we just ‘show up at the doorstep’ and let government officials decide if we get to come in?
1 The original news article was deleted from the Chinese News Agencies’ website but can be found in the Internet Archive.
2 Source: Goh, Gabriel. “COVID Epidemic Calculator“. Day 0 is at least 3 symptomatic cases per million people, meaning there are potentially hundreds infected given the incubation period. Parameters used for predictions: 106 mil. Population, a single initial case, contagiousness per person of 2.2, rate of transmission 0.73, incubation period 5.2 days and mortality rate 2%.
A note from me: I have sought to capture the empirical evidence and historical timeline as accurate as possible, but any errors in this blog post are my own.