[Note from Nate Breznau] This post originally appeared in DeGruyter’s blog. I had assigned it as a reading in my course, but when I followed the link recently it did not appear and resolved instead to the home page of the blog. I searched within the blog but it did not turn up. Did DeGruyter remove it? I also am unnable to find the author of the post as his website seems to no longer exist (according to what I think is his OSF page). [update] I Tweeted to them initially and they were quick to respond.
@degruyter_soc, we are trying to read the blogpost "Global inequalities in science are bigger than those in the economy" in our course, which once appeared in your blog https://t.co/k3pCKU0IkE, but it no longer appears to be there. Have you removed it?
Hi Nate, the article in question was published on the "Open Science" blog, which was recently discontinued. That is why the article is not available anymore – apologies for the confusion!
— De Gruyter Conversations (@DGConversations) June 24, 2021
Global inequalities in science are even bigger than those in the economy. Of course, to afford food is more important than to contribute to scientific discussion. Thus results of inequalities are less dramatic in case of scientific research, however, the case of Ebola might be worth to think over here. This pathogen was detected for the first time in 1976, quite a long time ago. Would the current therapy method for the disease that it causes be the same if it had been discovered in Alaska?
The lion’s share of all scientific articles published in established academic journals comes from a small number of countries, and some of these leading countries are really small and rich, when seen from a global perspective. According to World Bank Data, there were more than 21 thousand papers indexed by the Science Citation Index and Social Sciences Citation Index in 2013 that were published by Swiss researchers. This means that Switzerland was able to produce 2,603 top level papers for each one million of its inhabitants. Denmark, second in this ranking, achieved the result of 2,223 papers per million people, so 15% worse. The visualization of number of papers per million inhabitants on the global map shows the indubitable hegemony of rich, northern countries in science.
Reconstruction of original figure using the original figure thumbnail in a Google image search.
A country’s scientific publishing output per million people correlates very strongly with Gross Domestic Product per capita (Spearman 0.84!). In short, you have to be rich to have a significant input to science. This might be nothing new for you, but what is quite surprising for me, is that global inequalities in science are bigger than those in the economy.
I have calculated the Gini coefficient for 4 of the “development indicators” provided by the World Bank. First is the market capitalization of listed domestic companies, so commercial value of companies registered in a country, which I expected to be the most unequal on a global scale. The second one is Gross Domestic Product, that is a well established indicator of welfare and is known to be extremely unevenly distributed globally. I have also chosen electric power consumption, which is also a good indicator of general consumption level. The fourth indicator is the number of articles indexed by Thosmon Rueters services (this data is provided by the World Bank as well). All indicators were divided by the number of country’s inhabitants and then the Gini index was calculated.
In the result, I realized that the contribution to the scientific core is even more unequally distributed among countries than GDP and values of companies. And this is the most unevenly distributed factor that I have analysed. Countries are more equal in respect of their share in the global wealth than in their impact on global scientific discussion.
A lot of work has been done to inform citizens of Europe and North America about the dramatic scale of global inequalities. However, these inequalities are so big, that average people from wealthy countries still do not fully understand what it means “to live below the absolute poverty line”. So please, now try to imagine that inequalities in science are even bigger than that. Of course, to afford food is more important than to contribute to scientific discussion. Thus, the results of inequalities are less dramatic in the case of scientific research, however, the case of Ebola might be worth thinking about here. This pathogen was detected for the first time in 1976, so quite a long time ago. Would the current therapy method for disease that it causes be the same if it had been discovered on Alaska? The Ebola virus is the extreme and rare case, a lot of science has less to do with life or death issues.
However, my comparison to the drastically uneven world of global economy may let you imagine how different the chances of researchers from the Global South are from their colleagues in the Northern Countries. And factors that support hegemony of the North are not only economical ones. There are significant cultural and social barriers that enhance the unequal status quo (have a look here).
The WHO declared a Global Pandemic on Jan 30th, 2020, based on overwhelming evidence that the highly infectious Novel Coronavirus SARS-CoV-2 and the deadly COVID-19 disease that it causes threatened all of human kind. Despite this clear message, public and government responses varied dramatically by country, city and even neighborhood. Controlling the spread of any global pandemic requires large-scale, cohesive public responses. As there is no global governance, national governments were crucial institutional actors in the pandemic fight.
In Germany, the national government was quick to push German states to adopt cohesive measures in February, and to then ratchet these up in March as infections exploded in places like New York City and Italy and localized regions and events within Germany. In Figure 1, the dotted line are the deaths from COVID-19 and the solid green line is the degree of government lockdown measures. At least in the first wave, Germany was highly successful at curbing the spread of the virus. This contrasts sharply with Sweden, displayed in the middle panel of Figure 1.
Figure 1. Daily deaths per capita and government intervention
Known deaths from COVID-19 data from Johns Hopkins and Dong, Du and Gardner (2020) taken as the deaths per day divided by the population in one-hundred-thousands. Government intervention data from Oxford University Blavatnik School of Government measured as a scale of measures from none (-1) to all possible at the highest degree (+1) (e.g., travel restriction, banned public gatherings and stay-at-home orders).
In Sweden, the constitution prevented lockdown measures in non-war-times. Although the Swedish government encouraged its residents to follow pandemic safety guidelines, the lockdown measures were relatively lax and the infection rate and resulting deaths were among the worst in the world at the outbreak of the pandemic. The Swedish response and even relatively ‘good’ German response paled in comparison to the swift and effective lockdown in South Korea and most East Asian countries). In the lowest panel of Figure 1, the deaths per capita stays nearly at zero and remained there until the time of writing this.
Government response is not the only factor as made clear in the second wave of infection starting in October of 2020. Germany and Sweden had similar death rates in the second wave with a slightly stronger government intervention and slightly less deaths in Germany. However, in South Korea government lockdown was similar to Germany, but they had fraction of the infections and almost no deaths.
Government response is simply a method to control public behaviors. Ultimately the public are the arbiters of infection, and their behaviors explain different outcomes where governments take similar control measures. In Wuhan Province, China the public had little control over their behaviors as they were confined to their homes, subject to biosecurity protocols and ‘policed’ by both actual police and Communist Party-led neighborhood watches for at least 76 days. The lockdown halted the infection and death rates locally, but the virus had already hopped China’s borders leading to the pandemic. By contrast, once arrived in countries like Sweden or the United States, the residents were mostly free to behave as they pleased. The fate of the virus spread was essentially in the public’s hands because their behaviors – movements, contacts and (lack of) awareness – provide the only way the SARS-CoV-2 virus can spread or not.
This means that especially in liberal democratic systems where the governments cannot easily impose lockdown measures, studying human behavior is essential to understanding how to fight a pandemic. Social scientists regularly observe a correlation between sentiments and behaviors. The public forms attitudes toward ‘the virus’ and ‘a pandemic’ from the news and word-of-mouth. Therefore, the contents of media messages play a major role in shaping behaviors indirectly through the information contained in news and editorial articles.
Figure 2 shows how daily infections closely follow the sentiment in media messages. When sentiment is more positive (thick yellow line) it is likely that the public perceive less risk and then engage in less precautionary behavior leading to increases in infections (dashed purple line). At the same time, sentiment is more positive as government restrictions ease (thin green line), thus enabling less precautionary behavior like social gatherings and in-person work; in turn leading to more infections.
Figure 2. Media, government intervention and infection rates in Germany and the U.S.
Sentiment analysis of all available online media sources provided by RavenPack’s Coronavirus Media Monitor, standardized with a rolling average (thick yellow line); infection rate calculated as the 18-day lead deaths from Johns Hopkins data adjusted for the demographic composition of the population (dashed line); government intervention measured Oxford University Blavatnik School of Government.
What is also striking about Figure 2 is that infection rates in Germany show a weaker correlation with media sentiment than in the United States. This is most likely due to stronger government intervention in Germany, whereby individuals have less control over their decisions, or at least will face criminal punishment for not following government guidelines. The apparent association between media sentiment and infections should be caused by public behaviors, but cross-national behavioral data are scarce during the pandemic. However, during a brief window of opportunity from March 15th to April 7th, 2020, Thiemo Fetzer and colleagues fielded a survey asking about precautionary public behaviors in at least 80 countries. Figure 3 compares behaviors with average media sentiment in the last week across these countries and demonstrates a clear correlation between more positive sentiment in media contents and less precautionary behaviors.
Figure 3. Media and public precautionary behaviors in 80 countries, March 15th – April 7th
Media Sentiment provided by RavenPack’s Coronavirus Media Monitor and precautionary behaviors calculated as a scale from the Perceptions and Behaviors at the Onset of COVID-19 survey (Fetzer et al 2020).
National governments are in a tough position during pandemics. They cannot enforce lockdown measures beyond a certain ‘breaking’ point, or the public will simply rebel or ignore them in such large numbers that enforcement becomes impossible. It is therefore not unreasonable to conclude that at least in liberal democratic regimes, the most effective pandemic prevention measures, like those taken in Wuhan, are simply not possible. The old adage ‘give me liberty or give me death’ might therefore be reframed as ‘give me liberty and death’ in pandemic times.
Am 30. Januar 2020 erklärte die WHO einen ‚Global Health Emergency‘ basierend auf Hinweisen auf ein Virus, das sich schnell verbreitet. Das Coronavirus aus der SARS-Familie (Sars-CoV-2, und die Krankheit Covid-19). Die Beweise, mit denen die WHO diesen Notfall erklärte, stammten fast ausschließlich aus chinesischen Daten.
Die chinesischen Daten zeigten im Januar eine alarmierende Ausbreitungsrate, wie in Abbildung 1 dargestellt. Ohne die chinesischen Daten hätte die WHO wenig Anlass zur Sorge gehabt, da in allen anderen Ländern zusammen kaum 90 Fälle bekannt waren und kein einziger Todesfall.
Abbildung 1. Die Verbreitung von Covid-19, die zur Notstandserklärung der WHO am 30. Januar führte. Johns Hopkins Daten.
Anfang Januar ergriff die chinesische Regierung Maßnahmen, um Nachrichten und Daten1 im Zusammenhang mit dem Virus zu blockieren. Trotzdem gelang es chinesischen Wissenschaftler*innen, offenen wissenschaftlichen Praktiken zu folgen, einschließlich des Teilens partiell-genetischer Sequenzdaten mit der Welt. Dies ermöglichte der WHO, geeignete Maßnahmen zu ergreifen, und befähigte Wissenschaftler*innen in Deutschland, Tests zur Identifizierung des ,novel Coronavirus‘ zu entwickeln. Das deutsche Team veröffentlichte seine Methoden am 13. Januar auf der WHO-Website. Technologie und globale Kommunikation haben sich zu einem Punkt entwickelt, an dem Regierungen den freien Informationsfluss verlangsamen, aber nicht stoppen können.
Die gemeinsame Nutzung aller Daten und Erkenntnisse ist die beste Form der Wissenschaft, wird aber nicht immer praktiziert. Die Open Science Movement hat das Ziel, dies zu ändern. Wenn jeder auf der Welt gleichermaßen Zugang zu Theorie, Methoden, Daten und Ergebnissen aller anderen wissenschaftlichen Forschung hat, steigen Qualität und Effizienz exponentiell an. Dies zeigt sich in den offenen wissenschaftlichen Praktiken hinter dem globalen Kampf gegen Covid-19, die Leben retten und retten werden, möglicherweise Millionen von Leben.
Abbildung 2 ist eine Simulation, die vorhersagt, wie viele Menschen in einem bestimmten Land als Ergebnis des Zeitpunkts der staatlichen Intervention an dem Virus sterben würden. Intervention heißt wann die Regierungen die von der WHO empfohlenen Vorgehensweisen befolgen, wie z. B.: Anweisungen für den Aufenthalt zu Hause, Durchführung umfassender Tests und Quarantäne für diejenigen, die positiv auf das Virus getestet wurden und die, mit denen sie in Kontakt waren. “Tag 0” in Abbildung 2 ist der Moment, in dem mindestens 3 symptomatische Fälle pro Million Menschen auftreten, normalerweise etwa 2 Monate nach dem ersten Fall in einem Land, aber natürlich viel schneller, wenn mehrere Fälle gleichzeitig auftreten.
Abbildung 2. Die Auswirkungen staatlicher Interventionen auf die Reduzierung der Todesfälle durch Covid-19. Quelle: Gabriel Goh, und eigene Berechnungen des Autors (* vorhergesagte Todesfälle)
Der Leser sollte bedenken, dass Abbildung 2 eine vereinfachte Simulation ist. Die Realität ist äußerst komplex. Insbesondere gehen die Regierungen nicht an einem Tag vom normalen Betrieb zur vollständigen Stilllegung der Gesellschaft über, dies geschieht normalerweise schrittweise. Diese Simulation basiert jedoch auf den bekanntesten Modellen der prädiktiven Epidemiologie und zeigt, wie selbst ein Tag der Unentschlossenheit Tausende von Menschenleben kosten kann.
Als
Reaktion auf diesen Ausbruch in China und das rasche Auftreten von Covid-19
weltweit folgte Südkorea den standardisierten „Emergency Operating Procedures“
der WHO. Das heißt: möglichst viele Personen testen, alle Fälle isolieren,
Reisen und Versammlungen beschränken, nicht notwendige Geschäfte schließen. Das
Virus war eingedämmt und nur 200 Menschen starben. Natürlich
haben frühere Virusausbrüche in Südkorea die Bereitschaft verbessert. Ebenso
war Deutschland gut vorbereitet, weil es schnell Tests entwickelt hatte und weil es aus den Erfahrungen Italiens als
Europas „Ground Zero“ gelernt hatte.
Grob
gesagt hat Italien um den 15. Februar herum die Schwelle für „Tag 0“ in
Abbildung 2 überschritten. Als Land war es am wenigsten vorbereitet, weil es
das erste in Europa war und ein Ort ist, zu dem Menschen aus der ganzen Welt
als Touristen, wenn nicht als Fußballfans, strömen. Somit ist der Fall Italiens
keine Geschichte eines großen Versagens der Regierung, auch da es Gründe gab,
dem chinesischen Fall misstrauisch gegenüberzustehen.
Die
Schwelle für “Tag 0” lag in Deutschland um den 2. März herum, und
“Tag 0” war um den 8. März herum in New York, zumindest auf dem
Papier. New York begann jedoch erst am 1. März mit dem erfolgreichen Testen von
Personen, da die Anfang Februar veröffentlichten CDC-eigenen Testkits
fehlschlugen. ‘Tag 0’ in New York war wahrscheinlich Mitte Februar oder früher.
Dennoch hätte New York aus „pandemischer Sicht“ noch viel Zeit gehabt,
Maßnahmen zu ergreifen. Der Rest der Welt hatte seit Ende Januar, dank des
offenen Datenaustauschs auf der WHO-Website, genaue Tests
durchgeführt. Dies geschah jedoch weder in New York noch in den USA als Ganzes.
So wurde New York völlig unvorbereitet getroffen, aber nicht, weil das Virus
überraschend aufgetaucht war.
In
Kombination mit den Daten aus China, Südkorea und mehreren anderen Ländern
erklärte die WHO am 12. März, dass der globale Notfall nun eine „Global
Pandemic“ sei. New York hatte den Ausnahmezustand verhängt, aber erst ab dem
20. März Anordnungen für den Aufenthalt zu Hause erteilt. Erst eine Woche später wurden die meisten
Schulen geschlossen und die Polizei autorisiert, diese Anweisungen durchzusetzen
(der blaue Pfeil um „Tag 31“ in Abbildung 2). Trotz massiver offener
wissenschaftlicher Bemühungen, die durch die WHO kanalisiert wurden, haben New
York und ein Großteil der USA offensichtliche wissenschaftliche Beweise und Vorhersagen
einfach nicht beachtet. Dies ist umso schockierender, als Seattle und nicht New
York in den USA „Ground Zero“ war. Der gesamte Bundesstaat Washington hatte
frühzeitig und erfolgreich
Sofortmaßnahmen ergriffen.
Die
Überprüfung des Versagens von Ländern, Staaten oder Städten, vor oder am 30.
Januar (globaler Notfall) oder 12. März (Pandemie) sofort drastische
Notfallmaßnahmen zu ergreifen, ist nicht Gegenstand dieses Blogposts. Dank Open
Science Praktiken, der WHO und mehrerer Partnerorganisationen und Websites
hatte die Welt Zugang zu denselben Daten und Kenntnissen darüber, wie man auf
das Virus testet.
Die
Botschaft, die ich vermitteln möchte, ist, dass Open Science nicht ausreicht.
Ihre Grenzen liegen in den Regierungen. In vielen Ländern hat die Wissenschaft
wenig Platz in der Entscheidungsfindung der Regierung. Dies ist vielleicht in
einem dysfunktionalen autoritären Regime verständlich, in dem fast alle
politischen Entscheidungen getroffen werden, um die Macht aufrechtzuerhalten
und zu konzentrieren. Dies ist sicherlich ein Grund dafür, dass die schlimmsten
Schrecken des Virus in Afrika südlich der Sahara und in Zentralasien noch
bevorstehen. Aber es ist schockierend in Demokratien, in denen es eine Schar
von Wissenschaftler*innen und Agenturen gibt, die die Regierung dabei
beaufsichtigen und beraten sollen, was zu tun ist, um ihre Bevölkerung zu
schützen.
Die
Vereinigten Staaten hatten reichlich Informationen darüber, dass sich Covid-19
in den USA befand und sich schnell verbreitete, wie man wirksame Tests entwirft
und was genau zu tun ist, um die Ausbreitung des Virus und die Zahl der
Todesopfer zu verringern, Monate vor dem Ergreifen größerer Maßnahmen – dieselben
Informationen, die der Staat Washington zur Eindämmung der Ausbreitung nutzte.
Aber diese wissenschaftlichen Informationen, die in einem Umfang und einer
Geschwindigkeit geteilt wurden, die in der Weltgeschichte noch nie zuvor
gesehen wurden, reichten einfach nicht aus.
Die
Open Science Movement hat ethische Grundsätze, die ihrem offenen Zugang, den
offenen Daten, den offenen Methoden und Empfehlungen zum Austausch von zugrunde
liegen. Es ist nicht nur so, dass offene wissenschaftliche Praktiken die
Wissenschaft zuverlässiger und effektiver machen. Sie fördern soziale
Gerechtigkeit oder wissenschaftliche
Gerechtigkeit, wenn Sie so wollen. Wenn jede*r Wissenschaftler*in auf der
Welt auf alle Informationen zugreifen kann, über die jede*r andere
Wissenschaftler*in auf der Welt verfügt, besteht wissenschaftliche Gleichheit.
Während reiche Universitäten Elsevier boykottieren, können sich ärmere
Universitäten nicht einmal ein Abonnement leisten. Open Access würde also der
Welt eine globale Nord-Süd und eine dotierte vs. nicht dotierte
Universitätsgleichheit bringen. Aber es kann denjenigen, die potenzielle
Virusopfer sind, keine Gerechtigkeit bringen.
Im
Fall der Covid-19-Pandemie schien die offene Wissenschaft zunächst das Gezänke
und die Tiraden der Regierungen zu untergraben, konnte aber nur an der Tür
klingeln. Einige Regierungen weigerten sich einfach, die Tür zu öffnen und
Maßnahmen zu ergreifen. Dies wirft die Frage auf, ob die Open Science Bewegung politische
Handlungsprinzipien verabschieden muss, die über Maßnahmen zur Förderung von
Transparenz und Reproduzierbarkeit hinausgehen. Muss die Open Science Bewegung die
Regierungen dazu drängen, administrative, wenn nicht verfassungsrechtliche
Verfahren einzuführen, die die Regierungen bei einer Naturkatastrophe oder
einem Notfall wie einem Hurrikan oder einer Pandemie den Wissenschaftler*innen
gegenüber rechenschaftspflichtig machen?
Ich
sage ja aus ethischer Sicht. Aber es ist nicht so einfach. Sobald wir anfangen,
Dinge wie Verfahrensreformen voranzutreiben, werden tiefsitzende
Sonderinteressen einbezogen und es wird hässlich. Als Wissenschaftler*innen
sind wir wahrscheinlich nicht für Schlammschlachten und politisches Manövrieren
geeignet. Ganz zu schweigen davon, dass wir umso weniger Zeit für die
Wissenschaft haben, je mehr Zeit wir für Lobbying aufwenden. Einige von uns
haben die Fähigkeit, die Bewegung zu führen und Regierungen zu beeinflussen, aber die meisten von uns sind schlecht gerüstet, um die
Mächte zu bekämpfen, die hinter der Politik stehen.
Das
wirft die Frage nach dem Endspiel auf: Reicht es aus, den Regierungen die
richtigen Antworten zu geben, auch wenn sie sie ignorieren? Haben wir unsere
Pflicht als Wissenschaftler*innen erfüllt, wenn wir nur vor der Haustür
auftauchen und Regierungsbeamte entscheiden lassen, ob wir eintreten dürfen?
1 Der ursprüngliche Nachrichtenartikel wurde von der
Website der chinesischen Nachrichtenagenturen gelöscht, kann aber im Internet
Archive gefunden werden.
2 Quelle: Goh,
Gabriel. „COVID Epidemic Calculator“. Tag 0 ist
mindestens 3 symptomatische Fälle pro Million Menschen, was bedeutet, dass
aufgrund der Inkubationszeit möglicherweise Hunderte infiziert sind. Für
Vorhersagen verwendete Parameter: 106 mio. Bevölkerung, ein einziger Erstfall,
Ansteckungsgefahr pro Person von 2,2, Übertragungsrate 0,73, Inkubationszeit
5,2 Tage und Sterblichkeitsrate 2%.
Ein Hinweis von mir: Ich habe versucht, die empirischen Beweise und den historischen Zeitplan so genau wie möglich zu erfassen, aber alle Fehler in diesem Blog-Beitrag sind meine eigenen. Ich bin dankbar für die Kommentare von Lisa Heukamp.
My family is a mixture of Democrats, Republicans and swing voters. This can make for interesting emails, calls or reunions. It seems clear to me that partisan discussions, especially involving blame, are a no go. In fact, family in-fighting is pretty much like public in-fighting. It distracts us from some of our common problems. Like the unbridled increase in income and wealth inequality in the United States since the late 1970s. Especially the top 1%. The question is how to find common ground when one or both sides are past their breaking points.
I suggest three things that would benefit almost any American, except for the ultra rich (top 1%) or relatively rich (top 10%). These are things that encroach on freedom without touching on more polarizing topics.
1. End the winner-take-all electoral system. Why should the winner take all, when the winner is rich people? Low and middle-class workers have not had a real wage increase since the 1970s on average, but wall street provided huge profits.
There is nothing wrong with profits, but shouldn’t everyone working at profiting companies profit? A similar story unfolds in politics. The upper classes control both parties. Republicans favor the ultra-rich, and Democrats favor the quite-rich in general. We do not have parties that represent all voters’ interests. If you are lower to lower-middle class, these parties are both against you, for the most part. If you support the Tea Party, Libertarian, Social Democrats, Green Party, or other parties, you have no chance to get representation in government at the national level. We need a representative democracy where parties get power that equals their vote share. If Republicans get 55% of the vote they should get 55% of the legislative and executive positions. That is real democracy, where the government reflects the preferences of the people. When parties get proportional vote shares, they are forced to work together to solve problems. More than half of those who identify as Republicans and Democrats favored having a third party in a recent Gallup poll.
The two party system is now so deeply divided that democracy itself is faltering. The abuse of power the Constitution hoped to prevent is now rampant with executive dominance, a Supreme Court of partisan judges, and deep segregation of voter districts due to gerrymandering. Adding more parties will instantly restore coalitions and cooperation.
2. End Super PACs. This is how both parties came to be dominated by rich-peoples’ interests.
Citizens United and Speechnow.org made it possible that parties and candidates can get unlimited secret funding. This raised the stakes so high that the only way to get elected is to have over 1 billion dollars, or to accept donations to equal 1 billion dollars (that is one thousand times one million dollars – a $hit ton! The only way to get such big donations is to promise things to the rich that benefit them. In more plain English, this is known as corruption, by definition. These are simply ‘legal’ bribes being paid to politicians, made legal because of Citizens United.
“for the first time since at least the 1960s, the majority of Americans were not in the middle class”
– PEW Research Center
3. Overturn the 1987 repeal of the FCC Fairness Doctrine. Political conflict is an opportunity to create economic conflict.
Until 1987, news companies were legally bound to report news in a balanced manner, providing different sides of each story. Today they are allowed to say anything they want and claim it as ‘fact’ without any repercussion. All of our political and factual beliefs have been shaped by distortions of reality. For example, try this on for size at the next mixed-political gathering. Obama, what many political news agencies call the ‘socialist-Muslim’, was actually a relatively hawkish military president. He was the first president in history to have an American citizen assassinated without any trial. He also give a large pay and benefit raise to the military and made aggressive moves of the US to contain China militaristically and economically. These facts might perk the attention of even the most ‘libtard’-hating-family-members. But that is not my main point here. The media outlets are mostly owned by corporations with special interests in keeping you and I fighting over politics, so that corporations can keep paying low wages. The news in the US sows seeds of hate and misinformation so that working-class people end up in constant conflict. This conflict keeps the focus away from the ultra-wealthy making decisions that harm them, such as paying them miserable wages with low benefits. Why not end fake news?
In case you are not sure, what not to do. Abortion, gun control, racism, blame of any sort. Not gonna go over well in mixed political company. If you are not ultra wealthy, we are on the same side in the things that matter most – like getting fair wages for fair work.
Notes to the reader:
As news media companies tend to be on one side or the other, I tried to use neutral sources as much as possible in this post.
Full disclosure. I have never voted Republican. But I am no fan of the Democrats either. Having to choose the lesser of two evils is not an exciting political reality to face. Especially while watching the rich get richer, and the working-class continually get the shaft.
On January 30th, 2020, 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.
Figure 1. The spread of Covid-19 leading to the WHO emergency declaration Janurary 30th. Johns Hopkins data.
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.
Figure 2. The impact of government intervention in reducing deaths from Covid-19. Source: Gabriel Goh & author’s calculations (*indicates a predicted death toll)
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.
