As of late 2022 when I’m adding this section, our world is still heavily influenced by this health pandemic. We seem to have passed the worst[i], but it also seems too early to draw conclusions. We still do not know the long-term direct and indirect effects of the pandemic. E.g. whether the virus affects long after infection, why some people have symptoms much longer, what is the impact of delayed diagnosis of other conditions due to the healthcare congestion, or what is the physical, mental and social effect of a population mostly isolated for long periods of time.
Yet, this situation seems highly relevant to the core thesis of this book, the value of scientist outside academia and their own fields of study. In this section, I try to get some early lessons learned and review how the world looked at science to help overcome it.
Covid-19, or “covid” as many languages have normalized now, started in December 2019 (hence the number on the name). There are several hypotheses as to how it started, but several involve increasing pressure of natural habitats by agriculture, urbanization, tourism, … This forced animals, and animal feces, in more direct contact with humans, their food or water sources, increasing the chances that their viruses infected and adapted to us. It seems likely that corona originated on bats, perhaps infecting another animal before it passed to us.[ii] If this is confirmed, there is an important lesson on sustainability: A trend to recognize how we simply cannot consider nature, and Earth, as an externality to what we do. We cannot take care of people if we don’t take care of Earth; and vice versa. We cannot push natural habitats to a corner, or eradicate so many species, without exposing ourselves to how that ecosystem overflows or shifts and tries to adapt and survive. In this lights, wild animals encroaching cities and infecting us, can also be seen as human encroaching the stable nature of wild habitats and getting injured in the process. All in all, Covid thus would be a story of our drive to create better housing, better food, more comfortable lives on a particular location, without consideration to nature as an integral part of the system, that led to the evolution of an animal virus into humans, and our globally connected system spread it to create biggest and largest global economic, social and economic recession, with more than 6 million confirmed deaths, and erosion of quality of lives, especially for most vulnerable people.
Another example on our narrow view of maximizing outputs is the banana. The “Cavendish” banana is a cultivar made to maximize human taste and survive transportation unharmed. It is 99% of banana exports to developed countries, so the only banana you probably know is probably a Cavendish. One of the consequences of this cultivar, is that it doesn’t have seeds, so all Cavendish banana plants are clones excising the mother plant. Without sexual reproduction there is no mechanism to evolve disease resistance. Moreover, plantations are usually densely allocated. This makes them extremely vulnerable to any disease that learns to spread on them. Our drive to have perfect bananas made an extremely vulnerable plant. In fact the Cavendish only became the dominant around 1950, when the previous banana type humans liked, the “Gros Michel” was mostly wiped out by a fungus, the “Panama disease”. The same fungus that is now starting to spread and affect Cavendish[iii].
Back to covid, it started in China in November 2019, and soon exploded into the entire world. At the time, I was working with the World Bank and we started to look into the infection numbers from public sources. We were seeing covid spread to new countries daily. I was living on a tiny apartment in the center of Paris, and quickly realized it didn’t look good. I talked with my then fiancée and decided to book a flight home to rural Spain for the next day. We would rather weather that storm in the remote countryside than in a tiny apartment, especially if the socially impossible lockdowns we started to hear about arrived to Europe. We broke the rent lease of our place, paying the hefty price of last minute flights, and insisting on staying on an empty house back home in our village was a really hard decision to make when we, and the world, wanted to believe nothing would happen. Lockdowns sounded like something only dictatorships do. Turns out that our worst fears were right, and all flights stopped a mere 3 days later, and indeed Spain endured almost 3 months of lockdown. For our later discussion on Impact Science, it is important to note that these lockdowns quickly flipped from impossibly unlikely in the west, to socially accepted and widely respected when they were finally imposed.
I continued to work with the World Bank to estimate best options for extra resources in healthcare facilities and estimation of livelihoods impact. With Facebook and Google data we were getting signals everywhere that a substantial percentage of people moved less (to workplaces, to shops, to transportation hubs, …), sometimes forcibly (like in India) sometimes by choice. We saw wealthy neighbors reacting much quicker and strongly than poor ones, which both makes sense and suggest a very disproportional vulnerability and impact on poor people. The indirect effects looked very gloomy and potentially catastrophic as the world’s agricultural fields and temporary workers disappeared and left the harvest rot. The pandemic compounded the effects of the many dimensions of poverty, and rolled back the clock of development as services, supply chains, social work slowed. Meanwhile the public sector didn´t really know what to do, especially in democratic countries. Traditionally pandemics science experts recommend a heavy hand to stop transmissions stopping social events, imposing social norms like masks, or even halting transportation. Science demanded something governance, culture and law resisted.
We did not know at the time how severe or contagious this disease was. On a very aggressive virus, like Ebola, you must impose draconian measures to quarantine suspects and avoid transmission. It’s a clear case. Even stopping funerals, known propagate the virus across regions[iv]. A mild virus, like flu, does not warrant widespread lockdowns, but protection to vulnerable people. Not only people would rather get the flu than get locked at home, but as a public policy, a lockdown cost quickly explode in halted product and services we depend on, service industry employment, or disruption on our very interrelated supply chains. It is easy to forget how impossible it seemed to think that governments across Europe or America would need to go that far as imposing lockdowns, forcing masks to everyone or making gatherings illegal, from parties to weddings or religious events.
Our government in Spain gathered a group of experts on many fields from public health, law, data analytics, and economy. I was one of them. We had to evaluate and propose what to do, how to do it, and most importantly how long to keep each measure and what to do afterwards. There was a tremendous amount of uncertainty. We didn´t know much about the virus itself, the prospects of a vaccine, the long term effects, vulnerable populations, … But also our societies, and laws, were not prepared for the measures epidemiologists knew would help. Doing anything could be a huge error, but also not doing something. Could we technically and legally collect and share with government officials identifiable health information to do contact tracing? Could be lock down houses? Cities? The whole country? What about elections? How do we protect supply chains, emergency personnel? How can any of this work if it needs to last days, weeks or months? These were all difficult challenges for which we did not have enough data to make an informed decision, yet any delay incurred in exponential cost of lives and economic impact. The longer and the more effective anti-pandemic measures, the greater, slower and expensive the recovery would be. A one-day delay could mean weeks of exploding cases.
The most urgent need for most governments was preventing the collapse of health care facilities. Saturated hospitals with no available beds not only prevent treating more covid patients but also anyone else with any other medical need. This quickly became known as “flattening the curve”. We knew by then that contagion follows exponential growth with roughly 2 weeks incubation time. Any mistake of a few infected people transmitting the disease, and in two weeks you might face a wall of cases collapsing all resources. On the other side, imposing too strong measures might create social unrest in the short term and quick loss of employment, livelihoods and social welfare in the medium term on the service economy. Isolating people too hard and too long affects their mental and physical health, may be even weaking the immune system of babies[v]. An unnecessary hard lockdown could cost more than late one.
In both cases, at the World Bank, and with the Spanish government, my role as Impact Scientist was the same. I did not know about pandemics, public health, viruses or infections; but I had very valuable skills to offer. Some talked about contact tracing, and I was able to quickly ramp up and assess technological options, cryptographic privacy and some of the legal and practical challenges it poses, so we could raise it with our legal experts. There was a fluid back and forth between those experts in public health, data and tech, legal, governance, … This meant that usually we would receive a document with data and concerns with one expert, review it, quickly study and research online material, and come back to the team with further ideas, implications and request for expertise. Not have data can paralyze our capacity to decide in the dark, but in our digital age, we can easily get too much data that can confirm our wrong biases, or simply confuse us and paralyze our capacity to decide among the noise. The role of the impact scientist here is to be the sidekick of the decision makers to filter the noise, evaluate the signal, and constantly support providing tools for assessing what we have, what we need, and what we know we don’t know. And perhaps most important, avoid providing only the flaws or downsides of taking any decision, rather assess the reason what the choice, which is often not up the impact scientist, might work and how to make the best of it.
Slowly the world got several options for vaccines, trials, then massive vaccination campaigns, boosters, mask mandates, … Some countries choose mandatory vaccination, some opted for letting the virus run through their population to provide immunity while doing their best to protect the most vulnerable, … 2020 to 2022 (or longer) has been a widening array of public policy responses to the same threat. The jury is still out who did the right thing, or even who did the right thing with the information and constrains they had. As of late, more countries are shifting towards lifting most measures while the virus has not disappeared and continues to mutate to strains that affect people that are vaccinate or have had previous infections.
I don’t know if covid is a story of success when it comes to leverage science for impact. It certainly is for medicine, for vaccines, for public policy relevance. But is not clear our method to decide upon what actions to minimize impact were the right ones. May be at some point in the future I’ll complete this section with more lessons learned and move to the chapter of failures or successes, but for now I leave it here. It is a story of taking scientific reasoning outside academia seriously. This time we realized we needed scientist’s help next to the decision makers, but it’s not clear we did, or we were trusted, rightly or not.
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