Many years ago, in a world that was much unlike the world of today, I had a great Eureka moment. Like all great stories of discovery, this one starts with me being completely aloof and unaware of what was coming my way. Years later, this one moment (and the many months of hard, rigorous work that stemmed from it) would allow me to serve my people through science-based policy and mass media. It also give me a sneak peek into how Mexican politics and power disputes ultimately work.
It was a HOT day in 2016 and I was riding a PACKED bus on my way to pick up medicine from one of Mexico’s largest hospitals. After 30-some minutes standing tight in the corporal sea of warmth and sweat of this bus on rush hour, I saw the bus empty when we arrived at the hospital stop. Then it hit me: I’d been standing in what could potentially be a horrendous (but hot) soup of infection and disease vectors. Almost everyone on that bus was going to the hospital, so it was likely that a large fraction of them were patients seeking treatment. People who ride this bus line every day must get sick more often than people who ride other crowded buses, I thought.
After letting the thought marinate in my head for a few weeks, I reached out to Nestor Garcia-Chan, a professor of applied math at Universidad de Guadalajara (UdeG), and told him about my idea: where we live and where we work largely impact our exposure to infectious disease. Riding the bus with dozens of sick people every day is one extreme case of that concept, but what else is inbetween? Can this be tested mathematically?
The solution to this problem, in the context of influenza, is now published in the Proceedings of the Royal Society A. To reach it, I had to learn many tricks from probability theory, models of urban mobility, integro-differential equations, all while incorporating gigabytes of demographic, economic, and educational data from my hometown Guadalajara. In the end, my collaborators and I had a middle-complexity model with realistic urban geography but whose dynamics were remarkably tractable. Our results suggested that clustering of economic activities in city centers accelerates the spread of infectious disease.
By the time COVID-19 turned infectious disease modelling into the hottest of topics, the preprint for this project had been out for months and been featured by Forbes and Health Magazine. And then COVID hit. And I was sitting on what was perhaps the most sophisticated epidemiological model for any Mexican city. I needed to do some good with it, but what could I do? This story is about to get so much better, I swear.
My mentor Gerardo Chowell and I quickly jumped on the task to update our model and calibrate it to the specifics of COVID and the conditions of reduced mobility under economic lockdowns. I emailed people in the Jalisco state office for Public health, telling them that I’d spent the last 4 years trying to understand how infectious outbreaks spread in Guadalajara and how economic activity modulates that. I thought that they’d jump on the opportunity to talk to me, that I could save them great pains and help guide their decisions. But no one ever replied.
Armed with powerful science and a great deal of frustration, I (obviously) took things to the internet. Unable to influence government decisions, I’d have to be content with providing reliable information to people around me, so I made a banner and posted it on Facebook announcing that I’d be giving an introductory lecture in mathematical epidemiology. Within a few days, my announcement had been shared over 5,000 times, and the pressure to deliver an excellent lecture was growing. In the end, my lecture lasted ~90 minutes throughout which the live audience was at least 1000 viewers.
In the days following the lecture, people from all over Latin America showered me with gratitude and compliments. I felt immensely proud for bringing cutting edge science to people on the fringes of mainstream science communication (if finding high quality, accessible science on COVID was rare in March of 2020, finding it in Spanish was almost unthinkable). It felt like being in the right place at the right time; like I was fulfilling my purpose: not one that I had chosen, but one that had fallen on me and only me.
Among those who reached out after the lecture was Jorge, a young professor of molecular biology at UdeG. Jorge was a member of UdeG’s “situation room” on COVID-19; he invited me to join one of their meetings and bring a quantitative perspective, as everybody else were medical doctors, biologists, and chemists. My alma mater’s guiding body for this crisis needed someone like me, and I was ready to step up to the challenge. Even better, this group was actively communicating with the state government and influencing policy decisions. I started working 14 hour days to finish adjusting my model to COVID and contribute to this effort.
Unlike the majority of people, I was doing fantastically well in the early weeks and months of the pandemic. I had a purpose to wake up in the morning, an city of 5 million I could help, and a group of hard working people to walk that path with. A few days after *unofficially* joining UdeG’s situation room, I met with the Dean and shared some of the basic insights coming out of my model that could immediately help guide policy decisions. The day after, I was told that some of my slides would be used in a joint press conference with officials from the state government.
I remember sitting one morning to watch the announcement. Several statistics and key points from my work were cited. In a later interview, the state’s chief economic advisor said that my model “ought to be the base tool we use to define whether Government plans for economic reopening are viable or not.” At that time, we had upgraded the model to account for how different regulations on specific industries would impact urban mobility and the resulting rate of COVID spread. It was the perfect tool for politicians and bureaucrats, who could show colorful plots to business people and assure them that the Government was using cutting edge science to get the best possible outcomes. And that’s when things started going downhill.
As my model started to be referenced in press conferences and media reports as “the university’s model,” I started feeling uneasy. The number of people involved in translating my science into actions was growing uncontrollably. My work would be whatever they made of it.
Then I had a meeting with the Dean of UdeG’s school of economics (let’s call him Luis). Him and his colleagues came unprepared. There was no work in progress on their end, but they wanted to hear about my work. So I showed them simulations using a fictitious scenario in which business restrictions were gradually lifted over 3 months. Two days later, I found a news article where Luis was shown presenting the simulation results I’d sent him as his own, definite plan to ease economic restrictions in the state.
The point is, and I’ll spare you the details, many people started referencing my analyses to gain favor with others, or in some cases just to be done with their own job. Even though state representatives publicly claimed they would use my science to guide their policy decisions, none of them ever consulted with me directly. The focus was no longer to minimize deaths or maximize well being in the population, but to keep powerful players happy.
I can’t help but feel sorry for the great majority of Government workers, who were dealing with increasingly outrageous demands with little to no leadership or clarity coming from their higher ranks (plus unwillingness to consult with those who could help!) For example, state regulations on economic activities used the exact industry delineations I myself had made to use in my simulations. These definitions used NAICS codes, so they were concise and enforceable, but no one ever asked me for the logic I followed to classify them. I wish I’d known this was going to happen, because I would’ve classified sex shops as an essential service and then let journalists find out. Ultimately, there were hundreds of thousands of business owners whose regulations were largely influenced by a very large spreadsheet I (with no background in economics or public policy) filled while eating breakfast in my underwear thousands of miles away.
Once my science had entered the public discourse, I decided to focus on writing up the model updates we did for COVID to then submit them for publication. I also published an op-ed for El Pais, where Gerardo and I summarized our findings in the context of Mexico’s national policies. The Dean had become increasingly unavailable for me and other members of the UdeG response team, like the political push-pull he was playing had migrated away from science and onto other arenas.
All this may sound like the common disorderly Government response to COVID or almost any crisis. Maybe it is, and I just happened to have a very special seat to see it all unfold. Eventually, the State Congress would vote on a bill seeking a ~$310 million USD loan with uncertain interest rates to fund the economic reopening plan that my work had helped design and justify. On the day of the vote, one of my colleagues in the UdeG response team sent me a video that made this whole mess uniquely personal and profoundly disturbing to me.
It was a video of Representative Mara Robles and her intervention in Congress. She criticized the Government’s shift away from science and exposed ways in which the proposed loan was inconstitutional and nonsensical. As the bill was presented to Congress, acquiring a loan would take funds away from infrastructure projects for: healthcare for the uninsured, culture, sport facilities, child education, and the well being of indigenous people. It would also take away >10 million USD from the UdeG budget.
Representative Robles pointed out that the vast majority of PCR COVID tests in the state of Jalisco were being done free-of-charge by student and staff volunteers from UdeG using university facilities. All this labor, and my epidemiological model (she referred to it as “the university’s predictive model”) had been crucial to keep casecounts low. Now, after the university-government relationship had gone sour and been stained by power struggles, the state government was retaliating.
How this all felt to me was that my science had finally and entirely gotten out of my hands: my analyses had been presented as if they were a comprehensive, institutional plan to reopen the state economy. That plan would then be funded partly by taking away resources from some of the most vulnerable people in the state, and by taking away resources from the university itself. From my perspective, this was a piece of legislation guided by greed, incompetence, and anger: all diametrically opposite to the values that guided my scientific work.
I brought an idea to a gunfight and left terrified. This was my first direct peek into the workings of politics, power, crisis management, and high-stakes public relations. Translating technical insight into actionable plans should not be this difficult. Evidently, the tremendous uncertainty of a rapidly unfolding crisis complicates things, but the greater conflict rose from the profound insecurity and self-centeredness of factions that saw each other as latent enemies at every step until the gloves came off.
This all may resonate with the views that most people have of politicians. I was baffled by the disparity and disconnection between how the foot soldiers of major institutions (UdeG, state government) react to a crisis, and what the actions and motivations of their leaders actually are. If leaders do not dedicate themselves to the greater good, they will tend to hide their motivations. If the head moves without signaling where it goes, the body will not follow.
So here’s the big lesson I learned: only fight fights that you can feel proud for having fought. That’s the only way you will ever fight with all your strength.