There is no good reason for mosquitoes to exist in wealthy countries today. New technologies can get rid of the mosquito species that cause diseases like dengue, yellow fever, and malaria; and others can prevent them from carrying those diseases. worksinprogress.co/issue/mos… So why do mosquitoes still exist? In a new article for Works in Progress, Maya Rosen explains how state incapacity and risk aversion led to over a decade of delays and billions of unnecessary bites in the United States. Oxitec, for example, engineered an Aedes aegypti mosquito in such a way that its female offspring would die before adulthood. Through sustained releases, they showed they could reduce local mosquito populations by 80% or more, in field trials. They sought approval from the USDA in 2010, which then passed it to the FDA, which passed it to the EPA. In 2020, the EPA allowed them to conduct limited field trials in the Florida Keys. Two years ago, their experimental permit expired, and they still need another review before it can be used routinely. Their proposed field trial in California was withdrawn after public opposition. Another company, MosquitoMate, developed a technique involving Wolbachia, which also suppresses local mosquitoes. They then spent over fifteen years working through the system before finally receiving commercial registration nationwide in 2024. It seems that, sometimes, the barrier to getting rid of diseases isn’t technology at all. It’s policy and political will. New products shouldn’t get stuck in regulatory review for so long, especially if the delays are about basic questions like which agency has oversight over the product, rather than testing for safety. Though some of those uncertainties were being addressed, the broader changes haven’t materialized. After Biden’s 2022 executive order, the agencies planned to update how their coordinated framework was implemented. But Trump rescinded the order last year. Meanwhile, the EPA unit that reviews new mosquito control products is still a small office backlogged in reviewing a wide range of products. Mosquitoes are a policy choice. We can get rid of them. Will we choose to? worksinprogress.co/issue/mos…

1. I love the idea of curing most (or all?) diseases. I just wrote an article about it worksinprogress.news/p/futur… and gave a TED talk about it too! ted.com/talks/saloni_dattani… We should be a lot more ambitious with medical innovation, and the history of science makes me more, not less, optimistic about what’s possible. Many diseases we once thought were untreatable turned out not to be. 2. But what’s possible isn’t the same as what’s going to happen, and it’s very different from claiming it’s possible within a timeline of 10 years. Even if we grant exponential improvement in our technological capabilities, we still wouldn’t be able to cure most diseases within ten years, because technology is just one part of the equation. One clear illustration of this is Eroom’s law, where the cost of drug development has risen despite massive technological advancement. 3. In our episode ‘Will AI solve medicine?’ (harddrugs.worksinprogress.co…) Jacob Trefethen and I talk about how funding, institutions, incentives, better measurement, and a lack of data mean that we’ll still have massive gaps in solving diseases despite progress in AI. These problems fall most heavily on neglected diseases, like rare and tropical diseases, where funding is often the barrier to progress. But they also affect diseases whose clinical trials are long and onerous, disease progression itself is slow, or poorly measured. 4. All this means that, if we want to speed up medical innovation and scale it up worldwide, we should also be unblocking the other barriers to medical progress with clinical trial reform, better data collection, better measurement instruments, better funding models, and more. (See our ‘Clinical Trials Abundance blog’ for some ideas clinicaltrialsabundance.blog…) It’s going to take a while before we solve these to the point where we can cure most diseases! 5. I thought the policy proposals in ‘Policy on the AI Exponential’ relating to drug development were quite vague and too brief to be useful. But even if they were all implemented ideally, we would still be lacking cures for diseases for which financing or clinical trial infrastructure are the barrier, or where we’d need entirely new drug modalities to reach targets safely. FWIW, I’m fairly skeptical of the use of ‘synthetic control arms’ if we actually want reliably effective medicines, not just faster clinical trials. But platform trials, which include shared control groups that many drugs are tested against, probably get at the intention. 6. I’m very skeptical of the idea that “many of the steps in the clinical process that previously required expensive and slow experiments may soon be done via AI simulation or analysis” ... but maybe this depends on one’s definition of ‘many’. Human biology is extremely complicated and interactions between drugs and molecules in our body are very hard to predict, in part because of the number of combinations and biological states, and the level of measurement that’s possible in the lab; the way we get around this is through experimental data collection, i.e. clinical trials, but for some diseases, progression is very slow, so we’re not going to be able to collect that amount of data in time, even with perfect measurement. Unfortunately public health datasets typically don’t include much drug-human body data, with the exception of, e.g., prescription datasets, which would be somewhat useful for analyzing historical drugs. Getting pharma companies to pool sufficient individual participant data from clinical trials to improve these predictions, though, that would be exciting. 7. I sometimes worry about the deluge of AI ‘slop’ science that’s going to enter the academic literature. We already needed to reform peer review, catch scientific fraud, improve data quality and rigour before AI (see e.g. my articles ‘Real peer review has never been tried’ worksinprogress.co/issue/rea… and ‘The speed of science’ worksinprogress.co/issue/the…), but now it seems like science reform is even more important. I could imagine a Pangram-like model for AI-written science papers, but that seems like it merely catches the tip of the iceberg of how deep the slop might go, if it involves unpublished data, for example. If AI use increases exponentially, we might end up seeing an AI science slop tsunami, which could affect predictions based on the literature. What’s the solution to this?

BIG NEW piece by me. A few years ago, if you asked me to summarize my entire worldview in one article, I would’ve said it was too broad and too difficult. But this year, I decided to do it. worksinprogress.co/issue/fut… The central drama of the modern world is innovation against suffering, and we have made incredible progress so far. But we are doing far worse than we could. Millions still suffer from untreatable diseases, because of bottlenecks that we could fix. You can help fix them.

The timeline to develop covid vaccines blew most people’s predictions out of the water. But not everyone’s. In mid-2020, I wrote a forecast on why I believed they would most likely arrive within a year. 6 years on, I’ve written detailed retrospective clinicaltrialsabundance.blog… on what happened. Why were Covid vaccine trials so fast? To begin with, the virus was relatively easier to develop vaccines for, lots of research had already taken place, and a fast-growing epidemic meant trials reached their endpoints sooner. Beyond that, thousands of people around the world – including scientists, operators, policymakers, regulators, economists and government officials – worked differently to make sure vaccines became available faster. This was a coordinated effort that involved dedicated research funding, clinical trial networks, parallel trial phases, rolling regulatory review, and parts of the review being deferred rather than skipped. What eased the process were decisions to de-risk the process financially - through Operation Warp Speed and its analogs in other countries - with upfront funding and advance commitments to buy the vaccines if they worked. The speed of coronavirus vaccine trials also reveals how slow the usual system is. If clinical trials can be sped up so much, we should find ways to reform them outside of pandemics as well. We shouldn’t be dragging our feet for the sake of following the status quo. Every year of delays in testing is a year patients go without potentially lifesaving treatment, risking their lives. Rather than treating pandemic trials as exceptional, we should ask whether that speed should have been the norm all along. You can read my full post here: clinicaltrialsabundance.blog…