🔍 All your models fit your data equally well? Seems like there's nothing you can do? Except there is!. @HMorlon and I tackle this puzzle for phylogenetic birth-death models, and offer a strategy: first, embrace this uncertainty, then shrink it. 🧵 (1/9).

RT @NeelNanda5: I'm very impressed with the Sentinel newsletter: by far the best aggregator of global news I've found. Expert forecasters f….

RT @ignacioqevo: Happy to share our new preprint: `The rise, decline and fall of clades' with @JyAndreoletti, Daniele Silvestro and @HMorlo….

RT @OurWorldInData: Most OECD countries fail to reach the UN’s target for aid to the poorest countries—. In 1981, the major foreign aid don….

RT @kareem_carr: In honor of international women's day, let's take a moment to remember the most famous statistician in history. You've d….

RT @AsimovPress: Indoor air quality has been a problem for most of human history. From Viking longhouses to 19th-century London, the strug….

RT @AsimovPress: Flu pandemics have appeared every 10 to 50 years for millennia. Vaccines have been available for 80 years, but reduce inf….

RT @JesseTayRiver: I've mentioned previously that one of the more dangerous carbon monoxide scenarios is flue pipe disconnect because it do….

RT @Parthion: People sometimes ask me why I am so obsessed with lead poisoning - and sometimes I ask myself that too - but this paper sums….

RT @ARIA_research: Do you have an idea to tackle a critical bottleneck in science and technology that cannot be addressed through current i….

RT @EAForumPosts: "Announcing by Alix Pham (@alix_ph), Swan, Jérémy Andréoletti (@JyAndreoletti), and johantang🔸.

This project was part of the Biosecurity Fundamentals: Pandemics Course by @BlueDotImpact, which I highly recommend for anyone interested in the topic! 🥈It won runner-up in the 'Novel Research' category! (11/11).

- What alternative non-modelling interventions would be even more effective in achieving some of these objectives?. Conclusion: Epidemics are inevitable, but their impact doesn’t have to be! (10/11).

# Open Questions #. - How much time do we really have in different pandemic scenarios before things spiral?. - What’s the success rate for containing highly transmissible outbreaks?. - Which information do policymakers need the most during a pandemic? (9/11).

3. Build between pandemics: We don’t want to be patching models mid-crisis again. 4. Connect modellers and decision-makers: Great models are pointless if they’re not being used by decision-makers! (8/11).

# Key Recommendations #. 1. Focus on preparedness and early response models: Being ready before it’s too late. 2. Focus on catastrophic scenarios: Let’s make sure we can handle the worst, from 'stealth' to 'wildfire' pandemics. (7/11).

# Less Promising Intervention #. 😷 Assessing countermeasure effectiveness in real-time: Handy for long-term response adjustments, but arguably less impactful in the pivotal early stages. (6/11).

🧬 Detecting (stealth) outbreaks early: Early signs can help trigger mitigation measures, but might come too late to stop a fast-spreading pathogen. ⚖️ Forecasting joint health and economic impacts of policies: Aids to balance pandemic response strategies in real-time. (5/11).

# Promising Modelling Interventions #. 🌎 Understanding factors of pathogen (re-)emergence: Useful for prevention, but less relevant to worst-case scenarios?. 📈 Simulating realistic preparedness scenarios: Great for designing and stress-testing response strategies. (4/11).

# Most Promising Modelling Interventions #. 🦠 Assessing pathogen properties: Quickly figuring out characteristics like transmissibility and virulence. 🚨 Estimating the pandemic potential: Raising the alarm when an outbreak is likely to spiral into a full-blown pandemic. (3/11)

Link: (2/11).