Without gravity, virus-laden particles don't settle. They float indefinitely. Our modeling shows:
286× higher viral concentration in air vs Earth
~78% infection probability in 1 week (nearly 2× Earth)
HEPA filtration cuts airborne virus by 99.79%
Modeling the risk of airborne transmission of respiratory viruses in microgravity
www.nature.com/articles/s41...
Thank you!
🚀 As Artemis II astronauts journey around the Moon right now, our new paper asks a critical question:
What if a respiratory virus spreads inside a spacecraft?
📄 doi.org/10.1038/s415...
#ArtemisII #NASA
Our study on tracking Plasmodium knowlesi through fecal DNA
Our new paper just accepted in The Journal of Infectious Diseases! 🎉
We tracked Plasmodium knowlesi in wild macaque faeces across 9 countries in Southeast & South Asia — 4,752 samples, 8.2% positivity.
Great multinational collaboration led by Dr. Leshan 🦟🐒
🔗: doi.org/10.1093/infd...
Modeling the effectiveness of RT-PCR, RT-LAMP, and antigen testing strategies for COVID-19 control
link.springer.com/article/10.1...
Unraveling the drivers of leptospirosis risk in Thailand using machine learning
Our new study: journals.plos.org/plosntds/art...
Please share!
Amazing opportunity at @mcgill.ca
We are looking to recruit an internationally recognized, interdisciplinary scientist with a strong track record in innovation and research to direct a new program in climate, environment, and health
mcgill.wd3.myworkdayjobs.com/en-US/McGill...
Just few days left to apply to one of these postdoc positions in my infectious disease modelling Unit at @pasteur.fr in Paris!
🚨 Our latest paper is out today in @natcomms.nature.com
Surveillance of avian influenza through bird guano in remote regions of the global south to uncover transmission dynamics
www.nature.com/articles/s41...
🦜💩 Bird poo surveillance reveals global flu hotspots
A new study tracked avian influenza by analysing bird guano in 10 countries. It uncovered high H5N1 diversity, signs of antiviral resistance, & early circulation of strains later found in humans.
🔗 doi.org/10.1038/s414...
#SciComm #BirdFlu 🧪
🚨 Our latest paper is out today in @natcomms.nature.com
Surveillance of avian influenza through bird guano in remote regions of the global south to uncover transmission dynamics
www.nature.com/articles/s41...
🧵 NEW PREPRINT: Our team has developed a machine learning model to predict leptospirosis outbreaks in Thailand by identifying key environmental and socioeconomic risk factors. This could lead to better early warning systems for this neglected tropical disease.
Well done! Thanks @drleshan.bsky.social
Our new report on streptococcal toxic shock syndrome in Japan.
Our new study reveals streptococcus pyogenes persists seasonally in public environments across rural Japan, with peak concentrations in autumn/winter.
Environmental surveillance could be key to predicting outbreaks 👇
doi.org/10.1093/infd...
📝 Read our full preprint for comprehensive insights into leptospirosis risk prediction and the complex interplay of environmental and socioeconomic factors driving outbreaks in Thailand: ⤵️ doi.org/10.1101/2025...
Our approach demonstrates how machine learning can help unravel complex disease drivers when traditional modeling approaches struggle with highly correlated factors and limited data resolution.
😷 We also documented how COVID-19 disrupted leptospirosis surveillance in Thailand, with model performance declining during the pandemic (2020-2021) but recovering in 2022. This suggests significant underreporting during the pandemic years.
🌧️ While previous studies focused heavily on rainfall, our analysis revealed more complex climate interactions. Vapor pressure, maximum temperature, and precipitation during the driest month all influence outbreak patterns in different ways.
👨👩👧👦 Beyond agriculture, larger household size emerged as a critical risk factor, indicating leptospirosis disproportionately affects rural communities. Understanding these socioeconomic dimensions is crucial for targeted interventions.
🌾 Surprisingly, we found that rice production factors were the strongest predictors of leptospirosis risk. Traditional farming practices appear more conducive to disease transmission compared to mechanized methods, highlighting agriculture's role in outbreak dynamics.
🦠 Leptospirosis poses a significant public health challenge in Thailand, with complex transmission patterns influenced by rice farming, climate, and socioeconomic conditions. Our XGBoost model achieved high predictive accuracy (AUC>0.93) in identifying high-risk provinces.
🧵 NEW PREPRINT: Our team has developed a machine learning model to predict leptospirosis outbreaks in Thailand by identifying key environmental and socioeconomic risk factors. This could lead to better early warning systems for this neglected tropical disease.
Unraveling the drivers of leptospirosis risk in Thailand using machine learning www.medrxiv.org/content/10.1101/2025.03....
