Charin Modchang

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...

Unraveling the drivers of leptospirosis risk in Thailand using machine learning The research team built an artificial intelligence model using a technique called XGBoost to analyze 16 years of disease surveillance data alongside information about rainfall, temperature, rice farmi...

link.growkudos.com/1e4i5naqx34

Modeling the effectiveness of RT-PCR, RT-LAMP, and antigen testing strategies for COVID-19 control - BMC Infectious Diseases The COVID-19 pandemic has highlighted the crucial role of testing in mitigating disease transmission. This study evaluates the effectiveness and cost-efficiency of various testing strategies, includin...

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 Author summary Leptospirosis, a disease caused by Leptospira bacteria, poses a significant public health challenge in Thailand. The bacteria thrive in contaminated environments, particularly those ass...

Now published: journals.plos.org/plosntds/art...

Unraveling the drivers of leptospirosis risk in Thailand using machine learning Author summary Leptospirosis, a disease caused by Leptospira bacteria, poses a significant public health challenge in Thailand. The bacteria thrive in contaminated environments, particularly those ass...

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!

Surveillance of avian influenza through bird guano in remote regions of the global south to uncover transmission dynamics - Nature Communications Highly pathogenic avian influenza is an increasing global concern but its distribution in remote regions is not known. Here, the authors conduct an environmental influenza surveillance study in remote...

🚨 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...

Surveillance of avian influenza through bird guano in remote regions of the global south to uncover transmission dynamics - Nature Communications Highly pathogenic avian influenza is an increasing global concern but its distribution in remote regions is not known. Here, the authors conduct an environmental influenza surveillance study in remote...

🦜💩 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 🧪

Surveillance of avian influenza through bird guano in remote regions of the global south to uncover transmission dynamics - Nature Communications Highly pathogenic avian influenza is an increasing global concern but its distribution in remote regions is not known. Here, the authors conduct an environmental influenza surveillance study in remote...

🚨 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.

Streptococcus pyogenes surveillance through surface swab samples to track the emergence of streptococcal toxic shock syndrome in rural Japan Japan recently experienced a record surge in streptococcal toxic shock syndrome (STSS). Our environmental surveillance study shows seasonal S. pyogenes pea

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...

Unraveling the drivers of leptospirosis risk in Thailand using machine learning

📝 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 https://www.medrxiv.org/content/10.1101/2025.03.19.25324284v1

Community-based mpox and sexually transmitted disease surveillance using discarded condoms in the global south As COVID-19 restrictions have eased, the resumption of international travel and sexual interactions at large gatherings have fueled the continued spread of human mpox beyond its endemic regions.1–3 Th...

Community surveillance of discarded condoms might help track the mpox transmission: www.thelancet.com/journals/lan...

#mpox remains a public health emergency of international concern

The announcement follows the third meeting of the IHR Emergency Committee regarding the upsurge of mpox.

The Director-General concurred with the Committee’s advice.

More: bit.ly/4bgz4p3

Community-based mpox and sexually transmitted disease surveillance using discarded condoms in the global south As COVID-19 restrictions have eased, the resumption of international travel and sexual interactions at large gatherings have fueled the continued spread of human mpox beyond its endemic regions.1–3 Th...

Community surveillance of discarded condoms might help track the mpox transmission: www.thelancet.com/journals/lan...

Artificial intelligence for modelling infectious disease epidemics Nature - This Perspective considers the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science.

AI is poised to accelerate understanding in infectious diseases, but its value needs to be demonstrated through close collaboration between research, industry, society, and policy.

Paper free to read: rdcu.be/eaxEw

Summary here: www.ox.ac.uk/news/2025-02...

Why epidemic assumptions can matter so much…

An ongoing scientific question about the early dynamics of COVID is precisely when the first wave peaked in the UK – when did infections themselves actually start declining?