Sen Pei

Welcome to join Columbia Mailman seminar series on infectious disease modeling featuring Prof. Virginia Pitzer from Yale on 2/10 Tue at 12 pm EST! Open to the public over Zoom. For more information and registration: events.columbia.edu/cal/event/ev...

Frequent presymptomatic household transmission of influenza A but not influenza B virus - Nature Health Based on two waves of data collection from 748 households in Hong Kong, this analysis sheds light on the number of transmission events that occurred before manifestation of symptoms in influenza A and...

Presymptomatic transmission is a key determinant of the controllability of respiratory viruses. This nice study used household data to quantify presymptomatic transmission of influenza and Omicron. Important findings with implications for modeling and control.

www.nature.com/articles/s44...

Dont miss the ESPIDAM summer program in June 2026 in Stockholm, covering many key concepts for ID modelling:
stochastic models, AI for ID control, nowcasting and forecasting, phylodynamics, data analysis, network models, within-host models, health economics www.statistics.su.se/english/divi...

Here is the link to the paper:

doi.org/10.1073/pnas...

Takeaway: Pandemic respiratory viruses spread fast and stochastically, often before we can clearly detect them.
Preparedness needs to plan for uncertainty, and surveillance must be broad, not just focused on a few major hubs.

Grateful to an amazing group of collaborators across institutions!

We also ran simulations for future pandemics. Results suggest that wastewater surveillance limited to a few major hubs isn’t enough - broader coverage is needed to meaningfully slow early geographic spread.

Main finding: both pandemics spread to most US metro areas within weeks, leaving a very narrow window for early detection and containment.

The two viruses followed different transmission routes, but shared key spread hubs.

Key question: How fast did the last two pandemics spread in the US? Did they follow the same spatial transmission routes?

Using high-resolution disease data and human mobility, we built an ensemble inference framework that explicitly accounts for stochasticity and superspreading in early outbreaks.

Excited to share a new study published in PNAS! @pnas.org

We reconstructed the early, cryptic spatial spread of 2009 H1N1 influenza and SARS-CoV-2 across US metropolitan areas.

Link👉https://www.pnas.org/doi/10.1073/pnas.2518051123

#PandemicPreparedness #InfectiousDisease #HumanMobility

2025 was a challenging year for many of us. As the year comes to a close, let's pause to recognize and celebrate every accomplishment and milestone, big or small. Each step forward matters.

As we head into 2026, let’s keep climbing with aspiration, resilience, and strength!

This approach helps address common issues like filter divergence and underestimation of uncertainty in data assimilation. More importantly, we can reconstruct epidemic curves with time-varying Rt using forward simulations, which are essential for running counterfactual analyses.

Our results show that ensemble filter/smoother methods with adaptive inflation give more accurate and robust Rt estimates, especially around sudden changes in transmission dynamics.

Accurately estimating Rt and its uncertainty is central to understanding infectious disease dynamics and informing public health decisions. We systematically evaluated multiple data assimilation methods for estimating Rt using both synthetic epidemic simulations and real COVID-19 case data.

Glad to share our latest study, led by Han Yong Wunrow
@hwunrow.bsky.social, on estimating time-varying reproduction numbers (Rt) using data assimilation methods, now published in the Journal of the Royal Society Interface!

Link: royalsocietypublishing.org/rsif/article...

I am teaching Introduction to Network Science for a third year at Columbia Mailman! @cupublichealth.bsky.social Very grateful to have positive evaluations from students with diverse backgrounds. Welcome to join this small-size, engaged course if you are interested in networks and systems thinking!

Very interesting study on respiratory virus transmission in schools!

"Prolonged exposure in shared, poorly ventilated spaces, which potentially includes several infectious sources, drives respiratory virus transmission more than close contact."

www.nature.com/articles/s41...

Welcome to join Columbia Mailman @cupublichealth.bsky.social seminar series on infectious disease modeling featuring Prof. Mark Jit @markjit.bsky.social from NYU on 12/16 Tue at 12 pm EST! Open to the public over Zoom.

For more information and registration: 👉
events.columbia.edu/cal/event/ev...

Grateful to @natcomms.nature.com for featuring our AMRO inference study as one of the Editors’ Highlights in #PublicHealth: www.nature.com/collections/...

Safe travels! What a heavy snow ❄️

Sunny and warm in San Diego. Preparing for #EPIDEMICS10 next week!

Scientists Use Computer Model to Improve Hospitals’ Ability to Limit Spread of Drug-Resistant Infections A new analytical tool can improve a hospital’s ability to limit the spread of antibiotic-resistant infections compared to traditional methods like contact tracing.

Read a story from Mailman on this study: www.publichealth.columbia.edu/news/scienti...

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Huge thanks to my collaborators and co-authors for their incredible work on this project!

Code and examples are available here 👉 github.com/SenPei-CU/AM...

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We found that even limited sequence data can meaningfully improve carrier inference when integrated with other data. By linking patient mobility, genomics, EHR, and culture data, we move closer to spotting the silent spreaders of AMROs in hospitals and intervening more strategically. 5/

The inference framework was validated using both simulated outbreaks and real-world data on carbapenem-resistant Klebsiella pneumoniae in a large hospital. Inference with multiple data streams can better identify carriers and inform more effective target interventions. 4/

We then built an inference framework that combines patient movement, clinical cultures, whole-genome sequencing, and risk factors in electronic health records to estimate who is likely colonized. 3/

Asymptomatic carriers drive silent transmission of AMROs in hospitals, but they’re hard to detect due to sparse observations. We developed an agent-based model informed by real-world time-varying contact networks in hospitals to simulate AMRO transmission and community importation. 2/

Sharing our new study on identifying asymptomatic carriers of antimicrobial-resistant organisms (AMROs) in hospitals, out in @natcomms.nature.com. We combine patient mobility, clinical cultures, EHR, and genomics to identify hidden AMRO carriers. #AMR #HAI #HospitalEpi

Link 👉 rdcu.be/eQKKF

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A super interesting study on how ants alter their nest networks to prevent epidemics! Network topology metrics were used to measure the effect on disease transmission.

www.science.org/doi/full/10....

Excellent work on the ecology and spread of H5N1 in North America 👉

Fantastic work! Congratulations!