Sen Pei

Sen Pei

@senpei.bsky.social

Asst Prof @ColumbiaMSPH. A mix of Infectious Diseases, Environmental Health, Network Science & Complex Systems. Views are my own. Website: https://senpei-cu.github.io/

1.9K followers547 following127 posts

Welcome to join the Columbia Mailman Seminar Series on Infectious Disease Modeling, featuring Prof. Mauricio Santillana from Northeastern University & Harvard on 10/7 Wed at 12 pm ET! This is a hybrid event with both in-person and Zoom options, open to the public. Please register using the QR code!

πŸŒͺ️ We’re launching the Hurricane Mobility Tracker, an interactive dashboard showing how population movement changes before, during, and after hurricanes, using cellphone-derived foot-traffic data. Check it out and share with anyone who may be interested! πŸ”— hurricane-mobility.fly.dev

I’m delighted to be an invited speaker at the ARCANE Workshop on Modeling Antimicrobial Resistance, October 19-21, 2026 in Paris. The call for abstracts is now open! Submit your abstract here: arcane.sciencesconf.org Thanks to the organizers. Look forward to seeing many colleagues in Paris!

Congratulations to Dr. Han Yong Wunrow on successfully defending his PhD in Applied Math at Columbia APAM! Han Yong is the first PhD student I co-supervised. His research advances data-driven ID modeling using data assimilation and AI. Excited for his next chapter at Google!

Congratulations to Fintan Mooney, our brilliant doctoral student at Columbia EHS, for passing his qualification exam today! Fin is passionate about research on extreme weathers and population health. We look forward to his impactful works in this space!

Great weather in Boston. Excited to be at NetSci Conference 2026 this week! Our group has two presentations - welcome to stop by and chat. Looking forward to the fantastic science near the Charles River!

Really enjoyed presenting at the Columbia Global Research Stabilization Fund Spring Showcase today. RSF’s support last year was critical in keeping our epidemic forecasting work moving. Inspired by the breadth of research and looking forward to what comes next!

Sen Pei reposted

It was a great pleasure to host a visit by @theferrarilab.bsky.social ! Super interesting talk and tons of impactful works on measles and rubella.

The Ferrari Lab@theferrarilab.bsky.social

Got to give a talk today at Mailman School of Public Health with a view of the Hudson River* in the background. Thanks to @senpei.bsky.social for hosting me! *I grew up "up the river" in the shadow of Sing Sing Prison.

Honored to be elected as a Steering Committee Member of the MIDAS network @midas-network.bsky.social Grateful to give back to a community that has shaped my career, and looking forward to supporting early-career investigators and strengthening engagement with international scholars.

Sen Pei reposted

Two postdoc positions open at SUMOC (Sorbonne UniversitΓ© | INSERM), Paris, within the EPIcx lab. 🦠 Network epidemiology of healthcare-associated infections (ARCANE) 🧠 Coupled behavior–disease modeling (PREVIX) 2-y positions | Start June 2026 #epidemiology #networks #matrices

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...

nature.comFrequent presymptomatic household transmission of influenza A but not influenza B virus - Nature HealthBased 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...

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. LinkπŸ‘‰https://www.pnas.org/doi/10.1073/pnas.2518051123 #PandemicPreparedness #InfectiousDisease #HumanMobility

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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!

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. LinkπŸ‘‰https://www.pnas.org/doi/10.1073/pnas.2518051123 #PandemicPreparedness #InfectiousDisease #HumanMobility

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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.

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.

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. LinkπŸ‘‰https://www.pnas.org/doi/10.1073/pnas.2518051123 #PandemicPreparedness #InfectiousDisease #HumanMobility

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.

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!

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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.

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.

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.

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!

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

nature.comPublic healthOur recent highlights in the field of public health, including epidemiological studies investigating the social, environmental, genetic, and behavioural ...
Sen Pei@senpei.bsky.social

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 πŸ‘‰ rdcu.be/eQKKF 1/

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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 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/

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/

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/

Excellent work on the ecology and spread of H5N1 in North America πŸ‘‰

Louise H. Moncla@lhmoncla.bsky.social

Our lab's paper describing the North American H5N1 epizootic is out now in Nature! So thrilled to have this out, and congratulations to @lambod50.bsky.social for all the fantastic work on this: www.nature.com/articles/s41...

Fantastic talk by Dr. Holden Thorp, Editor-in-Chief of Science, on the future of scientific research. Especially inspiring to hear how many scientific giants pushed forward even when others tried to stop them. In times of uncertainty and challenge, do the work.

Our group is presenting at the MIDAS Network Annual Meeting! I will present on reconstructing the early spatial spread of pandemics in the US. Dr. Qing Yao will discuss the use of GNN to predict adaptive mobility for improving epidemic forecasts. Welcome to join us! @midas-network.bsky.social