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/
Website: https://senpei-cu.github.io/
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...
February 3, 2026 at 6:31 PM
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...
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...
www.nature.com/articles/s44...
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...
www.nature.com
January 28, 2026 at 2:34 PM
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...
www.nature.com/articles/s44...
Reposted by Sen Pei
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...
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...
ESPIDAM, the European Summer Program in Infectious Disease Analysis and Modelling - Stockholms universitet
www.statistics.su.se
January 19, 2026 at 10:05 AM
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...
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...
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!
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!
January 6, 2026 at 7:41 PM
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!
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.
January 6, 2026 at 7:41 PM
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.
The two viruses followed different transmission routes, but shared key spread hubs.
January 6, 2026 at 7:41 PM
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.
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.
Using high-resolution disease data and human mobility, we built an ensemble inference framework that explicitly accounts for stochasticity and superspreading in early outbreaks.
January 6, 2026 at 7:41 PM
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.
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
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
January 6, 2026 at 7:41 PM
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
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!
As we head into 2026, let’s keep climbing with aspiration, resilience, and strength!
December 31, 2025 at 4:36 PM
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!
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.
December 18, 2025 at 4:13 PM
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.
December 18, 2025 at 4:13 PM
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.
December 18, 2025 at 4:13 PM
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...
@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...
December 18, 2025 at 4:13 PM
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...
@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!
December 11, 2025 at 6:21 PM
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...
"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...
The relative contribution of close-proximity contacts, shared classroom exposure and indoor air quality to respiratory virus transmission in schools - Nature Communications
The relative importance of close-proximity interactions, shared space and air quality to the transmission of respiratory viruses is not well understood. Here, the authors investigate this question by ...
www.nature.com
December 10, 2025 at 3:50 PM
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...
"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...
For more information and registration: 👉
events.columbia.edu/cal/event/ev...
December 8, 2025 at 4:31 PM
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...
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/...
November 30, 2025 at 4:15 AM
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 ❄️
November 30, 2025 at 4:12 AM
Safe travels! What a heavy snow ❄️
Sunny and warm in San Diego. Preparing for #EPIDEMICS10 next week!
November 29, 2025 at 4:26 PM
Sunny and warm in San Diego. Preparing for #EPIDEMICS10 next week!
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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Code and examples are available here 👉 github.com/SenPei-CU/AM...
6/
November 19, 2025 at 8:23 PM
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...
6/
Code and examples are available here 👉 github.com/SenPei-CU/AM...
6/
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/
November 19, 2025 at 8:23 PM
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/
November 19, 2025 at 8:23 PM
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/
November 19, 2025 at 8:23 PM
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/