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Domenech de Cellès lab

@domenech-lab.bsky.social
214 followers 291 following 41 posts

A research group at @mpiib.bsky.social, led by Matthieu Domenech de Cellès, focused on vaccines, interactions, and the seasonality of infectious diseases. Website: www.mpiib-berlin.mpg.de/1953092/Inf…

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Domenech de Cellès lab @domenech-lab.bsky.social · 05/01/2026
Honored to join the Strategic Advisory Group of Experts (SAGE) Working Group on pertussis vaccines. Together with a dozen other colleagues, we will support the WHO by reviewing scientific evidence and drafting recommendations on pertussis vaccines. www.who.int/groups/strat...
who.int
Pertussis Vaccines
In the light of the resurgence of pertussis and the related increase in infant mortality in some countries a SAGE Working Group on Pertussis Vaccines was established.
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Domenech de Cellès lab @domenech-lab.bsky.social · 04/11/2025
Thank you!
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Domenech de Cellès lab @domenech-lab.bsky.social · 23/10/2025
💡What are the implications? Ans: Social contact structure can affect the impact of PCVs and should be taken into consideration when estimating vaccine impact. Congratulations to Anabelle Wong for publishing her PhD work and a big thank you to co-authors Sarah Kramer and Dan Weinberger! (6/6)
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Domenech de Cellès lab @domenech-lab.bsky.social · 23/10/2025
🔍 We found that varying the social contact matrix alone led to a range of time-to-elimination (3.8-6 years). We further found that such variation was largely explained by the social contact features (total contact rate and assortativity) of children under 5. (5/6)
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Domenech de Cellès lab @domenech-lab.bsky.social · 23/10/2025
👩🏻‍💻 We developed a compartmental transmission model to investigate the effect of social contact structure on the impact of PCVs (i.e., how fast PCVs eliminate vaccine-targeted serotypes). (4/6)
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Domenech de Cellès lab @domenech-lab.bsky.social · 23/10/2025
❓Many population factors can influence the dynamics of VT elimination, for example, social behaviours. So we asked, what is the effect of social contact structure on the PCV-induced VT elimination dynamics? (3/6)
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Domenech de Cellès lab @domenech-lab.bsky.social · 23/10/2025
🦠Pneumococcus is highly diverse but pneumococcal vaccines (PCVs) only target a fraction of the many serotypes. As PCVs reduced carriage of vaccine-targeted serotypes (VT), more carriage of non-vaccine-targeted serotypes (NVT) were observed — a phenomenon known as serotype replacement. (2/6)
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Domenech de Cellès lab @domenech-lab.bsky.social · 23/10/2025
🚨New paper alert: Sharing another latest study from our lab, "Assessing the effect of social contact structure on the impact of pneumococcal conjugate vaccines." nature.com/articles/s41... Read the 🧵to find out more! (1/6)
nature.com
Assessing the effect of social contact structure on the impact of pneumococcal conjugate vaccines - Scientific Reports
Scientific Reports - Assessing the effect of social contact structure on the impact of pneumococcal conjugate vaccines
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Domenech de Cellès lab @domenech-lab.bsky.social · 23/10/2025
A big thank you to co-authors Pej Rohani, Tine Dalby, and Anabelle Wong! (6/6)
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Domenech de Cellès lab @domenech-lab.bsky.social · 23/10/2025
💡What is the implication? True infection burden is probably somewhere between reported case number and seropositivity-based estimates. Mathematical models integrating both data streams may get us a better estimate in the future! (5/6)
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Domenech de Cellès lab @domenech-lab.bsky.social · 23/10/2025
🧑🏻‍💻 By fitting a methematical model to data from serosurveys during the whole-cell pertussis vaccine era, we found that the postive predictive value (PPV) of using seropositivity to estimate transmissible infections is low, esp. in young adults (20-39y) where PPV was <50%. (4/6)
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Domenech de Cellès lab @domenech-lab.bsky.social · 23/10/2025
📈 While seroprevalence data can aid estimation of the circulating pertussis infection burden, ignoring natural immune boosting would lead to an overestimation of cases and underestimation of vaccine impact. (3/6)
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Domenech de Cellès lab @domenech-lab.bsky.social · 23/10/2025
🩸Natural immune boosting occurs when pathogen triggers a detectable immune response in the (vaccinated or previously infected and recovered) host without causing a transmissible infection. (2/6)
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Domenech de Cellès lab @domenech-lab.bsky.social · 23/10/2025
Happy to share our @natcomms.nature.com paper 📢 “Natural immune boosting biases pertussis infection estimates in seroprevalence studies.” nature.com/articles/s41... Read the🧵 to find out more! (1/6)
nature.com
Natural immune boosting biases pertussis infection estimates in seroprevalence studies - Nature Communications
Estimating rates of pertussis infections is challenging due to the large proportion of asymptomatic infections and lack of a reliable serological correlate of protection. Here, the authors develop a t...
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Domenech de Cellès lab @domenech-lab.bsky.social · 17/07/2025
Massive congratulations to the new Dr. Laura Barrero Guevara (@labarreroguevara.bsky.social), who successfully defended her PhD on causal inference and infectious diseases yesterday, with summa cum laude!! Check out her work here rdcu.be/ewCNj and here doi.org/10.1093/infd... 🥳🎉
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Domenech de Cellès lab @domenech-lab.bsky.social · 10/12/2024
(10/10) Big thank you to our co-authors! Laura Barrero Guevara, Sarah Kramer and Tobias Kurth!
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Domenech de Cellès lab @domenech-lab.bsky.social · 10/12/2024
(9/10) Integrating #causalinference concepts with transmission models is necessary for inferring the effect of weather on infectious diseases and subsequently predicting the consequences of climate change on infectious diseases. Check out the paper here: www.nature.com/articles/s41... 🥳
nature.com
Causal inference concepts can guide research into the effects of climate on infectious diseases - Nature Ecology & Evolution
A series of case studies is used to illustrate how concepts from causal interference can be used to guide research into the effects of weather on the transmission and population dynamics of infectious...
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Domenech de Cellès lab @domenech-lab.bsky.social · 10/12/2024
(8/10) Fourth vignette: causal inference concepts can help to interpret the direct and indirect effects of weather on transmission. For example, temperature can affect transmission directly and indirectly (through humidity), and these effects vary by local climate.
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Domenech de Cellès lab @domenech-lab.bsky.social · 10/12/2024
(7/10) Third vignette: causal inference helps identify and avoid confounding bias. Gradients in climate across locations can masquerade as spatial spread of disease.
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Domenech de Cellès lab @domenech-lab.bsky.social · 10/12/2024
(6/10) Second vignette: causal inference can inform strategic choices of a study location to achieve the set-up of a natural experiment. By comparing temperate and tropical climates, we highlight how local conditions can help isolate the causal weather variable.
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Domenech de Cellès lab @domenech-lab.bsky.social · 10/12/2024
(5/10) First vignette: causal inference concepts can guide study design. Considering the complex causal paths between weather, transmission, and incidence, we show that measurement bias is a concern for time-series regression studies linking weather and incidence.
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Domenech de Cellès lab @domenech-lab.bsky.social · 10/12/2024
(4/10) Our new paper shows how applying causal inference concepts can help. We illustrate this with four short case studies based on our causal graph #dag ⬇️ linking weather, disease transmission, and reported cases.
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Domenech de Cellès lab @domenech-lab.bsky.social · 10/12/2024
(3/10) In practice, this often means using observational data—case counts and weather variables. Yet, interpreting such data can be challenging, as associations do not necessarily imply true causal effects.
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Domenech de Cellès lab @domenech-lab.bsky.social · 10/12/2024
(2/10) A key question arising from climate change is how it will impact the transmission of infectious diseases. Predicting these effects demands understanding how weather affects their transmission dynamics.
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Domenech de Cellès lab @domenech-lab.bsky.social · 10/12/2024
How does weather affect the transmission of #infectiousdiseases, and how can we predict the effects of #climatechange on them? Our new article in @natureportfolio.bsky.social Ecology & Evolution explores these questions using #causalinference and #transmissionmodels. See the 🧵for more! (1/10)
nature.com
Causal inference concepts can guide research into the effects of climate on infectious diseases - Nature Ecology & Evolution
A series of case studies is used to illustrate how concepts from causal interference can be used to guide research into the effects of weather on the transmission and population dynamics of infectious...
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Domenech de Cellès lab @domenech-lab.bsky.social · 27/11/2024
(7/7) Our study provides one of the first estimates of the strength and duration of the interaction between flu and RSV. We show how #mathematicalmodels can be vital to understanding virus-virus interactions. Check out the full paper here: www.nature.com/articles/s41...!
nature.com
Characterizing the interactions between influenza and respiratory syncytial viruses and their implications for epidemic control - Nature Communications
Influenza viruses and respiratory syncytial viruses may interfere with one another. Here, authors fit mathematical models of virus transmission, and find evidence of a bidirectional, moderate to stron...
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Domenech de Cellès lab @domenech-lab.bsky.social · 27/11/2024
(6/7) We also used our model to explore the potential for using live influenza #vaccines to control RSV outbreaks. We found that the effectiveness of this strategy is likely to depend on the size and timing of flu and RSV outbreaks in a given location.
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Domenech de Cellès lab @domenech-lab.bsky.social · 27/11/2024
(5/7) We found evidence of a moderate to strong, negative interaction between flu and RSV – being infected with either virus may provide protection against infection with the other. Our results also suggest this protection could last for anywhere from 1 to 5 months.
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Domenech de Cellès lab @domenech-lab.bsky.social · 27/11/2024
(4/7) Here, we used a mathematical model of #flu and #RSV cocirculation to estimate the strength and duration of the interaction between the two viruses. Specifically, we fitted our model to flu and RSV data from Hong Kong and Canada.
A schematic of a two-pathogen interacting transmission model.
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Domenech de Cellès lab @domenech-lab.bsky.social · 27/11/2024
(3/7) However, mathematical models can explicitly account for these complex and random processes.
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Domenech de Cellès lab @domenech-lab.bsky.social · 27/11/2024
(2/7) Characterizing interactions between viruses is surprisingly difficult. Many statistical methods fail when faced with data on infectious disease transmission, a complex and partly random process.
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Domenech de Cellès lab @domenech-lab.bsky.social · 27/11/2024
New paper alert!! Can infection with one virus protect against another? What does this mean for virus control? In our new paper in @NatureComms, we use #mathematicalmodelling to explore this for #influenza and #RSV: www.nature.com/articles/s41.... See the 🧵 for details! (1/7)
nature.com
Characterizing the interactions between influenza and respiratory syncytial viruses and their implications for epidemic control - Nature Communications
Influenza viruses and respiratory syncytial viruses may interfere with one another. Here, authors fit mathematical models of virus transmission, and find evidence of a bidirectional, moderate to stron...
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Domenech de Cellès lab @domenech-lab.bsky.social · 22/11/2024
(8/8) The optimal age for measles vaccination varies by population. Our method offers a way to tailor vaccination timing, potentially reducing measles cases. Check out the paper here: rdcu.be/d0ktY!
rdcu.be
Estimating the optimal age for infant measles vaccination
Nature Communications - Measles remains a significant public health concern despite the availability of a vaccine. Here, the authors use mathematical modelling to assess the optimal age group for...
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Domenech de Cellès lab @domenech-lab.bsky.social · 22/11/2024
(7/8) The social contact structure affects the optimal age: Which age groups socialize with which age groups substantially impacted the optimal ages, shifting the optimal age by up to 7 months.
The heat map shows the optimal ages for different social contact matrices. The optimal age differs between social contact matrices.
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Domenech de Cellès lab @domenech-lab.bsky.social · 22/11/2024
(6/8) Increased vaccination coverage leads to increased optimal ages: Increased vaccination leads to reduced transmission, reducing the risk of catching measles before getting vaccinated. A 10 % point increase in vaccine coverage increased the optimal age by 0.6 months.
The heat map shows the optimal ages for different vaccine coverage percentages. The optimal age increases with vaccine coverage.
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Domenech de Cellès lab @domenech-lab.bsky.social · 22/11/2024
(5/8) Increased transmission leads to decreased optimal ages: Increased transmission increases the risk of getting infected before vaccination, shifting the minimal overall risks to younger ages. Moving from low to high transmission decreased the optimal age by 3.7 months.
The heat map shows the ages of optima for different transmission levels. The optimal age decreases as the transmission level increases.
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Domenech de Cellès lab @domenech-lab.bsky.social · 22/11/2024
(4/8) Finding the optimal ages: We applied the method in various synthetic populations with varying characteristics that might affect the optimal age. We then identified the influential factors: the transmission level, vaccination coverage, and social contact structure.
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Domenech de Cellès lab @domenech-lab.bsky.social · 22/11/2024
(3/8) Developing a method: The optimal age to recommend measles #vaccination should minimize the combination of these risks, resulting in the fewest possible measles cases. Here, we develop a method using mathematical modeling to calculate this optimal age.
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Domenech de Cellès lab @domenech-lab.bsky.social · 22/11/2024
(2/8) Timing is crucial: The later a child is vaccinated, the more likely the #vaccine will protect them against measles, but this risks the child getting measles before being vaccinated.
Schematic showing the trade-off between the risk of vaccine failure and the risk of infection before vaccination, along with the combined risk of measles infection.
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Domenech de Cellès lab @domenech-lab.bsky.social · 22/11/2024
When should children get vaccinated against #measles? Is there an optimal age? If so, what affects this optimal age? In our new #NatureComms paper, led by @egoult.bsky.social, we explore these questions using #mathematicalmodeling: rdcu.be/d0ktY. Read the 🧵 for a summary of our findings 👇 (1/8)
rdcu.be
Estimating the optimal age for infant measles vaccination
Nature Communications - Measles remains a significant public health concern despite the availability of a vaccine. Here, the authors use mathematical modelling to assess the optimal age group for...
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Domenech de Cellès lab @domenech-lab.bsky.social · 22/11/2024
👋 Hello! This is the Bluesky of the Domenech de Cellès lab, focused on infectious disease epidemiology📉🦠 Can't wait to meet the community here!
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