Domenech de Cellès lab
@domenech-lab.bsky.social
A research group at @mpiib.bsky.social, led by Matthieu Domenech de Cellès, focused on vaccines, interactions, and the seasonality of infectious diseases.
Website: https://www.mpiib-berlin.mpg.de/1953092/Infectious-Disease-Epidemiology
Website: https://www.mpiib-berlin.mpg.de/1953092/Infectious-Disease-Epidemiology
💡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)
Congratulations to Anabelle Wong for publishing her PhD work and a big thank you to co-authors Sarah Kramer and Dan Weinberger! (6/6)
October 23, 2025 at 4:26 PM
💡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)
Congratulations to Anabelle Wong for publishing her PhD work and a big thank you to co-authors Sarah Kramer and Dan Weinberger! (6/6)
🔍 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)
October 23, 2025 at 4:26 PM
🔍 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)
👩🏻💻 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)
October 23, 2025 at 4:26 PM
👩🏻💻 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)
❓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)
October 23, 2025 at 4:26 PM
❓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)
🦠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)
October 23, 2025 at 4:26 PM
🦠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)
A big thank you to co-authors Pej Rohani, Tine Dalby, and Anabelle Wong! (6/6)
October 23, 2025 at 4:19 PM
A big thank you to co-authors Pej Rohani, Tine Dalby, and Anabelle Wong! (6/6)
💡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)
October 23, 2025 at 4:19 PM
💡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)
🧑🏻💻 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)
October 23, 2025 at 4:19 PM
🧑🏻💻 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)
📈 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)
October 23, 2025 at 4:19 PM
📈 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)
🩸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)
October 23, 2025 at 4:19 PM
🩸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)
(10/10) Big thank you to our co-authors! Laura Barrero Guevara, Sarah Kramer and Tobias Kurth!
December 10, 2024 at 2:35 PM
(10/10) Big thank you to our co-authors! Laura Barrero Guevara, Sarah Kramer and Tobias Kurth!
(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... 🥳
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...
www.nature.com
December 10, 2024 at 2:35 PM
(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... 🥳
(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.
December 10, 2024 at 2:35 PM
(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.
(7/10) Third vignette: causal inference helps identify and avoid confounding bias. Gradients in climate across locations can masquerade as spatial spread of disease.
December 10, 2024 at 2:35 PM
(7/10) Third vignette: causal inference helps identify and avoid confounding bias. Gradients in climate across locations can masquerade as spatial spread of disease.
(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.
December 10, 2024 at 2:35 PM
(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.
(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.
December 10, 2024 at 2:35 PM
(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.
(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.
December 10, 2024 at 2:35 PM
(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.
(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.
December 10, 2024 at 2:35 PM
(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.
(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.
December 10, 2024 at 2:35 PM
(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.
(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...!
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...
www.nature.com
November 27, 2024 at 5:02 PM
(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...!