Domenech de Cellès lab
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domenech-lab.bsky.social
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
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... 🥳🎉
July 17, 2025 at 10:10 AM
(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
(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
(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
(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
(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
(4/7) 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.
November 27, 2024 at 5:02 PM
(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.
November 22, 2024 at 2:52 PM
(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.
November 22, 2024 at 2:52 PM
(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.
November 22, 2024 at 2:52 PM
(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.
November 22, 2024 at 2:52 PM