Domenech de Cellès lab

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... 🥳🎉

(10/10) Big thank you to our co-authors! Laura Barrero Guevara, Sarah Kramer and Tobias Kurth!

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...

(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.

(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.

(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.

(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.

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...

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)

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...

(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...!

(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.

(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.

A schematic of a two-pathogen interacting transmission model.

(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.

(3/7) However, mathematical models can explicitly account for these complex and random processes.

(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.

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...

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)

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...

(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!

The heat map shows the optimal ages for different social contact matrices. The optimal age differs between social contact matrices.

(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 vaccine coverage percentages. The optimal age increases with vaccine coverage.

(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 ages of optima for different transmission levels. The optimal age decreases as the transmission level increases.

(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.

(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.

(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.

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.

(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.

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...

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)

👋 Hello! This is the Bluesky of the Domenech de Cellès lab, focused on infectious disease epidemiology📉🦠 Can't wait to meet the community here!