Big thank you to our co-authors! @LauraBarrero083, Sarah Kramer and @tobiaskurth.

Integrating causal inference concepts with transmission models is necessary for inferring the effect of weather on infectious diseases and subsequently predicting the consequences of #climatechange on infectious diseases.

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.

Third vignette: causal inference helps identify and avoid confounding bias. Gradients in climate across locations can masquerade as spatial spread of disease.

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.

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.

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.

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.

A key question arising from #climatechange is how it will impact the transmission of infectious diseases. Predicting these effects demands understanding how weather affects their transmission dynamics.

How does weather affect the transmission of infectious diseases, and how can we predict the effects of #climatechange on them? In our latest article published in @NatureEcoEvo, we explore these questions using #causalinference and #transmissionmodels.

(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: .

(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.

(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.

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:  See the 🧵 for details! (1/7).

(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: .

(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.

(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.