This approach enabled us to infer a mechanistic model of behaviour change for the 2022 Mpox outbreak in Berlin. You can preprint here: www.researchsquare.com/article/rs-6... 5/5

The trick: By deriving a formula for the edge existence probability, we can employ acceptance-rejection sampling to sample infection events without constructing the whole network each time. This approach allows to sample the infection tree exactly, with significantly less computational effort! 4/5

The problem: Contact updates and the spreading process occur on similar time scales. The effort of updating the network is dominating the effort of any exact sampling algo. So, the model is nice, but it's not practically feasible for examining dynamics or performing inference. 3/5

We were interested in a model for contact networks that naturally change over time and adapt according to the state of the spreading process. When there are many (diagnosed) cases, people may reduce their interactions in anticipation of infection. 2/5

Very pleased to see our latest work on sampling of spreading processes on adaptive temporal network published at @plos.org Complex Systems!
Read more at doi.org/10.1371/jour... or find some information below
1/5🧵

Thank you to @complenet.bsky.social for the opportunity to present! Excited to have shared my work on diseases spreading on adaptive networks.

I'm delighted that our latest research article has been published in Nature!

📖 Neue @nature.com Publikation: Wie schnell verbreiten sich #SARSCoV2 -Varianten?

Ein vom #RKI und Berliner Forschenden entwickeltes Modell zeigt, wie viele Menschen in bestimmten Regionen über einen gewissen Zeitraum empfänglich für verschiedene Virusvarianten sind.

www.nature.com/articles/s41...

How does #SARSCoV2 evolve? 🦠

A team at @freieuniversitaet.bsky.social studied how the pandemic continues to shape variant-specific population immunity.

Read the @nature.com article: go.nature.com/3X0A36Z
Listen to the discussion: bit.ly/4jUqeB4