Look at them go! hehehehe

RT @ChadNauseam: "A calculator app? Anyone could make that.". Not true. A calculator should show you the result of the mathematical expres….

Thank you to my co-authors from Kido Dynamics David Mateo and Alberto Hernando and of course to the ones from IFISC @_SandroMeloni_ and Jose J. Ramasco!.

Overall, we show via a simple model and hypotheses how critical mobility levels arise and how you can use them to explain the features and detriments of different epidemic containment policies. We hope this can be useful to inspire future more efficient containment policies.

In fact, just by knowing that there is a monotonously increasing critical mobility level, one can approximate it heuristically via tentative re-openings, allowing the system to auto-correct overshoots by simply holding the mobility in place. but never going back to restrict!

Thus, if one could just model or approximate this critical mobility level and its evolution throughout time, and use it to safely re-open until other solutions are found, you could avoid all this stress. And that's exactly what we propose!

But. it's not desirable to cause that much stress in order to contain an epidemic right? We have seen the effects of "epidemic burnout" in the recent COVID-19 pandemic, with individuals and businesses increasingly reluctant to take measures against the disease as time goes on.

Our model shows that traffic light policies can contain an epidemic if allowed to change between states rapidly enough as to not build epidemiological inertia between revisions, moving above and below the critical level as to average a mobility close to the critical one

We focused on the so-called "traffic light" systems where epidemic risk is classified into discrete categories depending on how much epidemic incidence has been observed recently and measures are taken according to in which category the system currently is in 🚦.

We have now characterised the desired critical mobility level and can re-enact some epidemic containment policies in light of these epidemic thresholds! 📄📄.

Depending on the immunity in the population and how much agents are allowed to travel, a phase diagram appears that separates the system into a stale and an outbreak phase, with the line dividing both phases being the sought-after critical mobility level for any situation!

Thanks to data provided by @kidodynamics we were able to create a minimal agent based model that embodies mobility and epidemic spreading around the Madrid region, reproducing expected mobility patterns thanks to their high quality data.

It all started with 2 main ideas: replicate the mechanisms behind epidemic oscillations observed recently and model epidemic outcomes via the postulated critical mobility levels in recent literature. #mobility #epidemiology #computationalsocialscience #physics.

⚠️NEW PUBLICATION ⚠️. "Critical mobility in policy making for epidemic containment" is out now in @SpringerNature @SciReports !. Happy to see this collaboration between @IFISC_mallorca and @kidodynamics come to light!. Details below 🧵.

Is this delaying the exodus to Mars? Asking for a friend.

@relatandohisto1 lo pillas?.

Con una cuadrícula pequeña, estas irregularidades van sumando y sumando y acabas con cada vez más costa hasta que superas a Andalucía.

Intuitivamente puedes entender que una cuadrícula grande se come los detalles finos de las rías y costas irregulares de Galicia, con lo cual te sale un tamaño similar al que ves en un mapa a lo lejos (que Galicia tiene menos costa que Andalucía).

Una forma de medir la dimensión fractal de una costa es usar cuadriculas. Pones una cuadrícula sobre el mapa y cuentas los cuadrados donde están las costas. Si usas una cuadrícula grande, Andalucía sale con más costa. Si usas una pequeña, sale Galicia.

Es decir, si uso una vara de medir más grande que otra, la costa gallega puede ser mayor, igual o menor que la andaluza porque la dimensión fractal de ambas costas es distinta.