Press release on Machine learning can help slow down future pandemics

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). Image by L. Natali.
The article Improving epidemic testing and containment strategies using machine learning has been featured in the News of the Faculty of Science of Gothenburg University.

Here the links to the press releases:
Swedish: Maskininlärning kan bidra till att bromsa framtida pandemier
English: Machine learning can help slow down future pandemics

Round Table Discussion on: Collective Behavior

The fourth roundtable was an opportunity for all students to discuss the topic “Collective Behavior” on Zoom with a panel of guests: Clemens Bechinger from the University of Konstanz, Ivo Buttinoni from Heinrich Heine University in Dusseldorf and Caroline Beck Adiels from Gothenburg University. The event was organized by Daniela Pérez, Danne van Roon, Davide Breoni, Jérémie Bertrand, Laura Natali and Liam Ruske on March 24th.

Although the guests had different background they seemed to agree on the fact that complex behavior can emerge from an ensemble of entities that obey a small number of simple rules. Indeed, minimalistic models such as the Vicsek model account for phase transition from a disordered motion to large scale motion and more; phenomena that appear to be universal.

A question on the role of intelligence and communication in collective behavior started the discussion. Although some animals or colony of bacteria may seem intelligent (e.g. escaping from a predator in a clever way or making long-lasting symbiotic microfilms), we must bear in mind that collective behavior is… collective, and rarely arises from decisions made individually. It may be said that in the animal kingdom, the need for survival requires a need to adapt and therefore to be intelligent, but this need for intelligence can be outsourced and solved at the level of the group rather than hardwired in the physical brain of each animal (or human).

It is also conceivable that one of the entities acts as a leader and ignites a collective behavior. Giovanni Volpe made an interesting remark, stating that a leader is the one who defines the objective function to be optimized by the group. The idea of leadership in collective behavior of microscopic systems remain largely unexplored by physicists.

After one hour of fruitful discussion and back and forth between the students and the guests, the session was finished and we resumed our activities with a better understanding of collective behavior. We thank the panelists for their inputs and attendance!

Improving epidemic testing and containment strategies using machine learning accepted in Machine Learning: Science and Technology

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). Image by L. Natali.
Improving epidemic testing and containment strategies using machine learning
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe
Machine Learning: Science and Technology (2021)
doi: 10.1088/2632-2153/abf0f7
arXiv: 2011.11717

Containment of epidemic outbreaks entails great societal and economic costs. Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these prediction, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.

Round Table Discussion on: Phoretic Propulsion Mechanism

During the second day of the experimental training, we organised the first round table discussion. The session was chaired by six of the students attending the training: Carolina van Baalen, Danne van Roon, Gülce Bayram, Harshith Bachimanchi, Laura Natali and Sandrine Heijnen.

The topic of the round table was phoretic propulsion mechanisms and we had four panelists – Juliane Simmchen, Frank Cichos, Ivo Buttinoni and Felix Ginot – and a guest speaker, Antoni Homs Corbera. After a brief introduction of the panelists, we had a chance to ask all the questions we collected from the other participants.

The discussion started with the definition of the term “phoresis” and continued with the simulation frameworks for phoretic colloids. It included a brief discussion of the complexity involved in these processes and the typical length scales at which interfacial effects are relevant.

The conclusion was “a common joke at conferences is that the phoresis starts when coffee is about to be served”. The real conclusion was that phoretic interaction needs very large gradients on the macroscopic scale and is hidden by diffusion on a very small scale.

All participants had the possibility to jump in and add upcoming questions. We ended the round table by discussing the possible applications of phoretic colloids, highlighting the environmental aspects like microplastics’ filtration in water.

We thank all the guests and participants for making it a successful discussion moment.

Improving epidemic testing and containment strategies using machine learning on ArXiv

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). Image by L. Natali.
Improving epidemic testing and containment strategies using machine learning
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe
arXiv: 2011.11717

Containment of epidemic outbreaks entails great societal and economic costs. Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these prediction, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.

Laura Natali presents her PhD project at the ActiveMatter online meeting, 10 September 2020

The ActiveMatter ESRs + PIs Online Meeting (if you want to know more read the main news here) took place on the 10th of last month. For the first time, the members of the ActiveMatter were in the “same place” also if just online. All the Early Stage Researchers were asked to introduce themselves in a short presentation video.

Laura Natali, ESR at the University of Gothenburg, also presented herself during the meeting.

The video lasts only five minutes and introduces Laura, her current project and the area of research she will study during the PhD. You can find the presentation below, and it is also on the ActiveMatter youtube channel .

ActiveMatter PIs+ESRs Online Meeting on 10 September 2020

The ActiveMatter PI+ESRs meeting took place on 10 September 2020. Because of the current travel restrictions and regulations imposed to hinder the spread of the CoViD-19 epidemics, the meeting was held online.

The aim of the meeting was to give an update to all the members on the progress of the ActiveMatter network.

Currently 12 of the 15 Early Stage Researchers (ESRs) have already been recruited and could started their project. During the meeting the ESRs had the opportunity to introduce themselves to the rest of the network and to present their research project.

The presentations of the ESRs have been uploaded on the Youtube channel of the ActiveMatter network and are available online.

Links to the individual presentations:
Liam Ruske, UOXF
Carolina van Baalen, ETH
Audrey Nsamela, ELVESYS
Danne van Roon, FC.ID
Chun-Jen Chen, UKONS
Sandrine Heijnen, UCL
Jesús Manuel Antúnez Dominguez, ELVESYS
David Bronte Ciriza, CNR
Laura Natali, UGOT
Ayten Gülce Bayram, UBIL
Davide Breoni, UDUS
Jérémie Mar Bertrand, EPFL

Pictures
(Screenshot by Caroline Beck Adiels)

(Screenshot by Giorgio Volpe)

(Screenshot by Giorgio Volpe)

(Screenshot by Agnese Callegari)

(Screenshot by Agnese Callegari)