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).
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 joins the Soft Matter Lab

Laura Natali starts her PhD at the Physics Department of the University of Gothenburg on 1st March 2020.

Laura has a Master degree in Physics, curriculum in Physics of Biosystems, from the University of Rome “La Sapienza”, where he submitted a Master thesis whose results can be found here.

In her PhD, she will focus on microswimmers and active polymers employing machine learning techniques.

Seminar on polymers under local active forces: a simplified stochastic model for motor-induced translocations by Laura Natali from Università di Roma “La Sapienza”, Nexus, 22 January 2020

Polymers under local active forces: a simplified stochastic model for motor-induced translocations.
Seminar by Laura Natali from the University of Rome “La Sapienza”, Rome, Italy.

Molecular motors are a wide class of enzymes that can transport even large macromolecules by converting chemical into mechanical energy, through the process of ATP-hydrolysis. Among those, active nanopore translocation is a common mechanism to carry proteins across biological membranes. The phospholipid bilayer is impenetrable for the folded protein, that requires to be unfolded and pulled across channels such as alpha-hemolysin [1]. Exploiting the translocation process, we can acquire information about the target proteins’ structure, such as the intermediate configurations between the folded and denatured structures [2]. Here follows the interest in modeling the motor proteins complex in a simple simulation setup.
First of all, we characterized the active polymer’s features in free space and subsequently, we confined it inside a nanopore model. The study in free space aims to investigate how activity affects both the global and the local polymer properties. The chosen model for the active force is an Active Ornstein-Uhlenbeck Particle, a model closely related to Active Brownian Particles [3]. The presence of the catalytic head affects the end-to-end distance of the polymer, which describes its degree of compactness. The active chain can be studied through the Rouse mode decomposition [4], which allowed us to analyze the dependence of the second moment of the end-to-end distance as a function of the persistence time of the activity. We considered also the distance between consecutive monomers, which provides an insight into the local effects of the active force and how it is transmitted along the chain.
In this work, we were inspired by an experiment employing the ClpXP complex [5], a protein-degradation machine responsible for unfolding and digesting malfunctioning proteins through unidirectional transport across the nanopore. In the model we propose, we enclose the polymer chain in a confining potential, simulating the effect of the nanopore. The energy generated by the molecular motor translocates the polymers, which means they cross the pore from side to side. In our setup, the effect of the molecular motor [6] is represented as a potential barrier combined with a region that makes active the monomers that are crossing it. The translocation pathways have a step-wise profile typical of their biological counterparts.

References:

  1. J. Nivala, D. B. Marks and M. Akeson, Nature Biotechnol., 2013, 31, 247
  2. M. Bacci, M. Chinappi, C. M. Casciola and F. Cecconi, J. Phys.Chem. B, 2012, 116, 4255–4262
  3. L. Caprini and U. M. B. Marconi, Soft matter, 2018, 14, 9044–9054.
  4. P. E. Rouse, J. Chem. Phys., 1953, 21, 1272.
  5. T. A. Baker and R. T. Sauer, Biochimica et Biophysica Acta (BBA) – Molecular Cell Research, 2012, 1823, 15–28.
  6. L. Caprini, U. Marini Bettolo Marconi, A. Puglisi and A. Vulpiani, J. Chemi. Phys., 2019, 150, 024902.

Place: Nexus room, Fysik Origo, Fysik
Time: 22 January, 2020, 11:00