Avaneesh Singh | Searcharxiv

arXiv2022

SUTRA: A Novel Approach to Modelling Pandemics with Applications to COVID-19

The Covid-19 pandemic has two key properties: (i) asymptomatic cases (both detected and undetected) that can result in new infections, and (ii) time-varying characteristics due to new variants, Non-Pharmaceutical Interventions etc. We develop a model called SUTRA (Susceptible, Undetected though infected, Tested positive, and Removed Analysis) that takes into account both of these two key properties. While applying the model to a region, two parameters of the model can be learnt from the number of daily new cases found in the region. Using the learnt values of the parameters the model can predict the number of daily new cases so long as the learnt parameters do not change substantially. Whenever any of the two parameters changes due to the key property (ii) above, the SUTRA model can detect that the values of one or both of the parameters have changed. Further, the model has the capability to relearn the changed parameter values, and then use these to carry out the prediction of the trajectory of the pandemic for the region of concern. The SUTRA approach can be applied at various levels of granularity, from an entire country to a district, more specifically, to any large enough region for which the data of daily new cases are available. We have applied the SUTRA model to thirty-two countries, covering more than half of the world's population. Our conclusions are: (i) The model is able to capture the past trajectories very well. Moreover, the parameter values, which we can estimate robustly, help quantify the impact of changes in the pandemic characteristics. (ii) Unless the pandemic characteristics change significantly, the model has good predictive capability. (iii) Natural immunity provides significantly better protection against infection than the currently available vaccines.

q-bio.PE↗

arXiv2021

Machine Learned Phase Transitions in a System of Anisotropic Particles on a Square Lattice

The area of Machine learning (ML) has seen exceptional growth in recent years. Successful implementation of ML methods in various branches of physics has led to new insights. These methods have been shown to classify phases in condensed matter systems. Here we study the classification problem of phases in a system of hard rigid rods on a square lattice around a continuous and a discontinuous phase transition. On comparing a number of methods we find that convolutional neural network (CNN) classifies the phases with the highest accuracy when only snapshots are given as inputs. We study how the system size affects the model performance. We further compare the performance of CNN in classifying the phases around a continuous and a discontinuous phase transition. Further, we show that one can even beat the accuracy of CNN with simpler models by using physics-guided features. Lastly, we show that the critical point in this system can be learned without any prior estimate by using only the information of the ordered phase (as training set). Our study reveals the ML techniques that have been successful in studying spin systems can be easily adapted to more complex systems.

cond-mat.stat-mech↗