Corpus ID: 221172745

Physics-informed machine learning for the COVID-19 pandemic: Adherence to social distancing and short-term predictions for eight countries

  title={Physics-informed machine learning for the COVID-19 pandemic: Adherence to social distancing and short-term predictions for eight countries},
  author={G. D. Barmparis and G. Tsironis},
The spread of COVID-19 during the initial phase of the first half of 2020 was curtailed to a larger or lesser extent through measures of social distancing imposed by most countries. In this work, we link directly, through machine learning techniques, infection data at a country level to a single number that signifies social distancing effectiveness. We assume that the standard SIR model gives a reasonable description of the dynamics of spreading, and thus the social distancing aspect can be… Expand
1 Citations
Discovering nonlinear resonances through physics-informed machine learning
For an ensemble of nonlinear systems that model for instance molecules or photonic systems we propose a method that finds efficiently the configuration that has prescribed transfer properties.Expand


Estimating the infection horizon of COVID-19 in eight countries with a data-driven approach
This work uses the quantitative landscape of the disease spreading in China as a benchmark and utilizes infection data from eight countries to estimate the complete evolution of the infection in each of these countries, based on a Gaussian spreading hypothesis. Expand
Rethinking case fatality ratios for covid-19 from a data-driven viewpoint
It is found that for each country, there is a unique value of the time lag between reported cases and deaths versus time, that yields the optimal correlation between them is a specific sense, and the resulting corrected CFR is actually constant over many months, for many countries, but also for the entire world. Expand
A contribution to the mathematical theory of epidemics
The present communication discussion will be limited to the case in which all members of the community are initially equally susceptible to the disease, and it will be further assumed that complete immunity is conferred by a single infection. Expand
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Abstract We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinearExpand
The first 100 days: Modeling the evolution of the COVID-19 pandemic
An expanded version of the original Kermack-McKendrick model, which includes a decaying value of the parameter β (the effective contact rate) due to externally imposed conditions, is considered, to which it is referred as the forced-SIR (FSIR) model. Expand
Handbook of exact solutions of ordinary differential equations, Chapmand and Hall/CRC, second edition
  • 2003