# Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread

arXiv: https://arxiv.org/abs/2010.05074 · HTML: https://mlsanity.com/paper/2010.05074/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [Physics-Informed Neural Networks for Quantum Eigenvalue Problems](https://mlsanity.com/paper/2203.00451/) (2022)
- [Unsupervised Reservoir Computing for Solving Ordinary Differential Equations](https://mlsanity.com/paper/2108.11417/) (2021)
- [DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks](https://mlsanity.com/paper/2209.07081/) (2022)
- [Uncertainty Quantification in Neural Differential Equations](https://mlsanity.com/paper/2111.04207/) (2021)
- [Semi-supervised Learning for COVID-19 Image Classification via ResNet](https://mlsanity.com/paper/2103.06140/) (2021)
- [Modeling the effect of the vaccination campaign on the Covid-19 pandemic](https://mlsanity.com/paper/2108.13908/) (2021)
- [First principles physics-informed neural network for quantum wavefunctions and eigenvalue surfaces](https://mlsanity.com/paper/2211.04607/) (2022)
- [A New Artificial Neuron Proposal with Trainable Simultaneous Local and Global Activation Function](https://mlsanity.com/paper/2101.06100/) (2021)

## Abstract (arXiv)

Studying the dynamics of COVID-19 is of paramount importance to understanding the efficiency of restrictive measures and develop strategies to defend against upcoming contagion waves. In this work, we study the spread of COVID-19 using a semi-supervised neural network and assuming a passive part of the population remains isolated from the virus dynamics. We start with an unsupervised neural network that learns solutions of differential equations for different modeling parameters and initial conditions. A supervised method then solves the inverse problem by estimating the optimal conditions that generate functions to fit the data for those infected by, recovered from, and deceased due to COVID-19. This semi-supervised approach incorporates real data to determine the evolution of the spread, the passive population, and the basic reproduction number for different countries.