# Data-driven approaches for predicting spread of infectious diseases through DINNs: Disease Informed Neural Networks

arXiv: https://arxiv.org/abs/2110.05445 · HTML: https://mlsanity.com/paper/2110.05445/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 69 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Automatic differentiation in machine learning: a survey](https://mlsanity.com/paper/1502.05767/) (2015)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations](https://mlsanity.com/paper/1708.00588/) (2017)
- [Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations](https://mlsanity.com/paper/1706.04702/) (2017)
- [The Power of Depth for Feedforward Neural Networks](https://mlsanity.com/paper/1512.03965/) (2015)
- [Solving differential equations with unknown constitutive relations as recurrent neural networks](https://mlsanity.com/paper/1710.02242/) (2017)

## Built on by (most cited first, 15 of 15)

- [From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning](https://mlsanity.com/paper/2410.13228/) (2024)
- [Generalizing to New Physical Systems via Context-Informed Dynamics Model](https://mlsanity.com/paper/2202.01889/) (2022)
- [Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing](https://mlsanity.com/paper/2211.07377/) (2022)
- [X-IL: Exploring the Design Space of Imitation Learning Policies](https://mlsanity.com/paper/2502.12330/) (2025)
- [Physics-Informed Neural Networks for Prognostics and Health Management of Lithium-Ion Batteries](https://mlsanity.com/paper/2301.00776/) (2023)
- [PINN Training using Biobjective Optimization: The Trade-off between Data Loss and Residual Loss](https://mlsanity.com/paper/2302.01810/) (2023)
- [Physics-Informed Regression: Parameter Estimation in Parameter-Linear Nonlinear Dynamic Models](https://mlsanity.com/paper/2508.19249/) (2025)
- [Environment Inference for Learning Generalizable Dynamical System](https://mlsanity.com/paper/2510.19784/) (2025)
- [A model learning framework for inferring the dynamics of transmission rate depending on exogenous variables for epidemic forecasts](https://mlsanity.com/paper/2410.11545/) (2024)
- [MP-PINN: A Multi-Phase Physics-Informed Neural Network for Epidemic Forecasting](https://mlsanity.com/paper/2411.06781/) (2024)
- [Exploration of Hepatitis B Virus Infection Dynamics through Physics-Informed Deep Learning Approach](https://mlsanity.com/paper/2503.10708/) (2025)
- [A Physics-Informed Neural Networks-Based Model Predictive Control Framework for SIR Epidemics](https://mlsanity.com/paper/2509.12226/) (2025)
- [Modeling COVID-19 Dynamics in German States Using Physics-Informed Neural Networks](https://mlsanity.com/paper/2510.06776/) (2025)
- [Neural Operator based Multi-Field Reconstruction of Inner Solar Boundary State](https://mlsanity.com/paper/2608.22782/) (2026)
- [Conformalized Physics-Informed Weak Regression for Parameter Estimation in Epidemic Models](https://mlsanity.com/paper/2610.19058/) (2026)

## Abstract (arXiv)

In this work, we present an approach called Disease Informed Neural Networks (DINNs) that can be employed to effectively predict the spread of infectious diseases. This approach builds on a successful physics informed neural network approaches that have been applied to a variety of applications that can be modeled by linear and non-linear ordinary and partial differential equations. Specifically, we build on the application of PINNs to SIR compartmental models and expand it a scaffolded family of mathematical models describing various infectious diseases. We show how the neural networks are capable of learning how diseases spread, forecasting their progression, and finding their unique parameters (e.g. death rate). To demonstrate the robustness and efficacy of DINNs, we apply the approach to eleven highly infectious diseases that have been modeled in increasing levels of complexity. Our computational experiments suggest that DINNs is a reliable candidate for effectively learn about the dynamics of spread and forecast its progression into the future from available real-world data.