# A Bayesian generative neural network framework for epidemic inference problems

arXiv: https://arxiv.org/abs/2111.03383 · HTML: https://mlsanity.com/paper/2111.03383/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 57 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 22 of 22)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Conditional Image Generation with PixelCNN Decoders](https://mlsanity.com/paper/1606.05328/) (2016)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [MADE: Masked Autoencoder for Distribution Estimation](https://mlsanity.com/paper/1502.03509/) (2015)
- [Solving Statistical Mechanics Using Variational Autoregressive Networks](https://mlsanity.com/paper/1809.10606/) (2018)
- [Neural Autoregressive Distribution Estimation](https://mlsanity.com/paper/1605.02226/) (2016)
- [Deep autoregressive models for the efficient variational simulation of many-body quantum systems](https://mlsanity.com/paper/1902.04057/) (2019)
- [Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys](https://mlsanity.com/paper/1506.03645/) (2015)
- [Epidemic processes in complex networks](https://mlsanity.com/paper/1408.2701/) (2014)
- [Variational Neural Annealing](https://mlsanity.com/paper/2101.10154/) (2021)
- [Finding Patient Zero: Learning Contagion Source with Graph Neural Networks](https://mlsanity.com/paper/2006.11913/) (2020)
- [Can co-location be used as a proxy for face-to-face contacts?](https://mlsanity.com/paper/1712.06346/) (2017)
- [Estimating the State of Epidemics Spreading with Graph Neural Networks](https://mlsanity.com/paper/2105.05060/) (2021)
- [Neural network based country wise risk prediction of COVID-19](https://mlsanity.com/paper/2004.00959/) (2020)
- [Network reconstruction from infection cascades](https://mlsanity.com/paper/1609.00432/) (2016)
- [Inferring the origin of an epidemic with a dynamic message-passing algorithm](https://mlsanity.com/paper/1303.5315/) (2013)
- [Epidemic mitigation by statistical inference from contact tracing data](https://mlsanity.com/paper/2009.09422/) (2020)
- [Bayesian inference of epidemics on networks via Belief Propagation](https://mlsanity.com/paper/1307.6786/) (2013)
- [Containing epidemic outbreaks by message-passing techniques](https://mlsanity.com/paper/1309.2805/) (2013)

## Built on by (most cited first, 5 of 5)

- [The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems](https://mlsanity.com/paper/2302.08347/) (2023)
- [Conditional diffusions for amortized neural posterior estimation](https://mlsanity.com/paper/2410.19105/) (2024)
- [Learning of networked spreading models from noisy and incomplete data](https://mlsanity.com/paper/2401.00011/) (2023)
- [Inference in conditioned dynamics through causality restoration](https://mlsanity.com/paper/2210.10179/) (2022)
- [Gated Graph Neural Networks for Learning Hidden Independent Cascade Dynamics](https://mlsanity.com/paper/2610.04033/) (2026)

## Abstract (arXiv)

The reconstruction of missing information in epidemic spreading on contact networks can be essential in the prevention and containment strategies. The identification and warning of infectious but asymptomatic individuals (i.e., contact tracing), the well-known patient-zero problem, or the inference of the infectivity values in structured populations are examples of significant epidemic inference problems. As the number of possible epidemic cascades grows exponentially with the number of individuals involved and only an almost negligible subset of them is compatible with the observations (e.g., medical tests), epidemic inference in contact networks poses incredible computational challenges. We present a new generative neural networks framework that learns to generate the most probable infection cascades compatible with observations. The proposed method achieves better (in some cases, significantly better) or comparable results with existing methods in all problems considered both in synthetic and real contact networks. Given its generality, clear Bayesian and variational nature, the presented framework paves the way to solve fundamental inference epidemic problems with high precision in small and medium-sized real case scenarios such as the spread of infections in workplaces and hospitals.