# Machine Learning for Infectious Disease Risk Prediction: A Survey

arXiv: https://arxiv.org/abs/2308.03037 · HTML: https://mlsanity.com/paper/2308.03037/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 39 of its 270 references that are in the map.
- First posted on arXiv 6 Aug 2023.

## Builds on (most cited first, 30 of 39)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Neural Processes](https://mlsanity.com/paper/1807.01622/) (2018)
- [Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting](https://mlsanity.com/paper/2103.07719/) (2021)
- [Attentive Neural Processes](https://mlsanity.com/paper/1901.05761/) (2019)
- [An introduction to domain adaptation and transfer learning](https://mlsanity.com/paper/1812.11806/) (2018)
- [Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2007.03113/) (2020)
- [Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection](https://mlsanity.com/paper/2003.10769/) (2020)
- [The Functional Neural Process](https://mlsanity.com/paper/1906.08324/) (2019)
- [Post-hoc Uncertainty Learning using a Dirichlet Meta-Model](https://mlsanity.com/paper/2212.07359/) (2022)
- [Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems](https://mlsanity.com/paper/2011.10616/) (2020)
- [An Uncertainty-aware Transfer Learning-based Framework for Covid-19 Diagnosis](https://mlsanity.com/paper/2007.14846/) (2020)
- [Interpretable Sequence Learning for COVID-19 Forecasting](https://mlsanity.com/paper/2008.00646/) (2020)
- [EINNs: Epidemiologically-informed Neural Networks](https://mlsanity.com/paper/2202.10446/) (2022)
- [Accurate estimation of influenza epidemics using Google search data via ARGO](https://mlsanity.com/paper/1505.00864/) (2015)
- [EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting](https://mlsanity.com/paper/2208.11517/) (2022)
- [When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting](https://mlsanity.com/paper/2106.03904/) (2021)
- [Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics](https://mlsanity.com/paper/2206.10696/) (2022)
- [Recurrent Attentive Neural Process for Sequential Data](https://mlsanity.com/paper/1910.09323/) (2019)
- [Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19](https://mlsanity.com/paper/2009.11407/) (2020)
- [DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and their Interactions](https://mlsanity.com/paper/2008.00115/) (2020)
- [4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection](https://mlsanity.com/paper/2007.11450/) (2020)
- [Inferring Metapopulation Propagation Network for Intra-city Epidemic Control and Prevention](https://mlsanity.com/paper/1806.08978/) (2018)
- [Predicting COVID-19 cases using Bidirectional LSTM on multivariate time series](https://mlsanity.com/paper/2009.12325/) (2020)
- [STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization](https://mlsanity.com/paper/2012.04747/) (2020)
- [A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA](https://mlsanity.com/paper/2109.12094/) (2021)

## Abstract (arXiv)

Infectious diseases, either emerging or long-lasting, place numerous people at risk and bring heavy public health burdens worldwide. In the process against infectious diseases, predicting the epidemic risk by modeling the disease transmission plays an essential role in assisting with preventing and controlling disease transmission in a more effective way. In this paper, we systematically describe how machine learning can play an essential role in quantitatively characterizing disease transmission patterns and accurately predicting infectious disease risks. First, we introduce the background and motivation of using machine learning for infectious disease risk prediction. Next, we describe the development and components of various machine learning models for infectious disease risk prediction. Specifically, existing models fall into three categories: Statistical prediction, data-driven machine learning, and epidemiology-inspired machine learning. Subsequently, we discuss challenges encountered when dealing with model inputs, designing task-oriented objectives, and conducting performance evaluation. Finally, we conclude with a discussion of open questions and future directions.