# Inter-Series Attention Model for COVID-19 Forecasting

arXiv: https://arxiv.org/abs/2010.13006 · HTML: https://mlsanity.com/paper/2010.13006/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2020.

## Built on by (most cited first, 18 of 18)

- [Dynamic Neural Networks: A Survey](https://mlsanity.com/paper/2102.04906/) (2021)
- [EINNs: Epidemiologically-informed Neural Networks](https://mlsanity.com/paper/2202.10446/) (2022)
- [EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting](https://mlsanity.com/paper/2208.11517/) (2022)
- [An Optimal Reduction of TV-Denoising to Adaptive Online Learning](https://mlsanity.com/paper/2101.09438/) (2021)
- [Empirical Quantitative Analysis of COVID-19 Forecasting Models](https://mlsanity.com/paper/2110.00174/) (2021)
- [Data-Centric Epidemic Forecasting: A Survey](https://mlsanity.com/paper/2207.09370/) (2022)
- [Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future](https://mlsanity.com/paper/2106.04420/) (2021)
- [MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting](https://mlsanity.com/paper/2308.15840/) (2023)
- [SIMLR: Machine Learning inside the SIR model for COVID-19 Forecasting](https://mlsanity.com/paper/2106.01590/) (2021)
- [#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs](https://mlsanity.com/paper/2108.03670/) (2021)
- [COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction](https://mlsanity.com/paper/2105.00620/) (2021)
- [Inter- and Intra-Series Embeddings Fusion Network for Epidemiological Forecasting](https://mlsanity.com/paper/2208.11515/) (2022)
- [Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies](https://mlsanity.com/paper/2209.04631/) (2022)
- [Predicting infections in the Covid-19 pandemic -- lessons learned](https://mlsanity.com/paper/2112.11187/) (2021)
- [COVID-19 Hospitalizations Forecasts Using Internet Search Data](https://mlsanity.com/paper/2202.03869/) (2022)
- [Leveraging Wastewater Monitoring for COVID-19 Forecasting in the US: a Deep Learning study](https://mlsanity.com/paper/2212.08798/) (2022)
- [Deep COVID-19 Forecasting for Multiple States with Data Augmentation](https://mlsanity.com/paper/2302.01155/) (2023)
- [Enhancing Epidemic Forecasting: Evaluating the Role of Mobility Data and Graph Convolutional Networks](https://mlsanity.com/paper/2506.11028/) (2025)

## Abstract (arXiv)

COVID-19 pandemic has an unprecedented impact all over the world since early 2020. During this public health crisis, reliable forecasting of the disease becomes critical for resource allocation and administrative planning. The results from compartmental models such as SIR and SEIR are popularly referred by CDC and news media. With more and more COVID-19 data becoming available, we examine the following question: Can a direct data-driven approach without modeling the disease spreading dynamics outperform the well referred compartmental models and their variants? In this paper, we show the possibility. It is observed that as COVID-19 spreads at different speed and scale in different geographic regions, it is highly likely that similar progression patterns are shared among these regions within different time periods. This intuition lead us to develop a new neural forecasting model, called Attention Crossing Time Series (ACTS), that makes forecasts via comparing patterns across time series obtained from multiple regions. The attention mechanism originally developed for natural language processing can be leveraged and generalized to materialize this idea. Among 13 out of 18 testings including forecasting newly confirmed cases, hospitalizations and deaths, ACTS outperforms all the leading COVID-19 forecasters highlighted by CDC.