# An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics

arXiv: https://arxiv.org/abs/2201.09394 · HTML: https://mlsanity.com/paper/2201.09394/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 23 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [Accurate estimation of influenza epidemics using Google search data via ARGO](https://mlsanity.com/paper/1505.00864/) (2015)
- [A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting](https://mlsanity.com/paper/1902.05113/) (2019)
- [A Recurrent Neural Network and Differential Equation Based Spatiotemporal Infectious Disease Model with Application to COVID-19](https://mlsanity.com/paper/2007.10929/) (2020)
- [A Spatial-Temporal Graph Based Hybrid Infectious Disease Model with Application to COVID-19](https://mlsanity.com/paper/2010.09077/) (2020)

## Abstract (arXiv)

We developed an integrated recurrent neural network and nonlinear regression spatio-temporal model for vector-borne disease evolution. We take into account climate data and seasonality as external factors that correlate with disease transmitting insects (e.g. flies), also spill-over infections from neighboring regions surrounding a region of interest. The climate data is encoded to the model through a quadratic embedding scheme motivated by recommendation systems. The neighboring regions' influence is modeled by a long short-term memory neural network. The integrated model is trained by stochastic gradient descent and tested on leish-maniasis data in Sri Lanka from 2013-2018 where infection outbreaks occurred. Our model outperformed ARIMA models across a number of regions with high infections, and an associated ablation study renders support to our modeling hypothesis and ideas.