# Deep Landscape Features for Improving Vector-borne Disease Prediction

arXiv: https://arxiv.org/abs/1904.01994 · HTML: https://mlsanity.com/paper/1904.01994/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

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

- [Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents](https://mlsanity.com/paper/2201.04236/) (2022)
- [Segmenting across places: The need for fair transfer learning with satellite imagery](https://mlsanity.com/paper/2204.04358/) (2022)
- [Neural networks for dengue forecasting: a systematic review](https://mlsanity.com/paper/2106.12905/) (2021)
- [DengueNet: Dengue Prediction using Spatiotemporal Satellite Imagery for Resource-Limited Countries](https://mlsanity.com/paper/2401.11114/) (2024)
- [A Fast Fourier Convolutional Deep Neural Network For Accurate and Explainable Discrimination Of Wheat Yellow Rust And Nitrogen Deficiency From Sentinel-2 Time-Series Data](https://mlsanity.com/paper/2306.17207/) (2023)

## Abstract (arXiv)

The global population at risk of mosquito-borne diseases such as dengue, yellow fever, chikungunya and Zika is expanding. Infectious disease models commonly incorporate environmental measures like temperature and precipitation. Given increasing availability of high-resolution satellite imagery, here we consider including landscape features from satellite imagery into infectious disease prediction models. To do so, we implement a Convolutional Neural Network (CNN) model trained on Imagenet data and labelled landscape features in satellite data from London. We then incorporate landscape features from satellite image data from Pakistan, labelled using the CNN, in a well-known Susceptible-Infectious-Recovered epidemic model, alongside dengue case data from 2012-2016 in Pakistan. We study improvement of the prediction model for each of the individual landscape features, and assess the feasibility of using image labels from a different place. We find that incorporating satellite-derived landscape features can improve prediction of outbreaks, which is important for proactive and strategic surveillance and control programmes.