Deep Landscape Features for Improving Vector-borne Disease Prediction
This work addresses the challenge of improving disease prediction for public health officials in regions at risk of mosquito-borne diseases, but it is incremental as it applies existing methods to new data.
The authors tackled the problem of predicting vector-borne disease outbreaks by incorporating landscape features from satellite imagery into epidemic models, finding that this approach can improve outbreak prediction for proactive surveillance and control.
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.