  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.
