  Black Sigatoka disease severely decreases global banana production, and
climate change aggravates the problem by altering fungal species distributions.
Due to the heavy financial burden of managing this infectious disease, farmers
in developing countries face significant banana crop losses. Though scientists
have produced mathematical models of infectious diseases, adapting these models
to incorporate climate effects is difficult. We present MR. NODE (Multiple
predictoR Neural ODE), a neural network that models the dynamics of black
Sigatoka infection learnt directly from data via Neural Ordinary Differential
Equations. Our method encodes external predictor factors into the latent space
in addition to the variable that we infer, and it can also predict the
infection risk at an arbitrary point in time. Empirically, we demonstrate on
historical climate data that our method has superior generalization performance
on time points up to one month in the future and unseen irregularities. We
believe that our method can be a useful tool to control the spread of black
Sigatoka.

Machine Learning;Machine Learning