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In this work, we present an approach called Disease Informed Neural Networks(DINNs) that can be employed to effectively predict the spread of infectiousdiseases. This approach builds on a successful physics informed neural networkapproaches that have been applied to a variety of applications that can bemodeled by linear and non-linear ordinary and partial differential equations.Specifically, we build on the application of PINNs to SIR compartmental modelsand expand it a scaffolded family of mathematical models describing variousinfectious diseases. We show how the neural networks are capable of learninghow diseases spread, forecasting their progression, and finding their uniqueparameters (e.g. death rate). To demonstrate the robustness and efficacy ofDINNs, we apply the approach to eleven highly infectious diseases that havebeen modeled in increasing levels of complexity. Our computational experimentssuggest that DINNs is a reliable candidate for effectively learn about thedynamics of spread and forecast its progression into the future from availablereal-world data.

Data-driven approaches for predicting spread of infectious diseases through DINNs: Disease Informed Neural Networks