Disease Outbreak Prediction and Spread Modelling

Achieving optimal measures at population level requires better models for problems such as disease outbreak prediction and disease spread dynamics. This involves analysing complex data to understand how diseases might emerge and propagate within a population over time.

Different types of Quantum Neural Networks (QNNs), including continuous-variable ones, have been applied for this purpose. One study used a COVID-19 time series data set which included confirmed cases, number of deaths, and number of recovered individuals to demonstrate the application of QNNs for outbreak prediction.

By applying quantum algorithms like QNNs to relevant data sets, the research demonstrates the potential of quantum computing to improve models for forecasting disease outbreaks and understanding their spread, which in turn helps in decision making on a public health and policy level.