Inference of Infection Interaction Networks from Time Series for COVID-19 Prediction

Prasse and Van Mieghem (2020) recently proposed a method to infer networks of interactions from time series of data, such as series of infection counts. In this research, the student will investigate the Network Inference-based Prediction Algorithm (NIPA) by Prasse and Van Mieghem (2020) in order to infer a COVID-19 interaction network from the daily infection data for states or municipalities of a country. By exploiting the inferred interaction network, the student will attempt to produce predictions for future COVID-19 outbreaks by following the procedure by Achterberg et al. (2022). The students will use publicly-available daily COVID-19 data.

Skills Required:

• Python,
• Applying statistical methods to test and validate predictions,
• Working with the provided dataset to extract relevant information.

References:

Hoven, T., Garcia-Robledo, A., & Zangiabady, M. (2024). Enhancing Epidemic Prediction Using Simulated Annealing for Parameter Optimization in Infection Network Inference. In 7th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2024.

Supervisor: Mahboobeh Zangiabady