[ECML-PKDD2022] EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting

The "Analysis of Information Networks" repository contains six exercises that explore key concepts in network analysis. From random network generation to link prediction and recommender systems, each exercise provides hands-on experience with metrics, visualizations, and real-world applications.

Spatial MultiAgent RL for Epidemic Control with Heterogeneous Risk Preferences

An uncertainty-driven probabilistic framework for modeling worm propagation in large-scale networks. It uses stochastic infection rates to capture bursty behavior, adaptive slowdowns, and defense mechanisms, improving prediction accuracy over traditional models while remaining safe, reproducible, and suitable for cybersecurity research.

A numerical simulation of the SVEIR epidemic model (Susceptible-Vaccinated-Exposed-Infected-Recovered) incorporating spatial diffusion. Solves the system of differential equations to analyze influenza spread and vaccination impact.

Code and data for "Faster Uptake, Slower Let-Down" (Risk Analysis, 2026) asymmetric community behavioral response to pandemic risk.

Notebooks used or made in development of prediksicovidjatim

A bi-virus epidemic model for networks with duty-cycled wireless sensors

Teaching-oriented ODE epidemic mini-lab with SI, SIR, and SEIR models, parameter sweeps, and interpretable metrics.

Epidemic spread simulation using cellular automata with intervention strategies and real-time visualisation in Python.

Bayesian discrete-time SIR/SEIR modelling, noisy epidemic observations, posterior predictive checks, and interpretable equation discovery.