[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.

MSc & BSc theses (CMC MSU): credit-portfolio management and epidemic compartment-network modeling.

Research code and reproducibility materials for a mobility-informed SIR model using subway ridership data, particle smoothing, and mobility-reduction scenarios to evaluate influenza transmission and the instantaneous reproduction number.

Spatial MultiAgent RL for Epidemic Control with Heterogeneous Risk Preferences

Agent-based model (R) simulating epidemic spread through a demographically structured population households, schools, workplaces & neighborhoods with stochastic disease-state transitions.

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

SIR epidemic model with formal property verification, exhaustive parameter tuning, and prediction on Influenza data (USA & Netherlands, 2009-2011). Computational Modelling

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.

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.

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.

Agent-based simulation of epidemic spread in R — stochastic disease-state agents interacting over a social network, with support for policy interventions like stay-at-home orders.

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