Predicting and Interpreting Bipartite Temporal Networks

A network, is defined as a collection of nodes interconnected by links. When this topology changes through time, we call it a temporal network. A specific class of networks, with only two types of nodes with no connections between one kind, is the bipartite network. An example is a telecommunications network, where nodes represent...

Modeling the Spread of Epidemics: Visualizing the Spread of COVID-19 and Applying a Markov-Modulated Process Model to Mobility Processes

This thesis consists of two parts which are connected by the central theme of epidemics. In the first part, a website is designed for forecasting the number of cases of COVID-19 in the Netherlands. The forecasting is performed using the Network-Inference-Based Prediction Algorithm (NIPA). The first part of the thesis documents the design process...

Developing a Markov-Modulated Process Model for Mobility Processes

A major factor in the spreading of viruses is human-to-human transmission, and human mobility is clearly linked to the spreading process of epidemics. If we hope to understand the evolution of an epidemic, then we must also understand the underlying mobility process and the interaction between the two.<br/><br/>We propose the Markov modulated...

Analyzing mobility models

By analyzing characteristics of human mobility and existing mobility models, we design a realistic mobility model which can represent more characteristics of human movement than before.