Improved Bounds on Information Dissemination by Manhattan Random Waypoint Model

With the popularity of portable wireless devices it is important to model and predict how information or contagions spread by natural human mobility -- for understanding the spreading of deadly infectious diseases and for improving delay tolerant communication schemes. Formally, we model this problem by considering $M$ moving agents, where each agent initially carries a \emph{distinct} bit of information. When two agents are at the same location or in close proximity to one another, they share all their information with each other. We would like to know the time it takes until all bits of information reach all agents, called the \textit{flood time}, and how it depends on the way agents move, the size and shape of the network and the number of agents moving in the network. We provide rigorous analysis for the \MRWP model (which takes paths with minimum number of turns), a convenient model used previously to analyze mobile agents, and find that with high probability the flood time is bounded by $O\big(N\log M\lceil(N/M) \log(NM)\rceil\big)$, where $M$ agents move on an $N\times N$ grid. In addition to extensive simulations, we use a data set of taxi trajectories to show that our method can successfully predict flood times in both experimental settings and the real world.Comment: 10 pages, ACM SIGSPATIAL 2018, Seattle, U

A Comparison of Agent-Based Models and Equation Based Models for Infectious Disease Epidemiology

There are two main methods that are used to model the spread of an infectious disease: agent-based modelling and equation based modelling. In this paper, we compare the results from an example implementation of each method, and show that although the agent-based model takes longer to setup and run, it provides additional information that is not available when using an equation based model. Specifically, the ability of the agent-based model to capture heterogeneous mixing and agent interactions enables it to give a better overall view of an outbreak. We compare the performance of both models by simulating a measles outbreak in 33 different Irish towns and measuring the outcomes of this outbreak

A simple empirical analysis on the link between socioeconomic status and spatial mobility

Considering spatial mobility as an important dimension of human capability with direct implications for well-being, this study examines the link between individuals’ socioeconomic status and their degree of mobility. We use data from the National Household Travel Survey (NHTS) conducted in United States in 2009 by the US Department of Transportation. We construct two complementary mobility variables: one translating the average distance individuals travel by day and the other capturing the average number of trips made by individuals in a day. Using both exploratory and multivariate linear regressions analyzes, our results show that socioeconomic status determines significantly individuals’ degree of mobility. We found that mobility is significantly higher among the most educated individuals and those with high income levels. It also appears strong heterogeneity in the mobility according to gender or individuals’ age. We found, in particular, that men are more mobile than women in terms of distance traveled. But conversely, women are much more mobile than men in terms of frequency of trips

Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors

Understanding and modeling the mobility of individuals is of paramount importance for public health. In particular, mobility characterization is key to predict the spatial and temporal diffusion of human-transmitted infections. However, the mobility behavior of a person can also reveal relevant information about her/his health conditions. In this paper, we study the impact of people mobility behaviors for predicting the future presence of flu-like and cold symptoms (i.e. fever, sore throat, cough, shortness of breath, headache, muscle pain, malaise, and cold). To this end, we use the mobility traces from mobile phones and the daily self-reported flu-like and cold symptoms of 29 individuals from February 20, 2013 to March 21, 2013. First of all, we demonstrate that daily symptoms of an individual can be predicted by using his/her mobility trace characteristics (e.g. total displacement, radius of gyration, number of unique visited places, etc.). Then, we present and validate models that are able to successfully predict the future presence of symptoms by analyzing the mobility patterns of our individuals. The proposed methodology could have a societal impact opening the way to customized mobile phone applications, which may detect and suggest to the user specific actions in order to prevent disease spreading and minimize the risk of contagion

Agentenbasierte Modellierung und Simulation im Pandemiemanagement

Mathematical models and computer simulations play a crucial role in the context of the COVID-19 crisis for knowledge about the possible course of the pandemic and for appropriate policy decisions. The paper presents results from an ethnographic study of a government-funded R & D project dealing with agent-based modeling and simulation (ABMS) in the context of pandemic management. Based on the assumption that the use of computer simulations in pandemic management is not only a means to an end for political or epidemiological goals but also plays a significant role in determining which goals and strategies appear politically legitimate, the paper reconstructs how insights into the pandemic are generated in ABMS and specifically in the researched project and made accessible for decision-making