An Agent-Based Model of Epidemic Spread Using Human Mobility and Social Network Information

Abstract

Abstract-The recent adoption of ubiquitous computing technologies has enabled capturing large amounts of human behavioral data. The digital footprints computed from these datasets provide information for the study of social and human dynamics, including social networks and mobility patterns, key elements for the effective modeling of virus spreading. Traditional epidemiologic models do not consider individual information and hence have limited ability to capture the inherent complexity of the disease spreading process. To overcome this limitation, agent-based models have recently been proposed as an effective approach to model virus spreading. However, most agent-based approaches to date have not included real-life data to characterize the agents' behavior. In this paper we propose an agent-based system that uses social interactions and individual mobility patterns extracted from call detail records to accurately model virus spreading. The proposed approach is applied to study the 2009 H1N1 outbreak in Mexico and to evaluate the impact that government mandates had on the spreading of the virus. Our simulations indicate that the restricted mobility due the government mandates reduced by 10% the peak number of individuals infected by the virus and postponed the peak of the pandemic by two days.

ICT4ICTD: Computational Social Science for Digital Development

“…For example, an ABM of 2009 H1N1 outbreak in Mexico showed that government mobility restrictions reduced the spread of the virus by about 10% and postponed it by about 2 days [71]. This policy is in agreement with the recommendation of the World Health Organization, which calls for the suspension of activities in educational, government and business units in case of a pandemic.…”

“…Humans conveniently produce relevant data as a byproduct of their digital life. Examples include the above-mentioned use of mobility patterns extracted from mobile phone call records to simulate the 2009 H1N1 outbreak in Mexico and to evaluate the impact that government policies had on the spreading of the virus [71], and the use of policy records to simulate the 2011 civil unrest in London [81]. This allows for a "more realistic representation of human behavior which includes the behavioral changes that might take place" during the dynamic under study [71;p.2].…”

Spatio-Temporal Modelling of Frequent Human Mobility Pattern to Analyse the Dynamics of Epidemic Disease

“…To overcome this limitation, agent based approaches are used widely to model spread of epidemic disease. In agent based modelling (ABM) individual human behaviour [12][13] and its inherent fuzziness are simulated by representing every person as a software agent. In ABM model each agent is characterized with a variety of variables that are related to spread of disease such as social characteristics, socio-economic status, health status, etc.…”

On the relation between socio-economic status and physical mobility

“…Thus, from our analysis, we could determine that in areas with lower socio-economic levels it would be easier to contain the epidemic than in areas with higher SEL, where individuals carry out their daily activities in larger geographical areas. It is important to highlight that although these results provide important insights to model and analyze the spreading of specific epidemics (like HIV or flu) many other variables need to be taken into account to build a realistic spread simulation as presented in [19].…”