Incorporating Social Relationships from Call Detail Records into Infectious Disease Spread Simulators Open Access

Shats, Ilya (2016)

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Traditionally, mathematical models and surveying have been central to studying the spread of infectious diseases. In this work, we used an anonymized call-detail-record (CDR) dataset, which contains metadata about phone calls, text messages, and data transmissions, as the foundation for predicting spread of influenza-like-illness (ILI) during the 2009 Flu Pandemic in Iceland. The CDR provides population's mobility patterns and in addition to a basic contact tracing, this data can be used to infer people's social networks. Here we show that social strength has an impact on disease spread, supporting the perhaps intuitive idea that an infected individual is likely to transmit the disease to people socially closest to him or her. To simulate ILI spread throughout populations, we built several discrete event simulators (written in the Python programming language) that are described in the second part of the thesis. Though there is still work to be done in improving the models' accuracy in predicting the spread, it is a step forward in the novel area of using cell phone metadata to model infectious disease dynamics.

Table of Contents

Section 1. Introduction 1

1.1 Motivation 1

1.2 Contribution 3

Section 2. Understanding the impact of social strength on disease spread 4

2.1 Description of data 4

2.2. Data Limitations 7

2.3. Data preprocessing 9

2.3.1. Building hashmaps from datasets 10

2.3.2. Dependency between variables 15

2.4. The impact of social strength 18

Section 3. Predicting disease spread with social strength 30

3.1. Seedset 31

3.2. Proposed Frameworks 31

3.2.1. Baseline 31

3.2.2. Disease Base Model 34

3.2.3. Disease Model 2 36

3.2.4. Social Network Base Model 37

3.2.5. Augmented Social Network Model 38

3.3. Evaluation 41

3.4. Results and Future Work 42

Section 4. Conclusion 47

References 48

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