Human population dynamics underlie infectious disease transmission. The movement of infected individuals can introduce pathogens into susceptible populations driving disease outbreaks. However, we have no systematic model for human movement dynamics and existing models are based on our understanding of travel in high-income countries. Policy makers and researchers urgently need a tractable quantification of the epidemiological impact of human travel that is informed by individual mobility data. Novel methods and data sets have driven a step change in our ability to characterize human travel, especially in low-income countries where the burden of infectious disease is high. The proposed research uses innovative sources of ?big data? from mobile phone call records to quantify mobility patterns of millions of individuals. The near ubiquity of mobile phones globally is producing individual level location data from entire populations. This research will focus on developing a data driven framework to model human mobility validated across multiple countries. We will then use our original method to quantify human travel to answer questions related to directly-transmitted and vector- borne disease control. Human travel fuels the size and spread of disease outbreaks, complicates local and global elimination efforts for vaccine-preventable diseases, and creates dynamic sources and sinks of local transmission where the disease would not otherwise exist. Using mathematical, statistical, and computational approaches we will provide data-driven estimates of the relationships between drivers of disease prevalence, incidence, and mobility to improve forecasting predictions across these infections. Computational advancements will be made through validating novel data streams, developing and parameterizing mathematical models, and performing data driven statistical analyses. The integration of novel behavioral data sets directly into epidemiological models is unorthodox and makes the work unsuitable for traditional funding mechanisms. Overall, this work will focus on a key global public health problem that is fundamentally linked to these types of data: how to account for human travel in the spread of infectious diseases?

Public Health Relevance

DISEASE EMERGENCE AND ELIMINATION: USING MOBILITY DATA TO INFORM SPATIAL DISEASE DYNAMICS Narrative/Relevance The movement of infected individuals can introduce pathogens into susceptible populations driving disease outbreaks; however, we do not have a tractable model of human travel that is informed by individual level data. This research will leverage unique mobile phone data sets to quantify human travel and directly relate these to infectious disease dynamics in novel ways that are applicable to public health disease control and elimination strategies.

National Institute of Health (NIH)
National Library of Medicine (NLM)
NIH Director’s New Innovator Awards (DP2)
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Special Emphasis Panel (ZRG1)
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Sim, Hua-Chuan
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Johns Hopkins University
Public Health & Prev Medicine
Schools of Public Health
United States
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