Abstract
Empiric quantification of human mobility patterns is paramount for better urban planning, understanding social network structure and responding to infectious disease threats, especially in light of rapid growth in urbanization and globalization. This need is of particular relevance for developing countries, since they host the majority of the global urban population and are disproportionally affected by the burden of disease. We used Global Positioning System (GPS) data-loggers to track the fine-scale (within city) mobility patterns of 582 residents from two neighborhoods from the city of Iquitos, Peru. We used ∼2.3 million GPS data-points to quantify age-specific mobility parameters and dynamic co-location networks among all tracked individuals. Geographic space significantly affected human mobility, giving rise to highly local mobility kernels. Most (∼80%) movements occurred within 1 km of an individual’s home. Potential hourly contacts among individuals were highly irregular and temporally unstructured. Only up to 38% of the tracked participants showed a regular and predictable mobility routine, a sharp contrast to the situation in the developed world. As a case study, we quantified the impact of spatially and temporally unstructured routines on the dynamics of transmission of an influenza-like pathogen within an Iquitos neighborhood. Temporally unstructured daily routines (e.g., not dominated by a single location, such as a workplace, where an individual repeatedly spent significant amount of time) increased an epidemic’s final size and effective reproduction number by 20% in comparison to scenarios modeling temporally structured contacts. Our findings provide a mechanistic description of the basic rules that shape human mobility within a resource-poor urban center, and contribute to the understanding of the role of fine-scale patterns of individual movement and co-location in infectious disease dynamics. More generally, this study emphasizes the need for careful consideration of human social interactions when designing infectious disease mitigation strategies, particularly within resource-poor urban environments.
Highlights
Routine movements of individuals within cities are of paramount importance for planning urban infrastructures [1], developing transport and commuting alternatives [1,2], improving wireless communication networks [3,4], promoting healthy lifestyles [5], and preventing or responding to emergence, propagation and persistence of infectious disease [6,7,8,9,10,11]
For each simulation we identified the contact structure emerging from the introduced infection and calculated the epidemic curve and the epidemic’s effective reproductive number (Re) [41]
Uncovering the basic mechanisms governing complex human behaviors in resource-poor urban environments is paramount for developing better infrastructure, fostering local economic development and responding to the emergence, transmission and propagation of infectious disease threats
Summary
Routine movements of individuals within cities are of paramount importance for planning urban infrastructures [1], developing transport and commuting alternatives [1,2], improving wireless communication networks [3,4], promoting healthy lifestyles [5], and preventing or responding to emergence, propagation and persistence of infectious disease [6,7,8,9,10,11]. Mathematical models of infectious diseases assumed individuals as having an equal chance of transmitting and getting exposed to disease agents (i.e., homogenous mixing), ignoring stochastic variations in transmission potential or heterogeneities in contact patterns [15]. Individual social structure and movement patterns play a significant role in modulating contact rates, affecting the transmission, spread and persistence of pathogens and drug resistance [10,11]. Most mathematical models of directly transmitted pathogens assume that contacts are fixed (edges in a contact network do not change over time or during the duration of an outbreak). Human movement and potential infectious contacts are highly dynamic, and theoretical models have shown that such heterogeneity can have profound impacts on the transmission and stability of disease outbreaks [19,20,21]. Accounting for human commuting behaviors in meta-population models (by simulating working-age individuals’ daily return to their home census district), significantly reduced the speed of propagation and the predicted impact of disease epidemics in comparison to models assuming irregular (probabilistic or random) movements [11,20,22,23]
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