About: Using GPS Technology to Quantify Human Mobility, Dynamic Contacts and Infectious Disease Dynamics in a Resource-Poor Urban Environment

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About: Using GPS Technology to Quantify Human Mobility, Dynamic Contacts and Infectious Disease Dynamics in a Resource-Poor Urban Environment Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Using GPS Technology to Quantify Human Mobility, Dynamic Contacts and Infectious Disease Dynamics in a Resource-Poor Urban Environment
Creator	Bisanzio, Donal Kitron, Uriel Paz-Soldan, Valerie Ramirez-Paredes, Jhon Kochel, Tadeusz Elder, John Halsey, Eric Morrison, Amy Scott, Thomas Stoddard, Steven Vazquez-Prokopec, Gonzalo
topic	covid:91781004fcf2142034de62e1212be3d885139060#this
Source	PMC
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.
has issue date	2013-04-08(xsd:dateTime)
bibo:doi	10.1371/journal.pone.0058802
bibo:pmid	23577059
has license	cc-by
sha1sum (hex)	91781004fcf2142034de62e1212be3d885139060
schema:url	https://doi.org/10.1371/journal.pone.0058802
resource representing a document's title	Using GPS Technology to Quantify Human Mobility, Dynamic Contacts and Infectious Disease Dynamics in a Resource-Poor Urban Environment
has PubMed Central identifier	PMC3620113
has PubMed identifier	23577059
schema:publication	PLoS One
resource representing a document's body	covid:91781004fcf2142034de62e1212be3d885139060#body_text
is http://vocab.deri.ie/void#inDataset of	proxy:http/ns.inria.fr/covid19/91781004fcf2142034de62e1212be3d885139060
is schema:about of	named entity 'SHARP' named entity 'USING' named entity 'REPEATEDLY' named entity 'RAPID' named entity 'THE CITY' named entity 'SITUATION' named entity 'SCALE' named entity 'CONTACTS' named entity 'TRACKED' named entity 'POTENTIAL' named entity 'DISEASE DYNAMICS' named entity 'INFECTIOUS DISEASE' named entity 'OCCURRED' named entity 'URBAN PLANNING' named entity 'NETWORKS' named entity 'FINE' named entity 'RELEVANCE' named entity 'STRUCTURED' named entity 'RISE' named entity 'GIVING' named entity 'LIGHT' named entity 'INCREASED' named entity 'CENTER' named entity 'RESOURCE' named entity 'SPACE' named entity 'EMPIRIC' named entity 'CONSIDERATION' named entity 'QUANTIFICATION' named entity 'NETWORK STRUCTURE' named entity 'NEIGHBORHOODS' named entity 'SOCIAL NETWORK' named entity 'DEVELOPING COUNTRIES' named entity 'SOCIAL INTERACTIONS' named entity 'USED' named entity 'URBANIZATION' named entity 'DYNAMIC' named entity 'CONTRIBUTE ' named entity 'FINDINGS' named entity 'DYNAMICS' named entity 'MOVEMENT' named entity 'MILLION' named entity 'INFECTIOUS DISEASE' named entity 'URBAN ENVIRONMENT' named entity 'social network' named entity 'urban planning' named entity 'space' named entity 'This' named entity 'study' named entity 'unstructured' named entity 'temporally' named entity 'Technology' named entity 'basic rules' named entity 'neighborhood' named entity 'shape' named entity 'Mobility' named entity 'Contacts' named entity 'RESOURCE' named entity 'USING' named entity 'QUANTIFY' covid:arg/91781004fcf2142034de62e1212be3d885139060 named entity 'SIGNIFICANT' named entity 'UP TO' named entity 'TO TRACK' named entity 'SHAPE' named entity 'PATTERNS' named entity 'INDIVIDUALS' named entity 'WHERE' named entity 'PATHOGEN' named entity 'PARAMETERS'

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