Evidence map›Paper›PMID 42285634›Full record

ArticleSpatial and spatio-temporal epidemiology2026

Assessing the validity and reliability of geotracking devices in urban settings of Nairobi, Kenya.

Daniel I Kakou, Phylis Busienei, Kelly K Baker, Sabin Gaire, Daniel K Sewell

Abstract read
In one paragraph

Article in Spatial and spatio-temporal epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Daniel I KakouDepartment of Biostatistics, University of Iowa, 145 N. Riverside Dr., Iowa City, 52242, Iowa, USA. Electronic address: danielisrael-kakou@uiowa.edu.
Phylis BusieneiPopulation Dynamics and Urbanization, African Population and Health Research Center, APHRC Campus, Nairobi, Kenya. Electronic address: pbusienei@aphrc.org.
Kelly K BakerDepartment of Epidemiology and Environmental Health, State University of New York at Buffalo, 401 Kimball Tower, Buffalo, 14214, NY, USA. Electronic address: kkbaker@buffalo.edu.
Sabin GaireDepartment of Biostatistics, University of Iowa, 145 N. Riverside Dr., Iowa City, 52242, Iowa, USA. Electronic address: sabin-gaire@uiowa.edu.
Daniel K SewellDepartment of Biostatistics, University of Iowa, 145 N. Riverside Dr., Iowa City, 52242, Iowa, USA. Electronic address: daniel-sewell@uiowa.edu.

Funding

Statistical and agent-based modeling of complex microbial systems: a means for understanding enteric disease transmission among children in urban neighborhoods of KenyaR01TW011795 · FIC · UNIVERSITY OF IOWA · PI BAKER, KELLY K, SEWELL, DANIEL · 2020 to 2024
$2.7M
FIC NIH HHS R01 TW011795
6 · The paper itself

Abstract

Spatial methods are critical to the understanding of infectious disease transmission and accurate exposure assessments. It is widely acknowledged that a One-Health approach is required to understand infectious disease processes, implying that knowledge of spatial patterns of both humans and animals is necessary. This is particularly so in urban areas and informal settlements where human-animal interactions are increasing. Towards this, we have described in prior work the feasibility of geotracker deployments using Tractive Global Positioning System (GPS) devices. This study extends that work by examining the validity (difference between true and reported locations) and reliability (reported locations remaining constant while in the same location) of data obtained from the deployment of tractive GPS devices in urban environments in Nairobi, Kenya. We recorded GPS data in three different types of locations - open spaces, alleyways, and inside homes - in each of two neighborhoods characterized by different socioeconomic statuses (SES). Our results from a set of generalized linear models indicated that the median distance between true and reported locations ranged from 9 m to 28 m, depending on housing density and location type, with the upper bound of 95% prediction intervals ranging from 16 m to 53 m, again depending on housing density and location type. In addition, Tractive devices showed very strong reliability in both our lower- and higher-housing density neighborhoods, with median distances ranging from 0-1 m between sequential reported locations while not moving. This implies that these geotracking devices are useful for understanding where humans and animals spend their time, but only up to a certain level of spatial granularity.

Indexed as

Geographic Information SystemsAnimalsHumansKenyaReproducibility of ResultsUrban PopulationEnvironmental healthGlobal Positioning SystemOne HealthSpatial exposureTractive deviceValidity and reliability of GPS distance measures

Identifiers

PMID42285634
PMCPMC13263551

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.