Evidence map›Paper›PMID 27471661›Full record

ArticleEPJ data science2016

Quantifying social contacts in a household setting of rural Kenya using wearable proximity sensors.

Moses C Kiti, Michele Tizzoni, Timothy M Kinyanjui, Dorothy C Koech, Patrick K Munywoki, Milosch Meriac, Luca Cappa, André Panisson, Alain Barrat, Ciro Cattuto and 1 more

Abstract read
In one paragraph

Article in EPJ data science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed.

  1. Trial
  2. Article
  3. Social interactions in isolated, confined, and extreme environments: A study of Antarctic winter teams using wearable sensors.Proceedings of the National Academy of Sciences of the United States of America · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Moses C Kiti *KEMRI - Wellcome Trust Research Programme, Kilifi, Kenya.
Michele Tizzoni *Data Science Laboratory, ISI Foundation, Via Alassio 11/c, Torino, 10126 Italy.
Timothy M KinyanjuiKEMRI - Wellcome Trust Research Programme, Kilifi, Kenya.
Dorothy C KoechKEMRI - Wellcome Trust Research Programme, Kilifi, Kenya.
Patrick K MunywokiKEMRI - Wellcome Trust Research Programme, Kilifi, Kenya.
Milosch MeriacBitmanufaktory Ltd, Cambridge, UK.
Luca CappaData Science Laboratory, ISI Foundation, Via Alassio 11/c, Torino, 10126 Italy.
André PanissonData Science Laboratory, ISI Foundation, Via Alassio 11/c, Torino, 10126 Italy.
Alain BarratData Science Laboratory, ISI Foundation, Via Alassio 11/c, Torino, 10126 Italy.
Ciro CattutoData Science Laboratory, ISI Foundation, Via Alassio 11/c, Torino, 10126 Italy.ORCID 0000-0001-9526-4364
D James NokesKEMRI - Wellcome Trust Research Programme, Kilifi, Kenya.

Funding

Wellcome Trust 077092Wellcome Trust 084633Wellcome Trust 098556Wellcome Trust 102975
6 · The paper itself

Abstract

Close proximity interactions between individuals influence how infections spread. Quantifying close contacts in developing world settings, where such data is sparse yet disease burden is high, can provide insights into the design of intervention strategies such as vaccination. Recent technological advances have enabled collection of time-resolved face-to-face human contact data using radio frequency proximity sensors. The acceptability and practicalities of using proximity devices within the developing country setting have not been investigated. We present and analyse data arising from a prospective study of 5 households in rural Kenya, followed through 3 consecutive days. Pre-study focus group discussions with key community groups were held. All residents of selected households carried wearable proximity sensors to collect data on their close (<1.5 metres) interactions. Data collection for residents of three of the 5 households was contemporaneous. Contact matrices and temporal networks for 75 individuals are defined and mixing patterns by age and time of day in household contacts determined. Our study demonstrates the stability of numbers and durations of contacts across days. The contact durations followed a broad distribution consistent with data from other settings. Contacts within households occur mainly among children and between children and adults, and are characterised by daily regular peaks in the morning, midday and evening. Inter-household contacts are between adults and more sporadic when measured over several days. Community feedback indicated privacy as a major concern especially regarding perceptions of non-participants, and that community acceptability required thorough explanation of study tools and procedures. Our results show for a low resource setting how wearable proximity sensors can be used to objectively collect high-resolution temporal data without direct supervision. The methodology appears acceptable in this population following adequate community engagement on study procedures. A target for future investigation is to determine the difference in contact networks within versus between households. We suggest that the results from this study may be used in the design of future studies using similar electronic devices targeting communities, including households and schools, in the developing world context. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1140/epjds/s13688-016-0084-2) contains supplementary material.

Indexed as

contact networkscontact patternshouseholdsinfectious disease controlrespiratory infectionswearable proximity sensors

Identifiers

PMID27471661
PMCPMC4944592

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.