Evidence map›Paper›PMID 28573009›Full record

ArticleRoyal Society open science2017

Characterizing and quantifying human movement patterns using GPS data loggers in an area approaching malaria elimination in rural southern Zambia.

Kelly M Searle, Jailos Lubinda, Harry Hamapumbu, Timothy M Shields, Frank C Curriero, David L Smith, Philip E Thuma, William J Moss

Abstract read
In one paragraph

Article in Royal Society open science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 1 pooled it
–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

38 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Mobility patterns, activity locations, and TB in Nairobi, Kenya.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2025
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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

8 authors.

Kelly M SearleDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.ORCID 0000-0003-1316-9342
Jailos LubindaMacha Research Trust, Macha, Choma District, Zambia.
Harry HamapumbuMacha Research Trust, Macha, Choma District, Zambia.
Timothy M ShieldsDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Frank C CurrieroDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
David L SmithInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Philip E ThumaMacha Research Trust, Macha, Choma District, Zambia.
William J MossDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.

Funding

TransmissionU19AI089680 · NIAID · JOHNS HOPKINS UNIVERSITY · PI WILLIAM J MOSS · 2010 to 2026
$25.6M
NIAID NIH HHS U19 AI089680
6 · The paper itself

Abstract

In areas approaching malaria elimination, human mobility patterns are important in determining the proportion of malaria cases that are imported or the result of low-level, endemic transmission. A convenience sample of participants enrolled in a longitudinal cohort study in the catchment area of Macha Hospital in Choma District, Southern Province, Zambia, was selected to carry a GPS data logger for one month from October 2013 to August 2014. Density maps and activity space plots were created to evaluate seasonal movement patterns. Time spent outside the household compound during anopheline biting times, and time spent in malaria high- and low-risk areas, were calculated. There was evidence of seasonal movement patterns, with increased long-distance movement during the dry season. A median of 10.6% (interquartile range (IQR): 5.8-23.8) of time was spent away from the household, which decreased during anopheline biting times to 5.6% (IQR: 1.7-14.9). The per cent of time spent in malaria high-risk areas for participants residing in high-risk areas ranged from 83.2% to 100%, but ranged from only 0.0% to 36.7% for participants residing in low-risk areas. Interventions targeted at the household may be more effective because of restricted movement during the rainy season, with limited movement between high- and low-risk areas.

Indexed as

GISGPS loggershuman mobilitymalaria eliminationmovement patternsZambia

Identifiers

PMID28573009
PMCPMC5451810

What OpenQuestion holds

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Registered trials

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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.