Treffer: Using step selection functions to analyse human mobility using telemetry data in infectious disease epidemiology: a case study of leptospirosis.
Title:
Using step selection functions to analyse human mobility using telemetry data in infectious disease epidemiology: a case study of leptospirosis.
Authors:
Ruiz Cuenca P; Centre for Health Informatics, Computing, and Statistics (CHICAS), Lancaster Medical School, Lancaster University, Lancaster, United Kingdom., Souza FN; Institute of Collective Health, Federal University of Bahia, Salvador, Brazil.; Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Ministério da Saúde, Salvador, Brazil., Coutinho do Nascimento R; Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Ministério da Saúde, Salvador, Brazil., Goncalves da Silva A; Institute of Collective Health, Federal University of Bahia, Salvador, Brazil., Eyre MT; Environmental Health Group, London School of Hygiene and Tropical Medicine, London, United Kingdom., Santana JO; Institute of Collective Health, Federal University of Bahia, Salvador, Brazil.; Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Ministério da Saúde, Salvador, Brazil., de Oliveira D; Institute of Collective Health, Federal University of Bahia, Salvador, Brazil.; Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Ministério da Saúde, Salvador, Brazil., Ribeiro de Souza EV; Institute of Collective Health, Federal University of Bahia, Salvador, Brazil., Palma FG; Institute of Collective Health, Federal University of Bahia, Salvador, Brazil., de Carvalho Santiago DC; Institute of Collective Health, Federal University of Bahia, Salvador, Brazil., Dos Santos Ribeiro P; Federal University of Bahia, National Institute of Science and Technology in Interdisciplinary and Transdiciplinary Studies in Ecology and Evolution, Salvador, Brazil.; Amsterdam University Medical Centre, Leptospirosis Reference Center, Medical Microbiology and Infection Control, Amsterdam, Netherlands., Ferreira Dos Santos PE; Institute of Collective Health, Federal University of Bahia, Salvador, Brazil., Khalil H; Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden., Read JM; Centre for Health Informatics, Computing, and Statistics (CHICAS), Lancaster Medical School, Lancaster University, Lancaster, United Kingdom., Cremonese C; Institute of Collective Health, Federal University of Bahia, Salvador, Brazil., Costa F; Institute of Collective Health, Federal University of Bahia, Salvador, Brazil.; Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Ministério da Saúde, Salvador, Brazil.; Department of Epidemiology of Microbial Diseases, School of Public Health, Yale University, New Haven, United States., Giorgi E; Centre for Health Informatics, Computing, and Statistics (CHICAS), Lancaster Medical School, Lancaster University, Lancaster, United Kingdom.
Source:
ELife [Elife] 2025 Dec 01; Vol. 14. Date of Electronic Publication: 2025 Dec 01.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: eLife Sciences Publications, Ltd Country of Publication: England NLM ID: 101579614 Publication Model: Electronic Cited Medium: Internet ISSN: 2050-084X (Electronic) Linking ISSN: 2050084X NLM ISO Abbreviation: Elife Subsets: MEDLINE
Imprint Name(s):
Original Publication: Cambridge, UK : eLife Sciences Publications, Ltd., 2012-
MeSH Terms:
Comments:
Update of: medRxiv. 2025 Sep 08:2025.04.28.25326582. doi: 10.1101/2025.04.28.25326582.. (PMID: 40343039)
References:
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J Anim Ecol. 2021 May;90(5):1027-1043. (PMID: 33583036)
Emerg Infect Dis. 2004 Jun;10(6):1016-22. (PMID: 15207052)
Elife. 2019 Oct 22;8:. (PMID: 31638575)
PLoS Negl Trop Dis. 2015 Sep 17;9(9):e0003898. (PMID: 26379143)
PLoS Negl Trop Dis. 2008 Apr 23;2(4):e228. (PMID: 18431445)
Vet Microbiol. 2010 Jan 27;140(3-4):287-96. (PMID: 19345023)
PLoS Negl Trop Dis. 2016 Jan 15;10(1):e0004275. (PMID: 26771379)
PLoS Negl Trop Dis. 2018 Aug 31;12(8):e0006752. (PMID: 30169513)
PLOS Glob Public Health. 2022 Dec 9;2(12):e0000408. (PMID: 36962720)
PLoS Negl Trop Dis. 2021 Mar 31;15(3):e0009256. (PMID: 33788864)
Nat Hum Behav. 2020 Aug;4(8):800-810. (PMID: 32424257)
Grant Information:
PhD studentship United Kingdom MRC_ Medical Research Council; R01 AI160780 United States AI NIAID NIH HHS; Fellowship Reckitt Global Hygiene Institute; 150142/2024-2 Conselho Nacional de Desenvolvimento Científico e Tecnológico; 10.35802/218987 United Kingdom WT_ Wellcome Trust; 1R01AI160780-01 National Institute of Allergy and Infectious Diseases; United Kingdom WT_ Wellcome Trust
Contributed Indexing:
Keywords: GPS; epidemiology; global health; human; human movement; infectious diseases; leptospirosis; urban health; zoonosis
Local Abstract: [plain-language-summary] Leptospirosis is a disease caused by Leptospira bacteria and can be transmitted to humans from other animals. It spreads through the urine of infected animals and can infect individuals who come into contact with contaminated water or soil. Previous research indicates that in urban slum settings, men face a higher risk of infection than women, which is believed to result from differences in behavior and access to certain locations rather than biological factors. However, data on human movement is typically gathered using mobile data or Google Location History, which often lack detailed information needed to understand movement and behavior at a more refined scale. GPS loggers are a growing tool for tracking animal movement. These small devices can be worn by individuals and record locations at regular preset time intervals, providing a much more detailed picture than conventional methods. Ruiz Cuenca et al. sought to determine if it was possible to analyze people’s movement through their neighborhoods by adapting existing methods used in ecology. For the movement analysis study, the researchers recruited adults who had been living in one of the study areas in Salvador, Brazil, for at least 6 months. People were tested for potential Leptospira infection and were asked to wear GPS loggers for continuous periods of up to 48 hours between March and November 2022. The GPS loggers recorded their location every 35 seconds. A target of 30 people per study area was chosen, balanced by gender and blind to their infection status. The analysis further focused on three environmental settings: community stream, open sewers and domestic rubbish piles. Ruiz Cuenca et al. used Step Selection Functions (SSFs), a relatively new model for studying the resource selection of animals moving through a landscape. The model compares the environmental attributes of observed steps with alternative random steps taken from the same starting point. The analyses indicate that step selection functions can be adapted to study how people travel through their neighborhoods. Although the methods used are still novel and results are not conclusive, no apparent difference in movement could be found between infection statuses or ages concerning the distances to stream, open sewer points or domestic rubbish piles. However, women tended to move closer to the central stream and farther from open sewer points than men, suggesting that women may avoid open sewers due to perceived risks, while men may not share these perceptions. Moreover, infected individuals were more likely to move outside the buffer zone for open sewers compared to non-infected individuals. Leptospirosis is strongly linked to human dwellings, and living near an open sewer may increase the risk of getting infected. A better understanding of how the movement of individuals could affect their risk of infection may enable the implementation of appropriate measures to reduce infection risk. However, further research is needed to fully understand where infections are happening, for example, by increasing the number of people participating in a study and evaluating perceived infection risks.
Local Abstract: [plain-language-summary] Leptospirosis is a disease caused by Leptospira bacteria and can be transmitted to humans from other animals. It spreads through the urine of infected animals and can infect individuals who come into contact with contaminated water or soil. Previous research indicates that in urban slum settings, men face a higher risk of infection than women, which is believed to result from differences in behavior and access to certain locations rather than biological factors. However, data on human movement is typically gathered using mobile data or Google Location History, which often lack detailed information needed to understand movement and behavior at a more refined scale. GPS loggers are a growing tool for tracking animal movement. These small devices can be worn by individuals and record locations at regular preset time intervals, providing a much more detailed picture than conventional methods. Ruiz Cuenca et al. sought to determine if it was possible to analyze people’s movement through their neighborhoods by adapting existing methods used in ecology. For the movement analysis study, the researchers recruited adults who had been living in one of the study areas in Salvador, Brazil, for at least 6 months. People were tested for potential Leptospira infection and were asked to wear GPS loggers for continuous periods of up to 48 hours between March and November 2022. The GPS loggers recorded their location every 35 seconds. A target of 30 people per study area was chosen, balanced by gender and blind to their infection status. The analysis further focused on three environmental settings: community stream, open sewers and domestic rubbish piles. Ruiz Cuenca et al. used Step Selection Functions (SSFs), a relatively new model for studying the resource selection of animals moving through a landscape. The model compares the environmental attributes of observed steps with alternative random steps taken from the same starting point. The analyses indicate that step selection functions can be adapted to study how people travel through their neighborhoods. Although the methods used are still novel and results are not conclusive, no apparent difference in movement could be found between infection statuses or ages concerning the distances to stream, open sewer points or domestic rubbish piles. However, women tended to move closer to the central stream and farther from open sewer points than men, suggesting that women may avoid open sewers due to perceived risks, while men may not share these perceptions. Moreover, infected individuals were more likely to move outside the buffer zone for open sewers compared to non-infected individuals. Leptospirosis is strongly linked to human dwellings, and living near an open sewer may increase the risk of getting infected. A better understanding of how the movement of individuals could affect their risk of infection may enable the implementation of appropriate measures to reduce infection risk. However, further research is needed to fully understand where infections are happening, for example, by increasing the number of people participating in a study and evaluating perceived infection risks.
Entry Date(s):
Date Created: 20251201 Date Completed: 20251201 Latest Revision: 20251204
Update Code:
20251204
PubMed Central ID:
PMC12668668
DOI:
10.7554/eLife.107153
PMID:
41324251
Database:
MEDLINE
Weitere Informationen
Background: Human movement plays a critical role in the transmission of infectious diseases, especially those with environmental drivers like leptospirosis-a zoonotic bacterial infection linked to mud and water contact. Using GPS loggers, we collected detailed telemetry data to understand how fine-scale movements can be analysed in the context of an infectious disease.
Methods: We recruited individuals living in urban slums in Salvador, Brazil, to analyse how they interact with environmental risk factors such as domestic rubbish piles, open sewers, and a local stream. We aimed to identify differences in movement patterns inside the study areas by gender, age, and leptospirosis serological status. Step selection functions, a spatio-temporal model used in animal movement ecology, estimated selection coefficients to represent the likelihood of movement toward specific environmental factors.
Results: With 128 participants wearing GPS devices for 24-48 hr, recording locations every 35 s during active daytime hours, we segmented movements into morning, midday, afternoon, and evening. Our results suggested women moved closer to the central stream and farther from open sewers compared to men, while serologically positive individuals avoided open sewers.
Conclusions: This study introduces a novel method for analysing human telemetry data in infectious disease research.
Funding: Funding provided by Wellcome Trust, UK Medical Research Council, Brazilian National Research Council, Reckitt Global Hygiene Institute, and National Institute of Allergy and Infectious Diseases.
(© 2025, Ruiz Cuenca et al.)
PR, FS, RC, AG, ME, JS, Dd, ER, FP, Dd, Pd, PF, HK, JR, CC, FC, EG No competing interests declared