TY - UNPB

T1 - Using step selection functions to analyse human mobility using telemetry data in infectious disease epidemiology: a case study of leptospirosis

AU - Cuenca, Pablo Ruiz

AU - Souza, Fabio Neves

AU - do Nascimento, Roberta Coutinho

AU - da Silva, Ariane Goncalves

AU - Eyre, Max T

AU - Santana, Juliet O

AU - de Oliveira, Daiana Santos

AU - de Souza, Emile Victoria Ribeiro

AU - Palma, Fabiana Almerinda G

AU - de Carvalho Santiago, Diogo César

AU - Dos Santos Ribeiro, Priscyla

AU - Read, Jonathan M

AU - Cremonese, Cleber

AU - Costa, Federico

AU - Giorgi, Emanuele

PY - 2025/5/1

Y1 - 2025/5/1

N2 - 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. 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. With 124 participants wearing GPS devices for 24 to 48 hours, recording locations every 35 seconds 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. This study introduces a novel method for analysing human telemetry data in infectious disease research, providing critical insights for targeted interventions.

AB - 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. 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. With 124 participants wearing GPS devices for 24 to 48 hours, recording locations every 35 seconds 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. This study introduces a novel method for analysing human telemetry data in infectious disease research, providing critical insights for targeted interventions.

KW - urban health

KW - human movement

KW - Leptospirosis

KW - infectious diseases

KW - zoonosis

KW - GPS

U2 - 10.1101/2025.04.28.25326582

DO - 10.1101/2025.04.28.25326582

M3 - Preprint

C2 - 40343039

T3 - medRxiv : the preprint server for health sciences

BT - Using step selection functions to analyse human mobility using telemetry data in infectious disease epidemiology: a case study of leptospirosis

PB - medRxiv

ER -