Sourcing Eco-Epidemiological Field Parameters to Describe a Slum Household-Based Pathogenic Leptospira Population Dynamics Simulation Model in Kenya: A Pseudo-Longitudinal Study Protocol

Abstract

Leptospirosis is a neglected zoonosis and an emerging urban slum health problem. There is a scarcity of population-based information on the distribution of the infection and transmission determinants for urban Leptospirosis in developing countries. This study aims to source dynamic ecological and epidemiological field parameters to describe a slum household-based pathogenic Leptospira population dynamics simulation model. The model will be analyzed to assess the role of slum-based biotic (human and animal) and abiotic (soil, water and weather) habitats in Leptospira propagation. Influential habitats would serve as key targets for interventions. To achieve this aim, we have designed a pseudo-longitudinal survey to be undertaken for two years in Kibera slums in Nairobi city Kenya. Human and animal blood and urine samples will be collected at the household level whereas soil and water samples will be collected at the community level. Attempts will be made to isolate Leptospira bacteria from urine, soil and water samples. Antibody detection in animal and human serum samples will utilize Microscopic Agglutination Test (MAT). Study outcomes will include (i) geo-referencing of adverse environmental attributes that support Leptospira growth, (ii) animal and human seasonal public health burden of Leptospirosis, (iii) seasonal variation in the distribution of Leptospira determinants in a slum setting, and (iv) a simulation model based on an ecological metapopulation framework to track Leptospira bacterial population dynamics in biotic and abiotic habitats in a slum setting. This is the first study in Kenya to describe the role of temporal dynamics of multihost community and adverse environmental attributes in Leptospira propagation in a slum setting. The study is strengthened by the pseudo-longitudinal design whose findings are expected to inform public health decision-making. Going forward, it will be possible to parameterize complex realistic Leptospira transmission models for understanding urban Leptospirosis dynamics