Accepted for/Published in: JMIR Research Protocols
Date Submitted: Dec 5, 2022
Date Accepted: Apr 30, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Spatio-Temporal Analysis of Leptospirosis Hotspot Areas and its Association with Hydroclimatic Factors in Selangor and Developing a Predictive Model: A Study Protocol
ABSTRACT
Background:
Leptospirosis is considered a neglected zoonotic disease (NZD) in temperate regions but an endemic disease in countries with tropical climates like South America, Southern Asia and Southeast Asia. There has been an increase in leptospirosis incidence in Malaysia from 1.45 to 25.94 cases per 100,000 population between 2005 to 2014. With concurrent increasing incidence in Selangor and frequent climate change dynamics, a study on the disease hotspot areas and its association with the hydroclimatic factors would further enhance disease surveillance and public health interventions.
Objective:
This study aims to examine the association between the spatio-temporal distribution of leptospirosis hotspot areas from 2011 to 2019 with the hydroclimatic factors in Selangor using the Geographical Information System (GIS) and remote sensing techniques to develop a leptospirosis hotspot predictive model.
Methods:
This will be an ecological cross-sectional study with GIS and remote sensing mapping and analysis concerning leptospirosis, using secondary data. Leptospirosis cases in Selangor from January 2011 to December 2019 shall be obtained from the Selangor State Health Department. Laboratory confirmed cases with data on the possible source of infection would be identified to be georeferenced according to their longitude and latitudes. Topographic data consisting of sub-district boundaries and the distribution of rivers in Selangor will be obtained from the Department of Survey and Mapping (JUPEM). The ArcGIS software will be used to evaluate the clustering of the cases and mapped using the Getis-Ord Gi* tool. The satellite images for rainfall and land surface temperature (LST) will be acquired from the Malaysian Space Agency (MySA) and processed to obtain the average monthly values. Meanwhile, the average monthly river hydrometric levels will be obtained from the Department of Drainage and Irrigation DID). Data is then inputted as thematic layers and in the ArcGIS software for further analysis. The Artificial Neural Network (ANN) analysis in Artificial Intelligence Phyton Software will be used to obtain the leptospirosis hotspot predictive model.
Results:
The leptospirosis distribution and clusters are expected to be significantly associated with the hydroclimatic factors of rainfall, land surface temperature, and the river hydrometric level.
Conclusions:
This study will explore the associations of leptospirosis hotspot areas with the hydroclimatic factors in Selangor, subsequently the development of a leptospirosis predictive model. Clinical Trial: NMRR ID-22-01548-C0Z (IIR)
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