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Updated: Jun 4, 2025

Author Spotlight: Leptospira DNA Detection in Water for Environmental Analysis and Disease Surveillance

Author Spotlight: Leptospira DNA Detection in Water for Environmental Analysis and Disease Surveillance

Published on: June 14, 2024

Predicting Leptospirosis Using Baseline Laboratory Tests and Geospatial Mapping of Acute Febrile Illness Cases

Mallika Sengupta¹, Aditya Kundu¹, Saikat Mandal²

¹Microbiology, All India Institute of Medical Sciences, Kalyani, IND.

|December 18, 2024

Summary

This summary is machine-generated.

Leptospirosis diagnosis is challenging due to nonspecific symptoms. Machine learning models, like KNN, showed moderate accuracy in predicting leptospirosis, while geographic mapping identified disease clusters.

Keywords:

acute febrile illness fever k-nearest neighbours leptospira machine learning model

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Area of Science:

Infectious Diseases
Epidemiology
Medical Informatics

Background:

Leptospirosis, a zoonotic infection caused by *Leptospira* bacteria, is reemerging globally.
The disease presents diagnostic challenges due to nonspecific symptoms and can lead to severe outcomes with high mortality.
Poor sanitation and urbanization are linked to increased risk in affected regions.

Purpose of the Study:

To investigate associations between laboratory parameters and leptospirosis diagnosis.
To identify spatial patterns and high-risk areas using geographic mapping.
To evaluate the utility of machine learning models for leptospirosis prediction.

Main Methods:

An observational retrospective study analyzed 325 patients with suspected leptospirosis over one year.
Laboratory investigations, geographic mapping, and machine learning (k-nearest neighbors - KNN) were employed.
IgM ELISA was used for laboratory confirmation of leptospirosis.

Main Results:

Of 325 patients, 43 (13.2%) tested positive for leptospirosis.
Geographic mapping revealed case clusters in West Bengal, India, with some cases from Tripura and Bangladesh.
No significant association was found between individual laboratory parameters and diagnosis; KNN showed 74% accuracy (AUC 0.6).

Conclusions:

Geographic mapping identified leptospirosis case clusters but no strong links with individual lab parameters.
Machine learning models, particularly KNN, offer moderate predictive accuracy for leptospirosis.
Overlapping clinical features with dengue and scrub typhus complicate diagnosis in endemic areas like West Bengal.