# Spatial analytics to elucidate the incubation period and drivers of visceral leishmaniasis: case of Turkana County in Kenya

## TL;DR

This study uses spatial analytics and machine learning to estimate the incubation period and identify risk factors for visceral leishmaniasis in Turkana County, Kenya.

## Contribution

The study introduces a novel data-driven approach combining statistical and machine learning models to estimate the incubation period and predict VL risk areas.

## Key findings

- AdaBoost was the best-performing model with an AUC of 71.2%.
- The optimal incubation period was predicted to be three months.
- Key predictors included age, humidity, greenness, and proximity to healthcare facilities.

## Abstract

Visceral leishmaniasis (VL) is a severe and neglected tropical disease of public health concern. VL is fatal if not treated. Kenya has experienced multiple outbreaks of the disease since 2017. The underlying drivers of the disease risk dynamics, as well as the incubation period, are not well understood.

We implemented statistical (spatial logistic regression and Bayesian spatial) and machine learning (random forest, support vector machine, AdaBoost, logistic regression, and extra trees) models to estimate the incubation period and predict areas of low/high risk in Turkana County, an endemic VL foci in Kenya. Two-year (2019–2020) patient data were sourced from 12 VL treatment centers in Turkana County. Environmental and weather data were sourced from satellites, while demographic data were extracted from the Kenyan Population and Housing Census 2019 dataset. The environmental and weather data were lagged up to 8 months to mimic the disease incubation period.

The AdaBoost was the best-performing classifier with an area under the curve of the receiver operating characteristic value of 71.2%. The model predicted three months as the optimal incubation period. Age, distance to a healthcare facility, mean monthly humidity, greenness, and total precipitation were identified as the five main predictors. The epidemiological risk map (for December 2024) was generated and deployed on the Web (https://dudumapper.icipe.org/). The Kerio Delta, Lokori, and the shores of the Lake Turkana regions were predicted to have a mid to high risk/number of cases.

These data-driven findings can improve the understanding of VL risk dynamics and support decision makers in the preparation, mitigation, and elimination of VL.