# Modeling the seasonal and climate-dependent dynamics of visceral leishmaniasis in Brazil: Implications for transmission and Control

**Authors:** Quinn H. Adams, Davidson H. Hamer, Lucy R. Hutyra, Gregory A. Wellenius, Kayoko Shioda

PMC · DOI: 10.1016/j.idm.2025.11.009 · Infectious Disease Modelling · 2025-11-25

## TL;DR

This study models how climate and seasons affect visceral leishmaniasis in Brazil, showing that vector control and environmental sanitation are most effective for reducing cases.

## Contribution

A climate-informed mechanistic model of visceral leishmaniasis transmission in Brazil, calibrated with real data and used to evaluate intervention impacts.

## Key findings

- Vector control and environmental sanitation interventions significantly reduce visceral leishmaniasis cases.
- Combined strategies are more effective than individual interventions in reducing disease incidence.
- Canine-focused interventions have limited impact compared to vector and environmental measures.

## Abstract

Visceral leishmaniasis (VL) is a parasitic, zoonotic neglected tropical disease that remains a persistent public health challenge in endemic regions of Brazil, including the state of Maranhão. Transmission dynamics are complex, involving interactions between Lutzomyia longipalpis sandflies, canine reservoirs, and human hosts, and are influenced by environmental and climatic variability. Mathematical models are critical tools for understanding these dynamics and identifying opportunities to effectively disrupt transmission.

Our objective was to develop and calibrate a climate-informed mechanistic model of VL transmission in Maranhão, Brazil, and to evaluate the potential impacts of vector, environmental, and reservoir-targeted interventions. The model incorporates seasonally varying sandfly biting rates and vector recruitment and explicitly accounts for climate variability through the El Niño-Southern Oscillation (ENSO). Transmission rates between populations (human, canine reservoir, and sandfly vector) were calibrated using monthly reported human VL cases from 2007 to 2019 in Maranhão. We simulated the impact of four potential interventions on VL incidence: increased vector mortality, environmental sanitation (reducing vector maturation), expanded canine treatment, and increased canine culling.

The model accurately reproduced the observed temporal trends in monthly human VL cases in Maranhão and quantified the nonlinear effects of potential interventions. Vector control was the most effective standalone strategy, with a 10 % increase in sandfly mortality reducing human cases by 43 %, and a 90 % increase leading to a 96 % decline. Environmental sanitation was similarly impactful, with a 50 % reduction in sandfly maturation lowering cases by 72 %, and a 90 % reduction leading to a 97 % decline. Canine-focused strategies were less effective: expanded canine treatment reduced human cases only up to 69 %, while increased euthanasia had only modest effects. A combined intervention strategy was more effective than any individual measure, reducing cases by 61 % at just a 10 % increase in coverage and achieving substantially greater declines at higher levels.

Climate variability and seasonal dynamics were key drivers of VL transmission in this setting. Our findings highlight the importance of integrating vector control and environmental management as core components of VL mitigation strategies. While canine-focused interventions may contribute incremental benefits, they are less effective than other interventions and are insufficient when implemented in isolation.

## Linked entities

- **Diseases:** visceral leishmaniasis (MONDO:0005445)
- **Species:** Lutzomyia longipalpis (taxon 7200)

## Full-text entities

- **Diseases:** neglected tropical disease (MESH:D058069), VL (MESH:D007898)
- **Species:** Canis lupus familiaris (dog, subspecies) [taxon 9615], Homo sapiens (human, species) [taxon 9606], Lutzomyia longipalpis (species) [taxon 7200]

## Full text

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## Figures

3 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12757486/full.md

## References

32 references — full list in the complete paper: https://tomesphere.com/paper/PMC12757486/full.md

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Source: https://tomesphere.com/paper/PMC12757486