Code analysis for "Impact of intensified control strategies on incidence of visceral leishmaniasis in a highly endemic district of Bihar, India: an interrupted time series analysis" Kumar, Siddiqui, Pollington et al.
this is a repository to design a deep learning model to detect leishmaniasis in microscopic images
Decision-support system for live detection of Leishmania parasites from microscopic images with Deep Learning
Aplicação mobile da Rede de Cuidado ao Calazar: análise de risco em leishmaniose visceral com explicabilidade SHAP. React Native, Expo e TypeScript.
Dataset for "Ecological study measuring the association between conflict, environmental factors, and annual global cutaneous and mucocutaneous leishmaniasis incidence (2005 - 2022)," published in PLOS NTD
KAN-based forecasting of monthly cutaneous leishmaniasis in Antioquia, Colombia, using log relative risk as the target and Kolmogorov–Arnold Networks to integrate climate/ENSO signals, seasonality, and autoregressive dynamics for reproducible, adaptive 12-month forecasts.
First open-source descriptor-augmented LLM for Neglected Tropical Disease drug discovery | OLMo-7B + QLoRA + DeepChem | Bioactivity & Toxicity prediction for Leishmaniasis, Chagas, Malaria, TB
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