Suchi Tyagi
Biographic Data
| ID | 9064087 |
|---|---|
| NAME | Suchi Tyagi |
| GIVEN NAMES | Suchi |
| FAMILY NAME | Tyagi |
| SIGNATURE | TYAGI S |
| AFFILIATIONS | Vector Control Research Centre |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Hybrid models combining trend and seasonality components with machine learning algorithms provide accurate forecasting of malaria incidence
Forecasting malaria incidence is vital for effective resource allocation during malaria elimination. In this study, we highlight robust models for forecasting incidence using climatic and malaria data from Goa, India. Multi-collinearity and Shapley Additive Explanations (SHAP) were used to identify most important predictors of malaria transmission among 15 climatic variables. Three machine-learning models (Support vector machines, Random Forest, …
Vivax malaria
Plasmodium vivax is geographically the most widely dispersed human malaria parasite species. It has shown resilience and a great deal of adaptability. Genomic studies suggest that P. vivax originated from Asia or Africa and moved to the rest of the world. Although P. vivax is evolutionarily an older species than Plasmodium falciparum , its biology, transmission, pathology, and control still require better elucidation. P. vivax poses problems for …
No prominent works on this page.
Vivax malaria
Plasmodium vivax is geographically the most widely dispersed human malaria parasite species. It has shown resilience and a great deal of adaptability. Genomic studies suggest that P. vivax originated from Asia or Africa and moved to the rest of the world. Although P. vivax is evolutionarily an older species than Plasmodium falciparum , its biology, transmission, pathology, and control still require better elucidation. P. vivax poses problems for …
Hybrid models combining trend and seasonality components with machine learning algorithms provide accurate forecasting of malaria incidence
Forecasting malaria incidence is vital for effective resource allocation during malaria elimination. In this study, we highlight robust models for forecasting incidence using climatic and malaria data from Goa, India. Multi-collinearity and Shapley Additive Explanations (SHAP) were used to identify most important predictors of malaria transmission among 15 climatic variables. Three machine-learning models (Support vector machines, Random Forest, …
Malaria (2 works) · Malaria Research and Control (2 works) · Biology (1 work) · Chloroquine (1 work) · Confidence interval (1 work) · Drug-Induced Hepatotoxicity and Protection (1 work) · Ensemble learning (1 work) · Extreme Learning Machine (1 work) · Forecasting Techniques and Applications (1 work) · Hemoglobinopathies and Related Disorders (1 work)