Predictions
ID
Lang
Model name
Repository
Description
UERJ-SARIMAX-2025-2
Validation test 3 for UF=RO (UERJ-SARIMAX-2025-2)
UERJ-SARIMAX-2025-2
Validation test 3 for UF=MT (UERJ-SARIMAX-2025-2)
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 1 in the state AL.
CNNLSTM_DengueForecast_Cases_Climate_Data_Driven_Ensemble
2025-2026 Forecast in SE
CNNLSTM_DengueForecast_Cases_Climate_Data_Driven_Ensemble
Validation test 3 in SE
UERJ-SARIMAX-2025-2
Validation test 2 for UF=MS (UERJ-SARIMAX-2025-2)
UERJ-SARIMAX-2025-2
Validation test 1 for UF=TO (UERJ-SARIMAX-2025-2)
Model 2 - Weekly and yearly (rw1) components
The model is founded on a structural decomposition designed for modeling counting series, employing a Poisson distribution. The log intensity is defined by the sum of weekly and yearly components, where the first one is defined as a AR(1) process and the last one is assumed to be iid.
UERJ-SARIMAX-2025-2
Validation test 3 for UF=AM (UERJ-SARIMAX-2025-2)
LSTM-RF model
LSTM-RF predictions for Mosqlimate Sprint 2025
UERJ-SARIMAX-2025-2
Validation test 3 for UF=AP (UERJ-SARIMAX-2025-2)
UERJ-SARIMAX-2025-2
Validation test 1 for UF=MA (UERJ-SARIMAX-2025-2)
CNNLSTM_DengueForecast_Cases_Climate_Data_Driven_Ensemble
Validation test 1 in SE
CNNLSTM_DengueForecast_Cases_Climate_Data_Driven_Ensemble
Validation test 2 in SE
UERJ-SARIMAX-2025-2
Validation test 3 for UF=GO (UERJ-SARIMAX-2025-2)
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state AM.
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2704302 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode 2704302
LSTM-RF model
LSTM-RF predictions for Mosqlimate Sprint 2025
LSTM-RF model
LSTM-RF predictions for Mosqlimate Sprint 2025
3054 predictions
https://api.mosqlimate.org/api/registry/predictions/?page=44&per_page=30&
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