Predictions
ID
Lang
Model name
Repository
Description
CNNLSTM_DengueForecast_Cases_Climate_Data_Driven_Ensemble
Validation test 1 in AL
UERJ-SARIMAX-2025-2
Validation test 1 for UF=MA (UERJ-SARIMAX-2025-2)
UERJ-SARIMAX-2025-2
Validation test 3 for UF=AP (UERJ-SARIMAX-2025-2)
UERJ-SARIMAX-2025-2
Validation test 2 for UF=MS (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.
UERJ-SARIMAX-2025-2
Validation test 2 for UF=RR (UERJ-SARIMAX-2025-2)
LSTM-RF model
LSTM-RF predictions for Mosqlimate Sprint 2025
CNNLSTM_DengueForecast_Cases_Climate_Data_Driven_Ensemble
Validation test 3 in SE
UERJ-SARIMAX-2025-2
Validation test 3 for UF=GO (UERJ-SARIMAX-2025-2)
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
CNNLSTM_DengueForecast_Cases_Climate_Data_Driven_Ensemble
Validation test 1 in SE
CNNLSTM_DengueForecast_Cases_Climate_Data_Driven_Ensemble
Validation test 2 in SE
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.
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 PR. 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.
2517 predictions
https://api.mosqlimate.org/api/registry/predictions/?page=22&per_page=30&
Page 22 of 84
