Predictions
ID
Lang
Model name
Repository
Description
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2800308 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode e das cidades clusterizadas com ele
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for validation test 1 in the state CE.
LSTM model for Infodengue Sprint
Predictions for 2023 in BA using the baseline architecture
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on RJ_info_dengue_test_1
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2507507 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode 2507507
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on TO_info_dengue_test_2
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 2 in the state MA.
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 2 in the state PA.
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on SC_info_dengue_test_2
LSTM model for Infodengue Sprint
Predictions for 2024 in PB using the baseline architecture
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 PB.
Model 1 - Weekly and yearly (iid) 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.
LNCC-CLiDENGO-2025-1
Validation test 3 for UF=RR (LNCC-CLiDENGO model)
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on PB_info_dengue_test_1
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on DF_info_dengue_test_1
Temp-SPI Interaction Model
Validation test 1 for three-way interaction model (SE)
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on SP_info_dengue_test_1
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 1 in the state SE.
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 1 in the state AL.
LaCiD/UFRN
Dengue predictions for SE using Validation Test 2
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on AM_info_dengue_test_1
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 1 in the state AC.
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for validation test 1 in the state AM.
2025 sprint test - Sarima
2025 - Sarima - Preditores da picada
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 1 in the state SE.
2025 sprint test - Sarima
2025 - Sarima - Preditores da picada
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 2 in the state DF.
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 follows a RW(1) process.
Model 1 - Weekly and yearly (iid) 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.
2569 predictions
https://api.mosqlimate.org/api/registry/predictions/?page=57&per_page=30&
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