Predictions
ID
Lang
Model name
Repository
Description
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 GO.
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state PA.
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 SE.
LaCiD/UFRN
Dengue predictions for PB using Validation Test 1
LSTM model for Infodengue Sprint
Predictions for 2023 in RS 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 RN.
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 PI.
LSTM model for Infodengue Sprint
Predictions for 2024 in SE 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 PE.
LSTM model for Infodengue Sprint
Predictions for 2024 in MT using the att_3 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 CE.
LSTM model for Infodengue Sprint
Predictions for 2024 in RJ using the baseline architecture
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state AC.
LSTM model for Infodengue Sprint
Predictions for 2024 in AC using the comb_att_n architecture
LaCiD/UFRN
Dengue predictions for MG using Validation Test 3
LSTM model for Infodengue Sprint
Predictions for 2023 in MT using the att_3 architecture
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state AP.
Model 2 - Weekly and yearly (rw1) 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 SC.
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 TO.
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 RR.
LSTM model for Infodengue Sprint
Predictions for 2023 in PE using the baseline architecture
LSTM model for Infodengue Sprint
Predictions for 2025 in RR using the comb_att_n 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 AP.
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 AC.
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 TO.
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 AP.
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.
2569 predictions
https://api.mosqlimate.org/api/registry/predictions/?page=45&per_page=30&
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