Predictions
ID
Lang
Model name
Repository
Description
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on MS_info_dengue_test_2
LSTM-RF model
LSTM-RF predictions for Mosqlimate Sprint 2025
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 AL.
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 RO.
Prophet model with PCA and vaiance threshold
predict for 2025 of the Prophet model in CE
LSTM model for Infodengue Sprint
Predictions for 2025 in ES 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 CE.
LaCiD/UFRN
Dengue predictions for CE using Validation Test 3
Temp-SPI Interaction Model
2024/25 forecast for three-way interaction model (DF)
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 GO.
LaCiD/UFRN
Dengue predictions for PE using Validation Test 3
Cornell PEH - NegBinom Baseline model
Validation 1 (NegBinom Baseline model)
UERJ-SARIMAX-2025-2
Forecast 2025-2026 for UF=CE (UERJ-SARIMAX-2025-2)
LaCiD/UFRN
Dengue predictions for RO using Validation Test 2
LaCiD/UFRN
Dengue predictions for AP using Validation Test 2
LaCiD/UFRN
Dengue predictions for SE using Validation Test 1
LaCiD/UFRN
Dengue predictions for RR using Validation Test 1
LaCiD/UFRN
Dengue predictions for AP using Validation Test 1
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. 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.
autoarima
arima forecast for RJ
3054 predictions
https://api.mosqlimate.org/api/registry/predictions/?page=102&per_page=30&
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