Predictions
ID
Lang
Model name
Repository
Description
LSTM model for Infodengue Sprint
Predictions for 2023 in MS using the att architecture
Chronos-Bolt
Validation set 3 for TO using Chronos-Bolt
Dengue oracle - M2
Validation test 2 for sprint 2025 preds in PA
Random Forest model with uncertainty computed with conformal prediction
Forecast de novos casos para o geocode 2800308 entre 2022-01-01 e 2023-01-01 usando apenas os dados de todos as cidades clusterizadas com 2800308 como input
Chronos-Bolt
Prediction of Season 25-26 for TO using Chronos-Bolt
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 1 in the state BA.
Dengue oracle - M1
Validation test 1 for sprint 2025 preds in RN
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on CE_info_dengue_test_2
UERJ-SARIMAX-2025-2
Validation test 2 for UF=AL (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 follows a RW(1) process.
CNNLSTM_DengueForecast_Cases_Climate_Data_Driven_Ensemble
Validation test 1 in MT
LSTM-RF model
LSTM-RF predictions for Mosqlimate Sprint 2025
Chronos-Bolt
Prediction of Season 25-26 for MT using Chronos-Bolt
Cornell PEH - NegBinom Baseline model
Validation 2 (NegBinom Baseline model)
Chronos-Bolt
Prediction of Season 25-26 for BA using Chronos-Bolt
Chronos-Bolt
Prediction of Season 25-26 for GO using Chronos-Bolt
Chronos-Bolt
Prediction of Season 25-26 for AM using Chronos-Bolt
Chronos-Bolt
Prediction of Season 25-26 for MA using Chronos-Bolt
Chronos-Bolt
Prediction of Season 25-26 for PI using Chronos-Bolt
LSTM-RF model
LSTM-RF predictions for Mosqlimate Sprint 2025
Chronos-Bolt
Prediction of Season 25-26 for SP using Chronos-Bolt
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.
LaCiD/UFRN
Dengue predictions for PI using Validation Test 2
2862 predictions
https://api.mosqlimate.org/api/registry/predictions/?page=11&per_page=30&
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