Predictions
ID
Lang
Model name
Repository
Description
LSTM-RF model
LSTM-RF predictions for Mosqlimate Sprint 2025
Temp-SPI Interaction Model
2024/25 forecast for three-way interaction model (RR)
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.
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.
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.
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.
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.
LaCiD/UFRN
Dengue predictions for RS using Validation Test 2
Random Forest model with uncertainty computed with conformal prediction
Forecast de novos casos para o geocode 2704302 entre 2022-01-01 e 2023-01-01 usando apenas os dados de todos as cidades clusterizadas com 2704302 como input
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.
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.
Random Forest model with uncertainty computed with conformal prediction
Forecast de novos casos para o geocode 2211001 entre 2022-01-01 e 2023-01-01 usando apenas os dados de todos as cidades clusterizadas com 2211001 como input
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.
LSTM model for Infodengue Sprint
Predictions for 2024 in RO using the comb_att_n architecture
Random Forest model with uncertainty computed with conformal prediction
Forecast de novos casos para o geocode 2304400 entre 2022-01-01 e 2023-01-01 usando apenas os dados de todos as cidades clusterizadas com 2304400 como input
LSTM model for Infodengue Sprint
Predictions for 2024 in RS using the baseline architecture
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2111300 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode 2111300
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=RN (LNCC-CLiDENGO model)
Temp-SPI Interaction Model
Validation test 1 for three-way interaction model (DF)
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on GO_info_dengue_2024_2025
LSTM model for Infodengue Sprint
Predictions for 2023 in MA using the baseline architecture
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.
UERJ-SARIMAX-2025-2
Validation test 1 for UF=AP (UERJ-SARIMAX-2025-2)
Cornell PEH - NegBinom Baseline model
Validation 2 (NegBinom Baseline model)
3054 predictions
https://api.mosqlimate.org/api/registry/predictions/?page=82&per_page=30&
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