Predictions
ID
Lang
Model name
Repository
Description
Example of Univariate neural prophet model
Example Forecast of new cases for 3304557 (Rio de Janeiro) between 2022-01-01 and 2023-07-02
Random Forest model with uncertainty computed with conformal prediction
Forecast de novos casos para o geocode 2507507 entre 2022-01-01 e 2023-01-01 usando apenas os dados de todos as cidades clusterizadas com 2507507 como input
LSTM model for Infodengue Sprint
Predictions for 2024 in PA using the comb_att_n architecture
LSTM model for Infodengue Sprint
Predictions for 2024 in MS using the att_3 architecture
LaCiD/UFRN
Dengue predictions for BA using Validation Test 3
LNCC-CLiDENGO-2025-1
Validation test 3 for UF=SC (LNCC-CLiDENGO model)
Temp-SPI Interaction Model
Validation test 1 for three-way interaction model (MA)
LaCiD/UFRN
Dengue predictions for DF using Validation Test 1
LSTM model for Infodengue Sprint
Predictions for 2025 in BA using the baseline architecture
Random Forest model with uncertainty computed with conformal prediction
Forecast de novos casos para o geocode 2611606 entre 2022-01-01 e 2023-01-01 usando apenas os dados de 2611606 como input
Temp-SPI Interaction Model
Validation test 2 for three-way interaction model (CE)
Univariate neural prophet model
Forecast de novos casos para o geocode 3106200 entre 2022-01-01 e 2023-07-02
Univariate neural prophet model
Forecast de novos casos para o geocode 2611606 entre 2022-01-01 e 2023-07-02
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 2211001 como input
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 3304557 entre 2022-01-01 e 2023-01-01 usando os dados do geocode e das cidades clusterizadas com ele
Univariate neural prophet model
Forecast de novos casos para o geocode 2507507 entre 2022-01-01 e 2023-07-02
Random Forest model with uncertainty computed with conformal prediction
Forecast de novos casos para o geocode 3106200 entre 2022-01-01 e 2023-01-01 usando apenas os dados de todos as cidades clusterizadas com 3106200 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.
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2927408 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode e das cidades clusterizadas com ele
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 3106200 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode e das cidades clusterizadas com ele
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2611606 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode e das cidades clusterizadas com ele
Temp-SPI Interaction Model
Validation test 2 for three-way interaction model (AC)
LNCC-CLiDENGO-2025-1
Validation test 3 for UF=RO (LNCC-CLiDENGO model)
2569 predictions
https://api.mosqlimate.org/api/registry/predictions/?page=51&per_page=30&
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