Predictions
ID
Lang
Model name
Repository
Description
LSTM model for Infodengue Sprint
Predictions for 2024 in MS using the att_3 architecture
Random Forest model with uncertainty computed with conformal prediction
Forecast de novos casos para o geocode 2927408 entre 2022-01-01 e 2023-01-01 usando apenas os dados de 2927408 como input
Random Forest model with uncertainty computed with conformal prediction
Forecast de novos casos para o geocode 2927408 entre 2022-01-01 e 2023-01-01 usando apenas os dados de todos as cidades clusterizadas com 2927408 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 2023 in DF using the att_3 architecture
Random Forest model with uncertainty computed with conformal prediction
Forecast de novos casos para o geocode 2408102 entre 2022-01-01 e 2023-01-01 usando apenas os dados de todos as cidades clusterizadas com 2408102 como input
Arima model (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202521
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 todos as cidades clusterizadas com 2611606 como input
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2304400 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode e das cidades clusterizadas com ele
LSTM model for Infodengue Sprint
Predictions for 2024 in TO using the baseline architecture
Example of Univariate neural prophet model
Example Forecast of new cases for 3304557 (Rio de Janeiro) between 2022-01-01 and 2023-07-02
LSTM model for Infodengue Sprint
Predictions for 2023 in AL using the baseline architecture
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
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 2111300 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode e das cidades clusterizadas com ele
LNCC-CLiDENGO-2025-1
Validation test 3 for UF=SC (LNCC-CLiDENGO model)
LaCiD/UFRN
Dengue predictions for BA using Validation Test 3
LSTM model for Infodengue Sprint
Predictions for 2024 in RN using the baseline architecture
LSTM model for Infodengue Sprint
Predictions for 2024 in PI using the baseline architecture
LaCiD/UFRN
Dengue predictions for DF using Validation Test 1
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.
Temp-SPI Interaction Model
Validation test 2 for three-way interaction model (CE)
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
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
Arima model (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202520
3054 predictions
https://api.mosqlimate.org/api/registry/predictions/?page=74&per_page=30&
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