Predictions
ID
Lang
Model name
Repository
Description
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for validation test 2 in the state BA.
LSTM model for Infodengue Sprint
Predictions for 2023 in AP using the comb_att architecture
LSTM model for Infodengue Sprint
Predictions for 2023 in DF using the baseline architecture
Arima model (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202543
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for validation test 1 in the state PR.
Univariate neural prophet model
Forecast de novos casos para o geocode 2408102 entre 2022-01-01 e 2023-07-02
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for validation test 1 in the state RO.
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 2 in the state AM.
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 1 in the state TO.
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2800308 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 PE using the baseline architecture
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 1 in the state RJ.
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for validation test 1 in the state CE.
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for validation test 2 in the state RO.
Arima model (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202516
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for validation test 2 in the state GO.
LSTM model for Infodengue Sprint
Predictions for 2024 in AC using the baseline architecture
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.
LSTM model for Infodengue Sprint
Predictions for 2023 in GO using the att architecture
LaCiD/UFRN
Dengue predictions for SP using Validation Test 2
LaCiD/UFRN
Dengue predictions for AC using Validation Test 1
LSTM model for Infodengue Sprint
Predictions for 2024 in PR using the baseline architecture
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for validation test 1 in the state SE.
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on SP_info_dengue_test_1
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 1 in the state AL.
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on TO_info_dengue_test_2
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on DF_info_dengue_test_1
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 1 in the state SE.
3054 predictions
https://api.mosqlimate.org/api/registry/predictions/?page=80&per_page=30&
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