Predictions
ID
Lang
Model name
Repository
Description
Random Forest model with uncertainty computed with conformal prediction
Forecast de novos casos para o geocode 3304557 entre 2022-01-01 e 2023-01-01 usando os dados de todos as cidades clusterizadas com 3304557 como input
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.
Arima model (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202504
Dengue oracle - M2
Validation test 3 for sprint 2025 preds in RO
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 2 in the state RS.
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.
Dengue oracle - M2
Validation test 2 for sprint 2025 preds in BA
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202517
2025 sprint test - Sarima
2025 - Sarima - Preditores da picada
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.
UERJ-SARIMAX-2025-2
Validation test 1 for UF=AM (UERJ-SARIMAX-2025-2)
LNCC-CLiDENGO-2025-1
Validation test 2 for UF=ES (Model3 CLiDENGO)
Dengue oracle - M1
Validation test 1 for sprint 2025 preds in GO
LSTM model for Infodengue Sprint
Predictions for 2023 in PI using the baseline architecture
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for validation test 1 in the state DF.
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2704302 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode e das cidades clusterizadas com ele
Arima model (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202542
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202514
Arima model (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202536
Arima model (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202522
2025 sprint test - Sarima
2025 - Sarima - Preditores da picada
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202503
Cornell PEH - NegBinom Baseline model
Validation 3 (NegBinom Baseline model)
Arima model (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202452
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.
3054 predictions
https://api.mosqlimate.org/api/registry/predictions/?page=1&per_page=30&
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