Predictions
ID
Lang
Model name
Repository
Description
Arima model (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202501
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 with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202529
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202538
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202527
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202533
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202525
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202528
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202523
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202520
Random Forest model with uncertainty computed with conformal prediction
Forecast de novos casos para o geocode 2800308 entre 2022-01-01 e 2023-01-01 usando apenas os dados de todos as cidades clusterizadas com 2800308 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 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.
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state MG.
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202522
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202544
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202543
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202541
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202540
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202539
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202537
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202535
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202534
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202524
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202532
LSTM model with climate covariates (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202530
Arima model (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202538
Arima model (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202537
Arima model (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202535
Arima model (3 weeks ahead)
Prediction 3 weeks ahead using data up to epiweek 202534
3054 predictions
https://api.mosqlimate.org/api/registry/predictions/?page=2&per_page=30&
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