Predictions


ID

Lang

Model name

Author

Repository

Predict date Type Model ID

Description

359

Model 1 - Weekly and yearly (iid) components

2024-08-29 Model 27

This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state MG. 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

2025-08-22 Model 157

Validation test 1 for UF=PA (UERJ-SARIMAX-2025-2)

UERJ-SARIMAX-2025-1

2025-08-08 Model 151

Validation test 1 for UF=PB (Model SARIMAX)

UERJ-SARIMAX-2025-1

2025-08-08 Model 151

Validation test 3 for UF=TO (Model SARIMAX)

44

Deep learning model using BI-LSTM Layers

2023-09-12 Model 6

Forecast de novos casos para o geocode 2507507 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode e das cidades clusterizadas com ele

34

Deep learning model using BI-LSTM Layers

2023-09-12 Model 6

Forecast de novos casos para o geocode 2304400 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode 2304400

308

Temp-SPI Interaction Model

2024-08-28 Model 22

2024/25 forecast for three-way interaction model (MT)

254

Model 1 - Weekly and yearly (iid) components

2024-08-15 Model 27

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 - M1

2025-08-12 Model 155

Validation test 3 for sprint 2025 preds in RJ

UERJ-SARIMAX-2025-1

2025-08-08 Model 151

Validation test 2 for UF=SE (Model SARIMAX)

563

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model

2024-09-09 Model 34

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on GO_info_dengue_test_2

ISI_Dengue_Model

2025-08-21 Model 134

2022-23 Dengue Forecast for Alagoas

LNCC-SURGE-2025-1

2025-08-03 Model 154

Validation test 3 for UF=PB (Model1 AR_p)

255

Model 1 - Weekly and yearly (iid) components

2024-08-15 Model 27

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

2025-08-22 Model 157

Validation test 2 for UF=RS (UERJ-SARIMAX-2025-2)

LNCC-SURGE-2025-1

2025-08-03 Model 154

Validation test 2 for UF=MA (Model1 AR_p)

357

Model 1 - Weekly and yearly (iid) components

2024-08-29 Model 27

This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state CE. 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.

LNCC-SURGE-2025-1

2025-08-03 Model 154

Validation test 1 for UF=GO (Model1 AR_p)

LNCC-CLiDENGO-2025-1

2025-08-03 Model 152

Validation test 1 for UF=GO (Model3 CLiDENGO)

358

Model 1 - Weekly and yearly (iid) components

2024-08-29 Model 27

This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state GO. 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-2025-1

2025-08-08 Model 151

Validation test 1 for UF=AL (Model SARIMAX)

247

Model 1 - Weekly and yearly (iid) components

2024-08-15 Model 27

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.

CNNLSTM_DengueForecast_Cases_Climate_Data_Driven_Ensemble

2025-08-05 Model 145

Validation test 3 in AP

360

Model 1 - Weekly and yearly (iid) components

2024-08-29 Model 27

This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state PR. 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

2025-08-12 Model 156

Validation test 3 for sprint 2025 preds in RJ

Dengue oracle - M2

2025-08-12 Model 156

Validation test 1 for sprint 2025 preds in PE

Dengue oracle - M1

2025-08-12 Model 155

Validation test 2 for sprint 2025 preds in AC

Dengue oracle - M2

2025-08-12 Model 156

Validation test 3 for sprint 2025 preds in SE

Dengue oracle - M1

2025-08-12 Model 155

Validation test 3 for sprint 2025 preds in PI

Dengue oracle - M2

2025-08-12 Model 156

Validation test 2 for sprint 2025 preds in MG

3054 predictions

https://api.mosqlimate.org/api/registry/predictions/?page=21&per_page=30&


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