Predictions

ID

Lang

Model name

Author

Repository

Description

257

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.

333

LSTM model for Infodengue Sprint

Predictions for 2025 in PB using the baseline architecture

307

Temp-SPI Interaction Model

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

357

Model 1 - Weekly and yearly (iid) components

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.

788

LSTM model for Infodengue Sprint

Predictions for 2024 in PR using the baseline architecture

280

BB-M

Prediction for CE in 2025

752

Model 2 - Weekly and yearly (rw1) components

This upload represents the epidemic prediction for validation test 2 in the state GO.

375

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 PR.

567

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on CE_info_dengue_2024_2025

596

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on DF_info_dengue_2024_2025

654

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on SC_info_dengue_test_2

563

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on GO_info_dengue_test_2

304

Temp-SPI Interaction Model

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

353

LSTM model for Infodengue Sprint

Predictions for 2025 in RO using the comb_att_n architecture

539

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 RO.

519

Model 1 - Weekly and yearly (iid) components

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

513

Model 1 - Weekly and yearly (iid) components

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

203

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.

267

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.

269

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.

259

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.

268

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.

374

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.

371

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 AM.

360

Model 1 - Weekly and yearly (iid) components

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.

488

LSTM model for Infodengue Sprint

Predictions for 2024 in RS using the baseline architecture

790

LSTM model for Infodengue Sprint

Predictions for 2024 in RJ using the baseline architecture

706

Model 1 - Weekly and yearly (iid) components

This upload represents the epidemic prediction for validation test 2 in the state RJ.

649

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on RJ_info_dengue_test_2

485

LSTM model for Infodengue Sprint

Predictions for 2024 in RJ using the baseline architecture