Predictions

ID

Lang

Model name

Author

Repository

Description

424

BB-M

Prediction for PE in 2023 (train 1)

423

BB-M

Prediction for PB in 2023 (train 1)

422

BB-M

Prediction for PA in 2023 (train 1)

421

BB-M

Prediction for MT in 2023 (train 1)

420

BB-M

Prediction for MS in 2023 (train 1)

419

BB-M

Prediction for MA in 2023 (train 1)

418

BB-M

Prediction for ES in 2023 (train 1)

417

BB-M

Prediction for DF in 2023 (train 1)

416

BB-M

Prediction for BA in 2023 (train 1)

415

BB-M

Prediction for AP in 2023 (train 1)

414

BB-M

Prediction for AL in 2023 (train 1)

413

BB-M

Prediction for AC in 2023 (train 1)

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.

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.

373

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 GO.

372

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 CE.

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.

370

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.

369

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 MG.

368

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 GO.

367

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.

366

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 AM.

365

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.

364

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.

363

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 GO.

362

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 CE.

361

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.

359

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 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.

358

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 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.

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.

356

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 AM. 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.

355

LSTM model for Infodengue Sprint

Predictions for 2025 in PA using the comb_att_n architecture

354

LSTM model for Infodengue Sprint

Predictions for 2025 in AC using the comb_att_n architecture

353

LSTM model for Infodengue Sprint

Predictions for 2025 in RO using the comb_att_n architecture

352

LSTM model for Infodengue Sprint

Predictions for 2025 in RR using the comb_att_n architecture

351

LSTM model for Infodengue Sprint

Predictions for 2025 in TO using the comb_att_n architecture

350

LSTM model for Infodengue Sprint

Predictions for 2025 in AP using the comb_att_n architecture

349

LSTM model for Infodengue Sprint

Predictions for 2025 in AM using the comb_att_n architecture

348

LSTM model for Infodengue Sprint

Predictions for 2025 in MS using the att_3 architecture

347

LSTM model for Infodengue Sprint

Predictions for 2025 in MT using the att_3 architecture

346

LSTM model for Infodengue Sprint

Predictions for 2025 in GO using the att_3 architecture

345

LSTM model for Infodengue Sprint

Predictions for 2025 in DF using the att_3 architecture

344

LSTM model for Infodengue Sprint

Predictions for 2025 in ES using the baseline architecture

343

LSTM model for Infodengue Sprint

Predictions for 2025 in RJ using the baseline architecture

342

LSTM model for Infodengue Sprint

Predictions for 2025 in MG using the baseline architecture

341

LSTM model for Infodengue Sprint

Predictions for 2025 in SP using the baseline architecture

340

LSTM model for Infodengue Sprint

Predictions for 2025 in PR using the baseline architecture

339

LSTM model for Infodengue Sprint

Predictions for 2025 in SC using the baseline architecture

338

LSTM model for Infodengue Sprint

Predictions for 2025 in RS using the baseline architecture