Predictions


ID

Lang

Model name

Author

Repository

Predict date Type Model ID

Description

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in AM

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in AC

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in PB

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in AL

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in RN

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in MT

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in RS

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in PE

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in MS

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in CE

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in DF

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in SC

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in ES

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in BA

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in RJ

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in PR

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in GO

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in MG

Modelo fourier-gravidade

2025-07-31 Model 143

Prediction in SP

LaCiD/UFRN

2025-07-30 Model 131

Dengue predictions for RO using Validation Test 2

LaCiD/UFRN

2025-07-30 Model 131

Dengue predictions for AP using Validation Test 2

LaCiD/UFRN

2025-07-30 Model 131

Dengue predictions for SE using Validation Test 1

LaCiD/UFRN

2025-07-30 Model 131

Dengue predictions for RR using Validation Test 1

LaCiD/UFRN

2025-07-30 Model 131

Dengue predictions for AP using Validation Test 1

356

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 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.

2025 sprint test - Sarima

2025-07-22 Model 108

2025 - Sarima - Preditores da picada

2025 sprint test - Sarima

2025-07-22 Model 108

2025 - Sarima - Preditores da picada

2025 sprint test - Sarima

2025-07-22 Model 108

2025 - Sarima - Preditores da picada

2025 sprint test - Sarima

2025-07-22 Model 108

2025 - Sarima - Preditores da picada

2025 sprint test - Sarima

2025-07-22 Model 108

2025 - Sarima - Preditores da picada

2783 predictions

https://api.mosqlimate.org/api/registry/predictions/?page=92&per_page=30&


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