Models
ID
Lang
Name
Uploaded at
Description
UERJ-SARIMAX-2025-2
2025-08-22
SARIMAX is a forecasting model for DENV dynamics through a seasonal autoregressive integrated moving average with exogenous inputs. This model was developed by Prof. Marcelo Rubens Amaral (UERJ, Brazil).
LNCC-CLiDENGO-2025-1
2025-08-03
CLimate Logistic DENGue Outbreak Simulator (CLiDENGO) is a forecasting model for DENV dynamics through a mechanistic climate-modulated beta-logistic growth differential equation. This model was developed by Prof. Americo Cunha Jr (LNCC/UERJ, Brazil) and Prof. Christian Soize (Université Gustave Eiffel, France), with the collaboration of Prof. Emanuelle Arantes Paixão (LNCC, Brazil) for model calibration.
LNCC-AR_p-2025-1
2025-08-03
AR_p is a forecasting model for DENV dynamics through an autoregressive process of order p. This model was developed by Prof. Paulo Antonio Andrade Esquef (LNCC, Brazil).
LNCC-SURGE-2025-1
2025-08-03
SURGE is a forecasting model for DENV dynamics through an average surge model. This model was developed by Prof. Paulo Antonio Andrade Esquef (LNCC, Brazil).
UERJ-SARIMAX-2025-1
2025-08-03
SARIMAX is a forecasting model for DENV dynamics through a seasonal autoregressive integrated moving average with exogenous inputs. This model was developed by Prof. Marcelo Rubens Amaral (UERJ, Brazil).
Dengue oracle - M1
2025-08-12
Baseline LSTM model with only cases, epiweek and population, predicting the parameters of a log-normal distribution.
Dengue oracle - M2
2025-08-12
Baseline LSTM model with covariates, predicting the parameters of a log-normal distribution.
CNNLSTM_DengueForecast_Cases_Climate_Data_Driven_Ensemble
2025-08-01
An ensemble deep learning model combining CNN and LSTM architectures, driven by reported dengue cases and climate data, designed for forecasting dengue incidence across Brazilian states.
Beat it
2025-08-01
Bayesian baseline model
Modelo fourier-gravidade
2025-07-31
This model integrates a gaussian wrapped with gravitational influence
KAUST GeoHealth Model
2025-07-31
KAUST GeoHealth using LSTM with climate covariates
Cornell PEH - NegBinom Baseline model
2025-07-31
Negative Binomial baseline model for the 2025 sprint
TSMixer ZKI-PH4 - sprint 2025
2025-07-31
TSmixer-based model using TiRex-forecasted features
LSTM-RF model
2025-07-31
Dengue forecasting model using LSTM-RF with climate covariates at ADM1 level
Imperial-TFT Model
2025-07-31
Temporal fusion transformer model for sprint 2025
GHR Model 2025
2025-07-31
Long-short-lag interaction model with monthly climate covariates, using SPEI-12, SPEI-3, 6-month temperature anomaly, 6-month absolute temperature, ONI and Köppen climate classification
ISI_Dengue_Model
2025-07-31
A Compartmental Model for studying the Dengue Epidemic dynamics in Brazilian States
Chronos-Bolt
2025-07-31
Chronos-Bolt model fine-tuned for 2025 Sprint
LaCiD/UFRN
2025-07-30
ARIMAX model used to participate in the 2nd Infodengue-Mosqlimate Dengue Challenge (IMDC)
2025 sprint test - Sarima
2025-07-22
test model for 2025 sprint
Proohet R Example
2025-05-08
test model for 2025 sprint test model for 2025 sprint test model for 2025 sprint test model for 2025 sprint
Example of Univariate neural prophet model
2023-12-06
In this repo, it's implemented a neural prophet model that based only on the times series of cases compute the cases in the next four weeks.
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
2024-09-09
2024_info_dengue_model_hybrid_CNN_LSTM_ensemble_RMSE_Penalties_final_ex_state_CE.ipynb
Model 2 - Weekly and yearly (rw1) components
2024-08-15
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.
Model 1 - Weekly and yearly (iid) components
2024-08-15
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.
BB-M
2024-08-19
Bayesian baseline random effects model
Temp-SPI Interaction Model
2024-08-14
Hierarchical Bayesian mixed model with three-way interaction between temperature, long-lag SPI and short-lag SPI
LSTM model with PCA and vaiance threshold
2024-08-15
using LSTM combined with PCA and vaiance threshold
Prophet model with PCA and vaiance threshold
2024-08-15
using Prophet model combined with PCA and vaiance threshold
LSTM model for Infodengue Sprint
2024-08-08
The models used to forecast the dengue cases in the 2024/2025. It's considered different architectures and set of predictors to predict the 52 weeks in different states
Univariate neural prophet model
2023-12-04
In this repo, it's implemented a neural prophet model that based only on the times series of cases compute the cases in the next four weeks.
Deep learning model using BI-LSTM Layers
2023-11-27
In this repo, in the path /models/neuralnetworks it's implemented a deep learning model with three lstm layers (the first one is bidirectional) interspersed with dropout layers and a dense layer in the output. This model computes the number of new cases in the next four weeks based on the last four weeks of data (cases and climate variables). The confidence interval of the predictions is computed using dropout and making multiple predictions to compute the ci of them.
Random Forest model with uncertainty computed with conformal prediction
2023-11-27
In this repo, in the path /models/gbt it's implemented a random forest regressor model that based on the last four weeks of data (cases and climate variables), compute the cases in the fourth week ahead. The predictions for multiple times are obtained in a rolling window fashion, i.e., the historical data window is moved forward one week at a time, predicting the next fourth week at each step. The confidence interval of the predictions are computed using the conformal prediction.
LSTM model
2024-05-10
In this repo, it's implemented a LSTM model that forecast the cases in the next four weeks and it used to gen probabilist forecasts according to a MEM baseline.
37 models
https://api.mosqlimate.org/api/registry/models/?page=1&per_page=50&
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