Data

Tools

Indexes

Applications

Experiments

Open Source Science

https://github.com/carlos1971salud/assessing-dengue-forecasting-methods

https://github.com/carlos1971salud/assessing-dengue-forecasting-methods

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: medrxiv.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (5.3%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: Carlos1971Salud
  • Language: R
  • Default Branch: main
  • Size: 32.2 KB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created 11 months ago · Last pushed 11 months ago

https://github.com/Carlos1971Salud/Assessing-Dengue-Forecasting-Methods/blob/main/ 

# Assessing-Dengue-Forecasting-Methods
codes for the paper *Assessing Dengue Forecasting Methods: A Comparative Study of Statistical Models and Machine Learning Techniques in Rio de Janeiro, Brazil* 


You can find the pre-print version of the paper [here]( https://medrxiv.org/cgi/content/short/2024.06.12.24308827v1).


There are 2 parts of the models: first is using the cases itself (no-cov); the other is including covariates (cov).

## no-cov

The data is in `data.csv`, only including time and the dengue cases.

Main function is `testing.R`.

All the models are in the `predict_functions.R`.

Using `ar_prediction_result <- predict_AR(data, window_size)` can get a table of results including the real cases and predicting cases.

Then using `print(combine_metrics(ar_prediction_result))` you can get a table of all 3 metrics of the model: MAE, MAPE, and RMSE.



## Cov

The data is stored in `data_with_covarites.csv`, including time, cases, humidity and temperature.

The main function is `testing.R`, you can call `sarimax_prediction_result <- predict_sarimax(data, window_size)` to get the same result table of  real cases and predicting cases, the same as the no-cov.

Then using the same function `print(combine_metrics(sarimax_prediction_result))` you can get the metrics of MAE, MAPE, and RMSE.

### aadiendo un comentario
## este cambio en en la web o en otro sitio remoto

Owner

  • Login: Carlos1971Salud
  • Kind: user

GitHub Events

Total
  • Push event: 1
  • Create event: 1
Last Year
  • Push event: 1
  • Create event: 1