Code and data of the "Large-scale Epidemiological modeling: Scanning for Mosquito-Borne Diseases Spatio-temporal Patterns in Brazil" paper
The Episcanner methodology implemented in the paper are implemented in the script: https://github.com/AlertaDengue/episcanner-downloader/blob/main/src/scanner/scanner.py that creates the data plotted in the online dashboard. To run the script is necessary to have the infodengue incidence data that can be accessed here.
The params files saved in the params folder can be downloaded using the example here. The name of the parameters file contains the disease and state names that they refer to.
The train_HGBR_model.ipynb contains the code used to train the regression model discussed in section 2-e) of the article. It also contains the data to generate the Figures 7 and 8. The notebook data/Access_data_from_api.ipynb contains examples of how the incidence data and climate data, used to create the matrix o features in features_sul.csv.gz, can be downloaded.
The gen_article_figures.ipynb contains the code to generate Figures 1, 2, 3, 4, 5, 6 e 9. This code used the parameters table.
The table of features used in the HGBR model is presented in the table below:
| Feature description | Type |
|---|---|
| year: year of the peak week that are being predicted. | temporal |
| cases_jan: sum of cases in the January of the year whose peak week are being predicted.. | epidemiological |
| cases_3rdQ: sum of cases on the third quarter of the previous year. | epidemiological |
| cases_4thQ: sum of cases on the fourth quarter of the previous year. | epidemiological |
| population: population in the previous year. | demographic |
| peak_week: peak week estimated on the previous year. | epidemiological |
| R0: reproduction number estimated on the previous year. | epidemiological |
| ep_dur: epidemic duration estimated on the previous year. | epidemiological |
| mean_temp_4thQ: average of the average temperature over the fourth quarter of the previous year. | climatic |
| temp_range_4thQ: average of the temperature amplitude over the fourth quarter of the previous year. | climatic |
| max_temp_4thQ: average of the maximum temperature over the fourth quarter of the previous year. | climatic |
| min_temp_4thQ: average of the minimum temperature over the fourth quarter of the previous year. | climatic |
| min_humid_4thQ: average of the minimum humidity over the fourth quarter of the previous year. | climatic |
| max_humid_4thQ: average of the maximum humidity over the fourth quarter of the previous year. | climatic |
| humid_range_4thQ: average of the humidity amplitude over the fourth quarter of the previous year. | climatic |
| enso_4thQ: average of the multivariate ENSO (El Niño-Southern Oscillation) in the fourth quarter of the previous year. | climatic |
| tot_precip_4thQ: sum of the total precipitation over the fourth quarter of the previous year. | climatic |
| rainy_days_4thQ: days with rain (precipitation above zero) over the fourth quarter of the previous year. | climatic |
| days_min_temp_4thQ: days with minimum temperature below 15-celsius degrees | climatic |
| days_temp_range_4thQ: days with temperature amplitude above celsius degrees. | climatic |
| days_mean_umid_4thQ: days with average humidity above 0.8. | climatic |
| jan_mean_temp: average of the average temperature over the january of the year whose peak week is being predicted. | climatic |
| jan_temp_range: average of the temperature amplitude over the january of the year whose peak week is being predicted. | climatic |
| jan_max_temp: average of the maximum temperature over the january of the year whose peak week is being predicted. | climatic |
| jan_min_temp: average of the minimum temperature over the january of the year whose peak week is being predicted. | climatic |
| jan_tot_precip: sum of total precipitation over the january of the year whose peak week is being predicted. | climatic |
| rainy_days_jan: days with precipitation over the january of the year whose peak week is being predicted. | climatic |
| enso_jan: average of the ENSO (El Niño-Southern Oscillation) over the January of the year whose peak week is being predicted. | climatic |
| latitude of the city center. | spatial |
| longitude of the city center. | spatial |
