Our Implementations

Reproducible Research defines good practices in scientific research methodology involving quantitative computational research. In this space we available some of our computational implementations.
  1. The Python codes and the data for the fitting procedures used in our paper AnŠlise de curvas epidÍmicas da Covid-19 via modelos generalizados de crescimento: Estudo de caso para as cidades de Recife e Teresina, submitted to Revista Brasileira de Epidemiologia, are available here:
    1. REC-CASOS.py
    2. REC-OBITOS.py
    3. REC_THE_OBITOS_DIARIOS.py
    4. THE-CASOS.py
    5. THE-OBITOS.py
    6. DadosCasosRecife.csv
    7. DadosObitosRecife.csv
    8. DadosCasosTeresina.csv
    9. DadosObitosTeresina.csv
  2. The Python codes and the data for the fitting procedures used in our paper Power law behaviour in the saturation regime of fatality curves of the COVID-19 pandemic, published in Scientific Reports (2021), are available here:
    1. inputs.py
    2. lmfit_BLM.py
    3. lmfit_CI.py
    4. inputs.txt
  3. The Python codes and the data for the fitting procedures used in our paper Modelling the epidemic growth of preprints on COVID-19 and SARS-CoV-2, published in Frontiers in Physics (2021), are available here:
    1. preprints_final.ipynb
    2. preprints1.csv
    3. preprints2.csv
  4. The Python codes and the data for the fitting procedures used in our paper Standard and anomalous waves of COVID-19: A multiple-wave growth model for epidemics, submitted to the Brazilian Journal of Physics (2021) and published as a preprint in medRxiv, are available here:
    1. 2-wave_fit.py
    2. 3-wave_fit.py
    3. Brazil_data.txt
    4. Canada_data.txt
    5. Germany_data.txt
    6. Iran_data.txt
    7. Italy_data.txt
    8. Japan_data.txt
    9. Mexico_data.txt
    10. South Africa_data.txt
    11. Sweden_data.txt
    12. US_data.txt