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 Complexity signatures in the COVID-19 epidemic: power law behaviour in the saturation regime of fatality curves, published in medRxiv, 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 medRxiv, are available here:
    1. preprintsv2.ipynb
    2. fig_1_2_preprints.csv
    3. fig_3_preprints.csv
    4. fig_4_preprints.csv