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.
- The Python codes and the data for the fitting procedures used in our paper
Análise de curvas epidêmicas 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:
- REC-CASOS.py
- REC-OBITOS.py
- REC_THE_OBITOS_DIARIOS.py
- THE-CASOS.py
- THE-OBITOS.py
- DadosCasosRecife.csv
- DadosObitosRecife.csv
- DadosCasosTeresina.csv
- DadosObitosTeresina.csv
- 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:
- inputs.py
- lmfit_BLM.py
- lmfit_CI.py
- inputs.txt
- 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:
- preprints_final.ipynb
- preprints1.csv
- preprints2.csv
- 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:
- 2-wave_fit.py
- 3-wave_fit.py
- Brazil_data.txt
- Canada_data.txt
- Germany_data.txt
- Iran_data.txt
- Italy_data.txt
- Japan_data.txt
- Mexico_data.txt
- South Africa_data.txt
- Sweden_data.txt
- US_data.txt