On the global time evolution of the Covid-19 pandemic: Logistic modeling
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DOI: 10.1016/j.techfore.2021.121387
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References listed on IDEAS
- Modis, Theodore, 1994. "Determination of the Uncertainties in S-Curve Logistic Fits," OSF Preprints n53pd, Center for Open Science.
- Debecker, Alain & Modis, Theodore, 2021. "Poorly known aspects of flattening the curve of COVID-19," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
- Devezas, Tessaleno, 2020. "The struggle SARS-CoV-2 vs. homo sapiens–Why the earth stood still, and how will it keep moving on?," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
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- Kraus, Sascha & Kumar, Satish & Lim, Weng Marc & Kaur, Jaspreet & Sharma, Anuj & Schiavone, Francesco, 2023. "From moon landing to metaverse: Tracing the evolution of Technological Forecasting and Social Change," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
- Zubcoff, Jose-Jacobo & Olcina, Jorge & Morales, Javier & Mazón, Jose-Norberto & Mayoral, Asunción M., 2023. "Usefulness of open data to determine the incidence of COVID-19 and its relationship with atmospheric variables in Spain during the 2020 lockdown," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
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