Table 3.
Epidemic Doubling Time in Selected Studies.
| Author | Country | Method | Dataset | Doubling Time | Tool Used | Recommendation by Author |
|---|---|---|---|---|---|---|
| [70] | India | Exponential Growth Model | February 2020–March 2021 | 1.7 to 46.2 days (based on districts) | Q-GIS software | no uniformity across country to analyze and study epidemics in future |
| [74] | China | Global Epidemic and Mobility Model (GLEAM) | By 23 January 2020 | 4.2 days | - | travel restrictions |
| [66] | Multi-Countries | Linear Regression and Support Vector Machine | 22 January 2020, to 12 July 2021 | Min = if (5000 cases) double in 5 days Max = if (163,840,000 cases) double in 140 days |
- | government and individuals aware about the severity |
| [75] | China | Exponential Growth Model | 1–23 January 2020 | 3.6 days | - | prevention measures were effective |
| [76] | South Africa | Susceptible–Exposed– Infectious–Recovered (SEIR) model |
By 23 November 2021 | 3.3 days | - | immune evasion is more concerning increased transmissibility |
| [78] | Argentina | Agent-based Model | Multiple Scenario | 2.0 to 7.14 days | social distancing measures |