Simple Time-Based R0 Case Projection
Estimate how case counts may change over time from an initial number of cases using a simple R0-based growth assumption and a serial interval.
Help notes
This tool provides a simple time-based projection of case counts using an R0-style growth assumption.
Inputs:
- Initial cases: the starting number of cases
- R0: the average number of secondary cases generated by one case
- Serial interval: the average time in days between successive cases in a chain of transmission
Method: The calculator converts R0 into an approximate daily growth factor using the serial interval, then projects case counts at selected time points.
Use this tool for:
- teaching and demonstration
- quick intuition about outbreak growth
- comparing faster and slower transmission scenarios
Do not use this tool as a full forecasting model. It does not account for:
- susceptible depletion
- immunity
- interventions
- behavior change
- reporting delays
- stochastic variation
- population size limits
Input
Enter values and review the calculated outputs.
Results
Daily growth factor
Not available
Approximate doubling time (days)
Not available
Projected cases at day 0
Not available
Projected cases at day 7
Not available
Projected cases at day 14
Not available
Projected cases at day 21
Not available
Projected cases at day 28
Not available
Interpretation
Interpret the outputs as approximate projected case counts under a simplified constant-growth assumption.
If R0 is greater than 1, projected cases increase over time. If R0 equals 1, case counts remain roughly stable. If R0 is less than 1, projected cases decrease over time.
The daily growth factor translates the chosen R0 and serial interval into an approximate day-to-day multiplier. The doubling time is only meaningful when the daily growth factor is greater than 1.
Because this is a simplified educational model, the results should be used for intuition and teaching rather than operational outbreak forecasting.