Goal

From the daily reported cases, create a stable Early Warning System based on each state's or district's healthcare infrastructure capacity that provides:

• Prediction of number of active cases in the next two weeks
• Days to critical (i.e. the number of days in which active cases will test health care infrastructure at current rate of growth)
• Warnings when the cases are low.

Summary of Method

Suppose a(t) is the total number of active cases at time t and λ(t) is the new infections per active infection per unit time at time t. Assuming constant recovery : μ(t) = 1 / 10 we can estimate

$$\lambda (t)=0.1+\frac{a(t+7)-a(t)}{7\cdot a(t)}$$
Note that at time t, λ(t) > 0.1 implies active cases will increase over time and λ(t) < 0.1 implies active cases will decrease over time. For prediction, we use average of last 4 calculated values of λ(t) on date of last data point as the growth rate for the future.

Early Warning Signals

• Below we plot the active cases as reported in Blue.
• For the past data we have picked a few critical instances where Growth Rate:= the number of new infection per unit time at time t exceeds recovery rate plot in red then surge in active cases predicted by the warning system. (Note: False Alarms do not happen)
• Finally at the current date we use the model to predict the active cases for the next 14 days. If the green curve is shooting upward then this is an early warning to the respective district.