From the daily reported cases, create a stable early warning system based on each district's health care 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); and
• early warning before the active cases increase substantially.

For details and limitations of the method we refer to Early Prediction of COVID Surge-Slides.

Summary:

Suppose $\active{t}$ is the total number of active cases at time $t$ and the relative growth rate $\ractive{t}$ is the number of new infections per active infection per unit time at time $t$. Assuming constant recovery : $\rinactive{t} \equiv 1/10$ we can estimate $$ \ractivehat{t} = 0.1 + \frac{ \active{t + 7} - \active{t} }{ 7 \cdot \active{t} } $$ Note that at time $t$, $\ractivehat{t} > 0.1$ implies active cases will increase over time and $\ractivehat{t} < 0.1$ implies active cases will decrease over time. To predict active cases at any given time, we average the last 4 calculated values of $\ractivehat{t}$ on that date and then use this average as the growth rate for the prediction.

Days: For the plots below, we have assumed that health infrastructure capacity in a district is proportional to its population. Hence we have used Days to 50 active cases per million Population and Days to 1500 active cases per million Population as markers for health care infrastructure capacity.
Days to Critical to previous peak: In the slides at: Estimated Growth Rate of Active Infections for districts in Karnataka are plotted along with days to critical, we assume the health-care infrastructure capacity to have been exceeded when it reaches the previously attained peak. For predicting the days to previous peak we do a smoothening of the data and use $1$ day lag instead of the $7$ day lag used otherwise.