$$ \newcommand{\sub}{_} \newcommand{\confirmed}[1]{x(#1)} \newcommand{\active}[1]{a(#1)} \newcommand{\dactive}[1]{a^{\prime}(#1)} \newcommand{\dconfirmed}[1]{x^{\prime}(#1)} \newcommand{\ractive}[1]{\lambda(#1)} \newcommand{\ractivehat}[1]{\hat{\lambda}(#1)} \newcommand{\rconfirmed}[1]{\gamma(#1)} \newcommand{\rincrement}[1]{\rho(#1)} \newcommand{\rincrementhat}[1]{\hat{\rho}(#1)} \newcommand{\rinactive}[1]{\mu(#1)} $$

Early Warning System for Indian States

(Based on Early Prediction of COVID Surge by Siva Athreya, Deepayan Sarkar, and Rajesh Sundaresan)

Goal: From the daily reported cases, create a stable early warning system based on each state's health care infrastructure capacity that provides:

Click here for Summary of Method

Suppose $\active{t}$ is the total number of active cases at time $t$ and $\ractive{t}$ 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. For prediction, we use average of last 4 calculated values of $\ractivehat{t}$ on date of last data point as the growth rate for the future.

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

Early warning Signals: