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

### 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:

• 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
• warnings when the cases are low.

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:

• 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 infections per active infection per unit time at time $t$ exceeds recovery rate plot in red the surge in active cases predicted by the warning system (Note: false alarms do 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.

#### Caution: The number of Active cases in each state should be taken into account while considering the alert.

We have divided the states into three categories
• Category 1: Predicted Days to 1500 cases per million population is less than 100 days and number of active cases is more than 100.
• Category 2: Growing and Daily Cases more than 50 Cases per million Population and number of active cases is more than 100.
• Category 3: Stable but Daily cases above 50 Cases per million Population and number of active cases is more than 100.
• Category 4: Stable or Number of active cases is below 100.
• Data in CSV

For all the graphs on this page, if you click on the image then it will display an interactive graph, where as you hover your mouse pointer over the graph annotations with details will be displayed.

#### Predicted Days to 1500 cases per million population is less than 100 days and number of active cases is more than 100.

Active Cases: 256, Growth Rate: 0.193
Days to 50 Cases per million population: 22
Days to 1500 Cases per million population is 62
Active Cases: 694, Growth Rate: 0.1853
Days to 50 Cases per million population: 27
Days to 1500 Cases per million population is 69

Active Cases: 696, Growth Rate: 0.1741
Days to 50 Cases per million population: 11
Days to 1500 Cases per million population is 60
Active Cases: 4939, Growth Rate: 0.1224
Days to 50 Cases per million population: 0
Days to 1500 Cases per million population is 81

Active Cases: 928, Growth Rate: 0.1401
Days to 50 Cases per million population: 0
Days to 1500 Cases per million population is 27
Active Cases: 2463, Growth Rate: 0.1623
Days to 50 Cases per million population: 6
Days to 1500 Cases per million population is 63

Active Cases: 319, Growth Rate: 0.1513
Days to 50 Cases per million population: 5
Days to 1500 Cases per million population is 74
Active Cases: 232, Growth Rate: 0.1742
Days to 50 Cases per million population: 30
Days to 1500 Cases per million population is 78

Active Cases: 27772, Growth Rate: 0.1366
Days to 50 Cases per million population: 0
Days to 1500 Cases per million population is 22
Active Cases: 440, Growth Rate: 0.1627
Days to 50 Cases per million population: 28
Days to 1500 Cases per million population is 85

Active Cases: 238, Growth Rate: 0.1722
Days to 50 Cases per million population: 0
Days to 1500 Cases per million population is 33
Active Cases: 908, Growth Rate: 0.1564
Days to 50 Cases per million population: 11
Days to 1500 Cases per million population is 74

Active Cases: 7458, Growth Rate: 0.1794
Days to 50 Cases per million population: 0
Days to 1500 Cases per million population is 38
Active Cases: 3653, Growth Rate: 0.1611
Days to 50 Cases per million population: 20
Days to 1500 Cases per million population is 78

Active Cases: 3777, Growth Rate: 0.1828
Days to 50 Cases per million population: 5
Days to 1500 Cases per million population is 48

#### Growing but Days to 1500 cases per million population is more than 100 days and number of active cases is more than 100.

Active Cases: 3089, Growth Rate: 0.1253
Days to 50 Cases per million population: 0
Days to 1500 Cases per million population is 108
Active Cases: 4438, Growth Rate: 0.1161
Days to 50 Cases per million population: 0

Active Cases: 462, Growth Rate: 0.1187
Days to 50 Cases per million population: 120
Active Cases: 24608, Growth Rate: 0.1211
Days to 50 Cases per million population: 0
Days to 1500 Cases per million population is 100

Active Cases: 858, Growth Rate: 0.1256
Days to 50 Cases per million population: 61
Active Cases: 702, Growth Rate: 0.1165
Days to 50 Cases per million population: 0

[Top], [Category 1], [Category 2], [Category 4]

#### Stable but Daily cases above 50 Cases per million Population and number of active cases is more than 100.

Active Cases: 171, Growth Rate: 0.0676
Days to 50 Cases per million population: 0

[Top], [Category 1], [Category 2],[Category 3]

#### Stable or Number of active cases is below 100.

Active Cases: 37, Growth Rate: 0.1643
Active Cases: 576, Growth Rate: 0.1717

Active Cases: 4, Growth Rate: 0.1732
Active Cases: 495, Growth Rate: 0.1573

Active Cases: 9, Growth Rate: 0.1821
Active Cases: 321, Growth Rate: 0.1671

Active Cases: 49, Growth Rate: 0.1324
Active Cases: 4, Growth Rate: 0.1821

Active Cases: 11, Growth Rate: 0.1974
Active Cases: 29, Growth Rate: -0.1301

Active Cases: 3, Growth Rate: 0.0036
Active Cases: 14, Growth Rate: 0.1329

Active Cases: 3762, Growth Rate: 0.1671
Active Cases: 5, Growth Rate: 0.2107