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

Early Warning System for Districts

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


We have divided the states into four categories:
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 Active cases per million population is less than 100 days

  • About the plots
    • 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 $ 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.
    • 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.
    • 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. Based on these we have divided the districts into four categories



    Active Active cases: 2, Growth Rate: 0.243
    Days to 50 Active cases per million population: 30
    Days to 1500 Active cases per million population is 56
    Days to Critical to previous peak
    Active Active cases: 9, Growth Rate: 0.17
    Days to 50 Active cases per million population: 41
    Days to 1500 Active cases per million population is 91
    Days to Critical to previous peak







    Growing and Alert Raised

  • About the plots
    • 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 $ 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.
    • 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.
    • 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. Based on these we have divided the districts into four categories



    Active Cases: 1, Growth Rate: 0.148
    Days to 50 Active Cases per million population: 141
    Days to Critical to previous peak
    Active Cases: 114, Growth Rate: 0.133
    Days to 50 Active Cases per million population: 0
    Days to 1500 Active Cases per million population is 99
    Days to Critical to previous peak


    Active Cases: 5, Growth Rate: 0.138
    Days to 50 Active Cases per million population: 65
    Days to 1500 Active Cases per million population is 155
    Days to Critical to previous peak
    Active Cases: 0, Growth Rate: 0.243
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak


    Active Cases: 10, Growth Rate: 0.113
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak
    Active Cases: 8, Growth Rate: 0.109
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak


    Active Cases: 0, Growth Rate: 0.1
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak
    Active Cases: 0, Growth Rate: 0.1
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak


    Active Cases: 0, Growth Rate: 0.243
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak
    Active Cases: 0, Growth Rate: 0.243
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak



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


    Stable but Need to be watched

  • About the plots
    • 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 $ 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.
    • 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.
    • 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. Based on these we have divided the districts into four categories



    Active Cases: 11, Growth Rate: 0.087
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak



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


    Stable

  • About the plots
    • 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 $ 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.
    • 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.
    • 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. Based on these we have divided the districts into four categories



    Active Cases: 0, Growth Rate: -0.007
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak
    Active Cases: 0, Growth Rate: 0.001
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak


    Active Cases: 2, Growth Rate: 0.076
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak
    Active Cases: 2, Growth Rate: 0.029
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak


    Active Cases: 1, Growth Rate: -0.055
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak
    Active Cases: 0, Growth Rate: 0.001
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak


    Active Cases: 0, Growth Rate: 0.001
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak
    Active Cases: 2, Growth Rate: 0.029
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak


    Active Cases: 0, Growth Rate: 0.001
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak
    Active Cases: 0, Growth Rate: 0.001
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak


    Active Cases: 3, Growth Rate: -0.025
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak
    Active Cases: 6, Growth Rate: 0.062
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak


    Active Cases: 1, Growth Rate: 0.025
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak
    Active Cases: 3, Growth Rate: 0.064
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak


    Active Cases: 0, Growth Rate: 0.043
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak
    Active Cases: 1, Growth Rate: 0.05
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak


    Active Cases: 0, Growth Rate: 0.001
    Days to 50 Active Cases per million population:
    Days to Critical to previous peak