Our primary aim is to understand infection spread across the states in India and districts of Karnataka. At the State level we provide run time monitoring of $R_t$, Active Cases, and provide an Early warning system for each state. For Karnataka, our analysis is detailed at the district level: we track Deceased data, Active cases, $R_t$, provide a one week prediction for them and an Early warning system for each.
We track: Timeline, Cases, Active Cases, $R_t$, Doubling time , Falling off Exponential. These pages contain interactive plots of timeline of infected-recovered-deceased, the timeline of infections in log-scale, the active cases, time varying estimate of the reproduction number, the doubling times and one that tracks if the infection growth is in the exponential phase respectively.
The tree-like graph tracks every infection in the state providing a detailed log of its parent or the cluster of origin. This effort was entirely inspired by Channel News Asia-interactive graph for the trace history of Singapore cases and we have borrowed the javascript code in its entirety from their website. We had initially followed the trace history of Telangana till March 26th, 2020.
The Trace History Graph for Karnataka has not been updated since June 26th, 2020 as there was a pause in Contact tracing data in the media bulletins.
In this page we track the various data provided in the Media Bulletins from Karnataka State to give a more comprehensive view of the COVID-19 infection in the state of Karnataka. In particular we track the cluster timelines, recovery and decea sed information, ICU timelines and information, the basic reproduction number, the infections in Karnataka across the cities, age distributions and testing data.
Mathematical models used to characterize early epidemic growth feature an exponential curve. This phase of exponential growth can be characterised by the doubling time. Doubling time is the time it takes for the number of infections to double from a given day. In this page we analyse doubling times for all of India and for each state. For worldwide study of doubling times, we refer the reader to Deepayan Sarkar's website on github.
A detailed explanation of the method and inferences with respect to the lockdown can be found in this expository note..
We use a notion similar to moving average of net increase over a symmetric 7-day window. On log-scale, we plot on each day the net increase from three days before that day to three days after that day versus the total number of infections up to that day. This graph can be used to understand if the infection growth has deviated from the exponential phase. In this page we plot various states and observe that when the exponential growth is arrested then the respective plots will veer off the straight line. This effort has been inspired in part by Aatish Bhatia and Minute Physics's website where they study COVID-19 trends worldwide.
A detailed explanation of the method and inferences with respect to the lockdown can be found in this expository note..
This csv file contains the counts of infected [both Indian and Foreign nationals], recovered and deceased for each state with at least one COVID-19 case. The rows correspond to different states and the columns represent the daily counts. Each day corresponds to four columns: Total Confirmed cases of Indian Nationals [TCIN], Total Confirmed cases of Foreign Nationals [TCFN], Recoveries [Cured] and Deaths [Death] for that day. This file is updated from the MOHFW website.
This csv file contains information about weekly dose 1 and dose 2 vaccination across all districts in Karnataka. The columns have data on:
The media bulletins on 9th May, 10th May and 11th May, and from 15th May onward, did not have patient-wise data of patients in ICU and the patients discharged. However, the consolidated counts had been given.Hence we have created a new csv file containing the total counts of Active, Recovered, Discharged and ICU patients. This file has the following information:
This csv file contains information on the patients regarding their hospitalization. Each row corresponds to a patient as indexed by the Media Bulletins and the columns correspond to different days, starting from 9th March, 2020. The entries in the cells are either H [implying they were hospitalized on that day], C [Cured], D [Deceased], ICU [required an ICU], ICUO [ICU and required Oxygen] or ICUV [ICU and required Ventilator support].
This csv file contains information about weekly reproduction numbers across all states and union territories in India. The columns have data on:
This csv file contains information about weekly reproduction numbers across all districts in Karnataka. The columns have data on:
The data are sourced from the Ministry of Health and Family Welfare (MOHFW) website and Media Bulletins published regularly by the Karnataka government. The data available on these websites is in the form of PDF files where it's difficult to extract automatically. We have collected this data and collated it to form the following csv files from where it can used easily by the public. Below we provide all the data in csv format.