Abhiti, Nitya, and Siva
Tracking the COVID-19 infections
There are six aspects to the portal
State Timelines
Doubling time
Exponential Growth
Karnataka: Understanding from detailed Media Briefs
Trace History of Karnataka (Paused Yesterday)
Data Analysis: Age distribution, ICU utilisation, Testing
Data Repository
The rate of exponential growth is seen in terms of the spacing of the points: Spaced further away imply the rate of growth is larger and closer together imply the rate of growth is smaller.
Detailed Notes are available on our website.
How long ago the infection count was half of the current count.
In Lockdown Phase 2, doubling time increases but at the end of Lockdown Phase 3 and 4, doubling time dips sharply.
If doubling time is more, infection spread is slower and vice versa.
For discrete data, we use linear interpolation to create a continuous graph and then calculate doubling time.
Detailed Notes are available on our website.
All India, States and Union Territories Timeline : Union Ministry of Health and Family Welfare.
Karnataka : State Union Ministry of Health and Family Welfare, Media Briefs.
Tamil Nadu and Kerala : State Union Ministry of Health and Family Welfare, Media Briefs.
Maharashtra : National Institute of Disaster Management.
All are provided in html or pdf format
All files are in csv format
Summary for All India
Karnataka Trace History
Karnataka Hospitalization information
Karnataka Hospitalization information - Consolidated
Karnataka Testing information
District-Wise Information (of 5 states)
-- Managed entirely by Ms. Kusuma, N.R. and Ms. Asha Latha at the Indian Statistical Institute, Bangalore centre.Publically available and open invitation for usage.
--Karnataka In Focus
Contain the following:
Testing Total tests and positives and Screening : At air and sea ports.
Test positive Cases : Date, the age, sex, district and either reason for contracting the disease or the reason for being tested.
Discharge Details: Recovered and deceased
Hospital Information: Consolidated information on number of patients in ICU
TJ Congregation in Delhi | Influenza like illness |
From USA | Severe Acute Respiratory Infections |
From South America | Unknown |
From Rest of Europe | Others |
From Middle East | Containment Zones |
From United Kingdom | Pharmaceutical company at Nanjangud |
From Rajasthan | From Southern States |
From Maharashtra | From Gujarat |
Deceased on Day 0: Tested positive posthumously.
The first generation nodes are called as parents of the cluster.
The children are the people who contracted the disease from the people labelled as parents, placed at depth two in the trace history chart.
Similarly, grandchildren and great grandchildren have depth three and four respectively.
For most infectious diseases, most of the new infections are assigned:
to very few individuals and
most infected individuals cause almost no infections.
Karnataka data for May 3rd and Descendants
$864$ patients observed by us here,
$683$ of then caused $0$ new infections.
20% of cases cause 80% of transmission
…
We order these individuals with regards to their individual infectiousness .
Consider the top $x$ fraction of these infections, and they cause $y$ fraction of the total infections (say).
$(x,y)$ has been plotted here.
We will analyse cluster data
For each cluster we have precise information of how the infection was assigned.
Compute the distribution of number of children per infected person.
Compute Mean of distribution = $R_0$
$R_0$ as a measure of the spread of the disease ?
Recall 20-80 Rule
Must take note of Variance.
Cluster | Size | Zeros | Maximum | R_{0} | Variance |
---|---|---|---|---|---|
Unknown | 1195 | 1080 | 27 | 0.2686 | 1.729 |
Pharmaceutical Company | 73 | 53 | 24 | 0.726 | 8.757 |
From the Southern States | 271 | 243 | 7 | 0.214 | 0.68 |
Others | 670 | 564 | 51 | 0.5299 | 6.817 |
TJ Congregation | 97 | 70 | 15 | 0.7732 | 3.823 |
SARI | 717 | 599 | 45 | 0.6862 | 8.822 |
ILI | 1211 | 1072 | 30 | 0.3543 | 2.553 |
Containment Zones | 290 | 252 | 7 | 0.3414 | 1.201 |
. . .
Stochastic effects in Transmission, one considers
Mixture of Poisson with Gamma
Negative Binomial with Mean $R_0$ and Dispersion $k$
Use log likelihood function of Negative Binomial given the data
Find the most likely value for $R_0$ and $k$ given the data
Calculus and Numerical approximation.
$R_0= 0.3414$
$k = 0.09345$
…
…
The above calculations are preliminary and are being verified.Cluster | Size | Maximum | R_{0} | Variance | k | p-value |
---|---|---|---|---|---|---|
Containment Zones | 293 | 7 | 0.3447 | 1.199 | 0.09345 | 0.5966 |
ILI | 1398 | 30 | 0.3369 | 2.355 | 0.06428 | 0.736 |
SARI | 746 | 45 | 0.6743 | 8.521 | 0.08023 | 0.4698 |
TJ Congregation | 97 | 15 | 0.7732 | 3.823 | 0.2138 | 0.1138 |
Others | 707 | 51 | 0.5191 | 6.502 | 0.09214 | 0.8318 |
From the Southern States | 286 | 7 | 0.2168 | 0.6897 | 0.08424 | 0.5409 |
Pharmaceutical Company | 73 | 24 | 0.726 | 8.757 | 0.1839 | 0.002671 |
Unknown | 1295 | 27 | 0.2625 | 1.637 | 0.05792 | 0.1225 |
Total | 4895 | 51 | 0.4029 | 3.682 | 0.07344 | 0.02011 |
Thanks to Karnataka Government Staff
June 27th Media Briefs, had 918 new cases, and did not have contact tracing
Hopefully they will start again.
Effective $R_0 < 1$, the cluster will die out.
Clearly shows effect of contact tracing and quarantine measures.
Dispersion $k$ can be used to understand super spreading events.
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