• District Wise Data Set

  This zipped file is a collection of files that contain data for each district in Karnataka, where each file contains all the information of all the people who died in that particular district.
  
  The columns have data on:

  1. The district name.
  2. The State P Code for that patient.
  3. The age of the patient, given in years.
  4. The sex of the patient.
  5. The description of the patient including why they were tested and which cluster they fall in.
  6. The symptoms of the patients.
  7. Any co-morbidities that the patients might have had.
  8. The date at which they were admitted to the hospital. For the patients who were brought dead or passed away at their residence, that has been mentioned.
  9. The date on which they passed away.
  10. The date of the media bulletin they appeared in.

• Stacked Age-Gender Distribution of Deceased across Districts
• District-Wise Age-Scatter Plot

  This graph is the scatter plot of the total age distribution across all districts.

  
• District-Wise Age-Scatter Plot

  The below box-plot, across districts, suggest that the median age of the deceased from every district is around 60 mostly, consistent with the general overall trends and across waves. We can see that the maximum number of outliers can be observed with Bengaluru Urban while Bidar has no outliers.

  

More than 4000 people die just after first day of hospitalisation. Days to decease for a majority of patients is within 2 weeks and and the mean is nearly 6 days.

• Wave-Wise Days to Decease
• Days to Decease: Data Set

  CSV File. From the media bulletin, we took the difference between the date of decease and admission date and call the difference as days to decease.
  
  The csv file conatains:

  1. The patient id,
  2. Name of District,
  3. Date of Admission,
  4. Date of Decease,
  5. The days to decease being the difference between the dates mentioned in third and second column.

• District-Wise: Days to Decease

  The graph below represents the Box-plot for days to decease in Karnataka(District-wise) based on the data reported.All District shows similar trend.

  
• Age-Gender: Days to Decease

  Below we plot the 95% t-confidence interval around the mean for days to decease across gender in various age bins.

  
• Age-Gender Wise-Days to Decease: Data Set

  CSV File.From the media bulletin, we took the difference between the date of decease and admission date and call the difference as days to decease.We then separated the deceased on the basis of age and gender and calculated the mean, variance, and standard deviation for days to decease.
  
  The column has data on:

  1. Age-Gender Category,
  2. Total deceased patients,
  3. t-value of Total deceased patients,
  4. Mean Days to decease,
  5. Variance of Days to decease,
  6. Standard deviation of Days to decease,
  7. Gender.

The above histogram is right-skewed indicating early reporting of deaths by the district administration. The mean time is nearly 7 days.

• Wave-Wise Days to Report
• Days to Decease: Data Set

  CSV File. From the media bulletin published by the state government, we took the difference between the date of decease and the media bulletin date and call the difference as the days to report.
  
  The column has data on:

  1. The patient id,
  2. Name of District,
  3. Date of Decease,
  4. Date of Report,
  5. The days to report being the difference between the dates mentioned in third and second column.

• District-Wise: Days to Report

  The graph below represents the Box-plot for days to report in Karnataka(District-wise) based on the data reported.Most districts have similar trend but some have high interquartile range along with high number of outliers.

  
• Age-Gender: Days to Report

  Below we plot the 95% t-confidence interval around the mean for days to report across gender in various age bins.

  

We have marked a line at 95% hospitalisation ratio in the scatter plots or at the minimum monthly hospilisation ratio if it is higher. This line may be viewed as an ideal hospitalisation ratio. In comparison with days to decease one may arrive at a standard for clinical treatment.

• Hospitalisation Ratio Data Set

  As per the media bulletin published by the state government, each individual death is reported either as hospitalised, brought dead,or died at residence.In the data file, for each individual district, we provide the count of total death, total deceased patients who were hospitalised, and deceased patients who were not hospitalised, that is, we added the deceased who were either brought dead or died at residence.Then we also calculate the ratio of hospitalisation and the ratio of unhospitalised patients and the values are listed in individual column of the csv file attached.
  
  The column has data on:

  1. Each row represents a district in Karnataka.
  2. Each row in this column represents total deaths in that district.
  3. Each row represents total deceased patients who were hospitalised in that district.
  4. Each row represents total deceased patients who were not-hospitalised in that district.
  5. Each row represents ratio of hospitalisation in that district.
  6. Each row represents ratio of un-hospitalised deceased patients in that district.

  Data in CSV

• District Wise Hospitalisation Ratio

• Bar Plot of Place of Death

  The graph below represents the Barplot of Place of Death for the state of Karnataka based on the data reported.

  

  The graph below represents the Barplot of Place of Death(District wise) in Karnataka based on the data reported.

  

  Most of the patients were actually diagnosed in hospitals before death.But Bengaluru urban shows a peak in death count in hospitals.

• Description of Source of Illness: Classification Details

  This csv file contains information about the 7 different clusters of description of sources of illness of deceased patients and the percentage of the number of deceased patients in each cluster of description of source of illness for each district in Karnataka. The 7 columns represent the following 7 clusters of description and the headings in each column are the name of the clusters of description.
  
  The column has data on:

  1. Influenza Like Illness
  2. Severe Acute Respiratory Illness
  3. Contact of Patient or Containment Zone
  4. Inter District Travel in Karnataka
  5. Domestic Travel outside of Karnataka
  6. International Travel
  7. Unknown (Contact under tracing or ongoing investigation)

  
  
  

  All the rows below the headings (except the last row) contain the percentage of the number of deceased patients in each of the 7 clusters of description of source of illness for each district. The last row represents the percentage of deceased patients in each of the 7 clusters of description in all of Karnataka.

• Description of Source of Illness: Data Set

  In the media bulletin for each deceased patient the source of illness is identified in the Description column. We have grouped the sources into 4 travel related clusters, SARI, ILI and Unknown. The specific clustering is provided in the following csv file.
  Clusters of Description of Source of Illness
  
  This CSV file contains information about the clusters of sources of illness based on the description associated with each patient in the Description column of the Media Bulletin. In the CSV file, the 7 columns represent the 7 clusters of description and the heading of each column is the name of that cluster of description of the source of illness. The rows below the headings are the description of the sources of illness classified under the corresponding cluster of description of the sources of illness.

• Karnataka Description Cluster Histogram
  

  The above bar graph represents the 7 clusters of the description of the sources of illness vs. the percentage of deceased patients in each of the clusters of description for all of Karnataka in total. As we can see, the percentage of deceased patients in the clusters of ILI (Influenza Like Illness), SARI (Severe Acute Respiratory Illness) and Unknown (Contact Under Tracing and Under Investigation), far exceed those in each of the other clusters. After clicking on the graph, hovering on the bars in the graph reveals the percentage of deceased patients in the given cluster. For clusters of description other than those related to ILI, SARI and Unknown, the bars are very short and hence hovering on them might not reveal the percentage of deceased patients. In such a case, please zoom on the graph to make visible the percentage of deceased patients in clusters with shorter bars while hovering on them.

• Symptom Cluster: Classification Details

  In the media bulletin in the Symptoms column the symptoms observed in each deceased patient is listed. To see the variation in symptoms of deceased patients across different districts, we first grouped symptoms into 10 clusters. The specific clustering is provided in the following csv file.
  Clusters of Symptoms
  
  This CSV file contains information about the different symptom clusters and the different sets of symptoms that are classified under each symptom cluster. In the CSV file, the 10 columns represent the 10 symptom clusters and the heading of each column is the name of that symptom cluster. The rows below the headings represent the different symptoms classified under the corresponding symptom clusters.

• Symptom Cluster: Data Set

  This csv file contains information about the 10 different clusters of symptoms of deceased patients and the percentage of the number of deceased patients in each symptom cluster for each district in Karnataka.
  
  The 10 columns represent the following 10 symptom clusters and the headings in each column are the name of the symptom clusters.
  

  1. Asymptomatic
  2. Abdominal Pain and Released Symptoms
  3. Chest pain and Pneumonia
  4. Body Pain and Fatigue
  5. Breathlessness and Respiratory Failure
  6. Fever, Cold or Cough without Breathlessness
  7. Fever, Cold or Cough with Breathlessness
  8. Anorexia and Decreased Appetite
  9. Acute Myocardial Infarction
  10. Altered Sensorium

  
  
  All the rows below the headings (except the last row) contain the percentage of the number of deceased patients in each of the 10 symptom clusters for each district. The last row represents the percentage of deceased patients in each of the 10 symptom clusters for all of Karnataka.

• Karnataka Symptom Cluster Histogram
  

  The above bar graph represents the ten symptom clusters vs. the percentage of deceased patients in each of the symptom clusters for all of Karnataka in total. As we can see, the percentage of deceased patients in the symptom clusters of breathlessness, cold, cough and fever, far exceed those in each of the other symptom clusters. After clicking on the graph, hovering on the bars in the graph reveals the percentage of deceased patients in the given symptom cluster. For symptom clusters other than those related to fever, cough, cold and breathlessness, the bars are very short and hence hovering on them might not reveal the percentage of deceased patients. In such a case, please zoom on the graph to make visible the percentage of deceased patients in clusters with shorter bars while hovering on them.

• Comorbidities Cluster: Classification Details

  In the media bulletin in the Comorbidities column the Comoorbidities observed for each deceased patient is listed. To see the variation in comorbidities of deceased patients across different districts, we first grouped the comorbidities into 15 clusters. The specific clustering is provided in the following csv file.
  
  Clusters of Comorbidities
  
  This CSV file contains information about the different clusters of comorbidities and the different sets of comorbidities that are classified under each comorbidity cluster. In the CSV file, the 15 columns represent the 15 comorbidities clusters and the heading of each column is the name of that comorbidity cluster. The rows below the headings are the comorbidities classified under the corresponding clusters of comorbidities

• Comorbidities Cluster: Data Set

  This csv file contains information about the 10 different clusters of comorbidities of deceased patients and the percentage of the number of deceased patients in each comorbidities cluster for each district in Karnataka. The 15 columns represent the following 15 comorbidities clusters and the headings in each column are the name of the comorbidities clusters.
  
  

  1. Respiratory Diseases
  2. Kidney Related Diseases
  3. Liver Related Diseases
  4. Heart and Blood Pressure Related Diseases
  5. Epilepsy
  6. Diabetes and Obesity
  7. Hormone and Gland Diseases
  8. Anaemia and Myasthenia
  9. Cancer
  10. Sepsis
  11. Pulmonary and Lung Diseases
  12. Brain Related Diseases
  13. Skin Diseases
  14. Pregnancy Related Illness
  15. Infectious Disease

  
  
  All the rows below the headings (except the last row) contain the percentage of the number of deceased patients in each of the 15 comorbidities clusters for each district. The last row represents the percentage of deceased patients in each of the 15 comorbidities clusters in all of Karnataka.

• Karnataka Comorbidities Cluster Histogram
  

  The above bar graph represents the 15 comorbidities clusters vs. the percentage of deceased patients in each of the comorbidities clusters for all of Karnataka in total. As we can see, the percentage of deceased patients in the comorbidities clusters of Diabetes and Obesity and Heart Disease, far exceed those in each of the other comorbidities clusters. After clicking on the graph, hovering on the bars in the graph reveals the percentage of deceased patients in the given comorbidities cluster. For comorbidities clusters other than those related to Heart Disease, Diabetes and Obesity and Kidney Diseases, the bars are very short and hence hovering on them might not reveal the percentage of deceased patients. In such a case, please zoom on the graph to make visible the percentage of deceased patients in clusters with shorter bars while hovering on them.