Covid Data Analytics : Case Study on India
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This document provides a case study and data analysis of COVID-19 in India using metrics to assess spread, risk, testing adequacy, comparative assessment between states, and clustering of districts based on cases. Figures and analyses examine trends in new daily cases, active cases, testing proportions, heat maps of top affected states, crude fatality rates, survival estimates, and clustering of states based on spread. The analyses aim to provide a multi-dimensional view and risk summary of COVID-19 patterns and progression in India.
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![Covid Data Analytics : Case Study on India
Bankar Prasad Vilas Subhasis Panda Vaibhav Anand Vineet Kumar
Indian Statistical Institute, Kolkata
https://covid-isical.tech contact@covid-isical.tech
Introduction
We provide a macro-level India specific analysis on COVID
related data as obtained from [1]. We built a dashboard
available at [2] by giving multi-dimensional view and risk
summary for India.
Spread Assessment Metrics
Increasing trend of occurrence of new cases is likely to
indicate that the virus is spreading.
Figure 1: Average confirmed cases / day in India for each
week (last 7 weeks)
Increasing trend of active cases indicates the possibility
of increased stress on resources at present or in the near
future.
Figure 2: Active cases in India for the last 7 weeks
Risk Assessment Metrics
Avg. Confirmed Cases to Avg. Recoveries in Consecutive
Weeks Traffic Intensity (term from queuing theory) —
• Leading indicator of stress on resources
• Values > 1 implies faster arrival of new cases
compared to recovery
Figure 3: Traffic Intensity for India for the last 7 weeks
Points plot of Test in Each Week and % positive in each
week Higher proportion positive test results on a larger
test population (possibly less targeting) are anomalous
and need investigation.
There are at least three possibilities:
• Rapid increase of rate of infection
• Carrying out tests in new (hitherto untested) areas
with high but unknown rate of infection
• Improper testing leading to many incorrectly positive
results
Figure 4: Assessing adequacy of testing in India
Comparative Assessment
Heat maps showing the top 10 States in India w.r.t con-
firmed (blue), recovered (green), and deceased (red), re-
spectively.
Figure 5: Heat map of last 15 days
Bounds on Death Rate
Crude Fatality Rate Calculated on daily basis using
P
Death
P
Confirmed
.
Figure 6: Crude Fatality Rate
Kaplan Meier Analysis The estimator of the survival func-
tion S(t) (the probability that life is longer than t is given
by:
d
S(t) =
Y
i: ti≤t
1 −
di
ni
with ti a time when at least one event happened, di the
number of events (e.g., deaths) that happened at time ti,
and ni the individuals known to have survived (have not
yet had an event or been censored) up to time ti.
Figure 7: KM Curve for Survival Estimation
Spread-based Clustering
Clustered Districts based on number of active cases and
deaths using k means and agglomerate clustering tech-
niques.
MH
TN
DL
GJ
KA
UP
TG
AP
WB
RJ
HR
BR
MP
AS
OR
JK
PB
KL
-0.5
0.0
0.5
1.0
-5.0 -2.5 0.0
Dim1 (94.8%)
Dim2
(3.5%)
cluster
a
a
a
a
1
2
3
4
Cluster plot
MH
TN
DL
PB
JK
KL
OR
BR
AS
MP
RJ
HR
KA
GJ
UP
WB
TG
AP
0
2
4
6
8
10
Cluster Dendrogram
hclust (*, "ward.D2")
d
Height
Figure 8: Clustering states based on infection spread
References
[1] https://api.covid19india.org/
[2] https://covid-isical.tech/
Summer School of Machine Learning at Skoltech, SMILES – Aug’ 2020](https://image.slidesharecdn.com/covidposter-210516162615/85/Covid-Data-Analytics-Case-Study-on-India-1-320.jpg)
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Ct
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t+1
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t
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t
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t
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3.4
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Complete the table below.
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Nt
Number of Events (Joint Failures)
Dt
Number Censored
Ct
Survival Probability
S
t+1
= S
t
*((N
t
-D
t
)/N
t
)
0
11
25
28
29
31
32
35
36
37
38
39
41
2.
cont.
Non-Obese
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Time, Weeks
Number at Risk
Nt
Number of Events (Joint Failures)
Dt
Number Censored
Ct
Survival Probability
S
t+1
= S
t
*((N
t
-D
t
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t
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0
11
27
31
32
34
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Let
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X
1
,...,Xn
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Y
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Y
1
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Xi
and
Yi
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A) The two-sample paired
t
-test with the null hypothesis that the means of
X
and
Y
differ.
B) The test with the null hypothesis that the Pearson correlation coefficient between
X
and
Y
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Y
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-test with
H
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X
is
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one-sided
two-sample
unpaired t
-test with the null hypothesis that the mean of
X
is
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0: The mean difference between
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DOI 10.1007/s12024-013-9452-4
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Abstract The modeling and analysis of the epidemic disease outbreaks in huge realistic populations is a data intensive task that requires immense computational resources. Such effective computational support becomes useful to study disease outbreak and to facilitate decision making. Epidemiology is one of the traditional approaches which are being used for studying and analyzing the outbreaks of epidemic diseases. Although useful for obtaining numbers of sick, infected and recovered individuals in a population, this traditional approach does not capture the complexity of human interactions. The model is only limited to the person to person interaction in order to track the surveillance of disease and it also having performance issues with large realistic data. In this paper we propose, the combination of computational epidemiology and modern data mining techniques with their comparative analysis for the Swine Flu prediction. The clustering algorithm K mean is used to make a group or cluster of Swine Flu suspects in a particular area. The Decision tree algorithm and Naive Bayes classifier are applied on the same inputs to find out the actual count of suspects and predict the possible surveillance of a Swine Flu in a nearby area from suspected area. The performances of these techniques are compared, based on accuracy. In our case the Naive Bayes classifier performs better than decision tree algorithm while finding the accurate count of suspects. Keywords: Computational Epidemiology, Aerosol-borne Disease, Clustering, Predictive analysis.
Biostatistics
This document provides an introduction to biostatistics. It defines biostatistics as the collection and analysis of data related to areas of research involving variables, observations, and their relationships. Biostatistics is concerned with vital events and factors affecting life from birth to death. It has applications in public health, clinical trials, genetics, ecology, and biological sequence analysis. The document discusses topics including population and sampling, methods of data collection, organization and presentation through classification, tabulation and graphs, measures of central tendency and dispersion, and examples of statistical calculations.
A New SDM Classifier Using Jaccard Mining Procedure (CASE STUDY: RHEUMATIC FE...
This document presents a new statistical data mining technique called the Jaccard Mining Procedure (JMP) for classification and prediction. The JMP applies several stages to training data, including calculating a Jaccard distance matrix and dividing attributes into Jaccard classes. It then links these stages to the Gini index measure to initiate a new classifier and predictor. The technique is demonstrated on a dataset of rheumatoid fever patients characterized by symptoms. Key steps include calculating the Jaccard matrix between attributes, dividing the matrix into Jaccard classes, and comparing classes to determine JMP classifiers while using the Gini index as a precision measure.
A new sdm classifier using jaccard mining procedure case study rheumatic feve...
In this paper, a new Statistical Data Mining (SDM) technique is proposed using Jaccard Mining Procedure
(JMP) contributing a novel classifier & predictor by applying very effective stages on the training data
depending on Jaccard (J) distance matrix Linked with the Gini Index Measure as precision measure for
initiating a new classifier and a new predictor, The proposed SDM technique using JMP is applied and
examined on a Rheumatic Fever Data to demonstrate its applicability.
Basic Statistics for application in Medical Assessment
- Percentages represent occurrences in proportions of 100 and are calculated by dividing the number of items by the total number and multiplying by 100.
- The mean, median, and mode are measures of central tendency used to understand data distribution. The mean is the average, median is the midpoint, and mode is the most frequent value.
- Standard deviation measures the spread of data around the average and is used to determine if data is normally distributed and identify outliers. Confidence intervals indicate the probable range of a population parameter.
- P-values represent the probability that results are due to chance, with lower p-values indicating higher significance. Parametric and non-parametric tests are used to analyze different types of
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Abstract- Statistical models include issues such as statistical characterization of numerical data, estimating the probabilistic future behaviour of a system based on past behaviour, extrapolation or interpolation of data based on some best-fit, error estimates of observations or model generated output. If the statistical model is used to analyse the survival data it is known as statistical model in survival analysis. There are different statistical data. Censored data is one of its kinds. Censoring means the actual survival time is unknown. Censoring may occur when a person does not experience the event before the study ends or lost to follow-up during the study period or withdraws from the study. For this type of censored data the suitable model is survival models. Survival models are classified as non-parametric, semi-parametric and parametric models. The survival probability can be obtained using these models. Using the health data of cancer registry in Tiruchirappalli, Tamil Nadu , a study on survival pattern of cancer patients was explored, the non-parametric modelling that is Kaplan-Meier method was used to estimate the survival probability and the comparison of survival probability of obtained by life table and Kaplan Meier methods for each stage of the disease were made. Log rank test has been used for the comparison between the estimates obtained at the different stages of the disease.
Science.abb3221
1) The document estimates that prior to travel restrictions on January 23rd in China, 86% of COVID-19 infections were undocumented and asymptomatic or mildly symptomatic.
2) These undocumented infections were estimated to be about half as contagious as documented cases, but due to their greater numbers, were the source of 79% of documented infections.
3) After travel restrictions and other control measures, the estimated proportion of documented infections rose to 65%, reproductive number fell below 1, and transmission rates were reduced, suggesting control measures were effective at identifying previously undocumented cases.
IRJET- Disease Prediction and Doctor Recommendation System
This document proposes a disease prediction and doctor recommendation system that uses machine learning and natural language processing. It uses a Naive Bayes classifier to predict the likelihood of diseases based on patient symptoms and medical data. It then recommends doctors for the predicted disease by analyzing reviews with CoreNLP and filtering by location, cost, experience or reviews. The system aims to help users receive accurate diagnoses and treatment recommendations efficiently. Future work could involve expanding the types of diseases and doctors covered as well as collecting more real-world patient and review data to improve accuracy.
Are age and week of first symptoms significant predictors
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Time trends & patterns, TB
The document discusses methods for assessing the quality of health surveillance data used to monitor disease trends and inform public health programs and policies. It describes key factors that can impact data quality, such as changes in case finding efforts, recording and reporting systems, and case definitions. The document outlines indicators and analytical approaches that can help identify issues with completeness, consistency, and reliability of notification data over time and across regions. This includes checks for unusual fluctuations, variations in notification rates, and consistency of case type proportions. The next steps proposed are to establish data quality review units, conduct in-depth analyses guided by quality checks, and develop online platforms to share best practices.
LGBTQ+ Adults: Unique Opportunities and Inclusive Approaches to Care
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Innovative Minds France's Most Impactful Healthcare Leaders.pdf
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Test positivity – Evaluation of a new metric to assess epidemic dispersal med...
Test positivity – Evaluation of a new metric to assess epidemic dispersal med...Olutosin Ademola Otekunrin
Epidemic control may be hampered when the percentage of asymptomatic cases is high. Seeking remedies for this problem, test positivity was explored between the first 60 to 90 epidemic days in six countries that reported their first COVID-19 case between February and March 2020: Argentina, Bolivia, Chile, Cuba, Mexico, and Uruguay.
Test positivity (TP) is the percentage of test-positive individuals reported on a given day out of all individuals tested the same day. To generate both country-specific and multi-country information, this study was implemented in two stages. First, the epidemiologic data of the country infected last (Uruguay) were analyzed. If at
least one TP-related analysis yielded a statistically significant relationship, later assessments would investigate the six countries. The Uruguayan data indicated (i) a positive correlation between daily TP and daily new cases (r = 0.75); (ii) a negative correlation between TP and the number of tests conducted per million inhabitants (TPMI, r = 0.66); and (iii) three temporal stages, which differed from one another in both TP and TPMI medians (p < 0.01) and, together, revealed a negative relationship between TPMI and TP. No significant relationship
was found between TP and the number of active or recovered patients. The six countries showed a positive correlation between TP and the number of deaths/million inhabitants (DMI, r = 0.65, p < 0.01). With one exception –a country where isolation was not pursued , all countries showed a negative correlation between
TP and TPMI (r = 0.74). The temporal analysis of country-specific policies revealed four patterns, characterized by: (1) low TPMI and high DMI, (2) high TPMI and low DMI; (3) an intermediate pattern, and (4) high TPMI and
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The issue of state estimation is considered for an SIR-SI model describing a vector-borne disease such as dengue fever, with seasonal variations and uncertainties in the transmission rates. Assuming continuous measurement of the number of new infectives in the host population per unit time, a class of interval observers with estimate-dependent gain is constructed, and asymptotic error bounds are provided. The synthesis method is based on the search for a common linear Lyapunov function for monotone systems representing the evolution of the estimation errors.
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Due to the advancements in various data acquisition and storage technologies, different disciplines have attained the ability to not only accumulate a wide variety of data but also to monitor observations over longer time periods. In many real-world applications, the primary objective of monitoring these observations is to estimate when a particular event of interest will occur in the future. One of the major difficulties in handling such problem is the presence of censoring, i.e., the event of interests is unobservable in some instance which is either because of time limitation or losing track. Due to censoring, standard statistical and machine learning based predictive models cannot readily be applied to analyze the data. An important subfield of statistics called survival analysis provides different mechanisms to handle such censored data problems. In addition to the presence of censoring, such time-to-event data also encounters several other research challenges such as instance/feature correlations, high-dimensionality, temporal dependencies, and difficulty in acquiring sufficient event data in a reasonable amount of time. To tackle such practical concerns, the data mining and machine learning communities have started to develop more sophisticated and effective algorithms that either complement or compete with the traditional statistical methods in survival analysis. In spite of the importance of this problem and relevance to real-world applications, this research topic is scattered across various disciplines. In this tutorial, we will provide a comprehensive and structured overview of both statistical and machine learning based survival analysis methods along with different applications. We will also discuss the commonly used evaluation metrics and other related topics. The material will be coherently organized and presented to help the audience get a clear picture of both the fundamentals and the state-of-the-art techniques.
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Forensic Science, Medicine, and
Pathology
ISSN 1547-769X
Forensic Sci Med Pathol
DOI 10.1007/s12024-013-9452-4
By Katherine R. N. Scafide, Daniel J. Sheridan, Jacquelyn Campbell, Valerie B. DeLeon & Matthew J. HayatComparative study of decision tree algorithm and naive bayes classifier for s...
Abstract The modeling and analysis of the epidemic disease outbreaks in huge realistic populations is a data intensive task that requires immense computational resources. Such effective computational support becomes useful to study disease outbreak and to facilitate decision making. Epidemiology is one of the traditional approaches which are being used for studying and analyzing the outbreaks of epidemic diseases. Although useful for obtaining numbers of sick, infected and recovered individuals in a population, this traditional approach does not capture the complexity of human interactions. The model is only limited to the person to person interaction in order to track the surveillance of disease and it also having performance issues with large realistic data. In this paper we propose, the combination of computational epidemiology and modern data mining techniques with their comparative analysis for the Swine Flu prediction. The clustering algorithm K mean is used to make a group or cluster of Swine Flu suspects in a particular area. The Decision tree algorithm and Naive Bayes classifier are applied on the same inputs to find out the actual count of suspects and predict the possible surveillance of a Swine Flu in a nearby area from suspected area. The performances of these techniques are compared, based on accuracy. In our case the Naive Bayes classifier performs better than decision tree algorithm while finding the accurate count of suspects. Keywords: Computational Epidemiology, Aerosol-borne Disease, Clustering, Predictive analysis.
Biostatistics
This document provides an introduction to biostatistics. It defines biostatistics as the collection and analysis of data related to areas of research involving variables, observations, and their relationships. Biostatistics is concerned with vital events and factors affecting life from birth to death. It has applications in public health, clinical trials, genetics, ecology, and biological sequence analysis. The document discusses topics including population and sampling, methods of data collection, organization and presentation through classification, tabulation and graphs, measures of central tendency and dispersion, and examples of statistical calculations.
A New SDM Classifier Using Jaccard Mining Procedure (CASE STUDY: RHEUMATIC FE...
This document presents a new statistical data mining technique called the Jaccard Mining Procedure (JMP) for classification and prediction. The JMP applies several stages to training data, including calculating a Jaccard distance matrix and dividing attributes into Jaccard classes. It then links these stages to the Gini index measure to initiate a new classifier and predictor. The technique is demonstrated on a dataset of rheumatoid fever patients characterized by symptoms. Key steps include calculating the Jaccard matrix between attributes, dividing the matrix into Jaccard classes, and comparing classes to determine JMP classifiers while using the Gini index as a precision measure.
A new sdm classifier using jaccard mining procedure case study rheumatic feve...
In this paper, a new Statistical Data Mining (SDM) technique is proposed using Jaccard Mining Procedure
(JMP) contributing a novel classifier & predictor by applying very effective stages on the training data
depending on Jaccard (J) distance matrix Linked with the Gini Index Measure as precision measure for
initiating a new classifier and a new predictor, The proposed SDM technique using JMP is applied and
examined on a Rheumatic Fever Data to demonstrate its applicability.
Basic Statistics for application in Medical Assessment
- Percentages represent occurrences in proportions of 100 and are calculated by dividing the number of items by the total number and multiplying by 100.
- The mean, median, and mode are measures of central tendency used to understand data distribution. The mean is the average, median is the midpoint, and mode is the most frequent value.
- Standard deviation measures the spread of data around the average and is used to determine if data is normally distributed and identify outliers. Confidence intervals indicate the probable range of a population parameter.
- P-values represent the probability that results are due to chance, with lower p-values indicating higher significance. Parametric and non-parametric tests are used to analyze different types of
Bachelor_thesis
This document describes how simulated data was generated to evaluate regression models for estimating influenza cases. A stochastic Susceptible-Infected-Recovered (SIR) model was used to simulate the spread of influenza in a population over time. The SIR model accounts for individuals moving between susceptible, infected, and recovered groups. Regression models were then fitted to the simulated surveillance data to estimate total influenza cases. An increasing sampling intensity for lab data posed challenges which were addressed by developing an additional model component. The Negative Binomial regression model performed best both with and without adjusting for changing sampling intensity.
Non-Parametric Survival Models
Abstract- Statistical models include issues such as statistical characterization of numerical data, estimating the probabilistic future behaviour of a system based on past behaviour, extrapolation or interpolation of data based on some best-fit, error estimates of observations or model generated output. If the statistical model is used to analyse the survival data it is known as statistical model in survival analysis. There are different statistical data. Censored data is one of its kinds. Censoring means the actual survival time is unknown. Censoring may occur when a person does not experience the event before the study ends or lost to follow-up during the study period or withdraws from the study. For this type of censored data the suitable model is survival models. Survival models are classified as non-parametric, semi-parametric and parametric models. The survival probability can be obtained using these models. Using the health data of cancer registry in Tiruchirappalli, Tamil Nadu , a study on survival pattern of cancer patients was explored, the non-parametric modelling that is Kaplan-Meier method was used to estimate the survival probability and the comparison of survival probability of obtained by life table and Kaplan Meier methods for each stage of the disease were made. Log rank test has been used for the comparison between the estimates obtained at the different stages of the disease.
Science.abb3221
1) The document estimates that prior to travel restrictions on January 23rd in China, 86% of COVID-19 infections were undocumented and asymptomatic or mildly symptomatic.
2) These undocumented infections were estimated to be about half as contagious as documented cases, but due to their greater numbers, were the source of 79% of documented infections.
3) After travel restrictions and other control measures, the estimated proportion of documented infections rose to 65%, reproductive number fell below 1, and transmission rates were reduced, suggesting control measures were effective at identifying previously undocumented cases.
IRJET- Disease Prediction and Doctor Recommendation System
This document proposes a disease prediction and doctor recommendation system that uses machine learning and natural language processing. It uses a Naive Bayes classifier to predict the likelihood of diseases based on patient symptoms and medical data. It then recommends doctors for the predicted disease by analyzing reviews with CoreNLP and filtering by location, cost, experience or reviews. The system aims to help users receive accurate diagnoses and treatment recommendations efficiently. Future work could involve expanding the types of diseases and doctors covered as well as collecting more real-world patient and review data to improve accuracy.
Are age and week of first symptoms significant predictors
This study analyzed the relationship between patient age, week of first symptoms, and time from admission to discharge using data from 4,644 patients. On average, patients were 57 years old and spent 10.8 days in the hospital after 32.2 weeks of first symptoms. Statistical analysis found a positive correlation between older patient age and longer hospital stays but no significant association between week of first symptoms and time to discharge. The study suggests that older patients generally require more time in the hospital than younger patients.
Time trends & patterns, TB
The document discusses methods for assessing the quality of health surveillance data used to monitor disease trends and inform public health programs and policies. It describes key factors that can impact data quality, such as changes in case finding efforts, recording and reporting systems, and case definitions. The document outlines indicators and analytical approaches that can help identify issues with completeness, consistency, and reliability of notification data over time and across regions. This includes checks for unusual fluctuations, variations in notification rates, and consistency of case type proportions. The next steps proposed are to establish data quality review units, conduct in-depth analyses guided by quality checks, and develop online platforms to share best practices.
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Covid Data Analytics : Case Study on India
- 1. Covid Data Analytics : Case Study on India Bankar Prasad Vilas Subhasis Panda Vaibhav Anand Vineet Kumar Indian Statistical Institute, Kolkata https://covid-isical.tech contact@covid-isical.tech Introduction We provide a macro-level India specific analysis on COVID related data as obtained from [1]. We built a dashboard available at [2] by giving multi-dimensional view and risk summary for India. Spread Assessment Metrics Increasing trend of occurrence of new cases is likely to indicate that the virus is spreading. Figure 1: Average confirmed cases / day in India for each week (last 7 weeks) Increasing trend of active cases indicates the possibility of increased stress on resources at present or in the near future. Figure 2: Active cases in India for the last 7 weeks Risk Assessment Metrics Avg. Confirmed Cases to Avg. Recoveries in Consecutive Weeks Traffic Intensity (term from queuing theory) — • Leading indicator of stress on resources • Values > 1 implies faster arrival of new cases compared to recovery Figure 3: Traffic Intensity for India for the last 7 weeks Points plot of Test in Each Week and % positive in each week Higher proportion positive test results on a larger test population (possibly less targeting) are anomalous and need investigation. There are at least three possibilities: • Rapid increase of rate of infection • Carrying out tests in new (hitherto untested) areas with high but unknown rate of infection • Improper testing leading to many incorrectly positive results Figure 4: Assessing adequacy of testing in India Comparative Assessment Heat maps showing the top 10 States in India w.r.t con- firmed (blue), recovered (green), and deceased (red), re- spectively. Figure 5: Heat map of last 15 days Bounds on Death Rate Crude Fatality Rate Calculated on daily basis using P Death P Confirmed . Figure 6: Crude Fatality Rate Kaplan Meier Analysis The estimator of the survival func- tion S(t) (the probability that life is longer than t is given by: d S(t) = Y i: ti≤t 1 − di ni with ti a time when at least one event happened, di the number of events (e.g., deaths) that happened at time ti, and ni the individuals known to have survived (have not yet had an event or been censored) up to time ti. Figure 7: KM Curve for Survival Estimation Spread-based Clustering Clustered Districts based on number of active cases and deaths using k means and agglomerate clustering tech- niques. MH TN DL GJ KA UP TG AP WB RJ HR BR MP AS OR JK PB KL -0.5 0.0 0.5 1.0 -5.0 -2.5 0.0 Dim1 (94.8%) Dim2 (3.5%) cluster a a a a 1 2 3 4 Cluster plot MH TN DL PB JK KL OR BR AS MP RJ HR KA GJ UP WB TG AP 0 2 4 6 8 10 Cluster Dendrogram hclust (*, "ward.D2") d Height Figure 8: Clustering states based on infection spread References [1] https://api.covid19india.org/ [2] https://covid-isical.tech/ Summer School of Machine Learning at Skoltech, SMILES – Aug’ 2020