COVID-19 PANDEMIC IN INDIA
What is Corona Virus?
Coronaviruses are a large family of viruses which may cause illness in animals or humans. In humans, several coronaviruses are known to cause respiratory infections ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). The most recently discovered coronavirus causes coronavirus disease COVID-19. The virus that causes COVID-19 is mainly transmitted through droplets generated when an infected person coughs, sneezes, or exhales. These droplets are too heavy to hang in the air, and quickly fall on floors or surfaces. You can be infected by breathing in the virus if you are within close proximity of someone who has COVID-19, or by touching a contaminated surface and then your eyes, nose or mouth.
COVID-19 INDIA LATEST NEWS!!
Coronavirus India Live Updates —Total Coronavirus cases in India has crossed the 4,73,000-mark while COVID 19 death toll is near 14,894 in the country. As per the data shared by the Ministry of Health and Family Welfare, the total number of COVID-19 cases in India now are 4,88,968 , with 2,16,968 active cases and 2,72,000 cured/discharged patients. As many as 15,359 people have died. The recovery rate in the country stands at 57.43 per cent, Health Ministry official said. The Global hunt for coronavirus COVID 19 vaccine is on even as large parts of the world is battling a war against the highly contagious disease. Total confirmed coronavirus cases around the world are 9.44 M. So far, 4,87,210 people have died due to COVID 19, according to Johns Hopkins University Coronavirus Resource Center data.
ABOUT THE DATA SET
This project gives a detailed analysis about the Covid -19 pandemic in India during the period between January and May. For this , we will be using multiple datasets from various sources . We will be using datasets like age_group_details , hospital_beds_in_India , Individual_details , per_day_cases , covid-19_in_India etc.
INITIAL PERIOD ANALYSIS of COVID-19 IN INDIA(JAN-MAR)
Here we will be using the age_group_details dataset which shows the total number of cases and it’s percentage for various age group .
After importing the dataset we can see that it has 4 columns that are Sno , AgeGroup , TotalCases , Percentage. Here we made two tables from the dataset , fread1 and fread2.
The following gives details about the percentage of the age group being affected in India in the months of january,february and march.
The dataframe fread2 here gives us the details about the different age groups and the percentage of affected people in that group. As we have printed the head of the table it only shows the top 5 rows of the table.
Graph portraying the current cases and the age groups affected in India
From the above graph we can clearly see that the older age group is at more risk of getting affected by the coronavirus. The maximum number of cases are found in the age group of 50–59 and 60–69.
To represent the above graph we have used a kdeplot from the seaborn library of python. The code for this is given below:-
Total number of cases,deaths,cured patients in India
To represent the total number of cases , death , cured patients in India and other details related to it , we have imported a dataset called covid_19_India.
These are the graphs showing the Ratio of Deaths that have taken place in the three months to the number of patients who have recovered and the information related to it . This is a pairplot which was made using the seaborn library of python.
Deaths vs Cured
From this pie chart we can see that death rates were around 60.2% while the cured rates were around 39.8% . So, it can be concluded that during the initital stage of this pandemic the recovery rates we low , below 50% .
The code for this pie chart is given below:-
Symptoms observed in hospitalized patients with COVID-19
Below we list the symptoms, with percentages representing the proportion of patients displaying that symptom, as observed in hospitalized patients tested and identified as having laboratory-confirmed COVID-19 infection. These findings refer to hospitalized patients, therefore generally representing serious or critical cases. The majority of cases of COVID-19 (about 80%) is mild.
The most common symptoms of COVID-19 are fever, tiredness, and dry cough. Some patients may have aches and pains, nasal congestion, runny nose, sore throat or diarrhea. These symptoms are usually mild and begin gradually. Some people become infected but don’t develop any symptoms and don’t feel unwell. Most people (about 80%) recover from the disease without needing special treatment. Around 1 out of every 6 people who gets COVID-19 becomes seriously ill and develops difficulty breathing. Older people, and those with underlying medical problems like high blood pressure, heart problems or diabetes, are more likely to develop serious illness. People with fever, cough and difficulty breathing should seek medical attention.
The above table shows us the information regarding the different symptoms observed on patients around the world and the percentage of patients having these symptoms while being affected from Covid-19.
This bar graph maps the various symptoms according to the percentage of people having these symptoms . On observing this graph carefully we can see that fever and dry-cough were the most common symptoms found in patients . This doesn’t mean that if you are having the other symptoms you are less prone to have coronavirus . Having any of these symptoms means that you should take care of yourself while self quarantining yourself and getting a covid-19 test.
This pie chart also gives the same information as above .
Details of Hospitals and healthcare facilities in India
First of all , here we have imported the dataset called the HospitalbedsinIndia . Here we can see various subplots giving the details about the numbers of beds across various states of India . The four graphs gives the information of beds in primary heath centers , community health centers , district hospitals and public health facilities.
The code for these subplots are given below:-
Percentage of Males and Females affected
Here we have first imported the Individualdetails dataset form kaggle . Then we have plotted a pie chart showing the number of males and females affected from this virus. On observing this pie chart we can see that around 69% of males and around 31% females are affected from this virus. From this we can conclude that males are somewhat more exposed to this virus than females .
The code for this pie chart is below :-
APRIL Analysis of COVID-19
Till now we have seen a detailed analysis about the pandemic from the period between January and March . From now on we will be analyzing the situation till April 30th. For this analysis we have imported the dataset having information till the 30th of APRIL.
The top 5 rows of the dataset looks like this:-
Data Preprocessing
Death value counts
CURED,TOTAL CASES, DEATHS
This pie chart shows the percentage of remaining positive cases , the patients cured and the deaths across India till 30th of April , 2020. We can see that around 18.4% of total cases observed has cured till now . While the death percentage is around 2.1% which has decreased from the previous few months . This means the number of people getting cured has increased and this seems to be a good sign. A lot of population still seem to be positive from this virus till now.
State wise Deaths
This bar graph gives the state wise deaths across different states of India. On close observation , we can note that the maximum deaths are from Maharashtra(400+ ) followed by Gujarat(200+)and Madhya Pradesh. While the northeastern states have the least number of deaths .
STATE-WISE CURED/MIGRATED/DISCHARGED
The bar graph here shows the number of patients cured across various states. The maximum number of people getting cured is in Maharashtra followed by Tamil Nadu , Delhi and Rajasthan.
State wise Mortality Rate
Mortality rate is a weighted average of the age-specific mortality rates per 100 000 persons, where the weights are the proportions of persons in the corresponding age groups of the WHO standard population.Mortality data allow health authorities to evaluate how they prioritize public health programs.
This bar graph gives the information about the mortality rate across various states of India . It can be seen observed from the graph that Meghalaya and Himachal Pradesh have equally highest mortality rate as compared to others. Odisha and Bihar seems to have the least mortality rate.
State wise active cases
This bar graph gives us the information about the number of active cases in various states of India . We can notice that Maharashtra has the highest number of active cases which is still increasing at an alarming rate. Madhya Pradesh and Gujarat also have a high number of active cases . While there’s some relief in the northeastern states of India.
Perday New Cases
Now we have imported the perdaycases dataset which gives us the infomation about the total cases and new cases of each day from January 1 to April 30.
The top 5 rows of the dataset is given below:-
APPALLING INCREASE in the COVID19 CASES PERDAY
This is a very important graph to analyse the situation . On closely observing the graph we can make a note that till 30th of March there was a very slight increase in the number of new cases everyday . But of after March , from the starting to April , the number of new cases began to increasing on an alarming rate which reached its peak value at the end of April by crossing more than 2500 new cases per day. One of the reason for this could be the more number of tests being done by the health authority across all over India.
IMPACT ON LIFESTYLE
This pandemic has affected our lifestyle in one way or the other . We are unable to lead our lives normally like we earlier used to do . We are now forced to lock ourselves in our home to quarantine ourselves . The GDP of the country is decreasing , we have no work to do , no schools , no college ,etc .
I have collected some of information about different lifestyles people are having now and percentage of people dealing with it.
To analyse this data in a more interactive way , let’s make a pie chart of this using python.
From the pie chart , we can we that more number of people are in the favour of not wasting which is a very good gesture shown by most of people . While some people are more environment conscious and more mindful of health. Some people also believe that using Indian made domestic products can also help . I personally appreciate the use of made in India products as they can really help to prevent the fall of GDP in India.
After this , we are going to import another dataset named complete_covid which gives us the information about the total number of confirmed cases, deaths , cured along with longitude and latitude of the place of the patient and the state from which they belong. This dataset is available on the kaggle website.
The first 5 rows of the dataset are:-
Heatmap giving information about deaths across various states
Heatmap giving information about Total confirmed cases across various states
From this heatmap, we can observe that the cases are increases at an alarming rate in India,Maharashtra has the maximum number of covid-19 positives followed by Delhi and Tamil Nadu.
The code for above heat map is given below:-
Representing the Deaths in India through Geographic Visualization
This map shows us the deaths of patients across various parts of India . From this we can conclude that the maximum number of deaths are from Maharashtra and it’s surrounding areas . While other parts of India also has a large number of death rate , which is increasing day by day .
Covid -19 ANALYSIS TILL MAY 28 , 2020
Till now we have analysed the initital stage of this pandemic and the scenario in April in detail . Now let’s analyse for the month of May . For this let’s import the dataset which has information till May 28th .
The top five rows of the dataset look like this .
Total confirmed cases till now
We can see that the number of confirmed cases till May 28 , are around 1,42,818. It’s getting to the end of the month of May and still the rate of patients getting affected from this virus is increasing day by day
We can conclude from the above bar graph that the Total number of confirmed cases has crossed 50000 in Maharashtra , which is very shocking as well a reason to worry about . Here we can see the top 5 states having the highest number of confirmed cases.
Here we can note that a lot of people are recovering from this disease . Maharashtra being the fastest one to have that number of recoveries . It is good to see that the number of patients getting cured is increased in each and very states across India. Here we can see the number of patients cured in the top 5 states of India.
Here we can see the top 5 states having the maximum number of deaths . On observing we can see that Maharashtra has the maximum number of death crossing the 1500 mark , followed by Gujarat and Rajasthan . The death rates are quite high in May as compared to the previous months . But it’s relief to see that the rate of recovery is also increasing .
CONCLUSION
The COVID-19 pandemic has demonstrated the interconnected nature of our world — and that no one is safe until everyone is safe. Only by acting in solidarity can communities save lives and overcome the devastating socio-economic impacts of the virus.
LinkedIN:-https://www.linkedin.com/in/saurav-borah-a7751818b/
Github:- https://github.com/SAURAVBORAH22
