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{{Short description|Statistics about the COVID-19 pandemic in India}} | |||
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This article contains statistics about the [[COVID-19 pandemic in India]]. [[COVID-19]] cases, deaths, recoveries, and other statistics are shown in nationwide and [[States and union territories of India|regional]] maps and graphs. | This article contains statistics about the [[COVID-19 pandemic in India]]. [[COVID-19]] cases, deaths, recoveries, and other statistics are shown in nationwide and [[States and union territories of India|regional]] maps and graphs. | ||
== Classifying COVID-19 deaths == | == Classifying COVID-19 deaths == | ||
The [[Indian Council of Medical Research]] (ICMR) governs classification of a COVID-19 related death in India. ICMR in turn follows WHO guidelines, recording COVID-19 deaths as U07.1 as per [[International Classification of Diseases]].<ref name=":1">{{Cite web|last1=Gupta|first1=Aashish|last2=Rajendran|first2=Dhanya|last3=S.|first3=Rukmini|date=11 May 2021|title=India is undercounting Covid-19 deaths. Here's how to work around the problem|url=https://scroll.in/article/994619/india-is-undercounting-covid-19-deaths-heres-how-to-work-around-the-problem | The [[Indian Council of Medical Research]] (ICMR) governs classification of a COVID-19 related death in India. ICMR in turn follows WHO guidelines, recording COVID-19 deaths as U07.1 as per [[International Classification of Diseases]].<ref name=":1">{{Cite web|last1=Gupta|first1=Aashish|last2=Rajendran|first2=Dhanya|last3=S.|first3=Rukmini|date=11 May 2021|title=India is undercounting Covid-19 deaths. Here's how to work around the problem|url=https://scroll.in/article/994619/india-is-undercounting-covid-19-deaths-heres-how-to-work-around-the-problem|access-date=2021-05-27|website=Scroll.in|language=en-US}}</ref> The National Centre for Disease Informatics and Research, ICMR, released a document titled "Guidance for appropriate recording of COVID-19 related deaths in India".<ref>{{Cite web|date=11 May 2021|others=PTI|title=ICMR issues guidance for appropriate recording of COVID-19 deaths to create robust data|url=https://www.newindianexpress.com/nation/2020/may/11/icmr-issues-guidance-for-appropriate-recording-of-covid-19-deaths-to-create-robust-data-2141917.html|access-date=2021-05-28|website=The New Indian Express}}</ref> | ||
In March 2020, the first two COVID-19 infected people to die in India officially died due to their co-morbidities and not COVID-19.<ref>{{Cite web|last=Pulla|first=Priyanka|date=2020-03-17|title=The Curious Case of the Deaths That Weren't Due to COVID-19|url=https://science.thewire.in/health/coronavirus-covid-19-comorbidity-diabetes-hypertension-community-transmission/|access-date=2021-05-29|website=The Wire Science|language=en-GB}}</ref> Around India, people dying of their co-morbidities are not be considered as a COVID-19 death, "if a comorbid patient dies then a committee of experts decides the primary and secondary causes of death [...] If that committee identifies the main cause of death as heart attack, then even if the patient was infected, such a death is not counted as being caused by Covid."<ref>{{Cite web|last=|first=|date=14 April 2021|title=Toll haze in Gujarat: If comorbid, Covid death not counted?|url=https://timesofindia.indiatimes.com/city/ahmedabad/toll-haze-if-comorbid-covid-death-not-counted/articleshow/82056748.cms | In March 2020, the first two COVID-19 infected people to die in India officially died due to their co-morbidities and not COVID-19.<ref>{{Cite web|last=Pulla|first=Priyanka|date=2020-03-17|title=The Curious Case of the Deaths That Weren't Due to COVID-19|url=https://science.thewire.in/health/coronavirus-covid-19-comorbidity-diabetes-hypertension-community-transmission/|access-date=2021-05-29|website=The Wire Science|language=en-GB}}</ref> Around India, people dying of their co-morbidities are not be considered as a COVID-19 death, "if a comorbid patient dies then a committee of experts decides the primary and secondary causes of death [...] If that committee identifies the main cause of death as heart attack, then even if the patient was infected, such a death is not counted as being caused by Covid."<ref>{{Cite web|last=|first=|date=14 April 2021|title=Toll haze in Gujarat: If comorbid, Covid death not counted?|url=https://timesofindia.indiatimes.com/city/ahmedabad/toll-haze-if-comorbid-covid-death-not-counted/articleshow/82056748.cms|access-date=2021-05-29|website=The Times of India|language=en|quote=CM Vijay Rupani had denied reports that the state government was hiding the true count of Covid fatalities. "As per the guidelines of the ICMR, if a comorbid patient dies then a committee of experts decides the primary and secondary causes of death," he said. "If that committee identifies the main cause of death as heart attack, then even if the patient was infected, such a death is not counted as being caused by Covid. The same system is followed across the entire country."}}</ref> Testing of dead bodies to COVID-19 is being done according to ICMR guidelines and government orders.<ref>{{Cite web|date=2020-05-21|others=PTI|title=Collect nasal samples from dead bodies for Covid-19 test before sending to mortuary: ICMR|url=https://www.hindustantimes.com/india-news/nasal-samples-from-dead-bodies-be-collected-for-covid-19-test-before-sending-to-mortuary-icmr/story-qvPy3WHlgUYl02WjExCRBK.html|access-date=2021-05-29|website=Hindustan Times|language=en}}</ref><ref>{{Cite web|date=2020-09-15|title=Coronavirus: Rapid Antigen Tests to be conducted on dead bodies|url=https://www.deccanherald.com/national/coronavirus-rapid-antigen-tests-to-be-conducted-on-dead-bodies-887750.html|access-date=2021-05-29|website=Deccan Herald|language=en}}</ref> On 17 May 2020, the Delhi government changed COVID-19 testing policy by stopping tests of dead bodies.<ref name=":2">{{Cite web|last=Johari|first=Aarefa|date=30 May 2020|title=376 burials in 25 days: What explains the surging death rate in Ahmedabad's burial grounds?|url=https://scroll.in/article/963293/376-burials-in-25-days-what-explains-the-surging-death-rate-in-ahmedabads-burial-grounds|access-date=2021-05-28|website=Scroll.in|language=en-US}}</ref> | ||
== National serological surveys == | == National serological surveys == | ||
India's first national [[Serological survey|serological (seroprevalence) survey]] was conducted by ICMR during May–June 2020; 0.73% sero-positivity was observed. The results were published on 10 September 2021 on the website of the ''[[Indian Journal of Medical Research]]''.<ref name=":5">{{Cite web|last=Ramachandran|first=R.|date=2020-09-23|title=Six Curious Questions About ICMR's First National Seroprevalence Survey Paper|url=https://science.thewire.in/health/icmr-national-covid-19-seroprevalence-survey-balram-bhargava-data-discrepancies/ | India's first national [[Serological survey|serological (seroprevalence) survey]] was conducted by ICMR during May–June 2020; 0.73% sero-positivity was observed. The results were published on 10 September 2021 on the website of the ''[[Indian Journal of Medical Research]]''.<ref name=":5">{{Cite web|last=Ramachandran|first=R.|date=2020-09-23|title=Six Curious Questions About ICMR's First National Seroprevalence Survey Paper|url=https://science.thewire.in/health/icmr-national-covid-19-seroprevalence-survey-balram-bhargava-data-discrepancies/|access-date=2021-05-28|website=The Wire Science|language=en-GB}}</ref> However the survey and subsequent press releases and the final publication saw a number of discrepancies.<ref name=":5" /><ref name=":6">{{Cite web|last=Banaji|first=Murad|date=2021-02-05|title=COVID-19: What the Third National Sero-Survey Result Does and Doesn't Tell Us|url=https://science.thewire.in/health/third-national-seroprevalence-survey-icmr-covid-19-rural-prevalence-test-positivity/|access-date=2021-05-28|website=The Wire Science|language=en-GB}}</ref> ICMR also clarified that the observed sero-positivity did not represent the entire population.<ref name=":5" /> While ICMR came out with a 49% sero-positivity rate for [[Ahmedabad]], the [[Ahmedabad Municipal Corporation]] conducted its own survey and came out with an average sero-positivity of 17.61%.<ref name=":5" /> The second serological survey, published in ''[[The Lancet]]'',<ref>{{Cite journal|last1=Murhekar|first1=Manoj V|last2=Bhatnagar|first2=Tarun|last3=Selvaraju|first3=Sriram|last4=Saravanakumar|first4=V|last5=Thangaraj|first5=Jeromie Wesley Vivian|last6=Shah|first6=Naman|last7=Kumar|first7=Muthusamy Santhosh|last8=Rade|first8=Kiran|last9=Sabarinathan|first9=R|last10=Asthana|first10=Smita|last11=Balachandar|first11=Rakesh|date=March 2021|orig-year=27 January 2021|title=SARS-CoV-2 antibody seroprevalence in India, August–September, 2020: findings from the second nationwide household serosurvey|journal=The Lancet Global Health|language=en|volume=9|issue=3|pages=e257–e266|doi=10.1016/S2214-109X(20)30544-1|pmc=7906675|pmid=33515512}}</ref> was conducted in August–September 2020; 6.6% sero-positivity was observed, i.e. for every case detected, about 15 went undetected.<ref name=":6" /> India's third national serological survey conducted in December 2020 and January 2021 revealed that only 3.5% of the total infections had been "detected" or "recorded".<ref name=":6" /> In other words, for every case detected, about 30 went undetected.<ref name=":4">{{Cite web|last1=Frayer|first1=Lauren|last2=Pathak|first2=Sushmita|date=30 April 2021|title=India Is Counting Thousands Of Daily COVID Deaths. How Many Is It Missing?|url=https://www.npr.org/sections/goatsandsoda/2021/04/30/992451165/india-is-counting-thousands-of-daily-covid-deaths-how-many-is-it-missing|access-date=2021-05-27|website=NPR.org|language=en}}</ref> | ||
== Reconciliation of data == | == Reconciliation of data == | ||
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=== Interactive maps === | === Interactive maps === | ||
{{Image frame|align=left|caption=Interactive map of confirmed COVID-19 deaths in India.<br>Click the play button in the top left to interact with the map.<br>Hover over the regions for values.|content= | {{Image frame|align=left|caption=Interactive map of confirmed COVID-19 deaths in India.<br />Click the play button in the top left to interact with the map.<br />Hover over the regions for values.|content= | ||
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{{Image frame|align=left|caption=Interactive map of confirmed COVID-19 cases in India.<br>Click the play button in the top left to interact with the map.<br>Hover over the regions for values.|content= | {{Image frame|align=left|caption=Interactive map of confirmed COVID-19 cases in India.<br />Click the play button in the top left to interact with the map.<br />Hover over the regions for values.|content= | ||
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=== Total confirmed cases, active cases, recoveries and deaths === | === Total confirmed cases, active cases, recoveries and deaths === | ||
<div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | <div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | ||
{{center| | |||
{{legend inline|#FF6347|Total confirmed cases}} | {{legend inline|#FF6347|Total confirmed cases}} | ||
{{legend inline|#FFC000|Active cases}} | {{legend inline|#FFC000|Active cases}} | ||
{{legend inline|#87CEEB|Recoveries}} | {{legend inline|#87CEEB|Recoveries}} | ||
{{legend inline|#A50026|Deaths}} | {{legend inline|#A50026|Deaths}} | ||
}} | |||
{{Switcher | {{Switcher | ||
|{{Graph:Chart | |{{Graph:Chart | ||
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=== Daily new cases === | === Daily new cases === | ||
<div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | <div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | ||
{{center| | |||
{{legend inline|#FF6347|New cases per day}} | {{legend inline|#FF6347|New cases per day}} | ||
{{legend inline|orange|7-day moving average of new cases per day}} | {{legend inline|orange|7-day moving average of new cases per day}} | ||
}} | |||
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| Line 110: | Line 111: | ||
=== Daily new deaths === | === Daily new deaths === | ||
<div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | <div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | ||
{{center| | |||
{{legend inline|#A50026|New deaths per day}} | {{legend inline|#A50026|New deaths per day}} | ||
{{legend inline|red|7-day moving average of new deaths per day}} | {{legend inline|red|7-day moving average of new deaths per day}} | ||
}} | |||
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|colors = #A50026, red }} | |colors = #A50026, red }} | ||
{{smalldiv|1= | |||
'''Notes:''' | |||
* On 17 June 2020, 1,672 backlogged deaths from Maharashtra and Delhi were added taking reported daily deaths to 2,003 on that day.<ref>{{Cite web|date=2020-06-17|title=India's death toll soars past 10K, backlog deaths raise count by 437 in Delhi, 1,409 in Maharashtra|url=https://www.hindustantimes.com/india-news/india-s-death-toll-soars-past-10k-backlog-deaths-raise-count-by-437-in-delhi-1-409-in-maharashtra/story-9GNbe7iMBKLsiHtByjRKCJ.html|access-date=2020-06-17|website=Hindustan Times|language=en}}</ref> | * On 17 June 2020, 1,672 backlogged deaths from Maharashtra and Delhi were added taking reported daily deaths to 2,003 on that day.<ref>{{Cite web|date=2020-06-17|title=India's death toll soars past 10K, backlog deaths raise count by 437 in Delhi, 1,409 in Maharashtra|url=https://www.hindustantimes.com/india-news/india-s-death-toll-soars-past-10k-backlog-deaths-raise-count-by-437-in-delhi-1-409-in-maharashtra/story-9GNbe7iMBKLsiHtByjRKCJ.html|access-date=2020-06-17|website=Hindustan Times|language=en}}</ref> | ||
* On 23 July 2020, Tamil Nadu reported 528 deaths including backlogged deaths, taking reported daily deaths to 1,129.<ref>{{Cite web|date=2020-07-23|title=45,720 new cases in 24 hrs, 13% positivity, single-day toll 1,129 after Tamil Nadu update|url=https://theprint.in/health/45720-new-cases-in-24-hrs-13-positivity-single-day-toll-1129-after-tamil-nadu-update/466882/|access-date=2020-08-05|website=The Print|language=en}}</ref> | * On 23 July 2020, Tamil Nadu reported 528 deaths including backlogged deaths, taking reported daily deaths to 1,129.<ref>{{Cite web|date=2020-07-23|title=45,720 new cases in 24 hrs, 13% positivity, single-day toll 1,129 after Tamil Nadu update|url=https://theprint.in/health/45720-new-cases-in-24-hrs-13-positivity-single-day-toll-1129-after-tamil-nadu-update/466882/|access-date=2020-08-05|website=The Print|language=en}}</ref> | ||
* On 10 June 2021, Bihar added 3,951 backlogged fatalities taking the day's death toll to 6,148 deaths.<ref>{{Cite web|last=Singh|first=Rohit Kumar|date=10 June 2021|title=Bihar govt revises Covid deaths by 72% after HC-ordered audit|url=https://www.indiatoday.in/coronavirus-outbreak/story/bihar-govt-revises-covid-deaths-by-72-per-cent-after-hc-ordered-audit-1813101-2021-06-10 | * On 10 June 2021, Bihar added 3,951 backlogged fatalities taking the day's death toll to 6,148 deaths.<ref>{{Cite web|last=Singh|first=Rohit Kumar|date=10 June 2021|title=Bihar govt revises Covid deaths by 72% after HC-ordered audit|url=https://www.indiatoday.in/coronavirus-outbreak/story/bihar-govt-revises-covid-deaths-by-72-per-cent-after-hc-ordered-audit-1813101-2021-06-10|access-date=2021-06-18|website=India Today|language=en}}</ref> | ||
* From 1 May to 7 June 2021, Maharashtra added 8,756 backlogged/reconciliation deaths.<ref>{{Cite web|last=Chakraborty|first=Rupsa|date=2021-06-10|title=Maharashtra: 9,000 more Covid deaths added after reconciliation|url=https://www.hindustantimes.com/cities/mumbai-news/maharashtra-9-000-more-covid-deaths-added-after-reconciliation-101623264250504.html | * From 1 May to 7 June 2021, Maharashtra added 8,756 backlogged/reconciliation deaths.<ref>{{Cite web|last=Chakraborty|first=Rupsa|date=2021-06-10|title=Maharashtra: 9,000 more Covid deaths added after reconciliation|url=https://www.hindustantimes.com/cities/mumbai-news/maharashtra-9-000-more-covid-deaths-added-after-reconciliation-101623264250504.html|access-date=2021-06-18|website=Hindustan Times|language=en}}</ref> | ||
* On 21 July 2021, Maharashtra added 3,509 reconciled deaths.<ref name=":HT MH">{{cite news |last1=Ghosh |first1=Poulomi |title=Maharashtra reconciles over 3,000 Covid deaths pushing India's daily toll to 3,998 |url=https://www.hindustantimes.com/india-news/maharashtra-reconciles-over-3-000-covid-deaths-pushing-india-s-daily-toll-to-3998-101626845777446.html |access-date=22 July 2021 |work=Hindustan Times |date=22 July 2021}}</ref> | * On 21 July 2021, Maharashtra added 3,509 reconciled deaths.<ref name=":HT MH">{{cite news |last1=Ghosh |first1=Poulomi |title=Maharashtra reconciles over 3,000 Covid deaths pushing India's daily toll to 3,998 |url=https://www.hindustantimes.com/india-news/maharashtra-reconciles-over-3-000-covid-deaths-pushing-india-s-daily-toll-to-3998-101626845777446.html |access-date=22 July 2021 |work=Hindustan Times |date=22 July 2021}}</ref> | ||
}} | |||
</div> | </div> | ||
=== Daily new recoveries === | === Daily new recoveries === | ||
<div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | <div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | ||
{{center| | |||
{{legend inline|#87CEEB |New recoveries per day}} | {{legend inline|#87CEEB |New recoveries per day}} | ||
}} | |||
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| Line 138: | Line 141: | ||
=== Daily new cases vs active cases === | === Daily new cases vs active cases === | ||
<div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | <div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | ||
{{center| | |||
{{legend inline|#5528A2|New cases as percentage of active cases}} | {{legend inline|#5528A2|New cases as percentage of active cases}} | ||
{{legend inline|#CC691E|7-day moving average of new cases as percentage of active cases}} | {{legend inline|#CC691E|7-day moving average of new cases as percentage of active cases}} | ||
{{legend inline|#82A43D|7-day moving CAGR of active cases}} | {{legend inline|#82A43D|7-day moving CAGR of active cases}} | ||
}} | |||
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}} | }} | ||
| Line 149: | Line 152: | ||
=== Confirmed cases by regions === | === Confirmed cases by regions === | ||
<div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | <div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | ||
{{center|'''>1,000,000'''}} | |||
{{Switcher|{{Graph:Lines|width = 850|height = 300|table = COVID-19 cases in India by State and UT.tab|type = time|xField = date|series = "maharashtra"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count | {{Switcher|{{Graph:Lines|width = 850|height = 300|table = COVID-19 cases in India by State and UT.tab|type = time|xField = date|series = "maharashtra"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count | ||
}}|Linear scale|{{Graph:Lines|width = 850|height = 300|table = COVID-19 cases in India by State and UT.tab|type = time|xField = date|series = "maharashtra"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count|yScale = log|yMin = 1|replaceZerosWith = 1 | }}|Linear scale|{{Graph:Lines|width = 850|height = 300|table = COVID-19 cases in India by State and UT.tab|type = time|xField = date|series = "maharashtra"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count|yScale = log|yMin = 1|replaceZerosWith = 1 | ||
}}|Logarithmic scale}} | }}|Logarithmic scale}} | ||
{{center|'''>100,000 and <1,000,000'''}} | |||
{{Switcher|{{Graph:Lines|width = 850|height = 300|table = COVID-19 cases in India by State and UT.tab|type = time|xField = date|series = "andhrapradesh","tamilnadu","karnataka","uttarpradesh", "delhi", "westbengal", "telangana", "bihar", "odisha", "assam", "gujarat", "kerala","rajasthan"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count | {{Switcher|{{Graph:Lines|width = 850|height = 300|table = COVID-19 cases in India by State and UT.tab|type = time|xField = date|series = "andhrapradesh","tamilnadu","karnataka","uttarpradesh", "delhi", "westbengal", "telangana", "bihar", "odisha", "assam", "gujarat", "kerala","rajasthan"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count | ||
}}|Linear scale|{{Graph:Lines|width = 850|height = 300|table = COVID-19 cases in India by State and UT.tab|type = time|xField = date|series = "andhrapradesh","tamilnadu","karnataka","uttarpradesh", "delhi", "westbengal", "telangana", "bihar", "odisha", "assam", "gujarat", "kerala","rajasthan"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count|yScale = log|yMin = 1|replaceZerosWith = 1 | }}|Linear scale|{{Graph:Lines|width = 850|height = 300|table = COVID-19 cases in India by State and UT.tab|type = time|xField = date|series = "andhrapradesh","tamilnadu","karnataka","uttarpradesh", "delhi", "westbengal", "telangana", "bihar", "odisha", "assam", "gujarat", "kerala","rajasthan"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count|yScale = log|yMin = 1|replaceZerosWith = 1 | ||
}}|Logarithmic scale}} | }}|Logarithmic scale}} | ||
{{center|'''>10,000 and <100,000'''}} | |||
{{Switcher|{{Graph:Lines|width = 850|height = 300|table = COVID-19 cases in India by State and UT.tab|type = time|xField = date|series = "haryana", "madhyapradesh", "punjab", "chhattisgarh", "jharkhand", "jammuandkashmir", "uttarakhand", "goa", "puducherry", "tripura"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count | {{Switcher|{{Graph:Lines|width = 850|height = 300|table = COVID-19 cases in India by State and UT.tab|type = time|xField = date|series = "haryana", "madhyapradesh", "punjab", "chhattisgarh", "jharkhand", "jammuandkashmir", "uttarakhand", "goa", "puducherry", "tripura"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count | ||
}}|Linear scale|{{Graph:Lines|width = 850|height = 300|table = COVID-19 cases in India by State and UT.tab|type = time|xField = date|series = "haryana", "madhyapradesh", "punjab", "chhattisgarh", "jharkhand", "jammuandkashmir", "uttarakhand", "goa", "puducherry", "tripura"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count|yScale = log|yMin = 1|replaceZerosWith = 1 | }}|Linear scale|{{Graph:Lines|width = 850|height = 300|table = COVID-19 cases in India by State and UT.tab|type = time|xField = date|series = "haryana", "madhyapradesh", "punjab", "chhattisgarh", "jharkhand", "jammuandkashmir", "uttarakhand", "goa", "puducherry", "tripura"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count|yScale = log|yMin = 1|replaceZerosWith = 1 | ||
| Line 165: | Line 168: | ||
=== Confirmed deaths by regions === | === Confirmed deaths by regions === | ||
<div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | <div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | ||
{{center|'''>10,000'''}} | |||
{{Switcher|{{Graph:Lines|width = 850|height = 300|table = COVID-19 deaths in India by State and UT.tab|type = time|xField = date|series = "maharashtra"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count | {{Switcher|{{Graph:Lines|width = 850|height = 300|table = COVID-19 deaths in India by State and UT.tab|type = time|xField = date|series = "maharashtra"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count | ||
}}|Linear scale|{{Graph:Lines|width = 850|height = 300|table = COVID-19 deaths in India by State and UT.tab|type = time|xField = date|series = "maharashtra"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count|yScale = log|yMin = 1|replaceZerosWith = 1 | }}|Linear scale|{{Graph:Lines|width = 850|height = 300|table = COVID-19 deaths in India by State and UT.tab|type = time|xField = date|series = "maharashtra"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count|yScale = log|yMin = 1|replaceZerosWith = 1 | ||
}}|Logarithmic scale}} | }}|Logarithmic scale}} | ||
{{center|'''>1,000 and <10,000'''}} | |||
{{Switcher|{{Graph:Lines|width = 850|height = 300|table = COVID-19 deaths in India by State and UT.tab|type = time|xField = date|series = "tamilnadu", "karnataka", "andhrapradesh","delhi", "uttarpradesh", "westbengal", "gujarat", "punjab", "madhyapradesh","rajasthan"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count | {{Switcher|{{Graph:Lines|width = 850|height = 300|table = COVID-19 deaths in India by State and UT.tab|type = time|xField = date|series = "tamilnadu", "karnataka", "andhrapradesh","delhi", "uttarpradesh", "westbengal", "gujarat", "punjab", "madhyapradesh","rajasthan"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count | ||
}}|Linear scale|{{Graph:Lines|width = 850|height = 300|table = COVID-19 deaths in India by State and UT.tab|type = time|xField = date|series = "tamilnadu", "karnataka", "andhrapradesh","delhi", "uttarpradesh", "westbengal", "gujarat", "punjab", "madhyapradesh","rajasthan"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count|yScale = log|yMin = 1|replaceZerosWith = 1 | }}|Linear scale|{{Graph:Lines|width = 850|height = 300|table = COVID-19 deaths in India by State and UT.tab|type = time|xField = date|series = "tamilnadu", "karnataka", "andhrapradesh","delhi", "uttarpradesh", "westbengal", "gujarat", "punjab", "madhyapradesh","rajasthan"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count|yScale = log|yMin = 1|replaceZerosWith = 1 | ||
}}|Logarithmic scale}} | }}|Logarithmic scale}} | ||
{{center|'''>100 and <1,000'''}} | |||
{{Switcher|{{Graph:Lines|width = 850|height = 300|table = COVID-19 deaths in India by State and UT.tab|type = time|xField = date|series = "haryana", "telangana", "jammuandkashmir", "bihar", "odisha", "chhattisgarh", "jharkhand", "assam", "kerala", "uttarakhand", "puducherry", "goa", "tripura"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count | {{Switcher|{{Graph:Lines|width = 850|height = 300|table = COVID-19 deaths in India by State and UT.tab|type = time|xField = date|series = "haryana", "telangana", "jammuandkashmir", "bihar", "odisha", "chhattisgarh", "jharkhand", "assam", "kerala", "uttarakhand", "puducherry", "goa", "tripura"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count | ||
}}|Linear scale|{{Graph:Lines|width = 850|height = 300|table = COVID-19 deaths in India by State and UT.tab|type = time|xField = date|series = "haryana", "telangana", "jammuandkashmir", "bihar", "odisha", "chhattisgarh", "jharkhand", "assam", "kerala", "uttarakhand", "puducherry", "goa", "tripura"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count|yScale = log|yMin = 1|replaceZerosWith = 1 | }}|Linear scale|{{Graph:Lines|width = 850|height = 300|table = COVID-19 deaths in India by State and UT.tab|type = time|xField = date|series = "haryana", "telangana", "jammuandkashmir", "bihar", "odisha", "chhattisgarh", "jharkhand", "assam", "kerala", "uttarakhand", "puducherry", "goa", "tripura"|xGrid = true|yGrid = true|legend = Legend|xAxis = Date|yAxis = Count|yScale = log|yMin = 1|replaceZerosWith = 1 | ||
| Line 183: | Line 186: | ||
<div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | <div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | ||
{{center| | |||
{{legend inline|#cd5c5c|Case fatality rate}} | {{legend inline|#cd5c5c|Case fatality rate}} | ||
}} | |||
{{Graph:Chart <!-- Formula = Total deaths till date/Total cases till date*100 -->|width = 850|height = 300|xAxisTitle = Date|yAxisTitle = Percentage|xAxisAngle = -45|type = line|xType = date|xGrid =|yGrid =|x = 2020-03-12, 2020-03-13, 2020-03-14, 2020-03-15, 2020-03-16, 2020-03-17, 2020-03-18, 2020-03-19, 2020-03-20, 2020-03-21, 2020-03-22, 2020-03-23, 2020-03-24, 2020-03-25, 2020-03-26, 2020-03-27, 2020-03-28, 2020-03-29, 2020-03-30, 2020-03-31, 2020-04-01, 2020-04-02, 2020-04-03, 2020-04-04, 2020-04-05, 2020-04-06, 2020-04-07, 2020-04-08, 2020-04-09, 2020-04-10, 2020-04-11, 2020-04-12, 2020-04-13, 2020-04-14, 2020-04-15, 2020-04-16, 2020-04-17, 2020-04-18, 2020-04-19, 2020-04-20, 2020-04-21, 2020-04-22, 2020-04-23, 2020-04-24, 2020-04-25, 2020-04-26, 2020-04-27, 2020-04-28, 2020-04-29, 2020-04-30, 2020-05-01, 2020-05-02, 2020-05-03, 2020-05-04, 2020-05-05, 2020-05-06, 2020-05-07, 2020-05-08, 2020-05-09, 2020-05-10, 2020-05-11, 2020-05-12, 2020-05-13, 2020-05-14, 2020-05-15, 2020-05-16, 2020-05-17, 2020-05-18, 2020-05-19, 2020-05-20, 2020-05-21, 2020-05-22, 2020-05-23, 2020-05-24, 2020-05-25, 2020-05-26, 2020-05-27, 2020-05-28, 2020-05-29, 2020-05-30, 2020-05-31, 2020-06-01, 2020-06-02, 2020-06-03, 2020-06-04, 2020-06-05, 2020-06-06, 2020-06-07, 2020-06-08, 2020-06-09, 2020-06-10, 2020-06-11, 2020-06-12, 2020-06-13, 2020-06-14, 2020-06-15, 2020-06-16, 2020-06-17, 2020-06-18, 2020-06-19, 2020-06-20, 2020-06-21, 2020-06-22, 2020-06-23, 2020-06-24, 2020-06-25, 2020-06-26, 2020-06-27, 2020-06-28, 2020-06-29, 2020-06-30, 2020-07-01, 2020-07-02, 2020-07-03, 2020-07-04, 2020-07-05, 2020-07-06, 2020-07-07, 2020-07-08, 2020-07-09, 2020-07-10, 2020-07-11, 2020-07-12, 2020-07-13, 2020-07-14, 2020-07-15, 2020-07-16, 2020-07-17, 2020-07-18, 2020-07-19, 2020-07-20, 2020-07-21, 2020-07-22, 2020-07-23, 2020-07-24, 2020-07-25, 2020-07-26, 2020-07-27, 2020-07-28, 2020-07-29, 2020-07-30, 2020-07-31, 2020-08-01, 2020-08-02, 2020-08-03, 2020-08-04, 2020-08-05, 2020-08-06, 2020-08-07, 2020-08-08, 2020-08-09, 2020-08-10, 2020-08-11, 2020-08-12, 2020-08-13, 2020-08-14, 2020-08-15, 2020-08-16, 2020-08-17, 2020-08-18, 2020-08-19, 2020-08-20, 2020-08-21, 2020-08-22, 2020-08-23, 2020-08-24, 2020-08-25, 2020-08-26, 2020-08-27, 2020-08-28, 2020-08-29, 2020-08-30, 2020-08-31, 2020-09-01, 2020-09-02, 2020-09-03, 2020-09-04, 2020-09-05, 2020-09-06, 2020-09-07, 2020-09-08, 2020-09-09, 2020-09-10, 2020-09-11, 2020-09-12, 2020-09-13, 2020-09-14, 2020-09-15, 2020-09-16, 2020-09-17, 2020-09-18, 2020-09-19, 2020-09-20, 2020-09-21, 2020-09-22, 2020-09-23, 2020-09-24, 2020-09-25, 2020-09-26, 2020-09-27, 2020-09-28, 2020-09-29, 2020-09-30, 2020-10-01, 2020-10-02, 2020-10-03, 2020-10-04, 2020-10-05, 2020-10-06, 2020-10-07, 2020-10-08, 2020-10-09, 2020-10-10, 2020-10-11, 2020-10-12, 2020-10-13, 2020-10-14, 2020-10-15, 2020-10-16, 2020-10-17, 2020-10-18, 2020-10-19, 2020-10-20, 2020-10-21, 2020-10-22, 2020-10-23, 2020-10-24, 2020-10-25, 2020-10-26, 2020-10-27, 2020-10-28, 2020-10-29, 2020-10-30, 2020-10-31, 2020-11-01, 2020-11-02, 2020-11-03, 2020-11-04, 2020-11-05, 2020-11-06, 2020-11-07, 2020-11-08, 2020-11-09, 2020-11-10, 2020-11-11, 2020-11-12, 2020-11-13, 2020-11-14, 2020-11-15, 2020-11-16, 2020-11-17, 2020-11-18, 2020-11-19, 2020-11-20, 2020-11-21, 2020-11-22, 2020-11-23, 2020-11-24, 2020-11-25, 2020-11-26, 2020-11-27, 2020-11-28, 2020-11-29, 2020-11-30, 2020-12-01, 2020-12-02, 2020-12-03, 2020-12-04, 2020-12-05, 2020-12-06, 2020-12-07, 2020-12-08, 2020-12-09, 2020-12-10, 2020-12-11, 2020-12-12, 2020-12-13, 2020-12-14, 2020-12-15, 2020-12-16, 2020-12-17, 2020-12-18, 2020-12-19, 2020-12-20, 2020-12-21, 2020-12-22, 2020-12-23, 2020-12-24, 2020-12-25, 2020-12-26, 2020-12-27, 2020-12-28, 2020-12-29, 2020-12-30, 2020-12-31, 2021-01-01, 2021-01-02, 2021-01-03, 2021-01-04, 2021-01-05, 2021-01-06, 2021-01-07, 2021-01-08, 2021-01-09, 2021-01-10, 2021-01-11, 2021-01-12, 2021-01-13, 2021-01-14, 2021-01-15, 2021-01-16, 2021-01-17, 2021-01-18, 2021-01-19, 2021-01-20, 2021-01-21, 2021-01-22, 2021-01-23, 2021-01-24, 2021-01-25, 2021-01-26, 2021-01-27, 2021-01-28, 2021-01-29, 2021-01-30, 2021-01-31, 2021-02-01, 2021-02-02, 2021-02-03, 2021-02-04, 2021-02-05, 2021-02-06, 2021-02-07, 2021-02-08, 2021-02-09, 2021-02-10, 2021-02-11, 2021-02-12, 2021-02-13, 2021-02-14, 2021-02-15, 2021-02-16, 2021-02-17, 2021-02-18, 2021-02-19, 2021-02-20, 2021-02-21, 2021-02-22, 2021-02-23, 2021-02-24, 2021-02-25, 2021-02-26, 2021-02-27, 2021-02-28, 2021-03-01, 2021-03-02, 2021-03-03, 2021-03-04, 2021-03-05, 2021-03-06, 2021-03-07, 2021-03-08, 2021-03-09, 2021-03-10, 2021-03-11, 2021-03-12, 2021-03-13, 2021-03-14, 2021-03-15, 2021-03-16, 2021-03-17, 2021-03-18, 2021-03-19, 2021-03-20, 2021-03-21, 2021-03-22, 2021-03-23, 2021-03-24, 2021-03-25, 2021-03-26, 2021-03-27, 2021-03-28, 2021-03-29, 2021-03-30, 2021-03-31, 2021-04-01, 2021-04-02, 2021-04-03, 2021-04-04, 2021-04-05, 2021-04-06|y = 1.35, 2.47, 2.38, 1.82, 1.75, 2.19, 1.99, 2.31, 1.79, 1.27, 1.94, 1.92, 1.93, 1.65, 2.31, 2.28, 2.07, 2.64, 2.56, 2.51, 2.24, 2.56, 2.43, 2.44, 2.32, 2.59, 2.59, 2.83, 2.88, 3.05, 3.21, 3.23, 3.46, 3.26, 3.29, 3.29, 3.27, 3.30, 3.22, 3.17, 3.18, 3.18, 3.16, 3.08, 3.12, 3.07, 3.12, 3.13, 3.17, 3.20, 3.26, 3.24, 3.24, 3.24, 3.39, 3.43, 3.37, 3.35, 3.32, 3.35, 3.29, 3.24, 3.25, 3.27, 3.23, 3.20, 3.16, 3.15, 3.13, 3.09, 3.06, 3.02, 2.97, 2.93, 2.90, 2.87, 2.86, 2.86, 2.84, 2.86, 2.84, 2.83, 2.82, 2.80, 2.80, 2.80, 2.81, 2.81, 2.81, 2.80, 2.80, 2.83, 2.86, 2.88, 2.87, 2.86, 2.89, 3.36, 3.33, 3.30, 3.28, 3.23, 3.22, 3.18, 3.17, 3.15, 3.12, 3.08, 3.04, 3.00, 2.98, 2.97, 2.95, 2.91, 2.88, 2.86, 2.82, 2.80, 2.78, 2.75, 2.72, 2.69, 2.67, 2.64, 2.62, 2.60, 2.57, 2.55, 2.53, 2.49, 2.46, 2.43, 2.41, 2.41, 2.38, 2.35, 2.31, 2.28, 2.25, 2.23, 2.21, 2.18, 2.15, 2.13, 2.11, 2.10, 2.09, 2.07, 2.05, 2.04, 2.01, 2.00, 1.99, 1.98, 1.96, 1.95, 1.94, 1.93, 1.92, 1.92, 1.91, 1.90, 1.89, 1.87, 1.86, 1.85, 1.84, 1.84, 1.83, 1.82, 1.81, 1.79, 1.78, 1.77, 1.76, 1.75, 1.74, 1.73, 1.72, 1.70, 1.70, 1.69, 1.68, 1.67, 1.66, 1.65, 1.64, 1.64, 1.63, 1.63, 1.62, 1.61, 1.61, 1.60, 1.60, 1.59, 1.59, 1.59, 1.58, 1.58, 1.57, 1.57, 1.57, 1.56, 1.56, 1.56, 1.55, 1.55, 1.55, 1.55, 1.54, 1.54, 1.54, 1.54, 1.53, 1.53, 1.53, 1.52, 1.52, 1.52, 1.52, 1.52, 1.52, 1.51, 1.51, 1.51, 1.51, 1.51, 1.50, 1.50, 1.50, 1.50, 1.50, 1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.48, 1.48, 1.48, 1.48, 1.48, 1.48, 1.47, 1.47, 1.47, 1.47, 1.47, 1.47, 1.47, 1.47, 1.47, 1.46, 1.46, 1.46, 1.46, 1.46, 1.46, 1.46, 1.46, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.45, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.44, 1.43, 1.43, 1.43, 1.43, 1.43, 1.43, 1.43, 1.43, 1.43, 1.43, 1.43, 1.43, 1.43, 1.43, 1.43, 1.43, 1.42, 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| Line 200: | Line 203: | ||
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}} | }} | ||
{{smalldiv|1= | |||
'''Notes:''' | |||
* Between 1 and 5 June 2021, 7.6 million tests were reconciled.<ref name=":11" /> | * Between 1 and 5 June 2021, 7.6 million tests were reconciled.<ref name=":11" /> | ||
}} | |||
</div> | </div> | ||
===Positive sampling rate=== | ===Positive sampling rate=== | ||
<div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | <div class='toccolours' style="display:inline-block; width:1000px; margin:1em 0;"> | ||
{{center| | |||
{{legend inline|#FA9703|Daily positive sampling ratio}} | {{legend inline|#FA9703|Daily positive sampling ratio}} | ||
{{legend inline|#975B02|Total positive sampling ratio}} | {{legend inline|#975B02|Total positive sampling ratio}} | ||
}} | |||
{{Graph:Chart <!-- Formula = confirmed cases/samples tested -->|width = 850|height = 300|xAxisTitle = Date|yAxisTitle = Positive|xAxisAngle = -45|type = line|xType = date|yAxisFormat = %|xGrid =|yGrid =|x = 2020-03-19, 2020-03-20, 2020-03-21, 2020-03-22, 2020-03-23, 2020-03-24, 2020-03-25, 2020-03-27, 2020-03-29, 2020-03-30, 2020-03-31, 2020-04-01, 2020-04-02, 2020-04-03, 2020-04-04, 2020-04-05, 2020-04-06, 2020-04-07, 2020-04-08, 2020-04-09, 2020-04-10, 2020-04-11, 2020-04-12, 2020-04-13, 2020-04-14, 2020-04-15, 2020-04-16, 2020-04-17, 2020-04-18, 2020-04-19, 2020-04-20, 2020-04-21, 2020-04-22, 2020-04-23, 2020-04-24, 2020-04-25, 2020-04-26, 2020-04-27, 2020-04-28, 2020-04-29, 2020-04-30, 2020-05-01, 2020-05-02, 2020-05-03, 2020-05-04, 2020-05-05, 2020-05-06, 2020-05-07, 2020-05-08, 2020-05-09, 2020-05-10, 2020-05-11, 2020-05-12, 2020-05-13, 2020-05-14, 2020-05-15, 2020-05-16, 2020-05-17, 2020-05-18, 2020-05-19, 2020-05-20, 2020-05-21, 2020-05-22, 2020-05-23, 2020-05-24, 2020-05-25, 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0.0854, 0.0856, 0.0852, 0.0850, 0.0847, 0.0843, 0.0843, 0.0842|colors = #FA9703, #975B02 | {{Graph:Chart <!-- Formula = confirmed cases/samples tested -->|width = 850|height = 300|xAxisTitle = Date|yAxisTitle = Positive|xAxisAngle = -45|type = line|xType = date|yAxisFormat = %|xGrid =|yGrid =|x = 2020-03-19, 2020-03-20, 2020-03-21, 2020-03-22, 2020-03-23, 2020-03-24, 2020-03-25, 2020-03-27, 2020-03-29, 2020-03-30, 2020-03-31, 2020-04-01, 2020-04-02, 2020-04-03, 2020-04-04, 2020-04-05, 2020-04-06, 2020-04-07, 2020-04-08, 2020-04-09, 2020-04-10, 2020-04-11, 2020-04-12, 2020-04-13, 2020-04-14, 2020-04-15, 2020-04-16, 2020-04-17, 2020-04-18, 2020-04-19, 2020-04-20, 2020-04-21, 2020-04-22, 2020-04-23, 2020-04-24, 2020-04-25, 2020-04-26, 2020-04-27, 2020-04-28, 2020-04-29, 2020-04-30, 2020-05-01, 2020-05-02, 2020-05-03, 2020-05-04, 2020-05-05, 2020-05-06, 2020-05-07, 2020-05-08, 2020-05-09, 2020-05-10, 2020-05-11, 2020-05-12, 2020-05-13, 2020-05-14, 2020-05-15, 2020-05-16, 2020-05-17, 2020-05-18, 2020-05-19, 2020-05-20, 2020-05-21, 2020-05-22, 2020-05-23, 2020-05-24, 2020-05-25, 2020-05-26, 2020-05-27, 2020-05-28, 2020-05-29, 2020-05-30, 2020-05-31, 2020-06-01, 2020-06-02, 2020-06-03, 2020-06-04, 2020-06-05, 2020-06-06, 2020-06-07, 2020-06-08, 2020-06-09, 2020-06-10, 2020-06-11, 2020-06-12, 2020-06-13, 2020-06-14, 2020-06-15, 2020-06-16, 2020-06-17, 2020-06-18, 2020-06-19, 2020-06-20, 2020-06-21, 2020-06-22, 2020-06-23, 2020-06-24, 2020-06-25, 2020-06-26, 2020-06-27, 2020-06-28, 2020-06-29, 2020-06-30, 2020-07-01, 2020-07-02, 2020-07-03, 2020-07-04, 2020-07-05, 2020-07-06, 2020-07-07, 2020-07-08, 2020-07-09, 2020-07-10, 2020-07-11, 2020-07-12, 2020-07-13, 2020-07-14, 2020-07-15, 2020-07-16, 2020-07-17, 2020-07-18, 2020-07-19, 2020-07-20, 2020-07-21, 2020-07-22, 2020-07-23, 2020-07-24, 2020-07-25, 2020-07-26, 2020-07-27, 2020-07-28, 2020-07-29, 2020-07-30, 2020-07-31, 2020-08-01, 2020-08-02, 2020-08-03, 2020-08-04, 2020-08-05, 2020-08-06, 2020-08-07, 2020-08-08, 2020-08-09, 2020-08-10, 2020-08-11, 2020-08-12, 2020-08-13, 2020-08-14, 2020-08-15, 2020-08-16, 2020-08-17, 2020-08-18, 2020-08-19, 2020-08-20, 2020-08-21, 2020-08-22, 2020-08-23, 2020-08-24, 2020-08-25, 2020-08-26, 2020-08-27, 2020-08-28, 2020-08-29, 2020-08-30, 2020-08-31, 2020-09-01, 2020-09-02, 2020-09-03, 2020-09-04, 2020-09-05, 2020-09-06|y1 = <!-- Daily positive sampling rate --> 0.0000, 0.0210, 0.0407, 0.0610, 0.0370, 0.0419, 0.0257, 0.0355, 0.0550, 0.0146, 0.0647, 0.0336, 0.0846, 0.0269, 0.0380, 0.0490, 0.0527, 0.0610, 0.0392, 0.0349, 0.0348, 0.0546, 0.0426, 0.0561, 0.0415, 0.0535, 0.0376, 0.0291, 0.0335, 0.0259, 0.0449, 0.0452, 0.0493, 0.0392, 0.0298, 0.0459, 0.0329, 0.0488, 0.0287, 0.0295, 0.0305, 0.0252, 0.0238, 0.0344, 0.0409, 0.0304, 0.0457, 0.0332, 0.0443, 0.0397, 0.0387, 0.0507, 0.0491, 0.0381, 0.0380, 0.0401, 0.0421, 0.0425, 0.0664, 0.0517, 0.0460, 0.0542, 0.0542, 0.0528, 0.0613, 0.0750, 0.0754, 0.0563, 0.0532, 0.0540, 0.0584, 0.0635, 0.0836, 0.0634, 0.0650, 0.0667, 0.0686, 0.0717, 0.0702, 0.0924, 0.0704, 0.0688, 0.0658, 0.0729, 0.0797, 0.0788, 0.0996, 0.0688, 0.0672, 0.0779, 0.0768, 0.0765, 0.0808, 0.1035, 0.0798, 0.0742, 0.0814, 0.0803, 0.0841, 0.0861, 0.1141, 0.0881, 0.0856, 0.0834, 0.0865, 0.0939, 0.0998, 0.1343, 0.0922, 0.0866, 0.0932, 0.0934, 0.0960, 0.1022, 0.1310, 0.0996, 0.0919, 0.1000, 0.1049, 0.0966, 0.1086, 0.1579, 0.1114, 0.1099, 0.1303, 0.1398, 0.1162, 0.1100, 0.0969, 0.0903, 0.1187, 0.1167, 0.0857, 0.1087, 0.1182, 0.1390, 0.0786, 0.0847, 0.0846, 0.0979, 0.1028, 0.0895, 0.1301, 0.0768, 0.0831, 0.0807, 0.0761, 0.0748, 0.0850, 0.0792, 0.0612, 0.0805, 0.0758, 0.0855, 0.0682, 0.0864, 0.1007, 0.0659, 0.0815, 0.0819, 0.0857, 0.0823, 0.0746, 0.0928, 0.0688, 0.0776, 0.0716, 0.0712, 0.0816, 0.0829|y2 = <!-- Total positive sampling rate --> 0.0132, 0.0157, 0.0204, 0.0213, 0.0258, 0.0251, 0.0267, 0.0332, 0.0370, 0.0358, 0.0363, 0.0429, 0.0431, 0.0449, 0.0444, 0.0447, 0.0478, 0.0474, 0.0463, 0.0458, 0.0467, 0.0467, 0.0471, 0.0478, 0.0497, 0.0487, 0.0465, 0.0457, 0.0441, 0.0433, 0.0440, 0.0436, 0.0443, 0.0434, 0.0433, 0.0430, 0.0430, 0.0426, 0.0418, 0.0412, 0.0405, 0.0392, 0.0387, 0.0385, 0.0387, 0.0392, 0.0387, 0.0390, 0.0392, 0.0392, 0.0391, 0.0401, 0.0402, 0.0401, 0.0401, 0.0402, 0.0403, 0.0408, 0.0418, 0.0421, 0.0425, 0.0430, 0.0436, 0.0441, 0.0448, 0.0458, 0.0465, 0.0468, 0.0471, 0.0476, 0.0481, 0.0487, 0.0497, 0.0501, 0.0506, 0.0511, 0.0517, 0.0523, 0.0529, 0.0537, 0.0542, 0.0546, 0.0550, 0.0555, 0.0561, 0.0567, 0.0576, 0.0579, 0.0582, 0.0587, 0.0592, 0.0597, 0.0603, 0.0612, 0.0617, 0.0620, 0.0626, 0.0631, 0.0636, 0.0643, 0.0653, 0.0658, 0.0663, 0.0668, 0.0673, 0.0680, 0.0688, 0.0700, 0.0705, 0.0709, 0.0714, 0.0720, 0.0726, 0.0733, 0.0744, 0.0750, 0.0754, 0.0760, 0.0768, 0.0773, 0.0781, 0.0796, 0.0803, 0.0810, 0.0822, 0.0835, 0.0844, 0.0850, 0.0854, 0.0856, 0.0863, 0.0871, 0.0870, 0.0876, 0.0883, 0.0893, 0.0889, 0.0888, 0.0887, 0.0890, 0.0893, 0.0893, 0.0901, 0.0897, 0.0896, 0.0893, 0.0889, 0.0884, 0.0884, 0.0881, 0.0874, 0.0872, 0.0869, 0.0868, 0.0863, 0.0863, 0.0865, 0.0860, 0.0859, 0.0858, 0.0858, 0.0857, 0.0854, 0.0856, 0.0852, 0.0850, 0.0847, 0.0843, 0.0843, 0.0842|colors = #FA9703, #975B02 | ||
}} | }} | ||
| Line 223: | Line 227: | ||
<section end="Statistics"/> | <section end="Statistics"/> | ||
{{small|Graph sources: Data from [https://www.mohfw.gov.in/ MoHFW] and [https://www.icmr.gov.in/ ICMR]}} | {{small|Graph sources: Data from [https://www.mohfw.gov.in/ MoHFW] and [https://www.icmr.gov.in/ ICMR]}} | ||
== Issues == | == Issues == | ||
| Line 233: | Line 233: | ||
==== Global mortality excess ==== | ==== Global mortality excess ==== | ||
According to the [[World Health Organization]] the global mortality excess for COVID-19 is significant, "while 1,813,188 COVID-19 deaths were reported in 2020... WHO estimates suggest an excess mortality of at least 3,000,000."<ref name=":7">{{Cite web|title=The true death toll of COVID-19: estimating global excess mortality|url=https://www.who.int/data/stories/the-true-death-toll-of-covid-19-estimating-global-excess-mortality|url-status=live|archive-url=https://web.archive.org/web/20210521131741/https://www.who.int/data/stories/the-true-death-toll-of-covid-19-estimating-global-excess-mortality|archive-date=21 May 2021|access-date=2021-05-28|website=www.who.int|publisher=World Health Organisation|language=en}}</ref> The worldwide average for underreporting COVID-19 deaths in cities is 30%.<ref name=":10">{{Cite web|last=Cohen|first=Jon|date=2021-04-30|title=Will India's devastating COVID-19 surge provide data that clear up its death 'paradox'?|url=https://www. | According to the [[World Health Organization]] the global mortality excess for COVID-19 is significant, "while 1,813,188 COVID-19 deaths were reported in 2020... WHO estimates suggest an excess mortality of at least 3,000,000."<ref name=":7">{{Cite web|title=The true death toll of COVID-19: estimating global excess mortality|url=https://www.who.int/data/stories/the-true-death-toll-of-covid-19-estimating-global-excess-mortality|url-status=live|archive-url=https://web.archive.org/web/20210521131741/https://www.who.int/data/stories/the-true-death-toll-of-covid-19-estimating-global-excess-mortality|archive-date=21 May 2021|access-date=2021-05-28|website=www.who.int|publisher=World Health Organisation|language=en}}</ref> The worldwide average for underreporting COVID-19 deaths in cities is 30%.<ref name=":10">{{Cite web|last=Cohen|first=Jon|date=2021-04-30|title=Will India's devastating COVID-19 surge provide data that clear up its death 'paradox'?|url=https://www.science.org/content/article/will-india-s-devastating-covid-19-surge-provide-data-clear-its-death-paradox|access-date=2021-05-29|website=Science {{!}} American Association for the Advancement of Science|language=en}}</ref> | ||
==== Legacy issues ==== | ==== Legacy issues ==== | ||
In India the accuracy of counting its dead is further complicated by its system of tracking deaths in its population in general. In 2018, about 86%% of the deaths in the country were are [https://casi.sas.upenn.edu/iit/aashishgupta registered] while medical certificates are given to around 22% of the deaths. Recently released reports by the Civil Registration suggest that death registration has improved further in 2019.<ref>{{Cite web|title=Civil Registration System|url=https://crsorgi.gov.in/annual-report.html|access-date=2021-06-20|website=crsorgi.gov.in}}</ref> Still, there are inaccuracies in the medical certificates.<ref name=":2" /> Death registration also varies. In 2019 the [[National Family Health Survey]] registration of deaths varied from 37% (Bihar) to 100% (Goa).<ref name=":3">{{Cite web|last1=NB|first1=Devikrishna|last2=Besra|first2=Bishwajeet|last3=Mishra|first3=Nand Lal|date=6 January 2021|title=Birth, death registration improves across states: NFHS-5|url=https://www.downtoearth.org.in/blog/governance/birth-death-registration-improves-across-states-nfhs-5-74920 | In India the accuracy of counting its dead is further complicated by its system of tracking deaths in its population in general. In 2018, about 86%% of the deaths in the country were are [https://casi.sas.upenn.edu/iit/aashishgupta registered] while medical certificates are given to around 22% of the deaths. Recently released reports by the Civil Registration suggest that death registration has improved further in 2019.<ref>{{Cite web|title=Civil Registration System|url=https://crsorgi.gov.in/annual-report.html|access-date=2021-06-20|website=crsorgi.gov.in}}</ref> Still, there are inaccuracies in the medical certificates.<ref name=":2" /> Death registration also varies. In 2019 the [[National Family Health Survey]] registration of deaths varied from 37% (Bihar) to 100% (Goa).<ref name=":3">{{Cite web|last1=NB|first1=Devikrishna|last2=Besra|first2=Bishwajeet|last3=Mishra|first3=Nand Lal|date=6 January 2021|title=Birth, death registration improves across states: NFHS-5|url=https://www.downtoearth.org.in/blog/governance/birth-death-registration-improves-across-states-nfhs-5-74920|access-date=2021-05-28|website=Down To Earth|language=en}}</ref> | ||
=== India's low COVID-19 death count === | === India's low COVID-19 death count === | ||
| Line 242: | Line 242: | ||
=== Accuracy === | === Accuracy === | ||
In April 2021 the [[Gujarat High Court]] and the [[Telangana High Court]] both commented on the need of states to provide accurate data with respect to the pandemic.<ref>{{Cite web|last=|first=|date=17 April 2021|title=Give real Covid picture, hiding will cause serious problem: Gujarat high court|url=https://timesofindia.indiatimes.com/city/ahmedabad/give-real-covid-picture-hiding-will-cause-serious-problem-gujarat-high-court/articleshow/82112391.cms | In April 2021 the [[Gujarat High Court]] and the [[Telangana High Court]] both commented on the need of states to provide accurate data with respect to the pandemic.<ref>{{Cite web|last=|first=|date=17 April 2021|title=Give real Covid picture, hiding will cause serious problem: Gujarat high court|url=https://timesofindia.indiatimes.com/city/ahmedabad/give-real-covid-picture-hiding-will-cause-serious-problem-gujarat-high-court/articleshow/82112391.cms|access-date=2021-05-29|website=The Times of India|language=en}}</ref><ref>{{Cite web|last=Mutha|first=Sagar Kumar|date=28 April 2021|title=Don't fudge Covid death numbers, HC tells Telangana|url=https://timesofindia.indiatimes.com/india/dont-fudge-covid-death-numbers-hc-tells-telangana/articleshow/82283731.cms|access-date=2021-05-28|website=The Times of India|language=en}}</ref> In May 2021, Prime Minister Modi made a comment related to death counts, "states should be encouraged to report their numbers transparently without any pressure of high numbers showing adversely on their efforts".<ref>{{Cite web|last=M|first=Kaunain Sheriff|date=2021-05-16|title=PM urges states: report numbers 'transparently'|url=https://indianexpress.com/article/india/modi-calls-for-audit-of-ventilators-provided-to-state-by-centre-after-reports-of-them-lying-unutilised-come-to-light-7316226/|access-date=2021-05-28|website=The Indian Express|language=en}}</ref> | ||
==== Undercounting ==== | ==== Undercounting ==== | ||
{{See also|Undercounting of COVID-19 pandemic deaths by country}} | {{See also|Undercounting of COVID-19 pandemic deaths by country}} | ||
Undercounting of cases and deaths during the COVID-19 pandemic in India is not unique to the country.<ref>{{Cite web|date=2021-04-22|title=Estimation of total mortality due to COVID-19|url=http://www.healthdata.org/special-analysis/estimation-excess-mortality-due-covid-19-and-scalars-reported-covid-19-deaths|url-status=live|archive-url=https://web.archive.org/web/20210521113522/http://www.healthdata.org/node/8660|archive-date=21 May 2021|access-date=2021-05-28|website=Institute for Health Metrics and Evaluation|language=en}}</ref><ref name=":7" /> Journalists,<ref name=":0">{{Cite news|last=Biswas|first=Soutik|date=2021-05-10|title=India's Covid crisis: The newsroom counting the uncounted deaths|language=en-GB|work=BBC News|url=https://www.bbc.com/news/world-asia-india-56969086|access-date=2021-05-27}}</ref> mathematicians,<ref name=":4" /> epidemiologists,<ref>{{Cite web|date=16 April 2021|title=Actual Covid-19 cases in India may be 5 to 10 times higher, says epidemiologist|url=https://www.indiatoday.in/coronavirus-outbreak/video/actual-covid-19-cases-in-india-may-5-10-times-higher-epidemiologist-1791602-2021-04-16 | Undercounting of cases and deaths during the COVID-19 pandemic in India is not unique to the country.<ref>{{Cite web|date=2021-04-22|title=Estimation of total mortality due to COVID-19|url=http://www.healthdata.org/special-analysis/estimation-excess-mortality-due-covid-19-and-scalars-reported-covid-19-deaths|url-status=live|archive-url=https://web.archive.org/web/20210521113522/http://www.healthdata.org/node/8660|archive-date=21 May 2021|access-date=2021-05-28|website=Institute for Health Metrics and Evaluation|language=en}}</ref><ref name=":7" /> Journalists,<ref name=":0">{{Cite news|last=Biswas|first=Soutik|date=2021-05-10|title=India's Covid crisis: The newsroom counting the uncounted deaths|language=en-GB|work=BBC News|url=https://www.bbc.com/news/world-asia-india-56969086|access-date=2021-05-27}}</ref> mathematicians,<ref name=":4" /> epidemiologists,<ref>{{Cite web|date=16 April 2021|title=Actual Covid-19 cases in India may be 5 to 10 times higher, says epidemiologist|url=https://www.indiatoday.in/coronavirus-outbreak/video/actual-covid-19-cases-in-india-may-5-10-times-higher-epidemiologist-1791602-2021-04-16|access-date=2021-05-27|website=India Today|language=en}}</ref> statisticians, and scientists have attempted,<ref>{{Cite web|last=Kumar|first=Chethan|date=14 April 2021|title=US 'undercounted' 3.4 lakh deaths, India 4.3 lakh: Study|url=https://timesofindia.indiatimes.com/india/us-undercounted-3-4-lakh-deaths-india-4-3-lakh-study/articleshow/82622386.cms|access-date=2021-05-27|website=The Times of India|language=en}}</ref> according to their expertise, to arrive at a truer number of the actual cases and deaths. The aim of this is to ultimately improve national and international responses to the pandemic.<ref>[https://www.ncdirindia.org/Downloads/CoD_COVID-19_Guidance.pdf Guidance for appropriate recording of COVID-19 related deaths in India.] ''National Centre for Disease Informatics and Research, Indian Council of Medical Research.'' [https://web.archive.org/web/20210510052021/https://www.ncdirindia.org/Downloads/CoD_COVID-19_Guidance.pdf Archived] on 10 May 2021 via— archive.org. "Patterns of disease and patterns of death can come from only standardised recording of clinical disease history and cause of death, and therefore epidemiological surveillance of disease and death are important. Robust data is needed from every district and state in India to measure the public health impact of COVID 19 and to plan for timely health interventions and protect communities."</ref><ref name=":8">{{Cite web|last=Banaji|first=Murad|date=2021-05-09|title=The Importance of Knowing How Many Have Died of COVID-19 in India|url=https://science.thewire.in/health/how-many-died-covid-19-india-undercounting-underreporting-prevent-next-surge/|access-date=2021-05-27|website=The Wire Science|language=en-GB}}</ref><ref>{{Cite web|date=3 May 2021|others=AP|title=Explained: Why India's Covid-19 data is vastly undercounted|url=https://timesofindia.indiatimes.com/india/explained-why-indias-covid-19-data-is-vastly-undercounted/articleshow/82366707.cms|access-date=2021-05-29|website=The Times of India|language=en}}</ref> Journalists have spent time at burial-grounds and crematoriums and counted in-person the number of burials and cremations. These manual counts have been compared to government figures and have been found significantly different.<ref name=":0" /><ref name=":1" /> India's national [[serological surveys]] also point to large numbers (ratios of one is to thirty) of cases remaining undetected.<ref name=":6" /><ref name=":4" /> The scale of under-reporting from one place to another can be very large.<ref name=":8" /> The undercounting may or may not be intentional. Rather undercounting may occur due to unreported COVID-19 cases, inefficiencies in the data collection system, inability to adapt to pandemic like situation and people not reporting deaths.<ref name=":1" /><ref>{{Cite web|last=Mulye|first=Prathamesh|date=4 May 2021|title=The lapses in India's Covid-19 data are a result of decades of callousness towards statistics|url=https://qz.com/india/2002374/covid-19-data-india-hasnt-cared-about-statistics-for-decades/|access-date=2021-05-27|website=Quartz|language=en}}</ref><ref name=":9">{{Cite news|last1=Gettleman|first1=Jeffrey|last2=Yasir|first2=Sameer|last3=Kumar|first3=Hari|last4=Raj|first4=Suhasini|last5=Loke|first5=Atul|date=2021-04-24|title=As Covid-19 Devastates India, Deaths Go Undercounted|language=en-US|work=The New York Times|url=https://www.nytimes.com/2021/04/24/world/asia/india-coronavirus-deaths.html|access-date=2021-05-29|issn=0362-4331}}</ref> | ||
*Undercounting of total cases and death figures was reported during the first wave in 2020. The discrepancies were detected by comparing official death counts released by the governments to the number of deaths reported in obituaries, at crematoria and burial grounds, etc. Some states were reported to have not added suspected cases to the final count contrary to WHO guidelines.<ref>{{cite news|url=https://www.bbc.com/news/world-asia-india-54985981|title=India coronavirus: How a group of volunteers 'exposed' hidden Covid-19 deaths|last=Biswas|first=Soutik|publisher=BBC|date=20 November 2020|access-date=19 April 2021}}</ref><ref>{{cite news|url=https://www.bbc.com/news/world-asia-india-53773070|title=Coronavirus: How many Covid-19 deaths is India missing?|last=Biswas|first=Soutik|publisher=BBC|date=18 August 2020|access-date=19 April 2021}}</ref><ref>{{cite news|url=https://science.thewire.in/health/india-mccd-comorbidities-covid-19-deaths-undercounting/|title=India Is Undercounting Its COVID-19 Deaths. This Is How.|last=Pulla|first=Priyanka|work=The Wire|date=4 August 2020|access-date=19 April 2021}}</ref> | *Undercounting of total cases and death figures was reported during the first wave in 2020. The discrepancies were detected by comparing official death counts released by the governments to the number of deaths reported in obituaries, at crematoria and burial grounds, etc. Some states were reported to have not added suspected cases to the final count contrary to WHO guidelines.<ref>{{cite news|url=https://www.bbc.com/news/world-asia-india-54985981|title=India coronavirus: How a group of volunteers 'exposed' hidden Covid-19 deaths|last=Biswas|first=Soutik|publisher=BBC|date=20 November 2020|access-date=19 April 2021}}</ref><ref>{{cite news|url=https://www.bbc.com/news/world-asia-india-53773070|title=Coronavirus: How many Covid-19 deaths is India missing?|last=Biswas|first=Soutik|publisher=BBC|date=18 August 2020|access-date=19 April 2021}}</ref><ref>{{cite news|url=https://science.thewire.in/health/india-mccd-comorbidities-covid-19-deaths-undercounting/|title=India Is Undercounting Its COVID-19 Deaths. This Is How.|last=Pulla|first=Priyanka|work=The Wire|date=4 August 2020|access-date=19 April 2021}}</ref> | ||
*Starting April 2021, reporters of the Gujarati newspaper [[Sandesh (Indian newspaper)|''Sandesh'']] based in seven cities in Gujarat including [[Ahmedabad]] and [[Gandhinagar]] went to mortuaries, hospitals and cremation sites and recorded what they saw. On 16 April, reporters in Ahmedabad counted over 200 bodies; the next day the governments official count was 25. For a number of days ''Sandesh'' reporters did this. The newspaper publishes the findings on a regular basis.<ref name=":0" /> ''[[The Hindu]]'' also pointed to similar discrepancies.<ref>{{Cite news|last=Langa|first=Mahesh|date=2021-04-18|title=COVID-19 deaths in Gujarat far exceed government figures|language=en-IN|work=The Hindu|url=https://www.thehindu.com/news/national/other-states/coronavirus-covid-19-deaths-in-gujarat-far-exceed-government-figures/article34352916.ece|access-date=2021-05-29|issn=0971-751X}}</ref> | *Starting April 2021, reporters of the Gujarati newspaper [[Sandesh (Indian newspaper)|''Sandesh'']] based in seven cities in Gujarat including [[Ahmedabad]] and [[Gandhinagar]] went to mortuaries, hospitals and cremation sites and recorded what they saw. On 16 April, reporters in Ahmedabad counted over 200 bodies; the next day the governments official count was 25. For a number of days ''Sandesh'' reporters did this. The newspaper publishes the findings on a regular basis.<ref name=":0" /> ''[[The Hindu]]'' also pointed to similar discrepancies.<ref>{{Cite news|last=Langa|first=Mahesh|date=2021-04-18|title=COVID-19 deaths in Gujarat far exceed government figures|language=en-IN|work=The Hindu|url=https://www.thehindu.com/news/national/other-states/coronavirus-covid-19-deaths-in-gujarat-far-exceed-government-figures/article34352916.ece|access-date=2021-05-29|issn=0971-751X}}</ref> | ||
* On 15 April 2021, a report in ''[[Dainik Bhaskar|Dainik Bhaskar's]]'' Bhopal edition stated that the official number of COVID-19 deaths in [[Bhopal]] during the last 5 days were 21, however cremations had taken place for 356 COVID-19 cases.<ref>{{Cite web|last=|first=|date=16 April 2021|title='Burning pyres telling the truth': Dainik Bhaskar says govt data on Covid deaths in Bhopal is a 'lie'|url=https://www.newslaundry.com/2021/04/16/burning-pyres-telling-the-truth-dainik-bhaskar-says-govt-data-on-covid-deaths-in-bhopal-is-a-lie | * On 15 April 2021, a report in ''[[Dainik Bhaskar|Dainik Bhaskar's]]'' Bhopal edition stated that the official number of COVID-19 deaths in [[Bhopal]] during the last 5 days were 21, however cremations had taken place for 356 COVID-19 cases.<ref>{{Cite web|last=|first=|date=16 April 2021|title='Burning pyres telling the truth': Dainik Bhaskar says govt data on Covid deaths in Bhopal is a 'lie'|url=https://www.newslaundry.com/2021/04/16/burning-pyres-telling-the-truth-dainik-bhaskar-says-govt-data-on-covid-deaths-in-bhopal-is-a-lie|access-date=2021-05-29|website=Newslaundry}}</ref> ''[[India Today]]'' and ''[[The New York Times]]'' found similar discrepancies in Bhopal in April.<ref>{{Cite web|last=Singh|first=Ravish Pal|date=14 April 2021|title=Bhopal crematoriums flooded with bodies: 187 cremated following Covid norms while records show only 5 deaths|url=https://www.indiatoday.in/cities/bhopal/story/bhopal-crematoriums-flooded-with-bodies-far-more-covid-deaths-than-official-tally-1790720-2021-04-14|access-date=2021-05-29|website=India Today|language=en}}</ref><ref name=":9" /> | ||
*On 24 April 2021, data scientist [[Bhramar Mukherjee]] said "From all the modeling we’ve done, we believe the true number of deaths is two to five times what is being reported."<ref name=":9" /> | *On 24 April 2021, data scientist [[Bhramar Mukherjee]] said "From all the modeling we’ve done, we believe the true number of deaths is two to five times what is being reported."<ref name=":9" /> | ||
*In April 2021, the ''Observer'' collected data from [[Muzaffarnagar]], Uttar Pradesh and found large differences in the official count and the numbers given by frontline workers.<ref>{{Cite web|last1=Ellis-Petersen|first1=Hannah|last2=Alam|first2=Mohammad Sartaj|date=2021-05-01|title='We're burning pyres all day': India accused of undercounting deaths|url=http://www.theguardian.com/world/2021/may/01/were-burning-pyres-all-day-india-accused-of-undercounting-deaths | *In April 2021, the ''Observer'' collected data from [[Muzaffarnagar]], Uttar Pradesh and found large differences in the official count and the numbers given by frontline workers.<ref>{{Cite web|last1=Ellis-Petersen|first1=Hannah|last2=Alam|first2=Mohammad Sartaj|date=2021-05-01|title='We're burning pyres all day': India accused of undercounting deaths|url=http://www.theguardian.com/world/2021/may/01/were-burning-pyres-all-day-india-accused-of-undercounting-deaths|access-date=2021-05-29|website=The Guardian|language=en}}</ref> | ||
* On 18 May 2021 ''[[Dainik Bhaskar]]'' published a story which was the outcome of a collaborative effort of 30 of its reporters. The story reported at least 2000 bodies along a 1140 km stretch of the [[River Ganga]].<ref>{{Cite web|last=|first=|date=18 May 2021|title='Uncountable corpses on banks of Ganga': Dainik Bhaskar's front-page story on deaths in UP|url=https://www.newslaundry.com/2021/05/18/uncountable-corpses-on-banks-of-ganga-dainik-bhaskars-front-page-story-on-deaths-in-up | * On 18 May 2021 ''[[Dainik Bhaskar]]'' published a story which was the outcome of a collaborative effort of 30 of its reporters. The story reported at least 2000 bodies along a 1140 km stretch of the [[River Ganga]].<ref>{{Cite web|last=|first=|date=18 May 2021|title='Uncountable corpses on banks of Ganga': Dainik Bhaskar's front-page story on deaths in UP|url=https://www.newslaundry.com/2021/05/18/uncountable-corpses-on-banks-of-ganga-dainik-bhaskars-front-page-story-on-deaths-in-up|access-date=2021-05-29|website=Newslaundry}}</ref> On 15 May, ''[[Reuters]]'' reported that they had accessed an official confirmation that COVID-19 deaths were being dumped in rivers.<ref>{{Cite web|date=2021-05-15|title=Bodies of COVID-19 victims among those dumped in India's Ganges -govt document|url=https://www.reuters.com/world/india/bodies-covid-19-victims-among-those-dumped-indias-ganges-govt-document-2021-05-15/|access-date=2021-05-29|website=Reuters}}</ref> | ||
* On 13 May 2021, the [[Institute for Health Metrics and Evaluation]] placed India's total deaths at 736,811 while the reported number of cases were 248,016.<ref>{{Cite web|last1=Ghose|first1=Anuprova|last2=Bajpai|first2=Isha|others=Factchecker.in|title=Explained: How Indian states misreport their Covid-19 death toll|url=https://scroll.in/article/994631/explained-how-indians-states-are-able-to-misreport-the-covid-19-death-toll | * On 13 May 2021, the [[Institute for Health Metrics and Evaluation]] placed India's total deaths at 736,811 while the reported number of cases were 248,016.<ref>{{Cite web|last1=Ghose|first1=Anuprova|last2=Bajpai|first2=Isha|others=Factchecker.in|title=Explained: How Indian states misreport their Covid-19 death toll|url=https://scroll.in/article/994631/explained-how-indians-states-are-able-to-misreport-the-covid-19-death-toll|access-date=2021-05-28|website=Scroll.in|language=en-US}}</ref> | ||
* On 25 May 2021, ''[[The New York Times]]'' has estimated a number from double the official figure, to over 1 million.<ref>{{Cite news|last1=Gamio|first1=Lazaro|last2=Glanz|first2=James|date=2021-05-25|title=Just How Big Could India's True Covid Toll Be?|language=en-US|work=The New York Times|url=https://www.nytimes.com/interactive/2021/05/25/world/asia/india-covid-death-estimates.html|access-date=2021-05-27|issn=0362-4331}}</ref> The worst case scenario according to NYT was 4.2 million deaths; 13.7 times India's official figure in the same time period.<ref>{{Cite web|last=Zompa|first=Tenzin|date=2021-05-26|title=At 'worst case', India's Covid deaths are 14 times higher than govt count, says NYT|url=https://theprint.in/india/at-worst-case-indias-covid-deaths-are-14-times-higher-than-govt-count-says-nyt/665718/|access-date=2021-05-28|website=ThePrint|language=en-US}}</ref> | * On 25 May 2021, ''[[The New York Times]]'' has estimated a number from double the official figure, to over 1 million.<ref>{{Cite news|last1=Gamio|first1=Lazaro|last2=Glanz|first2=James|date=2021-05-25|title=Just How Big Could India's True Covid Toll Be?|language=en-US|work=The New York Times|url=https://www.nytimes.com/interactive/2021/05/25/world/asia/india-covid-death-estimates.html|access-date=2021-05-27|issn=0362-4331}}</ref> The worst-case scenario according to NYT was 4.2 million deaths; 13.7 times India's official figure in the same time period.<ref>{{Cite web|last=Zompa|first=Tenzin|date=2021-05-26|title=At 'worst case', India's Covid deaths are 14 times higher than govt count, says NYT|url=https://theprint.in/india/at-worst-case-indias-covid-deaths-are-14-times-higher-than-govt-count-says-nyt/665718/|access-date=2021-05-28|website=ThePrint|language=en-US}}</ref> | ||
* On 12 June 2021, ''[[The Economist]]'' reported that Christopher Leffler of [[Virginia Commonwealth University]] analyses data on excess mortality from different parts of India, showing a rough estimate of between 1.8 million and 2.4 million total death since the start of the pandemic. Indian polling organization [[CVoter]], which conducted surveys throughout the pandemic, calculated that the likely death toll from | * On 12 June 2021, ''[[The Economist]]'' reported that Christopher Leffler of [[Virginia Commonwealth University]] analyses data on excess mortality from different parts of India, showing a rough estimate of between 1.8 million and 2.4 million total death since the start of the pandemic. Indian polling organization [[CVoter]], which conducted surveys throughout the pandemic, calculated that the likely death toll from COVID-19 by mid-May 2021 was around 1.83 million.<ref>{{cite web|url=https://www.economist.com/asia/2021/06/12/more-evidence-emerges-of-indias-true-death-toll-from-covid-19 |title=More evidence emerges of India's true death toll from covid-19 |website=Economist |date=12 June 2021}}</ref> | ||
==== Overcounting ==== | ==== Overcounting ==== | ||