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OPINION
Year : 2020  |  Volume : 18  |  Issue : 3  |  Page : 203-205

Riding the COVID-19 curves – Perspectives on cases in India


1 Department of Medicine, Trichy SRM Medical College Hospital and Research Centre, Tiruchirappalli, Tamil Nadu, India
2 Department of Medicine, Johns Hopkins School of Public Health, Baltimore, Maryland, USA

Date of Submission20-Apr-2020
Date of Decision24-Apr-2020
Date of Acceptance24-Apr-2020
Date of Web Publication22-May-2020

Correspondence Address:
Dr. S Joseph
Flat No. FF2, Fourth Floor, Justice Surya Residency Apartments, Surya Avenue, Aruna Nagar, Puthur, Tiruchirappalli - 620 017, Tamil Nadu
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/cmi.cmi_63_20

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How to cite this article:
Joseph S, George TK. Riding the COVID-19 curves – Perspectives on cases in India. Curr Med Issues 2020;18:203-5

How to cite this URL:
Joseph S, George TK. Riding the COVID-19 curves – Perspectives on cases in India. Curr Med Issues [serial online] 2020 [cited 2020 Oct 21];18:203-5. Available from: https://www.cmijournal.org/text.asp?2020/18/3/203/284739



Amidst the ongoing lockdown in India, there has been an increase in the number of cases from 571 to 7600 from March 25 to April 10, 2020 [Figure 1].[1] Many are concerned about these numbers for the country and even for the states in which they reside. Why are the cases rising even during a lockdown? What should people do? How should decision makers plan?
Figure 1: Cumulative cases in India over the last 1 month. X-axis plots the date and Y-axis denotes cumulative confirmed cases for each day. Source: www.covid19india.org as on 10-04-2020.

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We offer a perspective that explains that the absolute number of cases should not be taken out of context. The ongoing rise in cases could be due to disease factors and the testing measures. The disease has an incubation period of up to 2 weeks and hence despite the lockdown, people become symptomatic during the later weeks; there could be outbreaks due to inadequate social distancing measures and there may be ongoing community transmission. Regarding the testing measures, the first level of analysis is to relook at the numbers as a factor of the population. Second, we also point out that the identification of cases depends on the testing strategy. Identifying states with more than 100 cases (as of April 10),[2],[3],[4],[5],[6],[7],[8],[9],[10],[11],[12],[13],[14],[15] we see that the state with most cases [Table 1] is Maharashtra with 1574 (Telangana was excluded because we did not have data on the number of tests done). When we divide the tests and cases per state by their population in millions, we present a set of bar charts [Figure 2]. Immediately, we see a few patterns. Even though Maharashtra had the maximum cases, Delhi had the highest tests and cases per million population. When we divide the cases as a percent of the tests per million, we see that Tamil Nadu had the highest percentage.
Table 1. State-wise breakup of cases till April 10, 2020

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Figure 2: States by testing and cases. X-axis plots the state. Y-axis plots tests in tens/million population (blue bars), positive cases/million population (orange bars), and case/test ratio (gray bars) as a percentage for each state.

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What do these numbers represent? To understand this, we look at the second factor, testing strategies, adopted by the states. This may be viewed as a supply and demand scenario. States have to swiftly develop the capacity (supply) to test and have to devise protocols and people have to come forward (demand) for testing if they were symptomatic. The Indian Council of Medical Research (ICMR) had recommended testing for international travelers within the last 14 days, symptomatic contacts of laboratory-confirmed coronavirus disease 2019 (COVID-19) cases, symptomatic health-care workers, hospitalized patients with severe acute respiratory illness, and asymptomatic high-risk contacts.[16] At the time of writing this article, the ICMR had extended testing to include all symptomatic influenza-like illness in areas known to have hotspots.[17] These guidelines suggest testing for a higher risk population, one where the likelihood of positivity is high, also called the pretest probability. States adopted these recommendations differently. Some states such as Kerala adopted a more widespread community-based testing strategy with aggressive contact tracing, whereas others opted for a healthcare-based strategy, where tests were done only if patients presented to centers. Relooking at the charts, for most states, we see that the cases per population are closely associated with the tests per population. This may indicate that testing more identifies more cases. It is also because testing was focused and targeted toward high-risk individuals and contact tracing. Incidents such as the Nizamuddin hotspot in Delhi, where a religious event housed many devotees for several days and the Dharavi, a densely populated slum in Mumbai, were instances where testing was trying to catch up with the cases. However, in Kerala and Rajasthan, we see lower cases per million population for the rate of testing. Along with rapid, stringent, public health measure, here, extensive testing started early before the cluster consolidated into a hotspot. It is also interesting to note that large states such as Uttar Pradesh, which are six times more populous than Kerala, have had weak testing numbers and West Bengal, despite low numbers of testing has a relatively high case for tested percentage.

As we plot a graph of cases per million population and tests per million, we see an interesting linear relationship [Figure 3]. Although we note that these numbers are low, the states do not represent homogenous entities, and there are outliers, we see an interesting trend. This offers a perspective where increased testing is associated with increased case detection, and higher number of laboratories is associated with more people being tested [Figure 4]. These cannot be assumed to be causal at this stage, and there may be other variables that may significantly affect these outcomes.
Figure 3: Scatterplot with regression line of tests in tens per million (X-axis) and positive cases per million population (Y-axis).

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Figure 4: Scatterplot with regression line between number of laboratories in state (X-axis) and tests done (Y-axis).

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When we contrast India's numbers on a global stage, we see the numbers differently again [Table 2].[18] These countries are at different points on the epidemic curve and have adopted different testing strategies and public health responses over the past weeks but provide a comparison to India's COVID-19 profile. Hence, unless we understand the region, the number of cases by themselves does not represent the whole picture, and there are scientific, political, and resource implications of the respective contexts that have to be considered.
Table 2: Global testing patterns and cases detected

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In conclusion, we offer that the number of cases should not be interpreted in a simplistic manner but understood in the context of the state, its population, and the manner of testing done. The cases that increase may be the spillover effects prior to the lockdown or inadequate social distancing. It may indicate a surge in infections, where the testing was reactive, but may also reflect the proactiveness of states where higher testing access may be a surrogate for mobilization of more hospital resources and contact tracing. There appears to be a linear correlation where increased testing is associated with higher cases detected, and higher number of labs per state is associated with more test performed. Since the number of observations is small, the conclusions are not causal.

However, the management of positive cases will have implications on demand and access to testing. If people feel stigmatized and private facilities are not adequately supported, we may have a reduction in cases detected, despite an increase in the number of unlabeled illnesses. Furthermore, the strategy for testing may change when states acknowledge community transmission.

India is still in the early days of the COVID-19 impact, and the cases tell a story. Each story is of a person, where they live and what is happening in the region. It is for us to understand the story in the context and act accordingly. For policy makers, it may be a tough decision of extending the lockdown and ramp up mass testing; for health personnel, it may be increasing the capacity to face the surge in patients and for most of us, it may be just to stay home and be safe. However, these responses will be crucial in the coming weeks as India rides the COVID-19 curve.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Covid19 India Tracker. Available from: http://www.covid19india.org. [Last accessed on 2020 Apr 10].  Back to cited text no. 1
    
2.
Delhi Data. Available from: https://twitter.com/CMODelhi/sta tus/1248629443230175232. Available from: [Last accessed on 2020 Apr 10].  Back to cited text no. 2
    
3.
Kerala Data. Available from: https://dashboard.kerala.gov.in/t esting-view-public.php. [Last accessed on 2020 Apr 10].  Back to cited text no. 3
    
4.
Madhya Pradesh Data. Available from: https://twitter.com/JansamparkMP. [Last accessed on 2020 Apr 10].  Back to cited text no. 4
    
5.
Andhra Pradesh Data. Available from: http://hmfw.ap.gov.in/covid_dashboard.aspx. [Last accessed on 2020 Apr 10].  Back to cited text no. 5
    
6.
Uttar Pradesh Data. Available from: https://t.me/c/1428198946/1882. [Last accessed on 2020 Apr 10].  Back to cited text no. 6
    
7.
West Bengal Data. Available from: https://www.wbhealth.gov.in/upl oaded_files/corona/Bulletin_We st_Bengal_10.04_.2020_.pdf. [Last accessed on 2020 Apr 10].  Back to cited text no. 7
    
8.
Rajasthan Data. Available from: http://www.rajswasthya.nic.in/. [Last accessed on 2020 Apr 10].  Back to cited text no. 8
    
9.
Maharashtra Data. Available from: https://twitter.com/Maha_MEDD/sta tus/1248484537216065536. [Last accessed on 2020 Apr 10].  Back to cited text no. 9
    
10.
Jammu and Kashmir Data. Available from: https://twitter.com/diprjk/stat us/1248598584380887042. [Last accessed on 2020 Apr 10].  Back to cited text no. 10
    
11.
Haryana Data. Available from: http://www.nhmharyana.gov.in/Write ReadData/userfiles/file/CoronaVirus/Bulletin11042020Morning.pdf. [Last accessed on 2020 Apr 10].  Back to cited text no. 11
    
12.
Karnataka Data. Available from: https://karunadu.karnataka.g ov.in/hfw/kannada/nCovDocs/10-04-2020(English).pdf. [Last accessed on 2020 Apr 10].  Back to cited text no. 12
    
13.
Gujarat Data. Available from: https://gujcovid19.gujarat.gov.in/up loads/pressbrief1004102020081427569.pdf. [Last accessed on 2020 Apr 10].  Back to cited text no. 13
    
14.
Punjab Data. Available from: http://pbhealth.gov.in/Media bull etin-10-04-2020.pdf. [Last accessed on 2020 Apr 10].  Back to cited text no. 14
    
15.
Tamilnadu Data. Available from: https://stopcorona.tn.gov.in/wp-content/uploads/2020/03/Media-Bulletin-10-04-20-COVID-19-6-PM.pdf. [Last accessed on 2020 Apr 10].  Back to cited text no. 15
    
16.
ICMR Testing Strategy; 20 March, 2020. Available from: https://icmr.nic.in/sites/default/files/uplo ad_documents/2020-03-20_covi d19_test_v3.pdf. [Last accessed on 2020 Apr 10].  Back to cited text no. 16
    
17.
ICMR Testing Strategy; 9 April, 2020. Available from: https://icmr.nic.in/sites/default/file s/upload_documents/Strategey_for_CO VID19_Test_v4_09042020.pdf. [Last accessed on 2020 Apr 10].  Back to cited text no. 17
    
18.
World Testing Data. Available from: https://ourworldindata.org/coron avirus#our-database-on-co vid-19-testing-data. [Last accessed on 2020 Apr 10].  Back to cited text no. 18
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
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  [Table 1], [Table 2]



 

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