How much of SARS-CoV-2 Infections is India detecting? A model-based estimation
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Abstract
Background and Rationale
Amid SARS-CoV-2 outbreak, the low number of infections for a population size of 1.38 billion is widely discussed, but with no definite answers.
Methods
We used the model proposed by Bommer and Vollmer to assess the quality of official case records. The infection fatality rates were taken from Verity et al (2020). Age distribution of the population for India and states are taken from the Census of India (2011). Reported number of deaths and SARS-CoV-2 confirmed cases from https://www.covid19india.org . The reported numbers of samples tests were collected from the reports of the Indian Council for Medical Research (ICMR).
Results
The findings suggest that India is detecting just 3.6% of the total number of infections with a huge variation across its states. Among 13 states which have more than 100 COVID-19 cases, the detection rate varies from 81.9% (of 410 estimated infections) in Kerala to 0.8% (of 35487 estimated infections) in Madhya Pradesh and 2.4% (of 7431 estimated infections) in Gujarat.
Conclusion
As the study reports a lower number of deaths and higher recovery rates in the states with a high detection rate, thus suggest that India must enhance its testing capacity and go for widespread testing. Late detection puts patients in greater need of mechanical ventilation and ICU care, which imposes greater costs on the health system. The country should also adopt population-level random testing to assess the prevalence of the infection.
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SciScore for 10.1101/2020.04.09.20059014: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank…
SciScore for 10.1101/2020.04.09.20059014: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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