Time-dependent dynamic transmission potential and instantaneous reproduction number of COVID-19 pandemic in India
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Abstract
Introduction
Dynamic tools and methods to assess the ongoing transmission potential of COVID-19 in India are required. We aim to estimate time-dependent transmissibility of COVID-19 for India using a reproducible framework.
Methods
Daily COVID-19 case incidence time series for India and its states was obtained from https://api.covid19india.org/ and pre-processed. Bayesian approach was adopted to quantify transmissibility at a given location and time, as indicated by the instantaneous reproduction number (R eff ). Analysis was carried out in R version 4.0.2 using “EpiEstim_2.2-3” package. Serial interval distribution was estimated using “uncertain_si” algorithm with inputs of mean, standard deviation, minimum and maximum of mean serial interval as 5.1, 1.2, 3.9 and 7.5 days respectively; and mean, standard deviation, minimum, and maximum of standard deviations of serial interval as 3.7, 0.9, 2.3, and 4.7 respectively with 100 simulations and moving average of seven days.
Results
A total of 9,07,544 cumulative incident cases till July 13 th , 2020 were analysed. Daily COVID-19 incidence in the country was seen on the rise; however, transmissibility showed a decline from the initial phases of COVID-19 pandemic in India. The maximum R eff reached at the national level during the study period was 2.57 (sliding week ending April 4 th , 2020). R eff on July 13 th , 2020 for India was 1.16 with a range from 0.59 to 2.98 across various states/UTs.
Conclusion
R eff provides critical feedback for assessment of transmissibility of COVID-19 and thus is a potential dynamic decision support tool for on-ground public health decision making.
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SciScore for 10.1101/2020.07.15.20154971: (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: We detected the following sentences addressing limitations in the study:Limitations of the study. The present study was based on crowdsourced publicly available datasets. The data sources for the datasets include multiple official websites by the government of India and is likely to be close to the actual government data, real-time use of National Surveillance Data should be considered for public health …
SciScore for 10.1101/2020.07.15.20154971: (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: We detected the following sentences addressing limitations in the study:Limitations of the study. The present study was based on crowdsourced publicly available datasets. The data sources for the datasets include multiple official websites by the government of India and is likely to be close to the actual government data, real-time use of National Surveillance Data should be considered for public health decision making.13 Further, the present study did not categorise daily COVID-19 incidence between local and imported cases. As a result, it is likely that we might have overestimated the transmissibility of COVID-19 in certain states which have high rates of immigration from expatriates or within-country migration. The overestimation of Reff is justified considering its impact on decision making; however, the provision of modelling categorized cases within the algorithm is available and should be used for enhanced accuracy of the transmissibility estimates. The present study assumed serial interval parameters from the literature. These estimates are likely to be applicable to the Indian settings, health surveillance data from the existing public health programmes needs to be incorporated for developing country-specific decision support system. Further, assessment of the probability distribution function parameters based on empirical data from infector-infectee pairs in the country is required for fine-tuning of the country or state-specific COVID-19 management strategies. In studies on estimating epidemiological parameters in Hongkong, Singapore a...
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.
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- No protocol registration statement was detected.
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