A Bioinformatics Tool for Predicting Future COVID-19 Waves Based on a Retrospective Analysis of the Second Wave in India: Model Development Study

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Abstract

Background

Since the start of the COVID-19 pandemic, health policymakers globally have been attempting to predict an impending wave of COVID-19. India experienced a devastating second wave of COVID-19 in the late first week of May 2021. We retrospectively analyzed the viral genomic sequences and epidemiological data reflecting the emergence and spread of the second wave of COVID-19 in India to construct a prediction model.

Objective

We aimed to develop a bioinformatics tool that can predict an impending COVID-19 wave.

Methods

We analyzed the time series distribution of genomic sequence data for SARS-CoV-2 and correlated it with epidemiological data for new cases and deaths for the corresponding period of the second wave. In addition, we analyzed the phylodynamics of circulating SARS-CoV-2 variants in the Indian population during the study period.

Results

Our prediction analysis showed that the first signs of the arrival of the second wave could be seen by the end of January 2021, about 2 months before its peak in May 2021. By the end of March 2021, it was distinct. B.1.617 lineage variants powered the wave, most notably B.1.617.2 (Delta variant).

Conclusions

Based on the observations of this study, we propose that genomic surveillance of SARS-CoV-2 variants, complemented with epidemiological data, can be a promising tool to predict impending COVID-19 waves.

Related collections

Most cited references 48

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Effectiveness of Covid-19 Vaccines against the B.1.617.2 (Delta) Variant

Jamie Lopez Bernal, Nick Andrews, Charlotte Gower … (2021)

Background The B.1.617.2 (delta) variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (Covid-19), has contributed to a surge in cases in India and has now been detected across the globe, including a notable increase in cases in the United Kingdom. The effectiveness of the BNT162b2 and ChAdOx1 nCoV-19 vaccines against this variant has been unclear. Methods We used a test-negative case–control design to estimate the effectiveness of vaccination against symptomatic disease caused by the delta variant or the predominant strain (B.1.1.7, or alpha variant) over the period that the delta variant began circulating. Variants were identified with the use of sequencing and on the basis of the spike ( S ) gene status. Data on all symptomatic sequenced cases of Covid-19 in England were used to estimate the proportion of cases with either variant according to the patients’ vaccination status. Results Effectiveness after one dose of vaccine (BNT162b2 or ChAdOx1 nCoV-19) was notably lower among persons with the delta variant (30.7%; 95% confidence interval [CI], 25.2 to 35.7) than among those with the alpha variant (48.7%; 95% CI, 45.5 to 51.7); the results were similar for both vaccines. With the BNT162b2 vaccine, the effectiveness of two doses was 93.7% (95% CI, 91.6 to 95.3) among persons with the alpha variant and 88.0% (95% CI, 85.3 to 90.1) among those with the delta variant. With the ChAdOx1 nCoV-19 vaccine, the effectiveness of two doses was 74.5% (95% CI, 68.4 to 79.4) among persons with the alpha variant and 67.0% (95% CI, 61.3 to 71.8) among those with the delta variant. Conclusions Only modest differences in vaccine effectiveness were noted with the delta variant as compared with the alpha variant after the receipt of two vaccine doses. Absolute differences in vaccine effectiveness were more marked after the receipt of the first dose. This finding would support efforts to maximize vaccine uptake with two doses among vulnerable populations. (Funded by Public Health England.)

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Reduced sensitivity of SARS-CoV-2 variant Delta to antibody neutralization

Delphine Planas, David Veyer, Artem Baidaliuk … (2021)

The SARS-CoV-2 B.1.617 lineage was identified in October 2020 in India1-5. Since then, it has become dominant in some regions of India and in the UK, and has spread to many other countries6. The lineage includes three main subtypes (B1.617.1, B.1.617.2 and B.1.617.3), which contain diverse mutations in the N-terminal domain (NTD) and the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein that may increase the immune evasion potential of these variants. B.1.617.2-also termed the Delta variant-is believed to spread faster than other variants. Here we isolated an infectious strain of the Delta variant from an individual with COVID-19 who had returned to France from India. We examined the sensitivity of this strain to monoclonal antibodies and to antibodies present in sera from individuals who had recovered from COVID-19 (hereafter referred to as convalescent individuals) or who had received a COVID-19 vaccine, and then compared this strain with other strains of SARS-CoV-2. The Delta variant was resistant to neutralization by some anti-NTD and anti-RBD monoclonal antibodies, including bamlanivimab, and these antibodies showed impaired binding to the spike protein. Sera collected from convalescent individuals up to 12 months after the onset of symptoms were fourfold less potent against the Delta variant relative to the Alpha variant (B.1.1.7). Sera from individuals who had received one dose of the Pfizer or the AstraZeneca vaccine had a barely discernible inhibitory effect on the Delta variant. Administration of two doses of the vaccine generated a neutralizing response in 95% of individuals, with titres three- to fivefold lower against the Delta variant than against the Alpha variant. Thus, the spread of the Delta variant is associated with an escape from antibodies that target non-RBD and RBD epitopes of the spike protein.

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Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions

Zifeng Yang, Zhiqi Zeng, Ke Wang … (2020)

Background The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic. Methods We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic. Results We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction. Conclusions Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size.

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Author and article information

Contributors

Ashutosh Kumar:

ORCID: https://orcid.org/0000-0003-1589-9568

Department of AnatomyAll India Institute of Medical Sciences - PatnaPhulwari SharifPatna, 801507India91 612 245 ext 1335drashutoshkumar@aiimspatna.org

Journal

Journal ID (nlm-ta): JMIR Bioinform Biotech

Journal ID (iso-abbrev): JMIR Bioinform Biotech

Journal ID (publisher-id): JBB

Title: Jmir Bioinformatics and Biotechnology

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2563-3570

Publication date Collection: Jan-Dec 2022

Publication date (Electronic): 22 September 2022

Publication date PMC-release: 22 September 2022

Volume: 3

Issue: 1

Electronic Location Identifier: e36860

Affiliations

[1 ] Department of Anatomy All India Institute of Medical Sciences - Patna Patna India

[2 ] Department of Anatomy Dr B C Roy Multispeciality Medical Research Center Indian Institute of Technology-Kharagpur Kharagpur India

[3 ] India Health Lead Oxford Policy Management Limited Oxford United Kingdom

[4 ] Department of Anatomy Institute of Medical Sciences Banaras Hindu University Varanasi India

[5 ] Department of Zoology Banaras Hindu University Varanasi India

Author notes

Corresponding Author: Ashutosh Kumar drashutoshkumar@ 123456aiimspatna.org

Author information

Ashutosh Kumar https://orcid.org/0000-0003-1589-9568

Adil Asghar https://orcid.org/0000-0002-1404-1298

Prakhar Dwivedi https://orcid.org/0000-0001-8756-1676

Gopichand Kumar https://orcid.org/0000-0002-3100-8908

Ravi K Narayan https://orcid.org/0000-0003-2510-6744

Rakesh K Jha https://orcid.org/0000-0003-1292-5927

Rakesh Parashar https://orcid.org/0000-0002-2428-8622

Chetan Sahni https://orcid.org/0000-0002-4301-4643

Sada N Pandey https://orcid.org/0000-0003-3201-2287

Article

Publisher ID: v3i1e36860

DOI: 10.2196/36860

PMC ID: 9516867

SO-VID: ba2bf1dd-463e-4fda-ab2c-6a7e5a80e061

Copyright © ©Ashutosh Kumar, Adil Asghar, Prakhar Dwivedi, Gopichand Kumar, Ravi K Narayan, Rakesh K Jha, Rakesh Parashar, Chetan Sahni, Sada N Pandey. Originally published in JMIR Bioinformatics and Biotechnology (https://bioinform.jmir.org), 22.09.2022.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Bioinformatics and Biotechnology, is properly cited. The complete bibliographic information, a link to the original publication on https://bioinform.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 28 January 2022

Date revision requested : 20 July 2022

Date revision received : 26 August 2022

Date accepted : 12 September 2022

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