SARS-CoV-2 Seroprevalence in 12 Cities of India from July-December 2020

Velumani, A.; Nikam, Chaitili; Suraweera, Wilson; Fu, Sze Hang; Gelband, Hellen; Brown, Patrick; Bogoch, Isaac I.; Nagelkerke, Nico; Jha, Prabhat

doi:10.1101/2021.03.19.21253429

Cited by 21 publications

(27 citation statements)

References 29 publications

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“…This model also explicitly incorporates waning of immune protection following infection, parameterised using the results of recent longitudinal cohort studies (api.covid19india.org). The model integrates epidemiological data on daily COVID-19 mortality, serological data from the city spanning July -December 2020 5 and genomic sequence data from GISAID (with lineage classification carried out using the Pangolin software tool (https://pangolin.cog-uk.io/) 6 , (Figure 1b,c). There are substantial uncertainties in the date of the Delta variant's introduction into Mumbai, as well as the degree of COVID-19 death under-ascertainment in Mumbai to date.…”

Section: B16172 Delta Variant Growth Advantage Due To Re-infection and Increased Transmissibilitymentioning

confidence: 99%

SARS-CoV-2 B.1.617.2 Delta variant emergence and vaccine breakthrough

Mlčochová¹,

Kemp²,

Dhar

et al. 2021

Preprint

112

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The SARS-CoV-2 B.1.617.2 (Delta) variant was first identified in the state of Maharashtra in late 2020 and has spread throughout India, displacing the B.1.1.7 (Alpha) variant and other pre-existing lineages. Mathematical modelling indicates that the growth advantage is most likely explained by a combination of increased transmissibility and immune evasion. Indeed in vitro, the delta variant is less sensitive to neutralising antibodies in sera from recovered individuals, with higher replication efficiency as compared to the Alpha variant. In an analysis of vaccine breakthrough in over 100 healthcare workers across three centres in India, the Delta variant not only dominates vaccine-breakthrough infections with higher respiratory viral loads compared to non-delta infections (Ct value of 16.5 versus 19), but also generates greater transmission between HCW as compared to B.1.1.7 or B.1.617.1 (p=0.02). In vitro, the Delta variant shows 8 fold approximately reduced sensitivity to vaccine-elicited antibodies compared to wild type Wuhan-1 bearing D614G. Serum neutralising titres against the SARS-CoV-2 Delta variant were significantly lower in participants vaccinated with ChadOx-1 as compared to BNT162b2 (GMT 3372 versus 654, p<0001). These combined epidemiological and in vitro data indicate that the dominance of the Delta variant in India has been most likely driven by a combination of evasion of neutralising antibodies in previously infected individuals and increased virus infectivity. Whilst severe disease in fully vaccinated HCW was rare, breakthrough transmission clusters in hospitals associated with the Delta variant are concerning and indicate that infection control measures need continue in the post-vaccination era.

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Section: B16172 Delta Variant Growth Advantage Due To Re-infection and Increased Transmissibilitymentioning

confidence: 99%

SARS-CoV-2 B.1.617.2 Delta variant emergence and vaccine breakthrough

Mlčochová¹,

Kemp²,

Dhar

et al. 2021

Preprint

112

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“…Full mathematical details and a simulation study are given in Supplementary information (and code is available on GitHub). This model explicitly incorporates waning of immune protection following infection, parameterised using the results of recent longitudinal cohort studies 17,18 . The model integrates epidemiological data on daily COVID-19 mortality (api.covid19india.org), serological data from the city spanning July -December 2020 18 and genomic sequence data from GISAID (with lineage classification carried out using the Pangolin software tool (https://pangolin.coguk.io/) 19 (Figure 1b,c).…”

Section: Introductionmentioning

confidence: 99%

“…This model explicitly incorporates waning of immune protection following infection, parameterised using the results of recent longitudinal cohort studies 17,18 . The model integrates epidemiological data on daily COVID-19 mortality (api.covid19india.org), serological data from the city spanning July -December 2020 18 and genomic sequence data from GISAID (with lineage classification carried out using the Pangolin software tool (https://pangolin.coguk.io/) 19 (Figure 1b,c). Importantly, the model makes the assumption of a homogeneously mixed population and therefore ignores heterogeneities in contact patterns and degrees of exposures between different sub-groups such as healthcare or service workers where social distancing or working from home is not possible.…”

Section: Introductionmentioning

confidence: 99%

SARS-CoV-2 B.1.617.2 Delta variant replication, sensitivity to neutralising antibodies and vaccine breakthrough

Mlčochová¹,

Kemp²,

Dhar³

et al. 2021

Preprint

106

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The B.1.617 variant emerged in the Indian state of Maharashtra in late 2020 and has spread throughout India and to at least 40 countries. There have been fears that two key mutations seen in the receptor binding domain L452R and E484Q would have additive effects on evasion of neutralising antibodies. Here we delineate the phylogenetics of B.1.617 and spike mutation frequencies, in the context of others bearing L452R. The defining mutations in B.1.617.1 spike are L452R and E484Q in the RBD that interacts with ACE2 and is the target of neutralising antibodies. All B.1.617 viruses have the P681R mutation in the polybasic cleavage site region in spike. We report that B.1.617.1 spike bearing L452R, E484Q and P681R mediates entry into cells with slightly reduced efficiency compared to Wuhan-1. This spike confers modestly reduced sensitivity to BNT162b2 mRNA vaccine-elicited antibodies that is similar in magnitude to the loss of sensitivity conferred by L452R or E484Q alone. Furthermore we show that the P681R mutation significantly augments syncytium formation upon the B.1.617.1 spike protein, potentially contributing to increased pathogenesis observed in hamsters and infection growth rates observed in humans.

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“…As the second most populous nation in the world, with a population of close to 1.4 billion, the consequences of an explosion of COVID-19 cases in India could easily dwarf its impact anywhere else [38]. What remains unclear is the extent to which the Indian population has so far been infected by COVID-19 and whether any proximity to herd immunity through infection might slow later waves of disease [39][40][41]. Large-scale serological surveys (serosurveys) from the Indian Council of Medical Research (ICMR), adjusted for test sensitivity, estimate the overall fraction of those with a prior COVID-19 infection to be about 22% by December 2020 -January 2021 [42][43][44].…”

Section: Introductionmentioning

confidence: 99%

The INDSCI-SIM model for COVID-19 in India

Hazra

Pujari

Shekatkar

et al. 2021

Preprint

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Estimating the burden of COVID-19 in India is difficult because the extent to which cases and deaths have been undercounted is hard to assess. The INDSCI-SIM model is a 9-component, age-stratified, contact-structured compartmental model for COVID-19 spread in India. We use INDSCI-SIM, together with Bayesian methods, to obtain optimal fits to reported cases and deaths across the span of the first wave of the Indian pandemic, over the period Jan 30, 2020 to Feb 15, 2021. We account for lock-downs and other non-pharmaceutical interventions, an overall increase in testing as a function of time, the under-counting of cases and deaths, and a range of age-specific infection-fatality ratios. We first use our model to describe data from all individual districts of the state of Karnataka, benchmarking our calculations using data from serological surveys. We then extend this approach to aggregated data for Karnataka state. We model the progress of the pandemic across the cities of Delhi, Mumbai, Pune, Bengaluru and Chennai, and then for India as a whole. We estimate that deaths were undercounted by a factor between 2 and 5 across the span of the first wave, converging on 2.2 as a representative multiplier that accounts for the urban-rural gradient across the country. We also estimate an overall under-counting of cases by a factor of between 20 and 25 towards the end of the first wave. Our estimates of the infection fatality ratio (IFR) are in the range 0.05 - 0.15, broadly consistent with previous estimates but substantially lower than values that have been estimated for other LMIC countries. We find that approximately 40% of India had been infected overall by the end of the first wave, results broadly consistent with those from serosurveys. These results contribute to the understanding of the long-term trajectory of COVID-19 in India.

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SARS-CoV-2 Seroprevalence in 12 Cities of India from July-December 2020

Cited by 21 publications

References 29 publications

SARS-CoV-2 B.1.617.2 Delta variant emergence and vaccine breakthrough

SARS-CoV-2 B.1.617.2 Delta variant emergence and vaccine breakthrough

SARS-CoV-2 B.1.617.2 Delta variant replication, sensitivity to neutralising antibodies and vaccine breakthrough

The INDSCI-SIM model for COVID-19 in India

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