Model-based estimation of transmissibility and reinfection of SARS-CoV-2 P.1 variant

Coutinho, Renato Mendes; Marquitti, Flávia Maria Darcie; Ferreira, Leonardo Souto; Borges, Marcelo Eduardo; Silva, Rafael Lopes Paixão da; Canton, Otavio; Portella, Tatiana Pineda; Poloni, Silas; Franco, Caroline; Pluciński, Mateusz M.; Lessa, Fernanda C.; Silva, Antônio Augusto Moura da; Kraenkel, Roberto André; Veras, Maria Amélia de Sousa Mascena; Prado, Paulo Inácio

doi:10.1038/s43856-021-00048-6

Cited by 83 publications

(51 citation statements)

References 23 publications

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“…Early studies for the UK, where the Alpha variant (B.1.1.7 lineage) of SARS-CoV-2 was first noted in mid-November 2020, found an approximately 50% higher human-to-human transmissibility compared to the wild type of the virus 1 – 4 . Similar evidence was provided for the Beta variant (B.1.351 lineage) first detected in South Africa and the Gamma (P.1 lineage) variant 4 , 5 . Studies on a higher human-to-human-transmissibility of VOC were complemented by evidence on increased disease severity, fatality rates 6 – 8 and a larger risk of international disease transmission 9 .…”

Section: Introductionsupporting

confidence: 72%

“…Table 1 reports DiD estimation results, which compare the 7-day incidence rates in all NUTS-3 regions with and without confirmed VOC cases before and after the first VOC reporting in a region. To increase estimation power, data on the three VOC types have been pooled because the literature indicates similarly higher human-to-human transmissibility of these VOC compared to the wild type of the virus 1 – 5 . Further, in terms of health policy decisions, it is most important to identify potential higher human-to-human transmissibility and less which variant is responsible for it.…”

Section: Resultsmentioning

confidence: 99%

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Early-stage spatial disease surveillance of novel SARS-CoV-2 variants of concern in Germany with crowdsourced data

Mitze

Rode

2022

Sci Rep

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The emergence and rapid spread of novel variants of concern (VOC) of the coronavirus 2 constitute a major challenge for spatial disease surveillance. We explore the possibility to use close to real-time crowdsourced data on reported VOC cases (mainly the Alpha variant) at the local area level in Germany. The aim is to use these data for early-stage estimates of the statistical association between VOC reporting and the overall COVID-19 epidemiological development. For the first weeks in 2021 after international importation of VOC to Germany, our findings point to significant increases of up to 35–40% in the 7-day incidence rate and the hospitalization rate in regions with confirmed VOC cases compared to those without such cases. This is in line with simultaneously produced international evidence. We evaluate the sensitivity of our estimates to sampling errors associated with the collection of crowdsourced data. Overall, we find no statistical evidence for an over- or underestimation of effects once we account for differences in data representativeness at the regional level. This points to the potential use of crowdsourced data for spatial disease surveillance, local outbreak monitoring and public health decisions if no other data on new virus developments are available.

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Section: Introductionsupporting

confidence: 72%

Section: Resultsmentioning

confidence: 99%

Early-stage spatial disease surveillance of novel SARS-CoV-2 variants of concern in Germany with crowdsourced data

Mitze

Rode

2022

Sci Rep

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“…Several variants of concern (VOCs) of the severe acute respiratory virus 2 (SARS-CoV-2), the virus of COVID-19, emerged with increased transmissibility and immunity-escaping ability and posed new threats [1][2][3][4][5]. The SARS-CoV-2 P.1 variant that likely emerged from Amazonas, Brazil, was first detected by the Japanese National Institute of Infectious Diseases on 6 January 2021 from a returning traveller from Brazil [6]. As of June 2021, the P.1 variant (or Gamma variant) has spread to 55 countries globally and was once the dominant lineage in the South American region.…”

Section: Introductionmentioning

confidence: 99%

Modelling of Waning of Immunity and Reinfection Induced Antibody Boosting of SARS-CoV-2 in Manaus, Brazil

Wei

Musa

Zhao

et al. 2022

IJERPH

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It was reported that the Brazilian city, Manaus, likely exceeded the herd immunity threshold (presumably 60–70%) in November 2020 after the first wave of COVID-19, based on the serological data of a routine blood donor. However, a second wave started in November 2020, when an even higher magnitude of deaths hit the city. The arrival of the second wave coincided with the emergence of the Gamma (P.1) variant of SARS-CoV-2, with higher transmissibility, a younger age profile of cases, and a higher hospitalization rate. Prete et al. (2020 MedRxiv 21256644) found that 8 to 33 of 238 (3.4–13.9%) repeated blood donors likely were infected twice in Manaus between March 2020 and March 2021. It is unclear how this finding can be used to explain the second wave. We propose a simple model which allows reinfection to explain the two-wave pattern in Manaus. We find that the two waves with 30% and 40% infection attack rates, respectively, and a reinfection ratio at 3.4–13.9%, can explain the two waves well. We argue that the second wave was likely because the city had not exceeded the herd immunity level after the first wave. The reinfection likely played a weak role in causing the two waves.

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“…Shortly after the emergence of the Alpha variant in England in September 2020 [4], a number of studies aimed to estimate its transmission potential, compared to the previously circulating non-VOC lineages [4][5][6][7]. More recently, several papers have evaluated the transmissibility of VOCs compared to non-VOC lineages [8][9][10][11][12][13][14][15][16]. All of these studies have developed 1 new approaches to estimate the transmission advantages of new VOCs, often synthesising evidence from multiple data sources including genomic data.…”

Section: Introductionmentioning

confidence: 99%

A generic method and software to estimate the transmission advantage of pathogen variants in real-time : SARS-CoV-2 as a case-study

Bhatia

Wardle

Nash

et al. 2021

Preprint

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Recent months have demonstrated that emerging variants may set back the global COVID-19 response. The ability to rapidly assess the threat of new variants in real-time is critical for timely optimisation of control strategies.We extend the EpiEstim R package, designed to estimate the time-varying reproduction number (Rt), to estimate in real-time the effective transmission advantage of a new variant compared to a reference variant. Our method can combine information across multiple locations and over time and was validated using an extensive simulation study, designed to mimic a variety of real-time epidemic contexts.We estimate that the SARS-CoV-2 Alpha variant is 1.46 (95% Credible Interval 1.44-1.47) and 1.29, (95% CrI 1.29-1.30) times more transmissible than the wild type, using data from England and France respectively. We further estimate that Beta and Gamma combined are 1.25 (95% CrI 1.24-1.27) times more transmissible than the wildtype (France data). All results are in line with previous estimates from literature, but could have been obtained earlier and more easily with our off-the-shelf open-source tool.Our tool can be used as an important first step towards quantifying the threat of new variants in real-time. Given the popularity of EpiEstim, this extension will likely be used widely to monitor the co-circulation and/or emergence of multiple variants of infectious pathogens.Significance StatementEarly assessment of the transmissibility of new variants of an infectious pathogen is critical for anticipating their impact and designing appropriate interventions. However, this often requires complex and bespoke analyses relying on multiple data streams, including genomic data. Here we present a novel method and software to rapidly quantify the transmission advantage of new variants. Our method is fast and requires only routinely collected disease surveillance data, making it easy to use in real-time. The ongoing high level of SARS-CoV-2 circulation in a number of countries makes the emergence of new variants highly likely. Our work offers a powerful tool to help public health bodies monitor such emerging variants and rapidly detect those with increased transmissibility.

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Model-based estimation of transmissibility and reinfection of SARS-CoV-2 P.1 variant

Cited by 83 publications

References 23 publications

Early-stage spatial disease surveillance of novel SARS-CoV-2 variants of concern in Germany with crowdsourced data

Early-stage spatial disease surveillance of novel SARS-CoV-2 variants of concern in Germany with crowdsourced data

Modelling of Waning of Immunity and Reinfection Induced Antibody Boosting of SARS-CoV-2 in Manaus, Brazil

A generic method and software to estimate the transmission advantage of pathogen variants in real-time : SARS-CoV-2 as a case-study

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