Anton Barchuk reports personal fees from AstraZeneca, MSD, and Biocad outside the submitted work. Artur Isaev, Ekaterina Pomerantseva and Svetlana Zhikrivetskaya report a pending patent for the test system (ELISA) for detecting antibodies specific to the SARS-COV-2 in a biological sample. Other authors have no conflict of interest to declare. This does not alter our adherence to PLOS One policies on sharing data and materials. It also complies with the manuscript submission guidelines of PLOS One.
The COVID-19 pandemic in Russia has already resulted in 500,000 excess deaths, with more than 5.6 million cases registered officially by July 2021. Surveillance based on case reporting has become the core pandemic monitoring method in the country and globally. However, population-based seroprevalence studies may provide an unbiased estimate of the actual disease spread and, in combination with multiple surveillance tools, help to define the pandemic course. This study summarises results from four consecutive serological surveys conducted between May 2020 and April 2021 at St. Petersburg, Russia and combines them with other SARS-CoV-2 surveillance data.
We conducted four serological surveys of two random samples (May–June, July–August, October–December 2020, and February–April 2021) from adults residing in St. Petersburg recruited with the random digit dialing (RDD), accompanied by a telephone interview to collect information on both individuals who accepted and declined the invitation for testing and account for non-response. We have used enzyme-linked immunosorbent assay CoronaPass total antibodies test (Genetico, Moscow, Russia) to report seroprevalence. We corrected the estimates for non-response using the bivariate probit model and also accounted the test performance characteristics, obtained from independent assay evaluation. In addition, we have summarised the official registered cases statistics, the number of hospitalised patients, the number of COVID-19 deaths, excess deaths, tests performed, data from the ongoing SARS-CoV-2 variants of concern (VOC) surveillance, the vaccination uptake, and St. Petersburg search and mobility trends. The infection fatality ratios (IFR) have been calculated using the Bayesian evidence synthesis model.
After calling 113,017 random mobile phones we have reached 14,118 individuals who responded to computer-assisted telephone interviewing (CATI) and 2,413 provided blood samples at least once through the seroprevalence study. The adjusted seroprevalence in May–June, 2020 was 9.7% (95%: 7.7–11.7), 13.3% (95% 9.9–16.6) in July–August, 2020, 22.9% (95%: 20.3–25.5) in October–December, 2021 and 43.9% (95%: 39.7–48.0) in February–April, 2021. History of any symptoms, history of COVID-19 tests, and non-smoking status were significant predictors for higher seroprevalence. Most individuals remained seropositive with a maximum 10 months follow-up. 92.7% (95% CI 87.9–95.7) of participants who have reported at least one vaccine dose were seropositive. Hospitalisation and COVID-19 death statistics and search terms trends reflected the pandemic course better than the official case count, especially during the spring 2020. SARS-CoV-2 circulation showed rather low genetic SARS-CoV-2 lineages diversity that increased in the spring 2021. Local VOC (AT.1) was spreading till April 2021, but B.1.617.2 substituted all other lineages by June 2021. The IFR based on the excess deaths was equal to 1.04 (95% CI 0.80–1.31) for the adult population and 0.86% (95% CI 0.66–1.08) for the entire population.
Approximately one year after the COVID-19 pandemic about 45% of St. Petersburg, Russia residents contracted the SARS-CoV-2 infection. Combined with vaccination uptake of about 10% it was enough to slow the pandemic at the present level of all mitigation measures until the Delta VOC started to spread. Combination of several surveillance tools provides a comprehensive pandemic picture.
The COVID-19 pandemic in Russia has already resulted in 500,000 excess deaths [
Official case count and serological studies are not the only methods for SARS-CoV-2 surveillance. Cause-specific COVID-19 mortality is another valuable statistic to assess the pandemic impact. However, it may be biased in different healthcare settings, especially when definitions for COVID-19 death are not comparable. Using excess mortality, i.e. quantifying deaths from all causes relative to a recent historical benchmark, can help avoid this bias [
Novel auxiliary surveillance methods like search term trends to monitor the COVID-19 pandemic and mobility trends to monitor the effects of mitigation measures and population behaviour can also be helpful [
This study summarises the four consecutive rounds of population-based serological study based on two representative samples of adults residing in St. Petersburg, Russia, between May 2020 and April 2021. In addition, we combine the seroprevalence estimates with all other available surveillance data: official case count, hospitalisation data, SARS-CoV-2 VOCs monitoring data, COVID-19 specific mortality, excess mortality, vaccination uptake, mobility trends, and search term trends. Thus, we aim to assess whether different surveillance tools gave consistent insights for the course of the epidemics in the fourth largest European city with more than 5 million residents.
St. Petersburg serological study settings and design are described in detail in our previous report [
During the four surveys, we assessed anti-SARS-CoV-2 antibodies using three different assays. Even though our report was selected among studies of higher quality in the recent systematic review, a significant limitation was related to the absence of own test performance validation [
We summarised the data that included the official registered cases statistics, the number of patients hospitalised, the number of COVID-19 deaths, excess deaths, and tests performed for COVID-19 detection. Although this information was not available from one source, we used a combination of different sources to restore the pandemic course in St. Petersburg. We have also used the leading Russian search engine Yandex search history in St. Petersburg region to obtain search trends for three terms: “loss of smell”, “smell”, and “saturation”. In addition, Yandex provided mobility trends for St. Petersburg from the open data from Yandex, Apple, and Otomono (
We used the information on the official COVID-19 mortality and derived excess mortality to estimate the IFR. IFR was calculated for the four periods covered by our seroprevalence surveys. We treated the true number of deaths as an interval censored random variable bound downwards/upwards by the number of deaths 14 days after the cross-section start/end date (see
The sample size calculations and statistical analysis plan for the serological survey were described in detail in our previous report [
The Research Planning Board approved the study of the European University at St. Petersburg (on May 20, 2020) and the Ethics Committee of the Clinic “Scandinavia” (on May 26, 2020). All research was performed following the relevant guidelines and regulations. Eligible individuals were adults (older than 18 years). Written informed consent was obtained from all participants of the seroprevalence study. The study was registered with the following identifiers: Clinicaltrials.gov (NCT04406038, submitted on May 26, 2020, date of registration—May 28, 2020) and ISRCTN registry (ISRCTN11060415, submitted on May 26, 2020, date of registration—May 28, 2020). Official statistics, VOCs monitoring data, search terms trends, and mobility trends were obtained from open sources as aggregated data. Analysis based on open-source aggregated data does not require additional ethical permission in Russia.
All analyses were conducted in
The resulting 14,118 individuals responded to CATI questionnaire—6,400 in the first population sampling and 7,718 in the second (see
The adjusted seroprevalence in May–June 2020 was 9.7% (95%: 7.7–11.7) and increased to 13.3% (95% 9.9–16.6) in July–August 2020. We noticed a major increase through the third (22.9% 95%: 20.3–25.5) and between the third and fourth cross-sections of the seroprevalence study (see
Serosurvey cross-section | Seroprevalence estimate | |||
---|---|---|---|---|
Naïve | Adjusted for non-response | Adjusted for non-response and test characteristics | ||
(May 25, 2020—June 28, 2020) | 5951 / 988 | 10.6 (8.7–12.5) | 8.9 (7.1–10.8) | 9.7 (7.7–11.7) |
2 (July 20, 2020—August 8, 2020) | 5951 / 474 | 15.2 (12.0–18.4) | 12.2 (9.1–15.3) | 13.3 (9.9–16.6) |
3 (October 12, 2020—December 6, 2020) | 7110 / 1322 | 23.2 (20.9–25.5) | 21.0 (18.7–23.4) | 22.9 (20.3–25.5) |
4 (February 15, 2021—April 4, 2021) | 13412 / 1140 | 53.2 (50.3–56.1) | 40.4 (36.5–44.2) | 43.9 (39.7–48.0) |
Seroprevalence estimates adjusted through raking weights were similar and seroprevalence by different subgroups are available in
The SARS-CoV-2 antibodies test results trajectories showed that most individuals remained seropositive with a maximum follow-up of 10 months (
Grey lines are individual trajectories of study participants who tested positive at least once, excluding the 2020-07-20—2020-08-08 cross-section. Solid blue line is the loess smoother, blue areas report its 95% CI.
The number of cases officially registered in the spring 2020 was much lower than in the autumn and the winter 2020–2021. Number of SARS-CoV-2 tests reached its maximum in the winter 2020–2021 in contrast to a relatively low number of tests reported in the spring 2020. Official case statistics contrast the number of hospitalisations, official deaths, and excess deaths reported in the spring 2020. The official number of cases, the number of hospitalisation and deaths from COVID-19 never reached zero between and after the pandemic waves. The number of COVID-19 deaths and excess deaths from all causes peaked in both periods and was in line with hospitalisation dynamics (
(A) Weekly data of officially registered cases, tests performed, hospitalised cases, COVID-19 deaths, interpolated excess deaths (from monthly data), search trends, urban activity, and vaccination uptake combined with seroprevalence estimates; (B) Monthly data on SARS-CoV-2 variants monitoring during April-June 2020–2021.
Internet-based search terms trends were in line with pandemic dynamics. They reflected the changes in hospitalisation and death count better than the official case count, especially during the spring wave (
The SARS-CoV-2 circulating lineages diversity in 2020 was low. All samples from this period were attributed to the B.1 lineage and its sublineages. By autumn 2020 the number of PANGO lineages gradually increased with two Russian endemic—the B.1.397 and B.1.317. The Alpha VOC (B.1.1.7) was first detected in February 2021. The number of B.1.1.7 cases did not increase steeply but showed a gradual increase by April 2021. In February 2021, another lineage—AT.1, that has probably emerged in St. Petersburg was detected. The AT.1 was spreading rather quickly till April 2021, when B.1.617.2 (the Delta VOC) was first detected and substituted all other lineages by June 2021 (
Using excess deaths data, the IFR was equal to 1.04 (95% CI 0.80–1.31) for the adult population for the whole pandemic period. IFR based on the official COVID-19 deaths counts was lower and amounted to 0.43% (95% CI 0.11–0.82). When we considered the entire population of the city rather than the adult population for IFR, we obtained the estimate of 0.86% (95% CI 0.66–1.08) based on the excess deaths data. Full results for IFR are reported in
Our study is the first comprehensive attempt to characterise the pandemic dynamics in the fourth largest European metropolitan area. We used all available sources for surveillance, including population-based seroprevalence study, the monitoring of SARS-CoV-2 VOCs, data on registered cases and deaths, relevant search term trends and city activity. Combining this data provides an overall global picture how the pandemic evolved through 2020 and 2021 in St. Petersburg. In April 2021, approximately one year after COVID-19, we estimated that about 45% contracted the SARS-CoV-2 infection in St. Petersburg, roughly 2.2 mln residents. Together with more than 10% vaccination uptake to that moment, less than 45% susceptibles were there in the population. Nevertheless, it was enough to avoid a new pandemic wave in the absence of mitigation measures till the spread of the Delta VOC (B.1.617.2) at the end of May 2021.
The first year of the COVID-19 pandemic in St. Petersburg can be characterised by two waves of similar intensity but different lengths. In the spring 2020, the first pandemic wave resulted in unprecedented mitigation measures and population reaction that helped flatten the pandemic curve and preserve the healthcare system functionality. As a result, the daily registered number of cases have plateaued in summer. It helped reorganise the hospital capacities in St. Petersburg and prepare for the subsequent increase in the case count. The hospitals had to experience the entire load in autumn and winter, although many additional beds were allocated to COVID-19 patients. The number of daily cases plateaued during the winter holidays went down to 700–900 officially registered cases per day in the spring 2020. The halt of most mitigation measures has not resulted in the subsequent pandemic wave until the Delta VOC started to spread rapidly in May 2021. The new summer 2021 pandemic wave is yet to be analysed.
Possibly the number of individuals who already have antibodies to SARS-CoV-2, which was reported to be a strong protection marker from reinfection [
One of the surprising findings, which other studies reproduce [
Internet search term trends were quite reliably reflecting the pandemic’s progress and predicted the increase in the number of hospitalisation and deaths for both waves in St. Petersburg. However, the Internet search term trends to monitor pandemics should be considered with caution [
More than 20,000 excess deaths have already been reported in St. Petersburg during the pandemic year [
We continue to monitor the pandemic in St. Petersburg using all available sources and plan to run the following survey to estimate the number of individuals with antibodies to SARS-CoV-2 after the summer wave. In addition, we aim to detect the herd immunity threshold in St. Petersburg if any exists given the Delta VOC basic reproductive number and diminished vaccine effectiveness [
Several possible limitations of our serological survey may require further explanation. Small sample size and high non-response rate compared to the number of phone numbers generated pose a challenge in two cases. First, when the obtained sample is small enough to make the study underpowered. Our sample size calculations show that under the 50% hypothetical prevalence scenario, our sampling error does not exceed 3% [
In conclusion, our study provided an overall description of SARS-CoV-2 pandemic progression in the fourth largest European city—St. Petersburg, Russia, using all available surveillance sources, including a population-based serological study to assess the prevalence of antibodies to SARS-CoV-2. More than a half of the city’s population had antibodies to the new coronavirus by April 2021, most of them due to prior infection. That was enough to control the SARS-CoV-2 at the present level of the mitigation measures only until the Delta VOC universal spread. When compared against the number of overall excess deaths, our seroprevalence estimates align with the IFR of 0.86%. Furthermore, the combination of different surveillance sources, including internet search term trends, provide a clear picture of the course of the SARS-CoV-2 pandemic in St. Petersburg.
(PDF)
We acknowledge personal support from Vitaly Nesis. We thank Alla Samoletova (European University at St. Petersburg) for administrative support and management of the study, Yulia Stepantsova (Chursina) for coordinating phone-based interviews, Lizaveta Dubovik, and Irina Shubina for the science communication. We thank the interviewers, nurses, general practitioners, and the Clinic “Scandinavia” personnel. We also thank all study participants.
This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.
PONE-D-21-27726COVID-19 pandemic in Saint Petersburg, Russia: combining population-based serological study and surveillance dataPLOS ONE
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In particular, please address Reviewer 1's request to report the prevalence of different genotypes in Russia. Please also address all the comments by Reviewer 2, especially the one on the low participation rate for blood sample collection, and whether this is related to the high seroprevalence estimated for May-June 2020.
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Polymetal International plc funded the serological study. The main funder had no role in study design, data collection, data analysis, data interpretation, writing of the report or decision to submit the publication. The European University at St. Petersburg, clinic "Scandinavia", Smorodintsev Research Institute of Influenza and Genetico had access to the study data. The European University at St. Petersburg had final responsibility for the decision to submit for publication. Part of this study performed at Smorodintsev Research Institute of Influenza was funded by the Russian Ministry of Science and Higher Education as part of the Worldclass Research Center program: Advanced Digital Technologies (contract No. 075152020904, dated 16.11.2020).
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Polymetal International plc funded the serological study. The main funder had no role in study design, data collection, data analysis, data interpretation, writing of the report or decision to submit the publication. The European University at St. Petersburg,
clinic “Scandinavia”, Smorodintsev Research Institute of Influenza and Genetico had access to the study data. The European
University at St. Petersburg had final responsibility for the decision to submit for publication. Part of this study performed at
Smorodintsev Research Institute of Influenza was funded by the Russian Ministry of Science and Higher Education as part of
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the World class Research Center program: Advanced Digital Technologies (contract No. 075152020904, dated 16.11.2020)
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Polymetal International plc funded the serological study. The main funder had no role in study design, data collection, data analysis, data interpretation, writing of the report or decision to submit the publication. The European University at St. Petersburg, clinic "Scandinavia", Smorodintsev Research Institute of Influenza and Genetico had access to the study data. The European University at St. Petersburg had final responsibility for the decision to submit for publication. Part of this study performed at Smorodintsev Research Institute of Influenza was funded by the Russian Ministry of Science and Higher Education as part of the Worldclass Research Center program: Advanced Digital Technologies (contract No. 075152020904, dated 16.11.2020).
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Anton Barchuk reports personal fees from AstraZeneca, MSD, and Biocad outside the submitted work. Artur Isaev, Ekaterina Pomerantseva and Svetlana Zhikrivetskaya report a pending patent for the test system (ELISA) for detecting antibodies specific to the SARS-COV-2 in a biological sample. Other authors have no conflict of interest to declare. Other authors have no conflict of interest to declare.
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Reviewer #1: This work on the prevalence of COVID-19 in Saint Petersburg is very interesting. The conclusions are probable; the authors conclude that before the epidemic episode, due to the Delta variant, 50% of the population was already infected. It would be interesting to have a table that refers to seroprevalences obtained in other countries because this prevalence seems to me to be extremely high, as well as the ISR in relation to the whole population. One of the interesting things that would probably be worth discussing is the role of hand washing at the beginning of the epidemic in the seroprevalence. The subjects who started to wash their hands more often at the beginning of the epidemic have a lower seroprevalence which is seen in the second study but which disappears in the third. It would have been important to know if there was here, as in other places (this is something I have observed) a decrease in the precautionary gestures of hand washing or the use of hydro-alcoholic solutions as the epidemic progressed. It would be interesting to report, based on the Gisaids analysis, the prevalence of the different genotypes in Russia, in general, and more specifically in Saint Petersburg, using the standardized relative incidence of SARS-CoV-02 variant data.
Reviewer #2: Thank you for the opportunity to revise this manuscript. Please find below comments and suggestions thay I hope may help improve the manuscript:
1) The manuscript is understandable, but the quality of the English can be improved (there are several typos and a few sentences are not correct).
2) The Authors state they wanted to "assess the different surveillance tools validity", but in fact, validity was not formally assessed or quantified. What was done was to see whether the different surveillance tools gave consistent insights for the course of the epidemics. So that sentence should be rephrased.
3) Figure 1: what does "(incl. tested with other tests)" mean? Is that explained anywhere in the text? The manuscript is quite long and dense, and I may have missed it, but I recommend explain it in detail.
4) The main limitation is the very low participation rate (over 112,000 invited, and only 1,182 had blood samples on all occasions), implying that the potential for selection bias is just huge. I understand that the Authors did their best to account for non-respondency (this is explained in the Methods and again mentioned in the Discussion), but when around 1% of invited people complete the study, it would be unfair to say that all is fine. This should be acknowledged more clearly in the Discussion, for instance by detailing how the results could be affected (e.g. if acceptance was linked to one suspect to having been infected because of risky contacts etc.).
5) The seroprevalence looks very high (especially on the earliest time points), and I suspect that the selection bias may have been played a major role. A seroprevalence equal to 9.7% in May-June 2020 is hardly credible. How does it compare, for instance, with data in Northern Italy, where COVID first started to circulate in Europe, or to data regarding healthcare personnel, who were highly exposed because of their job?
6) I doubt that 45% infected and 10% vaccinated (i.e. 55% immune) would be enough to establish herd immunity and stop circulation by itself. It is true that pre-Delta variants were not particularly contagious, but even with a reproductive number equal to 2, you would need over 65% immune to reach some herd immunity. I suppose some other factor was at play (mitigation measures, individual protection through masks, less survival of the virus because based on climatic factors, ...). Please elaborate on this.
7) With 43.9% seropositivity and the population of Saint Petersburg, and given around 1% of infection fatality ratio, I would expect more deaths than reported (and mentioned by the Authors), closer to 25.000 than 20.000.
8) Internet search trends look to predict well the start of the epidemic wave, but definitely not its duration and, therefore, the overall burden of it. This should be acknowledged.
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PONE-D-21-27726 Response to Reviewers
COVID-19 pandemic in Saint Petersburg, Russia: combining surveillance and population-based serological study data in May 2020–April 2021
Anton Barchuk, Dmitriy Skougarevskiy, Alexei Kouprianov, Daniil Shirokov, Olga
Dudkina, Rustam Tursun-zade, Mariia Sergeeva, Varvara Tychkova, Andrey Komissarov,
Alena Zheltukhina, Dmitry Lioznov, Artur Isaev, Ekaterina Pomerantseva, Svetlana
Zhikrivetskaya, Yana Sofronova, Konstantin Blagodatskikh, Kirill Titaev, Lubov Barabanova,
and Daria Danilenko
Response to reviewers:
We would like to thank reviewers for the constructive and insightful comments on the manuscript. We have addressed all the raised issues and provided our itemized responses below.
Reviewer #1:
This work on the prevalence of COVID-19 in Saint Petersburg is very interesting. The conclusions are probable; the authors conclude that before the epidemic episode, due to the Delta variant, 50% of the population was already infected. It would be interesting to have a table that refers to seroprevalences obtained in other countries because this prevalence seems to me to be extremely high, as well as the ISR in relation to the whole population.
Reply: We would like to point out that the seroprevalence between February 15, 2021 – April 4, 2021, was 43.9 (39.7–48.0) among the adult population of Saint-Petersburg. We decided to avoid systematic comparisons with other countries as the mitigation measures, the basic pattern of contacts, and vaccination rates were different. Therefore, they cannot be easily accounted for in such a comparison. The quality of seroprevalence reports was also heterogeneous, as suggested by the systematic review published in The Lancet Global Health (Chen et al., 2021). For example, the seroprevalence in Manaus, Brazil, by October 2020 was 76%, but it is likely to be biased upwards (Sabino et al., 2021, Lancet). At the same time, seroprevalence in Geneva, Switzerland on 1 June–7 July 2021 was about 66%, but only about 30% of study participants had antibodies of infection origin (Stringhini et al., 2021, Eurosurveillance). The control measures in St. Petersburg were enforced less strictly than in Geneva. At the same time, vaccination rates were lower, and the autumn 2020 winter 2021 wave caused more hospitalization and deaths in Petersburg than in Geneva.
Text added: “Seroprevalence after the first wave in St. Petersburg was similar to other European cities, e.g., Geneva, Switzerland. However, subsequent epidemic waves caused more hospitalization and deaths in St. Petersburg. Seroprevalence in Geneva, Switzerland, measured on 1 June–7 July 2021, was about 66%, but only about 30% of study participants had antibodies of infection origin.”
One of the interesting things that would probably be worth discussing is the role of handwashing at the beginning of the epidemic in the seroprevalence. The subjects who started to wash their hands more often at the beginning of the epidemic have a lower seroprevalence which is seen in the second study but disappears in the third. It would have been important to know if there was here, as in other places (this is something I have observed) a decrease in the precautionary gestures of hand washing or the use of hydro-alcoholic solutions as the epidemic progressed.
Reply: It is an excellent discussion point, but unfortunately, the nature of our study (cross-sectional) doesn’t allow any conclusion on the causal nature of handwashing and the risk of infection. We will add this point to our discussion part.
Text added: There are other characteristics associated with seroprevalence, but the nature of our crosssectional study does not allow any causal conclusions.
It would be interesting to report, based on the Gisaids analysis, the prevalence of the different genotypes in Russia, in general, and more specifically in Saint Petersburg, using the standardized relative incidence of SARS-CoV-02 variant data.
Reply: Unfortunately, the information on different genotypes in Russia is limited due to a small number of sequences performed compared to other countries. However, It is worth mentioning that our report is authored by collaborators from Smorodintsev Research Institute of Influenza in St. Petersburg, Russia. They provided the majority of sequences reports from Russia to the Gisaid database, and we used this information in Figure 1B. For example, out of 408 B.1.1.7+Q sequences from Russia, 115 were from St. Petersburg. In addition, we have added a reference to the paper that describes the rise and spread of the SARS-CoV-2 AY.122 lineage in Russia (Klink et al. 2021).
Reference added: Klink GV, Safina K, Nabieva E, Shvyrev N, Garushyants S, Alekseeva E, Komissarov AB, Danilenko DM, Pochtovyi AA, Divisenko EV, Vasilchenko LA. The rise and spread of the SARS-CoV-2 AY. 122 lineage in Russia. medRxiv. 2021 Dec 5.
Reviewer #2:
The manuscript is understandable, but the quality of the English can be improved (there are several typos and a few sentences that are not correct).
Reply: We went through the manuscript one more time to correct errors and typos as suggested by the reviewer.
The Authors state they wanted to "assess the different surveillance tools validity", but in fact, validity was not formally assessed or quantified. What was done was to see whether the different surveillance tools gave consistent insights for the course of the epidemics. So that sentence should be rephrased.
Reply: We rephrased the sentence as suggested by the reviewer.
Text added: Thus, we aim to assess whether different surveillance tools gave consistent insights for the course of the epidemics in the fourth largest European city with more than 5 million residents.
Figure 1: what does "(incl. tested with other tests)" mean? Is that explained anywhere in the text? The manuscript is quite long and dense, and I may have missed it, but I recommend explain it in detail.
Reply: By “incl. tested with other tests,” we meant that we included individuals who were tested using antibody tests other than the one we used to calculate seroprevalence (we used several antibody tests to ensure consistent and internally valid results). We agree that this information is confusing and the final number of participants is clear from the flowchart, so we deleted this text.
The main limitation is the very low participation rate (over 112,000 invited, and only 1,182 had blood samples on all occasions), implying that the potential for selection bias is just huge. I understand that the Authors did their best to account for non-responding (this is explained in the Methods and again mentioned in the Discussion), but when around 1% of invited people complete the study, it would be unfair to say that all is fine. This should be acknowledged more clearly in the Discussion, for instance by detailing how the results could be affected (e.g. if acceptance was linked to one suspect to having been infected because of risky contacts, etc.).
Reply: We would like to point out that about 15,000 individuals were invited to the blood test. 112,000 is the total number of mobile phone numbers generated using the RDD (random digit dial) electronic system, not the number of invitations. RDD is a reliable method for conducting sociological surveys. To ensure our survey sample is not biased, we compared it to the 2016 round of the comprehensive monitoring of living conditions household survey by Russia’s Federal State Statistics Service.
The second step is an invitation to the blood sample, which around 2,300 participants accepted, and selection bias at this stage is inevitable. To understand the direction of non-response bias in our data, we estimated a binomial probit regression of individual agreement to participate in the study and offer their blood sample on observable characteristics. Our previous report described this study design in detail (Barchuk et al., 2021, Scientific Reports). Unfortunately, we did not have enough space to describe it in detail in this report. Nevertheless, some of the participant’s characteristics were linked both to an agreement to participate and to infection risk, and we took this into account in our study design and analysis. The analysis is described in detail in the supplementary material to our previous report (Barchuk et al., 2021, Scientific Reports).
Text added: Our previous report rigorously addressed possible selection related to
a low response rate in the serosurvey]. As a result, that report was selected among a few high-quality seroprevalence studies in the systematic review that addressed the quality of COVID-19 seroprevalence research [9].
The seroprevalence looks very high (especially on the earliest time points), and I suspect that the selection bias may have been played a major role. A seroprevalence equal to 9.7% in May-June 2020 is hardly credible. How does it compare, for instance, with data in Northern Italy, where COVID first started to circulate in Europe, or to data regarding healthcare personnel, who were highly exposed because of their job?
Reply: Seroprevalence equal to 9.7% in May-June 2020 in St. Petersburg is comparable to seroprevalence more than 10% in Madrid in April-May, 2020 (Pollan et al., 2020, Lancet) and to seroprevalence between 7 and 10% in Geneva, Switzerland in April-May 2020 (Stringhini et al., 2020, Lancet).
Text added: “Seroprevalence after the first wave in St. Petersburg was similar to other European cities, e.g., Geneva, Switzerland. However, subsequent epidemic waves caused more hospitalization and deaths in St. Petersburg. Seroprevalence in Geneva, Switzerland, measured on 1 June–7 July 2021, was about 66%, but only about 30% of study participants had antibodies of infection origin.”
I doubt that 45% infected and 10% vaccinated (i.e., 55% immune) would be enough to establish herd immunity and stop circulation by itself. Indeed, pre-Delta variants were not particularly contagious, but even with a reproductive number equal to 2, you would need over 65% immune to reach some herd immunity. I suppose some other factor was at play (mitigation measures, individual protection through masks, less survival of the virus based on climatic factors, ...). Please elaborate on this.
Reply: We agree that the statement about herd immunity is inaccurate. We need to mention that it’s the combination of population immunity and some level of mitigation measures, individual protection, and continued self-isolation in some social groups.
Text added: “Approximately one year after the COVID-19 pandemic about 45\\% of St.~Petersburg, Russia residents contracted the SARS-CoV-2 infection. Combined with vaccination uptake of about 10\\% it was enough to slow the pandemic at the present level of the mitigation measures until the Delta VOC started to spread.”
“That was enough to control the SARS-CoV-2 {without} at the present level of the mitigation measures only until the Delta VOC universal spread.”
With 43.9% seropositivity and the population of Saint Petersburg, and given around 1% of infection fatality ratio, I would expect more deaths than reported (and mentioned by the Authors), closer to 25.000 than 20.000.
Reply: We infer IFR based on the number of deaths (excess mortality or official statistics) and individuals infected (our seroprevalence estimates). First, we would like to highlight that we are only using information from the adult population, and an IFR of 1% is related to the adult population. Therefore, it is 0.86% for all citizens, as mentioned in Table S2.
Internet search trends look to predict well the start of the epidemic wave, but definitely not its duration and, therefore, the overall burden of it. This should be acknowledged.
Reply: We agree with this point and added this to the discussion section.
Text added: It should also be acknowledged that internet search trends may predict the start of the epidemic wave well, but not its duration or overall burden.
References
Barchuk A, Skougarevskiy D, Titaev K, Shirokov D, Raskina Y, Novkunkskaya A, Talantov P, Isaev A, Pomerantseva E, Zhikrivetskaya S, Barabanova L. Seroprevalence of SARS-CoV-2 antibodies in Saint Petersburg, Russia: a population-based study. Scientific reports. 2021 Jun 21;11(1):1-9.
Chen X, Chen Z, Azman AS, Deng X, Sun R, Zhao Z, Zheng N, Chen X, Lu W, Zhuang T, Yang J. Serological evidence of human infection with SARS-CoV-2: a systematic review and meta-analysis. The Lancet Global Health. 2021 May 1;9(5):e598-609.
Klink GV, Safina K, Nabieva E, Shvyrev N, Garushyants S, Alekseeva E, Komissarov AB, Danilenko DM, Pochtovyi AA, Divisenko EV, Vasilchenko LA. The rise and spread of the SARS-CoV-2 AY. 122 lineage in Russia. medRxiv. 2021 Dec 5.
Pollán M, Pérez-Gómez B, Pastor-Barriuso R, Oteo J, Hernán MA, Pérez-Olmeda M, Sanmartín JL, Fernández-García A, Cruz I, de Larrea NF, Molina M. Prevalence of SARS-CoV-2 in Spain (ENE-COVID): a nationwide, population-based seroepidemiological study. The Lancet. 2020 Aug 22;396(10250):535-44.
Sabino EC, Buss LF, Carvalho MP, Prete CA, Crispim MA, Fraiji NA, Pereira RH, Parag KV, da Silva Peixoto P, Kraemer MU, Oikawa MK. Resurgence of COVID-19 in Manaus, Brazil, despite high seroprevalence. The Lancet. 2021 Feb 6;397(10273):452-5.
Stringhini S, Wisniak A, Piumatti G, Azman AS, Lauer SA, Baysson H, De Ridder D, Petrovic D, Schrempft S, Marcus K, Yerly S. Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study. The Lancet. 2020 Aug 1;396(10247):313-9.
Stringhini S, Zaballa ME, Pullen N, Perez-Saez J, de Mestral C, Loizeau AJ, Lamour J, Pennacchio F, Wisniak A, Dumont R, Baysson H. Seroprevalence of anti-SARS-CoV-2 antibodies 6 months into the vaccination campaign in Geneva, Switzerland, 1 June to 7 July 2021. Eurosurveillance. 2021 Oct 28;26(43):2100830.
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COVID-19 pandemic in Saint Petersburg, Russia: combining population-based serological study and surveillance data
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COVID-19 pandemic in Saint Petersburg, Russia: combining population-based serological study and surveillance data
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