Prevalence of ARVI, influenza, and COVID-19 pathogens in individuals without symptoms of respiratory infection

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Introduction

The data collected over the year, starting from the COVID-19 outbreak that took place in China at the end of December 2019 and throughout its transition into the pandemic demonstrate that the pathogen can be transmitted by SARS-CoV-2 infected individuals without or with mild symptoms of acute respiratory infection (ARI). The results of meta-analyses [1][2] show that asymptomatic COVID-19 cases can account for 40–45%. In the meantime, these data were generally obtained from workers of healthcare facilities and restricted-access groups; therefore, they do not reflect the prevalence of asymptomatic infection in a population.

The rapid global spread of SARS-CoV-2 made it clear that extensive testing of population was of crucial significance for projection, effective implementation and correction of anti-epidemic measures. Nucleic acid amplification techniques take center stage in laboratory diagnostics of COVID-19 and in detection of its pathogen.

Based on the aforesaid, the area of special interest is analysis of prevalence of the COVID-19 pathogen among the healthy population and comparison with other pathogens of acute respiratory virus infection (ARVI), and influenza.

SARS-CoV-2 is primarily transmitted through respiratory droplets and contact routes, as the aerosols and droplets resulting from cough and sneezing settle down on objects and surfaces surrounding the infected person [3]. SARS-CoV-2 can replicate in cells of the gastrointestinal tract [4], therefore, the virus can be transmitted through a fecal-oral route.

Using of personal protective equipment (PPE), including medical masks, by both infected patients and healthy people is among the measures of nonspecific prevention of ARVI¹.

Until recently, no broad-scale studies have been conducted in Russia to assess the effectiveness of using medical masks and other PPE in a population to prevent ARVI, influenza, and COVID-19.

This study was intended to:

• measure and characterize the prevalence of ARVI, influenza and COVID-19 pathogens among people without ARI symptoms in different age groups during the inter-epidemic period and at the beginning of a typical ARVI epidemic season by using nucleic acid amplification techniques;
• trace changes in the epidemic situation through weekly monitoring of the prevalence of ARVI, influenza, and COVID-19 pathogens among people without ARI symptoms;
• assess the effectiveness of PPE for the purpose of prevention of COVID-19, ARVI, and influenza.

Materials and methods

The study was conducted as part of the pilot project of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing and was focused on weekly analysis of the prevalence dynamics for ARVI, influenza, and COVID-19 pathogens during the inter-epidemic period and at the beginning of the typical ARVI epidemic season from 1/8/2020 to 16/10/2020 in 26 regions of Russia.

The study participants were individuals who did not have any ARI symptoms within the last 2 weeks and at the time of the testing, and who signed their informed consent to participation in the study. The exclusion criteria were applicable to those who had visited other countries during the last 2 weeks as well as to restricted-access groups (military personnel, workers of long-term care facilities).

The laboratory tests were conducted by using the PCR method with real-time detection. To detect nucleic acids of 17 types of respiratory viruses: rhinovirus RNA, adenovirus DNA, RNA of human coronaviruses (229E, OC43, HKUI, NL63), bocavirus DNA, respiratory syncytial virus RNA, metapneumovirus RNA, parainfluenza virus RNA, influenza virus RNA, SARSCoV-2 RNA, we used AmpliSens ARVI-screen-FL, AmpliSens Influenza virus A/B-FL, and AmpliSens Cov-Bat-FL reagent kits (The Central Research Institute of Epidemiology of Rospotrebnadzor). Nasopharyngeal and oropharyngeal swabs served as the biological material for the study; the swabs were collected in accordance with the methodological recommendations MR 3.1.0117-17 "Laboratory Diagnostics of Influenza and Other ARVI by Using the Polymerase Chain Reaction Method", MR 3.1.0169-20 "Laboratory Diagnostics of COVID-19"; the informed consent was signed by all the participants.

For statistical processing and visual presentation of data, we used PASW Statistics 18 (SPSS) and Microsoft Excel 2010.

Results

The study included 14,119 individuals (including 4,582 children, 9,532 adult individuals; no age information was available in 5 cases). For calculations and their analysis, the participants were divided into groups by age, social (schoolchildren and students) and professional criteria.

The age groups included:

• children under 6 years old (n = 2,116);
• children aged 6–17 years (n = 2,466);
• individuals aged 18–25 years (n = 2,786);
• individuals aged 26–64 years (n = 4,609);
• people aged 64 years and over (n = 2,137).

During the monitoring period, the target pathogens (combined) were detected in 1,572 (11.1%) participants. The absolute number and proportion of the positive results are shown in Table 1. The prevalence of SARS-CoV-2 as well as ARVI and influenza pathogens in age groups is presented in Table 2.

 

At the beginning of the epidemic season, the target pathogens (combined) were detected in 14.7% (689 out of 4,674) of the people and during the inter-epidemic period they were detected in 9.35% (883 out of 9,445) of the people (p < 0.001).

Most of the detected cases of infection (7.32%) were caused by the rhinovirus. The proportion of individuals with rhinovirus infection was quite high both during the inter-epidemic period and at the beginning of the epidemic season – 6.61 and 8.75% of the participants, respectively.

SARS-CoV-2 was detected much less frequently: During the observation period, the virus was detected in 1.66% of examined persons. Yet, in autumn 2020, a statistically significant increase (8.3 times) in the proportion of the detected cases of SARS-CoV-2 infection (from 0.49 to 4.02%; p < 0.001) was recorded among the participants (Table 1) as compared to the proportion in August 2020.

The proportion of the detected cases with rhinovirus infection at the beginning of the epidemic season increased only 1.3 times as compared to the proportion in the inter-epidemic period (Table 1).

Parainfluenza viruses were detected equally frequently both during the inter-epidemic period and at the beginning of the epidemic season (1.09 and 1.05%). The prevalence of the other ARVI pathogens and influenza A and B viruses did not exceed 1% (Table 1).

Differences were found in the SARS-CoV-2 and rhinovirus detection dynamics. The chart of weekly dynamics (Fig. 1) shows an increase in the number of cases of rhinovirus infection starting from the 35th week and its decrease from the 40^th week. During that time, the number of positive SARS-CoV-2 cases increased uniformly from the 35^th week to the 42^nd week (the increase over the 41^st and 42^nd weeks was 39 and 21%, respectively). The prevalence of other target pathogens did not exceed 1%; any spread was within the margin of error.

Our data on the dynamics of rhinovirus infection and SARS-CoV-2 prevalence among individuals without ARVI symptoms are in agreement with the data published in the National Weekly Bulletin issued by
the Smorodintsev Research Institute of Influenza of the Ministry of Health of Russia regarding the frequency of diagnosis of rhinovirus infection and COVID-19 in people with ARVI symptoms². Based on the data from the Smorodintsev Research Institute of Influenza, the proportion of positive cases of rhinovirus infection starts decreasing, while the SARS-CoV-2 detection rate starts increasing from the 39^th week.

When making prognoses for the dynamics of an epidemic process, the priority attention should be given to the proper sample of participants, first of all, to the size of the sample to be able to identify statistically significant differences in detection rates of a pathogen.

To calculate the size of a sample group of participants, we used the following equation [5]:

$n=\frac{Z^{2}pq}{{∆}^2}$

where:
n — the size of the sample required for the study; 
Z — the critical value for Student’s t-test at the respective level of significance (when α = 0.05 Z = 1.96);
p — the proportion of cases with the target sign in a population;
q — the proportion of cases without the target sign (100 – p) in a population;
∆ — the maximum permissible error.

During the first weeks of monitoring, we identified prevalence rates for ARVI, influenza, and COVID-19 pathogens, which were used in calculations of the sample size. The minimum size of the sample for pathogens with the prevalence exceeding 1% was at least 1,961 people. This size of the sample is sufficient for identification of statistically significant differences in variables when conducting monitoring focused on weekly dynamics. These sample sizes stayed almost unchanged till the end of the study.

The questions regarding what age groups were involved in the ARVI epidemic process and if there were any special features attributable to COVID-19 were of special interest. In other words, it was important to analyze changes in the detection of different pathogens of ARVI, influenza, and COVID-19 in people of different age.

During the monitoring period, the maximum proportion of detection of the target pathogens (combined) was recorded in the age group of 3-5 years (18.9%). The rhinovirus was detected in most of the cases; it was detected in 15.66% of participants in the age group of 3–5 years; then, in the decreasing order, there were the group of children under 2 years (11.5%) and the group of children aged 6–17 years (10.5%); in adult groups, rhinovirus was detected significantly less frequently (p < 0.001; Table 2).

On the contrary, SARS-CoV-2 was detected more frequently in adult participants over 26 years of age and more rarely in younger children (2.0% vs 0.95%) (p < 0.01). In the groups of participants aged 6–17 years and 18–25 years, SARS-CoV-2 was detected in 1.66 and 1.26% of participants, respectively.

The proportion of adenovirus-infected children under 2 years old was significantly higher than that of the older children (6–17 years): 1.69 and 0.08% (p < 0.05; Table 2).

There were also differences in the dynamics of detection of SARS-CoV-2 and rhinovirus in different age groups (Fig. 2, 3).

The 40^th week demonstrated an increase in SARSCoV-2 infection among the school-aged children (6–17 years), adult individuals aged 18–25 years and individuals aged over 64 years. A week later, we observed an increase in the proportion of infected adults aged 26–64; by the 42^nd week, this proportion reached maximum values exceeding 2 and 3 times the proportion of SARS-CoV-2 infected children aged 6–17 and individuals aged 18–25, respectively. In the group of preschool children (0–5 years), no significant increase in the SARS-CoV-2 infection frequency was found in autumn (Fig. 2).

As for rhinovirus, on the contrary, individuals aged 26–64 years and over, as compared to other age groups, demonstrated the minimum infection level throughout the monitoring period. At the same time, children and individuals aged 18–25 demonstrated a significant increase in the rhinovirus detection rate by the 40^th week and a decrease to the initial level by the 42^nd week (Fig. 3).

Discussion

Thus, the study found that in autumn 2020, the epidemic process of SARS-CoV-2 spread initially affected schoolchildren (6–17 years), young adults (18– 25 years), and elderly people (>64 years) followed by adult people (26–64 years). Preschool children were involved in the epidemic process of SARS-CoV-2 spread to a lesser extent.

The prevalence of pathogens among students was estimated separately. At the beginning of the epidemic season, the students were infected with the above pathogens more rarely than children in total (11.5 and 19.7%; p < 0.001) and schoolchildren (11.5 and 18.9%; p < 0.001). Rhinovirus infection was detected in the students more rarely than in the children and schoolchildren (7.3, 15.5 and 13.2%; p < 0.001). At the same time, SARS-CoV-2 was detected in all 3 groups almost with the same frequency: 2.3, 2.4, and 3.7%. Thus, the obtained data cannot characterize students as a group of particular risk for ARI, including COVID-19.

It was found that preschool children contributed most heavily to spread of ARVI, while they are involved in spread of SARS-CoV-2 to a lesser extent as compared to other groups of the population.

To assess the PPE effectiveness, we conducted a survey among the participants who were asked to answer the following questions:

• "Do you use PPE?";
• "What PPE do you use (choose from the list)?";
• "Did you have close contact with an ARI patient within the last 2 weeks?".

The close contact with an ARI patient within the last 2 weeks was confirmed by 443 people. The list of PPE and the number of people who used the above PPE are shown in Table 3.

 

The analysis of the effectiveness of PPE (medical masks, gloves, antiseptic handwashing, respirator, protective shield, and their combinations) found that among the individuals who used PPE, the number of the infected with the target pathogens (ARVI, influenza, and COVID-19) generally was significantly smaller than among those who did not use PPE (9.6% vs 18.0%; p < 0.001) (Table 4). Using of PPE decreased the risk of infection with the target pathogens by 52%: the odds ratio (OR) = 0.48; 95% confidence interval (CI) 0.43–0.55.

 

It was found that using of PPE reduced the risk of infection with SARS-CoV-2 by 53% (p < 0.001; OR = 0.47; 95% CI, 0.35–0.63) and with rhinovirus — by 50% (OR = 0.5; 95% CI, 0.43–0.58) (Table 4).

Using of medical masks reduced the likelihood of being infected with any of the target pathogens 1.9 times; the risk of being infected when using medical masks decreased by 51% (18.0% vs 9.6%; p < 0.001; OR = 0.49; 95%, CI 0.41–0.57). When only medical masks were used, the likelihood of being infected with SARS-CoV-2 decreased by 34% (OR = 0.66; 95% CI, 0.47–0.93); the risk of rhinovirus infection decreased by 51% (OR = 0.49; 95% CI, 0.41–0.59). Using medical masks together with other PPE reduced the risk of infection with the target pathogens by 54% (18.0% vs 9.2%; p < 0.001; OR = 0.46; 95% CI, 0.41–0.53). Among different combinations of using PPE, the most frequent one was the combination of medical masks and gloves. Using of gloves together with medical masks demonstrates an advantage as compared to medical masks alone (7.1 vs 9.6% of infected; p = 0.007). For the other PPE or their combinations, no statistically significant difference in the groups was found, though it can be explained by small numbers of the compared samples.

The obtained data are in agreement with the results of the meta-analysis [6]. Liang et al. found that medical masks had a significant protective effect against ARVI pathogens: the risk of infection reduced by 65% (OR = 0.35; 95% CI, 0.24–0.51).

The protective properties of medical masks used for prevention of COVID-19 were demonstrated by experiment. Ueki et al. simulated the process of airborne transmission of SARS-CoV-2: They placed two mannequin heads facing each other in a tightly sealed test chamber. The distance between the heads was 50 cm. One of the mannequin heads mimicked a SARSCoV-2 source (it exhaled a mist of virus suspension containing SARS-CoV-2); the other mannequin head mimicked a recipient. The presence and viral loads in the recipient mannequin was detected and measured by using the PCR method and virus isolation in the cell culture [7]. When a medical mask was attached to the recipient mannequin, the number of viral particles reduced by 50% as compared to the situation when the recipient had no medical mask. When a medical mask was attached to the mannequin head exhaling a mist of virus suspension, the number of viral particles in the recipient reduced by 60%. During the experiment, the medical mask was attached most tightly to the "face" of the mannequin head.

In our study, among the participants who confirmed their close contact with ARI patients within the last 2 weeks, 24% of the participants had one of the target pathogens, though no statistically significant differences in the level of infection between the PPE users and non-users were found (p = 0.06). These results demonstrate that in addition to PPE, all the anti-epidemic measures must be instituted in the focus of infection, the most crucial of them being isolation of the infected³. If the sick person cannot be isolated, healthy people should use a respirator to have more effective protection during the prolonged exposure⁴.

In our study, we kept a strong focus on prevention of ARVI and COVID-19, in particular, among people whose professional activities are associated with a high level of social contacts. This group included 2,552 participants: checkout cashiers/sales staff from chain grocery stores, public transport employees (cab-drivers, ticket checkers, and conductors), security and passport control personnel at airports. It was found that during the onset of the epidemic season, the individuals whose work is associated with a high level of social contacts became infected more rarely than representatives of the same age group: SARS-CoV-2 was detected in 3.4% and 6.8% (p = 0.001), rhinovirus was detected in 4.0% and 5.7% (p = 0,09), all the pathogens collectively – in 9.0% and 13.8%, respectively (p = 0.001). Most likely, the lower level of infection is connected with stricter adherence to the anti-epidemic measures by employees whose work is associated with a higher risk of infection.

The data on the viral RNA load in individuals with COVID-19, but without ARI symptoms were of special interest.

Based on our data and with reference to amplification threshold cycle values and their dispersion (Fig. 4), the RNA loads in the tested participants ranged widely from the limit of detection to ~1010 RNA copies per mL of a sample.

Thus, individuals with asymptomatic COVID-19 infection and having high concentrations of SARSCoV-2 can become a dangerous source of infection, especially when they do not wear medical masks, as even a short-term contact with them can trigger airborne transmission of the pathogen.

Conclusion

This study made it possible to measure the prevalence of ARVI, influenza and COVID-19 pathogens among individuals without ARI symptoms; its findings were instrumental in assessment of the effectiveness of medical masks in a population for prevention of the above infections.

Throughout the monitoring period, the above pathogens dominated by rhinovirus were detected in 11.1% of the participants; the SARS-CoV-2 RNA was detected in 1.66% of the participants; the other viruses accounted for maximum 1%. It should be remembered that among the detected individuals with infection, there could be "presymptomatic" individuals, i.e. those who could have ARI symptoms displayed later, since the design of the study did not imply any subsequent monitoring of the infected.

The study found differences in SARS-CoV-2 and ARVI frequencies in different age groups: At the beginning of the epidemic season, the spread of the COVID-19 pathogen involved school-aged children, young adults, and individuals over 64 years of age; adults aged 26–64 were the last to join. Preschool children participated in the epidemic process of SARS-CoV-2 spread to a lesser extent. Preschool children contribute most heavily to ARVI spread; among individuals over 26 years old, the level of rhinovirus infection throughout the monitoring period was the lowest. SARS-CoV-2, on the contrary, was detected more frequently in adults over 26 years old.

Our study has demonstrated that individuals without ARVI symptoms can have a high concentration of SARS-CoV-2 RNA (up to 10¹⁰ RNA copies per mL of a nasopharyngeal swab sample); therefore, they can be a dangerous source of infection, especially when they do not wear medical masks, as airborne transmission of the pathogen can occur even during a short-term contact with the above individuals.

It was found that among the people using PPE, the number of those who were infected with the above pathogens was significantly smaller than among those who did not use PPE (9.6% vs 18.0%; p < 0.001); using of medical masks reduced the risk of infection by 51% (OR = 0.49; 95% CI, 0.41–0.57). Medical masks combined with other PPE reduced the risk of infection with the above viruses by 54% (OR = 0.46; 95% CI, 0.41–0.53).

As for SARS-CoV-2, using of PPE reduced the risk of infection by 53% (OR = 0.47; 95% CI, 0.35–0.63); among individuals who used medical masks, the likelihood of being infected with SARS-CoV-2 decreased by 34% (OR = 0.66; 95% CI, 0.47–0.93).

Thus, wearing of medical masks in public places is a mandatory and effective anti-epidemic measure, as masks when worn by the infected, including individuals without ARI symptoms, reduce the virus spread, while masks worn by healthy people reduce the likelihood of their infection.

Healthy people should use a respirator for more effective protection during the long contact with a patient, for example, at a COVID-19 focal site.

It was found that individuals whose work is associated with a high level of social contacts became infected more rarely than other representatives of the same age group: At the beginning of the epidemic season, SARSCoV-2 was detected in 3.4% and 6.8% of the tested people, respectively (p = 0.001), thus proving the effectiveness of anti-epidemic measures and demonstrating the commitment to their adherence by people whose work is associated with a higher risk of infection.

About the authors

S. B. Yatsyshina

Central Research Institute for Epidemiology

Author for correspondence.
Email: svetlana.yatsyshina@pcr.ms
ORCID iD: 0000-0003-4737-941X

Svetlana B. Yatsyshina — Cand. Sci. (Biol.), Head, Scientific group on the development of new diagnostic methods of ARI diagnostics.

Moscow

Россия

M. V. Mamoshina

Central Research Institute for Epidemiology

Email: fake@neicon.ru
ORCID iD: 0000-0002-1419-7807

Marina V. Mamoshina — junior researcher, Scientific group on the development of new diagnostic methods of ARI diagnostics.

Moscow

Россия

M. A. Elkina

Central Research Institute for Epidemiology

Email: fake@neicon.ru
ORCID iD: 0000-0003-4769-6781

Mariya A. Elkina — junior researcher, Scientific group on the de-velopment of new diagnostic methods of ARI diagnostics.

Moscow

Россия

G. V. Sharukho

Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing

Email: fake@neicon.ru
ORCID iD: 0000-0003-0772-8224

Galina V. Sharukho — D. Sci (Med.), Head, Departmen of the Fe-deral Service for Surveillance on Consumer Rights Protection and Human Wellbeing for Tyumen Region.

Tyumen

Россия

Yu. I. Raspopova

Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing

Email: fake@neicon.ru
ORCID iD: 0000-0002-5754-6755

Yulia I. Raspopova — Deputy head, Department of the Federal Ser-vice for Supervision of Consumer Rights Protection and Human Wel-fare in the Tyumen Region.

Tyumen

Россия

А. Ya. Folmer

Department of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing for Tyumen Region

Email: fake@neicon.ru
ORCID iD: 0000-0001-8323-6470

Aleksandr Ya. Folmer — Cand. Sci. (Med.), Chief physician, Center for Hygiene and Epidemiology in the Tyumen Region.

Tyumen

Россия

K. A. Agapov

Center of Hygiene and Epidemiology in Saint Petersburg

Email: fake@neicon.ru
ORCID iD: 0000-0002-8185-3624

Konstantin A. Agapov — Head, Laboratory of particularly dangerous and virological studies.

Saint Petersburg

Россия

I. M. Vladimirov

Center of Hygiene and Epidemiology in Saint Petersburg

Email: fake@neicon.ru
ORCID iD: 0000-0001-7030-1552

Ivan M. Vladimirov — epidemiologist.

Saint Petersburg

Россия

O. V. Zubareva

Department of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing for Volgograd region

Email: fake@neicon.ru
ORCID iD: 0000-0001-6863-0701

Olga V. Zubareva — Head.

Volgograd

Россия

I. S. Novikova

Department of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing for Volgograd region

Email: fake@neicon.ru
ORCID iD: 0000-0003-0718-0641

Irina S. Novikova — main specialist-expert of Epidemiological sur-veillance department.

Volgograd

Россия

O. B. Bondareva

Department of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing for Volgograd region

Email: fake@neicon.ru
ORCID iD: 0000-0002-7711-7608

Olga B. Bondareva — Head, Epidemiological surveillance depart-ment.

Volgograd

Россия

V. A. Gil

Department of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing for Volgograd region

Email: fake@neicon.ru
ORCID iD: 0000-0002-5691-0471

Valeria A. Gil — specialist-expert of epidemiological surveillance de-partment.

Volgograd

Россия

D. N. Kozlovskikh

Department of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing for Sverdlovsk Region

Email: fake@neicon.ru
ORCID iD: 0000-0003-0360-7695

Dmitry N. Kozlovskikh — Head.

Yekaterinburg

Россия

S. V. Romanov

Center of Hygiene and Epidemiology in the Sverdlovsk Region

Email: fake@neicon.ru
ORCID iD: 0000-0001-7357-9496

Sergey V. Romanov — Deputy chief physician.

Yekaterinburg

Россия

O. V. Dikonskaya

Department of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing for Sverdlovsk Region

Email: fake@neicon.ru
ORCID iD: 0000-0002-2249-4748

Olga V. Dikonskaya — Deputy Head.

Yekaterinburg

Россия

A. V. Ponomareva

Department of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing for Sverdlovsk Region

Email: fake@neicon.ru
ORCID iD: 0000-0002-5236-3458

Anzhelika V. Ponomareva — Deputy Head.

Yekaterinburg

Россия

I. V. Chistyakova

Center of Hygiene and Epidemiology in the Sverdlovsk Region

Email: fake@neicon.ru
ORCID iD: 0000-0002-3247-9269

Irina V. Chistyakova — Deputy chief physician.

Yekaterinburg

Россия

N. I. Kochneva

Department of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing for Sverdlovsk Region

Email: fake@neicon.ru
ORCID iD: 0000-0001-7316-854X

Natalia I. Kochneva — Chief specialist-expert of social and hygienic monitoring department.

Yekaterinburg

Россия

A. I. Yurovskikh

Center of Hygiene and Epidemiology in the Sverdlovsk Region

Email: fake@neicon.ru
ORCID iD: 0000-0002-1555-7931

Andrey I. Yurovskikh — Deputy chief physician.

Yekaterinburg

Россия

E. P. Kadnikova

Department of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing for Sverdlovsk Region

Email: fake@neicon.ru
ORCID iD: 0000-0001-8891-1922

Ekaterina P. Kadnikova — Head, Social and hygienic monitoring de-partment.

Yekaterinburg

Россия

A. S. Kilyachina

Center of Hygiene and Epidemiology in the Sverdlovsk Region

Email: fake@neicon.ru
ORCID iD: 0000-0003-1751-3462

Anastasia S. Kilyachina — Head, Laboratory of biological factors control.

Yekaterinburg

Россия

S. V. Luchinina

Department of Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing for Chelyabinsk region

Email: fake@neicon.ru
ORCID iD: 0000-0001-5705-8850

Svetlana V. Luchinina — D. Sci. (Med.), Deputy Head.

Chelyabinsk

Россия

R. R. Kosareva

Department of Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing for Chelyabinsk region

Email: fake@neicon.ru
ORCID iD: 0000-0001-5332-4218

Raisa R. Kosareva — Head, Epidemiological surveillance depart-ment.Chelyabinsk

Россия

G. G. Chirkova

Center for Hygiene and Epidemiology in the Chelyabinsk region

Email: fake@neicon.ru
ORCID iD: 0000-0001-7220-0456

Galina G. Chirkova — Head, Virological laboratory

Россия

N. N. Valeullina

Center for Hygiene and Epidemiology in the Chelyabinsk region

Email: fake@neicon.ru
ORCID iD: 0000-0002-0677-4571

Natalia N. Valeullina — Chief physician

Россия

L. A. Lebedeva

Center of Hygiene and Epidemiology in the Khabarovsky Kray

Email: fake@neicon.ru
ORCID iD: 0000-0003-2792-0424

Lyudmila A. Lebedeva — Head, Virological laboratory.

Khabarovsk

Россия

T. N. Detkovskaya

Department of Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing for Primorsky Kray

Email: fake@neicon.ru
ORCID iD: 0000-0002-7543-0633

Tatyana N. Detkovskaya — Head.

Vladivostok

Россия

E. I. Abbasova

Department of Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing for Primorsky Kray

Email: fake@neicon.ru
ORCID iD: 0000-0002-3278-9216

Elena I. Abbasova — Head, Epidemiological surveillance depart-ment.

Vladivostok

Россия

O. B. Romanova

Center of Hygiene and Epidemiology in the Primorsky Kray

Email: fake@neicon.ru
ORCID iD: 0000-0003-2290-8610

Olga B. Romanova — Chief physician.

Vladivostok

Россия

E. V. Pyatyrova

Center of Hygiene and Epidemiology in the Primorsky Kray

Email: fake@neicon.ru
ORCID iD: 0000-0002-6750-8920

Elena V. Pyatyrova — Deputy chief physician for the organization of expert activities.

Vladivostok

Россия

V. G. Akimkin

Central Research Institute for Epidemiology

Email: fake@neicon.ru
ORCID iD: 0000-0003-4228-9044

Vasily G. Akimkin — D. Sci. (Med.), Professor, Full Member of the Russian Academy of Sciences, Director.

Moscow

Россия

References

Supplementary files