COVID-19 : SARS-CoV-2 virus may find itself in competition with other seasonal coronaviruses

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Coronavirus infections are a common cause of mild colds, infecting thousands of people every year in the UK.

They mostly circulate in the winter in temperate regions where they are often referred to as ‘seasonal coronaviruses’. However, in contrast to many other infectious diseases, not much is known about how often, and in whom, these viruses cause illnesses requiring medical attention.

Data describing their patterns of infection are lacking because they are often not tested for.

Now, new research led by the MRC-University of Glasgow Centre for Virus Research and published in the Journal of Infectious Diseases, sheds light on when these viruses are most prevalent in different healthcare settings and how these viruses interact with other kinds of respiratory viruses.

The work – one of the most detailed studies of coronaviruses in a patient population – could be important for understanding and predicting the behaviour of COVID-19.

Researchers used unique data from over 70,000 NHS Greater Glasgow and Clyde patients with respiratory illness, attending general practice surgeries and hospitals between 2005 and 2017, who were tested for a panel of respiratory viruses, including common seasonal coronaviruses, to look for patterns related to age and seasonal frequency, and any variation between the different coronavirus types.

They found that different types of coronaviruses coexist in most winter seasons in the UK, although they exhibit structured seasonal patterns with some appearing to generate illnesses in the community at the same time. However, other coronaviruses appeared to circulate in their own unique pattern.

The findings could suggest that SARS-CoV-2, the coronavirus causing COVID-19, may find itself in competition with current seasonal coronaviruses and either struggle to persist in the long term, or, that it may push out one or more of the existing seasonal coronaviruses.

In the event of an emerging infectious disease pandemic such as COVID-19, in the absence of epidemiological information about the new pathogen, possible infection patterns are informed by other similar diseases.

These infection patterns help to inform modeling predictions of the disease spread and to assess control strategies.

So far COVID-19 appears to be more similar to flu than to seasonal coronaviruses in terms of the fraction of cases leading to severe illness and their older age profile, although this comparison is currently complicated by data biases.

Detailed information on seasonal coronaviruses will be important for predicting what will happen to COVID-19 in the long term, and its impact on other respiratory viruses.

Lead author Dr Sema Nickbakhsh, Research Associate at the CVR, said: “When data on age and seasonal risk profiles are lacking, particularly early on in an outbreak, we can learn from other infectious diseases that spread in a similar way.

“So by looking at the robust data we have on other coronaviruses from Scottish patients from 2005 to 2017, we can improve our understanding of normally-occurring seasonal coronaviruses, which is greatly needed to guide future COVID-19 science and to prepare for the post-pandemic era.”

The researchers also found that common seasonal coronaviruses were detected among all ages; which differs from COVID-19, where cases in children have rarely been reported.

It is unclear whether children are less susceptible to infection with SARS-CoV-2 virus; or whether they are susceptible, and spread infection, but are protected from severe illness requiring hospitalization.

Dr Nickbakhsh said: “More research is needed to understand whether infection with seasonal coronaviruses in young children provides lasting immunity, and whether seasonal coronaviruses can also protect against SARS-CoV-2.

“And as we move forward, studies investigating age patterns of exposure to SARS-CoV-2 in the community are needed, capturing people without or with mild symptoms, as well as those that are sick.”

The study also found that seasonal coronaviruses more often co-infect with particular common respiratory viruses, adenovirus and parainfluenza, than with other groups of respiratory virus. This suggests that coronaviruses are likely to form cooperative, rather than competitive, forms of relationships with other groups of respiratory virus.


n March 2020, the World Health Organization declared the global spread of coronavirus (CoV) disease 2019 (COVID-19), caused by a human CoV (severe acute respiratory syndrome CoV [SARS-CoV-2]) that emerged in China in December 2019, a pandemic [1].

Predicting the public health impact of pathogens with recently acquired human-to-human transmissibility is a challenge. Currently, the fate of COVID-19 remains unclear; understanding the likely age and seasonal profiles of infection risks will be critical to inform effective surveillance and control strategies.

During the early phase of an outbreak, in the absence of detailed country-specific knowledge, preliminary risk estimates may be gauged from endemic pathogens with similar modes of transmission. The infection incidence and levels of severe illness associated with COVID-19 remains unclear. In this instance, epidemiological data on seasonal CoVs (sCoVs) may provide valuable information about individuals and seasonal conditions favoured by, or limiting, an invading CoV.

To date, emergent zoonotic human CoVs associated with high case-fatality ratios have not achieved persistence in the human population. SARS-CoV emerged in 2002 and spread rapidly around the globe before being successfully contained in 2003 [2]. Conversely, Middle East respiratory syndrome CoV has continued to cause sporadic cases predominantly in healthcare settings since its discovery in 2012, but has not demonstrated sustained community transmission [3]. In contrast, CoV-229E, CoV-NL63, CoV-OC43, and CoV-HKU1 are common cocirculating sCoVs predominantly associated with mild infection of the upper respiratory tract [4].

A key determinant governing the invasion and persistence success of a new pathogen is the abundance of susceptible hosts. Such population susceptibility may be difficult to define owing to preexisting cross-protective immunity in individuals previously exposed to antigenically related pathogens, as demonstrated for pandemic influenza A H1N1 in 2009 [5]. Furthermore, the potential for heterologous interactions among taxonomically broad groups of respiratory viruses is also recognized [6–11]. A good epidemiological understanding of cocirculating viruses will provide valuable information on the potential for immune, or otherwise mediated, virus-virus interactions and the consequences for population susceptibility.

To date, epidemiological knowledge surrounding sCoVs has been limited for many settings owing to their historic association with mild illness. However, some laboratories have adopted sCoV testing as part of routine multiplex diagnostic screens [12–15], following an increased recognition of the associated disease spectrum. Our group previously reported on the comparative epidemiological characteristics of acute viral respiratory infections, and the potential for virus-virus interactions, based on multiplex reverse-transcription polymerase chain reaction (PCR) testing in the West of Scotland [6, 16]. In the current article, we provide further detail on sCoVs differentiated at the species level (sCoV types) over an extended time frame and discuss key potential implications for COVID-19 virus emergence in Scotland, United Kingdom.

The Study Population
Routine molecular testing for CoV-229E, CoV-OC43, and CoV-NL63 using multiplex real-time reverse-transcription PCR methods was conducted between 1 January 2005 and 30 September 2017 by the West of Scotland Specialist Virology Centre in NHS Greater Glasgow and Clyde, the largest Scottish National Health Service (NHS) board serving a population of approximately 1.2 million [17]. The respiratory virus screen also simultaneously detected influenza A virus, influenza B virus, respiratory syncytial virus (RSV), human adenoviruses (AdVs), human rhinoviruses, human metapneumovirus, and parainfluenza virus (PIV) types 1–4. The CoV-HKU1 assay was discontinued in 2012 owing to low levels of detection. Most clinical specimens (91%) were obtained from the upper respiratory tract (the majority being nasal and/or throat swab samples).

During the study period, 107 174 clinical respiratory samples, from 64 948 individual patients, were received by the West of Scotland Specialist Virology Centre for testing. For patients with ≥2 samples submitted (24.5% of patients), the PCR test data were aggregated into individual episodes, defined as a 30-day period from the collection date of the first sample. This generated 84 957 episodes of respiratory illness for analysis. Most episodes, 93% that occurred out with the 3 major waves of pandemic influenza A(H1N1)pdm09 virus circulation in the United Kingdom (summer 2009 and influenza seasons of 2009–2010 and 2010–2011), were tested for all 11 groups of respiratory virus. Of 84 957 episodes of respiratory illness, 10 438 were not tested for CoV (98% during the 3 major waves of pandemic influenza) and thus were excluded from analyses centered on sCoVs [18]. Among the remaining 74 519 episodes of illness, another 278 were either tested for CoV-HKU1 or the CoV was untyped; these episodes were excluded from analyses differentiating sCoV type. See Figure 1 for a summary of the data subsets.

Figure 1.

Data flow diagram summarizing patient subsets informing each analysis. Samples from 64 948 patients were subjected to molecular tested for respiratory viruses, performed with real-time multiplex reverse-transcription polymerase chain reaction in NHS Greater Glasgow and Clyde, Scotland, United Kingdom, between 1 January 2005 and 30 September 2017. Abbreviation: sCoV, seasonal coronavirus.
Data flow diagram summarizing patient subsets informing each analysis. Samples from 64 948 patients were subjected to molecular tested for respiratory viruses, performed with real-time multiplex reverse-transcription polymerase chain reaction in NHS Greater Glasgow and Clyde, Scotland, United Kingdom, between 1 January 2005 and 30 September 2017. Abbreviation: sCoV, seasonal coronavirus.

Statistical Modeling Analyses
Of 74 241 patient episodes of respiratory illness with sCoV subtyping, 8912 patients experienced multiple episodes over the study time frame. In such cases, we retained the first observed episode to remove patient-level clustering, leaving 56 276 patient observations for analysis (Figure 1). We used multivariable logistic regression to investigate associations between sCoV types (CoV-229E, CoV-OC43, and CoV-NL63) and patient age (categorical), sex (binary), healthcare service setting (binary; primary or secondary or tertiary services), time period with respect to the 3 major waves of pandemic influenza in the United Kingdom (categorical; prepandemic, January 2005 to April 2009; pandemic, May 2009 to February 2011; and postpandemic, March 2011 to September 2017) and season (categorical). Statistical interactions between patient covariates and healthcare service setting were assessed. An α level of 5% was used to determine statistical significance of all model coefficients. The fitted models, incorporating age-healthcare service interactions, were used to generate average predicted probabilities of virus detection by age and healthcare setting.

In addition, we used multivariable logistic regression to investigate interactions between each sCoV and other groups of respiratory viruses at the within-host scale. These analyses were based on 16 991 virus-positive episodes of respiratory illness, retaining the first observed episode of illness for patients with multiple episodes. Virus-negative patients were excluded to eliminate the influence of Berkson bias, which may lead to spurious inference of disease-disease associations when these are estimated from routine healthcare data [19]. Specifically, these analyses tested whether the odds of a given virus (“exposed”) coinfecting with a given sCoV differed from the average odds among the remaining groups of viruses (“nonexposed”), thereby assessing nonrandom mixing among the virus population.

Three models were fitted, one each for CoV-229E, CoV-OC43, and CoV-NL63 (response variables). The analyses adjusted for patient age, sex, healthcare service setting, time period with respect to pandemic influenza (as described above), and the monthly background prevalence of the sCoV (response variable) to eliminate spurious virus-virus associations owing to unrelated sources of seasonality. Holm’s method was used to correct P values for multiple comparisons (10 virus-virus interaction hypotheses per model) [20].

All analyses were conducted using R software version 3.4.4 [21]. Logistic regression modeling was conducted using the “glm” function, and predicted probabilities were computed using “ggaverage” from the “ggeffects” package [22].

RESULTS
Prevalence of sCoVs Among People With Respiratory Illness
Among 84 957 episodes of respiratory illness, 79.0% were sampled at secondary or tertiary healthcare services (hospital inpatients and outpatients), and 21.0% from primary healthcare services (general practice [GP]). The sex distribution was approximately equal, with 51.6% of patients female, and the median age was 33.1 years (interquartile range, 5.6–59.1 years).

The prevalence of sCoV detections overall was 4.0% among tested patients (2958 of 74 519), contributing to 10.7% (2958 of 27 734) of all respiratory virus detections. Figure 2 summarizes the contribution of sCoVs to the total viral detections in the patient population during each influenza season (October–May) from 2005 to 2016.

The most common virus detections during influenza seasons among virus-positive patients were human rhinoviruses (range, 15.3%–46.2%), influenza viruses (13.4%–34.0%, excluding pandemic influenza waves of 2009–2010 and 2010–2011), and RSV (10.1%–21.9%), followed by sCoVs (7.7%–7.4%) (Figure 2).

Figure 2.

Percentages of viral respiratory infections attributed to human coronaviruses and other common respiratory viruses during each influenza season (October–May) from 2005–2006 until 2016–2017, based on 84 957 episodes of respiratory illness. Influenza includes influenza A and influenza B viruses combined; and “other” includes human adenoviruses, human metapneumovirus, and parainfluenza viruses type 1–4. Note: Years of major pandemic influenza A H1N1 virus circulation (2009–2010 and 2010–2011) must be viewed with caution, owing to high levels of partial testing. Testing for CoV-HKU1was discontinued in 2012. Abbreviations: CoV, human coronaviruses (CoV-229E, CoV-OC43, CoV-NL63, and CoV-HKU1 combined); RSV, respiratory syncytial virus; RV, human rhinovirus.
Percentages of viral respiratory infections attributed to human coronaviruses and other common respiratory viruses during each influenza season (October–May) from 2005–2006 until 2016–2017, based on 84 957 episodes of respiratory illness. Influenza includes influenza A and influenza B viruses combined; and “other” includes human adenoviruses, human metapneumovirus, and parainfluenza viruses type 1–4. Note: Years of major pandemic influenza A H1N1 virus circulation (2009–2010 and 2010–2011) must be viewed with caution, owing to high levels of partial testing. Testing for CoV-HKU1was discontinued in 2012. Abbreviations: CoV, human coronaviruses (CoV-229E, CoV-OC43, CoV-NL63, and CoV-HKU1 combined); RSV, respiratory syncytial virus; RV, human rhinovirus.

Numbers of sCoV detections increased before pandemic influenza (March 2011 to September 2017), likely owing to enhanced virological testing of acute respiratory illnesses; the overall number of sCoV detections rose from 545 before, to 2072 following the pandemic influenza period. However, a decrease in prevalence among the tested population was observed, from 4.27% to 3.70%, and with varying patterns at the individual sCoV level (Supplementary Table 1).

The most prevalent detection was CoV-OC43, both before and following the pandemic influenza period (Supplementary Table 1). CoV-HKU1 was present at a very low prevalence of 0.3% overall (124 of 36 652 episodes tested until the assay was discontinued in 2012) and was therefore excluded from further analyses.

Difference Between Patients in Detection of sCoVs
Despite more sCoV detections in the hospital setting, the prevalence was greatest among the tested GP attendees (5.3%; 673 of 12 670) than among those in hospitals (3.7%; 2285 of 61 849). Figure 3 summarizes the age distributions. Cases of sCoV in children <5 years old and the elderly (>64 years) were disproportionately represented among patients admitted to the hospital, compared with a more uniform distribution among GP attendees, closely following the overall tested population (Figure 3A).

Different sex biases was found among adults depending on the healthcare setting, with more female patients in primary care versus more male patients in secondary or tertiary care (Figure 3B). This pattern was consistent when comparing the percentages of detections among sCoV-positive patients across sCoV types: 59.2% (CoV-229E), 55.6% (CoV-OC43), and 59.8% (CoV-NL63) of cases detected in primary care were in female patients, whereas 54.7% (CoV-229E), 51.1% (CoV-OC43), and 56.7% (CoV-NL63) cases detected in secondary or tertiary care were in male patients (Supplementary Table 2).

Age distributions of general practice (GP; primary care) and hospital (secondary/tertiary care) patients tested and positive for human coronavirus (CoV), (A) and percentages of female patients (B). Note the different y-axis scale for CoV cases in A. Hospital patients include inpatients and outpatients.
Age distributions of general practice (GP; primary care) and hospital (secondary/tertiary care) patients tested and positive for human coronavirus (CoV), (A) and percentages of female patients (B). Note the different y-axis scale for CoV cases in A. Hospital patients include inpatients and outpatients.

The median patient age (interquartile range) varied from 20.9 (2.7–50.2) years for CoV-NL63, to 39.9 (5.0–62.5) and 43.3 (16.5–60.4) years for CoV-OC43 and CoV-229E, respectively. The age-specific prevalences of sCoVs among the tested population are summarized in Supplementary Tables 3 and 4. More variation across ages was found in primary care patients for CoV-229E (coefficient of variation, 40.4%) and CoV-NL63 (33.8%) than for CoV-OC43 (13.6%), with less variation for patients in secondary or tertiary care (CV, 29.96% for CoV-229E, 28.0% for CoV-NL63, and 17.10% for CoV-OC43).

Statistical modeling analyses further confirmed differences in age and sex associations according to sCoV type, and a greater chance of sCoV detection among GP attendees than among patients admitted to the hospital (Supplementary Tables 5–7). No evidence of significant effect modification between patient age or sex and healthcare service setting was found (statistical interaction terms, P > .05; results not shown).

Figure 4 summarizes average age-specific predicted probabilities with statistical interactions incorporated. In summary, we observed a trend toward increasing probability of CoV-229E with age (Figure 4A), greater probabilities of CoV-OC43 at the extremities of age (Figure 4B), and decreasing probability of CoV-NL63 with age (Figure 4C).

These age patterns were broadly consistent across patient sex and healthcare settings, although we note that 95% confidence intervals overlapped across all ages except for patients in the hospital setting. A borderline significant sex effect was found for CoV-229E, with detections more likely among male patients (Supplementary Table 5).

Figure 4.

Average age-specific predicted probabilities of human coronavirus (CoV) detections by patient sex and healthcare service setting ( general practice [GP; primary care] or hospital [inpatients and outpatients; secondary or tertiary care]). Data were derived from multivariable logistic regression models incorporating statistical interactions between patient age and healthcare service (see Supplementary Tables 5–7 for model results without statistical interactions).
Average age-specific predicted probabilities of human coronavirus (CoV) detections by patient sex and healthcare service setting ( general practice [GP; primary care] or hospital [inpatients and outpatients; secondary or tertiary care]). Data were derived from multivariable logistic regression models incorporating statistical interactions between patient age and healthcare service (see Supplementary Tables 5–7 for model results without statistical interactions).

Variations in Seasonality Among sCoVs

Figure 5 shows the monthly prevalences of sCoVs detected among the patient population. These are winter pathogens in the United Kingdom, peaking on average between January and March. However, there were notable variations between sCoV types and between years. Overall, CoV-OC43 was the most prevalent detection among the tested population in each influenza season.

Differences were also observed in periodicities; before the first wave of pandemic influenza in 2009, CoV-229E peaked biennially, but it subsequently exhibited longer interpeak periods, particularly between 2013 and 2016.

Monthly prevalence of seasonal coronaviruses (sCoVs) detected among patients with respiratory illness virologically tested in NHS Greater Glasgow and Clyde, Scotland, United Kingdom, between January 2005 and September 2017. A, CoV-229E. B, CoV-OC43. C, CoV-NL63. D, Comparing all sCoV types.
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Monthly prevalence of seasonal coronaviruses (sCoVs) detected among patients with respiratory illness virologically tested in NHS Greater Glasgow and Clyde, Scotland, United Kingdom, between January 2005 and September 2017. A, CoV-229E. B, CoV-OC43. C, CoV-NL63. D, Comparing all sCoV types.

In contrast, CoV-OC43 and CoV-NL63 generally exhibited annual periodicity of varying magnitude. A considerable degree of synchrony is observed in the timing of the peak prevalence of CoV-OC43 and CoV-NL63 for most seasons, whereas CoV-229E was more distinctive in its temporal pattern. For example, low levels of CoV-229E in 2007 coincided with high magnitudes of CoV-OC43 and CoV-NL63, whereas the high prevalence of CoV-229E in 2010 coincided with low magnitudes of CoV-OC43 and CoV-NL63.

Interactions Between sCoVs and Other Respiratory Viruses
The cocirculation of sCoVs with other common respiratory virus raises the potential for ecological interactions, altering infection risks and the dynamics of population transmission. Our data did not permit analysis of potential within-host interactions among different sCoVs because of an absence of sCoV coinfections, but we did evaluate the potential for within-host interactions between sCoVs and other common respiratory viruses.

To do so, we analyzed the nonrandom mixing of respiratory viruses among virus-positive patients using multivariable logistic regression. We found a greater propensity for CoV-OC43 to coinfect with RSV (odds ratio, 1.68; 95% confidence interval, 1.05–2.63; uncorrected P = .03), AdV (2.93; 1.87–4.5, uncorrected P < .001), and PIV3 (2.38; 1.28–4.17; uncorrected P = .004) (Supplementary Table 8). The associations with AdV and PIV3 were supported after correction of P values for multiple comparisons (P < .001 and P = .04 respectively).

No evidence of interactions with other respiratory viruses was found for either CoV-229E or CoV-NL63. Assessment of PIV types was limited by small numbers of coinfections; these viruses were aggregated at the genus level for the CoV-229E analysis, and PIV2 was excluded from the CoV-NL63 analysis. See Supplementary Tables 9 and 10 for details and Supplementary Figure 1 for a summary.

The finding for PIVB (parainfluenza virus types 2 and 4 combined; the human rubulavirus genus) must be treated with caution, because the 95% confidence interval overlaps 1. The average age-specific predicted probabilities of sCoV coinfection for individuals with or without coinfection with each specific respiratory virus are given in Supplementary Table 11.


Source:
UKRI

Supplementary data

jiaa185_suppl_Supplementary_Material – pdf file

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