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Influenza illness is especially problematic in the elderly, infants, and individuals with chronic diseases, while RSV is mainly hazardous in infants. When assessing the age distribution of HCoV infection, we noted that HCoVs mainly infected infants and young children. More than fifty percent of HCoV-positive cases were in the 0–10 age group (Figure 2) while the remaining groups were around ten percent and below (Figure 2). HCoV-229E prevalence was low at all ages and absent in the 31–40 and 70+ age groups (Figure 2), probably due to its low prevalence compared to HCoV-OC43 and HCoV-NL63 (Figure 1A).
Among HIV-infected children, the RT-PCR viral panel was positive in 274 (53.0%) LRTI-episodes for at least one of the newly-tested viruses; Table 3. The prevalence of any of the newly-tested respiratory viruses was similar between PCV9- and placebo-recipients in HIV-infected children, except for WUPyV (11.2% vs. 6.3%; p = 0.047, respectively) and hBoV (12.5% vs. 7.0%; p = 0.034, respectively); Table S3. In HIV-infected children hRV was the most frequently detected virus (31.7%) followed by CoV-OC43 (12.2%), hBoV (9.5%), KIPyV (8.9%), WUPyV (8.5), CoV-NL63 (1.7%) and CoV-HKU1 (1.4%); Table 3. The newly-tested viruses were frequently identified as co-infecting viruses among HIV-infected children, including 49.4% of LRTI-episodes associated with hRV; Table 4. The most common viral co-infections with hRV included KIPyV (14.6%), WUPyV (11.6%), CoV-OC43 and hBoV (11.0%, each); Table 4. Among the 486 children on whom blood culture was done, bacteria were isolated on 38 (7.8%) occasions, 20 (52.6%) of which were associated with concomitant detection of one of the newly-tested viruses and 22 (57.9%) of any of the viruses.
In HIV-uninfected children at least one newly-tested virus was detected in 509 (54.0%) LRTI-episodes, with hRV also being the most common (32.0%), followed by hBoV (13.3%), WUPyV (11.9%), KIPyV (4.8%), CoV-OC43 (3.6%), CoV-NL63 (2.6%), CoV-HKU1 (1.6%) and CoV-229E (0.42%); Table 3. Comparing HIV-uninfected PCV9 and placebo recipients, differences in the prevalence of newly-tested viruses were evident for KIPyV (2.1% vs. 7.4%; p<0.001), CoV-HKU1 (0.21% vs. 2.9%; p = 0.001), CoV-OC43 (2.1% vs. 5.0%; p = 0.017) and hRV (36.2% vs. 27.9%; p = 0.007); Table S3. Of the 302 LRTI-episodes in which hRV was identified, 51.3% had at least one other virus detected, including 13.6% with RSV, 12.3% with WUPyV or hBoV and 2.0% (N = 6) with both WUPyV and hBoV; Table 5. The prevalence of bacteraemia in HIV-uninfected children among those with blood culture results was 2.7% (N = 24/881) of which 12 (50.0%) occurred in the presence of infection with one of the newly-tested viruses and 15 (62.5%) in presence of any of the studied viruses.
By multivariate analysis, adjusting for PCV-vaccination status, period of collection and age, single infections with a newly-tested virus were more frequent in HIV-infected (30.2%) than HIV-uninfected children (25.5%) (adjusted odds ratio [aOR] 1.3; p = 0.033); Table 3. Also, HIV-infected compared to HIV-uninfected children had a higher prevalence of KIPyV (aOR 2.14; p = 0.002) and CoV-OC43 (aOR 3.67; p<0.001) and a lower prevalence of hBoV (aOR 0.69; p = 0.043) and WUPyV (aOR 0.66; p = 0.035); Table 3. Concurrent bacteraemia and infection with at least one of the newly-tested viruses was more frequent in HIV-infected (7.7%) compared to HIV-uninfected children (2.5%, aOR 3.49; p = 0.001). There were no differences in the frequency of bacteraemia comparing children in whom newly-tested viruses were detected and those without viral detection both in HIV-infected (7.7% vs. 8.0%; p = 0.911) and HIV-uninfected children (2.5% vs. 2.9%; p = 0.713).
Analysis of the clinical symptoms reported by patients infected with influenza, RSV and HCoV, showed no significant differences in rates of diarrhea, vomiting, red throat and rhinitis cases between all viruses (Figure 3). In addition, the frequency of all symptoms assessed were similar or lower for HCoV infections compared to those of Influenza infections. Fewer HCoV patients had fever as compared to both influenza and RSV. More influenza-positive patients suffered from fatigue, headache, muscle pain, joint pain and trembling as compared to HCoV. Compared to RSV-infected patients, fever, cough and dyspnea were less frequent in HCoV-infected patients, while fatigue, headache, muscle pain, trembling and sore throat symptoms were more common among HCoV-infected patients (Figure 3). Other viruses responsible for upper respiratory tract infections were not analyzed (e.g., hPIV, hMPV, Rhinovirus, Adenoviruses, Bocaviruses).
During the follow-up period included in this analysis, there were a total of 2147 hospitalizations for LRTI, including 2094 (97.5%) in which NPA had been collected. Of the initial collected samples 69.7% were available for RT–PCR analysis. The same proportion of samples were available for RT–PCR from PCV9-recipients (69.2%) and placebo-recipients (70.2%; p = 0.621), from HIV-infected (78.2% vs. 73.1%; p = 0.130) and HIV-uninfected children (65.5% vs. 68.6%; p = 0.218); Table 1. A lower proportion of samples was, however, available for RT-PCR analysis (65.9% [1000/1518]) from the first period (February 2000–January 2001) compared to the second period (February 2001–January 2002) (79.9% [460/576]; p<0.001) of the study.
Children from whom NPA were available for RT–PCR testing compared to those in whom samples were unavailable were older (median age: 10 vs. 8 months; p<0.001), were 1.3-fold less likely to have tested positive for one of the previously-tested respiratory viruses (33.3% vs. 42.3%; p<0.001), had a higher prevalence of cyanosis (11.4% vs. 8.1%; p = 0.025), higher evidence of CXR-AC (26.6% vs. 22.1%; p = 0.041), higher C-reactive protein (CRP) levels (median: 15 vs. 12 mg/l; p = 0.003) and higher procalcitonin (PCT) concentration (median: 0.26 vs. 0.15 ng/ml; p = 0.006); Table S2. There were no other demographic, clinical or laboratory differences observed between the LRTI-episodes with NPA available versus unavailable for RT-PCR.
A total of 517 NPA samples from HIV-infected children were analysed by RT–PCR, including 45.0% from PCV9-recipients and 55.0% from placebo-recipients. Among HIV-uninfected children, 943 specimens were available for RT-PCR analysis, including 49.5% from PCV9-recipients and 50.5% from placebo-recipients. On admission HIV-infected children compared to HIV-uninfected were younger (median age: 9 vs. 11 months; p<0.001), more frequently presented with cyanosis (23.8% vs. 4.6%; p<0.001), had lower mean oxygen saturation (89.8% vs. 92.2%; p<0.001), had a higher median respiratory rate (54 vs. 48 breaths per minute; p<0.001), were more likely to present as pneumonia (88.8% vs. 49.6%; p<0.001) than bronchiolitis, had higher CRP (18 vs. 14 mg/l; p = 0.007) and PCT levels (0.47 vs. 0.17 ng/ml; p<0.001), had a longer hospital stay (4 vs. 1 median days; p<0.001), had a higher case-fatality rate (17.8% vs. 0.95%; p<0.001) and more frequently had bacteraemia (7.8% vs. 2.7; p<0.001); Table 2.
Of the 1460 specimens analysed, all but 13 (1 from HIV-infected and 12 from HIV-uninfected children) were previously tested for hMPV by nested-PCR and 1458 were tested by an immunofluorescence assay for RSV, which if found negative, were further tested for influenza A/B, PIV I–III and adenovirus as described.
The virus detection rates for different seasons are shown in Fig. 2. The proportion of positive viruses exhibited two waves corresponding to winter and spring, including Jan to Mar 2015, and Nov 2015 to Feb 2016. Similarly, the FluA virus also occurred more frequently in winter and spring. Conversely, EV infections were predominant between April and September. Other viruses occurred almost sporadically throughout the year without obvious seasonal trends, and a small number of ARI outpatients with virus infection were observed between June and September, excluding EV infections.
Totally, 1560 samples collected from patients with ARIs during the period from December 2013 to April 2017 were enrolled in the investigation. There were 824 males (52.8%) and 736 females (47.2%), and the patient’s ages ranged from 3 months to 15 years. The most numerous age group (43.2%) was between 1 and 3 years old. The age distribution is shown in Table 1.
Acute RVIs are responsible for causing significant levels of morbidity and mortality. The most common respiratory syndrome caused by these pathogens is ILI. A more severe presentation, named SARI, was also related to some RVs.16,17 In this study, we have examined the relationship between ILI and SARI cases, meteorological variables, and air pollution using multivariate time-series analyses. We found that ILI cases were inversely correlated with sunshine duration. In addition, SARI cases were significantly associated with mean temperature, sunshine duration, RH, and concentration of pollutants.
Seasonal cycles of infectious diseases have been attributed to changes in atmospheric variables, the prevalence or virulence of the pathogen, or the behavior of the host.18 Earlier investigations have demonstrated that lower temperatures and sunshine duration, conditions usually encountered in winter, were associated with admissions for RVI.12,19 Temperature was found to be highly inversely correlated with RSV, influenza A, and adenovirus frequency.12 Interestingly, we found a positive correlation between temperature and SARI cases. One possible explanation is that it was demonstrated that for every one degree Celsius rise in temperature, the risk of premature death and acute morbidity especially among respiratory patients is up to six times higher than in the rest of the population. Second, evidence is emerging that increasing temperature is associated with increases in air pollution, especially ground-level ozone, and can amplify the adverse effects of poor air quality.20 Taking this evidence into account, we could expect that higher temperatures may have increased concentration of pollutants, leading to more SARI cases. However, our data showed a decrease in air pollution during the months with a higher prevalence of SARI. The third hypothesis to explain the relationship between higher temperatures and SARI cases was related to El Niño Southern Oscillation (ENSO) phenomenon. ENSO undergoes cycles between warm phases (El Niño episodes) and reverse cold phases (La Niña episodes). In the southern region of Brazil, this phenomenon is associated with elevated temperatures and rainfall, especially in spring and in the period between May and July. Previous reports have determined that El Niño events were associated with increased hospitalizations and more severe influenza epidemics.21,22
Severe acute respiratory infection cases were found to be negatively related to RH in our study. Previous studies have demonstrated that higher RH decreases the survival of lipid-enveloped virus, like influenza A, influenza B, RSV, and PIV.23–25 The use of indoor heating in winter lowers the RH; breathing dry air could cause desiccation of the nasal mucosa, epithelial damage, and reduced mucociliary clearance, increasing the host susceptibility to RVIs.19 However, even in tropical regions with humid climate (RH >70%), a higher activity of influenza can be found. This observation could be explained by the variation of viral stability in different RH levels. The stability of aerosolized influenza virions is maximal at lower RH (20–40%), moderate at higher RH (60–80%), and minimum at a mid-range RH (50%).23
In a multivariate logistic regression model for IFI-positive patients, we found that AH was a protect factor for RVI. A recent study suggested that AH may better correlate with influenza virus survival and transmission. Unlike RH, AH measures the actual water vapor content of air irrespective of temperature and has a prominent wintertime low, both indoor and outdoor. Such findings suggest that humidification measures could be helpful decreasing survival and transmissibility of influenza.26
Air pollution has been associated with adverse health outcomes. Studies have suggested acute effects causing respiratory symptoms, cardiovascular events, hospital admissions, and mortality. Although the available evidences indicate associations between exposure to pollutants and increased risk of RVI, potential mechanisms mediating these effects are largely unexplored.27,28 Surprisingly, our results showed that SARI cases were associated with a decrease in mean concentration of pollutants. In fact, this could be a reflection of higher rainfall in the same period, as rain acts washing out or scattering pollutants from atmosphere.29 On the other hand, we cannot exclude an effect of indoor pollution. In the last years, indoor pollution has been recognized as an emerging health problem, as about 90% of our time is spent indoors where we are exposed to chemical and biological contaminants.30 We estimated indoor pollution indirectly in our study, questioning patients about the use of wood stoves and air conditioning, and the presence of mold in home. Our findings suggested that IFI-positive patients were more prone to live in a residence with mold growth. Dampness and mold are two important sources of indoor pollution, consistently associated with respiratory symptoms. Home dampness may be a marker for mold growth, dust mites, endotoxins, and reduced ventilation, which could increase concentrations of indoor pollutants.31 Cough, wheezing, and upper respiratory symptoms were associated with dampness and mold in a meta-analysis.32 According to these results, the prevalence of cough and wheezing was higher in patients with mold in home and IFI positive.
Air conditioning was also positively related to IFI test in this study. Air conditioning use was associated with fewer hospital admissions for cardiovascular diseases, chronic obstructive pulmonary disease, and pneumonia on days with high concentrations of PM10,33 as individuals are less exposed to outdoor pollutants. Nevertheless, the majority of virus transmission occurs within indoor, air-conditioned (i.e., cooler, lower humidity) environments that favor airborne virus survival and transmission.24,25 In hot and humid conditions, indoor transmission in air conditioning environments may account for most of the transmission.34
We found a prevalence of 22% of RV, which is higher than that previous studies have demonstrated (between 12% and 15% in adults).12,13 Moreover, the length of stay was lower in our IFI- and/or PCR-positive patients. This finding is consistent with existing knowledge that virus identification allows the prompt initiation of therapy when indicated and avoids the unnecessary use of antibiotics, decreasing the length of hospital stay.
The present study has some limitations. First, it was based on data collected from a single center, which may have potential biases because of the characteristics of the catchment population, like vaccination coverage. Second, it is also important to note that this investigation was performed in a group of hospitalized patients, which is a bias toward the most severe disease cases. Additionally, we do not have the concentrations of individual air pollutants, but it is implausible to reliably separate the effects of air pollutants because they frequently react with each other, sometimes potentiating individual effects.10,35 The short study period should also be considered a limitation. Finally, the use of molecular techniques (PCR) in all study patients could be useful, increasing the number of viruses detected, as limited sensitivity of IFI method is well known.10,36 Despite these limitations, this is the first study, to our knowledge, to analyze the relationship between RV, meteorological parameters, and air pollution in an adult population.
In conclusion, we found that in adult patients admitted to ER with respiratory complaints, at least 22% of infections were caused by RV. The correlations found among meteorological variables, air pollution, ILI/SARI cases, and RV demonstrated the relevance of climate factors as significant underlying contributors to the prevalence of RVI in a temperate region. There is still a need of additional investigations to clarify and confirm these data, perhaps using longer time-series observations.
In one paper, published in 2017, we investigated the relationship between diet and upper respiratory tract infections (URTI). Using data from the web-based food frequency questionnaire we found an inverse association between intake of vitamin C, vitamin E, docosahexaenoic (DHA) and arachidonic acid (AA) and risk of URTI among women, while intake of vitamin E and zinc was associated with an increased risk of URTI among men.
The study participant was the primary case in 183 of the 279 recorded household ILI events. One re-introduction of picornavirus into the household and five co-introductions were recorded, leaving 177 episodes for analysis. Transmission occurred in 39 (22.0%) episodes. Stratifying by presence of child (Figure 1), transmission occurred in 17 of 54 (31.5%) households in which at least one child was present and 22 of 123 (17.9%) households without children, a risk-ratio (RR) of 1.76 (1.02, 3.04), p = 0.045.
Female sex of the participant was associated with increased transmission: 30 of 108 (27.8%) episodes in women compared to 9 of 69 (13.0%) episodes in men, a risk ratio of 2.13 (1.08, 4.21), p = 0.021. The association with female sex remained (at borderline significance) when restricted to households without children (123 episodes): 23.3% transmission compared to 10.0% for males, a risk ratio of 2.33 (0.919, 5.90), p = 0.059. The risk ratio for female sex in households with children (54 episodes) was 1.76 (0.668, 4.66), p = 0.224.
Table 1 summarizes the descriptive statistics (and logistic model results) associated with presence or absence of transmission in the household for the 177 household ILI events in which the participant was the primary case.
There is marked co-linearity between the variables ‘presence of child in household’, ‘age-category’ and ‘household size’. For example, respondents aged 35 – 44 years had significantly greater odds of having a child in the household than those aged 18 – 24 years (OR 53.2 (13.0, 217)), while no participant aged more than 55 years lived with a child. The relationship between the household size distribution and presence or absence of children is depicted in Figure 2. We retained ‘presence of child in household’ in the final multivariate model for transmission due to its strong predictive role, intuitive appeal, presumed causal role in our observed (univariate) association with age-category, and previous research indicating an association between transmission and children. In the multivariate model, the observed increased risk of transmission with female sex remains (OR = 2.45 (1.01, 5.93), p = 0.047). Presence of children in the household is both the strongest and most statistically significant factor associated with transmission (OR = 2.63 (1.18, 5.88), p = 0.018).
A total of 426 outpatients with ARIs were enrolled from January 2015 to April 2016 in this study. Of them, 246 specimens (57.7%, 246/426) were positive for at least one virus, and single infections accounted for 89.8% (221/246) of cases. Coinfections were observed in 10.2% (25/246) of cases (Fig. 1a). Of the single virus infections, FluA virus was the most frequent virus, identified in 47.5% (105/221) of the cases, comprising 63 (60.0%) cases of sH3N2 virus and 42 (40.0%) cases of pandemic H1N1 (2009) virus patients, followed by EV (10.4%, 23/221), FluB (8.6%, 19/221), ADV (8.1%, 18/221), RhV (7.7%, 17/221), hMPV (5.4%, 12/221), and other viruses that were identified under 5.0%, respectively (Fig. 1b). Of the 25 coinfections, RhV + sH3N2 viruses were predominantly identified and accounted for 28.0% (7/25) of cases. RhV + EV, RhV + ADV and RhV + PIV-4 viruses equally accounted for 12.0% (3/25) of cases. ADV + hMPV, ADV + RSV-B, and FluB+PIV-3 also equally accounted for 8.0% (2/25) of cases. Other coinfections were identified in 4.0% (1/25) of cases (Fig. 1c). sH1N1 virus was not detected in this study.
The epidemic outbreak due to the SARS-CoV was the first worldwide epidemic of the 21st century. It began in Guangdong province of China in November 2002 and spread all over the world within just a few months. This new coronavirus was quickly identified thanks to a concerted international effort.
From November 2002 to July 2003, SARS-CoV affected more than 8000 people in all five continents and caused about 800 deaths. One of the striking features of this epidemic was its nosocomial propagation and the heavy burden of the health care workers. Moreover, the mortality rate was higher than 50% in aged (>60-year-old) populations.
SARS-CoV infection in humans typically causes an influenza-like syndrome such as malaise, rigors, tiredness and high fevers. In one-third of the infected patients, the clinical symptoms regress and patients recover, with, for some of them, persistent pulmonary lesions. In the remaining two-thirds of the infected patients, the disease progresses to an atypical pneumonia. Respiratory insufficiency leading to respiratory failure is the most common cause of death among those infected with SARS-CoV. Many of these patients also develop watery diarrhea with active virus shedding (until several weeks), which might increase the transmissibility of the virus and add another evidence of gastrointestinal tropism of HCoVs. Moreover, the SARS-CoV receptor, the angiotensin-converting enzyme 2 ACE-2, is present in lungs but also in the gastrointestinal tract.
SARS-CoV seemed predominantly transmitted by respiratory droplets over a relatively close distance. However, direct and indirect contact with respiratory secretions, feces or animal vectors could also lead to transmission, at least under some circumstances.
Respiratory viruses are the most common cause of infection among the general population. Every year, they cause millions of illnesses, thousands of hospitalizations and deaths,4 and impose a significant economic burden on health care systems.5 Respiratory viral infections are highly contagious, and the lack of a vaccine (with the exception of influenza) and absence of long‐lasting immunity favors the continued recurrence of outbreaks of these pathogens. Despite this ubiquitous circulation, very little is known about the prevalence of these infections among the global population, as the available estimates are based on syndromic surveillance (eg, in the United States, the CDC‐based National Respiratory and Enteric Virus Surveillance System (NREVSS) and FLUVIEW1, 2) and prospective studies are usually restricted to groups at risk.
Most of the existing literature focuses on young children because acute respiratory infections are a leading cause of childhood hospitalization and mortality worldwide.6, 7 Early life respiratory viral infections, principally due to rhinovirus and RSV, have been shown to be associated with the development of recurrent wheezing and asthma in infants and children.8, 9, 10 Conversely, the impact of these infectious agents on healthy adults and the role of asymptomatic infections on transmission dynamics have not been properly investigated. A recent study found high levels of asymptomatic respiratory infection among an ambulatory adult population in New York City.11
The work presented here is part of larger project attempting to document the prevalence of respiratory viral infections among different strata of the population, with a specific focus on the environmental, demographic, and genetic factors affecting susceptibility, symptomology and transmission. Here, we have shown that respiratory virus infections are present among all age groups, with almost all participants presenting with at least one viral infection per year and an overall rate of 17.5% of positivity among all collected samples. Infection appeared to be strongly connected with age, with young children presenting more than double the number of infections of other age groups. Adults with daily contact with children (parents and pediatric doctors) also had a higher number of infections than their counterparts without daily contact with children. Moreover, the distribution of respiratory virus infections for parents and pediatric doctors was very similar to the distribution observed in the children. These observations suggest children are a principal source of respiratory infection and confirm earlier studies that found day cares to be optimal environments for transmission.12, 13 Self‐identification with American Indian or Alaskan native race was also a factor influencing the number of respiratory viral infections. This association was likely due to the non‐mixed nature of our population, as nearly all of the participants self‐identifying as Alaskan native or American Indian were children or parents from one of the day care settings. Children were also associated with a higher risk for co‐infection than adults and teenagers, as has also been shown in earlier studies.14
A larger variety of viruses was found in children and their close contacts; however, rhinovirus and coronaviruses were the most frequently identified viral respiratory pathogens in all age groups. Together, these two viruses accounted for more than 70% of positive results. The presence of multiple subsequent infections with the same virus in many individuals suggests short‐lasting immunity or potential low cross‐immunity among multiple co‐circulating serotypes of the same pathogen. Previous studies on HRV report up to 20 different rhinovirus types (among more than one hundred known) circulating in a community during one season. Further, the prevailing strains can differ widely between locations, across seasons, and switch almost completely from year to year.15
Our estimates of incidence rates differ markedly from those built on syndromic surveillance data.16, 17 Among patients seeking care, some viruses like influenza are overrepresented and others, like coronaviruses, profoundly underrepresented. This asymmetry is likely due to the different pathogenicity of the viruses causing respiratory infections and underscores the importance of using of non‐syndromic surveillance data to capture the true overall prevalence of respiratory virus infection within the general population.
A limitation of this study is the low frequency of sampling in late spring/summer months, due to decreased participation of the enrolled individuals. Despite the lower number of samples collected during these months, seasonal and non‐seasonal patterns are clearly identifiable. Some viruses (influenza, RSV, coronavirus and HMPV) had a distinct peak during winter months, whereas others circulated year round. Such assessment of seasonality for different pathogens is important for planning vaccination and control strategies and to understand the dynamics of transmission.
Future work should involve analyses of differences in pathogenicity among respiratory viruses, as well as the impact of genetic, demographic, and environmental features on pathogenicity. Moreover, longitudinal sampling coupled with information on symptomology should be used to analyze the impact of asymptomatic infections and the role of asymptomatic carriers on transmission dynamics.
HRSV and IFV were the most frequently detected viruses with high incidence of 23.0% (358/1560) and 22.1% (344/1560), respectively, among all patients with ARIs. HRV was detected in 15.1% (235/1560), followed by HMPV, HPIV and HBoV with the detection rates higher than 5.0%. The positivity rates of HCoV and HAdV were lower than 5.0% (Fig 2).
The usage of molecular techniques for viral infections has improved the identification of mixed viral detection in a single sample. In this study, we assessed the incidence of viral mixed detection in Kuwait during three and a half consecutive years, September 2010 to April 2014 by PCR techniques in hospitalized children and adults with URTI and LRTI. The overall prevalence of viral mixed detection in Kuwait among hospitalized patients with RTI was 14%. The frequency of mixed viral detection was approximately 8% higher in LRTI than in URTI. From the published studies that use molecular diagnostics to report respiratory viral mixed detection, no other studies match our study population (children and adults) or clinical presentation (URTI and LRTI). A community-based study in Jinan, China, of 720 samples from inpatient and outpatients with RTI during a one-year period identified viral mixed detection in 95 samples (13.19%). Also, in this study the virus positive rate was approximately 20% higher in LRTIs than in URTIs. In a recent study 48% (140/292) of the samples from hospitalized children and adults with acute LRTI, viral mixed detection, were observed in 8% (22/292) of the samples. In another recent study of 131 samples from children aged 0–8 with acute RTI, 19 (14.5%) were identified with mixed viral detection.
The three principal pathogens involved in mixed viral detection were HRV, AdV, and HCoV-OC43. Similar results were reported, where they identified HRV, AdV, and HCoV-OC43 as the leading viruses involved in mixed detection. Other studies reported different groupings of leading viruses involved in mixed detection. Recent studies reported RSV, HRV, and AdV as the leading viruses involved in mixed detection among children [8, 31] and among children/adults. In another study the most prevalent viruses involved in mixed detection among children with RTI were HRV, PIV, and Flu viruses. These differences may be attributed to the panel of respiratory viruses tested, regional or environmental variability and the difference of the virus detection techniques.
Out of the 49 virally coinfected patients, 45 (12.8%) suffer from double viral detection and 4 (1.1%) triple viral detection. In an epidemiological study from Korea the mixed viral detection analysis showed 17.1% of double detection and 1.8% of triple detection, which is higher than our result probably due to the fact that they tested larger sample size and we tested a larger panel of viruses. Another study also reported double 20.3% and triple 3.9% viral detection among children with RSV infection. The most frequently detected combinations were HRV/AdV, HRV/HCoV-OC43, and HRV/FluA. The combination of HRV/AdV is the leading combination; this finding is directly comparable with those from previous reports [8, 30]. In this study, the majority of viral mixed detection was among children <1 years of age (20 patients or 5.7%). This is comparable with other recent studies [8–10, 31]. This may be due to an immature immune system of the infants and the absence of earlier exposure to respiratory viruses which could increase their susceptibility to a mixed infection.
In this study virus mixed detection was not identified between RSV and hMPV although a number of studies have found hMPV and RSV coinfection rates of approximately ~5–14% [20, 32, 33]. However, in a study conducted in Netherlands in hospitalized children with LRTI, no virus coinfection between RSV and hMPV was detected.
As shown in Table 2, HCoV-OC43 positive patients were most commonly coinfected with HRV and RSV. In a study conducted in China from 2006 to 2009 aimed to assess the overall prevalence of 10 respiratory viruses in children with acute LRTI, coronaviruses-positive samples were most commonly coinfected with HRV and RSV. Similar data describing a high rate of mixed detection of coronaviruses with RSV has also been previously described [36, 37].
Since the first identification of KIV and WUV, their viral sequences have been identified globally in respiratory samples from patients with RTI [38–41]. However WUV and KIV were found at similar rates in control individuals without respiratory diseases so the association between these polyomaviruses and respiratory diseases remains hypothetical [38, 40, 42]. A mixed detection rate of 74% has been identified for KIV and rates stretching from 68 to 79% for WUV [39–41]. In this study, hospitalized patients with a single WUV detection were diagnosed with bronchitis, bronchiolitis, and pneumonia (Table 1). In a study in Southern China, hospitalized children with a single WUV detection presented with cough, moderate fever, and wheezing and they were also diagnosed with pneumonia, bronchiolitis, URTI, and bronchitis. These findings suggest that polyomavirus can cause URTI and LRTI. In another study assessing the incidence and viral load of WUV and KIV in respiratory samples from immunocompromised and immunocompetent children revealed that the prevalence of WUV and KIV is similar in immunocompromised patients compared with that of the immunocompetent population. Nevertheless these data have to be confirmed in further studies.
Several studies have shown that Boca detection tends to be associated with other respiratory viruses such as HRV, AdV, and RSV [23, 35, 45]. In this study Boca virus mixed detection was identified with HRV, HCoV-OC43, HCoV-229E, and AdV (Table 2). Persistent viral shedding and high frequency of mixed detection have led to an argument over its role as a true pathogen [42, 46]. Other studies confirmed that Boca virus is most probably the cause of RTI if the patient has a single detection and high viral load in clinical samples [45, 47]. In this study, our patients who were diagnosed with a single Boca virus detection suffered from both URTI and LRTI (Table 1). Nevertheless, despite this debate it is becoming increasingly obvious that Boca virus is an important respiratory virus.
Our findings might indicate an association between respiratory virus mixed detection and the possibility of developing more severe LRTI such as bronchiolitis (P = 0.002) and pneumonia (P = 0.019) when compared with single detection. The relationship between mixed viral detection and disease/clinical severity is debatable. Earlier studies have reported that mixed detection with respiratory viruses increased the risk of hospitalization and pneumonia [8, 9, 13, 14], while other studies reported no association between mixed detection and disease/clinical severity [16, 49]. However, despite the availability of sensitive molecular assays, reports are still controversial concerning the role of mixed detection in the disease/clinical severity in comparison to single detection. A number of theories have been proposed to explain the association between mixed respiratory virus detection and RTI severity; these theories include alteration of immune responses after the primary infection [50, 51] and host vulnerability to multiple viruses.
The seasonal incidence of mixed viral detection was detectable throughout the year except for the month of July, with the peak incidence during the months of January, June, and November (43 incidences of detection or 42.1%).
In summary, our findings may indicate that viral mixed detection in patients with RTI is not uncommon and that mixed detection may increase the clinical severity of patients with pneumonia or bronchiolitis. Further investigations are necessary to investigate the determinants of disease severity in viral mixed detection in RTI.
Although this study has several limitations like the lack of study controls (matched hospitalizations without RTI necessary to estimate attributable disease), difference in RT-PCR sensitivity/specificity among targeted pathogens, and lack of systematic testing for potential bacterial pathogens, viral loads were not detected but these data provide representative results of mixed respiratory viral detection in Kuwait.
HCoVs are suspected to cause digestive dysfunctions. First, they have been associated with necrotizing enterocolitis in newborns, and diarrhea or other gastrointestinal symptoms have been shown to accompany coronavirus infections. Then, other findings such as the detection of viral particles and coronavirus RNA in stool samples, or the presence of HCoV OC43 antibodies in children with gastroenteritis, support this idea. However, despite these arguments, their implication in human intestinal infections is still controversial but should be considered to evaluate the potential routes of HCoVs spread.
Another debate is the potential involvement of HCoVs in central nervous system diseases such as multiple sclerosis. This is supported by a body of evidence, e.g. neurological symptoms in some HCoV OC43 infected patients, experimental infection of neural cells with HCoV 229E and OC43, detection of HCoV 229E and OC43 RNAs and antigens in brain of multiple sclerosis patients, or, more recently, neuroinvasive properties of HCoV OC43 after intranasal inoculation in mice. However, the precise and real implication of HCoVs in neural diseases has not yet been clearly demonstrated.
Furthermore, some studies reported also some heart troubles associated with HCoVs infections.
The raw sequence data of the samples, after removal of human reads have been deposited to Sequence Read Archive database (http://www.ncbi.nlm.nih.gov; accession number SRX6713943-SRX6714030).
Of the 46 HCoV-positive samples, 38 (82.6 %) were derived from patients with acute infections involving the upper respiratory tract such as the nose, sinuses, pharynx or larynx (Table 4). The remaining 8 (17.4 %) were from patients with lower respiratory tract infections. The most common clinical manifestations were fever and rhinorrhea, although several patients experienced tachypnea and hypoxemia.
A total of 4215 samples were collected and analyzed. Among them, 737 (17.5%) tested positive for one or more of the respiratory viruses. Seventy‐two samples tested positive for multiple viruses (10% of the positive tests). Across time, between 10% and 25% of the samples tested positive each week, this overall rate of positivity did not exhibit a trend or seasonality.
Rhinovirus and coronavirus were the most frequently identified viruses, present in 408 and 188 samples (55% and 25% of the positive samples), respectively, followed by adenovirus (11%), RSV (5%), influenza (5%), parainfluenza virus (4%), and HMPV (3%). Among these viruses, influenza, RSV, coronavirus, and HMPV were most prevalent in the winter months and had no documented incidence during the summer months. In contrast, rhinovirus, adenovirus, and parainfluenza circulated throughout the entire study period (the temporal distribution is showed in Figure 1 and Supplementary Figure S1).
We compared the results among the following four cohort groups: children, teenagers, adults with daily contact with children, and adults without daily contact with children. Children presented a significantly higher number of co‐infections than the other groups: 16% of positive results among children were positive for more than one virus vs 0%, 2%, and 6%, respectively, for teenagers, adults without children, and adults with children (significantly higher for the children, P < 0.0001).
The percentage of tests that were positive differed significantly among the groups: 36% for children, 15% for teenagers, 17% for the adults with children, and only 7% for adults without children. The odds of testing positive for children were six times higher than the odds of testing positive for adults without daily contacts with children (see Supplementary Figure S2 for raw numbers across the different locations and Table S1 for results of logistic regression). The analysis of viral events also confirmed a significant difference across groups, with children exhibiting the highest number of viral events and adults without children the lowest. Comparison of the number of viral events among the four groups is shown in Figure 2, together with P‐values for pairwise comparisons (Table 2).
We tested the effect of several baseline factors on the normalized number of infections. Gender, presence of pre‐existing respiratory conditions (any condition, but also separately asthma and allergy), choice of public vs private means of transportation, and self‐identification with Hispanic ethnicity did not have a significant association with the number of infections. In contrast, age group, living with other children, and self‐identification with American Indian race had a significant effect on the number of infections per 10 test (P‐values respectively 0, 0.05, and 0.01). Note that the majority (73%) of people self‐identifying as American Indian were children.
The distribution of viruses found across the different age groups was similar, with coronavirus and rhinovirus accounting for 70% to 82% of positive results. Children and adults with daily contacts with children had similar distributions of infecting pathogens, with higher percentages of adenovirus and parainfluenza than the other groups (Figure 3). Multiple subsequent infections with the same virus were frequently identified for HRV and coronavirus in all groups and for adenovirus among the children. In particular, multiple infections with rhinovirus were documented for 30% of the participants, with a maximum of 7 separate HRV events within a year.
The most prevalent phyla were Proteobacteria, Firmicutes Actinobacteria and Bacteroidetes, see Fig 6.
The normalized bacterial read count of the most prevalent phyla was not significantly different between patients with a PCR-target virus positive and PCR-target negative patients.
Pathogenic bacterial species detected with an abundance of >10% of the bacterial reads were: H. influenza (five samples); M. catarrhalis (20 samples); S. pneumoniae (one sample); and S. aureus (one sample). No apparent association with bacteriophages was found, or was a high abundance of bacteriophages associated with COPD exacerbations of viral cause.
We observed 12 pathogens that showed significant differences in incidence according to patient sex. Specifically, male patients across all ages and risk groups displayed higher PDRs for RSV, PIV (PIV1, PIV3, and PIV4), HRV and CP than female patients did. In contrast, female patients displayed higher PDRs for IBV, MP, HMPV, HCoV-229E, and ADV (Table 2).
In total, 1,733 disease reports were recorded in the present study and 1,843 nasal swabs were received, of which 48% tested positive for one or more of the analyzed viruses. In total, 1,461 sick reports could be merged with the laboratory data.
It is not surprising that the number of nasal swabs exceeded the number of disease reports. Some individuals, indeed, enjoyed the possibility to have a quick feedback on their test results and to get to know more about the virus description and its associated disease but they did not contribute to the study with their disease reports. This explains part of the 382 swabs that could not be matched, the remaining part is due to timing issues: when the number of days between arrival of the specimen to the lab and disease onset was greater than 15 or smaller than -5 (case where the specimen was sent before the registered onset of disease) laboratory results were not linked to symptoms reported. On the other hand, 272 sick reports could not be merged to nasal swabs. Apart from the same issue of timing, this fact might also be explained by certain symptoms reported. The absence of a nasal sample correlates well with the presence of vomiting and the absence of runny nose.
Further details on specimen results can be found in our Eurosurveillance paper. Briefly, when analyzing the seasonality, we found that the peak in the number of returned swabs was reached in the last week of September 2011, but the proportion of positive tests increased from mid-November until April. Among all samples tested, rhinovirus was the most common virus diagnosed (20.8%), followed by viruses belonging to coronavirus group (16.2%) and influenza (4.8%).
The average number of reported infections and virally diagnosed infections during the 9-month follow-up is reported in Table 3 stratified by the 10 largest occupational sub-major groups and gender. The differences in the number of reported infections were larger among the different occupational sub-major groups than between men and women. The occupational sub-major groups that reported the highest number of infections were those including health care workers (SSYK96 code 32) and preschool (SSYK96 code 33) and primary teachers (SSYK96 code 23) working with younger children. Corporate managers (SSYK96 code 12) reported instead the lowest numbers of infections.
Within 258 multi-occupancy households, 177 primary-participant introductions gave rise to 54 secondary cases among 391 potentially exposed individuals, a secondary household attack rate (SHAP) of 0.138. Of 102 exposed children, 22 developed ILI (SHAP = 0.216) compared to 32 of 289 adults (SHAP = 0.110), a risk-ratio of 1.95 (1.19, 3.19), p = 0.012. The adult SHAP did not differ by presence of children in the household (0.117 vs. 0.109, RR = 1.07 (0.486, 2.35)).
In households in which the participant was female, 41 secondary infections were reported among 238 exposed household members (SHAP = 0.172), compared with 13 secondary cases among 153 contacts in households in which the participant was male (SHAP = 0.085), a risk-ratio of 2.03 (1.12, 3.66), p = 0.016 (2-sided Fisher’s exact). In households with children, 27 secondary infections were reported among 162 exposed household members (SHAP = 0.167), compared with 27 secondary cases among 229 contacts in households without children (SHAP = 0.118), a risk-ratio of 1.41 (0.863, 2.32), p = 0.182.
A multivariate Poisson regression model (Table 2) was used to consider the influence of virus group and demographic characteristics on the number of reported secondary cases within a given household, offset against the number of potentially exposed household members. In correspondence with the logistic regression model for transmission, we include presence of children in the household rather than age-category or household size in the model. Consistent with the findings from the multivariate model for transmission and the univariate SHAP analyses, we observe a significant effect of sex. Presence of children in the household, while suggestive of an increase in the SHAP, is not statistically significant. In contrast to the analysis on transmission, we observe a positive association for the number of secondary cases with isolation of influenza (Incidence Risk Ratio (IRR) = 2.11 (0.992, 4.49), p = 0.052). Note that if, as an alternative to “no virus detected”, we use picornavirus as the reference group, we again find a positive association with isolation of influenza (IRR = 2.25 (0.960, 5.29, p = 0.062) in a multivariate model, full results not shown).
In a secondary analysis, we considered the influence of prior vaccination on the reported number of secondary household cases among participants testing positive for influenza compared with all other participants. In a Poisson model for secondary attacks including a statistical interaction between influenza detection (true/false) and vaccination status (placebo/vaccine), the IRR for influenza positive cases in those receiving placebo was 1.69 (0.421, 6.80), p = 0.459. The factor increase (interaction term) for the IRR for vaccinated participants was 3.10 (0.608, 15.8), p = 0.174, yielding a net IRR for vaccinated influenza-positive participants relative to vaccinated influenza-negative participants of 5.24 (2.17, 12.6), p < 0.001).
From the overall number of 850 hospitalized patients three hundred fifty one patients (47.8%) were diagnosed with viral respiratory infections, 210 (59.8%) of them were males and 141 (40.2%) were females. Results show that from the 351 patients 408 viruses were detected. Table 1 shows that HRV was the most detected virus in clinical respiratory specimens of patients with respiratory symptoms (41.6%), followed by FluA (15.1%), RSV (13.1%), and HCoV-OC43 (12.3%). Among the 351 hospitalized patients viral mixed detection was detected in 49 patients (14%). HRV was the most common virus associated with mixed detection (7.1%), followed by AdV (4%), HCoV-OC43 (3.7%), RSV (3.1%), and FluA (2.8%) (Table 1).
It was interesting to note that four patients had triple viral mixed detection. The first patient was infected with Boca, HCoV-OC43, and HRV, the second patient was diagnosed with WUV, Boca, and HCoV-229E, the third one was infected with HCoV-OC43, FluA, and HRV, and the fourth patient was infected with KIV, RSV, and hMPV.
Table 2 shows the frequency of viral mixed detection among the 49 patients. The highest combination of viral mixed detection was identified with HRV and AdV in 7 patients (2%), followed by HRV and HCoV-OC43 in 5 patients (1.4%), and HRV and FluA in 4 patients (1.1%).
From the 49 (14%) patients with mixed detection, 33 (9.4%) of them were males (32 patients (9.1%) with double detection and one patient (0.3%) with triple detection), and 16 (4.6%) were females (14 patients (4%) with double detection and 2 patients (0.6%) with triple detection) (Table 3).
In total, 20 of the 49 (5.7%) patients with viral mixed detection were aged <1 years (18 patients (5.1%) with double detection and 2 patients (0.6%) with triple detection), 17 patients (4.8%) were 1–14 years (16 patients (4.6%) with double detection and one patient (0.3%) with triple detection), and 12 patients (3.4%) were ≥15 with double viral detection (Table 3). Overall, the majority of viral mixed detection, reaching 8.5% (n = 30), was among children ≤5 years of age. Table 4 shows the distribution of median age, range, and IQ of patients with mixed detection for each virus. The median age was <1 years for Boca, HCoV-OC43, and RSV whereas for WU, AdV, FluA, PIV-3, HRV, and hMPV it was 1–11.5 years. Furthermore, for the rest of the respiratory viruses KI, HCoV-229E, and PIV-1 it was ≥15 years of age.
Mixed viral detection was identified in 17 patients (4.8%) with pneumonia (15 patients (4.3%) with double viral detection and 2 patients (0.6%) with triple detection), 15 patients (4.8%) with bronchiolitis (14 patients (4%) with double viral detection and one patient (0.3%) with triple detection), 10 patients (2.8%) with URTI all suffered from double viral detection, and 7 patients (2%) with respiratory distress (RD) all suffered from double viral mixed detection (Table 3). the majority of infections by the investigated respiratory viruses affected the lower respiratory tract (39 patients or 11.1%) rather than the upper respiratory tract (10 patients, or 2.8%). Pneumonia and bronchiolitis were the most frequent reason for hospitalization with viral mixed detection (32 patients or 9.1%). Table 1 compares the clinical manifestation of patients with mixed and single viral detection. There were statistical significance differences between mixed and single detection in patients diagnosed with bronchiolitis (P = 0.002) and pneumonia (P = 0.019).
The majority (32 patients or 9.1%) of hospitalized patients were admitted to wards, followed by pediatric intensive care unit (PICU) (11 patients or 3.1%), and intensive care unit (ICU) (6 patients or 1.7%) (Table 3).
The peak incidence of viral mixed detection was identified during the month of November (15 incidences of detection or 14.7%) followed by January and June (14 incidences of detection each or 13.7%). The lowest incidence was detected during the month of August (2 incidences of detection or 2%) and no viral mixed detection was identified during the month of July (Figure 1).
Among the 5833 samples analyzed, 637 (10.9 %) were positive for influenza A virus, 206 (3.5 %) were positive for influenza B virus, 201 (3.4 %) were positive for respiratory syncytial virus A, 91 (1.6 %) were positive for respiratory syncytial virus B, and 78 (1.3 %) were positive for adenovirus (Table 2). More importantly, 46 samples (0.79 %) tested positive for HCoV. Co-infection with other respiratory viruses was not observed in the HCoV-positive samples. All samples were negative when tested for MERS-CoV N gene.
Amongst the HCoV-positive samples, 56.5 % were from men and 43.5 % were from women (gender ratio 1.3:1) (Table 3). HCoV infection was detected in all age groups, and the mean age of HCoV-infected patients was 21.37 years (min. = 4 months, max. = 93 years, mean = 27.04 years). The percentage of HCoV infection per year was 0.81 % (23/2838) in 2012 and 0.77 % (23/2995) in 2013. There were no seasonal peaks associated with HCoV and no positive samples were identified in the typically dry months of April and November during both years (Fig. 1).
The majority of HCoV (25/46 or 54 %) were detected mainly in young children between the ages of 0–5 years and 15 % in the elderly aged over 60 years (Fig. 2). Approximately 80 % of the positive samples (37/46) were isolated from the NPS samples, while the rest (9/46) were from the NPA samples. Furthermore, 46 % (17/37) of all positive NPS and 89 % (8/9) of all positive NPA samples belonged to the 0–5 year age group.
To further differentiate the 46 HCoV-positive samples, the S gene was sequenced. Analysis showed that these HCoV strains belonged to one of the four HCoV species (Table 2; Fig. 3). In all, 19 (0.32 %) were positive for HCoV-HKU1, 19 (0.32 %) were positive for HCoV-NL63, 5 patients (0.09 %) were positive for HCoV-229E, and 3 (0.05 %) were positive for HCoV-OC43. Relative to all HCoV-positive samples, therefore, the predominant genotypes were 41.3 % for both HCoV-HKU1 and HCoV-NL63 (19/46), followed by 11 % HCoV-229E (5/46) and 6.5 % HCoV-OC43 (3/46). Interestingly, HCoV-NL63 and HCoV-HKU1 appeared sporadically during the study period and were detected mainly in March 2012 and July 2013, respectively.
Four of the seven human coronaviruses are endemic around the world but cause little more than the common cold. Currently, SARS-CoV-2 is a global epidemic, with the potential to be considered a pandemic. In one scenario, this outbreak may be contained, and the virus never seen again, like SARS-CoV. Alternatively, the virus may become an endemic virus with seasonality like influenza and the other human coronaviruses. However, it is too early to know whether SARS-CoV-2 spread will be affected by changing weather conditions. Nearly all cases of COVID-19 have been in China, where it is winter; whether cases will decrease as temperatures increase in the Northern Hemisphere, as is seen for influenza, remains to be seen.