Symptom profiles of community cases infected by influenza, RSV, rhinovirus, seasonal coronavirus, and SARS-CoV-2 variants of concern

Abstract

Following earlier lockdown time during the COVID-19 pandemic, the co-circulation of respiratory viruses with SARS-CoV-2 became obvious. It is critical to study the clinical features and symptoms of diverse respiratory viral infections in order to better detect cases and understand the evolutionary processes of SARS-CoV-2 variants. From 2006 to 2011, the Flu Watch and Virus Watch projects in England and Wales monitored the epidemiology of influenza, respiratory syncytial virus, rhinovirus, seasonal coronavirus, and SARS-CoV-2, respectively. The study compares the proportion of symptoms reported during various common respiratory viruses' infections. With the advent of new strains, SARS-CoV-2's symptom profile is increasingly resembling those of other respiratory viruses.

Notably, Omicron strains are linked to an increase in coughing, sore throat, runny nose, and sneezing. As SARS-CoV-2 becomes endemic, clinical surveillance will rely heavily on closely tracking the development of its symptomatology in relation to new variations.

Introduction

In February 2022, the UK government announced a new plan for "living with COVID-19," which many other nations also embraced. As a result of the cessation of all required non-pharmaceutical measures, previously suppressed respiratory viruses have begun to resurface alongside SARS-CoV-2. The CDC in the United States recorded an increase in flu-related hospitalizations and fatalities, exceeding the previous year's figures. Furthermore, there has been an alarming rise in respiratory syncytial virus (RSV) incidence in Europe, particularly among youngsters.

The decreasing availability of free testing will make detecting various respiratory viruses more difficult. There are no community-based studies comparing SARS-CoV-2 symptoms to those of other prevalent respiratory viruses, and doubts remain due to the appearance of numerous versions of concern during the pandemic.

The purpose of this research is to address this vacuum by detailing the symptoms of diverse respiratory diseases and evaluating how SARS-CoV-2 symptoms have altered with different variant waves. Furthermore, the study assesses the use of World Health Organization (WHO) standards for diagnosing various respiratory viruses in sick persons.

Methods

Flu Watch (2006-2011) and Virus Watch (2020-2022) cohort studies focusing on studying influenza and SARS-CoV-2. They looked on epidemiology, clinical aspects, risk factors for transmission, and immunity in home and community settings. Both studies enlisted the participation of all members of participating homes and performed weekly questionnaires beginning with the onset of symptoms.

Flu Watch

The Flu Watch research was carried out in England over six influenza seasons, following complete homes. The analysis covered seasonal influenza outbreaks from 2006 to 2009, as well as the first three waves of the 2009 pandemic. A total of 5,484 people were tracked for 118,158 person-weeks. On day two of any sickness resembling a "cough, cold, sore throat, or flu-like illness," participants gave nose swabs, which were subsequently examined using polymerase chain reaction (PCR) to identify influenza infection. PCR tests were also employed at various intervals throughout the trial to identify rhinovirus, RSV, and seasonal coronavirus. For analysis, only respiratory and constitutional symptoms such as sore throat, cough, runny nose, sneezing, fever, headache, and exhaustion were included.

Virus Watch

Virus Watch is a home community cohort research that has been running in England and Wales since the middle of June 2020. It has collected data from 58,566 people in 28,495 houses by February 2022. Participants completed weekly online surveys reporting any respiratory, constitutional, gastrointestinal, ophthalmic, or cutaneous symptoms, as well as any SARS-CoV-2 tests (LFT or PCR). The study looked at symptoms including coughing, sore throat, fever, and loss of smell and taste. The Virus Watch study protocol is available online13.

Data processing

Both studies focused on symptom data extraction and categorization into illness episodes. Cases without reported symptoms were excluded. The beginning of an illness episode was marked by the first day of symptom reporting, while the end was identified as the last day of reported symptoms. A seven-day symptom-free period served to distinguish separate illness episodes.

Swab results in Virus Watch were correlated with illnesses within seven days of symptom onset. The study connected self-reported SARS-CoV-2 test outcomes with data from the UK Second-Generation Surveillance System (SGSS). This linkage was facilitated using participants' unique personal identifiers, full names, dates of birth, and home addresses, which were recorded during interactions with the English National Health Service (NHS). The SGSS dataset includes SARS-CoV-2 test results from Pillar 1 (secondary care testing) and Pillar 2 (community testing).

SARS-CoV-2 variant designation

Testing for SARS-CoV-2 through LFT or PCR can detect the presence of the virus but does not identify the specific variant. To overcome this limitation, the researchers used national surveillance data and applied a threshold. They designated a variant to a particular case if that variant constituted at least 75% of all sequenced genomes in the region at the time when the case's symptoms appeared. Illnesses in regions and weeks without a dominant variant reaching the 75% threshold were excluded from the analysis. The variants were defined based on the UK Health Secretary Agency (UKHSA) definition, while the term "wild-type" referred to all SARS-CoV-2 variants circulating before the Alpha variant.

Analysis

The study examined the symptom profiles of individuals infected with different viruses, including SARS-CoV-2, seasonal coronaviruses, influenza, RSV, and rhinovirus. They compared the symptoms experienced during these illnesses and evaluated the compatibility of the WHO case definitions for Acute Respiratory Infection (ARI) and Influenza-Like Illness (ILI).

To account for differences in testing strategies, the researchers focused on SARS-CoV-2 cases submitted from participants meeting the ARI case definition. They used multivariate logistic regression to analyze the odds of experiencing various respiratory and constitutional symptoms based on different SARS-CoV-2 variants, while controlling for known confounders such as age, sex, clinical vulnerability, and immunity status.

The participants' past exposure to SARS-CoV-2 was determined based on whether they had a positive illness or vaccination within 14 to 90 days before experiencing symptoms. This determination followed guidelines from the UK Health Security Agency and the European Centre for Disease Control.

Ethics

The Virus Watch study obtained ethics approval from the Hampstead NHS Health Research Authority Ethics Committee with a specific approval number. Informed consent was obtained from all members of participating households, including consent for children. The consent process took place electronically during registration. The study followed relevant guidelines and regulations, and all consent forms have been properly archived by the institution.

Results

Flu Watch vs. Virus Watch

The article discusses the frequency of symptoms observed in SARS-CoV-2 and other respiratory viruses. The data presented in Table 1 and the radar plot in Fig. 1 show that cough was most common among influenza, rhinovirus, RSV, and SARS-CoV-2 Omicron BA5 illnesses, with the respective percentages of illnesses reporting cough being 91%, 87%, 83%, and 81%. Cough was significantly less common in Alpha, Omicron BA1, and wild-type SARS-CoV-2 variants, with frequencies of 60%, 61%, and 65%, respectively.

Regarding fever, it was found to be more common among influenza illnesses (74%) compared to other respiratory viruses. On average, only 17% of COVID-19 illnesses reported fever, but this proportion varied across SARS-CoV-2 variants, with Omicron BA1 and BA2 strains having significantly lower proportions compared to other variants.

Fig. 2 displays the frequency of illnesses meeting the Acute Respiratory Infection (ARI) and Influenza-Like Illness (ILI) case definitions by viruses. Most influenza, RSV, rhinovirus, and seasonal coronavirus illnesses met the ARI definition. However, fewer SARS-CoV-2 illnesses met the ARI definition, with proportions ranging from 75.7% for Alpha to 90.1% for Omicron BA5. The performance of the ILI case definition was generally low, with only 48.6% of influenza illnesses and 14.4% of illnesses from other respiratory viruses meeting the ILI case definition.

Over the course of the SARS-CoV-2 pandemic and with the emergence of new variants, there has been a gradual increase in the proportion of SARS-CoV-2 infections with upper respiratory symptoms. Since the Alpha variant became dominant, COVID-19 illnesses reported an increase in runny nose, sneezing, and sore throat. The rise of the Delta variant was associated with an 11.7 percentage points increase in reported upper respiratory tract symptoms compared to Alpha illnesses. Omicron BA2 was associated with an average increase of 11.3 percentage points compared to illnesses infected with the BA1 strain.

Fig. 3 shows that since the emergence of the Omicron strain, there has been a significant decrease in the proportion of SARS-CoV-2 cases reporting symptoms of loss of sense of smell or taste.

The study used multivariate logistic regressions (Fig. 4) to describe the odds of experiencing respiratory and constitutional symptoms by SARS-CoV-2 VOC with Omicron BA5 as the reference variant. Compared to Omicron BA5, all previously circulating strains were generally associated with decreased odds of cough and sore throat. Infections with any pre-Omicron strain (Delta, Alpha, wild-type) decreased the odds of experiencing sore throat by over 60%. Omicron BA2 was associated with increased odds of sneezing. Pre-Omicron strains were all associated with increased odds of loss of smell and loss of taste. Compared to Omicron BA5, all previously circulating strains except wild-type were associated with lower odds of experiencing fatigue, with Omicron BA1 showing the lowest odds ratios.

Overall, the article highlights the differences in symptom frequencies among various respiratory viruses and SARS-CoV-2 variants, shedding light on the changing nature of the COVID-19 pandemic as new variants emerge.

Discussion

The article describes the symptom profiles of multiple respiratory viruses co-circulating with SARS-CoV-2. Over time, the symptom profile of SARS-CoV-2 has become more similar to other respiratory viruses, with increased frequency of cough, sneezing, runny nose, and sore throat. The Omicron BA2 and BA5 variants of SARS-CoV-2 have shown a significant increase in illnesses meeting the WHO ARI case definition. Fever remains more common in influenza compared to other respiratory viruses studied. The growing similarity between symptoms of SARS-CoV-2 and other respiratory illnesses could make syndromic surveillance less effective, highlighting the importance of multi-pathogen virological surveillance.

The increase in cough and sneezing associated with the Omicron strains may contribute to their higher transmissibility compared to previous variants. Factors like changing levels of immunity in the population due to natural infections, vaccination, and immunity waning could also influence the symptomatology. However, it's unclear whether the observed changes in symptomatology are due to evolving SARS-CoV-2 variants or changing immunity levels in the population.

The study provides insights into the symptoms of SARS-CoV-2 variants, particularly Omicron BA5, compared to other respiratory viruses. It also compares symptom frequencies over different time periods and acknowledges potential limitations of the analysis. Understanding the relationships between symptomatology, immune mechanisms, and viral genetic evolution may help comprehend the transmission advantages of recent SARS-CoV-2 variants.

Data availability

The project aims to share aggregate data on the website www.ucl-virus-watch.net, along with a "Findings so far" section. Individual record-level data can be accessed on the Office of National Statistics (ONS) MDX Browser > Virus Watch—England and Wales @ 89201 (metadata.works). However, due to the sensitivity of health data, access to the data is restricted and can be obtained through the ONS Secure Research Service. The analysis code is publicly available on GitHub at https://github.com/CyGei/Symptom-profiles.

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