Fever and respiratory tract infections are among the leading causes of acute infectious diseases and mortality worldwide, with viral infections being the primary cause1,2. In recent years, a number of viral agents associated with fever and respiratory tract infections have emerged, including H5N132,SARS-CoV33, MERS34, H7N935 and SARS-Cov-25, imposing significant economic burdens and undermining public health globally. Fever clinics play a crucial role in triaging patients during the prevention, control, and treatment of emerging infectious diseases13. However, the detection and surveillance of respiratory pathogens in clinical laboratories still rely on traditional PCR for individual specimen testing36. In this study, we conducted mNGS detections on oropharyngeal swab specimens from fever clinic patients, employing sample pooling strategies to delineate the spectrum of pathogen distribution.
To assess the efficiency of pooled samples on virus detection rates, we employed tNGS to individually test 200 samples and compared the results with those obtained through mNGS. tNGS is a sequencing technology that employs target enrichment to selectively capture regions of interest (ROIs), enabling the successful identification of target pathogens37,38. Through target enrichment, tNGS enhances the proportion of microbial nucleic acids without the need to remove host material38. Particularly suitable for assessing viral distribution in samples, tNGS could specifically amplifies the load and coverage of fastidious and low-abundance pathogens, thereby increasing the credibility of detection39. Despite the mixing of 25 samples into one, the pooling results of mNGS exhibited a high concordance rate of around 80% with tNGS for RNA viruses, while the concordance rate for DNA viruses was less than 60%. Given that tNGS can detect low-abundance pathogens, its methodological advantage is highlighted in DNA detection from individual samples compared to mNGS, which involves DNA detection in pooled specimens with an extremely high host rate. This distinction may introduce a degree of bias in detection efficiency of DNA virus.
When examining the practical application of pooled sample testing in a clinical setting, it is crucial to consider the potential compromise in sensitivity that may occur due to dilution, particularly impacting samples with low viral loads19. Nevertheless, for respiratory virus surveillance, characterized by high viral loads in nasopharynx, this apprehension may be mitigated. Nonetheless, it is important to recognize that this approach may not be universally suitable for infections with potential low viral loads, prompting exploration of supplementary methodologies. In scenarios marked by potentially low viral loads, alternative strategies warrant consideration, as the current approach may not be optimally suited40. Although mixed-sample testing has significantly reduced the costs associated with the mNGS experimental process, it is still necessary to evaluate, from the perspectives of monitoring sustainability and effectiveness, how many samples can be pooled to achieve an optimal balance between improved accuracy and reduced detection costs.
Our study utilized the mNGS mixed detection method to monitor the prevalence of pathogens in cases of acute respiratory infections treated in the fever clinic from June 2022 to June 2023. We detected the circulation of different viruses in fever patients during this period. Influenza virus was found to be the most important pathogen causing fever and respiratory infections41. In June to early July 2022, we observed a high level of H3N2 presence in the pharyngeal swab specimens of patients, indicating its circulation and transmission within the population. Our data suggest a positive relation between the data from the Center for Disease Control revealed that the outbreak in Guangzhou was consistent with our monitoring results. This data suggests that our research strategy may be suitable as a viral monitoring scheme for both CDC and sentinel hospitals.
Respiratory viruses are a group of infectious agents transmitted through the respiratory tract, primarily causing illnesses such as the common cold, influenza, bronchitis, and pneumonia42. Common respiratory viruses include rhinoviruses, influenza viruses, coronaviruses, adenoviruses, among others42,43. Most clinical symptoms induced by respiratory viruses are asymptomatic or mild and have not received much attention in epidemic prevention and control in many countries. Consequently, there is a scarcity of molecular epidemiological data for respiratory viruses in these regions. It is imperative to conduct continuous monitoring of the epidemic trends and phylogenetic aspects of these viruses, as this holds significant importance for disease prevention and control strategies44.This study identified pathogen sequences in acute febrile respiratory specimens, detecting sequences of H3N2 influenza virus, SARS-CoV-2, NL63, RSV and HMPV. Both RSV and HMPV are the most common cause of acute respiratory infections for infants and young children. Given the lack of effective therapeutic drugs and vaccines in China, continuous monitoring of RSV and HMPV is crucial for its prevention and control. RSV is primarily categorized into subtypes A and B, with alternating predominance in recent years in the southern region of China. To investigate the sequence typing of RSV in this study, evolutionary trees were constructed for four RSV sequences, revealing that two RSV-A viruses belonged to the ON1 subtype, while one RSV-B virus belonged to the BA9 subtype. These results suggest that the surveillance method proposed in this study, compared to traditional PCR, can simultaneously provide molecular typing results of viruses. This offers greater assistance for subsequent epidemic monitoring and vaccine deployment.
To control the pandemic of COVID-19, China has taken a series of powerful non-pharmaceutical interventions (NPIs) against COVID-19 in accordance with state and community instructions, such as face mask mandates in public spaces, stay-at-home and lockdown, health screening at points of entry/exit, zero COVID-19 policy. These multifaceted NPIs were highly effective in limiting the spread of the SARS-CoV-2 among the population. However, it is unclear how the non-SARS-CoV-2 respiratory viral etiology and epidemiology changed among fever clinic patients during and after the COVID-19 pandemic. Based on global surveillance data for respiratory viruses such as influenza and RSV, during the COVID-19 pandemic, there has been a significant decrease in the prevalence levels of these non-SARS-CoV-2 respiratory viruses45,46. In our research findings, we observed a significant reduction in the diversity of respiratory viruses among patients attending the fever clinic during the COVID-19 pandemic in Guangzhou. Given that NPIs were designed to mitigate respiratory virus transmission, there have been notable disruptions to the typical seasonal circulation patterns of common respiratory virus infections, including by influenza virus and RSV. The decrease in community prevalence rates of non-SARS-CoV-2 respiratory viruses is undoubtedly influenced by multiple factors, including the collective implementation and sustained use of NPIs, reduced travel, changes in testing priorities and surveillance systems, among others47,48. These changes in the prevalence of other respiratory viruses cannot be fully explained, as in several tests from Dec 2022 to Mar 2023, the SDSMRN of SARS-CoV-2 were not notably high and, in some instances, undetectable. Indeed, studies have indicated that during the COVID-19 pandemic, only 2%-3% of COVID-19-positive patients experience co-infections with other respiratory viruses, whereas during the same period, 13.1% of patients infected with non-SARS-CoV-2 respiratory viruses exhibit co-infections49. These characterize this phenomenon, suggesting that coinfection with other respiratory viruses appears to be uncommon in patients with SARS-CoV‐2 infection. In fact, interactions between different viruses, such as virus interference, are also recognized as a factor that can affect the spread of respiratory viruses in the community50.However, the underlying reasons for this are currently unclear, with one potential explanation being that the immune response induced by SARS-CoV-2 and its interaction with the host may confer a competitive advantage over other respiratory viruses49. While the observed diversity changes in respiratory viruses using mNGS in mixed samples may differ from the concept of viral co-infection, our research results characterize significant differences in the respiratory virus composition among fever patients before and after the COVID-19 pandemic. This provides a novel understanding for discerning whether respiratory virus co-transmission entails antagonism or promotion.
This study has several limitations. Firstly, due to methodological constraints, we were unable to differentiate pathogens in each specimen, thus impeding our ability to discern the infection rates of various pathogens within this cohort of patients. Secondly, amid pandemic control measures, patients of all age groups sought treatment at the fever clinic, yet our experimental design lacked categorization of the included patients. This limitation hindered our understanding of the viral pathogen spectrum across different age groups, thereby compromising the effectiveness of pathogen surveillance. Subsequent research initiatives should refine grouping strategies to unveil respiratory virus profiles in febrile patients with distinct characteristics and features. This enhancement will contribute to more precise epidemic monitoring and control efforts. Moreover, as the pathogen detection approach utilized in this study involves mixed-sample sequencing using mNGS technology, it presents challenges in distinguishing the specific viral strain sequences of individual cases within mixed samples. Furthermore, issues such as sequencing depth and accuracy of species identification may potentially help to improve the accuracy of subtyping for certain viruses.
There are several aspects of the surveillance method utilized in this study that warrant further expansion in the future. It is widely recognized that mNGS not only facilitates the detection of viral pathogens but also enables the identification of bacterial and fungal pathogens, including their resistance and virulence genes38,51. Our study focused primarily on the preliminary analysis of viral pathogens and demonstrated the efficacy of this surveillance method in pathogen monitoring. We believe that, with further optimization, this mNGS-based surveillance strategy could significantly enhance the monitoring of bacteria or fungi, as well as drug-resistant strains, in future applications. Another noteworthy consideration is the distinct seasonal preferences exhibited by these respiratory viruses 52,53. For instance, in the southern region of China, influenza virus primarily peaks during the summer and winter seasons, while RSV predominantly circulates during the winter and spring52. HMPV, on the other hand, experiences heightened prevalence in the spring months of March and April. Furthermore, HPIV, and ADV were prevalent throughout the year52. Due to the limited timeframe encompassed by the study, the seasonal characteristics of these respiratory viruses were not adequately reflected. We aim to continue monitoring these seasonal changes in viruses through extended surveillance efforts. This will enable us to observe how the seasonal patterns of different respiratory viruses have been disrupted following the COVID-19 pandemic and identify the points at which these seasonal variations may eventually return to normalcy.
In conclusion, heightened surveillance of viruses is indispensable for predicting significant epidemic threats and comprehending the spatiotemporal dynamics of these pathogens. The monitoring strategy outlined in this study stands poised to enrich our understanding of epidemiology, outbreak patterns, and viral subtyping of respiratory viruses, thereby enhancing the capacity for timely detection and response to respiratory virus outbreaks, significantly improving viral surveillance.