Clinical presentation and detection of SARS-CoV-2, Influenza, and Other Viral URTIs Using rRT-PCR in a Tertiary Hospital in Pune: A Pilot Study

Clinical presentation and detection of SARS-CoV-2, Influenza, and Other Viral URTIs Using rRT-PCR in a Tertiary Hospital in Pune: A Pilot Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clinical presentation and detection of SARS-CoV-2, Influenza, and Other Viral URTIs Using rRT-PCR in a Tertiary Hospital in Pune: A Pilot Study Priyanka Jali, Varsha Potdar, Anil Pardeshi, Poonam Suryawanshi, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9278895/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Introduction Acute respiratory illness (ARI) is one of the most common illnesses globally, affecting all age groups and leading to both upper respiratory tract infections (URTIs) and lower respiratory tract infections (LRTIs). Severe Acute Respiratory Syndrome Corona virus 2 (SARS-CoV-2) and influenza are significant contributors to severe respiratory illnesses, causing fatalities. Both spread quickly through respiratory droplets and cause similar symptoms, leading to misleading diagnoses and treatments. Aim This study aims to differentiate symptoms and clinical parameters of SARS-CoV-2, Influenza A and B, and the Upper Respiratory Tract Infections (URTIs) due to other causes to understand disease severity better and provide effective treatment. Materials and Methods The study analysed 123 patients from March to November 2023 for routine SARS-CoV-2 and Influenza detection using a structured case reporting form. RNA extraction was performed using a Zybio kit, and a multiplex single-tube combo rRT-PCR assay was developed by the National Institute of Cholera. Results The study examined 123 individuals with upper respiratory tract infections (URTIs), 12 of whom had influenza A, 8 of whom had influenza B, and 6 of whom had SARS-CoV-2. It found no significant differences in age and comorbidities. Compared to male patients, females are more likely to be infected with influenza (A&B), and SARS-CoV-2 infection was more prevalent among males than females. Conclusion The study suggests potential benefits in differential diagnosis of COVID-19, influenza (A and B), and the upper respiratory tract infections (URTIs) due to other causes, though further research is needed to confirm these findings. Acute respiratory illness (ARI) SARS-CoV-2 Influenza upper respiratory tract infections (URTIs) respiratory biomarkers Figures Figure 1 Figure 2 INTRODUCTION Upper respiratory tract infections (URTIs) represent a significant public health burden globally and are among the most prevalent illnesses affecting individuals across all age demographics. Although most cases are confined to the upper airways and resolve spontaneously, a proportion may progress to involve the lower respiratory tract, manifesting as bronchiolitis or pneumonia, particularly in vulnerable populations such as young children and older adults [ 1 ]. Viral pathogens are the predominant causative agents of URTIs. While bacteria typically drive severe lower respiratory infections, viruses such as respiratory syncytial virus (RSV), influenza viruses, and more recently, novel coronaviruses like SARS-CoV-2, are major contributors to upper and mid-respiratory tract diseases [ 2 ]. Seasonal changes and widespread presence of these viruses create obstacles for clinicians and public health authorities in managing and tracking infections. Non-specific symptom presentation in viral respiratory infections presents a significant clinical dilemma. Symptoms like fever, sore throat, cough, myalgia, and rhinorrhoea commonly occur with influenza, SARS-CoV-2, and other viruses, reducing the diagnostic accuracy of syndromic approaches [ 3 ]. The occurrence of bacterial or fungal co-infections may interfere with accurate diagnosis and render therapeutic management more complex [ 4 ]. Establishing the viral etiology without delay is important for tailoring patient care, avoiding unwarranted antibiotic therapy, and initiating public health measures. Real-time RT-PCR has proven to be a pivotal technique for reliably detecting viruses responsible for respiratory illnesses [ 5 ]. The global spread of SARS-CoV-2 and the resulting COVID-19 pandemic has highlighted the persistent risk associated with emerging respiratory viruses, accounting for over 778 million reported cases and more than 7.09 million deaths worldwide [ 6 ], COVID-19 continues to impact health systems and economies. Seasonal influenza also remains a persistent global health issue, responsible for estimated 290,000-650,000 deaths annually despite established vaccination programs [ 7 ]. Even though the global availability of data on viral respiratory pathogens, there exists a paucity of region-specific studies, particularly from India’s western regions such as Pune. Understanding the regional burden, seasonality, and clinical profile of SARS-CoV-2, influenza, and other respiratory viruses is essential for tailoring local surveillance and response strategies [ 8 , 9 ]. Tertiary care centres are uniquely positioned to capture data on complex or referred respiratory infection cases. These institutions function as important surveillance hubs during respiratory virus outbreaks and seasonal surges, providing a clinical window into circulating viral patterns and the burden of co-infections [ 10 ]. Considering the substantial clinical similarities among respiratory viruses, evolving viral epidemiology, and the scarcity of region-specific data, it is essential to systematically assess the patterns of viral upper respiratory tract infections in tertiary care settings. Such data would inform regional preparedness plans and optimize clinical algorithms for diagnosis and treatment [ 11 ]. This pilot observational study sought to assess and differentiate the clinical presentation, epidemiological patterns, and diagnostic characteristics of SARS-CoV-2, Influenza A and B, and other viral upper respiratory tract infections (URTIs) among patients attending a tertiary care hospital in Pune. The study utilizes molecular testing methods to distinguish between viral pathogens and to characterize patterns of respiratory infections in the post-pandemic period. MATERIALS AND METHODS Study Design : This was a cross-sectional pilot study was carried out to assess the clinical and diagnostic characteristics of viral upper respiratory tract infections (URTIs) among adult patients seeking care at a tertiary hospital. The study adopted nucleic acid amplification techniques to detect SARS-CoV-2 and influenza viruses using validated rRT-PCR protocols. The study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Institutional Ethics Committee of ICMR–National Institute of Virology, Pune (IEC Approval No: NIV-IEC/Oct/2021/D-5). Written informed consent was obtained from all participants prior to their participation in the study. Setting The study was conducted in the outpatient department of a tertiary care hospital in Pune, India, between March 2023 and November 2023. Informed consent, sample collection, preliminary clinical evaluation was carried out at the hospital, whereas molecular diagnostic analysis was undertaken at the designated laboratory facility in National Institute of Virology (NIV), Pune. Study-Participants Eligibility Criteria The study included adult patients aged ≥ 18 years presenting with clinical symptoms suggestive of URTI (e.g., fever, cough, rhinorrhoea). Patients were recruited consecutively during the study period. Selection Method A total of 123 patients were enrolled using consecutive sampling, reflecting routine diagnostic testing for suspected viral URTIs. Written informed consent was obtained from all participants before sample collection and testing. Rationale for Inclusion Participants were included if they presented with URTI symptoms suspected to be caused by SARS-CoV-2 or influenza, as per clinician judgment. Variables The primary endpoint was defined as a virologically confirmed diagnosis of SARS-CoV-2, Influenza A, or Influenza B infection. The exposure variables and predictors included patient-level demographic and clinical characteristics such as age, sex, presence of comorbidities, and presenting symptoms like fever, cough, and rhinorrhoea. Diagnosis of viral infections was established using a standardized multiplex real-time reverse transcription PCR assay. The assay targeted the ORF1b region for SARS-CoV-2, the M1 gene segment for Influenza A, and the NS2 gene segment for Influenza B. Although variables like comorbidities and clinical severity that could influence outcomes were noted, the study did not incorporate stratification or covariate adjustment, in line with its exploratory framework. Data Sources and Measurement Data on demographics, clinical history, symptoms, and comorbidities were collected using a pre-tested and structured Case Reporting Form (CRF) designed for Severe Acute Respiratory Infection (SARI) surveillance. Sample Collection Nasopharyngeal and/or throat swabs were collected using CDC-recommended synthetic-head, plastic-shaft swabs, transported in 3 mL viral transport medium (VTM), and maintained at 4°C. Testing Procedures RNA was extracted using the Zybio Nucleic Acid Extraction Kit (Zybio Inc., China). A multiplex single-tube rRT-PCR assay developed by the National Institute of Cholera was used for simultaneous detection of SARS-CoV-2 and influenza viruses. Subtyping was performed using a CDC-based in-house duplex assay to identify A(H1N1) pdm09, A(H3), and B lineage strains (B/Yamagata, B/Victoria). Bias Efforts to reduce selection bias included consecutive sampling of all eligible URTI cases. Measurement bias was minimized by conducting all laboratory testing at an accredited national reference centre (NIV) using validated molecular protocols. The CRF used was standardized to reduce information bias. Sample-size A total of 123 patients were included in this pilot study, based on the duration of the study period and feasibility for molecular testing. The sample size was not calculated a priori, as the study was exploratory in nature. Quantitative Variables Quantitative variables (e.g., age, duration of illness) were summarized using mean ± standard deviation (SD) or median (interquartile range, IQR) based on distribution. For analytical purposes, variables were categorized based on clinically relevant groupings (e.g., age strata, presence, or absence of fever). Statistical Methods Descriptive analyses were performed to summarize patient demographics and clinical characteristics. Continuous variables were expressed as mean with standard deviation (SD) or as median with interquartile range (IQR), depending on the distribution of the data. Categorical variables were presented as frequencies and percentages. The Shapiro-Wilk test was employed to assess the normality of continuous variables. For group comparisons, one-way analysis of variance (ANOVA) was used to compare means across four diagnostic categories (SARS-CoV-2, Influenza A, Influenza B, and other/negative). In cases where data were not normally distributed, the Kruskal-Wallis test was used as a non-parametric alternative. Associations between categorical variables were evaluated using the chi-square test. A two-tailed p-value of less than 0.05 was considered statistically significant. All statistical analyses were conducted using Microsoft Excel (Microsoft 365), RStudio (Version 2023.03.1 + 446), and IBM SPSS Statistics Version 27. RESULTS Demographic, Clinical, and Comorbidity Profiles A total of 123 adult patients presenting with symptoms of upper respiratory tract infections (URTIs) were enrolled in this study. Based on rRT-PCR testing, 12 (9.8%) were positive for Influenza A, 8 (6.5%) for Influenza B, 6 (4.9%) for SARS-CoV-2, while the remaining 97 (78.9%) were classified under URTIs due to other causes (Fig. 1 ). The mean age varied across groups, ranging from 27.75 ± 10.18 years (Influenza B) to 44.71 ± 19.35 years (other URTIs), though the difference was not statistically significant ( p = 0.104). Males predominated in the SARS-CoV-2 group (83.3%), whereas the Influenza B group had predominantly female participants (87.5%) ( p = 0.062). Anthropometric parameters such as height, weight, and BMI showed no significant differences among the groups. Clinical vital signs including respiratory rate, blood pressure, pulse rate, and axillary temperature were comparable across groups (all p > 0.05). Similarly, the distribution of comorbid conditions including diabetes, hypertension, tuberculosis, and asthma did not differ significantly between the viral subgroups (all p > 0.21) The prevalence of comorbid conditions such as chronic lung disease, tuberculosis, diabetes, hypertension, asthma, heart disease, chronic liver disease, and HIV showed no statistically significant difference across the four groups ( p > 0.21). Notably, diabetes and hypertension were more frequently observed in the Influenza A and other URTI groups, though these differences did not reach statistical significance (diabetes p = 0.439; hypertension p = 0.597). Chronic lung disease, heart disease, and HIV were present only in the “other URTIs” group in small proportions. (Table 1 ). Table 1 Comparison of demographic, Clinical parameters, and comorbidities in enrolled study participants Influenza A Influenza B SARS CoV-2 URTIs due to other causes P-value n = 12 (9.76%) n = 8 (6.50%) n = 6 (4.88%) n = 97 (78.86%) [A] Demographic Parameters Age (in years) 43.08 ± 21.06 27.75 ± 10.18 37.00 ± 24.38 44.71 ± 19.35 0.104 Gender Male 5 (41.7%) 1 (12.5%) 5 (83.3%) 48 (50.5%) 0.062 Female 7 (58.3%) 7 (87.5%) 1 (16.7%) 49 (50.5%) Height (in Cm) 157.00 ± 10.73 158.00 ± 5.50 161.00 ± 6.81 158.31 ± 8.47 0.831 Weight (in Kg) 55.26 ± 10.42 56.75 ± 6.30 53.00 ± 14.97 57.66 ± 11.91 0.744 BMI 22.48 ± 4.02 22.74 ± 2.28 20.19 ± 4.07 22.96 ± 4.22 0.455 [B] Clinical Parameters Respiratory rate minute# 20.83 ± 3.13 19.75 ± 1.28 20.33 ± 0.82 21.58 ± 4.22 0.445 BP systolic(mmHg) 120.00 ± 15.95 111.25 ± 14.58 115.00 ± 8.37 118.30 ± 13.51 0.472 BP diastolic (mmHg)# 75.83 ± 7.93 72.50 ± 8.86 73.33 ± 5.16 74.01 ± 8.70 0.841 Pulse rate/minute 87.08 ± 21.36 80.00 ± 5.86 86.00 ± 12.52 88.49 ± 15.04 0.49 Axillary Temp. (°C)# 36.44 ± 0.54 36.48 ± 0.51 36.43 ± 0.64 36.58 ± 0.51 0.63 [C] Co morbidities Chronic lung disease 0 (0.0%) 0 (0.0%) 0 (0.0%) 2 (2.1%) 0.909 Tuberculosis 1 (8.3%) 1 (12.5%) 0 (0.0%) 4 (4.1%) 0.629 Diabetes 2 (16.7%) 0 (0.0%) 0 (0.0%) 16 (16.5%) 0.439 Hypertension 3 (25.0%) 0 (0.0%) 0 (0.0%) 23 (23.7%) 0.597 Asthma 2 (16.7%) 0 (0.0%) 0 (0.0%) 4 (4.1%) 0.219 Heart diseases 0 (0.0%) 0 (0.0%) 0 (0.0%) 9 (9.3%) 0.457 Chronic Liver Disease 1 (8.3%) 0 (0.0%) 0 (0.0%) 1 (1.0%) 0.281 HIV 0 (0.0%) 0 (0.0%) 0 (0.0%) 3 (3.1%) 0.844 Values represent mean ± SD for quantitative variables, Frequency (%) for categorical variables; Test used: One way ANOVA or Independent-Samples Kruskal-Wallis Test (indicated by #). P-value < 0.05; statistically significant* Symptomatology Across Viral URTI Groups Although most symptoms overlapped between groups, certain patterns showed statistically significant variation. Fever was commonly reported across all infections, with the highest prevalence in Influenza B (100%) and the lowest in other URTIs (77.3%), but this was not statistically significant ( p = 0.315). Cough was universally present in Influenza A and B cases (100%) but was significantly less frequent in SARS-CoV-2 (66.7%) and other URTIs (60.8%) ( p < 0.009). Statistically significant differences were also observed in sputum production ( p < 0.033), vomiting/nausea ( p < 0.026), and nasal discharge/stuffiness ( p < 0.041), with higher prevalence in Influenza and SARS-CoV-2 groups compared to others. Other symptoms such as sore throat, body ache, headache, breathlessness, and fatigue showed no significant group-wise variation (all p > 0.05) (Table 2 ). Table 2 Comparison of symptoms among enrolled study participants Symptoms Influenza A Influenza B SARS CoV-2 URTIs due to other causes P-value n = 12 (9.76%) n = 8 (6.50%) n = 6 (4.88%) n = 97 (78.86%) Fever 11 (91.7%) 8 (100.0%) 5 (83.3%) 75 (77.3%) 0.315 Rigors 2 (16.7%) 0 (0.0%) 0 (0.0%) 7 (7.2%) 0.448 Sputum production 6 (50.0%) 5 (62.5%) 4 (66.7%) 27 (27.8%) 0.033* Sore throat 1 (8.3%) 3 (37.5%) 3 (50.0%) 29 (29.9%) 0.254 Earache discharge 1 (8.3%) 0 (0.0%) 0 (0.0%) 3 (3.1%) 0.943 Body ache 8 (66.7%) 5 (62.5%) 3 (50.0%) 50 (51.5%) 0.735 Chest pain 1 (8.3%) 1 (12.5%) 2 (33.3%) 14 (14.4%) 0.884 Vomiting Nausea 7 (58.3%) 3 (37.5%) 3 (50.0%) 21 (21.6%) 0.026* Breathlessness difficulty breathing 3 (25.0%) 1 (12.5%) 3 (50.0%) 40 (41.2%) 0.277 Chills 8 (66.7%) 2 (25.0%) 2 (33.3%) 40 (41.2%) 0.246 Cough 12 (100.0%) 8 (100.0%) 4 (66.7%) 59 (60.8%) 0.009* Haemoptysis 1 (8.3%) 0 (0.0%) 0 (0.0%) 1 (1.0%) 0.281 Nasal discharge Stuffiness 1 (8.3%) 2 (25.0%) 3 (50.0%) 11 (11.3%) 0.041* Headache 8 (66.7%) 3 (37.5%) 3 (50.0%) 39 (40.2%) 0.352 Malaise Fatigue 0 (0.0%) 1 (12.5%) 2 (33.3%) 9 (9.3%) 0.159 Abdominal pain 3 (25.0%) 0 (0.0%) 1 (16.7%) 12 (12.4%) 0.419 Diarrhoea 1 (8.3%) 1 (12.5%) 1 (16.7%) 11 (11.3%) 0.962 Seizures 0 (0.0%) 0 (0.0%) 0 (0.0%) 1 (1.0%) 0.966 Wheeze 2 (16.7%) 0 (0.0%) 2 (33.3%) 9 (9.3%) 0.528 Apnoea 1 (8.3%) 0 (0.0%) 0 (0.0%) 8 (8.2%) 0.957 Values represent as Frequency (%) for categorical variables; test used: chi-square test. P-value < 0.05; statistically significant* Biochemical Parameter Differences Between Groups Biochemical profiling revealed several statistically significant differences in haematological parameters. Haemoglobin levels were comparable across groups ( p = 0.303), but total white blood cell (WBC) counts varied significantly ( p < 0.007), with the highest mean count in the other URTI group (9078.81 ± 5298.44/µL) and the lowest in Influenza B (4775.00 ± 1673.96/µL). Differential leukocyte analysis revealed significant differences in lymphocyte ( p < 0.002), monocyte ( p < 0.001), and neutrophil ( p < 0.002) percentages. SARS-CoV-2 patients showed marked lymphopenia and neutrophilia, whereas Influenza B patients had relatively higher lymphocyte and monocyte percentages. No significant differences were noted in eosinophil percentages or platelet counts ( p = 0.342 and p = 0.598, respectively) (Fig. 2 , Table 3 ). Table 3 Comparison of biochemical parameters among enrolled study participants Biochemical Parameters Influenza A Influenza B SARS CoV-2 URTIs due to other causes P-value n = 12 (9.76%) n = 8 (6.50%) n = 6 (4.88%) n = 97 (78.86%) HB (g/dl) 11.71 ± 2.86 12.06 ± 1.95 13.63 ± 2.35 11.15 ± 2.60 0.303 WBC Count (/µL) # 5990.91 ± 3170.95 4775.00 ± 1673.96 6050.00 ± 1202.08 9078.81 ± 5298.44 0.007* Lymphocytes (%) # 23.36 ± 6.70 34.00 ± 11.44 12.00 ± 6.93 17.42 ± 11.57 0.002* Monocytes (%)# 9.45 ± 1.57 10.13 ± 1.25 8.33 ± 4.51 7.46 ± 3.90 0.001* Neutrophil (%) # 66.00 ± 6.91 54.13 ± 11.23 78.67 ± 11.37 73.25 ± 15.07 0.002* Eosinophil (%) # 1.18 ± 2.14 1.75 ± 1.04 1.00 ± 1.00 1.69 ± 3.36 0.342 Platelet Count (/µL) 183909.09 ± 109656.24 206000.00 ± 81177.06 214666.67 ± 73500.57 230997.62 ± 118025.80 0.598 Values represent as Frequency (%) for categorical variables; test used: chi-square test. P-value < 0.05; statistically significant* DISCUSSION This single-centre pilot study aimed to delineate and compare the demographic, clinical, and haematological profiles of adult patients diagnosed with viral upper respiratory tract infections (URTIs), including Influenza A, Influenza B, SARS-CoV-2, and other etiologies, based on rRT-PCR detection. The comparative analysis across these four groups revealed both overlapping features and distinctive patterns in symptomatology and haematological markers, as outlined in Tables 1-3 . Demographic and Comorbidity Comparison There were no statistically significant differences observed in baseline demographic characteristics, clinical vitals, or comorbidities across the four groups. This finding suggests substantial overlap in the initial clinical presentation of viral URTIs regardless of etiology. These results are consistent with those reported by Zayet et al., who found no significant differences in comorbidities between COVID-19 and influenza A or B patients [12]. However, some epidemiological patterns emerged. Patients with Influenza B in our study were numerically younger compared to other groups, aligning with Kyung-Wook Hong's findings that Influenza B disproportionately affects socially active younger adults [13]. A gender-specific trend was also noted: SARS-CoV-2 infections were more frequent in males, consistent with earlier reports of male predominance in COVID-19 [14-16]. Conversely, females were more commonly affected by Influenza A and B in our cohort, corroborating observations by Morgan and Klein that adult women may be at greater risk for influenza than men [17]. Although these trends did not reach statistical significance in our dataset, they mirror well-documented demographic vulnerabilities and warrant continued surveillance. Symptom Profile Comparison Most symptoms including fever, sore throat, dyspnea, and body ache were common across all groups, reflecting the nonspecific clinical nature of viral URTIs. Nonetheless, statistically significant intergroup differences were observed for cough (p < 0.009), sputum production (p < 0.033), vomiting/nausea (p < 0.026), and nasal discharge/stuffiness (p < 0.041). These symptoms were more frequently reported in Influenza and SARS-CoV-2 cases, potentially serving as clinical differentiators in the absence of confirmatory testing. These findings partially align with Zayet et al., who reported that fever, fatigue, cough, and myalgia were common to both COVID-19 and influenza [12]. However, their study also identified statistically significant variations in symptoms such as conjunctival hyperaemia, sneezing, and sore throat, which were not significant in our sample. This disparity may stem from geographical or population differences and highlights the influence of sample size and clinical setting on symptom expression. Haematological Profile Comparison Our study revealed statistically significant intergroup differences in several haematological parameters, particularly white blood cell subtypes. SARS-CoV-2 patients exhibited marked lymphopenia compared to other groups, while Influenza B cases had elevated lymphocyte and monocyte percentages. Neutrophil percentages were highest in SARS-CoV-2 and lowest in Influenza B. These results are supported by findings from Chen et al., who reported that lymphopenia was more pronounced in severe COVID-19 and influenza compared to mild COVID-19. Additionally, their study found higher monocyte levels in influenza and increased neutrophils in both severe COVID-19 and influenza cases; both trends consistent with our findings [18]. In contrast to earlier reports by Chen et al., Li Y et al., and Mei Y et al., who observed significant differences in total white blood cell counts between groups [18-20], our study did not detect such differences. This could be attributed to the pilot nature and smaller sample size of our study, which may limit statistical power. Platelet counts, eosinophil percentages, and ESR values did not differ significantly between the four groups in our cohort. This contrasts with prior research suggesting that thrombocytopenia is common in severe COVID-19 and influenza [18, 21, 22]. Similarly, Chen et al. noted elevated eosinophils in severe influenza and COVID-19 compared to mild cases [18], while Pu SL et al., observed persistent ESR elevation even after clinical recovery from COVID-19 [23]. Li Y et al. also reported that COVID-19 patients had lower ESR compared to influenza cases [19]. The lack of these findings in our study could reflect our inclusion of mostly mild to moderate URTI cases, underscoring the need for stratification by illness severity in future research. Our results demonstrate that while clinical and demographic characteristics largely overlapped among viral URTI groups, symptom profiles and particularly haematological parameters revealed statistically significant differences. These differences, especially in lymphocyte and neutrophil counts, are consistent with known immune response patterns to influenza and SARS-CoV-2 and may support their use in early triage settings though confirmation via molecular diagnostics remains essential. Study - Limitations : This pilot study offers valuable preliminary insights into the comparative clinical and haematological profiles of common viral URTIs in an adult outpatient population, with standardized data collection and laboratory-confirmed diagnoses. However, the absence of disease severity stratification may have limited the interpretation of symptom and blood parameter variations across different illness spectrums. Self-reported symptoms and single-time-point vital or biochemical measurements could have introduced reporting and measurement biases. The study also did not assess other frequent respiratory pathogens like RSV or rhinovirus and lacked a healthy or bacterial-URTI control group, constraining comparative interpretation. Despite these limitations, the study highlights important trends that can inform larger, multi-pathogen surveillance studies and support early differential diagnosis strategies in tertiary care settings. Conclusion This pilot study offers initial insights into the clinical and haematological profiles of adult URTI cases caused by Influenza A, Influenza B, SARS-CoV-2, and other pathogens in a tertiary care setting in Pune. While most symptoms overlapped, significant differences in cough, sputum production, vomiting/nausea, and nasal discharge suggest some diagnostic utility. Notably, distinct leukocyte patterns especially lymphocyte, monocyte, and neutrophil percentages may aid in etiological differentiation. These findings highlight the importance of molecular diagnostics given symptom overlap. Despite its small sample size, the study provides a foundation for larger investigations to enhance the early identification and management of viral URTIs. Declarations Funding: The authors received no financial support for the research, authorship, and/or publication of this article. Conflicts of Interest: The authors declare that they have no conflicts of interest. Acknowledgements: The authors gratefully acknowledge the ICMR-National Institute of Virology, Pune, for facilitating diagnostic testing of clinical specimens submitted by Dr. D. Y. Patil Medical College, Hospital and Research Centre, Pune. Ethics Approval and Consent to Participate: The study was conducted in accordance with institutional and national ethical guidelines. Ethical approval was obtained from the Institutional Ethics Committee of ICMR-National Institute of Virology, Pune (IEC Approval No: NIV-IEC/Oct/2021/D-5). Written informed consent was obtained from all participants prior to inclusion in the study. Data Availability : The datasets generated and/or analysed during the current study are not publicly available due to confidentiality and ethical restrictions related to patient data but are available from the corresponding author on reasonable request. Consent for Publication : Not applicable Clinical Trial Number: Not applicable. Author Contributions: Conceptualization: PJ, VP, AP, ST Methodology: VV, DB, AP, VP, PJ Formal Analysis: AP, MG Investigation: PJ, AP, PS, MG, SS, HC, RP, SJ Data Curation: PJ, AP, PS, HC Validation: VP, MG, MB, DB, VV Resources: SK, VV Supervision: VP, SK, VV, MB, SD, ST Project Administration: ST Writing-Original Draft: PJ, AP Writing- Review & Editing: AP, VP, ST References Rudan I, Boschi-Pinto C, Biloglav Z, Mulholland K, Campbell H. Epidemiology, and etiology of childhood pneumonia. Bull World Health Organ. 2008 May;86(5):408-16. doi: 10.2471/blt.07.048769. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H, Cao B. Clinical course, and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020 Mar 28;395(10229):1054-1062. doi: 10.1016/S0140-6736(20)30566-3. Epub 2020 Mar 11. 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Zayet S, Kadiane-Oussou ND, Lepiller Q, Zahra H, Royer PY, Toko L, Gendrin V, Klopfenstein T. Clinical features of COVID-19 and influenza: a comparative study on Nord Franche-Comte cluster. Microbes and infection. 2020 Oct 1;22(9):481-8. Hong KW, Cheong HJ, Song JY, Noh JY, Yang TU, Kim WJ. Clinical manifestations of influenza A and B in children and adults at a tertiary hospital in Korea during the 2011–2012 season. Japanese journal of infectious diseases. 2015;68(1):20-6. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Xia JA. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The lancet. 2020 Feb 15;395(10223):507-13. Chaturvedi R, Lui B, Aaronson JA, White RS, Samuels JD. COVID-19 complications in males and females: recent developments. Journal of Comparative Effectiveness Research. 2022 Jun;11(9):689-98. Jin JM, Bai P, He W, Wu F, Liu XF, Han DM, Liu S, Yang JK. Gender differences in patients with COVID-19: focus on severity and mortality. Frontiers in public health. 2020 Apr 29; 8:545030. Morgan R, Klein SL. The intersection of sex and gender in the treatment of influenza. Current opinion in virology. 2019 Apr 1; 35:35-41. Chen J, Pan Y, Li G, Xu W, Zhang L, Yuan S, Xia Y, Lu P, Zhang J. Distinguishing between COVID‐19 and influenza during the early stages by measurement of peripheral blood parameters. Journal of Medical Virology. 2021 Feb;93(2):1029-37. Li Y, He H, Gao Y, Ou Z, He W, Chen C, Fu J, Xiong H, Chen Q. Comparison of clinical characteristics for distinguishing COVID-19 from influenza during the early stages in Guangdong, China. Frontiers in Medicine. 2021 Nov 11; 8:733999. Mei Y, Weinberg SE, Zhao L, Frink A, Qi C, Behdad A, Ji P. Risk stratification of hospitalized COVID-19 patients through comparative studies of laboratory results with influenza. EClinicalMedicine. 2020 Sep 1;26. Rohlfing AK, Rath D, Geisler T, Gawaz M. Platelets and COVID-19. Hämostaseologie. 2021 Oct;41(05):379-85. Jansen AG, Spaan T, Low HZ, Di Iorio D, van den Brand J, Tieke M, Barendrecht A, Rohn K, van Amerongen G, Stittelaar K, Baumgärtner W. Influenza-induced thrombocytopenia is dependent on the subtype and sialoglycan receptor and increases with virus pathogenicity. Blood advances. 2020 Jul 14;4(13):2967-78. Pu SL, Zhang XY, Liu DS, Ye BN, Li JQ. Unexplained elevation of erythrocyte sedimentation rate in a patient recovering from COVID-19: A case report. World journal of clinical cases. 2021 Feb 26;9(6):1394. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 12 May, 2026 Reviews received at journal 09 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 21 Apr, 2026 Editor invited by journal 21 Apr, 2026 Submission checks completed at journal 21 Apr, 2026 First submitted to journal 21 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Patil Vidyapeeth, (Deemed to be University), Pimpri, Pune","correspondingAuthor":true,"prefix":"","firstName":"Srikanth","middleName":"","lastName":"Tripathy","suffix":""}],"badges":[],"createdAt":"2026-03-31 11:09:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9278895/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9278895/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108398767,"identity":"e6c01f8a-761c-46ac-89fb-0e706ffb6510","added_by":"auto","created_at":"2026-05-04 08:35:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20481,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePercentage positive for viral infections in adults\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9278895/v1/1d43f6d59b42a5562c4520f8.png"},{"id":108493394,"identity":"81d06fcd-e849-451b-97f3-6a3a519d82f6","added_by":"auto","created_at":"2026-05-05 10:00:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":222165,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of viral respiratory tract infections between A] Lymphocytes B] WBC C] Monocytes and D] Neutrophils\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9278895/v1/41d33ee16eacf32c92a52bd7.png"},{"id":108495194,"identity":"b512af24-0c24-482c-9a62-dd2aed0b8623","added_by":"auto","created_at":"2026-05-05 10:09:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":611413,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9278895/v1/eb076f82-f681-44b9-90b8-fe76b8f08ea1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical presentation and detection of SARS-CoV-2, Influenza, and Other Viral URTIs Using rRT-PCR in a Tertiary Hospital in Pune: A Pilot Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eUpper respiratory tract infections (URTIs) represent a significant public health burden globally and are among the most prevalent illnesses affecting individuals across all age demographics. Although most cases are confined to the upper airways and resolve spontaneously, a proportion may progress to involve the lower respiratory tract, manifesting as bronchiolitis or pneumonia, particularly in vulnerable populations such as young children and older adults [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Viral pathogens are the predominant causative agents of URTIs. While bacteria typically drive severe lower respiratory infections, viruses such as respiratory syncytial virus (RSV), influenza viruses, and more recently, novel coronaviruses like SARS-CoV-2, are major contributors to upper and mid-respiratory tract diseases [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Seasonal changes and widespread presence of these viruses create obstacles for clinicians and public health authorities in managing and tracking infections.\u003c/p\u003e \u003cp\u003eNon-specific symptom presentation in viral respiratory infections presents a significant clinical dilemma. Symptoms like fever, sore throat, cough, myalgia, and rhinorrhoea commonly occur with influenza, SARS-CoV-2, and other viruses, reducing the diagnostic accuracy of syndromic approaches [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The occurrence of bacterial or fungal co-infections may interfere with accurate diagnosis and render therapeutic management more complex [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Establishing the viral etiology without delay is important for tailoring patient care, avoiding unwarranted antibiotic therapy, and initiating public health measures. Real-time RT-PCR has proven to be a pivotal technique for reliably detecting viruses responsible for respiratory illnesses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The global spread of SARS-CoV-2 and the resulting COVID-19 pandemic has highlighted the persistent risk associated with emerging respiratory viruses, accounting for over 778\u0026nbsp;million reported cases and more than 7.09\u0026nbsp;million deaths worldwide [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], COVID-19 continues to impact health systems and economies. Seasonal influenza also remains a persistent global health issue, responsible for estimated 290,000-650,000 deaths annually despite established vaccination programs [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEven though the global availability of data on viral respiratory pathogens, there exists a paucity of region-specific studies, particularly from India\u0026rsquo;s western regions such as Pune. Understanding the regional burden, seasonality, and clinical profile of SARS-CoV-2, influenza, and other respiratory viruses is essential for tailoring local surveillance and response strategies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Tertiary care centres are uniquely positioned to capture data on complex or referred respiratory infection cases. These institutions function as important surveillance hubs during respiratory virus outbreaks and seasonal surges, providing a clinical window into circulating viral patterns and the burden of co-infections [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Considering the substantial clinical similarities among respiratory viruses, evolving viral epidemiology, and the scarcity of region-specific data, it is essential to systematically assess the patterns of viral upper respiratory tract infections in tertiary care settings. Such data would inform regional preparedness plans and optimize clinical algorithms for diagnosis and treatment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This pilot observational study sought to assess and differentiate the clinical presentation, epidemiological patterns, and diagnostic characteristics of SARS-CoV-2, Influenza A and B, and other viral upper respiratory tract infections (URTIs) among patients attending a tertiary care hospital in Pune. The study utilizes molecular testing methods to distinguish between viral pathogens and to characterize patterns of respiratory infections in the post-pandemic period.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cb\u003eStudy Design\u003c/b\u003e: This was a cross-sectional pilot study was carried out to assess the clinical and diagnostic characteristics of viral upper respiratory tract infections (URTIs) among adult patients seeking care at a tertiary hospital. The study adopted nucleic acid amplification techniques to detect SARS-CoV-2 and influenza viruses using validated rRT-PCR protocols. The study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Institutional Ethics Committee of ICMR\u0026ndash;National Institute of Virology, Pune (IEC Approval No: NIV-IEC/Oct/2021/D-5). Written informed consent was obtained from all participants prior to their participation in the study.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSetting\u003c/strong\u003e \u003cp\u003eThe study was conducted in the outpatient department of a tertiary care hospital in Pune, India, between March 2023 and November 2023. Informed consent, sample collection, preliminary clinical evaluation was carried out at the hospital, whereas molecular diagnostic analysis was undertaken at the designated laboratory facility in National Institute of Virology (NIV), Pune.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy-Participants\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEligibility Criteria\u003c/strong\u003e \u003cp\u003eThe study included adult patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years presenting with clinical symptoms suggestive of URTI (e.g., fever, cough, rhinorrhoea). Patients were recruited consecutively during the study period.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSelection Method\u003c/strong\u003e \u003cp\u003eA total of 123 patients were enrolled using consecutive sampling, reflecting routine diagnostic testing for suspected viral URTIs. Written informed consent was obtained from all participants before sample collection and testing.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRationale for Inclusion\u003c/strong\u003e \u003cp\u003eParticipants were included if they presented with URTI symptoms suspected to be caused by SARS-CoV-2 or influenza, as per clinician judgment.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eVariables\u003c/strong\u003e \u003cp\u003eThe primary endpoint was defined as a virologically confirmed diagnosis of SARS-CoV-2, Influenza A, or Influenza B infection. The exposure variables and predictors included patient-level demographic and clinical characteristics such as age, sex, presence of comorbidities, and presenting symptoms like fever, cough, and rhinorrhoea. Diagnosis of viral infections was established using a standardized multiplex real-time reverse transcription PCR assay. The assay targeted the ORF1b region for SARS-CoV-2, the M1 gene segment for Influenza A, and the NS2 gene segment for Influenza B. Although variables like comorbidities and clinical severity that could influence outcomes were noted, the study did not incorporate stratification or covariate adjustment, in line with its exploratory framework.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eData Sources and Measurement\u003c/strong\u003e \u003cp\u003eData on demographics, clinical history, symptoms, and comorbidities were collected using a pre-tested and structured Case Reporting Form (CRF) designed for Severe Acute Respiratory Infection (SARI) surveillance.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSample Collection\u003c/strong\u003e \u003cp\u003eNasopharyngeal and/or throat swabs were collected using CDC-recommended synthetic-head, plastic-shaft swabs, transported in 3 mL viral transport medium (VTM), and maintained at 4\u0026deg;C.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTesting Procedures\u003c/strong\u003e \u003cp\u003eRNA was extracted using the Zybio Nucleic Acid Extraction Kit (Zybio Inc., China). A multiplex single-tube rRT-PCR assay developed by the National Institute of Cholera was used for simultaneous detection of SARS-CoV-2 and influenza viruses. Subtyping was performed using a CDC-based in-house duplex assay to identify A(H1N1) pdm09, A(H3), and B lineage strains (B/Yamagata, B/Victoria).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBias\u003c/strong\u003e \u003cp\u003eEfforts to reduce selection bias included consecutive sampling of all eligible URTI cases. Measurement bias was minimized by conducting all laboratory testing at an accredited national reference centre (NIV) using validated molecular protocols. The CRF used was standardized to reduce information bias.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSample-size\u003c/strong\u003e \u003cp\u003eA total of 123 patients were included in this pilot study, based on the duration of the study period and feasibility for molecular testing. The sample size was not calculated a priori, as the study was exploratory in nature.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eQuantitative Variables\u003c/strong\u003e \u003cp\u003eQuantitative variables (e.g., age, duration of illness) were summarized using mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (interquartile range, IQR) based on distribution. For analytical purposes, variables were categorized based on clinically relevant groupings (e.g., age strata, presence, or absence of fever).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatistical Methods\u003c/strong\u003e \u003cp\u003eDescriptive analyses were performed to summarize patient demographics and clinical characteristics. Continuous variables were expressed as mean with standard deviation (SD) or as median with interquartile range (IQR), depending on the distribution of the data. Categorical variables were presented as frequencies and percentages. The Shapiro-Wilk test was employed to assess the normality of continuous variables. For group comparisons, one-way analysis of variance (ANOVA) was used to compare means across four diagnostic categories (SARS-CoV-2, Influenza A, Influenza B, and other/negative). In cases where data were not normally distributed, the Kruskal-Wallis test was used as a non-parametric alternative. Associations between categorical variables were evaluated using the chi-square test. A two-tailed p-value of less than 0.05 was considered statistically significant. All statistical analyses were conducted using Microsoft Excel (Microsoft 365), RStudio (Version 2023.03.1\u0026thinsp;+\u0026thinsp;446), and IBM SPSS Statistics Version 27.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDemographic, Clinical, and Comorbidity Profiles\u003c/h2\u003e \u003cp\u003eA total of 123 adult patients presenting with symptoms of upper respiratory tract infections (URTIs) were enrolled in this study. Based on rRT-PCR testing, 12 (9.8%) were positive for Influenza A, 8 (6.5%) for Influenza B, 6 (4.9%) for SARS-CoV-2, while the remaining 97 (78.9%) were classified under URTIs due to other causes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age varied across groups, ranging from 27.75\u0026thinsp;\u0026plusmn;\u0026thinsp;10.18 years (Influenza B) to 44.71\u0026thinsp;\u0026plusmn;\u0026thinsp;19.35 years (other URTIs), though the difference was not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.104). Males predominated in the SARS-CoV-2 group (83.3%), whereas the Influenza B group had predominantly female participants (87.5%) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.062). Anthropometric parameters such as height, weight, and BMI showed no significant differences among the groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eClinical vital signs including respiratory rate, blood pressure, pulse rate, and axillary temperature were comparable across groups (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Similarly, the distribution of comorbid conditions including diabetes, hypertension, tuberculosis, and asthma did not differ significantly between the viral subgroups (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.21) The prevalence of comorbid conditions such as chronic lung disease, tuberculosis, diabetes, hypertension, asthma, heart disease, chronic liver disease, and HIV showed no statistically significant difference across the four groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.21). Notably, diabetes and hypertension were more frequently observed in the Influenza A and other URTI groups, though these differences did not reach statistical significance (diabetes \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.439; hypertension \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.597). Chronic lung disease, heart disease, and HIV were present only in the \u0026ldquo;other URTIs\u0026rdquo; group in small proportions. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of demographic, Clinical parameters, and comorbidities in enrolled study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfluenza A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInfluenza B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSARS CoV-2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eURTIs due to other causes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;12 (9.76%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;8 (6.50%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;6 (4.88%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;97 (78.86%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e[A] Demographic Parameters\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.08\u0026thinsp;\u0026plusmn;\u0026thinsp;21.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.75\u0026thinsp;\u0026plusmn;\u0026thinsp;10.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.00\u0026thinsp;\u0026plusmn;\u0026thinsp;24.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.71\u0026thinsp;\u0026plusmn;\u0026thinsp;19.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48 (50.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (58.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (87.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49 (50.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (in Cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157.00\u0026thinsp;\u0026plusmn;\u0026thinsp;10.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161.00\u0026thinsp;\u0026plusmn;\u0026thinsp;6.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e158.31\u0026thinsp;\u0026plusmn;\u0026thinsp;8.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (in Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.26\u0026thinsp;\u0026plusmn;\u0026thinsp;10.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.75\u0026thinsp;\u0026plusmn;\u0026thinsp;6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.00\u0026thinsp;\u0026plusmn;\u0026thinsp;14.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.66\u0026thinsp;\u0026plusmn;\u0026thinsp;11.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.19\u0026thinsp;\u0026plusmn;\u0026thinsp;4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.96\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e[B] Clinical Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory rate minute#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.58\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP systolic(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120.00\u0026thinsp;\u0026plusmn;\u0026thinsp;15.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111.25\u0026thinsp;\u0026plusmn;\u0026thinsp;14.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118.30\u0026thinsp;\u0026plusmn;\u0026thinsp;13.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP diastolic (mmHg)#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.83\u0026thinsp;\u0026plusmn;\u0026thinsp;7.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.50\u0026thinsp;\u0026plusmn;\u0026thinsp;8.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.01\u0026thinsp;\u0026plusmn;\u0026thinsp;8.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse rate/minute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.08\u0026thinsp;\u0026plusmn;\u0026thinsp;21.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.00\u0026thinsp;\u0026plusmn;\u0026thinsp;12.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.49\u0026thinsp;\u0026plusmn;\u0026thinsp;15.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAxillary Temp. (\u0026deg;C)#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e[C] Co morbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTuberculosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Liver Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eValues represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for quantitative variables, Frequency (%) for categorical variables; Test used: One way ANOVA or Independent-Samples Kruskal-Wallis Test (indicated by #). P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; statistically significant*\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSymptomatology Across Viral URTI Groups\u003c/h3\u003e\n\u003cp\u003eAlthough most symptoms overlapped between groups, certain patterns showed statistically significant variation. Fever was commonly reported across all infections, with the highest prevalence in Influenza B (100%) and the lowest in other URTIs (77.3%), but this was not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.315). Cough was universally present in Influenza A and B cases (100%) but was significantly less frequent in SARS-CoV-2 (66.7%) and other URTIs (60.8%) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.009). Statistically significant differences were also observed in sputum production (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.033), vomiting/nausea (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.026), and nasal discharge/stuffiness (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.041), with higher prevalence in Influenza and SARS-CoV-2 groups compared to others. Other symptoms such as sore throat, body ache, headache, breathlessness, and fatigue showed no significant group-wise variation (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of symptoms among enrolled study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSymptoms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfluenza A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInfluenza B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSARS CoV-2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eURTIs due to other causes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;12 (9.76%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;8 (6.50%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;6 (4.88%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;97 (78.86%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (91.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75 (77.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRigors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSputum production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27 (27.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.033*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSore throat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29 (29.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarache discharge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody ache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50 (51.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVomiting Nausea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (58.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21 (21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.026*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreathlessness difficulty breathing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59 (60.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaemoptysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNasal discharge Stuffiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.041*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39 (40.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalaise Fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiarrhoea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeizures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheeze\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApnoea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eValues represent as Frequency (%) for categorical variables; test used: chi-square test. P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; statistically significant*\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eBiochemical Parameter Differences Between Groups\u003c/h3\u003e\n\u003cp\u003eBiochemical profiling revealed several statistically significant differences in haematological parameters. Haemoglobin levels were comparable across groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.303), but total white blood cell (WBC) counts varied significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.007), with the highest mean count in the other URTI group (9078.81\u0026thinsp;\u0026plusmn;\u0026thinsp;5298.44/\u0026micro;L) and the lowest in Influenza B (4775.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1673.96/\u0026micro;L). Differential leukocyte analysis revealed significant differences in lymphocyte (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.002), monocyte (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and neutrophil (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.002) percentages. SARS-CoV-2 patients showed marked lymphopenia and neutrophilia, whereas Influenza B patients had relatively higher lymphocyte and monocyte percentages. No significant differences were noted in eosinophil percentages or platelet counts (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.342 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.598, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of biochemical parameters among enrolled study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBiochemical Parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfluenza A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInfluenza B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSARS CoV-2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eURTIs due to other causes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;12 (9.76%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;8 (6.50%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;6 (4.88%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;97 (78.86%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB (g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e12.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e13.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e11.15\u0026thinsp;\u0026plusmn;\u0026thinsp;2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC Count (/\u0026micro;L) #\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5990.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3170.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4775.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1673.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6050.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1202.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e9078.81\u0026thinsp;\u0026plusmn;\u0026thinsp;5298.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes (%) #\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e23.36\u0026thinsp;\u0026plusmn;\u0026thinsp;6.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e34.00\u0026thinsp;\u0026plusmn;\u0026thinsp;11.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e12.00\u0026thinsp;\u0026plusmn;\u0026thinsp;6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e17.42\u0026thinsp;\u0026plusmn;\u0026thinsp;11.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes (%)#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e7.46\u0026thinsp;\u0026plusmn;\u0026thinsp;3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil (%) #\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e66.00\u0026thinsp;\u0026plusmn;\u0026thinsp;6.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e54.13\u0026thinsp;\u0026plusmn;\u0026thinsp;11.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e78.67\u0026thinsp;\u0026plusmn;\u0026thinsp;11.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e73.25\u0026thinsp;\u0026plusmn;\u0026thinsp;15.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophil (%) #\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet Count (/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e183909.09\u0026thinsp;\u0026plusmn;\u0026thinsp;109656.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e206000.00\u0026thinsp;\u0026plusmn;\u0026thinsp;81177.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e214666.67\u0026thinsp;\u0026plusmn;\u0026thinsp;73500.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e230997.62\u0026thinsp;\u0026plusmn;\u0026thinsp;118025.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eValues represent as Frequency (%) for categorical variables; test used: chi-square test. P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; statistically significant*\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis single-centre pilot study aimed to delineate and compare the demographic, clinical, and haematological profiles of adult patients diagnosed with viral upper respiratory tract infections (URTIs), including Influenza A, Influenza B, SARS-CoV-2, and other etiologies, based on rRT-PCR detection. The comparative analysis across these four groups revealed both overlapping features and distinctive patterns in symptomatology and haematological markers, as outlined in \u003cstrong\u003eTables 1-3\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic and Comorbidity Comparison\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were no statistically significant differences observed in baseline demographic characteristics, clinical vitals, or comorbidities across the four groups. This finding suggests substantial overlap in the initial clinical presentation of viral URTIs regardless of etiology. These results are consistent with those reported by Zayet et al., who found no significant differences in comorbidities between COVID-19 and influenza A or B patients [12]. However, some epidemiological patterns emerged. Patients with Influenza B in our study were numerically younger compared to other groups, aligning with Kyung-Wook Hong\u0026apos;s findings that Influenza B disproportionately affects socially active younger adults [13]. A gender-specific trend was also noted: SARS-CoV-2 infections were more frequent in males, consistent with earlier reports of male predominance in COVID-19 [14-16]. Conversely, females were more commonly affected by Influenza A and B in our cohort, corroborating observations by Morgan and Klein that adult women may be at greater risk for influenza than men [17]. Although these trends did not reach statistical significance in our dataset, they mirror well-documented demographic vulnerabilities and warrant continued surveillance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSymptom Profile Comparison\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMost symptoms including fever, sore throat, dyspnea, and body ache were common across all groups, reflecting the nonspecific clinical nature of viral URTIs. Nonetheless, statistically significant intergroup differences were observed for cough (p \u0026lt; 0.009), sputum production (p \u0026lt; 0.033), vomiting/nausea (p \u0026lt; 0.026), and nasal discharge/stuffiness (p \u0026lt; 0.041). These symptoms were more frequently reported in Influenza and SARS-CoV-2 cases, potentially serving as clinical differentiators in the absence of confirmatory testing. These findings partially align with Zayet et al., who reported that fever, fatigue, cough, and myalgia were common to both COVID-19 and influenza [12]. However, their study also identified statistically significant variations in symptoms such as conjunctival hyperaemia, sneezing, and sore throat, which were not significant in our sample. This disparity may stem from geographical or population differences and highlights the influence of sample size and clinical setting on symptom expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHaematological Profile Comparison\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study revealed statistically significant intergroup differences in several haematological parameters, particularly white blood cell subtypes. SARS-CoV-2 patients exhibited marked lymphopenia compared to other groups, while Influenza B cases had elevated lymphocyte and monocyte percentages. Neutrophil percentages were highest in SARS-CoV-2 and lowest in Influenza B. These results are supported by findings from Chen et al., who reported that lymphopenia was more pronounced in severe COVID-19 and influenza compared to mild COVID-19. Additionally, their study found higher monocyte levels in influenza and increased neutrophils in both severe COVID-19 and influenza cases; both trends consistent with our findings [18]. In contrast to earlier reports by Chen et al., Li Y et al., and Mei Y et al., who observed significant differences in total white blood cell counts between groups [18-20], our study did not detect such differences. This could be attributed to the pilot nature and smaller sample size of our study, which may limit statistical power.\u003c/p\u003e\n\u003cp\u003ePlatelet counts, eosinophil percentages, and ESR values did not differ significantly between the four groups in our cohort. This contrasts with prior research suggesting that thrombocytopenia is common in severe COVID-19 and influenza [18, 21, 22]. Similarly, Chen et al. noted elevated eosinophils in severe influenza and COVID-19 compared to mild cases [18], while Pu SL et al., observed persistent ESR elevation even after clinical recovery from COVID-19 [23]. Li Y et al. also reported that COVID-19 patients had lower ESR compared to influenza cases [19]. The lack of these findings in our study could reflect our inclusion of mostly mild to moderate URTI cases, underscoring the need for stratification by illness severity in future research. Our results demonstrate that while clinical and demographic characteristics largely overlapped among viral URTI groups, symptom profiles and particularly haematological parameters revealed statistically significant differences. These differences, especially in lymphocyte and neutrophil counts, are consistent with known immune response patterns to influenza and SARS-CoV-2 and may support their use in early triage settings though confirmation via molecular diagnostics remains essential.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Study\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e: This pilot study offers valuable preliminary insights into the comparative clinical and haematological profiles of common viral URTIs in an adult outpatient population, with standardized data collection and laboratory-confirmed diagnoses. However, the absence of disease severity stratification may have limited the interpretation of symptom and blood parameter variations across different illness spectrums. Self-reported symptoms and single-time-point vital or biochemical measurements could have introduced reporting and measurement biases. The study also did not assess other frequent respiratory pathogens like RSV or rhinovirus and lacked a healthy or bacterial-URTI control group, constraining comparative interpretation. Despite these limitations, the study highlights important trends that can inform larger, multi-pathogen surveillance studies and support early differential diagnosis strategies in tertiary care settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis pilot study offers initial insights into the clinical and haematological profiles of adult URTI cases caused by Influenza A, Influenza B, SARS-CoV-2, and other pathogens in a tertiary care setting in Pune. While most symptoms overlapped, significant differences in cough, sputum production, vomiting/nausea, and nasal discharge suggest some diagnostic utility. Notably, distinct leukocyte patterns especially lymphocyte, monocyte, and neutrophil percentages may aid in etiological differentiation. These findings highlight the importance of molecular diagnostics given symptom overlap. Despite its small sample size, the study provides a foundation for larger investigations to enhance the early identification and management of viral URTIs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors gratefully acknowledge the ICMR-National Institute of Virology, Pune, for facilitating diagnostic testing of clinical specimens submitted by Dr. D. Y. Patil Medical College, Hospital and Research Centre, Pune.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe study was conducted in accordance with institutional and national ethical guidelines. Ethical approval was obtained from the Institutional Ethics Committee of ICMR-National Institute of Virology, Pune (IEC Approval No: NIV-IEC/Oct/2021/D-5). Written informed consent was obtained from all participants prior to inclusion in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e: The datasets generated and/or analysed during the current study are not publicly available due to confidentiality and ethical restrictions related to patient data but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: PJ, VP, AP, ST\u003c/p\u003e\n\u003cp\u003eMethodology: VV, DB, AP, VP, PJ\u003c/p\u003e\n\u003cp\u003eFormal Analysis: AP, MG\u003c/p\u003e\n\u003cp\u003eInvestigation: PJ, AP, PS, MG, SS, HC, RP, SJ\u003c/p\u003e\n\u003cp\u003eData Curation: PJ, AP, PS, HC\u003c/p\u003e\n\u003cp\u003eValidation: VP, MG, MB, DB, VV\u003c/p\u003e\n\u003cp\u003eResources: SK, VV\u003c/p\u003e\n\u003cp\u003eSupervision: VP, SK, VV, MB, SD, ST\u003c/p\u003e\n\u003cp\u003eProject Administration: ST\u003c/p\u003e\n\u003cp\u003eWriting-Original Draft: PJ, AP\u003c/p\u003e\n\u003cp\u003eWriting- Review \u0026amp; Editing: AP, VP, ST\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRudan I, Boschi-Pinto C, Biloglav Z, Mulholland K, Campbell H. 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Journal of Comparative Effectiveness Research. 2022 Jun;11(9):689-98. \u003c/li\u003e\n\u003cli\u003eJin JM, Bai P, He W, Wu F, Liu XF, Han DM, Liu S, Yang JK. Gender differences in patients with COVID-19: focus on severity and mortality. Frontiers in public health. 2020 Apr 29; 8:545030. \u003c/li\u003e\n\u003cli\u003eMorgan R, Klein SL. The intersection of sex and gender in the treatment of influenza. Current opinion in virology. 2019 Apr 1; 35:35-41. \u003c/li\u003e\n\u003cli\u003eChen J, Pan Y, Li G, Xu W, Zhang L, Yuan S, Xia Y, Lu P, Zhang J. Distinguishing between COVID‐19 and influenza during the early stages by measurement of peripheral blood parameters. Journal of Medical Virology. 2021 Feb;93(2):1029-37. \u003c/li\u003e\n\u003cli\u003eLi Y, He H, Gao Y, Ou Z, He W, Chen C, Fu J, Xiong H, Chen Q. Comparison of clinical characteristics for distinguishing COVID-19 from influenza during the early stages in Guangdong, China. Frontiers in Medicine. 2021 Nov 11; 8:733999. \u003c/li\u003e\n\u003cli\u003eMei Y, Weinberg SE, Zhao L, Frink A, Qi C, Behdad A, Ji P. Risk stratification of hospitalized COVID-19 patients through comparative studies of laboratory results with influenza. EClinicalMedicine. 2020 Sep 1;26. \u003c/li\u003e\n\u003cli\u003eRohlfing AK, Rath D, Geisler T, Gawaz M. Platelets and COVID-19. H\u0026auml;mostaseologie. 2021 Oct;41(05):379-85. \u003c/li\u003e\n\u003cli\u003eJansen AG, Spaan T, Low HZ, Di Iorio D, van den Brand J, Tieke M, Barendrecht A, Rohn K, van Amerongen G, Stittelaar K, Baumg\u0026auml;rtner W. Influenza-induced thrombocytopenia is dependent on the subtype and sialoglycan receptor and increases with virus pathogenicity. Blood advances. 2020 Jul 14;4(13):2967-78. \u003c/li\u003e\n\u003cli\u003ePu SL, Zhang XY, Liu DS, Ye BN, Li JQ. Unexplained elevation of erythrocyte sedimentation rate in a patient recovering from COVID-19: A case report. World journal of clinical cases. 2021 Feb 26;9(6):1394. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acute respiratory illness (ARI), SARS-CoV-2, Influenza, upper respiratory tract infections (URTIs), respiratory biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-9278895/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9278895/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcute respiratory illness (ARI) is one of the most common illnesses globally, affecting all age groups and leading to both upper respiratory tract infections (URTIs) and lower respiratory tract infections (LRTIs). Severe Acute Respiratory Syndrome Corona virus 2 (SARS-CoV-2) and influenza are significant contributors to severe respiratory illnesses, causing fatalities. Both spread quickly through respiratory droplets and cause similar symptoms, leading to misleading diagnoses and treatments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims to differentiate symptoms and clinical parameters of SARS-CoV-2, Influenza A and B, and the Upper Respiratory Tract Infections (URTIs) due to other causes to understand disease severity better and provide effective treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study analysed 123 patients from March to November 2023 for routine SARS-CoV-2 and Influenza detection using a structured case reporting form. RNA extraction was performed using a Zybio kit, and a multiplex single-tube combo rRT-PCR assay was developed by the National Institute of Cholera.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study examined 123 individuals with upper respiratory tract infections (URTIs), 12 of whom had influenza A, 8 of whom had influenza B, and 6 of whom had SARS-CoV-2. It found no significant differences in age and comorbidities. Compared to male patients, females are more likely to be infected with influenza (A\u0026amp;B), and SARS-CoV-2 infection was more prevalent among males than females.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study suggests potential benefits in differential diagnosis of COVID-19, influenza (A and B), and the upper respiratory tract infections (URTIs) due to other causes, though further research is needed to confirm these findings.\u003c/p\u003e","manuscriptTitle":"Clinical presentation and detection of SARS-CoV-2, Influenza, and Other Viral URTIs Using rRT-PCR in a Tertiary Hospital in Pune: A Pilot Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 08:35:27","doi":"10.21203/rs.3.rs-9278895/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-12T05:24:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-09T13:48:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91670829953950306878979026704958872098","date":"2026-05-07T16:54:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T19:21:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315423548617968028668609125194309810668","date":"2026-04-29T16:04:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11737330192384926637942694662787993948","date":"2026-04-23T08:47:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-22T01:30:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-22T01:27:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-21T16:13:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-21T06:59:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2026-04-21T06:52:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ecc2cd6-00da-4def-8412-adac15a56f90","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-12T05:24:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-09T13:48:13+00:00","index":51,"fulltext":""},{"type":"reviewerAgreed","content":"91670829953950306878979026704958872098","date":"2026-05-07T16:54:46+00:00","index":50,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T19:21:21+00:00","index":41,"fulltext":""},{"type":"reviewerAgreed","content":"315423548617968028668609125194309810668","date":"2026-04-29T16:04:21+00:00","index":39,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T05:39:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 08:35:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9278895","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9278895","identity":"rs-9278895","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}