Clinical and epidemiological characterization of viral respiratory pathogens in rural Ghana: The role of SARS-CoV-2 and Malaria in the immediate post-pandemic phase

Clinical and epidemiological characterization of viral respiratory pathogens in rural Ghana: The role of SARS-CoV-2 and Malaria in the immediate post-pandemic phase | 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 and epidemiological characterization of viral respiratory pathogens in rural Ghana: The role of SARS-CoV-2 and Malaria in the immediate post-pandemic phase Oumou Maiga Ascofaré, Lena Merkel, Isaac Darko Agyiri, Ruth Korankye, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8287787/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 18 You are reading this latest preprint version Abstract Background: Acute respiratory infections (ARIs) are a leading cause of morbidity and mortality worldwide with sub-Saharan Africa accounting for approximately 50% of the 5.8 million ARI-related deaths globally. Despite this substantial burden, pathogen-based surveillance remains limited in these regions. The post-pandemic phase presents a unique epidemiological landscape, with SARS-CoV-2 transitioning towards endemicity and potentially interacting with other circulating respiratory pathogens. This study aimed to characterize viral ARI etiology and the influence of SARS-CoV-2 serostatus and malaria co-infection on clinical outcomes during the immediate post-pandemic period in a rural Ghanaian setting. Methods: We conducted a prospective-observational study at a district hospital in Ghana between May 2022 and September 2023. Adults with acute respiratory infection were enrolled, and nasopharyngeal swabs were tested using a multiplex PCR panel detecting 22 respiratory, mainly viral, pathogens. SARS-CoV-2 serostatus and malaria infection status were determined, and broader clinical parameters were examined. Clinical severity was assessed using the PRIEST score, and participants were followed up for 28 days to evaluate symptom resolution. Multivariable logistic regression identified potential predictors of hospitalization. Results: Among 347 participants, the most frequently detected respiratory pathogens were rhinovirus/enterovirus (12.4%, n=43), acute SARS-CoV-2 (11.0%, n=38), and influenza A (6.3%, n=22). Among all participants, 79.8% (n=277) were seropositive for SARS-CoV-2 and 28.5% (n=99) were carrying malaria parasites. Hospitalization rates were comparable across SARS-CoV-2 serostatus, acute infection, and malaria status groups (25-30%). Higher PRIEST scores were associated with higher odds of hospitalization (OR: 1.50, 95% CI: 1.32-1.74), while COVID-19 vaccination was associated with lower odds (OR: 0.41, 95% CI: 0.18-0.93). Participants with acute SARS-CoV-2 infection experienced delayed symptom resolution. Conclusion: Post-pandemic ARI etiology in rural Ghana exhibits a diverse viral profile, with rhinovirus/enterovirus, SARS-CoV-2 and influenza A predominating. Clinical outcomes were more strongly associated with high PRIEST scores than with specific pathogens, with vaccination decreasing the odds for hospitalization. While this study did not identify pathogen-specific associations with clinical severity, sustained surveillance remains important for detecting shifts in viral circulation and informing tailored public health responses. Respiratory Infections Surveillance Malaria SARS-CoV-2 Ghana Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Acute respiratory infections (ARIs) represent a common cause of healthcare visits and hospitalizations among adults in sub-Saharan Africa (SSA), even before the Coronavirus Disease 2019 (COVID-19) pandemic 1,2 . The region accounts for approximately 50% of the 5.8 million annual ARI-related deaths globally 1 . Despite the burden of ARIs, pathogen-specific surveillance systems remain limited, especially in rural settings. During the pandemic, diagnostic capacities were temporarily expanded, however, as SARS-CoV-2 transitioned into the post-pandemic phase, the clinical and epidemiological landscape of ARIs may have shifted, potentially influenced by factors such as containment measures, changing patterns of pathogen circulation, and hybrid immunity from prior infection and vaccination 3 . In malaria-endemic regions, the overlap in epidemiology, infection incubation and clinical manifestation between malaria and SARS-CoV-2 has been reported to complicate diagnosis, clinical decision-making and health system responses, as they particularly present as acute febrile illnesses 4 . Clarifying this interaction is vital for rational therapeutical decisions and effective public health response in SSA 5,6 . Ghana experienced four distinct waves of SARS-CoV-2, each driven by different variants. The first wave, peaking in July 2020, was linked to the B.1.1 lineage. The second wave, in January 2021, was dominated by the Alpha variant (B.1.1.7), followed by a third wave in August 2021, driven by Delta (B.1.617.2). The fourth wave emerged in December 2021 with Omicron (B.1.1.529) as the predominant strain 7 . By December 2022, the Ghana Health Service reported a cumulative total of 171,048 COVID-19 cases and 1,461 related deaths 7 . During the same period, Ghana recorded between 5.3 million malaria cases in 2021 and 5.2 million cases in 2022 8 . Although several studies have examined the interplay between SARS-CoV-2 and ARI, very few have provided comprehensive data on pathogen-level etiology, clinical course and outcomes of viral respiratory infections in the immediate post-pandemic phase, particularly in malaria-endemic regions. Emerging evidence suggests that previous exposure to Plasmodium falciparum may attenuate immune responses potentially explaining the milder or asymptomatic COVID-19 presentation in malaria-endemic regions 4 . Additionally, there is some evidence suggesting that recent malaria-induced polyreactive antibodies may yield false-positive SARS-CoV-2 serology results 5,6 . This can potentially complicate the interpretation of diagnostic tests. Also, viral ARI co-infections and viral interference have been proposed as a factor influencing post-pandemic virus circulation globally, yet their pattern in SSA remains undocumented 9,10 . These gaps limit our ability to accurately interpret diagnostics and implement effective respiratory infection control strategies in malaria-endemic regions. This study contributes to addressing these gaps by characterizing circulating viral respiratory pathogens in the immediate post-pandemic period and assessing their clinical severity. We also examine how malaria co-infection as well as acute SARS-CoV-2 infection and serostatus relate to relevant outcomes in a rural Ghanaian setting. By leveraging multiplex molecular diagnostics, the study offers context-specific findings that will contribute to the evidence base needed to optimize diagnostic approaches, antimicrobial stewardship, and public health interventions in SSA. Further, the longitudinal nature of the study allowed for assessing duration of symptoms and length of therapy and hospitalization, and for capturing prospective data on disease progression or resolution. These insights are crucial not only for managing current respiratory illnesses, but also for strengthening resilience against future respiratory epidemics in SSA. Methods Study design, eligibility and study procedures This study was conducted at the Saint Francis Xavier Hospital, a rural district referral hospital located in Assin Foso in the Central Region of Ghana. The hospital serves a predominantly rural population of approximately 207,000 inhabitants. An observational, longitudinal design with a prospective follow-up from hospital presentation to 28 days was employed to capture trends in viral respiratory infection as the acute SARS-CoV-2 pandemic ended. Participant recruitment took place from 31 st May 2022 to 29 th June 2023. Study data were collected and managed using REDCap 11 (Research Electronic Data Capture). For follow up data capture at the community, data were collected offline and uploaded to the database at a later stage. Eligible participants were adults aged 16 years and older who presented to the outpatient or emergency department (ED) with ARI, broadly defined as the presence of at least two respiratory symptoms such as cough, shortness of breath, nasal congestion, or (history of) fever. Participants were screened using a standardized questionnaire based on WHO case definitions for ARI. Patients with respiratory complaints of cardiovascular origin confirmed by clinical assessment were excluded. At enrollment, trained healthcare staff administered structured questionnaires to collect demographic, clinical, and epidemiological data, including symptom onset, travel history, comorbidities, and prior COVID-19 vaccination status. The structured questionnaire used in this study was specifically developed for the CLEAR cohort and has not been previously published. It was adapted from WHO ARI and COVID-19 case investigation tools and tailored to the Ghanaian context (supplementary material S2). Vital signs were recorded and the Clinical Severity Score predicts adverse outcomes in adults presenting to ED adverse with suspected COVID-19 (PRIEST COVID-19) were assessed. Participants were followed up for 28 days via in-person visits or, if unreachable, via phone calls to document symptom resolution, additional healthcare visits, hospital admissions, and treatment outcomes. The daily follow-up period was discontinued if a participant remained symptom-free for 48 hours. Laboratory testing At enrolement, nasopharyngial swabs and blood samples were collected to perform the following tests. SARSCoV-2 infection was firstly detected using the Antigen Rapid Diagnostic Test (agRDT) Abbott Panbio TM COVID-19 Antigen Rapid Test Kit (# 41FK10CPT) according to the manufacturer. Serostatus (IgM, IgG) was determined using the FaStep Covid-19 Antibody Test Kit (CPT: 86328) from AssureTech Co. Nasopharyngeal swabs were collected and tested using the Biofire Respiratory 2.1 Panel, which detects 22 viral and atypical bacterial pathogens. SARS-CoV-2 testing was conducted using Polymerase Chain Reaction (PCR) on nasopharyngeal samples. Nasopharyngeal samples were extracted and purified using the QIAamp Viral RNA Mini Kit (QIAGEN, Hilden, Germany, Cat. #52906) according to manufacturer instructions. Purified extracts were used for real-time PCR using the RealStar® SARS-CoV-2 RT-PCR Kit 1.0 (Altona Diagnostics, Hamburg, Germany, Cat. #821005) using instructions stated by the manufacturer. The PCR assays were performed using the CFX96 Touch Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA). All samples meeting the cycle threshold (Ct) value cut-off of ≤26 were sequenced to determine the variants of the SARS-CoV-2. The sequencing was done on the Illumina iSeq100 platform. Library preparation was performed with the NEBNext® ARTIC SARS-CoV-2 FS library preparation kit with the ARTICv3 primers and following the manufacturers instructions. Variant calling and lineage determination were performed with an in-house Nextflow workflow that employed fastp for trimming the raw readss, bwa mem for mapping to the SARS-CoV-2 reference genome NC_045512.2, lofreq for variant calling, and pangolin (version 4.2) for variant classification. Malaria status was assessed through PCR targeting the gene varATS of the most common species Plasmodium falciparum using nucleic acid extracted from blood samples . Using the manufacturer's instructions, DNA was extracted and purified from whole blood samples using the Mag Maxi kit (LGC Genomics GmbH, Germany, Cat.# NAP43306). The ultra-sensitive varATS real-time PCR used for parasite detection was performed. The parasite load in positive samples from the varATS real-time PCR were determined using quantitative real-time PCR (RT-qPCR) targeting the 18s rRNA of Plasmodium falciparum . The standard curve and the starting quantity (SQ) for each sample were determined using the CFX Manager™ Dx Software, version 3.1 (Bio-Rad, Hercules, CA, USA). The SQ was used to determine the parasitaemia using the formula: parasitaemia = . The PCR assays were performed using the CFX96 Touch Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA). Additional blood samples were also collected to measure full blood count and extensive biochemistry testing including Inflammatory biomarkers. All laboratory assessments were conducted in the research laboratory at the research laboratory at Saint Francis Xavier Hospital in Assin Foso and at the Kumasi Centre for Collaborative research in Kumasi, Ghana. Sample size This study formed part of a larger longitudinal assessment which was initially designed as a test-negative study focusing on acute SARS-CoV-2 cases. However, as the pandemic evolved and such cases became rare, the analytical approach shifted to include seropositive individuals as cases. This change to the study design meant that it was not possible to conduct formal a priori sample size calculations for the current analytical objectives. The final sample size (N = 347) was determined by the maximum number of eligible participants who could be recruited within the available resources and timeframe of the study. This sample size was considered adequate for the primary descriptive objectives of characterising pathogen prevalence and clinical outcomes. For comparative analyses between subgroups (e.g. SARS-CoV-2 seropositive versus seronegative), the number of particpants recruited provided sufficient statistical power to detect clinically meaningful differences in main outcomes. Data Analysis For analysis, we compared study participants by serostatus, SARS-CoV-2 infection status, and malaria infection status. Serostatus was determined at enrollment: individuals were identified as seropositive if either the IgG or IgM RDT was positive, and seronegative if both tests were negative. Individuals were classified as having an acute SARS-CoV-2 infection if SARS-CoV-2 was detected using either multiplex PCR (BioFire) and/or RT-qPCR testing (Altona), whereas all other pathogens included in the analysis were detected exclusively by the BioFire panel. Malaria infection was determined using PCR. Descriptive statistics were used to summarize patient characteristics and pathogen prevalence. Illness duration was defined as the total number of days that symptoms were reported -since onset of symptoms- during daily follow up . Time-to-symptom resolution was analyzed using Kaplan–Meier methods, with differences between groups assessed using the log-rank test and subsequent adjustment for multiple testing using the benjamini-hochberg correction. Multivariable logistic regression models were employed to identify independent factors associated with hospitalization. To address potential confounding, the model was adjusted for age, gender, PRIEST score, total number of reported comorbidities, vaccination status (defined as having received at least one vaccine dose), serostatus, and exposure to a respiratory isolate. Exposure was operationalized as a positive result from the BioFire and included only isolates with a frequency of at least 5% among cases. We tested for multicollinerity using the variance inflation factor (VIF). The population attributable fraction (PAF), representing the proportion of hospital admissions attributable to specific pathogens, was estimated using maximum likelihood estimators from the multivariable logistic regression model. The extent of missing data was minimal. Malaria PCR results were unavailable for 9 participants. A small number of participants had missing values for key covariates, including vaccination status (n=14) and PRIEST score (n=83). In the latter group, PRIEST scores could not be calculated due to missing values in one or more of its components: respiratory rate (n=70), temperature (n=10), and oxygen saturation or heart rate (n=3). Additionally, hospitalization status was missing for one participant. Patterns of missingness were assessed and no substantial differences were observed in baseline characteristics between participants with complete data and those with missing data, supporting the assumption that data were missing at random (MAR). Consequently, a complete case analysis was performed, with participants excluded from specific models only if they were missing data for variables used in those models. Finally, BioFire results were excluded for one participant who tested positive for 13 pathogens, suggestive of probable sample contamination or a technical error. Data analysis was conducted using R software (version 4.3.1). Ethical approval Ethical approval for the study was obtained from and annually renewed by the institutional review board of the Kwame Nkrumah University of Sciences and Technology, School of Medical Sciences (CHRPE/AP/571/21), from the Ghana Health Service Ethics Review Committee (GHS-ERC 003/09/21), and the Ethics Committee of the Hamburg Medical Association (Ärztekammer Hamburg, #2021-100696-BO-ff). Written and voluntary informed consent was obtained from all participants, and for participants below 18 years of age, parental consent and informed assent were obtained before enrollment. Patient confidentiality was maintained by ensuring secure data storage, restricting access to authorized personnel only, and de-identifying the data prior to analysis. The study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. Results Prevalence and distribution of viral pathogens Among the 347 individuals with ARI tested, 215 (62.0%) were negative for all pathogens included in the panel. The remaining 132 participants (38.0%) tested positive for at least one pathogen, with 14 (11.1%) co-infections detected. In total, 14 different respiratory pathogens were detected in our study population. The most frequently detected pathogens were SARS-CoV-2 (13.0%, n=45), human rhinovirus/enterovirus (12.4%, n=43), and influenza A (6.3%, n=22). The distribution of single and multiple pathogen detections is shown in Figure 1. Characteristics of SARS-CoV-2 and Plasmodium falciparum infections Among the 347 individuals with ARI included in the study, 277 (79.8%) were seropositive and 70 (20.2%) were seronegative for SARS-CoV-2, of which acute SARS-CoV-2 infection was identified in 45 individuals (13.0%) (see supplementary material). Fourteen samples with Ct≤26 were sequenced and thirteen were classified as Omicron or probable Omicron variant. Three of them had a conflict score of 0.33, 0.5, 0.7 indicating that although Pangolin (Phylogenetic Assignment of Named Global Outbreak Lineages) 12 classified them as probable Omicron, they could also be in a difference variant class with varying likelihoods. One sample was not of sufficient quality to determine the variant type. All individuals were tested for Plasmodium falciparum malaria; 99 (28.5%) individuals tested positive and 239 (68.9%) tested negative. Malaria infection status was unavailable for 9 individuals (2.6%). One case, which tested positive for 13 different pathogens, was excluded due to a high likelihood of sample contamination or a technical issue. SARS-CoV-2 was detected using multiplex and simplex PCR testing combined. The top bar plot shows the number of cases testing positive for specific pathogen combinations (intersection size), with 215 individuals testing negative for all pathogens. The left bar plot indicates the total number of cases (set size) for each individual pathogen. The most frequent single-pathogen detections were SARS-CoV-2 (13.0%, n=45), human rhinovirus/enterovirus (10.1%, n=35), and influenza A (6,3%, n=22), while the most frequent co-infections involved human rhinovirus/enterovirus with human metapneumovirus or SARS-CoV-2 (both 0.9%, n = 3). All other co-infections were only detected once. Other coronaviruses correspond to detected NL63 and OC43. Atypical bacteria correspond to detected B ordetella pertussis and Mycoplasma pneumoniae . Parainfluenzaviruses correspond to detected types 3 and 4. From the 22 pathogens included in the Respiratory 2.1 Panel, no cases of Bordetella parapertussis , Chlamydia pneumoniae , coronavirus 229E, coronavirus HKU1, parainfluenza virus 1 and parainfluenza virus 2 were detected. Distribution by Malaria status and SARS-CoV-2 serostatus. As shown in Figure 2, relative proportions of respiratory pathogens varied across sub-groups defined by SARS-CoV-2 serostatus and malaria status. In Panel A, SARS-CoV-2 was notably more common in seronegative individuals (47.8% [95% CI: 26.2-69.4], n/N=11/23 among seronegative compared to 27.2% [95% CI: 19.3-35.1], n/N=34/125 among seropositive individuals), while rhinovirus/enterovirus (seronegative: 26.1% [95% CI: 7.1-45.1], n/N=6/23 vs. seropositive: 29.6% [95% CI: 21.5-37.7], n/N=37/125) and influenza A (seronegative: 13.0% [95% CI: -1.5-27.6], n/N=3/23 vs. seropositive: 15.2% [95% CI: 8.8-21.5], n/N=19/125) were more evenly distributed. The distribution by malaria status in Panel B remained relatively stable. Other respiratory viruses, including parainfluenza viruses, respiratory syncytial virus (RSV) and metapneumovirus, were detected at lower frequencies with minor differences between groups. Atypical bacteria and other coronaviruses were rarely observed (<5% of cases in any group). Among the 347 ARI cases in the study, the proportion of individuals requiring hospitalization was comparable across SARS-CoV-2 serostatus, acute infection, and malaria infection groups, with no significant differences observed (Figure 3.A). Hospitalization rates ranged from approximately 25% to 30% across all sub-groups, with overlapping confidence intervals. Among hospitalized patients, 90 individuals required oxygen therapy (Figure 3.B). The proportion of patients receiving oxygen therapy was higher among seropositive individuals (40%, 95% CI: 28.2–51.8) compared to seronegative individuals (20%, 95% CI: 0.8–39.2), although the difference was not statistically significant. It was also similar between those with and without acute SARS-CoV-2 infection (30.8%, 95% CI: 1.7–59.8 vs. 36.4%, 95% CI: 25.4–47.4) and between those with and without malaria infection (35.5%, 95% CI: 17.6–53.3 vs. 34.5%, 95% CI: 21.9–47.1). Additional Analyses The extensive examination of full blood count and biochemistry, including commonly used inflammatory biomarkers, did not yield a clinically significant difference across sub-groups (see supplement). Length of illness The cumulative incidence of symptom resolution during the initial 28 day follow-up period was examined in relation to serostatus, SARS-CoV-2 infection status, and malaria infection status. Of the 347 cases included, 321 (92 %) had symptoms resolved within 28 days from symptoms onset, while 26 cases did not. Participants with acute SARS-CoV-2 infection experienced a delayed symptom resolution compared to those without infection (p = 0.104 Figure 4A) suggesting prolonged symptom duration in the SARS-CoV-2-positive group. By day 10, more than half of the acute SARS-CoV-2-negative participants had recovered, whereas recovery in the acute SARS-CoV-2-positive group lagged behind, with some continuing to report symptoms beyond two weeks. However, there was no evidence for a difference in the time to symptom resolution between seropositive and seronegative participants (p = 0.903, Figure 4B), with the majority of participants (77.6%, n= 215 vs. 72.9%, n=51) achieving symptom resolution within the first two weeks. No evidence for a difference in symptom resolution was observed between malaria-positive and malaria-negative participants (p = 0.682, Figure 4C). Predictors of Hospital Admission Figure 5 shows the results of the logistic regression analysis identifying significant predictors of hospital admission. For this analysis, 100 (28.8%) of the initial 347 observations were excluded due to missing data in control or outcome variables. A higher PRIEST score was associated with increased odds of admission (OR: 1.50, 95% CI: 1.32–1.74). In contrast, SARS-CoV-2 vaccination was protective, reducing the odds of admission (OR: 0.42, 95% CI: 0.19–0.93). The intercept of the model was negative and statistically significant, indicating that the baseline probability of hospital admission among individuals with reference or minimal risk characteristics was significantly lower than 50%. Other variables, including age, sex, number of comorbidities, and pathogen detection (SARS-CoV-2, rhinovirus/enterovirus, influenza, malaria), did not show associations with hospitalization. Similarly, seropositivity for SARS-CoV-2 was not a strong predictor of admission (OR: 1.62, 95% CI: 0.65–4.20). Population attributable fractions SARS-CoV-2 had the highest population attributable fraction (PAF) for hospitalization at 2.02% (95% CI: 4.36 to 8.41), , followed by malaria at 1.49% (95% CI: -9.43 to 12.40), although these associations were not statistically significant (see supplementary material). By contrast, human rhinovirus/enterovirus (-4.28%, 95% CI: -9.78 to 1. .22) and influenza A/B (-0.70%, 95% CI: -5.82 to 4.42) exhibited negative PAFs, indicating a potential protective effect, although also not statistically significant. The effect size for influenza was particularly small, suggesting a minimal contribution to hospital admissions in this cohort. The wide confidence intervals reflect the limited sample size, thus, these estimates should be interpreted with caution. Discussion In this study, we characterize the evolving landscape of viral ARIs among adults in rural Ghana during the immediate COVID-19 post-pandemic phase. Rhinovirus/enterovirus, SARS-CoV-2, and influenza A were the most commonly detected viruses. SARS-CoV-2 infection was associated with delayed symptom resolution whereas neither serostatus nor malaria co-infection significantly influenced patient outcomes. As expected, we found that higher PRIEST scores predicted hospitalization, while prior COVID-19 vaccination was protective. Our findings partially align with pre- pandemic literature from sub-Saharan Africa (SSA) where viruses like RSV and influenza have historically dominated 1,13,14 . A meta-analysis from East Africa reported RSV, parainfluenza viruses, and adenoviruses as dominant pathogens 15,16 , while Ho et al. highlighted influenza viruses and RSV, with wide variability across settings 17 . The dominance of rhinovirus/enterovirus and SARS-CoV-2 may reflect a post pandemic shift in viral circulation. Also, the predominance of rhinovirus/enterovirus aligns with reports from high-income countries of its rapid resurgence after non-phamaceutical interventions were lifted 18 , while the persistence of SARS-CoV-2 may support its transition towards endemicity 3,19 . The concept of viral interference, may also underlie the suppression or delay of specific viruses such as RSV 20,21 . Our findings highlight the importance of continued respiratory pathogen-based surveillance in African settings to detect temporal changes and emerging threats. The high prevalence of SARS-CoV-2 among seronegative individuals underscores the protective effects of prior exposure or vaccination, aligning with global evidence of hybrid immunity 3,22 . While SARS-CoV-2 infection did not significantly increase hospitalization risk in our cohort, it was associated with prolonged symptom duration compared to those with non-SARS-CoV-2 infections, as seen in findings from other high income settings where acute infection correlates with slower recovery trajectories and increased risk of post-acute sequelae (PASC) 23 . This suggests that even in a malaria-endemic region with hybrid immunity, SARS-CoV-2 may disproportionately contribute to prolonged morbidity, potentially signaling underrecognized long COVID risk among mild cases. Expectedly, our findings confirm the predictive value for hospitalization of the PRIEST score — validated in high income settings — in a rural Sub-Saharan African context, where it could aid in clinical triage and resource allocation. In contrast, SARS-CoV-2 vaccination was associated with lower odds of hospitalization, reinforcing known protective effects even in settings with partial vaccination coverage 24,25 . During follow-up of participants, patients were followed for 12 months to assess PASC; these findings will be reported separately. The severity of ARI, as measured by hospitalization or the use of oxygen therapy, did not differ significantly by SARS-CoV-2 serostatus, acute infection, or malaria co-infection. This contrasts with patterns observed in HICs, where seronegativity and lack of vaccination are strong predictors of severe outcomes 26 . However, similar findings have been reported in SSA, where disease severity has been lower than anticipated, potentially due to a younger population, pre-existing cross-immunity, or under-recognition of hypoxia in resource-constrained settings 6,27 . We explored the contribution of individual pathogens to hospital admissions. Although P. falciparum and SARS-CoV-2 showed the highest PAF associated with hospitalization, the extremely wide confidence intervals preclude definitive conclusions due to limited sample size. However, multicenter case-control pneumonia studies such as PERCH have demonstrated the usefulness of this approach 14 . In the same Ghanaian setting, Krumkamp et al. estimated PAF in children where RSV, influenza A, S. pneumoniae and H. influenza where associated with admission 28 . Thus, given an adequate sample size, extending this methodology to adult populations could strengthen etiologic inference and inform clinical management. Although the study lacked the power to assess the interaction between malaria and SARS-CoV-2, some previous studies suggest that P. falciparum may modulate antiviral responses by dampening interferon signaling and promoting regulatory T-cell expansion 4 and ultimately leading to reduced COVID-19 severity whilst others report false-positive SARS-CoV-2 antibody results due to malaria-induced polyreactive antibodies 5 . We did not observe differences in pathogen distribution or severity by malaria status. However, the hypothesis of malaria-induced immunomodulation remains plausible and warrants further investigation. Inflammatory biomarkers consistently not elevated across all sub-group comparisons (serostatus, acute SARS-CoV and malaria status). Similar observations in endemic settings suggest that P. falciparum may not induce immune processes that elevate these biomarkers to the same extent as bacterial infections 29 . While not designed to confirm the transition to endemicity, our study showed that SARS-CoV-2 was frequently detected, suggesting ongoing circulation. Studies from similar regions support the claim of a shift towards endemicity which is characterized by recurrent seasonal waves, increasing hybrid immunity, and reduced mortality 3 . These claims are relevant especially in SSA, where asymptomatic transmission may strengthen community spread, with vulnerable groups, including the elderly and those with comorbidities, facing a disproportionate mortality risk. Furthermore, the burden of long COVID may be underrecognized in this setting. Controlled, longitudinal studies are needed to identify predictors of PASC and guide post-acute care strategies in African populations. Our study has several strengths: we systematically tested all participants with a broad PCR-based multiplex panel of respiratory pathogens, thereby minimising testing bias. Our analysis incorporated multiple indicators of infection status (acute infection, seropositivity and vaccination status) for SARS-CoV-2, enabling a nuanced evaluation of various immune states and their clinical implications. Further, we used validated clinical risk assessment tools, including the PRIEST score, enhancing the clinical applicability of our findings. Moreover, our study design captured both hospitalised and non-hospitalised patients, providing insights into the spectrum of ARI severity. This study has some limitations. Firstly, First, the use of a viral-only diagnostic panel likely contributed to the low overall pathogen detection rate and may have missed bacterial or fungal etiologies. The relatively low overall pathogen detection rate (36%) may be due to the specificity of our viral-only testing panel for the local pathogen ecology, or to the inclusion of patients with non-viral causes of ARI. Our findings may also be influenced by healthcare-seeking behavior, since our sample presented here comprised only individuals presenting to hospitals. Some analyses may have lacked statistical power to detect smaller effect sizes. The wide confidence intervals for PAF estimates reflect the limited precision of our effect estimates and the inherent uncertainty in attributing population-level disease burden to specific pathogens based on observational associations. Given the post hoc nature of our analytical approach, all analyses should be interpreted as exploratory. Furthermore, due to the short follow-up duration, seasonality could not be assessed. Our analysis of symptom resolution used standard survival analysis methods, though hospitalization during follow-up may constitute a competing risk that could influence recovery patterns. The application of competing risks regression models in future research would provide more robust estimates of symptom duration across different patient trajectories. To understand the full spectrum and impact of respiratory infections in adults, expanded panels including bacterial pathogens and host-response markers are needed, alongside larger, multi-site longitudinal surveillance. Such data will be invaluable in designing context-appropriate diagnostic and treatment algorithms that can improve patient care and resource allocation in these settings. Such approaches will be critical to prepare for future respiratory epidemics in SSA. Conclusions Our study shows the clinical and viral profile of acute respiratory infections in a rural, malaria-endemic setting during the immediate post-pandemic phase. With rhinovirus/enterovirus, SARS-CoV-2 and influenza A emerging as dominant pathogens in the viral ARI, we see a viral profile that might suggest the presence of several unrecognized circulating viruses during this period that could easily have been ascribed to SARS-CoV-2 in the absence of proper testing. In our cohort, clinical outcomes were primarily associated with disease severity and vaccination status rather than pathogen type. Although malaria infection was common, we found it not to be associated with the severity of illness. However, its presence may complicate the diagnosis of febrile patients and might influence serological interpretation of SARS-CoV-2. Expanding diagnostic strategies in endemic settings is vital. With over 60% of our ARI cases being pathogen-negative, our study finds that there are critical diagnostic gaps in current surveillance, emphasizing the need for expanded diagnostic capacities that include bacterial and fungal causes while improving sample timing. To translate these findings into impact, we recommend an urgent prioritization and continuous investment in decentralized diagnostic networks, adaptive vaccination strategies and routine respiratory surveillance that combines viral, bacterial and severity data. Abbreviations ARI: Acute respiratory infection BH: Benjamini–Hochberg COVID-19: Coronavirus disease 2019 Ct: Cycle threshold ED: Emergency department IgG/IgM: Immunoglobulin G / Immunoglobulin M KCCR: Kumasi Centre for Collaborative Research LMIC: Low- and middle-income countries PCR: Polymerase chain reaction PAF: Population attributable fraction PRIEST COVID 19: Clinical Severity Score predicts adverse outcomes in adults presenting to ED adverse with suspected COVID-19 RDT: Rapid diagnostic test RT-qPCR: Reverse-transcriptase quantitative PCR SARS-CoV-2: Severe acute respiratory syndrome coronavirus 2 SSA: Sub-Saharan Africa Declarations Ethics approval and consent to participate Ethical approval for this study was obtained and annually renewed by the Committee on Human Research, Publications and Ethics (CHRPE) of the Kwame Nkrumah University of Science and Technology, School of Medical Sciences (CHRPE/AP/571/21), the Ghana Health Service Ethics Review Committee (GHS-ERC 003/09/21), and the Ethics Committee of the Hamburg Medical Association (Ärztekammer Hamburg, #2021-100696-BO-ff). All participants (or guardians for those aged 16–17 years) provided written informed consent prior to enrolment. Illiterate participants provided thumbprint consent in the presence of an impartial witness. Participation was voluntary and could be withdrawn at any time. All study procedures adhered to the Declaration of Helsinki and Good Clinical Practice guidelines. Consent for publication Not applicable. This manuscript does not include any individual person’s identifiable data. Availability of data and materials The datasets generated and/or analysed during the current study contain potentially identifiable clinical information and are therefore not publicly available. De-identified participant-level data and analysis code can be made available from the corresponding author upon reasonable request, subject to data-sharing agreements and ethics approval. No microarray datasets, macromolecular structures, crystallographic data, or other data types were generated in this study. The RNA sequences generated in the context of a parallel study and are available in the European Nucleotide Archive (ENA) under accession number: PRJEB105234 or via this link: https://www.ebi.ac.uk/ena/browser/view/PRJEB105234. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This study was part of a project of the ‘Corona Global’ funding track within the Global Health Protection Programme (GHPP) with the financial support of the German Federal Ministry of Health (BMG) (FKZ 2521GHP920). Authors contributions- Conceptualization : OMA, JM, RS; Data Curation : LM, IDA, TH, LHR, AH, WL, EL; Formal Analysis and Methodology: OMA, EL, RS; Investigation: OMA, IDA, RK, PMA, JG, WOD, ENP, JOM, AAAA, MA, JHA, MA; Visualization: LM, LHR, EL; Funding acquisition: JM, RS; Project administration: OMA, TH, RS; Supervision: OMA, JHA, MA, JM, EL, RS; Writing – original draft: OMA, LM, IDA, LHA, EL, RS; Writing – review and editing: all authors. All authors approved the final manuscript. Acknowledgements Firstly, we would like to thank the study participants who voluntarly consented to take part in the study. We are grateful the support of the management and staff from the Saint Francis Xavier Hospital at Assin Foso, Ghana. We acknowledge and are deeply appreciative of the contributions of all the members of the Infectious Disease Epidemiology research group at KCCR, Ghana. We are grateful for the support of the study nurses Bright Tamakloe and Portia Kanadu who handled the calls to the participants for data collections during follow up visits. References Sirota, S. B. et al. Global, regional, and national burden of upper respiratory infections and otitis media, 1990–2021: a systematic analysis from the Global Burden of Disease Study 2021. The Lancet Infectious Diseases 25 , 36-51 (2025). https://doi.org:10.1016/S1473-3099(24)00430-4 WHO. WEEKLY BULLETIN ON OUTBREAKS AND OTHER EMERGENCIES. Week 23: 29 MAY-04 JUNE 2023. (2023). Cohen, L. E., Spiro, D. J. & Viboud, C. Projecting the SARS-CoV-2 transition from pandemicity to endemicity: Epidemiological and immunological considerations. PLoS pathogens 18 , e1010591 (2022). https://doi.org:10.1371/journal.ppat.1010591 Tapela, K. et al. Cellular immune response to SARS-CoV-2 and clinical presentation in individuals exposed to endemic malaria. 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H. & Brown, I. Rates of Co-infection Between SARS-CoV-2 and Other Respiratory Pathogens. JAMA (2020). https://doi.org:10.1001/jama.2020.6266 Kissler, S. M., Tedijanto, C., Goldstein, E., Grad, Y. H. & Lipsitch, M. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science (2020). https://doi.org:10.1126/science.abb5793 Gilbert-Girard, S. et al. Viral interference between severe acute respiratory syndrome coronavirus 2 and influenza A viruses. PLoS pathogens 20 , e1012017 (2024). https://doi.org:10.1371/journal.ppat.1012017 Zhang, S. et al. Spatial-temporal dynamics and virus interference of respiratory viruses: Insights from multi-pathogen surveillance in China. Journal of Infection 91 (2025). https://doi.org:10.1016/j.jinf.2025.106556 Nesteruk, I. Endemic characteristics of SARS-CoV-2 infection. Scientific reports 13 , 14841 (2023). https://doi.org:10.1038/s41598-023-41841-8 Bahmer, T. et al. Severity, predictors and clinical correlates of Post-COVID syndrome (PCS) in Germany: A prospective, multi-centre, population-based cohort study. EClinicalMedicine 51 , 101549 (2022). https://doi.org:https://doi.org/10.1016/j.eclinm.2022.101549 Anderson, R. M., Vegvari, C., Truscott, J. & Collyer, B. S. Challenges in creating herd immunity to SARS-CoV-2 infection by mass vaccination. The Lancet 396 , 1614-1616 (2020). https://doi.org:10.1016/S0140-6736(20)32318-7 Tartof, S. Y. et al. Durability of BNT162b2 vaccine against hospital and emergency department admissions due to the omicron and delta variants in a large health system in the USA: a test-negative case-control study. Lancet Respir Med 10 , 689-699 (2022). https://doi.org:10.1016/s2213-2600(22)00101-1 Ssentongo, P. et al. SARS-CoV-2 vaccine effectiveness against infection, symptomatic and severe COVID-19: a systematic review and meta-analysis. BMC Infectious Diseases 22 , 439 (2022). https://doi.org:10.1186/s12879-022-07418-y Kalungi, A., Kinyanda, E., Akena, D. H., Kaleebu, P. & Bisangwa, I. M. Less Severe Cases of COVID-19 in Sub-Saharan Africa: Could Co-infection or a Recent History of Plasmodium falciparum Infection Be Protective? Frontiers in Immunology 12 (2021). https://doi.org:10.3389/fimmu.2021.565625 Krumkamp, R. et al. Pathogens associated with hospitalization due to acute lower respiratory tract infections in children in rural Ghana: a case–control study. Scientific reports 13 , 2443 (2023). https://doi.org:10.1038/s41598-023-29410-5 Eriksson, U. K., van Bodegom, D., May, L., Boef, A. G. C. & Westendorp, R. G. J. Low C-Reactive Protein Levels in a Traditional West-African Population Living in a Malaria Endemic Area. PLOS ONE 8 , e70076 (2013). https://doi.org:10.1371/journal.pone.0070076 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx SuplementarymaterialS2.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 16 Mar, 2026 Reviews received at journal 05 Mar, 2026 Reviews received at journal 05 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviews received at journal 16 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviews received at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers invited by journal 07 Jan, 2026 Editor assigned by journal 05 Jan, 2026 Editor invited by journal 16 Dec, 2025 Submission checks completed at journal 16 Dec, 2025 First submitted to journal 16 Dec, 2025 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. 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Bars display pathogen-level counts (i.e., detections). Cases with multiple pathogens contribute one count to each pathogen detected. Panels are stratified by A. SARS-CoV-2 serostatus (tested=148) and B. malaria status (tested n=146). Within each stratum, percentages are calculated from pathogen-level detections among participants in that stratum.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8287787/v1/ce2e595c6f8e4aa710bcad72.png"},{"id":100009026,"identity":"02f6951a-11f4-442a-85a8-0ca00eef3c04","added_by":"auto","created_at":"2026-01-12 06:00:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64398,"visible":true,"origin":"","legend":"\u003cp\u003eClinical outcomes by SARS-CoV-2 sero- and acute infection status and malaria status. Proportion of hospitalized cases (Panel A) and receipt of oxygen therapy among hospitalized patients (Panel B), stratified by SARS-CoV-2 serostatus, acute SARS-CoV-2 infection status, and malaria infection status. Error bars indicate 95% confidence intervals.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8287787/v1/6195486dc71e4924f945fc1e.png"},{"id":100009021,"identity":"2c9df5d2-2096-40a9-85eb-85bf2404c94c","added_by":"auto","created_at":"2026-01-12 06:00:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":260976,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative incidence of symptom resolution among individuals with acute illness, stratified by SARS-CoV-2 infection status (Panel A), acute SARS-CoV-2 serostatus (Panel B), and malaria infection status (Panel C). The Kaplan-Meier plot displays the proportion of individuals whose symptoms resolved over the 28-day follow-up period. Groups are distinguished by color. Confidence intervals are shown as shaded ribbons, and statistical differences between groups were assessed using the log-rank test and adjusting for multiple hypothesis testing using the benjamini-hochberg correction (BH p-value shown). The event table below each panel indicates the number of individuals still at risk (with unresolved symptoms and not yet censored) at each time point.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8287787/v1/c1749fcc4e02b267bc820dec.png"},{"id":100009022,"identity":"501ea6c5-a2df-4686-8027-eb9001a39787","added_by":"auto","created_at":"2026-01-12 06:00:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":180005,"visible":true,"origin":"","legend":"\u003cp\u003ePredictors of hospital admission in patients with ARI. Forest plot displaying adjusted odds ratios (ORs) and 95% confidence intervals from a multivariable logistic regression model assessing the association with hospital admission (dependent variable). The model adjusts for age, sex, PRIEST score, total number of reported comorbidities, vaccination status (defined as receipt of at least one vaccine dose), SARS-CoV-2 serostatus, and exposure to a specific respiratory pathogens. Values greater than 1.0 indicate increased odds of hospital admission, while values less than 1.0 indicate reduced odds. The regression is conducted with 247 cases after exclusion of cases with missing data. We detect uncritical levels of VIF for all covariates with a VIF \u0026lt;5. The VIF was 1.68 for age, 1.18 sex, 1.47 PRIEST, 1.28 number of comorbidities, 1.36 vaccination status, and exposure to 1.03 SARS-Cov-2, 1.05 Human Rhinovirus/Enterovirus, 1.06 Influence A/B and 1.0 Malaria.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8287787/v1/0f325827c1c1096afff0c411.png"},{"id":100361820,"identity":"9676125a-3c4e-4e78-bcf7-725f93770daf","added_by":"auto","created_at":"2026-01-16 07:45:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1639356,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8287787/v1/8208e629-946a-4b02-9fc4-e9403d7228c1.pdf"},{"id":100009025,"identity":"83444800-a89a-44ad-a231-ed0cbe067853","added_by":"auto","created_at":"2026-01-12 06:00:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":35394,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8287787/v1/ba2e97b277bcb6207d4a2a82.docx"},{"id":100009062,"identity":"25683f74-ee09-4ef8-8256-69d4ab795920","added_by":"auto","created_at":"2026-01-12 06:00:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1295545,"visible":true,"origin":"","legend":"","description":"","filename":"SuplementarymaterialS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8287787/v1/132e71de2fc0db90cd130967.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical and epidemiological characterization of viral respiratory pathogens in rural Ghana: The role of SARS-CoV-2 and Malaria in the immediate post-pandemic phase","fulltext":[{"header":"Background","content":"\u003cp\u003eAcute respiratory infections (ARIs) represent a common cause of healthcare visits and hospitalizations among adults in sub-Saharan Africa (SSA), even before the Coronavirus Disease 2019 (COVID-19) pandemic \u003csup\u003e1,2\u003c/sup\u003e. The region accounts for approximately 50% of the 5.8 million annual ARI-related deaths globally\u003csup\u003e1\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the burden of ARIs, pathogen-specific surveillance systems remain limited, especially in rural settings. During the pandemic, diagnostic capacities were temporarily expanded, however, as SARS-CoV-2 transitioned into the post-pandemic phase, the clinical and epidemiological landscape of ARIs may have shifted, potentially influenced by factors such as containment measures, changing patterns of pathogen circulation, and hybrid immunity from prior infection and vaccination \u003csup\u003e3\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn malaria-endemic regions, the overlap in epidemiology, infection incubation and clinical manifestation between malaria and SARS-CoV-2 has been reported to complicate diagnosis, clinical decision-making and health system responses, as they particularly present as acute febrile illnesses\u003csup\u003e4\u003c/sup\u003e. Clarifying this interaction is vital for rational therapeutical decisions and effective public health response in SSA\u003csup\u003e5,6\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGhana experienced four distinct waves of SARS-CoV-2, each driven by different variants. The first wave, peaking in July 2020, was linked to the B.1.1 lineage. The second wave, in January 2021, was dominated by the Alpha variant (B.1.1.7), followed by a third wave in August 2021, driven by Delta (B.1.617.2). The fourth wave emerged in December 2021 with Omicron (B.1.1.529) as the predominant strain\u003csup\u003e7\u003c/sup\u003e. By December 2022, the Ghana Health Service reported a cumulative total of 171,048 COVID-19 cases and 1,461 related deaths\u003csup\u003e7\u003c/sup\u003e. During the same period, Ghana recorded between 5.3 million malaria cases in 2021 and 5.2 million cases in 2022\u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAlthough several studies have examined the interplay between SARS-CoV-2 and ARI, very few have provided comprehensive data on pathogen-level etiology, clinical course and outcomes of viral respiratory infections in the immediate post-pandemic phase, particularly in malaria-endemic regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmerging evidence suggests that previous exposure to \u003cem\u003ePlasmodium falciparum\u003c/em\u003e may attenuate immune responses potentially explaining the milder or asymptomatic COVID-19 presentation in malaria-endemic regions\u003csup\u003e4\u003c/sup\u003e. Additionally, there is some evidence suggesting that recent malaria-induced polyreactive antibodies may yield false-positive SARS-CoV-2 serology results\u003csup\u003e5,6\u003c/sup\u003e. This can potentially complicate the interpretation of diagnostic tests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlso, viral ARI co-infections and viral interference have been proposed as a factor influencing post-pandemic virus circulation globally, yet their pattern in SSA remains undocumented\u003csup\u003e9,10\u003c/sup\u003e. These gaps limit our ability to accurately interpret diagnostics and implement effective respiratory infection control strategies in malaria-endemic regions.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study contributes to addressing these gaps by characterizing circulating viral respiratory pathogens in the immediate post-pandemic period and assessing their clinical severity. We also examine how malaria co-infection as well as acute SARS-CoV-2 infection and serostatus relate to relevant outcomes in a rural Ghanaian setting. By leveraging multiplex molecular diagnostics, the study offers context-specific findings that will contribute to the evidence base needed to optimize diagnostic approaches, antimicrobial stewardship, and public health interventions in SSA. Further, the longitudinal nature of the study allowed for assessing duration of symptoms and length of therapy and hospitalization, and for capturing prospective data on disease progression or resolution. These insights are crucial not only for managing current respiratory illnesses, but also for strengthening resilience against future respiratory epidemics in SSA.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy design, eligibility and study procedures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted at the Saint Francis Xavier Hospital, a rural district referral hospital located in Assin Foso in the Central Region of Ghana. The hospital serves a predominantly rural population of approximately 207,000 inhabitants. An observational, longitudinal design with a prospective follow-up from hospital presentation to 28 days was employed to capture trends in viral respiratory infection as the acute SARS-CoV-2 pandemic ended. Participant recruitment took place from 31\u003csup\u003est\u003c/sup\u003e May 2022 to 29\u003csup\u003eth\u003c/sup\u003e June 2023.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudy data were collected and managed using REDCap\u003csup\u003e11\u003c/sup\u003e (Research Electronic Data Capture). For follow up data capture at the community, data were collected offline and uploaded to the database at a later stage.\u003c/p\u003e\n\u003cp\u003eEligible participants were adults aged 16 years and older who presented to the outpatient or emergency department (ED) with ARI, broadly defined as the presence of at least two respiratory symptoms such as cough, shortness of breath, nasal congestion, or (history of) fever. Participants were screened using a standardized questionnaire based on WHO case definitions for ARI. Patients with respiratory complaints of cardiovascular origin confirmed by clinical assessment were excluded.\u003c/p\u003e\n\u003cp\u003eAt enrollment, trained healthcare staff administered structured questionnaires to collect demographic, clinical, and epidemiological data, including symptom onset, travel history, comorbidities, and prior COVID-19 vaccination status. The structured questionnaire used in this study was specifically developed for the CLEAR cohort and has not been previously published. It was adapted from WHO ARI and COVID-19 case investigation tools and tailored to the Ghanaian context (supplementary material S2). Vital signs were recorded and the \u003cem\u003eClinical Severity Score predicts adverse outcomes in adults presenting to ED adverse with suspected COVID-19\u003c/em\u003e (PRIEST COVID-19) were assessed. Participants were followed up for 28 days via in-person visits or, if unreachable, via phone calls to document symptom resolution, additional healthcare visits, hospital admissions, and treatment outcomes. The daily follow-up period was discontinued if a participant remained symptom-free for 48 hours.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLaboratory testing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAt enrolement, nasopharyngial swabs and blood samples were collected to perform the following tests.\u003c/p\u003e\n\u003cp\u003eSARSCoV-2 infection was firstly detected using the Antigen Rapid Diagnostic Test (agRDT) Abbott Panbio\u003csup\u003eTM\u003c/sup\u003e COVID-19 Antigen Rapid Test Kit (# 41FK10CPT) according to the manufacturer. Serostatus (IgM, IgG) was determined using the FaStep Covid-19 Antibody Test Kit (CPT: 86328) from AssureTech Co.\u003c/p\u003e\n\u003cp\u003eNasopharyngeal swabs were collected and tested using the Biofire Respiratory 2.1 Panel, which detects 22 viral and atypical bacterial pathogens.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSARS-CoV-2 testing was conducted using Polymerase Chain Reaction (PCR) on nasopharyngeal samples. Nasopharyngeal samples were extracted and purified using the QIAamp Viral RNA Mini Kit (QIAGEN, Hilden, Germany, Cat. #52906) according to manufacturer instructions. Purified extracts were used for real-time PCR using the RealStar\u0026reg; SARS-CoV-2 RT-PCR Kit 1.0 (Altona Diagnostics, Hamburg, Germany, Cat. #821005) using instructions stated by the manufacturer. The PCR assays were performed using the CFX96 Touch Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA).\u003c/p\u003e\n\u003cp\u003eAll samples meeting the cycle threshold (Ct) value cut-off of \u0026le;26 were sequenced to determine the variants of the SARS-CoV-2. The sequencing was done on the Illumina iSeq100 platform. Library preparation was performed with the NEBNext\u0026reg; ARTIC SARS-CoV-2 FS library preparation kit with the ARTICv3 primers and following the manufacturers instructions. Variant calling and lineage determination were performed with an in-house Nextflow workflow that employed fastp for trimming the raw readss, bwa mem for mapping to the SARS-CoV-2 reference genome NC_045512.2, lofreq for variant calling, and pangolin (version 4.2) for variant classification.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMalaria status was assessed through PCR targeting the gene varATS of the most common species \u003cem\u003ePlasmodium falciparum\u0026nbsp;\u003c/em\u003eusing nucleic acid extracted from blood samples\u003cem\u003e.\u003c/em\u003e Using the manufacturer\u0026apos;s instructions, DNA was extracted and purified from whole blood samples using the Mag Maxi kit (LGC Genomics GmbH, Germany, Cat.# NAP43306). The ultra-sensitive \u003cem\u003evarATS\u0026nbsp;\u003c/em\u003ereal-time PCR used for parasite detection was performed. The parasite load in positive samples from the \u003cem\u003evarATS\u003c/em\u003e real-time PCR were determined using quantitative real-time PCR (RT-qPCR) targeting the \u003cem\u003e18s rRNA\u003c/em\u003e of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e. The standard curve and the starting quantity (SQ) for each sample were determined using the CFX Manager\u0026trade; Dx Software, version 3.1 (Bio-Rad, Hercules, CA, USA). The SQ was used to determine the parasitaemia using the formula:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eparasitaemia =\u003c/em\u003e\u0026nbsp;\u003cimg width=\"327\" height=\"33\" 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alt=\"image\"\u003e.\u003c/p\u003e\n\u003cp\u003eThe PCR assays were performed using the CFX96 Touch Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditional blood samples were also collected to measure full blood count and extensive biochemistry testing including Inflammatory biomarkers. All laboratory assessments were conducted in the research laboratory at the research laboratory at Saint Francis Xavier Hospital in Assin Foso and at the Kumasi Centre for Collaborative research in Kumasi, Ghana.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSample size\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study formed part of a larger longitudinal assessment which was initially designed as a test-negative study focusing on acute SARS-CoV-2 cases. However, as the pandemic evolved and such cases became rare, the analytical approach shifted to include seropositive individuals as cases. This change to the study design meant that it was not possible to conduct formal a priori sample size calculations for the current analytical objectives. The final sample size (N = 347) was determined by the maximum number of eligible participants who could be recruited within the available resources and timeframe of the study. This sample size was considered adequate for the primary descriptive objectives of characterising pathogen prevalence and clinical outcomes. For comparative analyses between subgroups (e.g. SARS-CoV-2 seropositive versus seronegative), the number of particpants recruited provided sufficient statistical power to detect clinically meaningful differences in main outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor analysis, we compared study participants by serostatus, SARS-CoV-2 infection status, and malaria infection status. Serostatus was determined at enrollment: individuals were identified as seropositive if either the IgG or IgM RDT was positive, and seronegative if both tests were negative. Individuals were classified as having an acute SARS-CoV-2 infection if SARS-CoV-2 was detected using either multiplex PCR (BioFire) and/or RT-qPCR testing (Altona), whereas all other pathogens included in the analysis were detected exclusively by the BioFire panel. Malaria infection was determined using PCR. Descriptive statistics were used to summarize patient characteristics and pathogen prevalence. Illness duration was defined as the total number of days that symptoms were reported -since onset of symptoms- during daily follow up .\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTime-to-symptom resolution was analyzed using Kaplan\u0026ndash;Meier methods, with differences between groups assessed using the log-rank test and subsequent adjustment for multiple testing using the benjamini-hochberg correction. Multivariable logistic regression models were employed to identify independent factors associated with hospitalization. To address potential confounding, the model was adjusted for age, gender, PRIEST score, total number of reported comorbidities, vaccination status (defined as having received at least one vaccine dose), serostatus, and exposure to a respiratory isolate. Exposure was operationalized as a positive result from the BioFire and included only isolates with a frequency of at least 5% among cases. We tested for multicollinerity using the variance inflation factor (VIF).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe population attributable fraction (PAF), representing the proportion of hospital admissions attributable to specific pathogens, was estimated using maximum likelihood estimators from the multivariable logistic regression model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe extent of missing data was minimal. Malaria PCR results were unavailable for 9 participants. A small number of participants had missing values for key covariates, including vaccination status (n=14) and PRIEST score (n=83). In the latter group, PRIEST scores could not be calculated due to missing values in one or more of its components: respiratory rate (n=70), temperature (n=10), and oxygen saturation or heart rate (n=3). Additionally, hospitalization status was missing for one participant. Patterns of missingness were assessed and no substantial differences were observed in baseline characteristics between participants with complete data and those with missing data, supporting the assumption that data were missing at random (MAR). Consequently, a complete case analysis was performed, with participants excluded from specific models only if they were missing data for variables used in those models. Finally, BioFire results were excluded for one participant who tested positive for 13 pathogens, suggestive of probable sample contamination or a technical error. Data analysis was conducted using R software (version 4.3.1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical approval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was obtained from and annually renewed by the institutional review board of the Kwame Nkrumah University \u0026nbsp;of Sciences and Technology, School of Medical Sciences (CHRPE/AP/571/21), from the Ghana Health Service Ethics Review Committee (GHS-ERC 003/09/21), and the Ethics Committee of the Hamburg Medical Association (\u0026Auml;rztekammer Hamburg, #2021-100696-BO-ff). Written and voluntary informed consent was obtained from all participants, and for participants below 18 years of age, parental consent and informed assent were obtained before enrollment. Patient confidentiality was maintained by ensuring secure data storage, restricting access to authorized personnel only, and de-identifying the data prior to analysis. The study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003ePrevalence and distribution of viral pathogens\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 347 individuals with ARI tested, 215 (62.0%) were negative for all pathogens included in the panel. The remaining 132 participants (38.0%) tested positive for at least one pathogen, with 14 (11.1%) co-infections detected. In total, 14 different respiratory pathogens were detected in our study population. The most frequently detected pathogens were SARS-CoV-2 (13.0%, n=45), human rhinovirus/enterovirus (12.4%, n=43), and influenza A (6.3%, n=22). The distribution of single and multiple pathogen detections is shown in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCharacteristics of SARS-CoV-2 and Plasmodium falciparum infections\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 347 individuals with ARI included in the study, 277 (79.8%) were seropositive and 70 (20.2%) were seronegative for SARS-CoV-2, of which acute SARS-CoV-2 infection was identified in 45 individuals (13.0%) (see supplementary material). Fourteen samples with Ct\u0026le;26 were sequenced and thirteen were classified as Omicron or probable Omicron variant. Three of them had a conflict score of 0.33, 0.5, 0.7 indicating that although Pangolin (Phylogenetic Assignment of Named Global Outbreak Lineages) \u003csup\u003e12\u003c/sup\u003e classified them as probable Omicron, they could also be in a difference variant class with varying likelihoods. One sample was not of sufficient quality to determine the variant type.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll individuals were tested for \u003cem\u003ePlasmodium falciparum\u003c/em\u003e malaria; 99 (28.5%) individuals tested positive and 239 (68.9%) tested negative. Malaria infection status was unavailable for 9 individuals (2.6%).\u003c/p\u003e\n\u003cp\u003eOne case, which tested positive for 13 different pathogens, was excluded due to a high likelihood of sample contamination or a technical issue. SARS-CoV-2 was detected using multiplex and simplex PCR testing combined. The top bar plot shows the number of cases testing positive for specific pathogen combinations (intersection size), with 215 individuals testing negative for all pathogens. The left bar plot indicates the total number of cases (set size) for each individual pathogen. The most frequent single-pathogen detections were SARS-CoV-2 (13.0%, n=45), human rhinovirus/enterovirus (10.1%, n=35), and influenza A (6,3%, n=22), while the most frequent co-infections involved human rhinovirus/enterovirus with human metapneumovirus or SARS-CoV-2 (both 0.9%, n = 3). All other co-infections were only detected once. Other coronaviruses correspond to detected NL63 and OC43. Atypical bacteria correspond to detected \u003cem\u003eB\u003c/em\u003e\u003cem\u003eordetella pertussis\u003c/em\u003e and \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e. Parainfluenzaviruses correspond to detected types 3 and 4. From the 22 pathogens included in the Respiratory 2.1 Panel, no cases of \u003cem\u003eBordetella parapertussis\u003c/em\u003e, \u003cem\u003eChlamydia pneumoniae\u003c/em\u003e, coronavirus 229E, coronavirus HKU1, parainfluenza virus 1 and parainfluenza virus 2 were detected.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDistribution by Malaria status and SARS-CoV-2 serostatus.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 2, relative proportions of respiratory pathogens varied across sub-groups defined by SARS-CoV-2 serostatus and malaria status. In Panel A, SARS-CoV-2 was notably more common in seronegative individuals (47.8% [95% CI: 26.2-69.4], n/N=11/23 \u0026nbsp;among seronegative compared to 27.2% [95% CI: 19.3-35.1], n/N=34/125 \u0026nbsp;among seropositive individuals), while rhinovirus/enterovirus (seronegative: 26.1% [95% CI: 7.1-45.1], n/N=6/23 vs. seropositive: 29.6% [95% CI: 21.5-37.7], n/N=37/125) and influenza A (seronegative: 13.0% [95% CI: -1.5-27.6], n/N=3/23 \u0026nbsp;vs. seropositive: 15.2% [95% CI: 8.8-21.5], n/N=19/125) were more evenly distributed. The distribution by malaria status in Panel B remained relatively stable. Other respiratory viruses, including parainfluenza viruses, respiratory syncytial virus (RSV) and metapneumovirus, were detected at lower frequencies with minor differences between groups. Atypical bacteria and other coronaviruses were rarely observed (\u0026lt;5% of cases in any group).\u003c/p\u003e\n\u003cp\u003eAmong the 347 ARI cases in the study, the proportion of individuals requiring hospitalization was comparable across SARS-CoV-2 serostatus, acute infection, and malaria infection groups, with no significant differences observed (Figure 3.A). Hospitalization rates ranged from approximately 25% to 30% across all sub-groups, with overlapping confidence intervals.\u003c/p\u003e\n\u003cp\u003eAmong hospitalized patients, 90 individuals required oxygen therapy (Figure 3.B). The proportion of patients receiving oxygen therapy was higher among seropositive individuals (40%, 95% CI: 28.2\u0026ndash;51.8) compared to seronegative individuals (20%, 95% CI: 0.8\u0026ndash;39.2), although the difference was not statistically significant. It was also similar between those with and without acute SARS-CoV-2 infection (30.8%, 95% CI: 1.7\u0026ndash;59.8 vs. 36.4%, 95% CI: 25.4\u0026ndash;47.4) and between those with and without malaria infection (35.5%, 95% CI: 17.6\u0026ndash;53.3 vs. 34.5%, 95% CI: 21.9\u0026ndash;47.1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdditional Analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe extensive examination of full blood count and biochemistry, including commonly used inflammatory biomarkers, did not yield a clinically significant difference across sub-groups (see supplement).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLength of illness\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe cumulative incidence of symptom resolution during the initial 28 day follow-up period was examined in relation to serostatus, SARS-CoV-2 infection status, and malaria infection status. Of the 347 cases included, 321 (92 %) had symptoms resolved within 28 days from symptoms onset, while 26 cases did not. Participants with acute SARS-CoV-2 infection experienced a delayed symptom resolution compared to those without infection (p = 0.104 Figure 4A) suggesting prolonged symptom duration in the SARS-CoV-2-positive group. By day 10, more than half of the acute SARS-CoV-2-negative participants had recovered, whereas recovery in the acute SARS-CoV-2-positive group lagged behind, with some continuing to report symptoms beyond two weeks. However, there was no evidence for a difference in the time to symptom resolution between seropositive and seronegative participants (p = 0.903, Figure 4B), with the majority of participants (77.6%, n= 215 vs. 72.9%, n=51) achieving symptom resolution within the first two weeks. No evidence for a difference in symptom resolution was observed between malaria-positive and malaria-negative participants (p = 0.682, Figure 4C).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePredictors of Hospital Admission\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFigure 5 shows the results of the logistic regression analysis identifying significant predictors of hospital admission. For this analysis, 100 (28.8%) of the initial 347 observations were excluded due to missing data in control or outcome variables. A higher PRIEST score was associated with increased odds of admission (OR: 1.50, 95% CI: 1.32\u0026ndash;1.74). In contrast, SARS-CoV-2 vaccination was protective, reducing the odds of admission (OR: 0.42, 95% CI: 0.19\u0026ndash;0.93). The intercept of the model was negative and statistically significant, indicating that the baseline probability of hospital admission among individuals with reference or minimal risk characteristics was significantly lower than 50%.\u003c/p\u003e\n\u003cp\u003eOther variables, including age, sex, number of comorbidities, and pathogen detection (SARS-CoV-2, rhinovirus/enterovirus, influenza, malaria), did not show associations with hospitalization. Similarly, seropositivity for SARS-CoV-2 was not a strong predictor of admission (OR: 1.62, 95% CI: 0.65\u0026ndash;4.20).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePopulation attributable fractions\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSARS-CoV-2 had the highest population attributable fraction (PAF) for hospitalization at 2.02% (95% CI: 4.36 to 8.41), , followed by malaria at 1.49% (95% CI: -9.43 to 12.40), although these associations were not statistically significant (see supplementary material). By contrast, human rhinovirus/enterovirus (-4.28%, 95% CI: -9.78 to 1. .22) and influenza A/B (-0.70%, 95% CI: -5.82 to 4.42) exhibited negative PAFs, indicating a potential protective effect, although also not statistically significant. The effect size for influenza was particularly small, suggesting a minimal contribution to hospital admissions in this cohort. The wide confidence intervals reflect the limited sample size, thus, these estimates should be interpreted with caution.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we characterize the evolving landscape of viral ARIs among adults in rural Ghana during the immediate COVID-19 post-pandemic phase. Rhinovirus/enterovirus, SARS-CoV-2, and influenza A were the most commonly detected viruses. SARS-CoV-2 infection was associated with delayed symptom resolution whereas neither serostatus nor malaria co-infection significantly influenced patient outcomes. As expected, we found that higher PRIEST scores predicted hospitalization, while prior COVID-19 vaccination was protective.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings partially align with pre- pandemic literature from sub-Saharan Africa (SSA) where viruses like RSV and influenza have historically dominated\u003csup\u003e1,13,14\u003c/sup\u003e. A meta-analysis from East Africa reported RSV, parainfluenza viruses, and adenoviruses as dominant pathogens\u003csup\u003e15,16\u003c/sup\u003e, while Ho et al. highlighted influenza viruses and RSV, with wide variability across settings \u003csup\u003e17\u003c/sup\u003e. The dominance of rhinovirus/enterovirus and SARS-CoV-2 may reflect a post pandemic shift in viral circulation. Also, the predominance of rhinovirus/enterovirus aligns with reports from high-income countries of its rapid resurgence after non-phamaceutical interventions were lifted \u003csup\u003e18\u003c/sup\u003e, while the persistence of SARS-CoV-2 may support its transition towards endemicity\u003csup\u003e3,19\u003c/sup\u003e. The concept of viral interference, may also underlie the suppression or delay of specific viruses such as RSV\u003csup\u003e20,21\u003c/sup\u003e. Our findings highlight the importance of continued respiratory pathogen-based surveillance in African settings to detect temporal changes and emerging threats.\u003c/p\u003e\n\u003cp\u003eThe high prevalence of SARS-CoV-2 among seronegative individuals underscores the protective effects of prior exposure or vaccination, aligning with global evidence of hybrid immunity \u003csup\u003e3,22\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWhile SARS-CoV-2 infection did not significantly increase hospitalization risk in our cohort, it was associated with prolonged symptom duration compared to those with non-SARS-CoV-2 infections, as seen in findings from other high income settings where acute infection correlates with slower recovery trajectories and increased risk of post-acute sequelae (PASC)\u003csup\u003e23\u003c/sup\u003e. This suggests that even in a malaria-endemic region with hybrid immunity, SARS-CoV-2 may disproportionately contribute to prolonged morbidity, potentially signaling underrecognized long COVID risk among mild cases.\u003c/p\u003e\n\u003cp\u003eExpectedly, our findings confirm the predictive value for hospitalization of the PRIEST score — validated in high income settings — in a rural Sub-Saharan African context, where it could aid in clinical triage and resource allocation.\u003c/p\u003e\n\u003cp\u003eIn contrast, SARS-CoV-2 vaccination was associated with lower odds of hospitalization, reinforcing known protective effects even in settings with partial vaccination coverage\u003csup\u003e24,25\u003c/sup\u003e. During follow-up of participants, patients were followed for 12 months to assess PASC; these findings will be reported separately.\u003c/p\u003e\n\u003cp\u003eThe severity of ARI, as measured by hospitalization or the use of oxygen therapy, did not differ significantly by SARS-CoV-2 serostatus, acute infection, or malaria co-infection. This contrasts with patterns observed in HICs, where seronegativity and lack of vaccination are strong predictors of severe outcomes\u003csup\u003e26\u003c/sup\u003e. However, similar findings have been reported in SSA, where disease severity has been lower than anticipated, potentially due to a younger population, pre-existing cross-immunity, or under-recognition of hypoxia in resource-constrained settings\u003csup\u003e6,27\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe explored the contribution of individual pathogens to hospital admissions. Although P.\u0026nbsp;falciparum and SARS-CoV-2 showed the highest PAF associated with hospitalization, the extremely wide confidence intervals preclude definitive conclusions due to limited sample size. However, multicenter case-control pneumonia studies such as PERCH have demonstrated the usefulness of this approach\u003csup\u003e14\u003c/sup\u003e. In the same Ghanaian setting, Krumkamp et al. estimated PAF in children where RSV, influenza A, \u003cem\u003eS. pneumoniae and H. influenza\u003c/em\u003e where associated with admission \u003csup\u003e28\u003c/sup\u003e. Thus, given an adequate sample size, extending this methodology to adult populations could strengthen etiologic inference and inform clinical management.\u003c/p\u003e\n\u003cp\u003eAlthough the study lacked the power to assess the interaction between malaria and SARS-CoV-2, some previous studies suggest that P. falciparum may modulate antiviral responses by dampening interferon signaling and promoting regulatory T-cell expansion \u003csup\u003e4\u003c/sup\u003e and ultimately leading to reduced COVID-19 severity whilst others report false-positive SARS-CoV-2 antibody results due to malaria-induced polyreactive antibodies \u003csup\u003e5\u003c/sup\u003e. We did not observe differences in pathogen distribution or severity by malaria status. However, the hypothesis of malaria-induced immunomodulation remains plausible and warrants further investigation.\u003c/p\u003e\n\u003cp\u003eInflammatory biomarkers consistently not elevated across all sub-group comparisons (serostatus, acute SARS-CoV and malaria status). Similar observations in endemic settings suggest that P. falciparum may not induce immune processes that elevate these biomarkers to the same extent as bacterial infections \u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWhile not designed to confirm the transition to endemicity, our study showed that SARS-CoV-2 was frequently detected, suggesting ongoing circulation. Studies from similar regions support the claim of a shift towards endemicity which is characterized by recurrent seasonal waves, increasing hybrid immunity, and reduced mortality\u003csup\u003e3\u003c/sup\u003e. These claims are relevant especially in SSA, where asymptomatic transmission may strengthen community spread, with vulnerable groups, including the elderly and those with comorbidities, facing a disproportionate mortality risk. Furthermore, the burden of long COVID may be underrecognized in this setting. Controlled, longitudinal studies are needed to identify predictors of PASC and guide post-acute care strategies in African populations.\u003c/p\u003e\n\u003cp\u003eOur study has several strengths: we systematically tested all participants with a broad PCR-based multiplex panel of respiratory pathogens, thereby minimising testing bias. Our analysis incorporated multiple indicators of infection status (acute infection, seropositivity and vaccination status) for SARS-CoV-2, enabling a nuanced evaluation of various immune states and their clinical implications. Further, we used validated clinical risk assessment tools, including the PRIEST score, enhancing the clinical applicability of our findings. Moreover, our study design captured both hospitalised and non-hospitalised patients, providing insights into the spectrum of ARI severity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has some limitations. Firstly, First, the use of a viral-only diagnostic panel likely contributed to the low overall pathogen detection rate and may have missed bacterial or fungal etiologies. The relatively low overall pathogen detection rate (36%) may be due to the specificity of our viral-only testing panel for the local pathogen ecology, or to the inclusion of patients with non-viral causes of ARI.\u003c/p\u003e\n\u003cp\u003eOur findings may also be influenced by healthcare-seeking behavior, since our sample presented here comprised only individuals presenting to hospitals. Some analyses may have lacked statistical power to detect smaller effect sizes. The wide confidence intervals for PAF estimates reflect the limited precision of our effect estimates and the inherent uncertainty in attributing population-level disease burden to specific pathogens based on observational associations. Given the post hoc nature of our analytical approach, all analyses should be interpreted as exploratory.\u003c/p\u003e\n\u003cp\u003eFurthermore, due to the short follow-up duration, seasonality could not be assessed. Our analysis of symptom resolution used standard survival analysis methods, though hospitalization during follow-up may constitute a competing risk that could influence recovery patterns. The application of competing risks regression models in future research would provide more robust estimates of symptom duration across different patient trajectories.\u003c/p\u003e\n\u003cp\u003eTo understand the full spectrum and impact of respiratory infections in adults, expanded panels including bacterial pathogens and host-response markers are needed, alongside larger, multi-site longitudinal surveillance. Such data will be invaluable in designing context-appropriate diagnostic and treatment algorithms that can improve patient care and resource allocation in these settings. Such approaches will be critical to prepare for future respiratory epidemics in SSA.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study shows the clinical and viral profile of acute respiratory infections in a rural, malaria-endemic setting during the immediate post-pandemic phase. With rhinovirus/enterovirus, SARS-CoV-2 and influenza A emerging as dominant pathogens in the viral ARI, we see a viral profile that might suggest the presence of several unrecognized circulating viruses during this period that could easily have been ascribed to SARS-CoV-2 in the absence of proper testing. In our cohort, clinical outcomes were primarily associated with disease severity and vaccination status rather than pathogen type.\u003c/p\u003e\n\u003cp\u003eAlthough malaria infection was common, we found it not to be associated with the severity of illness. However, its presence may complicate the diagnosis of febrile patients and might influence serological interpretation of SARS-CoV-2. Expanding diagnostic strategies in endemic settings is vital.\u003c/p\u003e\n\u003cp\u003eWith over 60% of our ARI cases being pathogen-negative, our study finds that there are critical diagnostic gaps in current surveillance, emphasizing the need for expanded diagnostic capacities that include bacterial and fungal causes while improving sample timing.\u003c/p\u003e\n\u003cp\u003eTo translate these findings into impact, we recommend an urgent prioritization and continuous investment in decentralized diagnostic networks, adaptive vaccination strategies and routine respiratory surveillance that combines viral, bacterial and severity data.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eARI: Acute respiratory infection\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;BH: Benjamini–Hochberg\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;COVID-19: Coronavirus disease 2019\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Ct: Cycle threshold\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;ED: Emergency department\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;IgG/IgM: Immunoglobulin G / Immunoglobulin M\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;KCCR: Kumasi Centre for Collaborative Research\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;LMIC: Low- and middle-income countries\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;PCR: Polymerase chain reaction\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;PAF: Population attributable fraction\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;PRIEST COVID 19: Clinical Severity Score predicts adverse outcomes in adults presenting to ED adverse with suspected COVID-19\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;RDT: Rapid diagnostic test\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;RT-qPCR: Reverse-transcriptase quantitative PCR\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;SARS-CoV-2: Severe acute respiratory syndrome coronavirus 2\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;SSA: Sub-Saharan Africa\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained and annually renewed by the Committee on Human Research, Publications and Ethics (CHRPE) of the Kwame Nkrumah University of Science and Technology, School of Medical Sciences (CHRPE/AP/571/21), the Ghana Health Service Ethics Review Committee (GHS-ERC 003/09/21), and the Ethics Committee of the Hamburg Medical Association (Ärztekammer Hamburg, #2021-100696-BO-ff).\u003c/p\u003e\n\u003cp\u003eAll participants (or guardians for those aged 16–17 years) provided written informed consent prior to enrolment. Illiterate participants provided thumbprint consent in the presence of an impartial witness. Participation was voluntary and could be withdrawn at any time. All study procedures adhered to the Declaration of Helsinki and Good Clinical Practice guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not include any individual person’s identifiable data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study contain potentially identifiable clinical information and are therefore not publicly available. De-identified participant-level data and analysis code can be made available from the corresponding author upon reasonable request, subject to data-sharing agreements and ethics approval.\u003c/p\u003e\n\u003cp\u003eNo microarray datasets, macromolecular structures, crystallographic data, or other data types were generated in this study. The RNA sequences generated in the context of a parallel study and are available in the European Nucleotide Archive (ENA) \u0026nbsp;under accession number: PRJEB105234 or via this link:\u0026nbsp;https://www.ebi.ac.uk/ena/browser/view/PRJEB105234.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was part of a project of the ‘Corona Global’ funding track within the Global Health Protection Programme (GHPP) with the financial support of the German Federal Ministry of Health (BMG) (FKZ 2521GHP920).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions-\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization\u003c/strong\u003e: OMA, JM, RS; \u003cstrong\u003eData Curation\u003c/strong\u003e: LM, IDA, TH, LHR, AH, WL, EL; \u003cstrong\u003eFormal Analysis and Methodology:\u003c/strong\u003e OMA, EL, RS; \u003cstrong\u003eInvestigation:\u003c/strong\u003e OMA, IDA, RK, PMA, JG, WOD, ENP, JOM, AAAA, MA, JHA, MA; \u003cstrong\u003eVisualization:\u003c/strong\u003e LM, LHR, EL; \u003cstrong\u003eFunding acquisition:\u0026nbsp;\u003c/strong\u003eJM, RS; \u003cstrong\u003eProject administration:\u003c/strong\u003e OMA, TH, RS; \u003cstrong\u003eSupervision:\u003c/strong\u003e OMA, JHA, MA, JM, EL, RS; \u003cstrong\u003eWriting – original draft:\u003c/strong\u003e OMA, LM, IDA, LHA, EL, RS; \u003cstrong\u003eWriting – review and editing:\u0026nbsp;\u003c/strong\u003eall authors. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, we would like to thank the study participants who voluntarly consented to take part in the study. We are grateful the support of the management and staff from the Saint Francis Xavier Hospital at Assin Foso, Ghana. We acknowledge and are deeply appreciative of the contributions of all the members of the Infectious Disease Epidemiology research group at KCCR, Ghana. We are grateful for the support of the study nurses Bright Tamakloe and Portia Kanadu who handled the calls to the participants for data collections during follow up visits.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSirota, S. 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Y.\u003cem\u003e et al.\u003c/em\u003e Durability of BNT162b2 vaccine against hospital and emergency department admissions due to the omicron and delta variants in a large health system in the USA: a test-negative case-control study. \u003cem\u003eLancet Respir Med\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, 689-699 (2022). https://doi.org:10.1016/s2213-2600(22)00101-1\u003c/li\u003e\n\u003cli\u003e Ssentongo, P.\u003cem\u003e et al.\u003c/em\u003e SARS-CoV-2 vaccine effectiveness against infection, symptomatic and severe COVID-19: a systematic review and meta-analysis. \u003cem\u003eBMC Infectious Diseases\u003c/em\u003e\u003cstrong\u003e22\u003c/strong\u003e, 439 (2022). https://doi.org:10.1186/s12879-022-07418-y\u003c/li\u003e\n\u003cli\u003e Kalungi, A., Kinyanda, E., Akena, D. H., Kaleebu, P. \u0026amp; Bisangwa, I. M. Less Severe Cases of COVID-19 in Sub-Saharan Africa: Could Co-infection or a Recent History of Plasmodium falciparum Infection Be Protective? \u003cem\u003eFrontiers in Immunology\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e (2021). https://doi.org:10.3389/fimmu.2021.565625\u003c/li\u003e\n\u003cli\u003e Krumkamp, R.\u003cem\u003e et al.\u003c/em\u003e Pathogens associated with hospitalization due to acute lower respiratory tract infections in children in rural Ghana: a case\u0026ndash;control study. \u003cem\u003eScientific reports\u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e, 2443 (2023). https://doi.org:10.1038/s41598-023-29410-5\u003c/li\u003e\n\u003cli\u003e Eriksson, U. K., van Bodegom, D., May, L., Boef, A. G. C. \u0026amp; Westendorp, R. G. J. Low C-Reactive Protein Levels in a Traditional West-African Population Living in a Malaria Endemic Area. \u003cem\u003ePLOS ONE\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, e70076 (2013). https://doi.org:10.1371/journal.pone.0070076\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-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Respiratory Infections, Surveillance, Malaria, SARS-CoV-2, Ghana","lastPublishedDoi":"10.21203/rs.3.rs-8287787/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8287787/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Acute respiratory infections (ARIs) are a leading cause of morbidity and mortality worldwide with sub-Saharan Africa accounting for approximately 50% of the 5.8 million ARI-related deaths globally.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite this substantial burden, pathogen-based surveillance remains limited in these regions. The post-pandemic phase presents a unique epidemiological landscape, with SARS-CoV-2 transitioning towards endemicity and potentially interacting with other circulating respiratory pathogens. This study aimed to characterize viral ARI etiology and the influence of SARS-CoV-2 serostatus and malaria co-infection on clinical outcomes during the immediate post-pandemic period in a rural Ghanaian setting.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We conducted a prospective-observational study at a district hospital in Ghana between May 2022 and September 2023. Adults with acute respiratory infection were enrolled, and nasopharyngeal swabs were tested using a multiplex PCR panel detecting 22 respiratory, mainly viral, pathogens. SARS-CoV-2 serostatus and malaria infection status were determined, and broader clinical parameters were examined. Clinical severity was assessed using the PRIEST score, and participants were followed up for 28 days to evaluate symptom resolution. Multivariable logistic regression identified potential predictors of hospitalization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among 347 participants, the most frequently detected respiratory pathogens were rhinovirus/enterovirus (12.4%, n=43), acute SARS-CoV-2 (11.0%, n=38), and influenza A (6.3%, n=22). Among all participants, 79.8% (n=277) were seropositive for SARS-CoV-2 and 28.5% (n=99) were carrying malaria parasites. Hospitalization rates were comparable across SARS-CoV-2 serostatus, acute infection, and malaria status groups (25-30%). Higher PRIEST scores were associated with higher odds of hospitalization (OR: 1.50, 95% CI: 1.32-1.74), while COVID-19 vaccination was associated with lower odds (OR: 0.41, 95% CI: 0.18-0.93). Participants with acute SARS-CoV-2 infection experienced delayed symptom resolution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Post-pandemic ARI etiology in rural Ghana exhibits a diverse viral profile, with rhinovirus/enterovirus, SARS-CoV-2 and influenza A predominating. Clinical outcomes were more strongly associated with high PRIEST scores than with specific pathogens, with vaccination decreasing the odds for hospitalization. While this study did not identify pathogen-specific associations with clinical severity, sustained surveillance remains important for detecting shifts in viral circulation and informing tailored public health responses.\u003c/p\u003e","manuscriptTitle":"Clinical and epidemiological characterization of viral respiratory pathogens in rural Ghana: The role of SARS-CoV-2 and Malaria in the immediate post-pandemic phase","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 06:00:22","doi":"10.21203/rs.3.rs-8287787/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-16T08:08:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T02:47:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-05T09:28:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312303622561163851750400174480005447816","date":"2026-03-05T01:56:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23045600484762623319192692076121782559","date":"2026-02-26T22:40:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11876127078987787570719039475663372594","date":"2026-02-26T14:26:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48131737222001822920258531501321060972","date":"2026-02-26T05:13:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335420107026275547194111101057657247151","date":"2026-02-03T08:33:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-16T23:24:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174158525813732531188262034433497798958","date":"2026-01-12T15:44:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165215438193383769463601946878362655864","date":"2026-01-08T09:06:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-07T15:13:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181670929844946241366039442684829197405","date":"2026-01-07T08:40:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-07T06:29:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-05T09:20:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-17T04:53:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-16T18:28:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-12-16T16:18:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a282d785-a30a-40ef-a3c0-02c31726ec04","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T18:53:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 06:00:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8287787","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8287787","identity":"rs-8287787","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}