Emergence of a Distinct Picobirnavirus Genotype Circulating in Patients Hospitalized with Acute Respiratory Illness

Picobirnaviruses (PBV) are found in a wide range of hosts and typically associated with gastrointestinal infections in immunocompromised individuals. Here, a divergent PBV genome was assembled from a patient hospitalized for acute respiratory illness (ARI) in Colombia. The RdRp protein branched with sequences previously reported in patients with ARI from Cambodia and China. Sputa from hospitalized individuals (n = 130) were screened by RT-qPCR which enabled detection and subsequent metagenomic characterization of 25 additional PBV infections circulating in Colombia and the US. Phylogenetic analysis of RdRp highlighted the emergence of two dominant lineages linked to the index case and Asian strains, which together clustered as a distinct genotype. Bayesian inference further established capsid and RdRp sequences as both significantly associated with ARI. Various respiratory-tropic pathogens were detected in PBV+ patients, yet no specific bacteria was common among them and four individuals lacked co-infections, suggesting PBV may not be a prokaryotic virus nor exclusively opportunistic, respectively. Competing models for the origin and transmission of this PBV genotype are presented that attempt to reconcile vectoring by a bacterial host with human pathogenicity. A high prevalence in patients with ARI, an ability to reassort, and demonstrated global spread indicate PBV warrant greater public health concern.

Plasma gp96 is a Novel Predictive Biomarker for Severe COVID-19

Early and effective identification of severe COVID-19 may allow us to improve the outcomes of associated severe acute respiratory illness with fever and respiratory symptoms. Some heat shock proteins (Hsps) are released during oxidative stress, cytotoxic injury, and viral infection and behave as danger-associated molecular patterns (DAMPs).

Modelling and Prediction of the Spread of COVID-19 in Cameroon and Assessing the Governmental Measures (March–September 2020)

COVID-19 is an acute respiratory illness in humans caused by a coronavirus, capable of producing severe symptoms and, in some cases, death, especially in older people and those with underlying health conditions. It was originally identified in China in 2019 and became a pandemic in 2020. On 6 March 2020, Cameroon recorded its first cases of infection with COVID-19. The Government of Cameroon (GOC) took 13 barrier measures on 18 March 2020. On 1 May 2020, 19 new measures were adopted, easing restrictions and encouraging economic activity. On 1 June, schools and universities were reopened, after which massive screening began to take place throughout the country. In this study, we have modelled the COVID-19 epidemic in Cameroon in order to assess the governmental measures of response and predict the behaviour of epidemic As a result of these measures, the pandemic evolved in three phases. The first phase began on 18 March and ended on 15 May 2020. During this phase, the actual curve of cumulative positive cases based on field data closely fit the theoretical curve resulting from mathematical modelling. In the beginning of May, we predicted that nearly 3000 positive cases would be declared by mid-May 2020. The actual data confirmed these predictions: there were 2954 cases as of 15 May 2020. The second phase, beyond mid-May 2020, encompasses the period when the GOC’s relaxation of measures takes effect. This phase was marked by an acceleration of the cumulative number of positive cases starting in the third week of May, postponing the expected peak by two weeks. Under Phase 2 conditions, the onset of the peak will occur in early June and extend through the first two weeks of June. However, a third phase occurs in the first week of June, with the reopening of schools and universities combined with massive screening; the peak is therefore expected in the second week of June (around 15 June). The GOC should, at this stage, strengthen its response plan by tripling the current coverage capacity to regain the first phase convergence conditions associated with the first 13 measures. The pandemic will begin its descent in the month of august, but COVID-19 will remain endemic for at least one year.

Acute respiratory illness and return to sport: a systematic review and meta-analysis by a subgroup of the IOC consensus on ‘acute respiratory illness in the athlete’

ObjectiveTo determine the days until return to sport (RTS) after acute respiratory illness (ARill), frequency of time loss after ARill resulting in >1 day lost from training/competition, and symptom duration (days) of ARill in athletes.DesignSystematic review and meta-analysis.Data sourcesPubMed, EBSCOhost, Web of Science, January 1990–July 2020.Eligibility criteriaOriginal research articles published in English on athletes/military recruits (15–65 years) with symptoms/diagnosis of an ARill and reporting any of the following: days until RTS after ARill, frequency (%) of time loss >1 day after ARill or symptom duration (days) of ARill.Results767 articles were identified; 54 were included (n=31 065 athletes). 4 studies reported days until RTS (range: 0–8.5 days). Frequency (%) of time loss >1 day after ARill was 20.4% (95% CI 15.3% to 25.4%). The mean symptom duration for all ARill was 7.1 days (95% CI 6.2 to 8.0). Results were similar between subgroups: pathological classification (acute respiratory infection (ARinf) vs undiagnosed ARill), anatomical classification (upper vs general ARill) or diagnostic method of ARinf (symptoms, physical examination, special investigations identifying pathogens).ConclusionsIn 80% of ARill in athletes, no days were lost from training/competition. The mean duration of ARill symptoms in athletes was 7 days. Outcomes were not influenced by pathological or anatomical classification of ARill, or in ARinf diagnosed by various methods. Current data are limited, and future studies with standardised approaches to definitions, diagnostic methods and classifications of ARill are needed to obtain detailed clinical, laboratory and specific pathogen data to inform RTS.PROSPERO registration numberCRD42020160479.

Trends and Intensity of Rhinovirus Invasions in Kilifi, Coastal Kenya Over a Twelve-Year Period, 2007-2018

Background Rhinoviruses (RVs) are ubiquitous pathogens and the principal etiological agents of common cold. Despite the high frequency of RV infections, data describing their long-term epidemiological patterns in a defined population remain limited. Methods Here, we analysed 1,070 VP4/VP2 genomic region sequences sampled at Kilifi County Hospital on the Kenya Coast. The samples were collected between 2007 and 2018 from hospitalised paediatric patients (< 60 months) with acute respiratory illness. Results Of 7,231 children enrolled, RV was detected in 1,497 (20.7%) and VP4/VP2 sequences were recovered from 1,070 samples (71.5%). A total of 144 different RV types were identified (67 Rhinovirus A, 18 Rhinovirus B and 59 Rhinovirus C) and at any month, several types co-circulated with alternating predominance. Within types multiple genetically divergent variants were observed. Ongoing RV infections through time appeared to be a combination of (i) persistent types (observed up to seven consecutive months), (ii) reintroduced genetically distinct variants and (iii) new invasions (average of eight new types, annually). Conclusion Sustained RV presence in the Kilifi community is mainly due to frequent invasion by new types and variants rather than continuous transmission of locally established types/variants.

LB18. Healthcare Utilization for Acute Respiratory Illness by Race/Ethnicity across Ambulatory, Emergency, and Hospital Settings

Background Given the disproportionate impact of COVID-19 among racial/ethnic minority groups across the United States on emergency visits, hospitalizations, and deaths, we examined healthcare utilization more broadly for acute respiratory illness (ARI across healthcare settings by racial/ethnic group. Methods Using data on 33,992,254 unique nonpharmacy healthcare encounters from the IBM Explorys Electronic Health Record database from January 1, 2020–May 1, 2021, across healthcare settings (ambulatory care or telehealth, emergency department, and hospitalizations) with nonmissing bridged racial/ethnic data. Encounters were classified as ARI based on ICD-10 and SNOMED codes and aggregated by month and US Census region. We estimated the population denominator as the total number of persons by bridged racial/ethnic group with encounters recorded during 2019. We both estimated the rate of ARI visits per 100,000 persons across healthcare settings and the rate ratio of ARI visits to non-ARI visits. We performed comparisons of these values by race/ethnicity, taking White persons as referent, using Poisson generalized estimating equations clustered within geographic regions. Results A total of 244,137 (6.5% of 3,745,135) hospitalizations, 237,873 (18% of 1,305,474) emergency visits, and 1,636,383 (5.7% of 28,941,645) ambulatory visits were associated with ARIs. We observed similar rates of ARI visits across race/ethnicity groups in all settings combined and in ambulatory settings, but higher rates of ARI hospitalization among Hispanic persons (IRR [95% CI]: 2.5 [1.7–3.7]) and higher rates of ARI emergency department visits among Black persons (2.5 [1.9–3.2]) (Figure). We also observed differences in the relative proportion of care received for ARI vs. other visits types by setting, for example with Black persons utilizing higher rates of hospital visits for ARI vs non-ARI care (2.2 [1.7–2.7]) but lower rates of ambulatory care for ARI (0.9 [0.7–0.96]). ARI Visits Per 100k Persons Conclusion Population rates of ARI visits and relative proportions of ARI vs. non ARI visits differed between racial/ethnic groups by setting. Understanding how utilization of care varies for ARI across settings can inform future monitoring efforts for health equity. Disclosures All Authors: No reported disclosures

Adapting the Surveillance Platform for Enteric and Respiratory Infectious Organisms at United States Veterans Affairs Medical Centers (SUPERNOVA) for COVID-19 Among Hospitalized Adults: Surveillance Protocol

Introduction: Early in the COVID-19 pandemic, the Centers for Disease Control and Prevention (CDC) rapidly initiated COVID-19 surveillance by leveraging existing hospital networks to assess disease burden among hospitalized inpatients and inform prevention efforts.Materials and Methods: The Surveillance Platform for Enteric and Respiratory Infectious Organisms at Veterans Affairs Medical Centers (SUPERNOVA) is a network of five United States Veterans Affairs Medical Centers which serves nearly 400,000 Veterans annually and conducts laboratory-based passive and active monitoring for pathogens associated with acute gastroenteritis and acute respiratory illness among hospitalized Veterans. This paper presents surveillance methods for adapting the SUPERNOVA surveillance platform to prospectively evaluate COVID-19 epidemiology during a public health emergency, including detecting, characterizing, and monitoring patients with and without COVID-19 beginning in March 2020. To allow for case-control analyses, patients with COVID-19 and patients with non-COVID-19 acute respiratory illness were included.Results: SUPERNOVA included 1,235 participants with COVID-19 and 707 participants with other acute respiratory illnesses hospitalized during February through December 2020. Most participants were male (93.1%), with a median age of 70 years, and 45.8% non-Hispanic Black and 32.6% non-Hispanic White. Among those with COVID-19, 28.2% were transferred to an intensive care unit, 9.4% received invasive mechanical ventilation, and 13.9% died. Compared with controls, after adjusting for age, sex, and race/ethnicity, COVID-19 case-patients had significantly higher risk of mortality, respiratory failure, and invasive mechanical ventilation, and longer hospital stays.Discussion: Strengths of the SUPERNOVA platform for COVID-19 surveillance include the ability to collect and integrate multiple types of data, including clinical and illness outcome information, and SARS-CoV-2 laboratory test results from respiratory and serum specimens. Analysis of data from this platform also enables formal comparisons of participants with and without COVID-19. Surveillance data collected during a public health emergency from this key U.S. population of Veterans will be useful for epidemiologic investigations of COVID-19 spectrum of disease, underlying medical conditions, virus variants, and vaccine effectiveness, according to public health priorities and needs.

A Review of Candidate Therapies for Beta Coronavirus a Molecular Research

Coronavirus disease 2019 (COVID-19) is an infectious illness caused by the coronavirus 2 that causes severe acute respiratory illness (SARS-CoV-2). The first instance of this virus was reported on November 17th, 2019 in Wuhan, China. The COVID-19 outbreak is evidenced with devastating consequences such as 34.9% rate of mortality in 27 countries. The metastasizing of COVID-19 all over the world is alarmed to cause significant losses of human life, and for this there is no specific vaccination or therapy for COVID-19 in particular. The therapies suggested at this time are adapted from the treatments of Severe Acute Respiratory Syndrome (SARV-CoV). For instance, the development for a particular therapy or vaccination for COVID-19 is an urgent requirement. The pattern of study is based on investigating the research papers for the period of 2012-2020, identifying all the potential aspects of medical research contributing for the development of treatment against diverse families of coronavirus. By analyzing this approach, this study is aimed to provide a directed approach for developing appropriate therapy for COVID-19.

Effects of Confounding Bias in COVID-19 and Influenza Vaccine Effectiveness Test-Negative Designs Due to Correlated Influenza and COVID-19 Vaccination Behaviors

Background: The test-negative design is commonly used to estimate influenza and COVID-19 vaccine effectiveness (VE). In these studies, correlated COVID-19 and influenza vaccine behaviors may introduce a confounding bias where controls are included with the other vaccine-preventable acute respiratory illness (ARI). We quantified the impact of this bias on VE estimates in studies where this bias is not addressed. Methods: We simulated study populations under varying vaccination probabilities, COVID-19 VE, influenza VE, and proportions of controls included with the other vaccine-preventable ARI. Mean bias was calculated as the difference between true and estimated VE. Absolute mean bias in VE estimates was classified as low (<10%), moderate (10% to <20%), and high (≥20%). Results: Where vaccination probabilities are positively correlated, COVID-19 and influenza VE test-negative studies with influenza and SARS-CoV-2 ARI controls, respectively, underestimate VE. For COVID-19 VE studies, mean bias was low for all scenarios where influenza represented ≤50% of controls. For influenza VE studies, mean bias was low for all scenarios where SARS-CoV-2 represented ≤10% of controls. Although bias was driven by the conditional probability of vaccination, low VE of the vaccine of interest and high VE of the confounding vaccine increase its magnitude. Conclusions: Where a low percentage of controls are included with the other vaccine-preventable ARI, bias in COVID-19 and influenza VE estimates is low. However, influenza VE estimates are likely more susceptible to bias. Researchers should consider potential bias and its implications in their respective study settings to make informed methodological decisions in test-negative VE studies.