SARS-CoV-2 and influenza co-infection throughout the COVID-19 pandemic: An assessment of co-infection rates and cohort characterization

BackgroundCase reports of patients infected with COVID-19 and influenza virus ("flurona") have raised questions around the prevalence and clinical significance of these reports. MethodsEpidemiological data from the HHS Protect Public Data Hub was analyzed to show trends in SARS-CoV-2 and influenza co-infection-related hospitalizations in the United States in relation to SARS-CoV-2 and influenza strain data from NCBI Virus and FluView. In addition, we retrospectively analyzed all cases of PCR-confirmed SARS-CoV-2 across the Mayo Clinic Enterprise from January 2020 to January 2022 and identified cases of influenza co-infections within two weeks of PCR-positive diagnosis date. Using a cohort from the Mayo Clinic with joint PCR testing data, we estimated the expected number of co-infection cases given the background prevalences of COVID-19 and influenza during the Wuhan (Original), Alpha, Delta, and Omicron waves of the pandemic. FindingsConsidering data from all states of the United States using HHS Protect Public Data Hub, hospitalizations due to influenza co-infection with SARS-CoV-2 were seen to be highest in January 2022 compared to all previous months during the COVID-19 pandemic. Among 171,639 SARS-CoV-2-positive cases analyzed at Mayo Clinic between January 2020 and January 2022, only 73 cases of influenza co-infection were observed. Identified coinfected patients were relatively young (mean age: 28.4 years), predominantly male, and had few comorbidities. During the Delta era (June 16, 2021 to December 13, 2021), there were 9 lab-confirmed co-infection cases observed compared to 13.9 expected cases (95% CI: [12.7, 15.2]), and during the Omicron era (December 14, 2021 to January 17, 2022), there were 54 lab-confirmed co-infection cases compared to 80.9 expected cases (95% CI: [76.6, 85.1]). ConclusionsReported co-infections of SARS-CoV-2 and influenza are rare. These co-infections have occurred throughout the COVID-19 pandemic and their prevalence can be explained by background rates of COVID-19 and influenza infection. Preliminary assessment of longitudinal EHR data suggests that most co-infections so far have been observed among relatively young and healthy patients. Further analysis is needed to assess the outcomes of "flurona" among subpopulations with risk factors for severe COVID-19 such as older age, obesity, and immunocompromised status. Significance StatementReports of COVID-19 and influenza co-infections ("flurona") have raised concern in recent months as both COVID-19 and influenza cases have increased to significant levels in the US. Here, we analyze trends in co-infection cases over the course of the pandemic to show that these co-infection cases are expected given the background prevalences of COVID-19 and influenza independently. In addition, from an initial analysis of these co-infection cases which have been observed at the Mayo Clinic, we find that these co-infection cases are extremely rare and have mostly been observed in relatively young, healthy patients.

Three doses of COVID-19 mRNA vaccination are safe based on adverse events reported in electronic health records

Recent reports on waning of COVID-19 vaccine induced immunity have led to the approval and roll-out of additional dose and booster vaccinations. At risk individuals are receiving additional vaccine dose(s), in addition to the regimen that was tested in clinical trials. The risks and the adverse event profiles associated with these additional vaccine doses are currently not well understood. Here, we performed a retrospective study analyzing vaccine-associated adverse events using electronic health records (EHRs) of individuals that have received three doses of mRNA-based COVID-19 vaccines (n = 47,999). By comparing symptoms reported in 2-week time periods after each vaccine dose and in a 2-week period before the 1st vaccine dose, we assessed the risk associated with 3rd dose vaccination, for both BNT162b2 and mRNA-1273. Reporting of severe adverse events remained low after the 3rd vaccine dose, with rates of pericarditis (0.01%, 0%-0.02% 95% CI), anaphylaxis (0.00%, 0%-0.01% 95% CI), myocarditis (0.00%, 0%-0.01% 95% CI), and cerebral venous sinus thrombosis (no cases), consistent with earlier studies. Significantly more individuals (p-value < 0.05) report low-severity adverse events after their 3rd dose compared with after their 2nd dose, including fatigue (4.92% after 3rd dose vs 3.47% after 2nd dose), lymphadenopathy (2.89% vs 2.07%), nausea (2.62% vs 2.04%), headache (2.47% vs 2.07%), arthralgia (2.12% vs 1.70%), myalgia (1.99% vs 1.63%), diarrhea (1.70% vs 1.24%), fever (1.11% vs 0.81%), vomiting (1.10% vs 0.80%), and chills (0.47% vs 0.36%). Our results show that although 3rd dose vaccination against SARS-CoV-2 infection led to increased reporting of low-severity adverse events, risk of severe adverse events remained comparable to the standard 2-dose regime. This study provides support for the safety of 3rd vaccination doses of individuals that are at high-risk of severe COVID-19 and breakthrough infection.

Comparison of two highly-effective mRNA vaccines for COVID-19 during periods of Alpha and Delta variant prevalence

Although clinical trials and real-world studies have affirmed the effectiveness and safety of the FDA-authorized COVID-19 vaccines, reports of breakthrough infections and persistent emergence of new variants highlight the need to vigilantly monitor the effectiveness of these vaccines. Here we compare the effectiveness of two full-length Spike protein-encoding mRNA vaccines from Moderna (mRNA-1273) and Pfizer/BioNTech (BNT162b2) in the Mayo Clinic Health System over time from January to July 2021, during which either the Alpha or Delta variant was highly prevalent. We defined cohorts of vaccinated and unvaccinated individuals from Minnesota (n = 25,589 each) matched on age, sex, race, history of prior SARS-CoV-2 PCR testing, and date of full vaccination. Both vaccines were highly effective during this study period against SARS-CoV-2 infection (mRNA-1273: 86%, 95%CI: 81-90.6%; BNT162b2: 76%, 95%CI: 69-81%) and COVID-19 associated hospitalization (mRNA-1273: 91.6%, 95% CI: 81-97%; BNT162b2: 85%, 95% CI: 73-93%). In July, vaccine effectiveness against hospitalization has remained high (mRNA-1273: 81%, 95% CI: 33-96.3%; BNT162b2: 75%, 95% CI: 24-93.9%), but effectiveness against infection was lower for both vaccines (mRNA-1273: 76%, 95% CI: 58-87%; BNT162b2: 42%, 95% CI: 13-62%), with a more pronounced reduction for BNT162b2. Notably, the Delta variant prevalence in Minnesota increased from 0.7% in May to over 70% in July whereas the Alpha variant prevalence decreased from 85% to 13% over the same time period. Comparing rates of infection between matched individuals fully vaccinated with mRNA-1273 versus BNT162b2 across Mayo Clinic Health System sites in multiple states (Minnesota, Wisconsin, Arizona, Florida, and Iowa), mRNA-1273 conferred a two-fold risk reduction against breakthrough infection compared to BNT162b2 (IRR = 0.50, 95% CI: 0.39-0.64). In Florida, which is currently experiencing its largest COVID-19 surge to date, the risk of infection in July after full vaccination with mRNA-1273 was about 60% lower than after full vaccination with BNT162b2 (IRR: 0.39, 95% CI: 0.24-0.62). Our observational study highlights that while both mRNA COVID-19 vaccines strongly protect against infection and severe disease, further evaluation of mechanisms underlying differences in their effectiveness such as dosing regimens and vaccine composition are warranted.

COVID-19 vaccines dampen genomic diversity of SARS-CoV-2: Unvaccinated patients exhibit more antigenic mutational variance

Variants of SARS-CoV-2 are evolving under a combination of immune selective pressure in infected hosts and natural genetic drift, raising a global alarm regarding the durability of COVID-19 vaccines. Here, we conducted longitudinal analysis over 1.8 million SARS-CoV-2 genomes from 183 countries or territories to capture vaccination-associated viral evolutionary patterns. To augment this macroscale analysis, we performed viral genome sequencing in 23 vaccine breakthrough COVID-19 patients and 30 unvaccinated COVID-19 patients for whom we also conducted machine-augmented curation of the electronic health records (EHRs). Strikingly, we find the diversity of the SARS-CoV-2 lineages is declining at the country-level with increased rate of mass vaccination (n = 25 countries, mean correlation coefficient = -0.72, S.D. = 0.20). Given that the COVID-19 vaccines leverage B-cell and T-cell epitopes, analysis of mutation rates shows neutralizing B-cell epitopes to be particularly more mutated than comparable amino acid clusters (4.3-fold, p < 0.001). Prospective validation of these macroscale evolutionary patterns using clinically annotated SARS-CoV-2 whole genome sequences confirms that vaccine breakthrough patients indeed harbor viruses with significantly lower diversity in known B cell epitopes compared to unvaccinated COVID-19 patients (2.3-fold, 95% C.I. 1.4-3.7). Incidentally, in these study cohorts, vaccinated breakthrough patients also displayed fewer COVID-associated complications and pre-existing conditions relative to unvaccinated COVID-19 patients. This study presents the first known evidence that COVID-19 vaccines are fundamentally restricting the evolutionary and antigenic escape pathways accessible to SARS-CoV-2. The societal benefit of mass vaccination may consequently go far beyond the widely reported mitigation of SARS-CoV-2 infection risk and amelioration of community transmission, to include stemming of rampant viral evolution.

Female-male differences in COVID vaccine adverse events have precedence in seasonal flu shots: a potential link to sex-associated baseline gene expression patterns

Nearly 150 million doses of FDA-authorized COVID vaccines have been administered in the United States. Sex-based differences of adverse events remain poorly understood, mandating the need for real-world investigation from Electronic Health Records (EHRs) and broader epidemiological data sets. Based on an augmented curation of EHR clinical notes of 31,064 COVID-vaccinated individuals (19,321 females and 11,743 males) in the Mayo Clinic, we find that nausea and vomiting were documented significantly more frequently in females than males after both vaccine doses (nausea: RRDose 1 = 1.67, pDose 1 <0.001, RRDose 2 = 2.2, pDose 1 < 0.001; vomiting: RRDose 1 = 1.58, pDose 1 < 0.001, RRDose 2 = 1.88, pDose 1 = 3.4x10-2). Conversely, fever, fatigue, and lymphadenopathy were more common in males after the first dose vaccination (fever RR = 0.62; p = 8.65x10-3; fatigue RR = 0.86, p = 2.89x10-2; lymphadenopathy RR = 0.61, p = 3.45x10-3). Analysis of the Vaccine Adverse Events Reporting System (VAERS) database further confirms that nausea comprises a larger fraction of total reports among females than males (RR: 1.58; p<0.001), while fever comprises a larger fraction of total reports among males than females (RR: 0.84; p<0.001). Importantly, increased reporting of nausea and fever among females and males, respectively, is also observed for prior influenza vaccines in the VAERS database, establishing that these differences are not unique to the recently developed COVID-19 vaccines. Investigating the mechanistic basis underlying these clinical findings, an analysis of bulk RNA-sequencing data from 12,158 human blood samples (8626 female, 3532 male) reveals 85 genes that are not only significantly different in their gene expression between females and males at baseline, but also have established literature-based associations to COVID-19 as well as the vaccine-related adverse events of clinical consequence. The NLRP3 inflammasome and the NR3C1 glucocorticoid receptor emerge as particularly promising baseline links to sex-associated vaccine adverse events, warranting targeted investigation of these signaling pathways and associated cell types. From a public health standpoint, our clinical findings shall aid in educating patients on the sex-associated risks they should expect for COVID-19 vaccines and also promote better clinical management of vaccine-associated adverse events.

Real-time analysis of a mass vaccination effort via an Artificial Intelligence platform confirms the safety of FDA-authorized COVID-19 vaccines

As the COVID-19 vaccination campaign unfolds as one of the most rapid and widespread in history, it is important to continuously assess the real world safety of the FDA-authorized vaccines. Curation from large-scale electronic health records (EHRs) allows for near real-time safety evaluations that were not previously possible. Here, we advance context- and sentiment-aware deep neural networks over the multi-state Mayo Clinic enterprise (Minnesota, Arizona, Florida, Wisconsin) for automatically curating the adverse effects mentioned by physicians in over 108,000 EHR clinical notes between December 1st 2020 to February 8th 2021. We retrospectively compared the clinical notes of 31,069 individuals who received at least one dose of the Pfizer/BioNTech or Moderna vaccine to those of 31,069 unvaccinated individuals who were propensity matched by demographics, residential location, and history of prior SARS-CoV-2 testing. We find that vaccinated and unvaccinated individuals were seen in the the clinic at similar rates within 21 days of the first or second actual or assigned vaccination dose (first dose Odds Ratio = 1.13, 95% CI: 1.09-1.16; second dose Odds Ratio = 0.89, 95% CI: 0.84-0.93). Further, the incidence rates of all surveyed adverse effects were similar or lower in vaccinated individuals compared to unvaccinated individuals after either vaccine dose. Finally, the most frequently documented adverse effects within 7 days of each vaccine dose were arthralgia (Dose 1: 0.59%; Dose 2: 0.39%), diarrhea (Dose 1: 0.58%; Dose 2: 0.33%), erythema (Dose 1: 0.51%; Dose 2: 0.31%), myalgia (Dose 1: 0.40%; Dose 2: 0.34%), and fever (Dose 1: 0.27%; Dose 2: 0.31%). These remarkably low frequencies of adverse effects recorded in EHRs versus those derived from active solicitation during clinical trials (arthralgia: 24-46%; erythema: 9.5-14.7%; myalgia: 38-62%; fever: 14.2-15.5%) emphasize the rarity of vaccine-associated adverse effects requiring clinical attention. This rapid and timely analysis of vaccine-related adverse effects from contextually rich EHR notes of 62,138 individuals, which was enabled through a large scale Artificial Intelligence (AI)-powered platform, reaffirms the safety and tolerability of the FDA-authorized COVID-19 vaccines in practice.

Augmented Curation of Unstructured Clinical Notes from a Massive EHR System Reveals Specific Phenotypic Signature of Impending COVID-19 Diagnosis

Understanding temporal dynamics of COVID-19 patient symptoms could provide fine-grained resolution to guide clinical decision-making. Here, we use deep neural networks over an institution-wide platform for the augmented curation of clinical notes from 77,167 patients subjected to COVID-19 PCR testing. By contrasting Electronic Health Record (EHR)-derived symptoms of COVID-19-positive (COVIDpos; n=2,317) versus COVID-19-negative (COVIDneg; n=74,850) patients for the week preceding the PCR testing date, we identify anosmia/dysgeusia (27.1-fold), fever/chills (2.6-fold), respiratory difficulty (2.2-fold), cough (2.2-fold), myalgia/arthralgia (2-fold), and diarrhea (1.4-fold) as significantly amplified in COVIDpos over COVIDneg patients. The combination of cough and fever/chills has 4.2-fold amplification in COVIDpos patients during the week prior to PCR testing, and along with anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19. This study introduces an Augmented Intelligence platform for the real-time synthesis of institutional biomedical knowledge. The platform holds tremendous potential for scaling up curation throughput, thus enabling EHR-powered early disease diagnosis.