SARS-CoV-2 coinfections: Could influenza and the common cold be beneficial?

The burden, clinical features and outcomes of SARS-CoV-2, Influenza and co-infections during concurrently out-of-season outbreaks in Brazil

OBJECTIVES

Little is known about the burden and the clinical presentation and prognosis of individuals with Influenza and SARS-CoV-2 during concurrent outbreaks. We aimed to describe the burden, clinical characteristics and outcomes of hospitalized adults during the Influenza A/H3N2 and Omicron outbreaks in Brazil.

STUDY DESIGN

Cross-sectional analysis of national surveillance data.

METHODS

We described the health system burden and clinical features of confirmed cases of Influenza and/or SARS-CoV-2 reported in the national surveillance system during the Influenza A H3N2 out-of-season outbreak and the first Omicron surge between November 2021 and March 2022 in Brazil. A multilevel mixed-effects logistic regression model adjusted by a priori defined confounders was used to evaluate the association between the infection type and resource use and mortality.

RESULTS

The outbreaks occurred simultaneously across all Brazilian regions. Coinfected patients had clinical features from both infections. Influenza coinfected cases had similar odds for requiring ICU admission (adjusted odds ratio, aOR 0.96, 95 % CI, 0.80-1.15, p = 0.634), mechanical ventilation (aOR 0.88, 95 % CI, 0.70-1.11, p = 0.290), and in-hospital mortality (aOR 1.02, 95 % CI, 0.84-1.23, p = 0.847) compared to COVID-19 only. Influenza had lower odds for requiring ICU admission, mechanical ventilation and in-hospital mortality compared to COVID-19 only.

CONCLUSIONS

Simultaneous surges of Influenza and SARS-CoV-2 increased the pressure on the health system of Brazil. Coinfection was not associated with higher resource use or death; Influenza was associated with better outcomes, compared to COVID-19.

Exploring the Clinical Characteristics and Outcomes of Rhinovirus Infection in Hospitalized Children Compared with Other Respiratory Viruses

BACKGROUND

Acute viral respiratory tract infections constitute a significant challenge in pediatric healthcare globally, with rhinovirus representing one of the primary etiological agents. In this context, we conducted a study with the objective of identifying the clinical characteristics and outcomes of rhinovirus infection in comparison with other respiratory viruses in children hospitalized in one of the largest pediatric hospitals in the capital of Romania.

METHODS

We conducted a retrospective study among children hospitalized for influenza-like illness symptoms and who were tested by multiplex RT-PCR with a nasopharyngeal swab between May 2020 and December 2021.

RESULTS

A total of 496 children were eligible for inclusion in the study, and the positivity rate for at least one virus was 58.5%. The rhinovirus was identified in 138 patients (median age 12.5 months), representing 27.8% of all children tested and 49.3% of all positive samples. Although the clinical features of children with rhinovirus were dominated by cough (63.7%) and dyspnea (51.6%), no symptoms were identified that were strongly associated with rhinovirus infection in comparison to other respiratory viruses. The probability of receiving an antibiotic prescription was 1.92 times lower (p = 0.011) in children who tested positive for rhinovirus compared to children with negative RT-PCR results. The incidence of acute bronchiolitis or acute bronchitis, acute respiratory failure, and acute otitis media was higher among rhinovirus-positive children than among those who tested negative via RT-PCR. However, the incidence of these conditions was similar among children who tested positive for other respiratory viruses.

CONCLUSIONS

Rhinovirus was the most prevalent virus identified in children hospitalized with influenza-like illness symptoms. The utilization of multiplex RT-PCR molecular tests is instrumental in elucidating etiology with precision and implementation of these advanced diagnostic methods, which can bring significant benefits in practice. A positive result for rhinovirus helps to reduce the unnecessary administration of antibiotics and optimizes patient management, thus decreasing the risk of severe complications such as acute respiratory failure and acute otitis media.

Viral interference between severe acute respiratory syndrome coronavirus 2 and influenza A viruses

Some respiratory viruses can cause a viral interference through the activation of the interferon (IFN) pathway that reduces the replication of another virus. Epidemiological studies of coinfections between SARS-CoV-2 and other respiratory viruses have been hampered by non-pharmacological measures applied to mitigate the spread of SARS-CoV-2 during the COVID-19 pandemic. With the ease of these interventions, SARS-CoV-2 and influenza A viruses can now co-circulate. It is thus of prime importance to characterize their interactions. In this work, we investigated viral interference effects between an Omicron variant and a contemporary influenza A/H3N2 strain, in comparison with an ancestral SARS-CoV-2 strain and the 2009 pandemic influenza A/H1N1 virus. We infected nasal human airway epitheliums with SARS-CoV-2 and influenza, either simultaneously or 24 h apart. Viral load was measured by RT-qPCR and IFN-α/β/λ1/λ2 proteins were quantified by immunoassay. Expression of four interferon-stimulated genes (ISGs; OAS1/IFITM3/ISG15/MxA) was also measured by RT-droplet digital PCR. Additionally, susceptibility of each virus to IFN-α/β/λ2 recombinant proteins was determined. Our results showed that influenza A, and especially A/H3N2, interfered with both SARS-CoV-2 viruses, but that SARS-CoV-2 did not significantly interfere with A/H3N2 or A/H1N1. Consistently with these results, influenza, and particularly the A/H3N2 strain, caused a higher production of IFN proteins and expression of ISGs than SARS-CoV-2. SARS-CoV-2 induced a marginal IFN production and reduced the IFN response during coinfections with influenza. All viruses were susceptible to exogenous IFNs, with the ancestral SARS-CoV-2 and Omicron being less susceptible to type I and type III IFNs, respectively. Thus, influenza A causes a viral interference towards SARS-CoV-2 most likely through an IFN response. The opposite is not necessarily true, and a concurrent infection with both viruses leads to a lower IFN response. Taken together, these results help us to understand how SARS-CoV-2 interacts with another major respiratory pathogen.

From intramuscular to nasal: unleashing the potential of nasal spray vaccines against coronavirus disease 2019

Coronavirus disease 2019, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has affected 700 million people worldwide since its outbreak in 2019. The current pandemic strains, including Omicron and its large subvariant series, exhibit strong transmission and stealth. After entering the human body, the virus first infects nasal epithelial cells and invades host cells through the angiotensin-converting enzyme 2 receptor and transmembrane serine protease 2 on the host cell surface. The nasal cavity is an important body part that protects against the virus. Immunisation of the nasal mucosa produces immunoglobulin A antibodies that effectively neutralise viruses. Saline nasal irrigation, a type of physical therapy, can reduce the viral load in the nasal cavity and prevent viral infections to some extent. As a commonly used means to fight SARS-CoV-2, the intramuscular (IM) vaccine can induce the human body to produce a systemic immune response and immunoglobulin G antibody; however, the antibody is difficult to distribute to the nasal mucosa in time and cannot achieve a good preventive effect. Intranasal (IN) vaccines compensate for the shortcomings of IM vaccines, induce mucosal immune responses, and have a better effect in preventing infection. In this review, we discuss the nasal defence barrier, the harm caused by SARS-CoV-2, the mechanism of its invasion into host cells, nasal cleaning, IM vaccines and IN vaccines, and suggest increasing the development of IN vaccines, and use of IN vaccines as a supplement to IM vaccines.

SARS-CoV-2 co-detection with influenza and human respiratory syncytial virus in Ethiopia: Findings from the severe acute respiratory illness (SARI) and influenza-like illness (ILI) sentinel surveillance, January 01, 2021, to June 30, 2022

SARS-CoV-2 co-infection with the influenza virus or human respiratory syncytial virus (RSV) may complicate its progress and clinical outcomes. However, data on the co-detection of SARS-CoV-2 with other respiratory viruses are limited in Ethiopia and other parts of Africa to inform evidence-based response and decision-making. We analyzed 4,989 patients' data captured from the national severe acute respiratory illness (SARI) and influenza-like illness (ILI) sentinel surveillance sites over 18 months period from January 01, 2021, to June 30, 2022. Laboratory specimens were collected from the patients and tested for viral respiratory pathogens by real-time, reverse transcription polymerase chain reaction (RT-PCR) at the national influenza center. The median age of the patients was 14 years (IQR: 1-35 years), with a slight preponderance of them being at the age of 15 to less than 50 years. SARS-CoV-2 was detected among 459 (9.2%, 95% CI: 8.4-10.0) patients, and 64 (1.3%, 95% CI: 1.0-1.6) of SARS-CoV-2 were co-detected either with Influenza virus (54.7%) or RSV (32.8%) and 12.5% were detected with both of the viruses. A substantial proportion (54.7%) of SARS-CoV-2 co-detection with other respiratory viruses was identified among patients in the age group from 15 to less than 50 years. The multivariable analysis found that the odds of SARS-CoV-2 co-detection was higher among individuals with the age category of 20 to 39 years as compared to those less than 20 years old (AOR: 1.98, 95%CI:1.15-3.42) while the odds of SARS-CoV-2 co-detection was lower among cases from other regions of the country as compared to those from Addis Ababa (AOR:0.16 95%CI:0.07-0.34). Although the SARS-CoV-2 co-detection with other respiratory viral pathogens was minimal, the findings of this study underscore that it is critical to continuously monitor the co-infections to reduce transmission and improve patient outcomes, particularly among the youth and patients with ILI.

Prevalence and genetic diversity of human rhinovirus among patients with acute respiratory infections in China, 2012-2021

To understand the prevalence of rhinovirus (RV) among acute respiratory infection (ARI) patients, 10-year ARI surveillance in multiple provinces of China were conducted during 2012-2021. Of 15 645 ARI patients, 1180 (7.54%) were confirmed to have RV infection and 820 (69.49%) were children under 5 years of age. RV typing was performed on the 527 VP1 gene sequences, and species A, B, and C accounted for 73.24%, 4.93%, and 21.82%, respectively. Although no significant difference in the proportions of age groups or disease severity was found between RV species, RV-C was more frequently detected in children under 5 years of age, RV-A was more frequently detected in elderly individuals (≥60), and the proportions of pneumonia in RV-A and RV-C patients were higher than those in RV-B patients. The epidemic peak of RV-A was earlier than that of RV-C. A total of 57 types of RV-A, 13 types of RV-B, and 35 types of RV-C were identified in RV-infected patients, and two uncertain RV types were also detected. The findings showed a few differences in epidemiological and clinical features between RV species in ARI patients, and RV-A and RV-C were more prevalent than RV-B.

Vaccination against influenza viruses reduces infection, not hospitalization or death, from respiratory COVID-19: A systematic review and meta-analysis

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 and has brought a huge burden in terms of human lives. Strict social distance and influenza vaccination have been recommended to avoid co-infections between influenza viruses and SARS-CoV-2. Scattered reports suggested a protective effect of influenza vaccine on COVID-19 development and severity. We analyzed 51 studies on the capacity of influenza vaccination to affect infection with SARS-CoV-2, hospitalization, admission to Intensive Care Units (ICU), and mortality. All subjects taken into consideration did not receive any anti-SARS-CoV-2 vaccine, although their status with respect to previous infections with SARS-CoV-2 is not known. Comparison between vaccinated and not-vaccinated subjects for each of the four endpoints was expressed as odds ratio (OR), with 95% confidence intervals (CIs); all analyses were performed by DerSimonian and Laird model, and Hartung-Knapp model when studies were less than 10. In a total of 61 029 936 subjects from 33 studies, influenza vaccination reduced frequency of SARS-CoV-2 infection [OR plus 95% CI = 0.70 (0.65-0.77)]. The effect was significant in all studies together, in health care workers and in the general population; distance from influenza vaccination and the type of vaccine were also of importance. In 98 174 subjects from 11 studies, frequency of ICU admission was reduced with influenza vaccination [OR (95% CI) = 0.71 (0.54-0.94)]; the effect was significant in all studies together, in pregnant women and in hospitalized subjects. In contrast, in 4 737 328 subjects from 14 studies hospitalization was not modified [OR (95% CI) = 1.05 (0.82-1.35)], and in 4 139 660 subjects from 19 studies, mortality was not modified [OR (95% CI) = 0.76 (0.26-2.20)]. Our study emphasizes the importance of influenza vaccination in the protection against SARS-CoV-2 infection.

Global prevalence of respiratory virus infections in adults and adolescents during the COVID-19 pandemic: A systematic review and meta-analysis

OBJECTIVES

Since the beginning of the COVID-19 pandemic, efforts have been made to contain the spread of the virus. However, the epidemiological burden of other respiratory viruses during the pandemic is unclear. We aim to address the epidemiology of respiratory viruses on adults/adolescents since the beginning of the pandemic.

METHODS

We systematically searched five databases and performed a meta-analysis to explore the pooled prevalence of respiratory viruses in different geographical regions, age groups, and periods and compared the prevalence between COVID-19 cases and non-COVID-19 patients.

RESULTS

Enteroviruses/rhinoviruses were highly prevalent compared to other viruses. Different viruses were dominant in different regions. No significant differences in prevalence were found between different age groups, except for human metapneumovirus. There was an increase in prevalence of non-SARS-CoV-2 viruses in the second half of the pandemic (July 2021-December 2022). Comparison of COVID-19 and non-COVID patients showed a higher prevalence in the non-COVID group, significant for influenza, seasonal coronaviruses, and human parainfluenza viruses.

CONCLUSION

Our findings indicate that enteroviruses/rhinoviruses were less impacted by healthcare measures compared with other respiratory viruses. The relaxation of measures in the second half led to an increased pooled prevalence of infections. Several factors may explain the lower prevalence among individuals infected with COVID-19.

Stability of a delayed SARS-CoV-2 reactivation model with logistic growth and adaptive immune response

This paper develops and analyzes a SARS-CoV-2 dynamics model with logistic growth of healthy epithelial cells, CTL immune and humoral (antibody) immune responses. The model is incorporated with four mixed (distributed/discrete) time delays, delay in the formation of latent infected epithelial cells, delay in the formation of active infected epithelial cells, delay in the activation of latent infected epithelial cells, and maturation delay of new SARS-CoV-2 particles. We establish that the model's solutions are non-negative and ultimately bounded. We deduce that the model has five steady states and their existence and stability are perfectly determined by four threshold parameters. We study the global stability of the model's steady states using Lyapunov method. The analytical results are enhanced by numerical simulations. The impact of intracellular time delays on the dynamical behavior of the SARS-CoV-2 is addressed. We noted that increasing the time delay period can suppress the viral replication and control the infection. This could be helpful to create new drugs that extend the delay time period.

Modeling SARS-CoV-2 and HBV co-dynamics with optimal control

Clinical reports have shown that chronic hepatitis B virus (HBV) patients co-infected with SARS-CoV-2 have a higher risk of complications with liver disease than patients without SARS-CoV-2. In this work, a co-dynamical model is designed for SARS-CoV-2 and HBV which incorporates incident infection with the dual diseases. Existence of boundary and co-existence endemic equilibria are proved. The occurrence of backward bifurcation, in the absence and presence of incident co-infection, is investigated through the proposed model. It is noted that in the absence of incident co-infection, backward bifurcation is not observed in the model. However, incident co-infection triggers this phenomenon. For a special case of the study, the disease free and endemic equilibria are shown to be globally asymptotically stable. To contain the spread of both infections in case of an endemic situation, the time dependent controls are incorporated in the model. Also, global sensitivity analysis is carried out by using appropriate ranges of the parameter values which helps to assess their level of sensitivity with reference to the reproduction numbers and the infected components of the model. Finally, numerical assessment of the control system using various intervention strategies is performed, and reached at the conclusion that enhanced preventive efforts against incident co-infection could remarkably control the co-circulation of both SARS-CoV-2 and HBV.

The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

Evaluation of Four Rapid Antigen Tests for the Detection of SARS-CoV-2 Infection with Nasopharyngeal Swabs

Owing to the high transmissibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, the capacity of testing systems based on the gold standard real-time reverse transcription–polymerase chain reaction (rRT-PCR) is limited. Rapid antigen tests (RATs) can substantially contribute to the prevention of community transmission, but their further assessment is required. Here, using 1503 nasopharyngeal swabs, we compared the diagnostic performance of four RAT kits (Abbott Panbio™ COVID-19 Ag Rapid Test, SD Biosensor Standard™ Q COVID-19 Ag Test, Humasis COVID-19 Ag Test, and SG Medical Acrosis COVID-19 Ag Test) to the cycle threshold (Ct) values obtained from rRT-PCR. The precision values, area under the curve values, SARS-CoV-2 variant detection ability, and non-SARS-CoV-2 specificity of all four kits were similar. An assay using the Acrosis kit had a significantly better positive detection rate with a higher recall value and cut-off value than that using the other three RAT kits. During the current COVID-19 pandemic, the Acrosis kit is an effective tool to prevent the spread of SARS-CoV-2 in communities.

Mathematical Modeling of SARS-CoV-2 Omicron Wave under Vaccination Effects

Over the course of the COVID-19 pandemic millions of deaths and hospitalizations have been reported. Different SARS-CoV-2 variants of concern have been recognized during this pandemic and some of these variants of concern have caused uncertainty and changes in the dynamics. The Omicron variant has caused a large amount of infected cases in the US and worldwide. The average number of deaths during the Omicron wave toll increased in comparison with previous SARS-CoV-2 waves. We studied the Omicron wave by using a highly nonlinear mathematical model for the COVID-19 pandemic. The novel model includes individuals who are vaccinated and asymptomatic, which influences the dynamics of SARS-CoV-2. Moreover, the model considers the waning of the immunity and efficacy of the vaccine against the Omicron strain. This study uses the facts that the Omicron strain has a higher transmissibility than the previous circulating SARS-CoV-2 strain but is less deadly. Preliminary studies have found that Omicron has a lower case fatality rate compared to previous circulating SARS-CoV-2 strains. The simulation results show that even if the Omicron strain is less deadly it might cause more deaths, hospitalizations and infections. We provide a variety of scenarios that help to obtain insight about the Omicron wave and its consequences. The proposed mathematical model, in conjunction with the simulations, provides an explanation for a large Omicron wave under various conditions related to vaccines and transmissibility. These results provide an awareness that new SARS-CoV-2 variants can cause more deaths even if their fatality rate is lower.

SARS-CoV-2 Related Viral Respiratory Co-Infections: A Narrative Review

Background

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the new coronavirus originating from Wuhan, China, responsible for the illness known as coronavirus disease 2019 (COVID-19). Early experience and the recent literature have shown that co-infection of SARS-CoV-2 with another respiratory virus might occur. Similar symptoms of acute respiratory infections (ARIs) and COVID-19 represent a challenge for diagnostic and therapeutic efficacy and may modify COVID-19 outcomes.

Materials and Methods

We reviewed the literature on the epidemic pattern and major learning points on important aspects of SARS-CoV-2-related viral respiratory co-infections during the COVID-19 pandemic. Databases such as PubMed, Scopus, Science Direct, and Google Scholar were used to conduct a comprehensive search.

Results

The circulation of respiratory viruses changed as the COVID-19 epidemic continues. Phenomena like viral interference, resource competition, and differences in virus-host range might explain why simultaneous viral respiratory infections have seemed to vanish with the spread of SARS-CoV-2.

Conclusion

Key research to be conducted during this pandemic should include the simultaneous screening of other respiratory pathogens with many available commercial platforms for transmission containment and appropriate clinical management.

Global dynamics of IAV/SARS-CoV-2 coinfection model with eclipse phase and antibody immunity

Coronavirus disease 2019 (COVID-19) and influenza are two respiratory infectious diseases of high importance widely studied around the world. COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), while influenza is caused by one of the influenza viruses, A, B, C, and D. Influenza A virus (IAV) can infect a wide range of species. Studies have reported several cases of respiratory virus coinfection in hospitalized patients. IAV mimics the SARS-CoV-2 with respect to the seasonal occurrence, transmission routes, clinical manifestations and related immune responses. The present paper aimed to develop and investigate a mathematical model to study the within-host dynamics of IAV/SARS-CoV-2 coinfection with the eclipse (or latent) phase. The eclipse phase is the period of time that elapses between the viral entry into the target cell and the release of virions produced by that newly infected cell. The role of the immune system in controlling and clearing the coinfection is modeled. The model simulates the interaction between nine compartments, uninfected epithelial cells, latent/active SARS-CoV-2-infected cells, latent/active IAV-infected cells, free SARS-CoV-2 particles, free IAV particles, SARS-CoV-2-specific antibodies and IAV-specific antibodies. The regrowth and death of the uninfected epithelial cells are considered. We study the basic qualitative properties of the model, calculate all equilibria, and prove the global stability of all equilibria. The global stability of equilibria is established using the Lyapunov method. The theoretical findings are demonstrated via numerical simulations. The importance of considering the antibody immunity in the coinfection dynamics model is discussed. It is found that without modeling the antibody immunity, the case of IAV and SARS-CoV-2 coexistence will not occur. Further, we discuss the effect of IAV infection on the dynamics of SARS-CoV-2 single infection and vice versa.

Mathematical modeling to study the impact of immigration on the dynamics of the COVID-19 pandemic: A case study for Venezuela

We propose two different mathematical models to study the effect of immigration on the COVID-19 pandemic. The first model does not consider immigration, whereas the second one does. Both mathematical models consider five different subpopulations: susceptible, exposed, infected, asymptomatic carriers, and recovered. We find the basic reproduction number R₀ using the next-generation matrix method for the mathematical model without immigration. This threshold parameter is paramount because it allows us to characterize the evolution of the disease and identify what parameters substantially affect the COVID-19 pandemic outcome. We focus on the Venezuelan scenario, where immigration and emigration have been important over recent years, particularly during the pandemic. We show that the estimation of the transmission rates of the SARS-CoV-2 are affected when the immigration of infected people is considered. This has an important consequence from a public health perspective because if the basic reproduction number is less than unity, we can expect that the SARS-CoV-2 would disappear. Thus, if the basic reproduction number is slightly above one, we can predict that some mild non-pharmaceutical interventions would be enough to decrease the number of infected people. The results show that the dynamics of the spread of SARS-CoV-2 through the population must consider immigration to obtain better insight into the outcomes and create awareness in the population regarding the population flow.

Characterizing viral within-host diversity in fast and non-equilibrium demo-genetic dynamics

High-throughput sequencing has opened the route for a deep assessment of within-host genetic diversity that can be used, e.g., to characterize microbial communities and to infer transmission links in infectious disease outbreaks. The performance of such characterizations and inferences cannot be analytically assessed in general and are often grounded on computer-intensive evaluations. Then, being able to simulate within-host genetic diversity across time under various demo-genetic assumptions is paramount to assess the performance of the approaches of interest. In this context, we built an original model that can be simulated to investigate the temporal evolution of genotypes and their frequencies under various demo-genetic assumptions. The model describes the growth and the mutation of genotypes at the nucleotide resolution conditional on an overall within-host viral kinetics, and can be tuned to generate fast non-equilibrium demo-genetic dynamics. We ran simulations of this model and computed classic diversity indices to characterize the temporal variation of within-host genetic diversity (from high-throughput amplicon sequences) of virus populations under three demographic kinetic models of viral infection. Our results highlight how demographic (viral load) and genetic (mutation, selection, or drift) factors drive variations in within-host diversity during the course of an infection. In particular, we observed a non-monotonic relationship between pathogen population size and genetic diversity, and a reduction of the impact of mutation on diversity when a non-specific host immune response is activated. The large variation in the diversity patterns generated in our simulations suggests that the underlying model provides a flexible basis to produce very diverse demo-genetic scenarios and test, for instance, methods for the inference of transmission links during outbreaks.

Severe acute respiratory syndrome coronavirus 2 and influenza A virus co-infection alters viral tropism and haematological composition in Syrian hamsters

The ongoing coronavirus disease 2019 pandemic and its overlap with the influenza season lead to concerns over severe disease caused by the influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) co-infections. Using a Syrian hamster co-infection model with SARS-CoV-2 and the pandemic influenza virus A/California/04/2009 (H1N1), we found (a) more severe disease in co-infected animals, compared to those infected with influenza virus alone but not SARS-CoV-2 infection alone; (b) altered haematological changes in only co-infected animals and (c) altered influenza virus tropism in the respiratory tracts of co-infected animals. Overall, our study revealed that co-infection with SARS-CoV-2 and influenza virus is associated with altered disease severity and tissue tropism, as well as haematological changes, compared to infection with either virus alone.

Stability analysis of within-host SARS-CoV-2/HIV coinfection model

The world has been suffering from the coronavirus disease 2019 (COVID-19) since late 2019. COVID-19 is caused by a virus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The human immunodeficiency virus (HIV) coinfection with SARS-CoV-2 has been reported in many patients around the world. This has raised the alarm for the importance of understanding the dynamics of coinfection and its impact on the lives of patients. As in other pandemics, mathematical modeling is one of the important tools that can help medical and experimental studies of COVID-19. In this paper, we develop a within-host SARS-CoV-2/HIV coinfection model. The model consists of six ordinary differential equations. It depicts the interactions between uninfected epithelial cells, infected epithelial cells, free SARS-CoV-2 particles, uninfected CD4+ T cells, infected CD4+ T cells, and free HIV particles. We confirm that the solutions of the developed model are biologically acceptable by proving their nonnegativity and boundedness. We compute all possible steady states and derive their positivity conditions. We choose suitable Lyapunov functions to prove the global asymptotic stability of all steady states. We run some numerical simulations to enhance the global stability results. Based on our model, weak CD4+ T cell immune response or low CD4+ T cell counts in SARS-CoV-2/HIV coinfected patient increase the concentrations of infected epithelial cells and SARS-CoV-2 viral load. This causes the coinfected patient to suffer from severe SARS-CoV-2 infection. This result agrees with many studies which showed that HIV patients are at greater risk of suffering from severe COVID-19 when infected. More studies are needed to understand the nature of SARS-CoV-2/HIV coinfection and the role of different immune responses during infection.

Global Stability of a Humoral Immunity COVID-19 Model with Logistic Growth and Delays

The mathematical modeling and analysis of within-host or between-host coronavirus disease 2019 (COVID-19) dynamics are considered robust tools to support scientific research. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of COVID-19. This paper proposes and investigates a within-host COVID-19 dynamics model with latent infection, the logistic growth of healthy epithelial cells and the humoral (antibody) immune response. Time delays can affect the dynamics of SARS-CoV-2 infection predicted by mathematical models. Therefore, we incorporate four time delays into the model: (i) delay in the formation of latent infected epithelial cells, (ii) delay in the formation of active infected epithelial cells, (iii) delay in the activation of latent infected epithelial cells, and (iv) maturation delay of new SARS-CoV-2 particles. We establish that the model’s solutions are non-negative and ultimately bounded. This confirms that the concentrations of the virus and cells should not become negative or unbounded. We deduce that the model has three steady states and their existence and stability are perfectly determined by two threshold parameters. We use Lyapunov functionals to confirm the global stability of the model’s steady states. The analytical results are enhanced by numerical simulations. The effect of time delays on the SARS-CoV-2 dynamics is investigated. We observe that increasing time delay values can have the same impact as drug therapies in suppressing viral progression. This offers some insight useful to develop a new class of treatment that causes an increase in the delay periods and then may control SARS-CoV-2 replication.

Co-infection of SARS-CoV-2 with other viral respiratory pathogens in Yogyakarta, Indonesia: A cross-sectional study

Background

Growing evidence shows that viral co-infection is found repeatedly in patients with Coronavirus Disease–2019 (COVID-19). This is the first report of SARS-CoV-2 co-infection with viral respiratory pathogens in Indonesia.

Methods

Over a one month period of April to May 2020, SARS-CoV-2 positive nasopharyngeal swabs in our COVID-19 referral laboratory in Yogyakarta, Indonesia, were tested for viral respiratory pathogens by real-time, reverse transcription polymerase chain reaction (RT-PCR). Proportion of co-infection reported in percentage.

Results

Fifty-nine samples were positive for other viral respiratory pathogens among a total of 125 samples. Influenza A virus was detected in 32 samples, Influenza B in 16 samples, Human metapneumovirus in 1 sample, and adenovirus in 10 samples. We did not detect any co-infection with respiratory syncytial virus. Nine (7.2%) patients had co-infection with more than two viruses.

Conclusion

Viral co-infection with SARS-CoV-2 is common. These results will provide a helpful reference for diagnosis and clinical treatment of patients with COVID-19.

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Viral co-infection with SARS-CoV-2 is common.

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Co-infection by two viral respiratory pathogens is prevalent.

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Pandemic transmission control may also impact in other viral respiratory infections.

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Early identification of co-infection is necessary, given differences in treatment.

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Simple laboratory diagnosis algorithm may be applied to screen viral co-infection.

SARS-CoV-2 and Influenza Viruses: Strategies to Cope with Co-infection and Bioinformatics Perspective

Almost a century after the devastating pandemic of the Spanish flu, humankind is facing the relatively comparable global outbreak of COVID‐19. COVID‐19 is an infectious disease caused by SARS‐CoV‐2 with an unprecedented transmission pattern. In the face of the recent repercussions of COVID‐19, many have argued that the clinical experience with influenza through the last century may have tremendous implications in the containment of this newly emerged viral disease. During the last 2 years, from the emergence of COVID‐19, tremendous advances have been made in diagnosing and treating coinfections. Several approved vaccines are available now for the primary prevention of COVID‐19 and specific treatments exist to alleviate symptoms. The present review article aims to discuss the pathophysiology, diagnosis, and treatment of SARS‐CoV‐2 and influenza A virus coinfection while delivering a bioinformatics‐based insight into this subject matter.

Mathematical Modeling to Study Optimal Allocation of Vaccines against COVID-19 Using an Age-Structured Population

Vaccination against the coronavirus disease 2019 (COVID-19) started in early December of 2020 in the USA. The efficacy of the vaccines vary depending on the SARS-CoV-2 variant. Some countries have been able to deploy strong vaccination programs, and large proportions of their populations have been fully vaccinated. In other countries, low proportions of their populations have been vaccinated, due to different factors. For instance, countries such as Afghanistan, Cameroon, Ghana, Haiti and Syria have less than 10% of their populations fully vaccinated at this time. Implementing an optimal vaccination program is a very complex process due to a variety of variables that affect the programs. Besides, science, policy and ethics are all involved in the determination of the main objectives of the vaccination program. We present two nonlinear mathematical models that allow us to gain insight into the optimal vaccination strategy under different situations, taking into account the case fatality rate and age-structure of the population. We study scenarios with different availabilities and efficacies of the vaccines. The results of this study show that for most scenarios, the optimal allocation of vaccines is to first give the doses to people in the 55+ age group. However, in some situations the optimal strategy is to first allocate vaccines to the 15–54 age group. This situation occurs whenever the SARS-CoV-2 transmission rate is relatively high and the people in the 55+ age group have a transmission rate 50% or less that of those in the 15–54 age group. This study and similar ones can provide scientific recommendations for countries where the proportion of vaccinated individuals is relatively small or for future pandemics.

Treatment of Respiratory Viral Coinfections

With the advent of rapid multiplex PCR, physicians have been able to test for multiple viral pathogens when a patient presents with influenza-like illness. This has led to the discovery that many respiratory infections are caused by more than one virus. Antiviral treatment of viral coinfections can be complex because treatment of one virus will affect the time course of the other virus. Since effective antivirals are only available for some respiratory viruses, careful consideration needs to be given on the effect treating one virus will have on the dynamics of the other virus, which might not have available antiviral treatment. In this study, we use mathematical models of viral coinfections to assess the effect of antiviral treatment on coinfections. We examine the effect of the mechanism of action, relative growth rates of the viruses, and the assumptions underlying the interaction of the viruses. We find that high antiviral efficacy is needed to suppress both infections. If high doses of both antivirals are not achieved, then we run the risk of lengthening the duration of coinfection or even of allowing a suppressed virus to replicate to higher viral titers.

Global analysis of within-host SARS-CoV-2/HIV coinfection model with latency

The coronavirus disease 2019 (COVID-19) is a respiratory disease caused by a virus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this paper, we analyze a within-host SARS-CoV-2/HIV coinfection model. The model is made up of eight ordinary differential equations. These equations describe the interactions between healthy epithelial cells, latently infected epithelial cells, productively infected epithelial cells, SARS-CoV-2 particles, healthy CD 4 + T cells, latently infected CD 4 + T cells, productively infected CD 4 + T cells, and HIV particles. We confirm that the solutions of the developed model are bounded and nonnegative. We calculate the different steady states of the model and derive their existence conditions. We choose appropriate Lyapunov functions to show the global stability of all steady states. We execute some numerical simulations to assist the theoretical contributions. Based on our results, weak CD 4 + T cell immunity in SARS-CoV-2/HIV coinfected patients causes an increase in the concentrations of productively infected epithelial cells and SARS-CoV-2 particles. This may lead to severe SARS-CoV-2 infection in HIV patients. This result agrees with many studies that discussed the high risk of severe infection and death in HIV patients when they get SARS-CoV-2 infection. On the other hand, increasing the death rate of infected epithelial cells during the latency period can reduce the severity of SARS-CoV-2 infection in HIV patients. More studies are needed to understand the dynamics of SARS-CoV-2/HIV coinfection and find better ways to treat this vulnerable group of patients.

Understanding Rhinovirus Circulation and Impact on Illness

Rhinoviruses (RVs) have been reported as one of the main viral causes for severe respiratory illnesses that may require hospitalization, competing with the burden of other respiratory viruses such as influenza and RSV in terms of severity, economic cost, and resource utilization. With three species and 169 subtypes, RV presents the greatest diversity within the Enterovirus genus, and despite the efforts of the research community to identify clinically relevant subtypes to target therapeutic strategies, the role of species and subtype in the clinical outcomes of RV infection remains unclear. This review aims to collect and organize data relevant to RV illness in order to find patterns and links with species and/or subtype, with a specific focus on species and subtype diversity in clinical studies typing of respiratory samples.

Compliance with safety measures and risk of COVID-19 transmission among healthcare workers

Aim:

This study aimed to determine the compliance of healthcare workers (HCWs) with the hospital safety measures and the prevalence of hospital-acquired COVID-19 infection among them.

Methodology:

HCWs at King Abdullah University Hospital (KAUH) assigned for COVID-19 patients between 18 March and 10 June 2020 were tested for past infection using total anti-SARS-CoV-2 immunoglobulin assay, demographic data and compliance with safety measures were assessed using a questionnaire.

Results:

A total of 340 HCWs participated in the study, 260 were close direct care. Three HCWs tested positive for total anti-SARS-CoV-2 immunoglobulin. Close direct care were more compliant with personal protective guidelines than those providing direct care.

Conclusion:

HCWs compliance with personal protective guidelines might explain the low prevalence of COVID-19 infection in hospital settings.

Hospitals around the globe have implemented safety measures in order to decrease the risk of spreading the virus among healthcare workers (HCWs); our aim in this study is to assess the compliance of HCWs to the safety measures introduced in our hospital and the prevalence of contracting COVID-19 among them using total anti-SARS-CoV-2 immunoglobulin assay. A total of 113 physicians and 227 nurses participated in the study; results showed a high level of compliance among HCWs working in close direct care and a relatively lower level of compliance among those providing direct care. Three of the HCWs tested positive for the total immunoglobulin assay, indicating the importance of adhering to the safety measures to decrease the risk of contracting the virus.

Estimating the impact of influenza on the epidemiological dynamics of SARS-CoV-2

As in past pandemics, co-circulating pathogens may play a role in the epidemiology of coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In particular, experimental evidence indicates that influenza infection can up-regulate the expression of ACE2-the receptor of SARS-CoV-2 in human cells-and facilitate SARS-CoV-2 infection. Here we hypothesized that influenza impacted the epidemiology of SARS-CoV-2 during the early 2020 epidemic of COVID-19 in Europe. To test this hypothesis, we developed a population-based model of SARS-CoV-2 transmission and of COVID-19 mortality, which simultaneously incorporated the impact of non-pharmaceutical control measures and of influenza on the epidemiological dynamics of SARS-CoV-2. Using statistical inference methods based on iterated filtering, we confronted this model with mortality incidence data in four European countries (Belgium, Italy, Norway, and Spain) to systematically test a range of assumptions about the impact of influenza. We found consistent evidence for a 1.8-3.4-fold (uncertainty range across countries: 1.1 to 5.0) average population-level increase in SARS-CoV-2 transmission associated with influenza during the period of co-circulation. These estimates remained robust to a variety of alternative assumptions regarding the epidemiological traits of SARS-CoV-2 and the modeled impact of control measures. Although further confirmatory evidence is required, our results suggest that influenza could facilitate the spread and hamper effective control of SARS-CoV-2. More generally, they highlight the possible role of co-circulating pathogens in the epidemiology of COVID-19.

Decline of Influenza and Respiratory Viruses With COVID-19 Public Health Measures: Alberta, Canada

OBJECTIVE

To determine the incidence of influenza and noninfluenza respiratory viruses (NIRVs) pre-/post-implementation of public health measures aimed to decrease coronavirus disease 2019 (COVID-19) transmission using population-based surveillance data. We hypothesized that such measures could reduce the burden of respiratory viruses (RVs) transmitting via the same routes.

PATIENTS AND METHODS

An interrupted time-series analysis of RV surveillance data in Alberta, Canada, from May 2017 to July 2020 was conducted. The burden of influenza and NIRVs before and after intervention initiation at week 11 was compared. The analysis was adjusted for seasonality, overdispersion, and autocorrelation.

RESULTS

During the study period, an average of 708 and 4056 weekly respiratory multiplex molecular panels were conducted pre-/post-intervention, respectively. We found significant reductions in test positivity rates in the postintervention period for influenza (-94.3%; 95% CI, -93.8 to 97.4%; P<.001) and all NIRVs (-76.5%; 95% CI, -77.3 to -75.8%; P<.001) in the crude model, and -86.2% (95% CI, -91.5 to -77.4%: P<.001) and -75% (95% CI, -79.7 to -69.3%; P<.001), respectively, in the adjusted models. Subanalyses for individual viruses showed significant decreases in respiratory syncytial virus, human metapneumovirus, enterovirus/rhinovirus, and parainfluenza. For non-severe acute respiratory coronavirus 2 human coronaviruses, the decline was not statistically significant after adjustment (-22.3%; 95% CI, -49.3 to +19%, P=.246).

CONCLUSION

The implementation of COVID-19 public health measures likely resulted in reduced transmission of common RVs. Although drastic lockdowns are unlikely to be required given widespread COVID-19 vaccination, targeted implementation of such measures can lower RV disease burden. Studies to evaluate relative contributions of individual interventions are warranted.

The associations of previous influenza/upper respiratory infection with COVID-19 susceptibility/morbidity/mortality: a nationwide cohort study in South Korea

We aimed to investigate the associations of previous influenza/URI with the susceptibility of COVID-19 patients compared to that of non-COVID-19 participants. A nationwide COVID-19 cohort database was collected by the Korea National Health Insurance Corporation. A total of 8,070 COVID-19 patients (1 January 2020 through 4 June 2020) were matched with 32,280 control participants. Severe COVID-19 morbidity was defined based on the treatment histories of the intensive care unit, invasive ventilation, and extracorporeal membrane oxygenation and death. The susceptibility/morbidity/mortality associated with prior histories of 1-14, 1-30, 1-90, 15-45, 15-90, and 31-90 days before COVID-19 onset were analyzed using conditional/unconditional logistic regression. Prior influenza infection was related to increased susceptibility to COVID-19 (adjusted odds ratio [95% confidence interval] = 3.07 [1.61-5.85] for 1-14 days and 1.91 [1.54-2.37] for 1-90 days). Prior URI was also associated with increased susceptibility to COVID-19 (6.95 [6.38-7.58] for 1-14 days, 4.99 [4.64-5.37] for 1-30 days, and 2.70 [2.55-2.86] for 1-90 days). COVID-19 morbidity was positively associated with influenza (3.64 [1.55-9.21] and 3.59 [1.42-9.05]) and URI (1.40 [1.11-1.78] and 1.28 [1.02-1.61]) at 1-14 days and 1-30 days, respectively. Overall, previous influenza/URI did not show an association with COVID-19 mortality. Previous influenza/URI histories were associated with increased COVID-19 susceptibility and morbidity. Our findings indicate why controlling influenza/URI is important during the COVID-19 pandemic.

Computational simulations to dissect the cell immune response dynamics for severe and critical cases of SARS-CoV-2 infection

BACKGROUND

COVID-19 is a global pandemic leading to high death tolls worldwide day by day. Clinical evidence suggests that COVID-19 patients can be classified as non-severe, severe, and critical cases. In particular, studies have highlighted the relationship between lymphopenia and the severity of the illness, where CD8+ T cells have the lowest levels in critical cases. However, a quantitative understanding of the immune responses in COVID-19 patients is still missing.

OBJECTIVES

In this work, we aim to elucidate the key parameters that define the course of the disease deviating from severe to critical cases. The dynamics of different immune cells are taken into account in mechanistic models to elucidate those that contribute to the worsening of the disease.

METHODS

Several mathematical models based on ordinary differential equations are proposed to represent data sets of different immune response cells dynamics such as CD8+ T cells, NK cells, and also CD4+ T cells in patients with SARS-CoV-2 infection. Parameter fitting is performed using the differential evolution algorithm. Non-parametric bootstrap approach is introduced to abstract the stochastic environment of the infection.

RESULTS

The mathematical model that represents the data more appropriately is considering CD8+ T cell dynamics. This model had a good fit to reported experimental data, and in accordance with values found in the literature. The NK cells and CD4+ T cells did not contribute enough to explain the dynamics of the immune responses.

CONCLUSIONS

Our computational results highlight that a low viral clearance rate by CD8+ T cells could lead to the severity of the disease. This deregulated clearance suggests that it is necessary immunomodulatory strategies during the course of the infection to avoid critical states in COVID-19 patients.

Global dynamics of SARS-CoV-2/cancer model with immune responses

The world is going through a critical period due to a new respiratory disease called coronavirus disease 2019 (COVID-19). This disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Mathematical modeling is one of the most important tools that can speed up finding a drug or vaccine for COVID-19. COVID-19 can lead to death especially for patients having chronic diseases such as cancer, AIDS, etc. We construct a new within-host SARS-CoV-2/cancer model. The model describes the interactions between six compartments: nutrient, healthy epithelial cells, cancer cells, SARS-CoV-2 virus particles, cancer-specific CTLs, and SARS-CoV-2-specific antibodies. We verify the nonnegativity and boundedness of its solutions. We outline all possible equilibrium points of the proposed model. We prove the global stability of equilibria by constructing proper Lyapunov functions. We do some numerical simulations to visualize the obtained results. According to our model, lymphopenia in COVID-19 cancer patients may worsen the outcomes of the infection and lead to death. Understanding dysfunctions in immune responses during COVID-19 infection in cancer patients could have implications for the development of treatments for this high-risk group.

Personalized Virus Load Curves for Acute Viral Infections

We introduce an explicit function that describes virus-load curves on a patient-specific level. This function is based on simple and intuitive model parameters. It allows virus load analysis of acute viral infections without solving a full virus load dynamic model. We validate our model on data from mice influenza A, human rhinovirus data, human influenza A data, and monkey and human SARS-CoV-2 data. We find wide distributions for the model parameters, reflecting large variability in the disease outcomes between individuals. Further, we compare the virus load function to an established target model of virus dynamics, and we provide a new way to estimate the exponential growth rates of the corresponding infection phases. The virus load function, the target model, and the exponential approximations show excellent fits for the data considered. Our virus-load function offers a new way to analyze patient-specific virus load data, and it can be used as input for higher level models for the physiological effects of a virus infection, for models of tissue damage, and to estimate patient risks.

Interactions between SARS-CoV-2 and influenza, and the impact of coinfection on disease severity: a test-negative design

BACKGROUND

The impact of SARS-CoV-2 alongside influenza is a major concern in the northern hemisphere as winter approaches.

METHODS

Test data for influenza and SARS-CoV-2 from national surveillance systems between 20 January 2020 and 25 April 2020 were used to estimate influenza infection on the risk of SARS-CoV-2 infection. A test-negative design was used to assess the odds of SARS-CoV-2 in those who tested positive for influenza compared with those who tested negative. The severity of SARS-CoV-2 was also assessed using univariable and multivariable analyses.

RESULTS

The risk of testing positive for SARS-CoV-2 was 58% lower among influenza-positive cases and patients with a coinfection had a risk of death of 5.92 (95% confidence interval: 3.21-10.91) times greater than among those with neither influenza nor SARS-CoV-2. The odds of ventilator use or death and intensive care unit admission or death were greatest among coinfected patients.

CONCLUSIONS

Coinfection of these viruses could have a significant impact on morbidity, mortality and health-service demand.

The Positive Rhinovirus/Enterovirus Detection and SARS-CoV-2 Persistence beyond the Acute Infection Phase: An Intra-Household Surveillance Study

We aimed to assess the duration of nasopharyngeal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA persistence in adults self-confined at home after acute infection; and to identify the associations of SARS-CoV-2 persistence with respiratory virus co-detection and infection transmission. A cross-sectional intra-household study was conducted in metropolitan Barcelona (Spain) during the time period of April to June 2020. Every adult who was the first family member reported as SARS-CoV-2-positive by reverse transcription polymerase chain reaction (RT-PCR) as well as their household child contacts had nasopharyngeal swabs tested by a targeted SARS-CoV-2 RT-PCR and a multiplex viral respiratory panel after a 15 day minimum time lag. Four-hundred and four households (404 adults and 708 children) were enrolled. SARS-CoV-2 RNA was detected in 137 (33.9%) adults and 84 (11.9%) children. Rhinovirus/Enterovirus (RV/EV) was commonly found (83.3%) in co-infection with SARS-CoV-2 in adults. The mean duration of SARS-CoV-2 RNA presence in adults’ nasopharynx was 52 days (range 26–83 days). The persistence of SARS-CoV-2 was significantly associated with RV/EV co-infection (adjusted odds ratio (aOR) 9.31; 95% CI 2.57–33.80) and SARS-CoV-2 detection in child contacts (aOR 2.08; 95% CI 1.24–3.51). Prolonged nasopharyngeal SARS-CoV-2 RNA persistence beyond the acute infection phase was frequent in adults quarantined at home during the first epidemic wave; which was associated with RV/EV co-infection and could enhance intra-household infection transmission.

Analysis of Delayed Vaccination Regimens: A Mathematical Modeling Approach

The first round of vaccination against coronavirus disease 2019 (COVID-19) began in early December of 2020 in a few countries. There are several vaccines, and each has a different efficacy and mechanism of action. Several countries, for example, the United Kingdom and the USA, have been able to develop consistent vaccination programs where a great percentage of the population has been vaccinated (May 2021). However, in other countries, a low percentage of the population has been vaccinated due to constraints related to vaccine supply and distribution capacity. Countries such as the USA and the UK have implemented different vaccination strategies, and some scholars have been debating the optimal strategy for vaccine campaigns. This problem is complex due to the great number of variables that affect the relevant outcomes. In this article, we study the impact of different vaccination regimens on main health outcomes such as deaths, hospitalizations, and the number of infected. We develop a mathematical model of COVID-19 transmission to focus on this important health policy issue. Thus, we are able to identify the optimal strategy regarding vaccination campaigns. We find that for vaccines with high efficacy (>70%) after the first dose, the optimal strategy is to delay inoculation with the second dose. On the other hand, for a low first dose vaccine efficacy, it is better to use the standard vaccination regimen of 4 weeks between doses. Thus, under the delayed second dose option, a campaign focus on generating a certain immunity in as great a number of people as fast as possible is preferable to having an almost perfect immunity in fewer people first. Therefore, based on these results, we suggest that the UK implemented a better vaccination campaign than that in the USA with regard to time between doses. The results presented here provide scientific guidelines for other countries where vaccination campaigns are just starting, or the percentage of vaccinated people is small.

A Global Analysis of Delayed SARS-CoV-2/Cancer Model with Immune Response

Coronavirus disease 2019 (COVID-19) is a respiratory disease caused by SARS-CoV-2. It appeared in China in late 2019 and rapidly spread to most countries of the world. Cancer patients infected with SARS-CoV-2 are at higher risk of developing severe infection and death. This risk increases further in the presence of lymphopenia affecting the lymphocytes count. Here, we develop a delayed within-host SARS-CoV-2/cancer model. The model describes the occurrence of SARS-CoV-2 infection in cancer patients and its effect on the functionality of immune responses. The model considers the time delays that affect the growth rates of healthy epithelial cells and cancer cells. We provide a detailed analysis of the model by proving the nonnegativity and boundedness of the solutions, finding steady states, and showing the global stability of the different steady states. We perform numerical simulations to highlight some important observations. The results indicate that increasing the time delay in the growth rate of cancer cells reduced the size of tumors and decreased the likelihood of deterioration in the condition of SARS-CoV-2/cancer patients. On the other hand, lymphopenia increased the concentrations of SARS-CoV-2 particles and cancer cells, which worsened the condition of the patient.

Analysis of Key Factors of a SARS-CoV-2 Vaccination Program: A Mathematical Modeling Approach

The administration of vaccines against the coronavirus disease 2019 (COVID-19) started in early December of 2020. Currently, there are only a few approved vaccines, each with different efficacies and mechanisms of action. Moreover, vaccination programs in different regions may vary due to differences in implementation, for instance, simply the availability of the vaccine. In this article, we study the impact of the pace of vaccination and the intrinsic efficacy of the vaccine on prevalence, hospitalizations, and deaths related to the SARS-CoV-2 virus. Then we study different potential scenarios regarding the burden of the COVID-19 pandemic in the near future. We construct a compartmental mathematical model and use computational methodologies to study these different scenarios. Thus, we are able to identify some key factors to reach the aims of the vaccination programs. We use some metrics related to the outcomes of the COVID-19 pandemic in order to assess the impact of the efficacy of the vaccine and the pace of the vaccine inoculation. We found that both factors have a high impact on the outcomes. However, the rate of vaccine administration has a higher impact in reducing the burden of the COVID-19 pandemic. This result shows that health institutions need to focus on increasing the vaccine inoculation pace and create awareness in the population about the importance of COVID-19 vaccines.

Impact of a New SARS-CoV-2 Variant on the Population: A Mathematical Modeling Approach

Several SARS-CoV-2 variants have emerged around the world, and the appearance of other variants depends on many factors. These new variants might have different characteristics that can affect the transmissibility and death rate. The administration of vaccines against the coronavirus disease 2019 (COVID-19) started in early December of 2020 and in some countries the vaccines will not soon be widely available. For this article, we studied the impact of a new more transmissible SARS-CoV-2 strain on prevalence, hospitalizations, and deaths related to the SARS-CoV-2 virus. We studied different scenarios regarding the transmissibility in order to provide a scientific support for public health policies and bring awareness of potential future situations related to the COVID-19 pandemic. We constructed a compartmental mathematical model based on differential equations to study these different scenarios. In this way, we are able to understand how a new, more infectious strain of the virus can impact the dynamics of the COVID-19 pandemic. We studied several metrics related to the possible outcomes of the COVID-19 pandemic in order to assess the impact of a higher transmissibility of a new SARS-CoV-2 strain on these metrics. We found that, even if the new variant has the same death rate, its high transmissibility can increase the number of infected people, those hospitalized, and deaths. The simulation results show that health institutions need to focus on increasing non-pharmaceutical interventions and the pace of vaccine inoculation since a new variant with higher transmissibility, such as, for example, VOC-202012/01 of lineage B.1.1.7, may cause more devastating outcomes in the population.

Co-infection of SARS-COV-2 and Influenza A Virus: A Case Series and Fast Review

Coronavirus disease 2019 (COVID-19) occurs in the influenza season and has become a global pandemic. The present study aimed to examine severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) co-infection with influenza A virus (IAV) in an attempt to provide clues for the antiviral interventions of co-infected patients. We described two patients who were co-infected with SARS-CoV-2 and IAV treated at Wuhan Union Hospital, China. In addition, we performed a review in PubMed, Web of Science and CNKI (from January 1 up to November 1, 2020) with combinations of the following key words: “COVID-19, SARS-COV-2, influenza A and co-infection”. A total of 28 co-infected patients were enrolled in the analysis. Of the 28 patients, the median age was 54.5 years (IQR, 34.25–67.5) and 14 cases (50.0%) were classified as severe types. The most common symptoms were fever (85.71%), cough (82.14%) and dyspnea (60.71%). Sixteen patients had lymphocytopenia on admission and 23 patients exhibited abnormal radiological changes. The median time from symptom onset to hospital admission was 4 days (IQR, 3–6), and the median time of hospital stay was 14 days (IQR, 8.5–16.75). In conclusion, patients with SARS-COV-2 and IAV co-infection were similar to those infected with SARS-COV-2 alone in symptoms and radiological images. SARS-COV-2 co-infection with IAV could lead to more severe clinical condition but did not experience longer hospital stay compared with patients infected with SARS-COV-2 alone.

Qualitative analysis of a mathematical model with presymptomatic individuals and two SARS-CoV-2 variants

The SARS-CoV-2 continues to spread across the world. During this COVID-19 pandemic, several variants of the SARS-CoV-2 have been found. Some of these new variants like the VOC-202012/01 of lineage B.1.1.7 or the most recently B.1.617 emerging in India have a higher infectiousness than those previously prevalent. We propose a mathematical model based on ordinary differential equations to investigate potential consequences of the appearance of a new more transmissible SARS-CoV-2 strain in a given region. The proposed mathematical model incorporates the presymptomatic and asymptomatic subpopulations in addition to the more usual susceptible, exposed, infected, and recovered subpopulations. This is important from a realistic point of view since it has been found recently that presymptomatic and asymptomatic individuals are relevant spreaders of the SARS-CoV-2. Using the next-generation matrix method, we find the basic reproduction number, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal {R}}_{0}$$\end{document}R0, an important threshold parameter that provides insight regarding the evolution and outcome of a certain instance of the COVID-19 pandemic. The local and global stability of system equilibria are also presented. In particular, for the global stability we construct a Lyapunov functional and use the LaSalle invariant principle to prove that if the basic reproduction ratio is less than unity, the infection-free equilibrium is globally asymptotically stable. On the other hand, if \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal {R}}_{0}>1$$\end{document}R0>1 the endemic equilibrium is globally asymptotically stable. Finally, we present numerical simulations to numerically support the analytic results and to show the impact of the introduction of a new more contagious SARS-CoV-2 variant in a population.

Shedding of SARS-CoV-2 in feces and urine and its potential role in person-to-person transmission and the environment-based spread of COVID-19

The recent detection of SARS-CoV-2 RNA in feces has led to speculation that it can be transmitted via the fecal-oral/ocular route. This review aims to critically evaluate the incidence of gastrointestinal (GI) symptoms, the quantity and infectivity of SARS-CoV-2 in feces and urine, and whether these pose an infection risk in sanitary settings, sewage networks, wastewater treatment plants, and the wider environment (e.g. rivers, lakes and marine waters). A review of 48 independent studies revealed that severe GI dysfunction is only evident in a small number of COVID-19 cases, with 11 ± 2% exhibiting diarrhea and 12 ± 3% exhibiting vomiting and nausea. In addition to these cases, SARS-CoV-2 RNA can be detected in feces from some asymptomatic, mildly- and pre-symptomatic individuals. Fecal shedding of the virus peaks in the symptomatic period and can persist for several weeks, but with declining abundances in the post-symptomatic phase. SARS-CoV-2 RNA is occasionally detected in urine, but reports in fecal samples are more frequent. The abundance of the virus genetic material in both urine (ca. 102-105 gc/ml) and feces (ca. 102-107 gc/ml) is much lower than in nasopharyngeal fluids (ca. 105-1011 gc/ml). There is strong evidence of multiplication of SARS-CoV-2 in the gut and infectious virus has occasionally been recovered from both urine and stool samples. The level and infectious capability of SARS-CoV-2 in vomit remain unknown. In comparison to enteric viruses transmitted via the fecal-oral route (e.g. norovirus, adenovirus), the likelihood of SARS-CoV-2 being transmitted via feces or urine appears much lower due to the lower relative amounts of virus present in feces/urine. The biggest risk of transmission will occur in clinical and care home settings where secondary handling of people and urine/fecal matter occurs. In addition, while SARS-CoV-2 RNA genetic material can be detected by in wastewater, this signal is greatly reduced by conventional treatment. Our analysis also suggests the likelihood of infection due to contact with sewage-contaminated water (e.g. swimming, surfing, angling) or food (e.g. salads, shellfish) is extremely low or negligible based on very low predicted abundances and limited environmental survival of SARS-CoV-2. These conclusions are corroborated by the fact that tens of million cases of COVID-19 have occurred globally, but exposure to feces or wastewater has never been implicated as a transmission vector.

Shedding of SARS-CoV-2 in feces and urine and its potential role in person-to-person transmission and the environment-based spread of COVID-19

The recent detection of SARS-CoV-2 RNA in feces has led to speculation that it can be transmitted via the fecal-oral/ocular route. This review aims to critically evaluate the incidence of gastrointestinal (GI) symptoms, the quantity and infectivity of SARS-CoV-2 in feces and urine, and whether these pose an infection risk in sanitary settings, sewage networks, wastewater treatment plants, and the wider environment (e.g. rivers, lakes and marine waters). A review of 48 independent studies revealed that severe GI dysfunction is only evident in a small number of COVID-19 cases, with 11 ± 2% exhibiting diarrhea and 12 ± 3% exhibiting vomiting and nausea. In addition to these cases, SARS-CoV-2 RNA can be detected in feces from some asymptomatic, mildly- and pre-symptomatic individuals. Fecal shedding of the virus peaks in the symptomatic period and can persist for several weeks, but with declining abundances in the post-symptomatic phase. SARS-CoV-2 RNA is occasionally detected in urine, but reports in fecal samples are more frequent. The abundance of the virus genetic material in both urine (ca. 10²-10⁵ gc/ml) and feces (ca. 10²-10⁷ gc/ml) is much lower than in nasopharyngeal fluids (ca. 10⁵-10¹¹ gc/ml). There is strong evidence of multiplication of SARS-CoV-2 in the gut and infectious virus has occasionally been recovered from both urine and stool samples. The level and infectious capability of SARS-CoV-2 in vomit remain unknown. In comparison to enteric viruses transmitted via the fecal-oral route (e.g. norovirus, adenovirus), the likelihood of SARS-CoV-2 being transmitted via feces or urine appears much lower due to the lower relative amounts of virus present in feces/urine. The biggest risk of transmission will occur in clinical and care home settings where secondary handling of people and urine/fecal matter occurs. In addition, while SARS-CoV-2 RNA genetic material can be detected by in wastewater, this signal is greatly reduced by conventional treatment. Our analysis also suggests the likelihood of infection due to contact with sewage-contaminated water (e.g. swimming, surfing, angling) or food (e.g. salads, shellfish) is extremely low or negligible based on very low predicted abundances and limited environmental survival of SARS-CoV-2. These conclusions are corroborated by the fact that tens of million cases of COVID-19 have occurred globally, but exposure to feces or wastewater has never been implicated as a transmission vector.

Shedding of SARS-CoV-2 in feces and urine and its potential role in person-to-person transmission and the environment-based spread of COVID-19

The recent detection of SARS-CoV-2 RNA in feces has led to speculation that it can be transmitted via the fecal-oral/ocular route. This review aims to critically evaluate the incidence of gastrointestinal (GI) symptoms, the quantity and infectivity of SARS-CoV-2 in feces and urine, and whether these pose an infection risk in sanitary settings, sewage networks, wastewater treatment plants, and the wider environment (e.g. rivers, lakes and marine waters). A review of 48 independent studies revealed that severe GI dysfunction is only evident in a small number of COVID-19 cases, with 11 ± 2% exhibiting diarrhea and 12 ± 3% exhibiting vomiting and nausea. In addition to these cases, SARS-CoV-2 RNA can be detected in feces from some asymptomatic, mildly- and pre-symptomatic individuals. Fecal shedding of the virus peaks in the symptomatic period and can persist for several weeks, but with declining abundances in the post-symptomatic phase. SARS-CoV-2 RNA is occasionally detected in urine, but reports in fecal samples are more frequent. The abundance of the virus genetic material in both urine (ca. 10²-10⁵ gc/ml) and feces (ca. 10²-10⁷ gc/ml) is much lower than in nasopharyngeal fluids (ca. 10⁵-10¹¹ gc/ml). There is strong evidence of multiplication of SARS-CoV-2 in the gut and infectious virus has occasionally been recovered from both urine and stool samples. The level and infectious capability of SARS-CoV-2 in vomit remain unknown. In comparison to enteric viruses transmitted via the fecal-oral route (e.g. norovirus, adenovirus), the likelihood of SARS-CoV-2 being transmitted via feces or urine appears much lower due to the lower relative amounts of virus present in feces/urine. The biggest risk of transmission will occur in clinical and care home settings where secondary handling of people and urine/fecal matter occurs. In addition, while SARS-CoV-2 RNA genetic material can be detected by in wastewater, this signal is greatly reduced by conventional treatment. Our analysis also suggests the likelihood of infection due to contact with sewage-contaminated water (e.g. swimming, surfing, angling) or food (e.g. salads, shellfish) is extremely low or negligible based on very low predicted abundances and limited environmental survival of SARS-CoV-2. These conclusions are corroborated by the fact that tens of million cases of COVID-19 have occurred globally, but exposure to feces or wastewater has never been implicated as a transmission vector.

The Second Wave of COVID-19 Pandemic Strikes during the Flu Season: An Awareness Perspective

Coinfection with both SARS-CoV-2 and influenza viruses seems to be a real and severe problem. However, coinfection is far from a simple matter, and cannot be considered having more unfavorable outcomes as a direct consequence. In reality, the aftermath is powerfully nuanced by the presence of risk factors and specific molecular mechanisms. Our objective was to raise awareness around the unpredictable association between COVID-19 pandemics and the upcoming flu season, and make arguments about the need to develop new routine testing protocols for both viruses, at least during the period with an expected high incidence. Our reasoning is built around the various impacts that the whole range of risk groups, common immunological mechanisms, and complex interactions, such as influenza vaccination, will have on patients' prognosis. We show that the more flawed clinical course is due to managing only one of the infections (and, subsequently, neglecting the other condition).

Initial Inoculum and the Severity of COVID-19: A Mathematical Modeling Study of the Dose-Response of SARS-CoV-2 Infections

SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) causes a variety of responses in those who contract the virus, ranging from asymptomatic infections to acute respiratory failure and death. While there are likely multiple mechanisms triggering severe disease, one potential cause of severe disease is the size of the initial inoculum. For other respiratory diseases, larger initial doses lead to more severe outcomes. We investigate whether there is a similar link for SARS-CoV-2 infections using the combination of an agent-based model (ABM) and a partial differential equation model (PDM). We use the model to examine the viral time course for different sizes of initial inocula, generating dose-response curves for peak viral load, time of viral peak, viral growth rate, infection duration, and area under the viral titer curve. We find that large initial inocula lead to short infections, but with higher viral titer peaks; and that smaller initial inocula lower the viral titer peak, but make the infection last longer.

In-host Mathematical Modelling of COVID-19 in Humans

COVID-19 pandemic has underlined the impact of emergent pathogens as a major threat to human health. The development of quantitative approaches to advance comprehension of the current outbreak is urgently needed to tackle this severe disease. Considering different starting times of infection, mathematical models are proposed to represent SARS-CoV-2 dynamics in infected patients. Based on the target cell limited model, the within-host reproductive number for SARS-CoV-2 is consistent with the broad values of human influenza infection. The best model to fit the data was including immune cell response, which suggests a slow immune response peaking between 5 to 10 days post-onset of symptoms. The model with the eclipse phase, time in a latent phase before becoming productively infected cells, was not supported. Interestingly, model simulations predict that SARS-CoV-2 may replicate very slowly in the first days after infection, and viral load could be below detection levels during the first 4 days post infection. A quantitative comprehension of SARS-CoV-2 dynamics and the estimation of standard parameters of viral infections is the key contribution of this pioneering work. These models can serve for future evaluation of control theoretical approaches to tailor new drugs against COVID-19.