Competitive exclusion during co-infection as a strategy to prevent the spread of a virus: A computational perspective

Prevalence and Seasonal Patterns of 16 Common Viral Respiratory Pathogens during the COVID-19 Pandemic in Gauteng Province, South Africa, 2020-2021

Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The COVID-19 pandemic resulted in widespread morbidity and mortality, but generally, the diagnosis of other respiratory viruses was limited. This study aimed to assess the prevalence of other respiratory viruses during the 2020/2021 pandemic among patients of all ages who accessed care at public healthcare facilities in Gauteng Province, South Africa. Laboratory diagnosis for respiratory viruses, with or without SARS-CoV-2, was conducted via multiplex real-time polymerase chain reactions using respiratory specimens. A total of 1776 patients were included from 1 April 2020 to 31 March 2021, of which 766 (43.1%) were positive for respiratory viruses other than SARS-CoV-2. RV (368/1776; 20.7%) was the most prevalent, followed by RSV (304/1776; 17.1%), AdV (112/1776; 6.3%) and EV (105/1776; 5.9%). hCoV-OC43 (39/1776; 2.2%) was the most prevalent common coronavirus. SARS-CoV-2 co-infections were detected in 4.8% (24/500) of patients. Only 27.1% (482/1776) of patients were admitted to high-care or intensive care units. A decrease in respiratory virus detections was observed, except for RSV, EV and hCoV-OC43. RSV prevalence increased in 2021, while influenza A/B viruses remained undetected.

Investigating epidemiological distribution (temporality and intensity) of respiratory pathogens following COVID-19 de-escalation process in Catalonia, September 2016-June 2021: Analysis of regional surveillance data

BACKGROUND

Following the low incidence rates of non-SARS-CoV-2 respiratory viruses registered during the strict lockdown enforced in the pandemic, a resurgence of several endemic viruses in Catalonia (Spain) was noted during the early summer of 2021.

OBJECTIVES

In this study, we investigated whether the circulation of non-SARS-CoV-2 respiratory viruses in Catalonia, assessed by Microbiological Reporting System of Catalonia (MRSC) and the Epidemiological Surveillance Network of Catalonia, was affected by the strict lockdown measures, as well as, the implication of the Coronavirus Disease 19 (COVID-19) de-escalation process in the late season outbreaks registered during the 2020-2021 season.

STUDY DESIGN

A retrospective comparison of epidemic patterns in the respiratory viruses' incidence, using regional public health surveillance data from MRSC, was performed between weeks 26/2016 to week 27/2021. Data were expressed as the weekly total number of test positivity for individual viruses. A segmented negative binomial regression model was conducted, with two parameters included (level and trend) for each segment of the time series (2020 pre-lockdown, 2020 post-lockdown and 2021). Results were reported as a unit changed in the strict lockdown.

RESULTS

A total of 51588 confirmed cases of the different respiratory viruses were included in the analysis, the majority were influenza cases (63.7%). An immediate reduction in the weekly number of cases was observed in 2020 after the COVID-19 outbreak for human adenovirus virus (HAdV) (β2 = -2.606; P <0.01), human parainfluenza virus (HPIV) (β2 = -3.023; P <0.01), influenza virus (IFV) (β2 = -1.259; P <0.01), but not for respiratory syncytial virus (RSV), where the number of cases remained unchanged. During 2020, a significant negative trend was found for RSV (β3 = -0.170, P <0.01), and a positive trend for HAdV (β3 = 0.075, P <0.01). During 2021, a significant reduction in the weekly number of cases was also observed for all respiratory viruses, and a borderline non-significant reduction for HPIV (β3 = -0.027; P = 0.086). Moreover, significant positive trends were found for each viral pathogen, except for influenza during 2020-2021 season, where cases remained close to zero. The respiratory viruses increased activity and their late season epidemic start particularly affected children under 6 years old.

CONCLUSIONS

Our data not only provides evidence that occurrence of different respiratory virus infections was affected by the strict lockdown taken against SARS-CoV-2 but it also shows a late resurgence of seasonal respiratory viruses' cases during the 2020-2021 season following the relaxation of COVID-19-targeted non-pharmaceutical interventions.

Epidemiology and burden of Severe Acute Respiratory Infections (SARI) in the aftermath of COVID-19 pandemic: A prospective sentinel surveillance study in a Tunisian Medical ICU, 2022/2023

BACKGROUND

Severe Acute Respiratory Infections (SARI) caused by influenza and other respiratory viruses pose significant global health challenges, and the COVID-19 pandemic has further strained healthcare systems. As the focus shifts from the pandemic to other respiratory infections, assessing the epidemiology and burden of SARI is crucial for healthcare planning and resource allocation. Aim: to understand the impact of the post-pandemic period on the epidemiology of SARI cases, clinical outcomes, and healthcare resource utilization in Tunisia.

METHODS

This is a prospective study conducted in a Tunisian MICU part of a national sentinel surveillance system, focusing on enhanced SARI surveillance. SARI cases from week 39/2022, 26 September to week 19/2023, 13 May were included, according to a standardized case definition. Samples were collected for virological RT-PCR testing, and an electronic system ensured standardized and accurate data collection. Descriptive statistics were performed to assess epidemiology, trends, and outcomes of SARI cases, and univariate/multivariate analyses to assess factors associated with mortality.

RESULTS

Among 312 MICU patients, 164 SARI cases were identified during the study period. 64(39%) RT-PCR were returned positive for at least one pathogen, with influenza A and B strains accounting for 20.7% of cases at the early stages of the influenza season. The MICU experienced a significant peak in admissions during weeks 1-11/2023, leading to resource mobilization and the creation of a surge unit. SARI cases utilized 1664/3120 of the MICU-stay days and required 1157 mechanical ventilation days. The overall mortality rate among SARI cases was 22.6%. Age, non-COPD, and ARDS were identified as independent predictors of mortality.

CONCLUSIONS

The present study identified a relatively high rate of SARI cases, with 39% positivity for at least one respiratory virus, with influenza A and B strains occurring predominantly during the early stages of the influenza season. The findings shed light on the considerable resource utilization and mortality associated with these infections, underscoring the urgency for proactive management and efficient resource allocation strategies.

Mathematical Modeling of the Lethal Synergism of Coinfecting Pathogens in Respiratory Viral Infections: A Review

Influenza A virus (IAV) infections represent a substantial global health challenge and are often accompanied by coinfections involving secondary viruses or bacteria, resulting in increased morbidity and mortality. The clinical impact of coinfections remains poorly understood, with conflicting findings regarding fatality. Isolating the impact of each pathogen and mechanisms of pathogen synergy during coinfections is challenging and further complicated by host and pathogen variability and experimental conditions. Factors such as cytokine dysregulation, immune cell function alterations, mucociliary dysfunction, and changes to the respiratory tract epithelium have been identified as contributors to increased lethality. The relative significance of these factors depends on variables such as pathogen types, infection timing, sequence, and inoculum size. Mathematical biological modeling can play a pivotal role in shedding light on the mechanisms of coinfections. Mathematical modeling enables the quantification of aspects of the intra-host immune response that are difficult to assess experimentally. In this narrative review, we highlight important mechanisms of IAV coinfection with bacterial and viral pathogens and survey mathematical models of coinfection and the insights gained from them. We discuss current challenges and limitations facing coinfection modeling, as well as current trends and future directions toward a complete understanding of coinfection using mathematical modeling and computer simulation.

Multiple cohort study of hospitalized SARS-CoV-2 in-host infection dynamics: Parameter estimates, identifiability, sensitivity and the eclipse phase profile

Within-host SARS-CoV-2 modelling studies have been published throughout the COVID-19 pandemic. These studies contain highly variable numbers of individuals and capture varying timescales of pathogen dynamics; some studies capture the time of disease onset, the peak viral load and subsequent heterogeneity in clearance dynamics across individuals, while others capture late-time post-peak dynamics. In this study, we curate multiple previously published SARS-CoV-2 viral load data sets, fit these data with a consistent modelling approach, and estimate the variability of in-host parameters including the basic reproduction number, R₀, as well as the best-fit eclipse phase profile. We find that fitted dynamics can be highly variable across data sets, and highly variable within data sets, particularly when key components of the dynamic trajectories (e.g. peak viral load) are not represented in the data. Further, we investigated the role of the eclipse phase time distribution in fitting SARS-CoV-2 viral load data. By varying the shape parameter of an Erlang distribution, we demonstrate that models with either no eclipse phase, or with an exponentially-distributed eclipse phase, offer significantly worse fits to these data, whereas models with less dispersion around the mean eclipse time (shape parameter two or more) offered the best fits to the available data across all data sets used in this work. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Cervical Microbiome in Women Infected with HPV16 and High-Risk HPVs

Human papillomavirus type 16 (HPV16) and/or high-risk (Hr-) HPV are the main causes of cervical cancer. Another element that may contribute to the development of cervical cancer is the microbiota. To date, no study has investigated the entire cervical microbiome, which consists of bacteria, fungi, and viruses. In this study, cervical samples with different histopathology (CIN1, CIN2, and CIN3), with or without HPV16 and Hr-HPVs infection, were enrolled. From bacterial community analysis, 115 bacterial species were found and separated into 2 distinct categories based on Lactobacillus abundance: Lactobacilli-dominated (LD) and non-Lactobacilli-dominated (NLD) groups. The LD group had significantly less bacterial diversity than the NLD group. In addition, the variety of bacteria was contingent on the prevalence of HPV infection. Among distinct histological groups, an abundance of L. iners (>60% of total Lactobacillus spp.) was discovered in both groups. A few fungi, e.g., C. albicans, were identified in the fungal community. The viral community analysis revealed that the presence of HPV considerably reduced the diversity of human viruses. Taken together, when we analyzed all our results collectively, we discovered that HPV infection was a significant determinant in the diversity of bacteria and human viruses in the cervix.

Human Virome in Cervix Controlled by the Domination of Human Papillomavirus

Although other co-viral infections could also be considered influencing factors, cervical human papillomavirus (HPV) infection is the main cause of cervical cancer. Metagenomics have been employed in the NGS era to study the microbial community in each habitat. Thus, in this investigation, virome capture sequencing was used to examine the virome composition in the HPV-infected cervix. Based on the amount of HPV present in each sample, the results revealed that the cervical virome of HPV-infected individuals could be split into two categories: HPV-dominated (HD; ≥60%) and non-HPV-dominated (NHD; <60%). Cervical samples contained traces of several human viral species, including the molluscum contagiosum virus (MCV), human herpesvirus 4 (HHV4), torque teno virus (TTV), and influenza A virus. When compared to the HD group, the NHD group had a higher abundance of several viruses. Human viral diversity appears to be influenced by HPV dominance. This is the first proof that the diversity of human viruses in the cervix is impacted by HPV abundance. However, more research is required to determine whether human viral variety and the emergence of cancer are related.

Analysis of the competition among viral strains using a temporal interaction-driven contagion model

The temporal dynamics of social interactions were shown to influence the spread of disease. Here, we model the conditions of progression and competition for several viral strains, exploring various levels of cross-immunity over temporal networks. We use our interaction-driven contagion model and characterize, using it, several viral variants. Our results, obtained on temporal random networks and on real-world interaction data, demonstrate that temporal dynamics are crucial to determining the competition results. We consider two and three competing pathogens and show the conditions under which a slower pathogen will remain active and create a second wave infecting most of the population. We then show that when the duration of the encounters is considered, the spreading dynamics change significantly. Our results indicate that when considering airborne diseases, it might be crucial to consider the duration of temporal meetings to model the spread of pathogens in a population.

Superinfection exclusion: A viral strategy with short-term benefits and long-term drawbacks

Viral superinfection occurs when multiple viral particles subsequently infect the same host. In nature, several viral species are found to have evolved diverse mechanisms to prevent superinfection (superinfection exclusion) but how this strategic choice impacts the fate of mutations in the viral population remains unclear. Using stochastic simulations, we find that genetic drift is suppressed when superinfection occurs, thus facilitating the fixation of beneficial mutations and the removal of deleterious ones. Interestingly, we also find that the competitive (dis)advantage associated with variations in life history parameters is not necessarily captured by the viral growth rate for either infection strategy. Putting these together, we then show that a mutant with superinfection exclusion will easily overtake a superinfecting population even if the latter has a much higher growth rate. Our findings suggest that while superinfection exclusion can negatively impact the long-term adaptation of a viral population, in the short-term it is ultimately a winning strategy.