Influenza interaction with cocirculating pathogens, and its impact on surveillance, pathogenesis and epidemic profile: a key role for mathematical modeling

ABSTRACT Evidence is mounting that influenza virus, a major contributor to the global disease burden, interacts with other pathogens infecting the human respiratory tract. Taking into account interactions with other pathogens may be critical to determining the real influenza burden and the full impact of public health policies targeting influenza. That necessity is particularly true for mathematical modeling studies, which have become critical in public health decision-making, despite their usually focusing on lone influenza virus acquisition and infection, thereby making broad oversimplifications regarding pathogen ecology. Herein, we review evidence of influenza virus interaction with bacteria and viruses, and the modeling studies that incorporated some of these. Despite the many studies examining possible associations between influenza and Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Neisseria meningitides , respiratory syncytial virus, human rhinoviruses, human parainfluenza viruses, etc., very few mathematical models have integrated other pathogens alongside influenza. A notable exception is the recent modeling of the pneumococcus-influenza interaction, which highlighted potential influenza-related increased pneumococcal transmission and pathogenicity. That example demonstrates the power of dynamic modeling as an approach to test biological hypotheses concerning interaction mechanisms and estimate the strength of those interactions. We explore how different interference mechanisms may lead to unexpected incidence trends and misinterpretations. Using simple transmission models, we illustrate how existing interactions might impact public health surveillance systems and demonstrate that the development of multipathogen models is essential to assess the true public health burden of influenza, and help improve planning and evaluation of control measures. Finally, we identify the public health needs, surveillance, modeling and biological challenges, and propose avenues of research for the coming years. Author Summary Influenza is a major pathogen responsible for important morbidity and mortality burdens worldwide. Mathematical models of influenza virus acquisition have been critical to understanding its epidemiology and planning public health strategies of infection control. It is increasingly clear that microbes do not act in isolation but potentially interact within the host. Hence, studying influenza alone may lead to masking effects or misunderstanding information on its transmission and severity. Herein, we review the literature on bacterial and viral species that interact with the influenza virus, interaction mechanisms, and mathematical modeling studies integrating interactions. We report evidence that, beyond the classic secondary bacterial infections, many pathogenic bacteria and viruses probably interact with influenza. Public health relevance of pathogen interactions is detailed, showing how potential misreading or a narrow outlook might lead to mistaken public health decisionmaking. We describe the role of mechanistic transmission models in investigating this complex system and obtaining insight into interactions between influenza and other pathogens. Finally, we highlight benefits and challenges in modeling, and speculate on new opportunities made possible by taking a broader view: including basic science, clinical relevance and public health

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

Phase boundaries in mixtures of membrane-forming amphiphiles and micelle-forming amphiphiles

AbstractThe phase behavior of mixtures of phospholipids and detergents in aqueous solutions is an issue of basic importance for understanding the solubilization and reconstitution of biological membranes. We review the existing knowledge on the compositionally induced reversible transformation of phospholipid bilayers into lipid-detergent mixed micelles. First, we describe the experimental protocols used for preparation of such mixtures and emphasize the scope and limitations of the various techniques used for evaluation of the microstructures of the self-assembled amphiphiles in the mixture. Subsequently, we interpret the existing data in terms of the spontaneous curvature of the amphiphiles and the finite size of the mixed micelles. These considerations lead to a general description of the phase behavior, which forms the basis for a rational approach to solubilization and reconstitution experiments

Youth Evaluations of CVE/PVE Programming in Kenya in Context

Despite the military efforts of the Kenyan, Ethiopian, and Somali Federal governments, the collaboration of the African Union Mission in Somalia (AMISOM) forces with US and coalition forces, and despite the enormous tactical and strategic set-backs that al-Shabaab has faced over the last five years, its insurgency in the Horn of Africa (HoA) remains resilient. The Kenyan government’s approach to stemming domestic recruitment to al-Shabaab remains fixated on law enforcement control and surveillance. As a result, many Somali communities are subject to daily crackdowns, interrogations, and discriminatory profiling practices whose negative effects are only heightened by current tribal and clan-based tensions in the country. Current scholarly evaluations of Kenya’s ‘Countering Violent Extremism’ (CVE) & ‘Preventing Violent Extremism’ (PVE) policies tend to adhere to three major approaches: top-down evaluations by elites repeatedly locating the protection of national security in inter-agency cooperation; bottom-up CVE/PVE evaluations placing primacy on the voices of Muslim community elders, such as imams, social workers, parents, and community leaders for interventions with at-risk youth; and social scientific evaluations of CVE/PVE policy through empirical exploration of the push and pull factors of youth recruitment into militancy. To date, there is a dearth of studies asking what Kenyan youth leaders think about CVE/PVE policies especially in light of the fact that they are often the main targets of al-Shabaab attacks. This study has one key objective: to use input from Kenyan youth to evaluate the effectiveness, suitability, and appropriateness of Kenya’s current CVE/PVE policies in order to dissect their utility, inefficiencies, and possible harms, and contribute to the academic and policy discussions on the best CVE/PVE policy mix

Predicting consequences of COVID-19 control measure de-escalation on nosocomial transmission and mortality: a modelling study in a French rehabilitation hospital

Introduction: Infection control measures are effective for nosocomial COVID-19 prevention but bear substantial health-economic costs, motivating their “de-escalation” in settings at low risk of SARS-CoV-2 transmission. Yet consequences of de-escalation are difficult to predict, particularly in light of novel variants and heterogeneous population immunity. Aim: To estimate how infection control measure de-escalation influences nosocomial COVID-19 risk. Methods: An individual-based transmission model was used to simulate SARS-CoV-2 outbreaks and control measure de-escalation in a French long-term care hospital with multi-modal control measures in place (testing and isolation, universal masking, single-occupant rooms). Estimates of COVID-19 case fatality rates (CFRs) from reported outbreaks were used to quantify excess COVID-19 mortality due to de-escalation. Results: In a population fully susceptible to infection, de-escalating both universal masking and single rooms resulted in hospital-wide outbreaks of 114 (95% CI: 103–125) excess infections, compared with five (three to seven) excess infections when de-escalating only universal masking or 15 (11–18) when de-escalating only single rooms. When de-escalating both measures and applying CFRs from the first wave of COVID-19, excess patient mortality ranged from 1.57 (1.41–1.71) to 9.66 (8.73–10.57) excess deaths/1000 patient-days. By contrast, when applying CFRs from subsequent pandemic waves and assuming susceptibility to infection among 40–60% of individuals, excess mortality ranged from 0 (0–0) to 0.92 (0.77–1.07) excess deaths/1000 patient-days. Conclusions: The de-escalation of bundled COVID-19 control measures may facilitate widespread nosocomial SARS-CoV-2 transmission. However, excess mortality is probably limited in populations at least moderately immune to infection and given CFRs resembling those estimated during the ‘post-vaccine’ era