Assessing the potential impact of transmission during prolonged viral shedding on the effect of lockdown relaxation on COVID-19.

A key parameter in epidemiological modeling which characterizes the spread of an infectious disease is the generation time, or more generally the distribution of infectiousness as a function of time since infection. There is increasing evidence supporting a prolonged viral shedding window for COVID-19, but the transmissibility in this phase is unclear. Based on this, we develop a generalized Susceptible-Exposed-Infected-Resistant (SEIR) model including an additional compartment of chronically infected individuals who can stay infectious for a longer duration than the reported generation time, but with infectivity reduced to varying degrees. Using the incidence and fatality data from different countries, we first show that such an assumption also yields a plausible model in explaining the data observed prior to the easing of the lockdown measures (relaxation). We then test the predictive power of this model for different durations and levels of prolonged infectiousness using the incidence data after the introduction of relaxation in Switzerland, and compare it with a model without the chronically infected population to represent the models conventionally used. We show that in case of a gradual easing on the lockdown measures, the predictions of the model including the chronically infected population vary considerably from those obtained under a model in which prolonged infectiousness is not taken into account. Although the existence of a chronically infected population still remains largely hypothetical, we believe that our results provide tentative evidence to consider a chronically infected population as an alternative modeling approach to better interpret the transmission dynamics of COVID-19

Sex and gender in infection and immunity: addressing the bottlenecks from basic science to public health and clinical applications

Although sex and gender are recognized as major determinants of health and immunity, their role israrely considered in clinical practice and public health. We identified six bottlenecks preventing theinclusion of sex and gender considerations from basic science to clinical practice, precision medicineand public health policies. (i) A terminology-related bottleneck, linked to the definitions of sex andgender themselves, and the lack of consensus on how to evaluate gender. (ii) A data-relatedbottleneck, due to gaps in sex-disaggregated data, data on trans/non-binary people and genderidentity. (iii) A translational bottleneck, limited by animal models and the underrepresentation ofgender minorities in biomedical studies. (iv) A statistical bottleneck, with inappropriate statisticalanalyses and results interpretation. (v) An ethical bottleneck posed by the underrepresentation ofpregnant people and gender minorities in clinical studies. (vi) A structural bottleneck, as systemicbias and discriminations affect not only academic research but also decision makers. We specifyguidelines for researchers, scientific journals, funding agencies and academic institutions to addressthese bottlenecks. Following such guidelines will support the development of more efficient andequitable care strategies for all

Understanding the decline of incident, active tuberculosis in people with HIV in Switzerland

BACKGROUND: People with human immunodeficiency virus type 1 (HIV) (PWH) are frequently coinfected with Mycobacterium tuberculosis (MTB) and at risk for progressing from asymptomatic latent TB infection (LTBI) to active tuberculosis (TB). LTBI testing and preventive treatment (TB specific prevention) are recommended, but its efficacy in low transmission settings is unclear. METHODS: We included PWH enrolled from 1988 to 2022 in the Swiss HIV Cohort study (SHCS). The outcome, incident TB, was defined as TB ≥6 months after SHCS inclusion. We assessed its risk factors using a time-updated hazard regression, modeled the potential impact of modifiable factors on TB incidence, performed mediation analysis to assess underlying causes of time trends, and evaluated preventive measures. RESULTS: In 21,528 PWH, LTBI prevalence declined from 15.1% in 2001 to 4.6% in 2021. Incident TB declined from 90.8 cases/1000 person-years in 1989 to 0.1 in 2021. A positive LTBI test showed a higher risk for incident TB (HR 9.8, 5.8-16.5) but only 10.5% of PWH with incident TB were tested positive. Preventive treatment reduced the risk in LTBI test positive PWH for active TB (relative risk reduction, 28.1%, absolute risk reduction 0.9%). On population level, the increase of CD4 T-cells and reduction of HIV viral load were the main driver of TB decrease. CONCLUSIONS: TB specific prevention is effective in selected patient groups. On a population level, control of HIV-1 remains the most important factor for incident TB reduction. Accurate identification of PWH at highest risk for TB is an unmet clinical need

Sex and gender in infection and immunity: addressing the bottlenecks from basic science to public health and clinical applications

Although sex and gender are recognized as major determinants of health and immunity, their role is rarely considered in clinical practice and public health. We identified six bottlenecks preventing the inclusion of sex and gender considerations from basic science to clinical practice, precision medicine and public health policies. (i) A terminology-related bottleneck, linked to the definitions of sex and gender themselves, and the lack of consensus on how to evaluate gender. (ii) A data-related bottleneck, due to gaps in sex-disaggregated data, data on trans/non-binary people and gender identity. (iii) A translational bottleneck, limited by animal models and the underrepresentation of gender minorities in biomedical studies. (iv) A statistical bottleneck, with inappropriate statistical analyses and results interpretation. (v) An ethical bottleneck posed by the underrepresentation of pregnant people and gender minorities in clinical studies. (vi) A structural bottleneck, as systemic bias and discriminations affect not only academic research but also decision makers. We specify guidelines for researchers, scientific journals, funding agencies and academic institutions to address these bottlenecks. Following such guidelines will support the development of more efficient and equitable care strategies for all

Sex and gender in infection and immunity: addressing the bottlenecks from basic science to public health and clinical applications.

Although sex and gender are recognized as major determinants of health and immunity, their role is rarely considered in clinical practice and public health. We identified six bottlenecks preventing the inclusion of sex and gender considerations from basic science to clinical practice, precision medicine and public health policies. (i) A terminology-related bottleneck, linked to the definitions of sex and gender themselves, and the lack of consensus on how to evaluate gender. (ii) A data-related bottleneck, due to gaps in sex-disaggregated data, data on trans/non-binary people and gender identity. (iii) A translational bottleneck, limited by animal models and the underrepresentation of gender minorities in biomedical studies. (iv) A statistical bottleneck, with inappropriate statistical analyses and results interpretation. (v) An ethical bottleneck posed by the underrepresentation of pregnant people and gender minorities in clinical studies. (vi) A structural bottleneck, as systemic bias and discriminations affect not only academic research but also decision makers. We specify guidelines for researchers, scientific journals, funding agencies and academic institutions to address these bottlenecks. Following such guidelines will support the development of more efficient and equitable care strategies for all

Quantifying the impact of treatment history on plasmid-mediated resistance evolution in human gut microbiota

To understand how antibiotic use affects the risk of a resistant infection, we present a computational model of the population dynamics of gut microbiota including antibiotic resistance-conferring plasmids. We then describe how this model is parameterized based on published microbiota data. Finally, we investigate how treatment history affects the prevalence of resistance among opportunistic enterobacterial pathogens. We simulate treatment histories and identify which properties of prior antibiotic exposure are most influential in determining the prevalence of resistance. We find that resistance prevalence can be predicted by 3 properties, namely the total days of drug exposure, the duration of the drug-free period after last treatment, and the center of mass of the treatment pattern. Overall this work provides a framework for capturing the role of the microbiome in the selection of antibiotic resistance and highlights the role of treatment history for the prevalence of resistance

Hosts, microbiomes, and the evolution of critical windows

The absence of microbial exposure early in life leaves individuals vulnerable to immune overreaction later in life, manifesting as immunopathology, autoimmunity, or allergies. A key factor is thought to be a "critical window" during which the host's immune system can "learn" tolerance, and beyond which learning is no longer possible. Animal models indicate that many mechanisms have evolved to enable critical windows, and that their time limits are distinct and consistent. Such a variety of mechanisms, and precision in their manifestation suggest the outcome of strong evolutionary selection. To strengthen our understanding of critical windows, we explore their underlying evolutionary ecology using models encompassing demographic and epidemiological transitions, identifying the length of the critical window that would maximize fitness in different environments. We characterize how direct effects of microbes on host mortality, but also indirect effects via microbial ecology, will drive the optimal length of the critical window. We find that indirect effects such as magnitude of transmission, duration of infection, rates of reinfection, vertical transmission, host demography, and seasonality in transmission all have the effect of redistributing the timing and/or likelihood of encounters with microbial taxa across age, and thus increasing or decreasing the optimal length of the critical window. Declining microbial population abundance and diversity are predicted to result in increases in immune dysfunction later in life. We also make predictions for the length of the critical window across different taxa and environments. Overall, our modeling efforts demonstrate how critical windows will be impacted over evolution as a function of both host-microbiome/pathogen interactions and dispersal, raising central questions about potential mismatches between these evolved systems and the current loss of microbial diversity and/or increases in infectious disease