Pursuing equitable access to vaccines for the next epidemic (preprint)

To mitigate the pandemic stemming from COVID-19, numerous nations have initiated extensive vaccination campaigns for their citizens since late 2020. While affluent countries have predominantly received vaccine allocations, fewer doses have been dispatched to nations with lower average incomes. This unequal distribution not only widens the disparity between wealthy and impoverished regions but also prolongs the pandemic, evident in the emergence of new viral variants. Our research delves into the correlation between the duration of the pandemic and the timing of vaccine distribution between two countries with migratory ties. By using a pair of coupled Susceptible- Infected-Recovered-Deceased (SIRD) models incorporating vaccination data, we demonstrate that timely sharing of vaccines benefits both nations, regardless of the presence of viral variants. This underscores that in the realm of vaccine distribution, self-interest and altruism are not mutually exclusive.

The Rise and Fall of Omicron BA.1 Variant as Seen in Wastewater Supports Epidemiological Model Predictions (preprint)

The COVID-19 pandemic caused by the SARS-CoV-2 virus has inflicted significant mortality and morbidity worldwide. Continuous virus mutations have led to the emergence of new variants. The Omicron BA.1 sub-lineage prevailed as the dominant variant globally at the beginning of 2022 but was subsequently replaced by BA.2 in numerous countries. Wastewater-based epidemiology (WBE) offers an efficient tool for capturing viral shedding from infected individuals, enabling early detection of potential pandemic outbreaks without relying solely on community cooperation and clinical testing resources. This study integrated RT-qPCR assays for detecting general SARS-CoV-2 and its variants levels in wastewater into a modified triple susceptible-infected-recovered-susceptible (SIRS) model. The emergence of the Omicron-BA.1 variant was observed, replacing the presence of its predecessor, the Delta variant. Comparative analysis between the wastewater data and the modified SIRS model effectively described the BA.1 and subsequent BA.2 waves, with the decline of the Delta variant aligning with its diminished presence below the detection threshold in wastewater. This study demonstrates the potential of WBE as a valuable tool for future pandemics. Furthermore, by analyzing the sensitivity of different variants to model parameters, we are able to deduce real-life values of cross-variant immunity probabilities, emphasizing the asymmetry in their strength.

Modeling correlated uncertainties in stochastic compartmental models (preprint)

We consider compartmental models of communicable disease with uncertain contact rates. Stochastic fluctuations are often added to the contact rate to account for uncertainties. White noise, which is the typical choice for the fluctuations, leads to significant underestimation of the disease severity. Here, starting from reasonable assumptions on the social behavior of individuals, we model the contacts as a Markov process which takes into account the temporal correlations present in human social activities. Consequently, we show that the mean-reverting Ornstein-Uhlenbeck (OU) process is the correct model for the stochastic contact rate. We demonstrate the implication of our model on two examples: a Susceptibles-Infected-Susceptibles (SIS) model and a Susceptibles-Exposed-Infected-Removed (SEIR) model of the COVID-19 pandemic. In particular, we observe that both compartmental models with white noise uncertainties undergo transitions that lead to the systematic underestimation of the spread of the disease. In contrast, modeling the contact rate with the OU process significantly hinders such unrealistic noise-induced transitions. For the SIS model, we derive its stationary probability density analytically, for both white and correlated noise. This allows us to give a complete description of the model's asymptotic behavior as a function of its bifurcation parameters, i.e., the basic reproduction number, noise intensity, and correlation time. For the SEIR model, where the probability density is not available in closed form, we study the transitions using Monte Carlo simulations. Our study underscores the necessity of temporal correlations in stochastic compartmental models and the need for more empirical studies that would systematically quantify such correlations.

Genetic Variants within SARS-CoV-2 Human Receptor Genes May Contribute to Variable Disease Outcomes in Different Ethnicities.

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved into a global pandemic, with an alarming infectivity and mortality rate. Studies have examined genetic effects on SARS-CoV-2 disease susceptibility and severity within Eurasian populations. These studies identified contrasting effects on the severity of disease between African populations. Genetic factors can explain some of the diversity observed within SARS-CoV-2 disease susceptibility and severity. Single nucleotide polymorphisms (SNPs) within the SARS-CoV-2 receptor genes have demonstrated detrimental and protective effects across ethnic groups. For example, the TT genotype of rs2285666 (Angiotensin-converting enzyme 2 (ACE2)) is associated with the severity of SARS-CoV-2 disease, which is found at higher frequency within Asian individuals compared to African and European individuals. In this study, we examined four SARS-CoV-2 receptors, ACE2, Transmembrane serine protease 2 (TMPRSS2), Neuropilin-1 (NRP1), and Basigin (CD147). A total of 42 SNPs located within the four receptors were reviewed: ACE2 (12), TMPRSS2 (10), BSG (CD147) (5), and NRP1 (15). These SNPs may be determining factors for the decreased disease severity observed within African individuals. Furthermore, we highlight the absence of genetic studies within the African population and emphasize the importance of further research. This review provides a comprehensive summary of specific variants within the SARS-CoV-2 receptor genes, which can offer a better understanding of the pathology of the SARS-CoV-2 pandemic and identify novel potential therapeutic targets.

The multifactorial complexities of autoimmune development in Pemphigus vulgaris: Critical evaluation of the role of environmental and lifestyle "exposome" factors.

Pemphigus vulgaris (PV) is a potentially life-threatening blistering disorder characterized by autoantibodies directed against cell-cell adhesion molecules that serves as an excellent model to study human autoimmune development. Numerous studies have identified specific Human Leukocyte Antigen (HLA) genes, in particular DRB1*0402 and DQB1*0503, that confer disease risk. Although HLA is required, it is not sufficient for the initiation of disease. As with all autoimmune diseases, the etio-pathogenesis of PV is complex, meaning it is multifactorial. Susceptibility is polygenic, and the search for non-HLA disease-linked genes continues. Moreover, twin studies across autoimmune conditions indicate that non-genetic environmental and lifestyle factors, which can be collectively grouped under the term "exposome", are also major contributors to disease development. The literature presents evidence for the potential role of multiple triggers such as medications, infections, stress, diet, immunizations, and sleep to influence the etiology, pathophysiology, and prognosis of PV. However, a clear understanding of the degree to which specific factors impact PV is lacking. In this investigation, we comprehensively review the environmental elements listed above and consider the strength of evidence for these factors. The overall goals of this work are to provide greater insights into the factors that influence disease susceptibility, disease development and disease course and ultimately help to better guide clinicians and inform patients in the management of PV.

Coronavirus Disease 2019, Diabetes, and Inflammation: A Systemic Review.

People with cardiometabolic diseases [namely type 2 diabetes (T2D), obesity, or metabolic syndrome] are more susceptible to coronavirus disease 2019 (COVID-19) infection and endure more severe illness and poorer outcomes. Hyperinflammation has been suggested as a common pathway for both diseases. To examine the role of inflammatory biomarkers shared between COVID-19 and cardiometabolic diseases, we reviewed and evaluated published data using PubMed, SCOPUS, and World Health Organization COVID-19 databases for English articles from December 2019 to February 2022. Of 248 identified articles, 50 were selected and included. We found that people with diabetes or obesity have (i) increased risk of COVID-19 infection; (ii) increased risk of hospitalization (those with diabetes have a higher risk of intensive care unit admissions) and death; and (iii) heightened inflammatory and stress responses (hyperinflammation) to COVID-19, which worsen their prognosis. In addition, COVID-19-infected patients have a higher risk of developing T2D, especially if they have other comorbidities. Treatments controlling blood glucose levels and or ameliorating the inflammatory response may be valuable for improving clinical outcomes in these patient populations. In conclusion, it is critical for health care providers to clinically evaluate hyperinflammatory states to drive clinical decisions for COVID-19 patients.

Perceptive movement of susceptible individuals with memory.

The perception of susceptible individuals naturally lowers the transmission probability of an infectious disease but has been often ignored. In this paper, we formulate and analyze a diffusive SIS epidemic model with memory-based perceptive movement, where the perceptive movement describes a strategy for susceptible individuals to escape from infections. We prove the global existence and boundedness of a classical solution in an n-dimensional bounded smooth domain. We show the threshold-type dynamics in terms of the basic reproduction number [Formula: see text]: when [Formula: see text], the unique disease-free equilibrium is globally asymptotically stable; when [Formula: see text], there is a unique constant endemic equilibrium, and the model is uniformly persistent. Numerical analysis exhibits that when [Formula: see text], solutions converge to the endemic equilibrium for slow memory-based movement and they converge to a stable periodic solution when memory-based movement is fast. Our results imply that the memory-based movement cannot determine the extinction or persistence of infectious disease, but it can change the persistence manner.

Murine Coronavirus Disease 2019 Lethality Is Characterized by Lymphoid Depletion Associated with Suppressed Antigen-Presenting Cell Functionality.

The disease severity of coronavirus disease 2019 (COVID-19) varies considerably from asymptomatic to serious, with fatal complications associated with dysregulation of innate and adaptive immunity. Lymphoid depletion in lymphoid tissues and lymphocytopenia have both been associated with poor disease outcomes in patients with COVID-19, but the mechanisms involved remain elusive. In this study, human angiotensin-converting enzyme 2 (hACE2) transgenic mouse models susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection were used to investigate the characteristics and determinants of lethality associated with the lymphoid depletion observed in SARS-CoV-2 infection. The lethality of Wuhan SARS-CoV-2 infection in K18-hACE2 mice was characterized by severe lymphoid depletion and apoptosis in lymphoid tissues related to fatal neuroinvasion. The lymphoid depletion was associated with a decreased number of antigen-presenting cells (APCs) and their suppressed functionality below basal levels. Lymphoid depletion with reduced APC function was a specific feature observed in SARS-CoV-2 infection but not in influenza A infection and had the greatest prognostic value for disease severity in murine COVID-19. Comparison of transgenic mouse models resistant and susceptible to SARS-CoV-2 infection revealed that suppressed APC function could be determined by the hACE2 expression pattern and interferon-related signaling. Thus, we demonstrated that lymphoid depletion associated with suppressed APC function characterizes the lethality of COVID-19 mouse models. Our data also suggest a potential therapeutic approach to prevent the severe progression of COVID-19 by enhancing APC functionality.

Drivers of SARS-CoV-2 testing behaviour: a modelling study using nationwide testing data in England.

During the COVID-19 pandemic, national testing programmes were conducted worldwide on unprecedented scales. While testing behaviour is generally recognised as dynamic and complex, current literature demonstrating and quantifying such relationships is scarce, despite its importance for infectious disease surveillance and control. Here, we characterise the impacts of SARS-CoV-2 transmission, disease susceptibility/severity, risk perception, and public health measures on SARS-CoV-2 PCR testing behaviour in England over 20 months of the pandemic, by linking testing trends to underlying epidemic trends and contextual meta-data within a systematic conceptual framework. The best-fitting model describing SARS-CoV-2 PCR testing behaviour explained close to 80% of the total deviance in NHS test data. Testing behaviour showed complex associations with factors reflecting transmission level, disease susceptibility/severity (e.g. age, dominant variant, and vaccination), public health measures (e.g. testing strategies and lockdown), and associated changes in risk perception, varying throughout the pandemic and differing between infected and non-infected people.

Impact of SARS-CoV-2 infection and COVID-19 on patients with inborn errors of immunity.

Since the arrival of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, its characterization as a novel human pathogen, and the resulting coronavirus disease 2019 (COVID-19) pandemic, over 6.5 million people have died worldwide-a stark and sobering reminder of the fundamental and nonredundant roles of the innate and adaptive immune systems in host defense against emerging pathogens. Inborn errors of immunity (IEI) are caused by germline variants, typically in single genes. IEI are characterized by defects in development and/or function of cells involved in immunity and host defense, rendering individuals highly susceptible to severe, recurrent, and sometimes fatal infections, as well as immune dysregulatory conditions such as autoinflammation, autoimmunity, and allergy. The study of IEI has revealed key insights into the molecular and cellular requirements for immune-mediated protection against infectious diseases. Indeed, this has been exemplified by assessing the impact of SARS-CoV-2 infection in individuals with previously diagnosed IEI, as well as analyzing rare cases of severe COVID-19 in otherwise healthy individuals. This approach has defined fundamental aspects of mechanisms of disease pathogenesis, immunopathology in the context of infection with a novel pathogen, and therapeutic options to mitigate severe disease. This review summarizes these findings and illustrates how the study of these rare experiments of nature can inform key features of human immunology, which can then be leveraged to improve therapies for treating emerging and established infectious diseases.

Autoantibodies targeting type I interferons: Prevalence, mechanisms of induction, and association with viral disease susceptibility.

The type I IFN (IFN-I) system is essential to limit severe viral disease in humans. Thus, IFN-I deficiencies are associated with serious life-threatening infections. Remarkably, some rare individuals with chronic autoimmune diseases develop neutralizing autoantibodies (autoAbs) against IFN-Is thereby compromising their own innate antiviral defenses. Furthermore, the prevalence of anti-IFN-I autoAbs in apparently healthy individuals increases with age, such that ∼4% of those over 70 years old are affected. Here, I review the literature on factors that may predispose individuals to develop anti-IFN-I autoAbs, such as reduced self-tolerance caused by defects in the genes AIRE, NFKB2, and FOXP3 (among others), or by generally impaired thymus function, including thymic involution in the elderly. In addition, I discuss the hypothesis that predisposed individuals develop anti-IFN-I autoAbs following "autoimmunization" with IFN-Is generated during some acute viral infections, systemic inflammatory events, or chronic IFN-I exposure. Finally, I highlight the enhanced susceptibility that individuals with anti-IFN-I autoAbs appear to have towards viral diseases such as severe COVID-19, influenza, or herpes (e.g., varicella-zoster virus, herpes simplex virus, cytomegalovirus), as well as adverse reactions to live-attenuated vaccines. Understanding the mechanisms underlying development and consequences of anti-IFN-I autoAbs will be key to implementing effective prophylactic and therapeutic measures.

VA's implementation of universal screening and evaluation for the suicide risk identification program in November 2020 -Implications for Veterans with prior mental health needs.

IMPORTANCE: United States Veterans are at higher risk for suicide than non-Veterans. Veterans in rural areas are at higher risk than their urban counterparts. The coronavirus pandemic intensified risk factors for suicide, especially in rural areas. OBJECTIVE: To examine associations between Veterans Health Administration's (VA's) universal suicide risk screening, implemented November 2020, and likelihood of Veterans being screened, and receiving follow-up evaluations, as well as post-screening suicidal behavior among patients who used VA mental health services in 2019. METHODS: VA's Suicide Risk Identification Strategy (Risk ID), implemented October 2018, is a national, standardized process for suicide risk screening and evaluation. In November 2020, VA expanded Risk ID, requiring annual universal suicide screening. As such, we are evaluating outcomes of interest before and after the start of the policy among Veterans who had ≥1 VA mental health care visit in 2019 (n = 1,654,180; rural n = 485,592, urban n = 1,168,588). Regression-adjusted outcomes were compared 6 months pre-universal screening and 6, 12 and 13 months post-universal screening implementation. MEASURES: Item-9 on the Patient Health Questionnaire (I-9, VA's historic suicide screener), Columbia- Suicide Severity Risk Scale (C-SSRS) Screener, VA's Comprehensive Suicide Risk Evaluation (CSRE), and Suicide Behavior and Overdose Report (SBOR). RESULTS: 12 months post-universal screening implementation, 1.3 million Veterans (80% of the study cohort) were screened or evaluated for suicide risk, with 91% the sub-cohort who had at least one mental health visit in the 12 months post-universal screening implementation period were screened or evaluated. At least 20% of the study cohort was screened outside of mental health care settings. Among Veterans with positive screens, 80% received follow-up CSREs. Covariate-adjusted models indicated that an additional 89,160 Veterans were screened per month via the C-SSRS and an additional 30,106 Veterans/month screened via either C-SSRS or I-9 post-universal screening implementation. Compared to their urban counterparts, 7,720 additional rural Veterans/month were screened via the C-SSRS and 9,226 additional rural Veterans/month were screened via either the C-SSRS or I-9. CONCLUSION: VA's universal screening requirement via VA's Risk ID program increased screening for suicide risk among Veterans with mental health care needs. A universal approach to screening may be particularly advantageous for rural Veterans, who are typically at higher risk for suicide but have fewer interactions with the health care system, particularly within specialty care settings, due to higher barriers to accessing care. Insights from this program offer valuable insights for health systems nationwide.

Modeling transmission of pathogens in healthcare settings.

PURPOSE OF REVIEW: Mathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings. RECENT FINDINGS: The COVID-19 pandemic has led to a dramatic shift in the modeling landscape as the healthcare community has raced to characterize the transmission dynamics of SARS-CoV-2 and develop effective interventions. Inequities in COVID-19 outcomes have inspired new efforts to quantify how structural bias impacts both health outcomes and model parameterization. Meanwhile, developments in the modeling of methicillin-resistant Staphylococcus aureus, Clostridioides difficile, and other nosocomial infections continue to advance. Machine learning continues to be applied in novel ways, and genomic data is being increasingly incorporated into modeling efforts. SUMMARY: As the type and amount of data continues to grow, mathematical, statistical, and computational modeling will play an increasing role in healthcare epidemiology. Gaps remain in producing models that are generalizable to a variety of time periods, geographic locations, and populations. However, with effective communication of findings and interdisciplinary collaboration, opportunities for implementing models for clinical decision-making and public health decision-making are bound to increase.

Mobility-Guided Estimation of COVID-19 Transmission Rates.

It is of critical importance to estimate changing disease-transmission rates and their dependence on population mobility. A common approach to this problem involves fitting daily transmission rates using a susceptible-exposed-infected-recovered-(SEIR) model (regularizing to avoid overfitting) and then computing the relationship between the estimated transmission rate and mobility. Unfortunately, there are often several very different transmission-rate trajectories that can fit the reported cases well, meaning that the choice of regularization determines the final solution (and thus the mobility-transmission rate relationship) selected by the SEIR model. Moreover, the classical approaches to regularization-penalizing the derivative of the transmission rate trajectory-do not correspond to realistic properties of pandemic spread. Consequently, models fitted using derivative-based regularization are often biased toward underestimating the current transmission rate and future deaths. In this work, we propose mobility-driven regularization of the SEIR transmission rate trajectory. This method rectifies the artificial regularization problem, produces more accurate and unbiased forecasts of future deaths, and estimates a highly interpretable relationship between mobility and the transmission rate. For this analysis, mobility data related to the coronavirus disease 2019 pandemic was collected by Safegraph (San Francisco, California) from major US cities between March and August 2020.

Simplified within-host and Dose-response Models of SARS-CoV-2.

Understanding the mechanistic dynamics of transmission is key to designing more targeted and effective interventions to limit the spread of infectious diseases. A well-described within-host model allows explicit simulation of how infectiousness changes over time at an individual level. This can then be coupled with dose-response models to investigate the impact of timing on transmission. We collected and compared a range of within-host models used in previous studies and identified a minimally-complex model that provides suitable within-host dynamics while keeping a reduced number of parameters to allow inference and limit unidentifiability issues. Furthermore, non-dimensionalised models were developed to further overcome the uncertainty in estimates of the size of the susceptible cell population, a common problem in many of these approaches. We will discuss these models, and their fit to data from the human challenge study (see Killingley et al. (2022)) for SARS-CoV-2 and the model selection results, which has been performed using ABC-SMC. The parameter posteriors have then used to simulate viral-load based infectiousness profiles via a range of dose-response models, which illustrate the large variability of the periods of infection window observed for COVID-19.

Comparative performance of between-population vaccine allocation strategies with applications for emerging pandemics.

Vaccine allocation decisions during emerging pandemics have proven to be challenging due to competing ethical, practical, and political considerations. Complicating decision making, policy makers need to consider vaccine allocation strategies that balance needs both within and between populations. When vaccine stockpiles are limited, doses should be allocated in locations to maximize their impact. Using a susceptible-exposed-infectious-recovered (SEIR) model we examine optimal vaccine allocation decisions across two populations considering the impact of characteristics of the population (e.g., size, underlying immunity, heterogeneous risk structure, interaction), vaccine (e.g., vaccine efficacy), pathogen (e.g., transmissibility), and delivery (e.g., varying speed and timing of rollout). Across a wide range of characteristics considered, we find that vaccine allocation proportional to population size (i.e., pro-rata allocation) performs either better or comparably to nonproportional allocation strategies in minimizing the cumulative number of infections. These results may argue in favor of sharing of vaccines between locations in the context of an epidemic caused by an emerging pathogen, where many epidemiologic characteristics may not be known.

Development of animal models for emerging infectious diseases by breaking the barrier of species susceptibility to human pathogens.

Outbreaks of emerging infectious diseases pose a serious threat to public health security, human health and economic development. After an outbreak, an animal model for an emerging infectious disease is urgently needed for studying the etiology, host immune mechanisms and pathology of the disease, evaluating the efficiency of vaccines or drugs against infection, and minimizing the time available for animal model development, which is usually hindered by the nonsusceptibility of common laboratory animals to human pathogens. Thus, we summarize the technologies and methods that induce animal susceptibility to human pathogens, which include viral receptor humanization, pathogen-targeted tissue humanization, immunodeficiency induction and screening for naturally susceptible animal species. Furthermore, the advantages and deficiencies of animal models developed using each method were analyzed, and these will guide the selection of susceptible animals and potentially reduce the time needed to develop animal models during epidemics.

Autoimmunity in Down's syndrome via cytokines, CD4 T cells and CD11c+ B cells.

Down's syndrome (DS) presents with a constellation of cardiac, neurocognitive and growth impairments. Individuals with DS are also prone to severe infections and autoimmunity including thyroiditis, type 1 diabetes, coeliac disease and alopecia areata1,2. Here, to investigate the mechanisms underlying autoimmune susceptibility, we mapped the soluble and cellular immune landscape of individuals with DS. We found a persistent elevation of up to 22 cytokines at steady state (at levels often exceeding those in patients with acute infection) and detected basal cellular activation: chronic IL-6 signalling in CD4 T cells and a high proportion of plasmablasts and CD11c+TbethighCD21low B cells (Tbet is also known as TBX21). This subset is known to be autoimmune-prone and displayed even greater autoreactive features in DS including receptors with fewer non-reference nucleotides and higher IGHV4-34 utilization. In vitro, incubation of naive B cells in the plasma of individuals with DS or with IL-6-activated T cells resulted in increased plasmablast differentiation compared with control plasma or unstimulated T cells, respectively. Finally, we detected 365 auto-antibodies in the plasma of individuals with DS, which targeted the gastrointestinal tract, the pancreas, the thyroid, the central nervous system, and the immune system itself. Together, these data point to an autoimmunity-prone state in DS, in which a steady-state cytokinopathy, hyperactivated CD4 T cells and ongoing B cell activation all contribute to a breach in immune tolerance. Our findings also open therapeutic paths, as we demonstrate that T cell activation is resolved not only with broad immunosuppressants such as Jak inhibitors, but also with the more tailored approach of IL-6 inhibition.