Epidemic models are invaluable tools to understand and implement strategies to control the spread of infectious diseases, as well as to inform public health policies and resource allocation. However, current modeling approaches have limitations that reduce their practical utility, such as the exclusion of human behavioral change in response to the epidemic or ignoring the presence of undetected infectious individuals in the population. These limitations became particularly evident during the COVID-19 pandemic, underscoring the need for more accurate and informative models. To address these challenges, we develop a novel Bayesian epidemic modeling framework to better capture the complexities of disease spread by incorporating behavioral responses and undetected infections. In particular, our framework makes three contributions: (1) leveraging additional data on hospitalizations and deaths in modeling the disease dynamics, (2) accounting for data uncertainty arising from the large presence of asymptomatic and undetected infections, and (3) allowing the population behavioral change to be dynamically influenced by multiple data sources (cases and deaths). We thoroughly investigate the properties of the proposed model via simulation, and illustrate its utility on COVID-19 data from Montréal and Miami.

Sex differences exist in the immune responses to infections and in the prevalence and severity of autoimmune and allergic diseases. These sex differences may be caused by sex hormones and/or variable inactivation of X chromosome genes. The 4 core genotypes mice allow for distinction between the effects of sex hormones and chromosomes on physiology and disease pathology. In the FCG mouse model, the Sry gene is deleted from the Y chromosome and inserted into chromosome 3 as multiple copies of a transgene, allowing for phenotypic male and female mice with XX and XY chromosomes. We sought to investigate the role of sex hormones and chromosomes in respiratory syncytial virus infection and allergen-induced airway inflammation. However, in performing these studies, we found that the immune response in FCG males (XXM and XYM) was significantly blunted. XXF and XYF had 4-fold more CD3+CD4+ T cells and over 7-fold more CD3+CD8+ T cells compared to XXM and XYM in the lungs following stimulus. CD4+ and CD8+ T cells were also significantly decreased in XXM and XYM mice in the lungs, spleen, and peripheral blood at baseline with no effect on B cells, NK cells, or myeloid cells. Thymic T cell numbers were similar among groups, and bone marrow progenitors were unchanged between groups. Overall, the translocation of Sry to chromosome 3 resulted in dramatically decreased immune responses to RSV infection and allergen-challenge, indicating that FCG male mice do not mount appropriate immune responses to a respiratory virus infection.

Silicosis is caused by occupational exposure to respirable crystalline silica (CS) particles and is characterized by chronic inflammation and progressive pulmonary fibrosis. To date, the underlying mechanisms of silicosis remain poorly understood and effective treatment options are limited. Stimulator of interferon genes (STING) has been identified by us and others as a critical initiator of inflammatory responses and lung damage during silicosis. In this study, we investigate the role of STING pathway in regulating programmed death-ligand 1 (PD-L1) expression and epithelial-mesenchymal transition (EMT) during silicosis, and further explore the underlying mechanisms and profibrotic significance of this axis. Our findings demonstrate that STING signaling regulates PD-L1 expression and promotes EMT progression in the silicosis mouse model. Mechanistically, CS-activated macrophage STING signaling induces PD-L1 expression and drives the EMT process in alveolar epithelial cells by the TGF-β1/Smad2/3 signaling pathway. Furthermore, we demonstrate that epithelial PD-L1 mediates CS-induced EMT and fibrogenic activity in alveolar epithelia. Collectively, these findings extend our mechanistic understanding of how STING promotes silicotic fibrosis and identify potential therapeutic targets for silicosis.