Near real time monitoring and forecasting for COVID-19 situational awareness.

In the opening months of the pandemic, the need for situational awareness was urgent. Forecasting models such as the Susceptible-Infectious-Recovered (SIR) model were hampered by limited testing data and key information on mobility, contact tracing, and local policy variations would not be consistently available for months. New case counts from sources like John Hopkins University and the NY Times were systematically reliable. Using these data, we developed the novel COVID County Situational Awareness Tool (CCSAT) for reliable monitoring and decision support. In CCSAT, we developed a retrospective seven-day moving window semantic map of county-level disease magnitude and acceleration that smoothed noisy daily variations. We also developed a novel Bayesian model that reliably forecasted county-level magnitude and acceleration for the upcoming week based on population and new case count data. Together these formed a robust operational update including county-level maps of new case rate changes, estimates of new cases in the upcoming week, and measures of model reliability. We found CCSAT provided stable, reliable estimates across the seven-day time window, with the greatest errors occurring in cases of anomalous, single day spikes. In this paper, we provide CCSAT details and apply it to a single week in June 2020.

Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling.

The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-reporting of active cases, thus introducing bias in the estimation of model parameters. To infer more accurate parameter estimates and to reduce the uncertainty of these estimates, we extend the SIR and SEIR epidemiological models with two time-varying parameters that capture the transmission rate and the rate at which active cases are reported to health officials. Using two real data sets of COVID-19 cases, we perform Bayesian inference via our SIR and SEIR models with time-varying transmission and reporting rates and via their standard counterparts with constant rates; our approach provides parameter estimates with more realistic interpretation, and 1-week ahead predictions with reduced uncertainty. Furthermore, we find consistent under-reporting in the number of active cases in the data that we consider, suggesting that the initial phase of the pandemic was more widespread than previously reported.

Mathematical model of broadly reactive plasma cell production.

Strain-specific plasma cells are capable of producing neutralizing antibodies that are essential for clearance of challenging pathogens. These neutralizing antibodies also function as a main defense against disease establishment in a host. However, when a rapidly mutating pathogen infects a host, successful control of the invasion requires shifting the production of plasma cells from strain-specific to broadly reactive. In this study, we develop a mathematical model of germinal center dynamics and use it to predict the events that lead to improved breadth of the plasma cell response. We examine scenarios that lead to germinal centers that are composed of B-cells that come from a single strain-specific clone, a single broadly reactive clone or both clones. We find that the initial B-cell clonal composition, T-follicular helper cell signaling, increased rounds of productive somatic hypermutation, and B-cell selection strength are among the mechanisms differentiating between strain-specific and broadly reactive plasma cell production during infections. Understanding the contribution of these factors to emergence of breadth may assist in boosting broadly reactive plasma cells production.

The effect of enrofloxacin on enteric Escherichia coli: Fitting a mathematical model to in vivo data.

Antimicrobial drugs administered systemically may cause the emergence and dissemination of antimicrobial resistance among enteric bacteria. To develop logical, research-based recommendations for food animal veterinarians, we must understand how to maximize antimicrobial drug efficacy while minimizing risk of antimicrobial resistance. Our objective is to evaluate the effect of two approved dosing regimens of enrofloxacin (a single high dose or three low doses) on Escherichia coli in cattle. We look specifically at bacteria above and below the epidemiological cutoff (ECOFF), above which the bacteria are likely to have an acquired or mutational resistance to enrofloxacin. We developed a differential equation model for the antimicrobial drug concentrations in plasma and colon, and bacteria populations in the feces. The model was fit to animal data of drug concentrations in the plasma and colon obtained using ultrafiltration probes. Fecal E. coli counts and minimum inhibitory concentrations were measured for the week after receiving the antimicrobial drug. We predict that the antimicrobial susceptibility of the bacteria above the ECOFF pre-treatment strongly affects the composition of the bacteria following treatment. Faster removal of the antimicrobial drugs from the colon throughout the study leads to improved clearance of bacteria above the ECOFF in the low dose regimen. If we assume a fitness cost is associated with bacteria above the ECOFF, the increased fitness costs leads to reduction of bacteria above the ECOFF in the low dose study. These results suggest the initial E. coli susceptibility is a strong indicator of how steers respond to antimicrobial drug treatment.

On modelling environmentally transmitted pathogens.

Many pathogens are able to replicate or survive in abiotic environments. Disease transmission models that include environmental reservoirs and environment-to-host transmission have used a variety of functional forms and modelling frameworks without a clear connection to pathogen ecology or space and time scales. We present a conceptual framework to organize microparasites based on the role that abiotic environments play in their lifecycle. Mean-field and individual-based models for environmental transmission are analysed and compared. We show considerable divergence between both modelling approaches when conditions do not facilitate well mixing and for pathogens with fast dynamics in the environment. We conclude with recommendations for modelling environmentally transmitted pathogens based on the pathogen lifecycle and time and spatial scales of the host-pathogen system under consideration.

Shifts in the Gut Metabolome and Clostridium difficile Transcriptome throughout Colonization and Infection in a Mouse Model.

Antibiotics alter the gut microbiota and decrease resistance to Clostridium difficile colonization; however, the mechanisms driving colonization resistance are not well understood. Loss of resistance to C. difficile colonization due to antibiotic treatment is associated with alterations in the gut metabolome, specifically, with increases in levels of nutrients that C. difficile can utilize for growth in vitro. To define the nutrients that C. difficile requires for colonization and pathogenesis in vivo, we used a combination of mass spectrometry and RNA sequencing (RNA Seq) to model the gut metabolome and C. difficile transcriptome throughout an acute infection in a mouse model at the following time points: 0, 12, 24, and 30 h. We also performed multivariate-based integration of the omics data to define the signatures that were most important throughout colonization and infection. Here we show that amino acids, in particular, proline and branched-chain amino acids, and carbohydrates decrease in abundance over time in the mouse cecum and that C. difficile gene expression is consistent with their utilization in vivo. This was also reinforced by the multivariate-based integration of the omics data where we were able to discriminate the metabolites and transcripts that support C. difficile physiology between the different time points throughout colonization and infection. This report illustrates how important the availability of amino acids and other nutrients is for the initial stages of C. difficile colonization and progression of disease. Future studies identifying the source of the nutrients and engineering bacteria capable of outcompeting C. difficile in the gut will be important for developing new targeted bacterial therapeutics. IMPORTANCE Clostridium difficile is a bacterial pathogen of global significance that is a major cause of antibiotic-associated diarrhea. Antibiotics deplete the indigenous gut microbiota and change the metabolic environment in the gut to one favoring C. difficile growth. Here we used metabolomics and transcriptomics to define the gut environment after antibiotics and during the initial stages of C. difficile colonization and infection. We show that amino acids, in particular, proline and branched-chain amino acids, and carbohydrates decrease in abundance over time and that C. difficile gene expression is consistent with their utilization by the bacterium in vivo. We employed an integrated approach to analyze the metabolome and transcriptome to identify associations between metabolites and transcripts. This highlighted the importance of key nutrients in the early stages of colonization, and the data provide a rationale for the development of therapies based on the use of bacteria that specifically compete for nutrients that are essential for C. difficile colonization and disease.

Germinal center dynamics during acute and chronic infection.

The ability of the immune system to clear pathogens is limited during chronic virus infections where potent long-lived plasma and memory B-cells are produced only after germinal center B-cells undergo many rounds of somatic hypermutations. In this paper, we investigate the mechanisms of germinal center B-cell formation by developing mathematical models for the dynamics of B-cell somatic hypermutations. We use the models to determine how B-cell selection and competition for T follicular helper cells and antigen influences the size and composition of germinal centers in acute and chronic infections. We predict that the T follicular helper cells are a limiting resource in driving large numbers of somatic hypermutations and present possible mechanisms that can revert this limitation in the presence of non-mutating and mutating antigen.

Modeling the Mechanisms by Which HIV-Associated Immunosuppression Influences HPV Persistence at the Oral Mucosa.

Human immunodeficiency virus (HIV)-infected patients are at an increased risk of co-infection with human papilloma virus (HPV), and subsequent malignancies such as oral cancer. To determine the role of HIV-associated immune suppression on HPV persistence and pathogenesis, and to investigate the mechanisms underlying the modulation of HPV infection and oral cancer by HIV, we developed a mathematical model of HIV/HPV co-infection. Our model captures known immunological and molecular features such as impaired HPV-specific effector T helper 1 (Th1) cell responses, and enhanced HPV infection due to HIV. We used the model to determine HPV prognosis in the presence of HIV infection, and identified conditions under which HIV infection alters HPV persistence in the oral mucosa system. The model predicts that conditions leading to HPV persistence during HIV/HPV co-infection are the permissive immune environment created by HIV and molecular interactions between the two viruses. The model also determines when HPV infection continues to persist in the short run in a co-infected patient undergoing antiretroviral therapy. Lastly, the model predicts that, under efficacious antiretroviral treatment, HPV infections will decrease in the long run due to the restoration of CD4+ T cell numbers and protective immune responses.