Integrating Epigenetic Priors For Improving Computational Identification of Transcription Factor Binding Sites

Transcription factors and histone modifications play critical roles in tissue-specific gene expression. Identifying binding sites is key in understanding the regulatory interactions of gene expression. Nave computational approaches uses solely DNA sequence data to construct models known as Position Weight Matrices. However, the various assumptions and the lack of background genomic information leads to a high false positive rate. In an attempt to improve the predictive performance of a PWM, we use a Hidden Markov Model to incorporate chromatin structure, in particular histone modifications. The HMM captures physical interactions between distinct HMs. Indeed, the integration of sequence based PWM models and chromatin modifications improve the predictive ability of the integrative model

Cost-effectiveness of a potential Zika vaccine candidate: a case study for Colombia

Background: A number of Zika vaccine platforms are currently being investigated, some of which have entered clinical trials. We sought to evaluate the cost-effectiveness of a potential Zika vaccine candidate under the WHO Vaccine Target Product Profile for outbreak response, prioritizing women of reproductive age to prevent microcephaly and other neurological disorders. Methods: Using an agent-based simulation model of ZIKV transmission dynamics in a Colombian population setting, we conducted cost-effectiveness analysis with and without pre-existing herd immunity. The model was parameterized with estimates associated with ZIKV infection, risks of microcephaly in different trimesters, direct medical costs, and vaccination costs. We assumed that a single dose of vaccine provides a protection efficacy in the range 60% to 90% against infection. Cost-effectiveness analysis was conducted from a government perspective. Results: Under a favorable scenario when the reproduction number is relatively low (R0 = 2.2) and the relative transmissibility of asymptomatic infection is 10% compared with symptomatic infection, a vaccine is cost-saving (with negative incremental cost-effective ratio; ICER) for vaccination costs up to US$6 per individual without herd immunity, and up to US$4 per individual with 8% herd immunity. For positive ICER values, vaccination is highly cost-effective for vaccination costs up to US$10 (US$7) in the respective scenarios with the willingness-to-pay of US$6610 per disability-adjusted life-year, corresponding to the average per capita GDP of Colombia between 2013 and 2017. Our results indicate that the effect of other control measures targeted to reduce ZIKV transmission decreases the range of vaccination costs for cost-effectiveness due to reduced returns of vaccine-induced herd immunity. In all scenarios investigated, the median reduction of microcephaly exceeded 64% with vaccination. Conclusions: Our study suggests that a Zika vaccine with protection efficacy as low as 60% could significantly reduce the incidence of microcephaly. From a government perspective, Zika vaccination is highly cost-effective, and even cost-saving in Colombia if vaccination costs per individual is sufficiently low. Efficacy data from clinical trials and number of vaccine doses will be important requirements in future studies to refine our estimates, and conduct similar studies in other at-risk populations. Keywords: Zika, Microcephaly, Vaccination, Agent-based modeling, Cost-effectivenessYork University Librarie

Cost-Effectiveness Analysis Using Agent-Based Modelling: A General Framework with Case Studies

In recent years, agent-based modelling (ABM) has been increasingly used to elucidate complex adaptive systems. An ABM is a structural computational system that consists of a collection of abstract objects (agents) embedded in a virtual environment that interact based on a set of prescribed rules. While traditional approaches such as differential equation-based compartmental models span a vast literature, they often impose restrictive assumptions such as homogeneity and determinism that limit their application to real settings. ABM overcomes these limitations through a bottom-up approach in which macro dynamics emerge from micro level phenomena. During the past decade, there has been a surge of interest in the use of ABM in human health and disease dynamics. While this is rapidly growing, its application to other relevant areas such as health economics is still in infancy, and frameworks that could systematically apply ABM are still lacking. In this thesis, we develop a general framework for cost-effectiveness analysis in which ABM is designed to project the system dynamics. We argue that ABM improves the empirical reliability of policy-oriented simulation models and that it presents an ideal tool to address the complexity of disease processes, project the impact of interventions and inform their optimal implementation. We use this framework in an epidemiological context to quantify the economic impact of vaccination strategies for prevention of infectious diseases. We present two case studies for a human-to-human infection transmission (i.e., Haemophilus influenzae) and a vector-borne disease (i.e., Zika). In each case, we detail the construction of ABM and its utilization to conduct Bayesian cost-effectiveness analysis of potential vaccine candidates. In addition to uncovering important characteristics of these diseases in epidemic dynamics, we present their first cost-effectiveness analysis and implications for vaccination strategies in different populations settings

Projecting hospital utilization during the COVID-19 outbreaks in the United States

Data deposition: The computational system is available in Github (https://github.com/affans/ncov2019odemodel).In the wake of community coronavirus disease 2019 (COVID-19) transmission in the United States, there is a growing public health concern regarding the adequacy of resources to treat infected cases. Hospital beds, intensive care units (ICUs), and ventilators are vital for the treatment of patients with severe illness. To project the timing of the outbreak peak and the number of ICU beds required at peak, we simulated a COVID-19 outbreak parameterized with the US population demographics. In scenario analyses, we varied the delay from symptom onset to self-isolation, the proportion of symptomatic individuals practicing self-isolation, and the basic reproduction number R0. Without self-isolation, when R0 =2.5, treatment of critically ill individuals at the outbreak peak would require 3.8 times more ICU beds than exist in the United States. Self-isolation by 20% of cases 24 h after symptom onset would delay and flatten the outbreak trajectory, reducing the number of ICU beds needed at the peak by 48.4% (interquartile range 46.4-50.3%), although still exceeding existing capacity. When R0 =2, twice as many ICU beds would be required at the peak of outbreak in the absence of self-isolation. In this scenario, the proportional impact of self-isolation within 24 h on reducing the peak number of ICU beds is substantially higher at 73.5% (interquartile range 71.4-75.3%). Our estimates underscore the inadequacy of critical care capacity to handle the burgeoning outbreak. Policies that encourage self-isolation, such as paid sick leave, may delay the epidemic peak, giving a window of time that could facilitate emergency mobilization to expand hospital capacity.S.M.M. acknowledges support from the Canadian Institutes of Health Research (grant OV4-170643; Canadian 2019 Novel Coronavirus Rapid Research), and the Natural Sciences and Engineering Research Council of Canada. A.P.G. gratefully acknowledges funding from the NIH (grant UO1-GM087719), the Burnett and Stender families’ endowment, the Notsew Orm Sands Foundation, NIH grant 1R01AI151176-01, and National Science Foundation grant RAPID-2027755. M.C.F. was supported by the NIH grant K01 AI141576.Integrative Biolog

The impact of vaccination on COVID-19 Outbreaks in the United States

Background: Global vaccine development efforts have been accelerated in response to the devastating COVID-19 pandemic. We evaluated the impact of a 2-dose COVID-19 vaccination campaign on reducing incidence, hospitalizations, and deaths in the United States (US). Methods: We developed an agent-based model of SARS-CoV-2 transmission and parameterized it with US demographics and age-specific COVID-19 outcomes. Healthcare workers and high-risk individuals were prioritized for vaccination, while children under 18 years of age were not vaccinated. We considered a vaccine efficacy of 95% against disease following 2 doses administered 21 days apart achieving 40% vaccine coverage of the overall population within 284 days. We varied vaccine efficacy against infection, and specified 10% pre-existing population immunity for the base-case scenario. The model was calibrated to an effective reproduction number of 1.2, accounting for current non-pharmaceutical interventions in the US. Results: Vaccination reduced the overall attack rate to 4.6% (95% CrI: 4.3% - 5.0%) from 9.0% (95% CrI: 8.4% - 9.4%) without vaccination, over 300 days. The highest relative reduction (54-62%) was observed among individuals aged 65 and older. Vaccination markedly reduced adverse outcomes, with non-ICU hospitalizations, ICU hospitalizations, and deaths decreasing by 63.5% (95% CrI: 60.3% - 66.7%), 65.6% (95% CrI: 62.2% - 68.6%), and 69.3% (95% CrI: 65.5% - 73.1%), respectively, across the same period. Conclusions: Our results indicate that vaccination can have a substantial impact on mitigating COVID-19 outbreaks, even with limited protection against infection. However, continued compliance with non-pharmaceutical interventions is essential to achieve this impact.Integrative Biolog

Additional file 1: of Cost-effectiveness of a potential Zika vaccine candidate: a case study for Colombia

Details of the model and its analysis with additional simulation results. (PDF 3785 kb

Projecting influenza vaccine effectiveness: A simulation study.

The impact of influenza vaccination is largely measured by estimating vaccine effectiveness (VE), which vary in different seasons. Strain mutations and waning immunity present two key mechanisms affecting VE. We sought to quantify the relative effect of these mechanisms by projecting VE and the reduction of illness due to vaccination. We developed a stochastic age-structured agent-based simulation model of influenza transmission dynamics to encapsulate intraseason waning of immunity post-vaccination, and mutation-induced antigenic distance between circulating strains and vaccine strains. Parameterizing the model with published estimates, we projected the temporal and overall VE during an epidemic season, and estimated the reduction of infection for high (70%) and low (30%) vaccine efficacies to reflect the levels of vaccine-induced protection in randomized control trials. Both temporal and overall VE decreased as the attack rate increased, with lower median values for epidemics starting with strains that were antigenically more distant from vaccine strains. We observed a higher rate of temporal decline with considerably lower median values of the overall VE in the presence of intraseason waning of immunity compared with only the antigenic distance effect. The highest benefit of vaccination in preventing influenza infection was achieved at moderate attack rates in the range of 6%-15%. The results show that even when VE is relatively low in the population and almost negligible for older age groups (i.e., 50+ years), vaccination can still prevent significant illness in high-risk individuals; thereby reducing healthcare resource utilization and economic burden. Our study indicates that early vaccination remains an important strategy for alleviating the burden of seasonal influenza. Policy discussions on optimal timing of vaccination to reduce the effect of intraseason waning of immunity should be considered in the context of strain mutations within the epidemic course