BackgroundInfluenza viruses are a major cause of morbidity and mortality worldwide. Vaccination remains a powerful tool for preventing or mitigating influenza outbreaks. Yet, vaccine supplies and daily administration capacities are limited, even in developed countries. Understanding how such constraints can alter the mitigating effects of vaccination is a crucial part of influenza preparedness plans. Mathematical models provide tools for government and medical officials to assess the impact of different vaccination strategies and plan accordingly. However, many existing models of vaccination employ several questionable assumptions, including a rate of vaccination proportional to the population at each point in time.MethodsWe present a SIR-like model that explicitly takes into account vaccine supply and the number of vaccines administered per day and places data-informed limits on these parameters. We refer to this as the non-proportional model of vaccination and compare it to the proportional scheme typically found in the literature.ResultsThe proportional and non-proportional models behave similarly for a few different vaccination scenarios. However, there are parameter regimes involving the vaccination campaign duration and daily supply limit for which the non-proportional model predicts smaller epidemics that peak later, but may last longer, than those of the proportional model. We also use the non-proportional model to predict the mitigating effects of variably timed vaccination campaigns for different levels of vaccination coverage, using specific constraints on daily administration capacity.ConclusionsThe non-proportional model of vaccination is a theoretical improvement that provides more accurate predictions of the mitigating effects of vaccination on influenza outbreaks than the proportional model. In addition, parameters such as vaccine supply and daily administration limit can be easily adjusted to simulate conditions in developed and developing nations with a wide variety of financial and medical resources. Finally, the model can be used by government and medical officials to create customized pandemic preparedness plans based on the supply and administration constraints of specific communities.

The COVID-19 pandemic has become a crucial public health problem in the world that disrupted the lives of millions in many countries including the United States. In this study, we present a decision analytic approach which is an efficient tool to assess the effectiveness of early social distancing measures in communities with different population characteristics. First, we empirically estimate the reproduction numbers for two different states. Then, we develop an age-structured compartmental simulation model for the disease spread to demonstrate the variation in the observed outbreak. Finally, we analyze the computational results and show that early trigger social distancing strategies result in smaller death tolls; however, there are relatively larger second waves. Conversely, late trigger social distancing strategies result in higher initial death tolls but relatively smaller second waves. This study shows that decision analytic tools can help policy makers simulate different social distancing scenarios at the early stages of a global outbreak. Policy makers should expect multiple waves of cases as a result of the social distancing policies implemented when there are no vaccines available for mass immunization and appropriate antiviral treatments.

No abstract

We present a decision analytic framework that uses a mathematical model of Chlamydia trachomatis transmission dynamics in two interacting populations using ordinary differential equations. A public health survey informs model parametrization, and analytical findings guide the computational design of the decision-making process. The potential impact of jail-based screen-treat (S-T) programs on community health outcomes is presented. Numerical experiments are conducted for a case study population to quantify the effect and evaluate the cost-effectiveness of considered interventions. Numerical experiments show the effectiveness of increased jail S-T rates on community cases when resources for a community S-T program stays constant. Although this effect decreases when higher S-T rates are in place, jail-based S-T programs are cost-effective relative to community-based programs. Summary of Contribution: Public health programs have been developed to control community-wide infectious diseases and to reduce prevalence of sexually transmitted diseases (STD). These programs can consist of screening and treatment of diseases and behavioral interventions. Public correctional facilities play an important role in operational execution of these public health programs. However, because of lack of capacity and resources, public health programs using correctional facilities are questioned by policy-makers in terms of their costs and benefits. In this article, we present an analytical framework using a computational epidemiology model for supporting public health policy making. The system represents the dynamics of Chlamydia trachomatis transmission in two interacting populations, with an ordinary differential equations-based simulation model. The theoretical epidemic control conditions are derived and numerically tested, which guide the design of simulation experiments. Then cost-effectiveness of the potential policies is analyzed. We also present an extensive sensitivity analyses on model parameters. This study contributes to the computational epidemiology literature by presenting an analytical framework to guide effective simulation experimentation for policy decision making. The presented methodology can be applied to other complex policy and public health problems.

This is an open letter concerning the recent launch of the new open access journal, eNeuro.We welcome the diversification of journal choices for authors looking for open access venues, as well as the willingness of eNeuro to accept negative results and study replications, its membership in the Neuroscience Peer Review Consortium, the publication of peer review syntheses alongside articles, and the requirement that molecular data be publicly available.