Noise and Nonlinearity in Measles Epidemics: Combining Mechanistic and Statistical Approaches to Population Modeling

We present and evaluate an approach to analyzing population dynamics data using semimechanistic models. These models incorporate reliable information on population structure and underlying dynamic mechanisms but use nonparametric surface-fitting methods to avoid unsupported assumptions about the precise form of rate equations. Using historical data on measles epidemics as a case study, we show how this approach can lead to better forecasts, better characterizations of the dynamics, and better understanding of the factors causing complex population dynamics relative to either mechanistic models or purely descriptive statistical time-series models. The semimechanistic models are found to have better forecasting accuracy than either of the model types used in previous analyses when tested on data not used to fit the models. The dynamics are characterized as being both nonlinear and noisy, and the global dynamics are clustered very tightly near the border of stability (dominant Lyapunov exponent λ < 0). However, locally in state space the dynamics oscillate between strong short-term stability and strong short-term chaos (i.e., between negative and positive local Lyapunov exponents). There is statistically significant evidence for short-term chaos in all data sets examined. Thus the nonlinearity in these systems is characterized by the variance over state space in local measures of chaos versus stability rather than a single summary measure of the overall dynamics as either chaotic or nonchaotic

~115 GeV and ~143 GeV Higgs mass considerations within the Composite Particles Model

The radiatively generated Higgs mass is obtained by requiring that leading "divergences" are cancelled in both 2D and 4D. This predicts one or more viable modes; the k=1 mode mass is m_H\cong2/3 m_t\cong115GeV whereas the k=2 mode is m_H\cong143GeV. These findings are interpreted within the Composite Particles Model (CPM), [Popovic 2002, 2010], with the massive top quark being a composite structure composed of 3 fundamental O quarks (O\bar{O}O) and the massive Higgs scalar being a color-neutral meson like structure composed of 2 fundamental O quarks (\bar{O}O). The CPM predicts that the Z mass generation is mediated primarily by a top - anti top whereas the Higgs mass is generated primarily by a O - anti O interactions. The relationship [Popovic 2010] between top Yukawa coupling and strong QCD coupling, obtained by requiring that top - anti top channel is neither attractive or repulsive at tree level at \surd s\congM_Z, defines the Z mass. In addition, this relationship indirectly defines the electroweak symmetry breaking (EWSB) vacuum expectation value (VEV), the CPM Higgs mass and potentially the EWSB scale.Comment: 11 pages, 3 figures, slightly updated second version: Lagrangian explicitly specified, OOO->O\bar{O}O and a few other typos correcte

Modeling inpatient and outpatient antibiotic stewardship interventions to reduce the burden of Clostridioides difficile infection in a regional healthcare network

Antibiotic exposure can lead to unintended outcomes, including drug-drug interactions, adverse drug events, and healthcare-associated infections like Clostridioides difficile infection (CDI). Improving antibiotic use is critical to reduce an individual's CDI risk. Antibiotic stewardship initiatives can reduce inappropriate antibiotic prescribing (e.g., unnecessary antibiotic prescribing, inappropriate antibiotic selection), impacting both hospital (healthcare)-onset (HO)-CDI and community-associated (CA)-CDI. Previous computational and mathematical modeling studies have demonstrated a reduction in CDI incidence associated with antibiotic stewardship initiatives in hospital settings. Although the impact of antibiotic stewardship initiatives in long-term care facilities (LTCFs), including nursing homes, and in outpatient settings have been documented, the effects of specific interventions on CDI incidence are not well understood. We examined the relative effectiveness of antibiotic stewardship interventions on CDI incidence using a geospatially explicit agent-based model of a regional healthcare network in North Carolina. We simulated reductions in unnecessary antibiotic prescribing and inappropriate antibiotic selection with intervention scenarios at individual and network healthcare facilities, including short-term acute care hospitals (STACHs), nursing homes, and outpatient locations. Modeled antibiotic prescription rates were calculated using patient-level data on antibiotic length of therapy for the 10 modeled network STACHs. By simulating a 30% reduction in antibiotics prescribed across all inpatient and outpatient locations, we found the greatest reductions on network CDI incidence among tested scenarios, namely a 17% decrease in HO-CDI incidence and 7% decrease in CA-CDI. Among intervention scenarios of reducing inappropriate antibiotic selection, we found a greater impact on network CDI incidence when modeling this reduction in nursing homes alone compared to the same intervention in STACHs alone. These results support the potential importance of LTCF and outpatient antibiotic stewardship efforts on network CDI burden and add to the evidence that a coordinated approach to antibiotic stewardship across multiple facilities, including inpatient and outpatient settings, within a regional healthcare network could be an effective strategy to reduce network CDI burden

Controlling Pandemic Flu: The Value of International Air Travel Restrictions

BACKGROUND: Planning for a possible influenza pandemic is an extremely high priority, as social and economic effects of an unmitigated pandemic would be devastating. Mathematical models can be used to explore different scenarios and provide insight into potential costs, benefits, and effectiveness of prevention and control strategies under consideration. METHODS AND FINDINGS: A stochastic, equation-based epidemic model is used to study global transmission of pandemic flu, including the effects of travel restrictions and vaccination. Economic costs of intervention are also considered. The distribution of First Passage Times (FPT) to the United States and the numbers of infected persons in metropolitan areas worldwide are studied assuming various times and locations of the initial outbreak. International air travel restrictions alone provide a small delay in FPT to the U.S. When other containment measures are applied at the source in conjunction with travel restrictions, delays could be much longer. If in addition, control measures are instituted worldwide, there is a significant reduction in cases worldwide and specifically in the U.S. However, if travel restrictions are not combined with other measures, local epidemic severity may increase, because restriction-induced delays can push local outbreaks into high epidemic season. The per annum cost to the U.S. economy of international and major domestic air passenger travel restrictions is minimal: on the order of 0.8% of Gross National Product. CONCLUSIONS: International air travel restrictions may provide a small but important delay in the spread of a pandemic, especially if other disease control measures are implemented during the afforded time. However, if other measures are not instituted, delays may worsen regional epidemics by pushing the outbreak into high epidemic season. This important interaction between policy and seasonality is only evident with a global-scale model. Since the benefit of travel restrictions can be substantial while their costs are minimal, dismissal of travel restrictions as an aid in dealing with a global pandemic seems premature

Description of the methodology used in an ongoing pediatric care interventional study of children born with cleft lip and palate in South America [NCT00097149]

BACKGROUND: The contribution of birth defects, including cleft lip and palate, to neonatal and infant mortality and morbidity is substantial. As other mortality and morbidity causes including infections, hygiene, prematurity, and nutrition are eradicated in less developed countries, the burden of birth defects will increase proportionally. METHODS/DESIGN: We are using cleft lip and palate as a sentinel birth defect to evaluate its burden on neonatal and infant health and to assess the effectiveness of systematic pediatric care during the first month and first two years of life in decreasing this burden. The neonatal intervention, consisting of weekly pediatric evaluation and referral to appropriate care, is delivered to about 696 infants born with cleft lip and/or palate in 47 hospitals in South America. Neonatal mortality in this group will be compared to that in a retrospective control group of about 464 infants born with cleft lip and/or palate in the same hospitals. The subgroup of infants with isolated clefts of both the lip and palate (about 264) is also randomized into two groups, intervened and non-intervened, and further followed up over 2 years. Intervened cases are evaluated by pediatricians every three months and referred for appropriate care. The intervened and non-intervened cases will be compared over study outcomes to evaluate the intervention effectiveness. Non-intervened cases are matched and compared to healthy controls to assess the burden of cleft lip and palate. Outcomes include child's neurological and physical development and family social and economic conditions. DISCUSSION: Large-scale clinical trials to improve infant health in developing countries are commonly suggested, making it important to share the methods used in ongoing studies with other investigators implementing similar research. We describe here the content of our ongoing pediatric care study in South America. We hope that this may help researchers targeting this area to plan their studies more effectively and encourage the development of similar research efforts to target other birth defects or infant outcomes such as prematurity and low birth weight

Real-time numerical forecast of global epidemic spreading: Case study of 2009 A/H1N1pdm

Background Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches. Methods We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability. Results Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model. Conclusions Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models