Predictive Modeling of Cholera Outbreaks in Bangladesh

Despite seasonal cholera outbreaks in Bangladesh, little is known about the relationship between environmental conditions and cholera cases. We seek to develop a predictive model for cholera outbreaks in Bangladesh based on environmental predictors. To do this, we estimate the contribution of environmental variables, such as water depth and water temperature, to cholera outbreaks in the context of a disease transmission model. We implement a method which simultaneously accounts for disease dynamics and environmental variables in a Susceptible-Infected-Recovered-Susceptible (SIRS) model. The entire system is treated as a continuous-time hidden Markov model, where the hidden Markov states are the numbers of people who are susceptible, infected, or recovered at each time point, and the observed states are the numbers of cholera cases reported. We use a Bayesian framework to fit this hidden SIRS model, implementing particle Markov chain Monte Carlo methods to sample from the posterior distribution of the environmental and transmission parameters given the observed data. We test this method using both simulation and data from Mathbaria, Bangladesh. Parameter estimates are used to make short-term predictions that capture the formation and decline of epidemic peaks. We demonstrate that our model can successfully predict an increase in the number of infected individuals in the population weeks before the observed number of cholera cases increases, which could allow for early notification of an epidemic and timely allocation of resources.Comment: 43 pages, including appendices, 5 figures, 1 table in the main tex

Optical Atomic Clock Comparison through Turbulent Air

We use frequency comb-based optical two-way time-frequency transfer (O-TWTFT) to measure the optical frequency ratio of state-of-the-art ytterbium and strontium optical atomic clocks separated by a 1.5 km open-air link. Our free-space measurement is compared to a simultaneous measurement acquired via a noise-cancelled fiber link. Despite non-stationary, ps-level time-of-flight variations in the free-space link, ratio measurements obtained from the two links, averaged over 30.5 hours across six days, agree to $6\times10^{-19}$, showing that O-TWTFT can support free-space atomic clock comparisons below the $10^{-18}$ level

PREDICTIVE MODELING OF CHOLERA OUTBREAKS IN BANGLADESH.

Despite seasonal cholera outbreaks in Bangladesh, little is known about the relationship between environmental conditions and cholera cases. We seek to develop a predictive model for cholera outbreaks in Bangladesh based on environmental predictors. To do this, we estimate the contribution of environmental variables, such as water depth and water temperature, to cholera outbreaks in the context of a disease transmission model. We implement a method which simultaneously accounts for disease dynamics and environmental variables in a Susceptible-Infected-Recovered-Susceptible (SIRS) model. The entire system is treated as a continuous-time hidden Markov model, where the hidden Markov states are the numbers of people who are susceptible, infected, or recovered at each time point, and the observed states are the numbers of cholera cases reported. We use a Bayesian framework to fit this hidden SIRS model, implementing particle Markov chain Monte Carlo methods to sample from the posterior distribution of the environmental and transmission parameters given the observed data. We test this method using both simulation and data from Mathbaria, Bangladesh. Parameter estimates are used to make short-term predictions that capture the formation and decline of epidemic peaks. We demonstrate that our model can successfully predict an increase in the number of infected individuals in the population weeks before the observed number of cholera cases increases, which could allow for early notification of an epidemic and timely allocation of resources

Effects of Nesiritide and Predictors of Urine Output in Acute Decompensated Heart Failure Results From ASCEND-HF (Acute Study of Clinical Effectiveness of Nesiritide and Decompensated Heart Failure)

<p>Objectives This study sought to determine if nesiritide increases diuresis in congestive heart failure patients.</p><p>Background In the ASCEND-HF (Acute Study of Clinical Effectiveness of Nesiritide and Decompensated Heart Failure), 7,141 patients hospitalized with acute decompensated heart failure (ADHF) were randomized to receive nesiritide or placebo for 24 to 168 h, in addition to standard care. There were minimal effects of nesiritide on survival, future hospitalizations, and symptoms. However, whether or not nesiritide increases diuresis in ADHF patients is unknown.</p><p>Methods Urine output was measured in 5,864 subjects; of these, 5,320 received loop diuretics and had dose data recorded. Loop diuretics other than furosemide were converted to furosemide equivalent doses. A total of 4,881 patients had complete data. We used logistic regression models to identify the impact of nesiritide on urine output and the factors associated with high urine output.</p><p>Results Median (25th, 75th percentiles) 24-h urine output was 2,280 (1,550, 3,280) ml with nesiritide and 2,200 (1,550, 3,200) ml with placebo (p = NS). Loop diuretic dose (furosemide equivalent) was 80 (40, 140) mg with both nesiritide and placebo. Diuretic dose was a strong predictor of urine output. Other independent predictors included: male sex, greater body mass index, higher diastolic blood pressure, elevated jugular venous pressure, recent weight gain, and lower blood urea nitrogen. The addition of nesiritide did not change urine output. None of the interaction terms between nesiritide and predictors affected the urine output prediction.</p><p>Conclusions Nesiritide did not increase urine output in patients with ADHF. Higher diuretic dose was a strong predictor of higher urine output, but neurohormonal activation (as evidenced by blood urea nitrogen concentration) and lower blood pressure limited diuresis. (c) 2013 by the American College of Cardiology Foundation</p>

Designing a Defined-Contribution Plan: What to Learn from Aircraft Designers

Why are pension plans not designed in the same way as commercial aircraft? At first blush, this question might seem a strange one to ask. It is also, however, an instructive one, and many similarities exist between the two things. Given the astounding success of aircraft design over the last century, we show that designers of pension plans have much to learn from aircraft designers. This article spells out these lessons by using the framework of designing a commercial aircraft to illustrate how a personal defined-contribution (DC) pension plan should be designed if it is to achieve its objective of delivering an adequate and secure pension to the retired pension plan member. Understanding the process of designing an aircraft can greatly enhance one’s understanding of how an optimal DC pension plan might be designed and can considerably simplify the task of the pension plan trustees, sponsoring employers, and regulators who oversee personal DC pension plans

Descriptive statistics for the study population.

<p><i>Footnotes</i></p><p>N (%), Number of individuals (percentage of the column total in the first row); NA, Not applicable; SD, Standard deviation.</p>a<p>Excluded because one or more members did not have vibriocidal antibody titer measurements. This does not include the 24 households that were excluded for having only one member.</p>b<p>Defined as the base-ten logarithm of the vibriocidal antibody titer of the initial serum specimen collected on day 2 of the household observation period. For one individual, the serum specimen collected on day 4 was used due to a missing measurement for day 2.</p><p>Descriptive statistics for the study population.</p

Survey data and specimen collection schedule for each study household, relative to the enrollment date of the household's index cholera infection (study day 1).

<p>The “*” denotes the day on which stool/rectal swab specimens were only collected from the index cholera infections.</p

Covariate effects estimated by the univariate and multivariate transmission models.

<p>Covariate effects estimated by the univariate and multivariate transmission models.</p