A Simulation Study of Predicting Flush Date

For Canada's boreal forest region, the accurate modelling of the timing of the appearance of aspen leaves is important to forest fire management, as it signifies the end of the spring fire season that occurs after snowmelt. This article compares two methods, a midpoint rule and a conditional expectation method used to estimate the true flush date for interval-censored data from a large set of fire-weather stations in Alberta, Canada. The conditional expectation method uses the interval censored kernel density estimator of Braun et al. (2005 Braun , J. , Duchesne , T. , Stafford , J. E. ( 2005 ). Local likelihood density estimation for interval censored data . Canadian Journal of Statistics 33 : 39 – 60 .[Crossref], [Web of Science ®] , [Google Scholar]). The methods are compared via simulation, where true flush dates were generated from a normal distribution and then converted into intervals by adding and subtracting exponential random variables. The simulation parameters were estimated from the data set and several scenarios were considered. The study reveals that the conditional expectation method is never worse than the midpoint method, and that there is a significant advantage to this method when the intervals are large. An illustration of the methodology applied to the Alberta data set is also provided.     

Perspectives: Abortion and Genocide: The Unbridgeable Gap

This article examines The Center for Bio-Ethical Reform's claimthat abortion is genocide, assessing it against legal, trait-basedand "dynamic process" definitions of genocide. The purpose ofthis exercise is not to give credence to what many consideran outrageous claim, nor is it to merely refute this claim basedupon a close reading of existing definitions of genocide; instead,by subjecting The Center for Bio-Ethical Reform's claim to anethical and performative evaluation, our goal is to illustratehow the term genocide can be "misused." In the end, we arguethat The Center for Bio-Ethical Reform uses the term genocidefor its own totalizing and essentializing purposes, and in doingso engages in practices that share an affinity with the exclusionarydiscourses that help make genocide thinkable.     

An investigation into social media syndromic monitoring

Tweets offer us early information on initial stages of diseases, since people often tweet the early symptoms of feeling unwell prior to presenting to an emergency department if their symptoms become more severe. Even when people do present at an emergency department, it generally takes over 24 hours for their information to be collected, diagnosed and transferred for analysis at a centralized location. The advantage of utilizing tweets is that they offer information on syndromes in real-time. This paper investigates the value of carrying out multivariate syndromic surveillance using daily counts of keywords. The dynamic bi-plot is used to detect unexpected changes in the daily counts. These methods can be easily generalized to hourly tweet syndromic counts. By following Twitter users that suffer certain symptoms over time we can better understand the burden of these health issues and better understand emerging health issues. Monitoring people who present with symptoms but are just not sick enough to go to emergency departments provides us with additional information not gathered by emergency departments.