A Mathematical Investigation Of The Drivers Of Lyme Disease Ecology At Two Ecologically Contrasting Sites

INTRODUCTION: Lyme disease is an emerging tick-borne disease of increasing concern in the United States. In order to create and to implement effective public health interventions for Lyme disease, there must be a better understanding of the factors driving pathogen transmission. OBJECTIVES: The primary objectives of this study were to determine how presence/absence of “super-spreaders” and observed differences between two ecologically contrasting sites influence Borrelia burgdorferi transmission. METHODS: A next generation matrix R0 model was parameterized with field data from an island site (Block Island, Rhode Island) and a mainland site (Connecticut) in order to generate R0 estimates. A local elasticity analysis was performed in order to identify crucial parameters. RESULTS: Super-spreaders caused the majority of pathogen transmission but did not greatly influence total transmission. R0 estimates were greater for the island site than for the mainland site, and island R0 estimates increased from 2013 to 2014. Model sensitivity to parameter values also varied across sites and years. CONCLUSION: The dynamics of B. burgdorferi transmission may differ across sites and over time within a single site. Additional research is necessary to validate the results of this model and to identify predictors of certain transmission patterns in order to inform public health strategies, particularly as the effects of climate change intensify

Reducing Detailed Vehicle Energy Dynamics to Physics-Like Models

The energy demand of vehicles, particularly in unsteady drive cycles, is affected by complex dynamics internal to the engine and other powertrain components. Yet, in many applications, particularly macroscopic traffic flow modeling and optimization, structurally simple approximations to the complex vehicle dynamics are needed that nevertheless reproduce the correct effective energy behavior. This work presents a systematic model reduction pipeline that starts from complex vehicle models based on the Autonomie software and derives a hierarchy of simplified models that are fast to evaluate, easy to disseminate in open-source frameworks, and compatible with optimization frameworks. The pipeline, based on a virtual chassis dynamometer and subsequent approximation strategies, is reproducible and is applied to six different vehicle classes to produce concrete explicit energy models that represent an average vehicle in each class and leverage the accuracy and validation work of the Autonomie software.Comment: 40 pages, 9 figure

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Mathematics makes me wonder

This paper draws a picture of Mathematics education in the Philippines for Years 1 to 10. The factors included for this endeavor are the curriculum, the results of various national exams and the results of the studies undertaken by the International Association for the Evaluation of Educational Achievement

On the existence of calendar anomalies and persistence in the daily returns of the PSEi

The future of the stock market may never be predicted consistently, nor its past behavior understood entirely, but any knowledge gained from observing it could help decide on a sound investment strategy. In this study, I looked at the daily returns of the Philippine Stock Exchange index (PSEi) from March 1, 1990, to January 31, 2017, and see how the data relates to the mathematically verifiable aspects of the noise theory and efficient market theory (EMT). In relation to the noise theory, I looked at the occurrences of anomalies. For the EMT, I made use of discrete-time Markov chains to determine some trends. The study results showed that most stock market anomalies are present while persistent behavior is hardly present in the dataset. Furthermore, I applied day ahead time domain forecasting methods starting with the simple moving average models to autoregressive moving average models. The augmented Dickey-Fuller test indicate that the daily returns are a stationary series although the ACF and PACF plots have consistently shown non-zero correlations for lags 1, 9, 12, 13. I have obtained AR(1) and ARMA(1,2) processes for the data and both models indicate the same forecasting accuracy via the Diebold-Mariano test. Although these time domain processes were unable to predict the random noise in the data, these processes were accurate in predicting the signs of the values as supported by the Pesaran-Timmermann test. © 2018 by De La Salle University

Long-range dependence of stationary processes in single-server queues

The stationary processes of waiting times {W n }n = 1,2,... in a GI/G/1 queue and queue sizes at successive departure epochs {Q n}n = 1,2,... in an M/G/1 queue are long-range dependent when 3 \u3c κ S \u3c 4, where κ S is the moment index of the independent identically distributed (i.i.d.) sequence of service times. When the tail of the service time is regularly varying at infinity the stationary long-range dependent process {W n } has Hurst index 1/2(5-κ S ), i.e. sup {h : lim sup n→∞\, var(W1+⋯+Wn)/n 2h = ∞} = 5 - κS}/2 If this assumption does not hold but the sequence of serial correlation coefficients {ρ n } of the stationary process {W n } behaves asymptotically as cn -α for some finite positive c and α ∈ (0,1), where α = κ S - 3, then {W n } has Hurst index 1/2(5-κ S ). If this condition also holds for the sequence of serial correlation coefficients {r n } of the stationary process {Q n } then it also has Hurst index 1/2(5κ S ). © Springer Science+Business Media, LLC 2007

Long-Range Dependence of Markov Processes

Long-range dependence in discrete and continuous time Markov chains over a countable state space is defined via embedded renewal processes brought about by visits to a fixed state. In the discrete time chain, solidarity properties are obtained and long-range dependence of functionals are examined. On the other hand, the study of LRD of continuous time chains is defined via the number of visits in a given time interval. Long-range dependence of Markov chains over a non-countable state space is also carried out through positive Harris chains. Embedded renewal processes in these chains exist via visits to sets of states called proper atoms. ¶ Examples of these chains are presented, with particular attention given to long-range dependent Markov chains in single-server queues, namely, the waiting times of GI/G/1 queues and queue lengths at departure epochs in M/G/1 queues. The presence of long-range dependence in these processes is dependent on the moment index of the lifetime distribution of the service times. The Hurst indexes are obtained under certain conditions on the distribution function of the service times and the structure of the correlations. These processes of waiting times and queue sizes are also examined in a range of M/P/2 queues via simulation (here, P denotes a Pareto distribution)

Forecasting day-ahead electricity prices of Singapore through ARIMA and wavelet-ARIMA

The changes observed in the electricity markets over the past decade brought about developments in the field of electricity modeling. In this paper, traditional AutoRegressive Integrated Moving Average (ARIMA) models and Wavelet-ARIMA models arc applied to the Singapore electricity market, Asia\u27s first liberalized electricity market. Forecasting will be done for each electricity price modelling technique and the adequacy of the models is tested through forecast accuracy. The comparison of forecast accuracy of the models is done across different data behaviors. Copyright © 2012 De La Salle University, Philippines