Using approximate Bayesian computation to estimate transmission rates of nosocomial pathogens

In this paper, we apply a simulation based approach for estimating transmission rates of nosocomial pathogens. In particular, the objective is to infer the transmission rate between colonised health-care practitioners and uncolonised patients (and vice versa) solely from routinely collected incidence data. The method, using approximate Bayesian computation, is substantially less computer intensive and easier to implement than likelihood-based approaches we refer to here. We find through replacing the likelihood with a comparison of an efficient summary statistic between observed and simulated data that little is lost in the precision of estimated transmission rates. Furthermore, we investigate the impact of incorporating uncertainty in previously fixed parameters on the precision of the estimated transmission rates.

Socio-environmental variability and Barmah Forest virus disease transmission : a review of epidemiological evidence and future prospects

Barmah Forest Virus (BFV) disease is the most rapidly emerging mosquito-borne disease in Australia. BFV transmission depends on factors such as climate, virus, vector and the human population. However, the impact of climatic and social factors on BFV remains to be determined. This paper provided an overview of current research and discusses the future research directions on the BFV transmission. These research findings could be regarded as an impetus towards BFV prevention and control strategies.

Analysis of the damping properties of high-transmission-ratio Cycloidal drives

Cycloidal drives are widely used in today’s industries for drives where large reduction ratios are required. Drive-train dynamics plays an important role in their design. This paper presents a new methodology for assessing damping characteristics of Cycloidal drives and compares the natural frequencies obtained from experiments and theoretical/numerical calculations using Fast-Fourier-Transforms.

Impact of wind power development on transmission planning at Midwest ISO

The Midwest Independent Transmission System Operator (MISO) has experienced significant amounts of wind power development within the last decade. The MISO footprint spans the majority of the upper Midwest region of the country, from the Dakotas to Indiana and as far east as Michigan. These areas have a rich wind energy resource. States in the MISO footprint have passed laws or set goals that require load serving entities to supply a portion of their load using renewable energy. In order to meet these requirements, significant investments are needed to build the transmission infrastructure necessary to deliver the power from these often remote wind energy resources to the load centers. This paper presents some of the transmission planning related work done at MISO which was largely influenced by current and future needs for increased wind power generation in the footprint. Specifically, topics covered are generator interconnection, long-term planning coordination, and cost-allocation for new transmission lines.

Long term transmission planning to meet renewable energy targets in Australia

Australia is rich in renewable energy resources such as wind, solar and geothermal. Geographical diversity of these renewable resources combined with developing climate change policies poses a great challenge for the long term interconnection planning. Intermittency of wind and solar potentially driving the development of new transmission lines bring additional complexity to power system operations and planning. This paper provides an overview of generation and transmission planning studies in Australia to meet 20% renewable energy target by 2020. Appraisal of the effectiveness of dispersed energy storage, non schedulable peaking plants, wide area controls and demand management techniques to aid the penetration of renewables is presented in this paper

Could BFFB mode breath aerosol play a role in H5N1 transmission?

Recent findings concerning exhaled aerosol size distributions and the regions in the respiratory tract in which they are generated could have significant implications for human to human spread of lower respiratory tract-specific infections. Even in healthy people, measurable quantities of aerosol are routinely generated from the Lower Respiratory Tract (LRT) during breathing(1-3). We have found that there at least three modes in the exhaled aerosol size distribution of healthy adults(4) (see Figure 1). These modes each have a characteristic size and arise from different parts of the respiratory tract. The respiratory bronchioles produce aerosol during breathing, the larynx during speech and the oral cavity also during speech. The model of the resulting droplet size distribution is therefore called the Bronchial Laryngeal Oral (B.L.O.) tri-modal model of expired aerosol.

Risk control in transmission system expansion planning with wind generators

With the advent of large-scale wind farms and their integration into electrical grids, more uncertainties, constraints and objectives must be considered in power system development. It is therefore necessary to introduce risk-control strategies into the planning of transmission systems connected with wind power generators. This paper presents a probability-based multi-objective model equipped with three risk-control strategies. The model is developed to evaluate and enhance the ability of the transmission system to protect against overload risks when wind power is integrated into the power system. The model involves: (i) defining the uncertainties associated with wind power generators with probability measures and calculating the probabilistic power flow with the combined use of cumulants and Gram-Charlier series; (ii) developing three risk-control strategies by specifying the smallest acceptable non-overload probability for each branch and the whole system, and specifying the non-overload margin for all branches in the whole system; (iii) formulating an overload risk index based on the non-overload probability and the non-overload margin defined; and (iv) developing a multi-objective transmission system expansion planning (TSEP) model with the objective functions composed of transmission investment and the overload risk index. The presented work represents a superior risk-control model for TSEP in terms of security, reliability and economy. The transmission expansion planning model with the three risk-control strategies demonstrates its feasibility in the case study using two typical power systems

Passenger transmission and productiveness of transit lines with high loads

Deterministic transit capacity analysis applies to planning, design and operational management of urban transit systems. The Transit Capacity and Quality of Service Manual (1) and Vuchic (2, 3) enable transit performance to be quantified and assessed using transit capacity and productive capacity. This paper further defines important productive performance measures of an individual transit service and transit line. Transit work (p-km) captures the transit task performed over distance. Passenger transmission (p-km/h) captures the passenger task delivered by service at speed. Transit productiveness (p-km/h) captures transit work performed over time. These measures are useful to operators in understanding their services’ or systems’ capabilities and passenger quality of service. This paper accounts for variability in utilized demand by passengers along a line and high passenger load conditions where passenger pass-up delay occurs. A hypothetical case study of an individual bus service’s operation demonstrates the usefulness of passenger transmission in comparing existing and growth scenarios. A hypothetical case study of a bus line’s operation during a peak hour window demonstrates the theory’s usefulness in examining the contribution of individual services to line productive performance. Scenarios may be assessed using this theory to benchmark or compare lines and segments, conditions, or consider improvements.

Can slide positivity rates predict malaria transmission?

Background: Malaria is a significant threat to population health in the border areas of Yunnan Province, China. How to accurately measure malaria transmission is an important issue. This study aimed to examine the role of slide positivity rates (SPR) in malaria transmission in Mengla County, Yunnan Province, China. Methods: Data on annual malaria cases, SPR and socio-economic factors for the period of 1993 to 2008 were obtained from the Center for Disease Control and Prevention (CDC) and the Bureau of Statistics, Mengla, China. Multiple linear regression models were conducted to evaluate the relationship between socio-ecologic factors and malaria incidence. Results: The results show that SPR was significantly positively associated with the malaria incidence rates. The SPR (beta = 1.244, p = 0.000) alone and combination (SPR, beta = 1.326, p < 0.001) with other predictors can explain about 85% and 95% of variation in malaria transmission, respectively. Every 1% increase in SPR corresponded to an increase of 1.76/100,000 in malaria incidence rates. Conclusion: SPR is a strong predictor of malaria transmission, and can be used to improve the planning and implementation of malaria elimination programmes in Mengla and other similar locations. SPR might also be a useful indicator of malaria early warning systems in China.

One-year delayed effect of fog on malaria transmission : a time-series analysis in the rain forest area of Mengla County, south-west China

Background: Malaria is a major public health burden in the tropics with the potential to significantly increase in response to climate change. Analyses of data from the recent past can elucidate how short-term variations in weather factors affect malaria transmission. This study explored the impact of climate variability on the transmission of malaria in the tropical rain forest area of Mengla County, south-west China. Methods: Ecological time-series analysis was performed on data collected between 1971 and 1999. Auto-regressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Results: At the time scale of months, the predictors for malaria incidence included: minimum temperature, maximum temperature, and fog day frequency. The effect of minimum temperature on malaria incidence was greater in the cool months than in the hot months. The fog day frequency in October had a positive effect on malaria incidence in May of the following year. At the time scale of years, the annual fog day frequency was the only weather predictor of the annual incidence of malaria. Conclusion: Fog day frequency was for the first time found to be a predictor of malaria incidence in a rain forest area. The one-year delayed effect of fog on malaria transmission may involve providing water input and maintaining aquatic breeding sites for mosquitoes in vulnerable times when there is little rainfall in the 6-month dry seasons. These findings should be considered in the prediction of future patterns of malaria for similar tropical rain forest areas worldwide.