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When are solar refrigerators less costly than on-grid refrigerators: A simulation modeling study.

ABSTRACT: Gavi recommends solar refrigerators for vaccine storage in areas with less than eight hours of electricity per day, and WHO guidelines are more conservative. The question remains: Can solar refrigerators provide value where electrical outages are less frequent?Using a HERMES-generated computational model of the Mozambique routine immunization supply chain, we simulated the use of solar versus electric mains-powered refrigerators (hereafter referred to as "electric refrigerators") at different locations in the supply chain under various circumstances.At their current price premium, the annual cost of each solar refrigerator is 132% more than each electric refrigerator at the district level and 241% more at health facilities. Solar refrigerators provided savings over electric refrigerators when one-day electrical outages occurred more than five times per year at either the district level or the health facilities, even when the electric refrigerator holdover time exceeded the duration of the outage. Two-day outages occurring more than three times per year at the district level or more than twice per year at the health facilities also caused solar refrigerators to be cost saving. Lowering the annual cost of a solar refrigerator to 75% more than an electric refrigerator allowed solar refrigerators to be cost saving at either level when one-day outages occurred more than once per year, or when two-day outages occurred more than once per year at the district level or even once per year at the health facilities.Our study supports WHO and Gavi guidelines. In fact, solar refrigerators may provide savings in total cost per dose administered over electrical refrigerators when electrical outages are less frequent. Our study identified the frequency and duration at which electrical outages need to occur for solar refrigerators to provide savings in total cost per dose administered over electric refrigerators at different solar refrigerator prices.

SUBMITTER: Haidari LA

PROVIDER: S-EPMC5548121 | biostudies-other | 2017 Apr

REPOSITORIES: biostudies-other

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Project description:Grid cells are a special class of spatial cells found in the medial entorhinal cortex (MEC) characterized by their strikingly regular hexagonal firing fields. This spatially periodic firing pattern is originally considered to be independent of the geometric properties of the environment. However, this notion was contested by examining the grid cell periodicity in environments with different polarity (Krupic et al., 2015) and in connected environments (Carpenter et al., 2015). Aforementioned experimental results demonstrated the dependence of grid cell activity on environmental geometry. Analysis of grid cell periodicity on practically infinite variations of environmental geometry imposes a limitation on the experimental study. Hence we analyze the dependence of grid cell periodicity on the environmental geometry purely from a computational point of view. We use a hierarchical oscillatory network model where velocity inputs are presented to a layer of Head Direction cells, outputs of which are projected to a Path Integration layer. The Lateral Anti-Hebbian Network (LAHN) is used to perform feature extraction from the Path Integration neurons thereby producing a spectrum of spatial cell responses. We simulated the model in five types of environmental geometries such as: (1) connected environments, (2) convex shapes, (3) concave shapes, (4) regular polygons with varying number of sides, and (5) transforming environment. Simulation results point to a greater function for grid cells than what was believed hitherto. Grid cells in the model encode not just the local position but also more global information like the shape of the environment. Furthermore, the model is able to capture the invariant attributes of the physical space ingrained in its LAHN layer, thereby revealing its ability to classify an environment using this information. The proposed model is interesting not only because it is able to capture the experimental results but, more importantly, it is able to make many important predictions on the effect of the environmental geometry on the grid cell periodicity and suggesting the possibility of grid cells encoding the invariant properties of an environment.

Project description:BACKGROUND:Mental health service managers must take into account multiple factors when making decisions about the best way to deliver care to clients across increasingly larger service areas. This task is made more difficult by the lack of evidence and tools historically available to inform these decisions. In recent decades, the increasing availability of epidemiological and service use data for mental illness has solved the problem of evidence, but there still exists a challenge to make these data easily accessible and understandable for managers. OBJECTIVE:This study aims to develop a simulation modeling tool to allow managers to explore various service configurations in virtual reality, enabling predictions to be made about the cost and quality of care. METHODS:This is a longitudinal, mixed-methods case study, comprising overlapping intervention and evaluation phases. In partnership with senior managers of a mental health program, the researchers will develop a series of simulation models in Arena to address key strategic issues facing the service. Thematic and content analyses of semistructured interviews, meeting observations, and document analysis will be used to evaluate the process of model implementation and the outcomes for both researchers and managers. The study is being conducted in Australia. RESULTS:Data collection has been ongoing since late 2013. To date, 3 prototype simulation models have been developed and presented to senior managers, and 18 evaluation interviews have been conducted. The project is expected to conclude in late 2018. CONCLUSIONS:Findings of this study have the potential to shape decision making in mental health service delivery, by providing key examples of how to integrate patient data using simulation modeling. In addition, the results will provide key insights into how researchers and consultants can effectively implement simulation modeling in real-world health care organizations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):RR1-10.2196/11119.

Project description:BACKGROUND: Graphical models were identified as a promising new approach to modeling high-dimensional clinical data. They provided a probabilistic tool to display, analyze and visualize the net-like dependence structures by drawing a graph describing the conditional dependencies between the variables. Until now, the main focus of research was on building Gaussian graphical models for continuous multivariate data following a multivariate normal distribution. Satisfactory solutions for binary data were missing. We adapted the method of Meinshausen and Bühlmann to binary data and used the LASSO for logistic regression. Objective of this paper was to examine the performance of the Bolasso to the development of graphical models for high dimensional binary data. We hypothesized that the performance of Bolasso is superior to competing LASSO methods to identify graphical models. METHODS: We analyzed the Bolasso to derive graphical models in comparison with other LASSO based method. Model performance was assessed in a simulation study with random data generated via symmetric local logistic regression models and Gibbs sampling. Main outcome variables were the Structural Hamming Distance and the Youden Index.We applied the results of the simulation study to a real-life data with functioning data of patients having head and neck cancer. RESULTS: Bootstrap aggregating as incorporated in the Bolasso algorithm greatly improved the performance in higher sample sizes. The number of bootstraps did have minimal impact on performance. Bolasso performed reasonable well with a cutpoint of 0.90 and a small penalty term. Optimal prediction for Bolasso leads to very conservative models in comparison with AIC, BIC or cross-validated optimal penalty terms. CONCLUSIONS: Bootstrap aggregating may improve variable selection if the underlying selection process is not too unstable due to small sample size and if one is mainly interested in reducing the false discovery rate. We propose using the Bolasso for graphical modeling in large sample sizes.

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When are solar refrigerators less costly than on-grid refrigerators: A simulation modeling study.

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