121 research outputs found
Marketecture: A Simulation-Based Framework for Studying Experimental Deregulated Power Markets
In this paper, we present MARKETECTURE, an agent-based, microeconomic, scalable model for studying deregulated power markets. Features that distinguish it from previously studied models include: the ability to generate individualistic, demographics based, elastic demand profiles; a highly configurable system that supports different matching algorithms for buyers and sellers, different market clearing mechanisms; ability to aggregate individuals to different classes; an electrical grid to physically clear the economic contracts etc. This paper describes the model and its various features in detail. A case study is done for the city of Portland, Oregon, to evaluate the performance and efficiency of the market under different market clearing algorithms and sellers’ strategies. We analyze the structural properties of the market under different scenarios to validate our model. Our results show that if Vickrey auction clearing mechanism can induce the sellers to reveal their true production costs and bid at competitive level, the market performance can be almost pareto-efficient. The weighted average clearing method in the poolco market results in the lowest market clearing price (MCP). However, the market clearing quantity (MCQ) is also low which results in deadweight loss to the society. Our findings also show that the different orders of market execution (bilateral and poolco) can significantly affect the performance of the markets
Epidemiological and economic impact of pandemic influenza in Chicago: Priorities for vaccine interventions.
The study objective is to estimate the epidemiological and economic impact of vaccine interventions during influenza pandemics in Chicago, and assist in vaccine intervention priorities. Scenarios of delay in vaccine introduction with limited vaccine efficacy and limited supplies are not unlikely in future influenza pandemics, as in the 2009 H1N1 influenza pandemic. We simulated influenza pandemics in Chicago using agent-based transmission dynamic modeling. Population was distributed among high-risk and non-high risk among 0-19, 20-64 and 65+ years subpopulations. Different attack rate scenarios for catastrophic (30.15%), strong (21.96%), and moderate (11.73%) influenza pandemics were compared against vaccine intervention scenarios, at 40% coverage, 40% efficacy, and unit cost of $28.62. Sensitivity analysis for vaccine compliance, vaccine efficacy and vaccine start date was also conducted. Vaccine prioritization criteria include risk of death, total deaths, net benefits, and return on investment. The risk of death is the highest among the high-risk 65+ years subpopulation in the catastrophic influenza pandemic, and highest among the high-risk 0-19 years subpopulation in the strong and moderate influenza pandemics. The proportion of total deaths and net benefits are the highest among the high-risk 20-64 years subpopulation in the catastrophic, strong and moderate influenza pandemics. The return on investment is the highest in the high-risk 0-19 years subpopulation in the catastrophic, strong and moderate influenza pandemics. Based on risk of death and return on investment, high-risk groups of the three age group subpopulations can be prioritized for vaccination, and the vaccine interventions are cost saving for all age and risk groups. The attack rates among the children are higher than among the adults and seniors in the catastrophic, strong, and moderate influenza pandemic scenarios, due to their larger social contact network and homophilous interactions in school. Based on return on investment and higher attack rates among children, we recommend prioritizing children (0-19 years) and seniors (65+ years) after high-risk groups for influenza vaccination during times of limited vaccine supplies. Based on risk of death, we recommend prioritizing seniors (65+ years) after high-risk groups for influenza vaccination during times of limited vaccine supplies
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Characterizing the Interaction Between Routing and MAC Protocols in Ad-Hoc Networks
We empirically study the effect of mobility on the performance of protocols designed for wireless ad-hoc networks. An important objective is to study the interaction of the Routing and MAC layer protocols under different mobility parameters. We use three basic mobility models: grid mobility model, random waypoint model, and exponential correlated random model. The performance of protocols is measured in terms of (i) latency, (ii) throughput, (iii) number of packets received, (iv) long term fairness and (v) number of control packets at the MAC and routing layer level. Three different commonly studied routing protocols are used: AODV, DSR and LAR1. Similarly three well known MAC protocols are used: MACA, 802.1 1 and CSMA. Our main contribution is simulation based experiments coupled with rigorous statistical analysis to characterize the interaction of MAC layer protocols with routing layer protocols in ad-hoc networks. From the results, we can conclude the following: e No single MAC or Routing protocol dominated the other protocols in their class. Probably more interestingly, no MAURouting protocol combination was better than other combinations over all scenarios and response variables. 0 In general, it is not meaningful to speak about a MAC or a routing protocol in isolation. Presence of interaction leads to trade-offs between the amount of control packets generated by each layer. The results raise the possibility of improving the performance of a particular MAC layer protocol by using a cleverly designed routing protocol or vice-versa. Thus in order to improve the performanceof a communication network, it is important to study the entire protocol stack as a single algorithmic construct; optimizing individual layers in the seven layer OS1 stack will not yield performance improvements beyond a point. A methodological contribution of this paper is the use of statistical methods such as analysis of variance (ANOVA), to characterize the interaction between the protocols, mobility patterns and speed. Such methods allow us to analyze complicated experiments with large input space in a systematic manner
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EFFECT OF MOBILITY ON PERFORMANCE OF WIRELESS AD-HOC NETWORK PROTOCOLS.
We empirically study the effect of mobility on the performance of protocols designed for wireless adhoc networks. An important ohjective is to study the interaction of the Routing and MAC layer protocols under different mobility parameters. We use three basic mobility models: grid mobility model, random waypoint model, and exponential correlated random model. The performance of protocols was measured in terms of (i) latency, (ii) throughput, (iii) number of packels received, (iv) long term fairness and (v) number of control packets at the MAC layer level. Three different commonly studied routing protocols were used: AODV, DSR and LAR1. Similarly three well known MAC protocols were used: MACA, 802.1 1 and CSMA. The inair1 conclusion of our study include the following: 1. 'I'he performance of the: network varies widely with varying mobility models, packet injection rates and speeds; and can ba in fact characterized as fair to poor depending on the specific situation. Nevertheless, in general, it appears that the combination of AODV and 802.1 I is far better than other combination of routing and MAC protocols. 2. MAC layer protocols interact with routing layer protocols. This concept which is formalized using statistics implies that in general it is not meaningful to speak about a MAC or a routing protocol in isolation. Such an interaction leads to trade-offs between the amount of control packets generated by each layer. More interestingly, the results wise the possibility of improving the performance of a particular MAC layer protocol by using a cleverly designed routing protocol or vice-versa. 3. Routing prolocols with distributed knowledge about routes are more suitable for networks with mobility. This is seen by comparing the performance of AODV with DSR or LAR scheme 1. In DSli and IAR scheme 1, information about a computed path is being stored in the route query control packct. 4. MAC layer protocols have varying performance with varying mobility models. It is not only speed that influences the performance but also node degree and connectivity of the dynamic network that affects the protocol performance. 'The main implication of OUI' work is that performance analysis of protocols at a given level in the protocol stack need to be studied not locally in isolation but as a part of the complete protocol stack. The results suggest that in order to improve the pcrlormance of a communication network, it will be important to study the entire protocol stack as a single algorithmic construct; optimizing individual layers in the 7 layer OS1 stilck will not yield performance improvements beyond a point. A methodological contribution of this paper is the use of statistical methods such as design of experinierits arzd aiialysis qf variance methods to characterize the interaction between the protocols, mobility patterns and speed. This allows us to mako much more informed conclusions about the performance of thc protocols than would have been possible by merely running these experiments and observing the data. These ideas are of independtmt interest and are applicable in other contexts wherein one experimentally analyzes algorithms
Demographics, perceptions, and socioeconomic factors affecting influenza vaccination among adults in the United States.
OBJECTIVE: The study objective is to analyze influenza vaccination status by demographic factors, perceived vaccine efficacy, social influence, herd immunity, vaccine cost, health insurance status, and barriers to influenza vaccination among adults 18 years and older in the United States. BACKGROUND: Influenza vaccination coverage among adults 18 years and older was 41% during 2010-2011 and has increased and plateaued at 43% during 2016-2017. This is below the target of 70% influenza vaccination coverage among adults, which is an objective of the Healthy People 2020 initiative. METHODS: We conducted a survey of a nationally representative sample of adults 18 years and older in the United States on factors affecting influenza vaccination. We conducted bivariate analysis using Rao-Scott chi-square test and multivariate analysis using weighted multinomial logistic regression of this survey data to determine the effect of demographics, perceived vaccine efficacy, social influence, herd immunity, vaccine cost, health insurance, and barriers associated with influenza vaccination uptake among adults in the United States. RESULTS: Influenza vaccination rates are relatively high among adults in older age groups (73.3% among 75Â + year old), adults with education levels of bachelor's degree or higher (45.1%), non-Hispanic Whites (41.8%), adults with higher incomes (52.8% among adults with income of over $150,000), partnered adults (43.2%), non-working adults (46.2%), and adults with internet access (39.9%). Influenza vaccine is taken every year by 76% of adults who perceive that the vaccine is very effective, 64.2% of adults who are socially influenced by others, and 41.8% of adults with health insurance, while 72.3% of adults without health insurance never get vaccinated. Facilitators for adults getting vaccinated every year in comparison to only some years include older age, perception of high vaccine effectiveness, higher income and no out-of-pocket payments. Barriers for adults never getting vaccinated in comparison to only some years include lack of health insurance, disliking of shots, perception of low vaccine effectiveness, low perception of risk for influenza infection, and perception of risky side effects. CONCLUSION: Influenza vaccination rates among adults in the United States can be improved towards the Healthy People 2020 target of 70% by increasing awareness of the safety, efficacy and need for influenza vaccination, leveraging the practices and principles of commercial and social marketing to improve vaccine trust, confidence and acceptance, and lowering out-of-pocket expenses and covering influenza vaccination costs through health insurance
Combining participatory influenza surveillance with modeling and forecasting
Background:
Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact.
Objectives:
Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions.
Methods:
We describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), InfluenzaNet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using InfluenzaNet and Flu Near You).
Results:
WISDM based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. InfluenzaNet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities; and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40% when compared to the estimates of the model that only incorporates CDC historical information.
Conclusions:
While the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long term forecasting of Influenza activity in data poor parts of the world
Semantic network analysis of vaccine sentiment in online social media.
OBJECTIVE: To examine current vaccine sentiment on social media by constructing and analyzing semantic networks of vaccine information from highly shared websites of Twitter users in the United States; and to assist public health communication of vaccines. BACKGROUND: Vaccine hesitancy continues to contribute to suboptimal vaccination coverage in the United States, posing significant risk of disease outbreaks, yet remains poorly understood. METHODS: We constructed semantic networks of vaccine information from internet articles shared by Twitter users in the United States. We analyzed resulting network topology, compared semantic differences, and identified the most salient concepts within networks expressing positive, negative, and neutral vaccine sentiment. RESULTS: The semantic network of positive vaccine sentiment demonstrated greater cohesiveness in discourse compared to the larger, less-connected network of negative vaccine sentiment. The positive sentiment network centered around parents and focused on communicating health risks and benefits, highlighting medical concepts such as measles, autism, HPV vaccine, vaccine-autism link, meningococcal disease, and MMR vaccine. In contrast, the negative network centered around children and focused on organizational bodies such as CDC, vaccine industry, doctors, mainstream media, pharmaceutical companies, and United States. The prevalence of negative vaccine sentiment was demonstrated through diverse messaging, framed around skepticism and distrust of government organizations that communicate scientific evidence supporting positive vaccine benefits. CONCLUSION: Semantic network analysis of vaccine sentiment in online social media can enhance understanding of the scope and variability of current attitudes and beliefs toward vaccines. Our study synthesizes quantitative and qualitative evidence from an interdisciplinary approach to better understand complex drivers of vaccine hesitancy for public health communication, to improve vaccine confidence and vaccination coverage in the United States
Impact of demographic disparities in social distancing and vaccination on influenza epidemics in urban and rural regions of the United States.
BACKGROUND: Self-protective behaviors of social distancing and vaccination uptake vary by demographics and affect the transmission dynamics of influenza in the United States. By incorporating the socio-behavioral differences in social distancing and vaccination uptake into mathematical models of influenza transmission dynamics, we can improve our estimates of epidemic outcomes. In this study we analyze the impact of demographic disparities in social distancing and vaccination on influenza epidemics in urban and rural regions of the United States. METHODS: We conducted a survey of a nationally representative sample of US adults to collect data on their self-protective behaviors, including social distancing and vaccination to protect themselves from influenza infection. We incorporated this data in an agent-based model to simulate the transmission dynamics of influenza in the urban region of Miami Dade county in Florida and the rural region of Montgomery county in Virginia. RESULTS: We compare epidemic scenarios wherein the social distancing and vaccination behaviors are uniform versus non-uniform across different demographic subpopulations. We infer that a uniform compliance of social distancing and vaccination uptake among different demographic subpopulations underestimates the severity of the epidemic in comparison to differentiated compliance among different demographic subpopulations. This result holds for both urban and rural regions. CONCLUSIONS: By taking into account the behavioral differences in social distancing and vaccination uptake among different demographic subpopulations in analysis of influenza epidemics, we provide improved estimates of epidemic outcomes that can assist in improved public health interventions for prevention and control of influenza
Comparing Effectiveness of Top-Down and Bottom-Up Strategies in Containing Influenza
This research compares the performance of bottom-up, self-motivated behavioral interventions with top-down interventions targeted at controlling an “Influenza-like-illness”. Both types of interventions use a variant of the ring strategy. In the first case, when the fraction of a person's direct contacts who are diagnosed exceeds a threshold, that person decides to seek prophylaxis, e.g. vaccine or antivirals; in the second case, we consider two intervention protocols, denoted Block and School: when a fraction of people who are diagnosed in a Census Block (resp., School) exceeds the threshold, prophylax the entire Block (resp., School). Results show that the bottom-up strategy outperforms the top-down strategies under our parameter settings. Even in situations where the Block strategy reduces the overall attack rate well, it incurs a much higher cost. These findings lend credence to the notion that if people used antivirals effectively, making them available quickly on demand to private citizens could be a very effective way to control an outbreak
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