In this paper, we conduct a systematic study of human-initiated cascading failures in three critical inter-dependent societal infrastructures due to behavioral adaptations in response to a crisis. We ...focus on three closely coupled socio-technical networks here: (i) cellular and mesh networks, (ii) transportation networks and (iii) mobile call networks. In crises, changes in individual behaviors lead to altered travel, activity and calling patterns, which influence the transport network and the loads on wireless networks. The interaction between these systems and their co-evolution poses significant technical challenges for representing and reasoning about these systems. In contrast to system dynamics models for studying these interacting infrastructures, we develop interaction-based models in which individuals and infrastructure elements are represented in detail and are placed in a common geographic coordinate system. Using the detailed representation, we study the impact of a chemical plume that has been released in a densely populated urban region. Authorities order evacuation of the affected area, and this leads to individual behavioral adaptation wherein individuals drop their scheduled activities and drive to home or pre-specified evacuation shelters as appropriate. They also revise their calling behavior to communicate and coordinate among family members. These two behavioral adaptations cause flash-congestion in the urban transport network and the wireless network. The problem is exacerbated with a few, already occurring, road closures. We analyze how extended periods of unanticipated road congestion can result in failure of infrastructures, starting with the servicing base stations in the congested area. A sensitivity analysis on the compliance rate of evacuees shows non-intuitive effect on the spatial distribution of people and on the loading of the base stations. For example, an evacuation compliance rate of 70% results in higher number of overloaded base stations than the evacuation compliance rate of 90%.
Introduction The wealth index is widely used as a proxy for a household’s socioeconomic position (SEP) and living standard. This work constructs a wealth index for the Mopeia district in Mozambique ...using data collected in year 2021 under the BOHEMIA (Broad One Health Endectocide-based Malaria Intervention in Africa) project. Methods We evaluate the performance of three alternative approaches against the Demographic and Health Survey (DHS) method based wealth index: feature selection principal components analysis (PCA), sparse PCA and robust PCA. The internal coherence between four wealth indices is investigated through statistical testing. Validation and an evaluation of the stability of the wealth index are performed with additional household income data from the BOHEMIA Health Economics Survey and the 2018 Malaria Indicator Survey data in Mozambique. Results The Spearman’s rank correlation between wealth index ventiles from four methods is over 0.98, indicating a high consistency in results across methods. Wealth rankings and households’ income show a strong concordance with the area under the curve value of ~0.7 in the receiver operating characteristic analysis. The agreement between the alternative wealth indices and the DHS wealth index demonstrates the stability in rankings from the alternative methods. Conclusions This study creates a wealth index for Mopeia, Mozambique, and shows that DHS method based wealth index is an appropriate proxy for the SEP in low-income regions. However, this research recommends feature selection PCA over the DHS method since it uses fewer asset indicators and constructs a high-quality wealth index.
In this paper, we examine the causal relationship between the per capita electricity consumption and the per capita GDP for Bangladesh using cointegration and vector error correction model. Our ...results show that there is unidirectional causality from per capita GDP to per capita electricity consumption. However, the per capita electricity consumption does not cause per capita GDP in case of Bangladesh. The finding has significant implications from the point of view of energy conservation, emission reduction and economic development.
We study the feedback processes between individual behavior, disease prevalence, interventions and social networks during an influenza pandemic when a limited stockpile of antivirals is shared ...between the private and the public sectors. An economic model that uses prevalence-elastic demand for interventions is combined with a detailed social network and a disease propagation model to understand the feedback mechanism between epidemic dynamics, market behavior, individual perceptions, and the social network. An urban and a rural region are simulated to assess the robustness of results. Results show that an optimal split between the private and public sectors can be reached to contain the disease but the accessibility of antivirals from the private sector is skewed towards the richest income quartile. Also, larger allocations to the private sector result in wastage where individuals who do not need it are able to purchase it but who need it cannot afford it. Disease prevalence increases with household size and total contact time but not by degree in the social network, whereas wastage of antivirals decreases with degree and contact time. The best utilization of drugs is achieved when individuals with high contact time use them, who tend to be the school-aged children of large families.
Decoupling the environmental attributes of renewable energy (RE) generation from the physical unit of energy is an innovative mechanism for marketing green or renewable power. The introduction of ...‘Tradable Renewable Energy Credits’ (TRECs) allows the green power attributes of energy to be sold or traded separately from the physical unit of energy. Since the green power certificate system removes potential locational and physical bottlenecks, both suppliers and consumers gain flexibility in the marketplace. The TREC is also an efficient tool to meet ‘Renewable Portfolio Standard’ (RPS) required by different states in the US. This paper discusses the RPS requirements for different states and examines the implications of an integrated TREC market. It offers a competitive setting to the consumers to pay for renewable energy and a cost effective tool to support renewable energy generation Grace and Wiser, 2002. This paper also highlights some practical difficulties that should be addressed in order to establish an efficient integrated TREC market.
Quite often symptoms of malaria go unrecognized or untreated. According to the Multilateral Initiative on Malaria, 70% of the malaria cases that are treated at home are mismanaged. Up to 82% of all ...malaria episodes in sub-Saharan Africa are treated outside the formal health sector. Fast and appropriate diagnosis and treatment of malaria is extremely important in reducing morbidity and mortality.
Data from 70 different countries is pooled together to construct a panel dataset of health and socio-economic variables for a time span of (1960-2004). The generalized two-stage least squares and panel data models are used to investigate the impact of information and communication network (ICN) variables on malaria death probability. The intensity of ICN is represented by the number of telephone main lines per 1,000 people and the number of television sets per 1,000 people.
The major finding is that the intensity of ICN is associated with reduced probability of deaths of people that are clinically identified as malaria infected. The results are robust for both indicators i.e. interpersonal and mass communication networks and for all model specifications examined.
The results suggest that information and communication networks can substantially scale up the effectiveness of the existing resources for malaria prevention. Resources spent in preventing malaria are far less than needed. Expanded information and communication networks will widen the avenues for community based "participatory development", that encourages the use of local information, knowledge and decision making. Timely information, immediate care and collective knowledge based treatment can be extremely important in reducing child mortality and achieving the millennium development goal.
Mixed-type observations, such as continuous measurements, discrete counts, and binary outcomes, are commonly present in many applications. The change-point detection with mixed-type observations is ...challenging since it is difficult to quantify the hidden association among mixed-type observations. In this work, we propose a latent process method to model the mixed observations in a joint manner, and effectively detect the changes. Bayesian parameter estimation and inference are developed for the proposed method by combining the discrete particle filter (DPF) and sequential Monte Carlo (SMC) algorithms. Such an algorithm can efficiently update the high dimensional proposal distribution and can exploit the discrete and continuous natures of the latent processes simultaneously. The performance of the proposed method is illustrated by several numerical examples and a case study of civil-unrest data.
Overuse and misuse of prescription opioids have become significant public health burdens in the United States. About 11.5 million people are estimated to have misused prescription opioids for ...nonmedical purposes in 2016. This has led to a significant number of drug overdose deaths in the United States. Previous studies have examined spatiotemporal clusters of opioid misuse, but they have been restricted to circular shaped regions.
The goal of this study was to identify spatiotemporal hot spots of opioid users and opioid prescription claims using Medicare data.
We examined spatiotemporal clusters with significantly higher number of beneficiaries and rate of prescriptions for opioids using Medicare payment data from the Centers for Medicare & Medicaid Services. We used network scan statistics to detect significant clusters with arbitrary shapes, the Kulldorff scan statistic to examine the significant clusters for each year (2013, 2014, and 2015) and an expectation-based version to examine the significant clusters relative to past years. Regression analysis was used to characterize the demographics of the counties that are a part of any significant cluster, and data mining techniques were used to discover the specialties of the anomalous providers.
We examined anomalous spatial clusters with respect to opioid prescription claims and beneficiary counts and found some common patterns across states: the counties in the most anomalous clusters were fairly stable in 2014 and 2015, but they have shrunk from 2013. In Virginia, a higher percentage of African Americans in a county lower the odds of the county being anomalous in terms of opioid beneficiary counts to about 0.96 in 2015. For opioid prescription claim counts, the odds were 0.92. This pattern was consistent across the 3 states and across the 3 years. A higher number of people in the county with access to Medicaid increased the odds of the county being in the anomalous cluster to 1.16 in both types of counts in Virginia. A higher number of people with access to direct purchase of insurance plans decreased the odds of a county being in an anomalous cluster to 0.85. The expectation-based scan statistic, which captures change over time, revealed different clusters than the Kulldorff statistic. Providers with an unusually high number of opioid beneficiaries and opioid claims include specialties such as physician's assistant, nurse practitioner, and family practice.
Our analysis of the Medicare claims data provides characteristics of the counties and provider specialties that have higher odds of being anomalous. The empirical analysis identifies highly refined spatial hot spots that are likely to encounter prescription opioid misuse and overdose. The methodology is generic and can be applied to monitor providers and their prescription behaviors in regions that are at a high risk of abuse.
Infectious diseases such as Influenza and Ebola pose a serious threat to everyone but certain demographics and cohorts face a higher risk of infection than others. This research provides a ...computational framework for studying health disparities among cohorts based on individual level features, such as age, gender, income, etc. We apply this framework to find health disparities among subpopulations in an influenza epidemic and evaluate vaccination prioritization strategies to achieve specific objectives. We explore the heterogeneities in individuals’ demographic and socioeconomic attributes as the potential cause of health disparities. An agent-based model is used to simulate an influenza epidemic over a synthetic social contact network of the Montgomery County in Southwest Virginia to identify infected cases which are then labeled with a specific clinical outcome by using a predefined probability distribution based on age and risk level. We divide the population into age and income based cohorts and measure the direct and indirect economic impact of vaccination for each cohort. Simulation-based results find strong health disparities across age and income groups. Various vaccine distribution strategies are considered and outcomes are measured through metrics such as death count, total number of infections, net return per capita, net return per dollar spent and net return per vaccinated person. The results, framework, and methodology developed here can assist public health policy makers in efficiently allocating limited pharmaceutical resources.
According to the U.S. Energy Information Administration (EIA), the residential sector accounts for one-third of the country's energy consumption. This number is steadily increasing, posing a ...challenge to energy regulators as well as suppliers. To manage the growing demand for energy, there is a need for energy system optimization, especially on the demand side. This paper uses a first principles approach to build a high-resolution energy demand model, which can be used as a test bed by academicians as well as policy makers for performing such optimizations. This framework generates activity-based, building-level, time-dependent demand profiles. The model associates appliance usage with each household activity and calculates energy consumption based on the appliance energy rating, the duration of the energy consuming activity, and the type of activity performed by each household member. It also accounts for shared activities among household members to avoid double counting. Additionally, passive energy consumptions such as space heating/cooling, lighting, etc., are measured. Finally, validation of the results obtained by this model against real-world data for Virginia is carried out. The results indicate that the modeling framework is robust and can be extended to other parts of the U.S. and beyond.
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