Browsing by Author "Marathe, Achla"

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    An activity-based energy demand modeling framework for buildings: A bottom-up approach
    Subbiah, Rajesh (Virginia Tech, 2013-05-23)
    Energy consumption by buildings, due to various factors such as temperature regulation, lighting, poses a threat to our environment and energy resources. In the United States, statistics reveal that commercial and residential buildings combined contribute about 40 percent of the overall energy consumption, and this figure is expected to increase. In order to manage the growing demand for energy, there is a need for energy system optimization, which would require a realistic, high-resolution energy-demand model. In this work, we investigate and model the energy consumption of buildings by taking into account physical, structural, economic, and social factors that influence energy use. We propose a novel activity based modeling framework that generates an energy demand profile on a regular basis for a given nominal day.  We use this information to generate a building-level energy demand profile at highly dis-aggregated level. We then investigate the different possible uses of generated demand profiles in different What-if scenarios like urban-area planning, demand-side management, demand sensitive pricing, etc. We also provide a novel way to resolve correlational and consistency problems in the generation of individual-level and building-level "shared" activities which occur due to individuals\' interactions.
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    Alternative approaches for creating a wealth index: the case of Mozambique
    Xie, Kexin; Marathe, Achla; Deng, Xinwei; Ruiz-Castillo, Paula; Imputiua, Saimado; Elobolobo, Eldo; Mutepa, Victor; Sale, Mussa; Nicolas, Patricia; Montana, Julia; Jamisse, Edgar; Munguambe, Humberto; Materrula, Felisbela; Casellas, Aina; Rabinovich, Regina; Saute, Francisco; Chaccour, Carlos J.; Sacoor, Charfudin; Rist, Cassidy (BMJ, 2023-08)
    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.
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    ‘Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators
    Ramakrishnan, Naren; Butler, Patrick; Self, Nathan; Khandpur, Rupinder P.; Saraf, Parang; Wang, Wei; Cadena, Jose; Vullikanti, Anil Kumar S.; Korkmaz, Gizem; Kuhlman, Christopher J.; Marathe, Achla; Zhao, Liang; Ting, Hua; Huang, Bert; Srinivasan, Aravind; Trinh, Khoa; Getoor, Lise; Katz, Graham; Doyle, Andy; Ackermann, Chris; Zavorin, Ilya; Ford, Jim; Summers, Kristen; Fayed, Youssef; Arredondo, Jaime; Gupta, Dipak; Mares, David; Muthia, Sathappan; Chen, Feng; Lu, Chang-Tien (2014)
    We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the uptick and downtick of incidents during the June 2013 protests in Brazil. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.
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    BOHEMIA a cluster randomized trial to assess the impact of an endectocide-based one health approach to malaria in Mozambique: baseline demographics and key malaria indicators
    Ruiz-Castillo, Paula; Imputiua, Saimado; Xie, Kexin; Elobolobo, Eldo; Nicolas, Patricia; Montaña, Julia; Jamisse, Edgar; Munguambe, Humberto; Materrula, Felisbela; Casellas, Aina; Deng, Xinwei; Marathe, Achla; Rabinovich, Regina; Saute, Francisco; Chaccour, Carlos; Sacoor, Charfudin (2023-06-04)
    Background Many geographical areas of sub-Saharan Africa, especially in rural settings, lack complete and up-to-date demographic data, posing a challenge for implementation and evaluation of public health interventions and carrying out large-scale health research. A demographic survey was completed in Mopeia district, located in the Zambezia province in Mozambique, to inform the Broad One Health Endectocide-based Malaria Intervention in Africa (BOHEMIA) cluster randomized clinical trial, which tested ivermectin mass drug administration to humans and/or livestock as a potential novel strategy to decrease malaria transmission. Methods The demographic survey was a prospective descriptive study, which collected data of all the households in the district that accepted to participate. Households were mapped through geolocation and identified with a unique identification number. Basic demographic data of the household members was collected and each person received a permanent identification number for the study. Results 25,550 households were mapped and underwent the demographic survey, and 131,818 individuals were registered in the district. The average household size was 5 members and 76.9% of households identified a male household head. Housing conditions are often substandard with low access to improved water systems and electricity. The reported coverage of malaria interventions was 71.1% for indoor residual spraying and 54.1% for universal coverage of long-lasting insecticidal nets. The median age of the population was 15 years old. There were 910 deaths in the previous 12 months reported, and 43.9% were of children less than 5 years of age. Conclusions The study showed that the district had good coverage of vector control tools against malaria but sub-optimal living conditions and poor access to basic services. The majority of households are led by males and Mopeia Sede/Cuacua is the most populated locality in the district. The population of Mopeia is young (< 15 years) and there is a high childhood mortality. The results of this survey were crucial as they provided the household and population profiles and allowed the design and implementation of the cluster randomized clinical trial. Trial registration NCT04966702.
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    BOHEMIA: Broad One Health Endectocide-based Malaria Intervention in Africa—a phase III cluster-randomized, open-label, clinical trial to study the safety and efficacy of ivermectin mass drug administration to reduce malaria transmission in two African settings
    Chaccour, Carlos; Casellas, Aina; Hammann, Felix; Ruiz-Castillo, Paula; Nicolas, Patricia; Montaña, Julia; Mael, Mary; Selvaraj, Prashant; Duthaler, Urs; Mrema, Sigilbert; Kakolwa, Mwaka; Lyimo, Issa; Okumu, Fredros; Marathe, Achla; Schürch, Roger; Elobolobo, Eldo; Sacoor, Charfudin; Saute, Francisco; Xia, Kang; Jones, Caroline; Rist, Cassidy; Maia, Marta; Rabinovich, N. R. (2023-02-21)
    Background Residual malaria transmission is the result of adaptive mosquito behavior that allows malaria vectors to thrive and sustain transmission in the presence of good access to bed nets or insecticide residual spraying. These behaviors include crepuscular and outdoor feeding as well as intermittent feeding upon livestock. Ivermectin is a broadly used antiparasitic drug that kills mosquitoes feeding on a treated subject for a dose-dependent period. Mass drug administration with ivermectin has been proposed as a complementary strategy to reduce malaria transmission. Methods A cluster randomized, parallel arm, superiority trial conducted in two settings with distinct eco-epidemiological conditions in East and Southern Africa. There will be three groups: human intervention, consisting of a dose of ivermectin (400 mcg/kg) administered monthly for 3 months to all the eligible population in the cluster (>15 kg, non-pregnant and no medical contraindication); human and livestock intervention, consisting human treatment as above plus treatment of livestock in the area with a single dose of injectable ivermectin (200 mcg/kg) monthly for 3 months; and controls, consisting of a dose of albendazole (400 mg) monthly for 3 months. The main outcome measure will be malaria incidence in a cohort of children under five living in the core of each cluster followed prospectively with monthly RDTs Discussion The second site for the implementation of this protocol has changed from Tanzania to Kenya. This summary presents the Mozambique-specific protocol while the updated master protocol and the adapted Kenya-specific protocol undergo national approval in Kenya. BOHEMIA will be the first large-scale trial evaluating the impact of ivermectin-only mass drug administration to humans or humans and cattle on local malaria transmission.
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    Change Detection and Analysis of Data with Heterogeneous Structures
    Chu, Shuyu (Virginia Tech, 2017-07-28)
    Heterogeneous data with different characteristics are ubiquitous in the modern digital world. For example, the observations collected from a process may change on its mean or variance. In numerous applications, data are often of mixed types including both discrete and continuous variables. Heterogeneity also commonly arises in data when underlying models vary across different segments. Besides, the underlying pattern of data may change in different dimensions, such as in time and space. The diversity of heterogeneous data structures makes statistical modeling and analysis challenging. Detection of change-points in heterogeneous data has attracted great attention from a variety of application areas, such as quality control in manufacturing, protest event detection in social science, purchase likelihood prediction in business analytics, and organ state change in the biomedical engineering. However, due to the extraordinary diversity of the heterogeneous data structures and complexity of the underlying dynamic patterns, the change-detection and analysis of such data is quite challenging. This dissertation aims to develop novel statistical modeling methodologies to analyze four types of heterogeneous data and to find change-points efficiently. The proposed approaches have been applied to solve real-world problems and can be potentially applied to a broad range of areas.
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    Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches
    Brownstein, John S.; Marathe, Achla (JMIR Publications, 2017)
    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. Objective: 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. The 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.
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    Comparing Effectiveness of Top-Down and Bottom-Up Strategies in Containing Influenza
    Marathe, Achla; Lewis, Bryan L.; Barrett, Christopher L.; Chen, Jiangzhuo; Marathe, Madhav V.; Eubank, Stephen; Ma, Yifei (Public Library of Science, 2011-09-22)
    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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    Complex situation analysis system that generates a social contact network, uses edge brokers and service brokers, and dynamically adds brokers
    (United States Patent and Trademark Office, 2013-04-16)
    A system for generating a representation of a situation is disclosed. The system comprises one or more computer-readable media including computer-executable instructions that are executable by one or more processors to implement a method of generating a representation of a situation. The method comprises receiving input data regarding a target population. The method further comprises constructing a synthetic data set including a synthetic population based on the input data. The synthetic population includes a plurality of synthetic entities. Each synthetic entity has a one-to-one correspondence with an entity in the target population. Each synthetic entity is assigned one or more attributes based on information included in the input data. The method further comprises receiving activity data for a plurality of entities in the target population.
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    Demographics, perceptions, and socioeconomic factors affecting influenza vaccination among adults in the United States
    Abbas, Kaja M.; Kang, Gloria J.; Chen, Daniel; Werre, Stephen R.; Marathe, Achla (PeerJ, 2018-07-13)
    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 75Cyear 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.
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    Detail in network models of epidemiology: are we there yet?
    Eubank, Stephen; Barrett, Christopher L.; Beckman, Richard J.; Bisset, Keith R.; Durbeck, L.; Kuhlman, Christopher J.; Lewis, Bryan L.; Marathe, Achla; Marathe, Madhav V.; Stretz, P. (Taylor & Francis, 2010)
    Network models of infectious disease epidemiology can potentially provide insight into how to tailor control strategies for specific regions, but only if the network adequately reflects the structure of the region’s contact network. Typically, the network is produced by models that incorporate details about human interactions. Each detail added renders the models more complicated and more difficult to calibrate, but also more faithful to the actual contact network structure. We propose a statistical test to determine when sufficient detail has been added to the models and demonstrate its application to the models used to create a synthetic population and contact network for the USA.
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    Discovery of under immunized spatial clusters using network scan statistics
    Cadena, Jose; Falcone, David; Marathe, Achla; Vullikanti, Anil (2019-02-04)
    Background Clusters of under-vaccinated children are emerging in a number of states in the United States due to rising rates of vaccine hesitancy and refusal. As the measles outbreaks in California and other states in 2015 and in Minnesota in 2017 showed, such clusters can pose a significant public health risk. Prior methods have used publicly-available school immunization data for analysis (except for a few, which use private healthcare patient records). School immunization data has limited demographic information—as a result, such analyses are not able to provide demographic characteristics of significant clusters. Further, the resolution of the clusters identified by prior methods is limited since they are typically restricted to disks or well-rounded shapes. Methods We use realistic population models for Minnesota (MN) and Washington (WA) state, which provide a model of activities for all individuals in the population. We combine this with school level immunization data for these two states, to estimate vaccine coverage at the level of census block groups. A scan statistic method defined on networks is used for finding significant clusters of under-immunized block groups, without any restrictions on shape. Further we provide the demographic characteristics of these clusters. Results We find 2 significant under-vaccinated clusters in MN and 3 in WA. These are very irregular in shape, in contrast to the circular disks reported in prior work, which rely on the SatScan approach. Some of the clusters found by our method are not contained in those computed using SatScan, a state-of-the-art software tool used in similar studies in other states. Conclusions The emergence of under-immunized clusters is a growing concern for public health agencies because they can act as reservoirs of infection and increase the risk of infection into the wider population. Higher resolution clusters computed using our network based approach and population models provide new insights on the structure and characteristics of such clusters and enable targeted interventions.
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    Discrete Event Simulation of Mobility and Spatio-Temporal Spectrum Demand
    Chandan, Shridhar (Virginia Tech, 2014-02-05)
    Realistic mobility and cellular traffic modeling is key to various wireless networking applications and have a significant impact on network performance. Planning and design, network resource allocation and performance evaluation in cellular networks require realistic traffic modeling. We propose a Discrete Event Simulation framework, Diamond - (Discrete Event Simulation of Mobility and Spatio-Temporal Spectrum Demand) to model and analyze realistic activity based mobility and spectrum demand patterns. The framework can be used for spatio-temporal estimation of load, in deciding location of a new base station, contingency planning, and estimating the resilience of the existing infrastructure. The novelty of this framework lies in its ability to capture a variety of complex, realistic and dynamically changing events effectively. Our initial results show that the framework can be instrumental in contingency planning and dynamic spectrum allocation.
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    Disparities in spread and control of influenza in slums of Delhi: findings from an agent-based modelling study
    Adiga, Abhijin; Chu, Shuyu; Kuhlman, Christopher J.; Lewis, Bryan L.; Marathe, Achla; Nordberg, Eric K.; Swarup, Samarth; Vullikanti, Anil; Wilson, Mandy L. (BMJ Publishing Group, 2017-11-03)
    Objectives: This research studies the role of slums in the spread and control of infectious diseases in the National Capital Territory of India, Delhi, using detailed social contact networks of its residents. Methods: We use an agent-based model to study the spread of influenza in Delhi through person-to-person contact. Two different networks are used: one in which slum and non-slum regions are treated the same, and the other in which 298 slum zones are identified. In the second network, slum-specific demographics and activities are assigned to the individuals whose homes reside inside these zones. The main effects of integrating slums are that the network has more home-related contacts due to larger family sizes and more outside contacts due to more daily activities outside home. Various vaccination and social distancing interventions are applied to control the spread of influenza. Results: Simulation-based results show that when slum attributes are ignored, the effectiveness of vaccination can be overestimated by 30%–55%, in terms of reducing the peak number of infections and the size of the epidemic, and in delaying the time to peak infection. The slum population sustains greater infection rates under all intervention scenarios in the network that treats slums differently. Vaccination strategy performs better than social distancing strategies in slums. Conclusions: Unique characteristics of slums play a significant role in the spread of infectious diseases. Modelling slums and estimating their impact on epidemics will help policy makers and regulators more accurately prioritise allocation of scarce medical resources and implement public health policies.
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    A Distributed Approach to EpiFast using Apache Spark
    Kannan, Vijayasarathy (Virginia Tech, 2015-08-04)
    EpiFast is a parallel algorithm for large-scale epidemic simulations, based on an interpretation of the stochastic disease propagation in a contact network. The original EpiFast implementation is based on a master-slave computation model with a focus on distributed memory using message-passing-interface (MPI). However, it suffers from few shortcomings with respect to scale of networks being studied. This thesis addresses these shortcomings and provides two different implementations: Spark-EpiFast based on the Apache Spark big data processing engine and Charm-EpiFast based on the Charm++ parallel programming framework. The study focuses on exploiting features of both systems that we believe could potentially benefit in terms of performance and scalability. We present models of EpiFast specific to each system and relate algorithm specifics to several optimization techniques. We also provide a detailed analysis of these optimizations through a range of experiments that consider scale of networks and environment settings we used. Our analysis shows that the Spark-based version is more efficient than the Charm++ and MPI-based counterparts. To the best of our knowledge, ours is one of the preliminary efforts of using Apache Spark for epidemic simulations. We believe that our proposed model could act as a reference for similar large-scale epidemiological simulations exploring non-MPI or MapReduce-like approaches.
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    Economic and Social Impact of Influenza Mitigation Strategies by Demographic Class
    Barrett, Christopher L.; Bisset, Keith R.; Leidig, Jonathan; Marathe, Achla; Marathe, Madhav V. (Elsevier, 2011-03-01)
    Background—We aim to determine the economic and social impact of typical interventions proposed by the public health officials and preventive behavioral changes adopted by the private citizens in the event of a “flu-like” epidemic. Method—We apply an individual-based simulation model to the New River Valley area of Virginia for addressing this critical problem. The economic costs include not only the loss in productivity due to sickness but also the indirect cost incurred through disease avoidance and caring for dependents. Results—The results show that the most important factor responsible for preventing income loss is the modification of individual behavior; it drops the total income loss by 62% compared to the base case. The next most important factor is the closure of schools which reduces the total income loss by another 40%. Conclusions—The preventive behavior of the private citizens is the most important factor in controlling the epidemic.
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    Effect of modelling slum populations on influenza spread in Delhi
    Chen, Jiangzhuo; Chu, Shuyu; Chungbaek, Youngyun; Khan, Maleq; Kuhlman, Christopher J.; Marathe, Achla; Mortveit, Henning; Vullikanti, Anil; Xie, Dawen (BMJ, 2016-01-01)
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    Epidemiological and economic impact of pandemic influenza in Chicago: Priorities for vaccine interventions
    Dorratoltaj, Nargesalsadat; Marathe, Achla; Lewis, Bryan L.; Swarup, Samarth; Eubank, Stephen G.; Abbas, Kaja M. (PLOS, 2017-06-01)
    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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    EpiViewer: An Epidemiological Application For Exploring Time Series Data
    Thorve, Swapna (Virginia Tech, 2018-11)
    Visualization plays an important role in epidemic time series analysis and forecasting. Viewing time series data plotted on a graph can help researchers identify anomalies and unexpected trends that could be overlooked if the data were reviewed in tabular form. However,there are challenges in reviewing data sets from multiple data sources (data can be aggregated in different ways and measure different criteria which can make a direct comparison between time series difficult. In the face of an emerging epidemic, the ability to visualize time series from various sources and organizations and to reconcile these datasets based on different criteria could be key in developing accurate forecasts and identifying effective interventions. Many tools have been developed for visualizing temporal data; however, none yet supports all the functionality needed for easy collaborative visualization and analysis of epidemic data. In this thesis, we develop EpiViewer, a time series exploration dashboard where users can upload epidemiological time series data from a variety of sources and compare, organize, and track how data evolves as an epidemic progresses. EpiViewer provides an easy-to-use web interface for visualizing temporal datasets either as line charts or bar charts. The application provides enhanced features for visual analysis, such as hierarchical categorization, zooming, and filtering, to enable detailed inspection and comparison of multiple time series on a single canvas. Finally, EpiViewer provides a built-in statistical Epi-features module to help users interpret the epidemiological curves.
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    Feedback Between Behavioral Adaptations and Disease Dynamics
    Chen, Jiangzhuo; Marathe, Achla; Marathe, Madhav V. (Springer Nature, 2018-08-20)
    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.