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Dr. Ali Asgary
Faculty of Liberal Arts & Professional Studies, York University, Toronto, ON M3J 1P3, Canada

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0 IT applications in disaster and emergency management

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Editorial
Published: 26 July 2021 in International Journal of Environmental Research and Public Health
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COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

ACS Style

Bruce Mellado; Jianhong Wu; Jude Kong; Nicola Bragazzi; Ali Asgary; Mary Kawonga; Nalamotse Choma; Kentaro Hayasi; Benjamin Lieberman; Thuso Mathaha; Mduduzi Mbada; Xifeng Ruan; Finn Stevenson; James Orbinski. Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa. International Journal of Environmental Research and Public Health 2021, 18, 7890 .

AMA Style

Bruce Mellado, Jianhong Wu, Jude Kong, Nicola Bragazzi, Ali Asgary, Mary Kawonga, Nalamotse Choma, Kentaro Hayasi, Benjamin Lieberman, Thuso Mathaha, Mduduzi Mbada, Xifeng Ruan, Finn Stevenson, James Orbinski. Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa. International Journal of Environmental Research and Public Health. 2021; 18 (15):7890.

Chicago/Turabian Style

Bruce Mellado; Jianhong Wu; Jude Kong; Nicola Bragazzi; Ali Asgary; Mary Kawonga; Nalamotse Choma; Kentaro Hayasi; Benjamin Lieberman; Thuso Mathaha; Mduduzi Mbada; Xifeng Ruan; Finn Stevenson; James Orbinski. 2021. "Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa." International Journal of Environmental Research and Public Health 18, no. 15: 7890.

Journal article
Published: 09 July 2021 in International Journal of Environmental Research and Public Health
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The impact of the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic has been and is still vast, affecting not only global human health and stretching healthcare facilities, but also profoundly disrupting societal and economic systems worldwide. The nature of the way the virus spreads causes cases to come in further recurring waves. This is due a complex array of biological, societal and environmental factors, including the novel nature of the emerging pathogen. Other parameters explaining the epidemic trend consisting of recurring waves are logistic–organizational challenges in the implementation of the vaccine roll-out, scarcity of doses and human resources, seasonality, meteorological drivers, and community heterogeneity, as well as cycles of strengthening and easing/lifting of the mitigation interventions. Therefore, it is crucial to be able to have an early alert system to identify when another wave of cases is about to occur. The availability of a variety of newly developed indicators allows for the exploration of multi-feature prediction models for case data. Ten indicators were selected as features for our prediction model. The model chosen is a Recurrent Neural Network with Long Short-Term Memory. This paper documents the development of an early alert/detection system that functions by predicting future daily confirmed cases based on a series of features that include mobility and stringency indices, and epidemiological parameters. The model is trained on the intermittent period in between the first and the second wave, in all of the South African provinces.

ACS Style

Finn Stevenson; Kentaro Hayasi; Nicola Bragazzi; Jude Kong; Ali Asgary; Benjamin Lieberman; Xifeng Ruan; Thuso Mathaha; Salah-Eddine Dahbi; Joshua Choma; Mary Kawonga; Mduduzi Mbada; Nidhi Tripathi; James Orbinski; Bruce Mellado; Jianhong Wu. Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory. International Journal of Environmental Research and Public Health 2021, 18, 7376 .

AMA Style

Finn Stevenson, Kentaro Hayasi, Nicola Bragazzi, Jude Kong, Ali Asgary, Benjamin Lieberman, Xifeng Ruan, Thuso Mathaha, Salah-Eddine Dahbi, Joshua Choma, Mary Kawonga, Mduduzi Mbada, Nidhi Tripathi, James Orbinski, Bruce Mellado, Jianhong Wu. Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory. International Journal of Environmental Research and Public Health. 2021; 18 (14):7376.

Chicago/Turabian Style

Finn Stevenson; Kentaro Hayasi; Nicola Bragazzi; Jude Kong; Ali Asgary; Benjamin Lieberman; Xifeng Ruan; Thuso Mathaha; Salah-Eddine Dahbi; Joshua Choma; Mary Kawonga; Mduduzi Mbada; Nidhi Tripathi; James Orbinski; Bruce Mellado; Jianhong Wu. 2021. "Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory." International Journal of Environmental Research and Public Health 18, no. 14: 7376.

Journal article
Published: 07 July 2021 in Electronics
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Research on SARS-CoV-2 and its social implications have become a major focus to interdisciplinary teams worldwide. As interest in more direct solutions, such as mass testing and vaccination grows, several studies appear to be dedicated to the operationalization of those solutions, leveraging both traditional and new methodologies, and, increasingly, the combination of both. This research examines the challenges anticipated for preventative testing of SARS-CoV-2 in schools and proposes an artificial intelligence (AI)-powered agent-based model crafted specifically for school scenarios. This research shows that in the absence of real data, simulation-based data can be used to develop an artificial intelligence model for the application of rapid assessment of school testing policies.

ACS Style

Svetozar Valtchev; Ali Asgary; Michael Chen; Felippe Cronemberger; Mahdi Najafabadi; Monica Cojocaru; Jianhong Wu. Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data. Electronics 2021, 10, 1626 .

AMA Style

Svetozar Valtchev, Ali Asgary, Michael Chen, Felippe Cronemberger, Mahdi Najafabadi, Monica Cojocaru, Jianhong Wu. Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data. Electronics. 2021; 10 (14):1626.

Chicago/Turabian Style

Svetozar Valtchev; Ali Asgary; Michael Chen; Felippe Cronemberger; Mahdi Najafabadi; Monica Cojocaru; Jianhong Wu. 2021. "Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data." Electronics 10, no. 14: 1626.

Preprint content
Published: 20 April 2021
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Background As COVID-19 vaccination coverage increases, public health and industries are contemplating re-opening measures of public spaces, including theme-parks. To re-open, theme-parks must provide public health mitigation plans. Questions on implementation of public health mitigation strategies such as park cleaning, COVID-19 testing, and enforcement of social distancing and the wearing of personal protective equipment (PPE) in the park remain. Methods We have developed a mathematical model of COVID-19 transmission in a theme-park that considers direct human-human and indirect environment-human transmission of the virus. The model thus tracks the changing infection/disease landscape of all visitors, workers, and environmental reservoirs in a theme park setting. Findings Models results show that theme-park public health mitigation must include mechanisms that reduce virus contamination of the environment to ensure that workers and visitors are protected from COVID-19 transmission in the park. Thus, cleaning rates and mitigation of human-environment contact increases in importance. Conclusion Our findings have important practical implications in terms of public health as policy- and decision-makers are equipped with a mathematical tool that can guide theme-parks in developing public health mitigation strategies for a safe re-opening.

ACS Style

Elena Aruffo; Safia Athar; Angie Raad; Afsar Ali; Mohammed Althubyani; Christopher Chow; Mahmuda Ruma; Chao Liu; Jianhong Wu; Ali Asgary; Jude D. Kong; Jane M. Heffernan. COVID-19 transmission in a theme-park. 2021, 1 .

AMA Style

Elena Aruffo, Safia Athar, Angie Raad, Afsar Ali, Mohammed Althubyani, Christopher Chow, Mahmuda Ruma, Chao Liu, Jianhong Wu, Ali Asgary, Jude D. Kong, Jane M. Heffernan. COVID-19 transmission in a theme-park. . 2021; ():1.

Chicago/Turabian Style

Elena Aruffo; Safia Athar; Angie Raad; Afsar Ali; Mohammed Althubyani; Christopher Chow; Mahmuda Ruma; Chao Liu; Jianhong Wu; Ali Asgary; Jude D. Kong; Jane M. Heffernan. 2021. "COVID-19 transmission in a theme-park." , no. : 1.

Journal article
Published: 12 January 2021 in BMC Public Health
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Background School testing for SARS-CoV-2 infection has become an important policy and planning issue as schools were reopened after the summer season and as the COVID-19 pandemic continues. Decisions to test or not to test and, if testing, how many tests, how often and for how long, are complex decisions that need to be taken under uncertainty and conflicting pressures from various stakeholders. Method We have developed an agent-based model and simulation tool that can be used to analyze the outcomes and effectiveness of different testing strategies and scenarios in schools with various number of classrooms and class sizes. We have applied a modified version of a standard SEIR disease transmission model that includes symptomatic and asymptomatic infectious populations, and that incorporates feasible public health measures. We also incorporated a pre-symptomatic phase for symptomatic cases. Every day, a random number of students in each class are tested. If they tested positive, they are placed in self-isolation at home when the test results are provided. Last but not least, we have included options to allow for full testing or complete self-isolation of a classroom with a positive case. Results We present sample simulation results for parameter values based on schools and disease related information, in the Province of Ontario, Canada. The findings show that testing can be an effective method in controlling the SARS-CoV-2 infection in schools if taken frequently, with expedited test results and self-isolation of infected students at home. Conclusions Our findings show that while testing cannot eliminate the risk and has its own challenges, it can significantly control outbreaks when combined with other measures, such as masks and other protective measures.

ACS Style

Ali Asgary; Monica Gabriela Cojocaru; Mahdi M. Najafabadi; Jianhong Wu. Simulating preventative testing of SARS-CoV-2 in schools: policy implications. BMC Public Health 2021, 21, 1 -18.

AMA Style

Ali Asgary, Monica Gabriela Cojocaru, Mahdi M. Najafabadi, Jianhong Wu. Simulating preventative testing of SARS-CoV-2 in schools: policy implications. BMC Public Health. 2021; 21 (1):1-18.

Chicago/Turabian Style

Ali Asgary; Monica Gabriela Cojocaru; Mahdi M. Najafabadi; Jianhong Wu. 2021. "Simulating preventative testing of SARS-CoV-2 in schools: policy implications." BMC Public Health 21, no. 1: 1-18.

Preprint content
Published: 06 January 2021
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The COVID-19 pandemic has been particularly threatening to the patients with end-stage kidney disease (ESKD) on intermittent hemodialysis and their care providers. Hemodialysis patients who receive life-sustaining medical therapy in healthcare settings, face unique challenges as they need to be at a dialysis unit three or more times a week, where they are confined to specific settings and tended to by dialysis nurses and staff with physical interaction and in close proximity. Despite the importance and critical situation of the dialysis units, modelling studies of the SARS-CoV-2 spread in these settings are very limited. In this paper, we have used a combination of discrete event and agent-based simulation models, to study the operations of a typical large dialysis unit and generate contact matrices to examine outbreak scenarios. We present the details of the contact matrix generation process and demonstrate how the simulation calculates a micro-scale contact matrix comprising the number and duration of contacts at a micro-scale time step. We have used the contacts matrix in an agent-based model to predict disease transmission under different scenarios. The results show that micro-simulation can be used to estimate contact matrices, which can be used effectively for disease modelling in dialysis and similar settings.

ACS Style

Mohammadali Tofighi; Ali Asgary; Asad A. Merchant; Mohammad Ali Shafiee; Mahdi M. Najafabadi; Nazanin Nadri; Mehdi Aarabi; Jane Heffernan; Jianhong Wu. Modelling COVID -19 transmission in a hemodialysis centre using simulation generated contacts matrices. 2021, 1 .

AMA Style

Mohammadali Tofighi, Ali Asgary, Asad A. Merchant, Mohammad Ali Shafiee, Mahdi M. Najafabadi, Nazanin Nadri, Mehdi Aarabi, Jane Heffernan, Jianhong Wu. Modelling COVID -19 transmission in a hemodialysis centre using simulation generated contacts matrices. . 2021; ():1.

Chicago/Turabian Style

Mohammadali Tofighi; Ali Asgary; Asad A. Merchant; Mohammad Ali Shafiee; Mahdi M. Najafabadi; Nazanin Nadri; Mehdi Aarabi; Jane Heffernan; Jianhong Wu. 2021. "Modelling COVID -19 transmission in a hemodialysis centre using simulation generated contacts matrices." , no. : 1.

Journal article
Published: 31 December 2020 in International Journal of Environmental Research and Public Health
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Planning for mass vaccination against SARS-Cov-2 is ongoing in many countries considering that vaccine will be available for the general public in the near future. Rapid mass vaccination while a pandemic is ongoing requires the use of traditional and new temporary vaccination clinics. Use of drive-through has been suggested as one of the possible effective temporary mass vaccinations among other methods. In this study, we present a machine learning model that has been developed based on a big dataset derived from 125K runs of a drive-through mass vaccination simulation tool. The results show that the model is able to reasonably well predict the key outputs of the simulation tool. Therefore, the model has been turned to an online application that can help mass vaccination planners to assess the outcomes of different types of drive-through mass vaccination facilities much faster.

ACS Style

Ali Asgary; Svetozar Zarko Valtchev; Michael Chen; Mahdi M. Najafabadi; Jianhong Wu. Artificial Intelligence Model of Drive-Through Vaccination Simulation. International Journal of Environmental Research and Public Health 2020, 18, 268 .

AMA Style

Ali Asgary, Svetozar Zarko Valtchev, Michael Chen, Mahdi M. Najafabadi, Jianhong Wu. Artificial Intelligence Model of Drive-Through Vaccination Simulation. International Journal of Environmental Research and Public Health. 2020; 18 (1):268.

Chicago/Turabian Style

Ali Asgary; Svetozar Zarko Valtchev; Michael Chen; Mahdi M. Najafabadi; Jianhong Wu. 2020. "Artificial Intelligence Model of Drive-Through Vaccination Simulation." International Journal of Environmental Research and Public Health 18, no. 1: 268.

Journal article
Published: 09 November 2020 in Healthcare
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Several research and development teams around the world are working towards COVID-19 vaccines. As vaccines are expected to be developed and produced, preparedness and planning for mass vaccination and immunization will become an important aspect of the pandemic management. Mass vaccination has been used by public health agencies in the past and is being proposed as a viable option for COVID-19 immunization. To be able to rapidly and safely immunize a large number of people against SARS-CoV-2, different mass vaccination options are available. Drive-through facilities have been successfully used in the past for immunization against other diseases and for testing during COVID-19. In this paper we introduce a drive-through vaccination simulation tool that can be used to enhance the planning, design, operation, and feasibility and effectiveness assessment of such facilities. The simulation tool is a hybrid model that integrates discrete event and agent-based modeling techniques. The simulation outputs visually and numerically show the average processing and waiting times and the number of cars and people that can be served (throughput values) under different numbers of staff, service lanes, screening, registration, immunization, and recovery times.

ACS Style

Ali Asgary; Mahdi Najafabadi; Richard Karsseboom; Jianhong Wu. A Drive-through Simulation Tool for Mass Vaccination during COVID-19 Pandemic. Healthcare 2020, 8, 469 .

AMA Style

Ali Asgary, Mahdi Najafabadi, Richard Karsseboom, Jianhong Wu. A Drive-through Simulation Tool for Mass Vaccination during COVID-19 Pandemic. Healthcare. 2020; 8 (4):469.

Chicago/Turabian Style

Ali Asgary; Mahdi Najafabadi; Richard Karsseboom; Jianhong Wu. 2020. "A Drive-through Simulation Tool for Mass Vaccination during COVID-19 Pandemic." Healthcare 8, no. 4: 469.

Journal article
Published: 16 May 2020 in Biology
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Since the beginning of the COVID-19 pandemic, most Canadian provinces have gone through four distinct phases of social distancing and enhanced testing. A transmission dynamics model fitted to the cumulative case time series data permits us to estimate the effectiveness of interventions implemented in terms of the contact rate, probability of transmission per contact, proportion of isolated contacts, and detection rate. This allows us to calculate the control reproduction number during different phases (which gradually decreased to less than one). From this, we derive the necessary conditions in terms of enhanced social distancing, personal protection, contact tracing, quarantine/isolation strength at each escalation phase for the disease control to avoid a rebound. From this, we quantify the conditions needed to prevent epidemic rebound during de-escalation by simply reversing the escalation process.

ACS Style

Biao Tang; Francesca Scarabel; Nicola Luigi Bragazzi; Zachary McCarthy; Michael Glazer; Yanyu Xiao; Jane M. Heffernan; Ali Asgary; Nicholas Hume Ogden; Jianhong Wu. De-Escalation by Reversing the Escalation with a Stronger Synergistic Package of Contact Tracing, Quarantine, Isolation and Personal Protection: Feasibility of Preventing a COVID-19 Rebound in Ontario, Canada, as a Case Study. Biology 2020, 9, 100 .

AMA Style

Biao Tang, Francesca Scarabel, Nicola Luigi Bragazzi, Zachary McCarthy, Michael Glazer, Yanyu Xiao, Jane M. Heffernan, Ali Asgary, Nicholas Hume Ogden, Jianhong Wu. De-Escalation by Reversing the Escalation with a Stronger Synergistic Package of Contact Tracing, Quarantine, Isolation and Personal Protection: Feasibility of Preventing a COVID-19 Rebound in Ontario, Canada, as a Case Study. Biology. 2020; 9 (5):100.

Chicago/Turabian Style

Biao Tang; Francesca Scarabel; Nicola Luigi Bragazzi; Zachary McCarthy; Michael Glazer; Yanyu Xiao; Jane M. Heffernan; Ali Asgary; Nicholas Hume Ogden; Jianhong Wu. 2020. "De-Escalation by Reversing the Escalation with a Stronger Synergistic Package of Contact Tracing, Quarantine, Isolation and Personal Protection: Feasibility of Preventing a COVID-19 Rebound in Ontario, Canada, as a Case Study." Biology 9, no. 5: 100.

Article
Published: 12 February 2020 in International Journal of Disaster Risk Science
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This study investigated how small and medium enterprises (SMEs) in a country perceive major global risks. The aim was to explore how country attributes and circumstances affect SME assessments of the likelihood, impacts, and rankings of global risks, and to find out if SME risk assessment and rankings differ from the global rankings. Data were gathered using an online survey of manufacturing SMEs in Turkey. The results show that global economic risks and geopolitical risks are of major concern for SMEs, and environmental risks are at the bottom of their ranking. Among the economic risks, fiscal crises in key economies and high structural unemployment or underemployment were found to be the highest risks for the SMEs. Failure of regional or global governance, failure of national governance, and interstate conflict with regional consequences were found to be among the top geopolitical risks for the SMEs. The SMEs considered the risk of large-scale cyber-attacks and massive incident of data fraud/theft to be relatively higher than other global technological risks. Profound social instability and failure of urban planning were among the top societal risks for the SMEs. Although the global environmental and disaster risks were ranked lowest on the list, man-made environmental damage and disasters and major natural hazard-induced disasters were ranked the highest among this group of risks. Overall, the results show that SMEs at a country level, for example Turkey, perceive global risks differently than the major global players.

ACS Style

Ali Asgary; Ali Ihsan Ozdemir; Hale Özyürek. Small and Medium Enterprises and Global Risks: Evidence from Manufacturing SMEs in Turkey. International Journal of Disaster Risk Science 2020, 11, 59 -73.

AMA Style

Ali Asgary, Ali Ihsan Ozdemir, Hale Özyürek. Small and Medium Enterprises and Global Risks: Evidence from Manufacturing SMEs in Turkey. International Journal of Disaster Risk Science. 2020; 11 (1):59-73.

Chicago/Turabian Style

Ali Asgary; Ali Ihsan Ozdemir; Hale Özyürek. 2020. "Small and Medium Enterprises and Global Risks: Evidence from Manufacturing SMEs in Turkey." International Journal of Disaster Risk Science 11, no. 1: 59-73.

Journal article
Published: 31 October 2019 in International Journal of Emergency Services
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Purpose Responding to emergency incidents by emergency response organizations such as fire, ambulance and police during large disaster and emergency events is very important. The purpose of this paper is to provide some insights into response patterns during the 2013 ice storm in the city of Vaughan, Ontario, Canada, using temporal and spatial analyses. Design/methodology/approach The City of Vaughan Fire and Rescue Service data set containing all responses to fire and other emergency incidents from January 1, 2009 to December 31, 2016 was used. The 2013 Southern Ontario ice storm occurred from December 20, 2013 to January 1, 2014, and, for this study, December 20–31 is considered the “study period.” Temporal, spatial and spatiotemporal analyses of responses during the study period are carried out and are compared with the same period in other years (2009–2012 and 2014–2016). Findings The findings show that temporal patterns of response attributes changed significantly during the 2013 ice storm. Similarly, the spatial pattern of responses during the 2013 ice storm showed some major differences with other years. The spatiotemporal analyses also demonstrate significant variations in responses in the city during different hours of the day in the ice storm days. Originality/value This study is the first study to examine the spatiotemporal patterns of responses made by a fire department during the 2013 ice storm in Canada. It provides some insights into the differences between response volumes, temporal and spatial distributions during large emergency events (e.g. ice storm) and normal situations. The results will help in mitigating the number of responses in the future through public education and technological changes. Moreover, the results will provide fire departments with information that could help them prepare for such events by possible reallocation of resources.

ACS Style

Maryam Shafiei Sabet; Ali Asgary; Adriano O. Solis. Emergency calls during the 2013 southern Ontario ice storm: case study of Vaughan. International Journal of Emergency Services 2019, 8, 292 -314.

AMA Style

Maryam Shafiei Sabet, Ali Asgary, Adriano O. Solis. Emergency calls during the 2013 southern Ontario ice storm: case study of Vaughan. International Journal of Emergency Services. 2019; 8 (3):292-314.

Chicago/Turabian Style

Maryam Shafiei Sabet; Ali Asgary; Adriano O. Solis. 2019. "Emergency calls during the 2013 southern Ontario ice storm: case study of Vaughan." International Journal of Emergency Services 8, no. 3: 292-314.

Journal article
Published: 04 September 2019 in IEEE Transactions on Engineering Management
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This article describes an interactive holographic visualization of volcanic eruption application for Microsoft HoloLens device. The aim of the project is to use this technology to visualize different eruptive phenomena on an active volcano for public education, emergency training, preparedness planning purposes, and raising awareness among tourists. We have selected La Fossa volcano on Vulcano island (Italy) as a case study and, thus, the application is named HoloVulcano. Unity game engine and Microsoft Visual Studio were used to develop the HoloVulcano augmented/virtual reality visualization application. The current version of HoloVulcano visualizes volcanic phenomena typically associated with unrest (fumaroles) and explosive eruptions (e.g. eruptive plumes, ejection of ballistic blocks, bombs, and pyroclastic density currents). The eruption types are developed based on existing literature using Unity game engine's particle systems component. HoloVulcano is a Microsoft HoloLens device application. Wearing the HoloLens, users can interact with the application through voice, gazing, and gestures and view different volcanic phenomena from different sites and angles on the island. HoloVulcano can be used by emergency managers and teachers for training, emergency exercises, and public education.

ACS Style

Ali Asgary; Costanza Bonadonna; Corine Frischknecht. Simulation and Visualization of Volcanic Phenomena Using Microsoft Hololens: Case of Vulcano Island (Italy). IEEE Transactions on Engineering Management 2019, 67, 545 -553.

AMA Style

Ali Asgary, Costanza Bonadonna, Corine Frischknecht. Simulation and Visualization of Volcanic Phenomena Using Microsoft Hololens: Case of Vulcano Island (Italy). IEEE Transactions on Engineering Management. 2019; 67 (3):545-553.

Chicago/Turabian Style

Ali Asgary; Costanza Bonadonna; Corine Frischknecht. 2019. "Simulation and Visualization of Volcanic Phenomena Using Microsoft Hololens: Case of Vulcano Island (Italy)." IEEE Transactions on Engineering Management 67, no. 3: 545-553.

Chapter
Published: 29 August 2018 in Understanding Complex Systems
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The bioenergy sector has been experiencing significant growth in the last two decades. That said, the industry faces many challenges, mainly focused around the understanding of feedstock supply risk. Developers and investors cannot properly price risk without raw material supply chain risk understanding, making the development of the bioenergy industry slower than it would otherwise be. Currently biofuel, or wood pellet, production in Ontario requires wood chips supplied by existing sawmills. The supply of wood chips in turn depends on the supply of timber. A model was developed here simulating the timber supply chain in Southern Ontario. The objective of the simulation was to show the applicability of computer simulation methods in determining the most resilient areas from a perspective of a developer looking to build a new biofuel plant. The simulation presented here, developed in AnyLogic 7.3.5, is considered a base simulation. That is, it can be improved upon to simulate different disturbances or add/change experiment assumptions. The simulation is therefore a first version of a useful tool that has a potential to improve the understanding of risk among biofuel developers and investors.

ACS Style

Marcin Lewandowski; Ali Asgary. Risk Assessment of the Timber Supply Chain in Southern Ontario Using Agent-Based Simulation. Understanding Complex Systems 2018, 317 -332.

AMA Style

Marcin Lewandowski, Ali Asgary. Risk Assessment of the Timber Supply Chain in Southern Ontario Using Agent-Based Simulation. Understanding Complex Systems. 2018; ():317-332.

Chicago/Turabian Style

Marcin Lewandowski; Ali Asgary. 2018. "Risk Assessment of the Timber Supply Chain in Southern Ontario Using Agent-Based Simulation." Understanding Complex Systems , no. : 317-332.

Journal article
Published: 03 April 2017 in Disaster Prevention and Management: An International Journal
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Purpose Disaster mutual assistance (DMA) or mutual aid is a reciprocal arrangement between organizations that permits and prearranges one company to access resources from another company to recover from disaster impacts faster. As a practical tool to access response resources quickly, DMA can be an important element of an effective emergency management process, but the decision to provide (or not to provide) DMA is challenging and involves a number of factors. The purpose of this paper is to present the results of a study conducted to identify DMA decision criteria and their weights based on electricity companies operating in North America. Design/methodology/approach The authors employed a combination of Delphi and analytical hierarchy process (AHP) methods. Delphi method identified the decision criteria that should be considered before electricity utilities enact their DMA agreements. A standard AHP calculated the weights of identified DMA criteria. Findings In total, 11 criteria were identified and classified into three main groups: responding criteria, requesting criteria and disaster criteria. Of the 11, “Emergency Conditions” within the responding criteria group, “Extent of Damage” of the requesting criteria group, and “Size of Disaster”, associated with the disaster criteria group, had the highest weight. Three other factors (“Work Safety Practice”, “Natural Hazards” and “Availability of Resources”) carried a noticeable weight difference, while the remaining factors were weighted relatively lower. Practical implications At present, a decision to provide mutual assistance is highly subjective, based on “gut feel”, and dependent on interpersonal relationships between the requestor and the provider. However, mobilizing and dispatching electricity industry crews is a risky and costly operation for both requesting and responding companies and requires careful assessment for which a cost-benefit threshold has not been developed. This cost-benefit perspective is often frowned upon owing to the intended altruistic nature of DMA agreements and its influence on decision makers. The developed criteria in this study are intended to assist electricity companies in making a more informed and quantifiable decision when deliberating a request for mutual assistance. These criteria may also be used by assistance-requesting companies to better identify electricity companies that are more likely to provide assistance to them. Originality/value This study contributes to the literature by examining the current state of DMA in electricity utilities, identifying decision criteria and weighing such criteria to enable electricity companies in making more objective decisions, thereby, increasing the overall effectiveness of their disaster management process.

ACS Style

Ali Asgary; Ben Pantin; Bahareh Emamgholizadeh Saiiar; Jianhong Wu. Developing disaster mutual assistance decision criteria for electricity industry. Disaster Prevention and Management: An International Journal 2017, 26, 230 -240.

AMA Style

Ali Asgary, Ben Pantin, Bahareh Emamgholizadeh Saiiar, Jianhong Wu. Developing disaster mutual assistance decision criteria for electricity industry. Disaster Prevention and Management: An International Journal. 2017; 26 (2):230-240.

Chicago/Turabian Style

Ali Asgary; Ben Pantin; Bahareh Emamgholizadeh Saiiar; Jianhong Wu. 2017. "Developing disaster mutual assistance decision criteria for electricity industry." Disaster Prevention and Management: An International Journal 26, no. 2: 230-240.

Book chapter
Published: 17 March 2017 in Global Changes and Natural Disaster Management: Geo-information Technologies
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Significant number of disaster risk reduction projects is implemented around the world. Each of these projects can provide guidelines for future projects and can be adopted by other communities if their information is properly communicated and shared. These projects can also become a useful source of knowledge for teaching and training of future disaster risk reduction professionals. This chapter describes a disaster risk reduction web mapping project called MapDRR that uses ESRI Webapp Builder to map more than 600 disaster risk reduction projects around the world. This application uses advances in web mapping to add more values to the existing online information about the disaster risk reduction projects. MapDRR covers a diverse range of DRR cases implemented in many different countries to reduce the risk of different types of disasters at local, regional, national, and international levels. MapDRR provides both textual and contextual (visual) information to users by zooming into each project. MapDRR is enhanced regularly to include more DRR cases, countries.

ACS Style

Ali Asgary; Daryoush Kari. Communicating Disaster Risk Reduction Through Web-Map Applications. Global Changes and Natural Disaster Management: Geo-information Technologies 2017, 91 -99.

AMA Style

Ali Asgary, Daryoush Kari. Communicating Disaster Risk Reduction Through Web-Map Applications. Global Changes and Natural Disaster Management: Geo-information Technologies. 2017; ():91-99.

Chicago/Turabian Style

Ali Asgary; Daryoush Kari. 2017. "Communicating Disaster Risk Reduction Through Web-Map Applications." Global Changes and Natural Disaster Management: Geo-information Technologies , no. : 91-99.

Journal article
Published: 01 January 2017 in International Journal of Business Continuity and Risk Management
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Recent large-scale electricity disruptions in North America like the 2013 ice storm and hurricane Sandy (2012) highlighted the merits of mutual assistance as a disaster recovery and continuity mechanism. The decision to provide assistance requires careful consideration of multiple factors in a timely manner. This paper examines and tests the validity and reliability of a disaster mutual assistance decision support tool that is being developed for Canadian electricity companies using a multi-criteria decision method and simulation tools. Participating experts from utility companies were provided with a number of mutual assistance scenarios and their decisions to provide assistance with and without using the decision support tool were analysed. Findings show that the decisions suggested by the tool are consistent with experts' decisions and this tool has the potential to inform decisions during mutual assistance operations.

ACS Style

Ali Asgary; Jenaro Nosedal Sanchez; Ben Pantin; Jianhong Wu; Maryam Shafiei Sabet. Testing and validating a disaster mutual assistance decision support tool for electricity companies. International Journal of Business Continuity and Risk Management 2017, 7, 292 .

AMA Style

Ali Asgary, Jenaro Nosedal Sanchez, Ben Pantin, Jianhong Wu, Maryam Shafiei Sabet. Testing and validating a disaster mutual assistance decision support tool for electricity companies. International Journal of Business Continuity and Risk Management. 2017; 7 (4):292.

Chicago/Turabian Style

Ali Asgary; Jenaro Nosedal Sanchez; Ben Pantin; Jianhong Wu; Maryam Shafiei Sabet. 2017. "Testing and validating a disaster mutual assistance decision support tool for electricity companies." International Journal of Business Continuity and Risk Management 7, no. 4: 292.

Journal article
Published: 01 January 2017 in International Journal of Business Continuity and Risk Management
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ACS Style

Maryam Shafiei Sabet; Jianhong Wu; Ali Asgary; Jenaro Nosedal Sanchez; Ben Pantin. Testing and validating a disaster mutual assistance decision support tool for electricity companies. International Journal of Business Continuity and Risk Management 2017, 7, 292 .

AMA Style

Maryam Shafiei Sabet, Jianhong Wu, Ali Asgary, Jenaro Nosedal Sanchez, Ben Pantin. Testing and validating a disaster mutual assistance decision support tool for electricity companies. International Journal of Business Continuity and Risk Management. 2017; 7 (4):292.

Chicago/Turabian Style

Maryam Shafiei Sabet; Jianhong Wu; Ali Asgary; Jenaro Nosedal Sanchez; Ben Pantin. 2017. "Testing and validating a disaster mutual assistance decision support tool for electricity companies." International Journal of Business Continuity and Risk Management 7, no. 4: 292.

Journal article
Published: 01 October 2016 in Big Data & Information Analytics
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ACS Style

Jianhong Wu; Ali Asgary. ADERSIM-IBM partnership in big data. Big Data & Information Analytics 2016, 1, 277 -278.

AMA Style

Jianhong Wu, Ali Asgary. ADERSIM-IBM partnership in big data. Big Data & Information Analytics. 2016; 1 (4):277-278.

Chicago/Turabian Style

Jianhong Wu; Ali Asgary. 2016. "ADERSIM-IBM partnership in big data." Big Data & Information Analytics 1, no. 4: 277-278.

Journal article
Published: 01 January 2016 in Big Data & Information Analytics
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ACS Style

Jimmy Huang; Ali Asgary; Jianhong Wu. Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM). Big Data & Information Analytics 2016, 1, 1 .

AMA Style

Jimmy Huang, Ali Asgary, Jianhong Wu. Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM). Big Data & Information Analytics. 2016; 1 (1):1.

Chicago/Turabian Style

Jimmy Huang; Ali Asgary; Jianhong Wu. 2016. "Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM)." Big Data & Information Analytics 1, no. 1: 1.

Research article
Published: 13 July 2012 in Scandinavian Journal of Public Health
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Aims: This study sought to contribute to the existing literature on pandemic influenza vaccination studies by providing additional evidences of households’ willingness to pay (WTP) for protection against influenza during a pandemic situation from North America. Methods: A standard dichotomous-choice contingent valuation survey was designed and completed in a sample of 306 individuals living in the Greater Toronto Area, Ontario, Canada. Results: This study shows that, on average, households are willing to pay $417.35 for immediate pandemic influenza (H1N1) vaccination. Results show that the vaccine price, age, gender, occupation, organisation, annual family income, receiving annual flu shot, having additional insurance, having someone with a serious illness in the house, knowledge about pandemics, trusting official information on pandemics, supporting government expenditure, and rating government pandemic planning have significant effects on the decision to accept the vaccine bids. Conclusions: The results reconfirm the findings of similar studies that influenza vaccine programmes are highly cost-effective despite the high programme cost, because people’s WTP (benefits) for this programme is much higher than the actual costs. Pandemic influenza vaccination programmes should consider the demographic and economic status of the target population as such characteristics have significant impacts on the benefits that people place on such programmes.

ACS Style

Ali Asgary. Assessing households’ willingness to pay for an immediate pandemic influenza vaccination programme. Scandinavian Journal of Public Health 2012, 40, 412 -417.

AMA Style

Ali Asgary. Assessing households’ willingness to pay for an immediate pandemic influenza vaccination programme. Scandinavian Journal of Public Health. 2012; 40 (5):412-417.

Chicago/Turabian Style

Ali Asgary. 2012. "Assessing households’ willingness to pay for an immediate pandemic influenza vaccination programme." Scandinavian Journal of Public Health 40, no. 5: 412-417.