scholarly journals Agent-Based Simulation Framework for Epidemic Forecasting during Hajj Seasons in Saudi Arabia

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 325
Author(s):  
Sultanah Mohammed Alshammari ◽  
Mohammed Hassan Ba-Aoum ◽  
Nofe Ateq Alganmi ◽  
Arwa AbdulAziz Allinjawi
Keyword(s):  
Public Health ◽  
Saudi Arabia ◽  
Disease Transmission ◽  
Simulation Framework ◽  
Agent Based Simulation ◽  
Agent Based ◽  
The World ◽  
Hajj Season ◽  
The Impact ◽  
Epidemic Forecasting

The religious pilgrimage of Hajj is one of the largest annual gatherings in the world. Every year approximately three million pilgrims travel from all over the world to perform Hajj in Mecca in Saudi Arabia. The high population density of pilgrims in confined settings throughout the Hajj rituals can facilitate infectious disease transmission among the pilgrims and their contacts. Infected pilgrims may enter Mecca without being detected and potentially transmit the disease to other pilgrims. Upon returning home, infected international pilgrims may introduce the disease into their home countries, causing a further spread of the disease. Computational modeling and simulation of social mixing and disease transmission between pilgrims can enhance the prevention of potential epidemics. Computational epidemic models can help public health authorities predict the risk of disease outbreaks and implement necessary intervention measures before or during the Hajj season. In this study, we proposed a conceptual agent-based simulation framework that integrates agent-based modeling to simulate disease transmission during the Hajj season from the arrival of the international pilgrims to their departure. The epidemic forecasting system provides a simulation of the phases and rituals of Hajj following their actual sequence to capture and assess the impact of each stage in the Hajj on the disease dynamics. The proposed framework can also be used to evaluate the effectiveness of the different public health interventions that can be implemented during the Hajj, including size restriction and screening at entry points.

scholarly journals Bat-Borne Coronaviruses in Jordan and Saudi Arabia: A Threat to Public Health?

Viruses ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 1413
Author(s):  
Laith N. AL-Eitan ◽  
Amneh H. Tarkhan ◽  
Mansour A. Alghamdi ◽  
Denise A. Marston ◽  
Guanghui Wu ◽  
...  
Keyword(s):  
Public Health ◽  
Saudi Arabia ◽  
Disease Transmission ◽  
Emerging Infectious Diseases ◽  
Emerging Pathogens ◽  
Kingdom Of Saudi Arabia ◽  
Mass Gatherings ◽  
Mass Migration ◽  
Close Proximity ◽  
Hajj Season

Emerging infectious diseases are of great concern to public health, as highlighted by the ongoing coronavirus disease 2019 (COVID-19) pandemic. Such diseases are of particular danger during mass gathering and mass influx events, as large crowds of people in close proximity to each other creates optimal opportunities for disease transmission. The Hashemite Kingdom of Jordan and the Kingdom of Saudi Arabia are two countries that have witnessed mass gatherings due to the arrival of Syrian refugees and the annual Hajj season. The mass migration of people not only brings exotic diseases to these regions but also brings new diseases back to their own countries, e.g., the outbreak of MERS in South Korea. Many emerging pathogens originate in bats, and more than 30 bat species have been identified in these two countries. Some of those bat species are known to carry viruses that cause deadly diseases in other parts of the world, such as the rabies virus and coronaviruses. However, little is known about bats and the pathogens they carry in Jordan and Saudi Arabia. Here, the importance of enhanced surveillance of bat-borne infections in Jordan and Saudi Arabia is emphasized, promoting the awareness of bat-borne diseases among the general public and building up infrastructure and capability to fill the gaps in public health preparedness to prevent future pandemics.

scholarly journals Impact of Timing of and Adherence to Social Distancing Measures on COVID-19 Burden in the US: A Simulation Modeling Approach

2020 ◽  
Author(s):  
Oguzhan Alagoz ◽  
Ajay K. Sethi ◽  
Brian W. Patterson ◽  
Matthew Churpek ◽  
Nasia Safdar
Keyword(s):  
New York ◽  
Simulation Model ◽  
Disease Transmission ◽  
Major Effect ◽  
Agent Based Simulation ◽  
Population Demographics ◽  
Agent Based ◽  
Social Distancing ◽  
The People ◽  
The Impact

ABSTRACTBackgroundAcross the U.S., various social distancing measures were implemented to control COVID-19 pandemic. However, there is uncertainty in the effectiveness of such measures for specific regions with varying population demographics and different levels of adherence to social distancing. The objective of this paper is to determine the impact of social distancing measures in unique regions.MethodsWe developed COVid-19 Agent-based simulation Model (COVAM), an agent-based simulation model (ABM) that represents the social network and interactions among the people in a region considering population demographics, limited testing availability, imported infections from outside of the region, asymptomatic disease transmission, and adherence to social distancing measures. We adopted COVAM to represent COVID-19-associated events in Dane County, Wisconsin, Milwaukee metropolitan area, and New York City (NYC). We used COVAM to evaluate the impact of three different aspects of social distancing: 1) Adherence to social distancing measures; 2) timing of implementing social distancing; and 3) timing of easing social distancing.ResultsWe found that the timing of social distancing and adherence level had a major effect on COVID-19 occurrence. For example, in NYC, implementing social distancing measures on March 5, 2020 instead of March 12, 2020 would have reduced the total number of confirmed cases from 191,984 to 43,968 as of May 30, whereas a 1-week delay in implementing such measures could have increased the number of confirmed cases to 1,299,420. Easing social distancing measures on June 1, 2020 instead of June 15, 2020 in NYC would increase the total number of confirmed cases from 275,587 to 379,858 as of July 31.ConclusionThe timing of implementing social distancing measures, adherence to the measures, and timing of their easing have major effects on the number of COVID-19 cases.Primary Funding SourceNational Institute of Allergy and Infectious Diseases Institute

Exploring the World Trade Organisation's Trade and Environment/Public Health Jurisprudence as a Model for Incorporating a Trade–Labour Linkage into the Organisation's Multilateral Trade Regime: Should African Countries Accept a Policy Shift?

2017 ◽  
Vol 25 (1) ◽  
pp. 47-65
Author(s):  
Tapiwa V. Warikandwa ◽  
Patrick C. Osode
Keyword(s):  
Public Health ◽  
Developing Countries ◽  
World Trade ◽  
African Countries ◽  
Trade Regime ◽  
Multilateral Trade ◽  
Appellate Body ◽  
The World ◽  
Labour Standards ◽  
The Impact

The incorporation of a trade-labour (standards) linkage into the multilateral trade regime of the World Trade Organisation (WTO) has been persistently opposed by developing countries, including those in Africa, on the grounds that it has the potential to weaken their competitive advantage. For that reason, low levels of compliance with core labour standards have been viewed as acceptable by African countries. However, with the impact of WTO agreements growing increasingly broader and deeper for the weaker and vulnerable economies of developing countries, the jurisprudence developed by the WTO Panels and Appellate Body regarding a trade-environment/public health linkage has the potential to address the concerns of developing countries regarding the potential negative effects of a trade-labour linkage. This article argues that the pertinent WTO Panel and Appellate Body decisions could advance the prospects of establishing a linkage of global trade participation to labour standards without any harm befalling developing countries.

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

scholarly journals Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility

2021 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Lennart Adenaw ◽  
Markus Lienkamp
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
User Behavior ◽  
Transport Sector ◽  
Simulation Framework ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Charging Station ◽  
Spatio Temporal

In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.

scholarly journals Process Modeling and Simulation of Tableting—An Agent-Based Simulation Methodology for Direct Compression

Pharmaceutics ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 996
Author(s):  
Niels Lasse Martin ◽  
Ann Kathrin Schomberg ◽  
Jan Henrik Finke ◽  
Tim Gyung-min Abraham ◽  
Arno Kwade ◽  
...  
Keyword(s):  
Raw Materials ◽  
Pilot Scale ◽  
Physical Models ◽  
Agent Based Simulation ◽  
Validation Data ◽  
Agent Based ◽  
Industrial Processing ◽  
Digital Twins ◽  
Product And Process ◽  
The Impact

In pharmaceutical manufacturing, the utmost aim is reliably producing high quality products. Simulation approaches allow virtual experiments of processes in the planning phase and the implementation of digital twins in operation. The industrial processing of active pharmaceutical ingredients (APIs) into tablets requires the combination of discrete and continuous sub-processes with complex interdependencies regarding the material structures and characteristics. The API and excipients are mixed, granulated if required, and subsequently tableted. Thereby, the structure as well as the properties of the intermediate and final product are influenced by the raw materials, the parametrized processes and environmental conditions, which are subject to certain fluctuations. In this study, for the first time, an agent-based simulation model is presented, which enables the prediction, tracking, and tracing of resulting structures and properties of the intermediates of an industrial tableting process. Therefore, the methodology for the identification and development of product and process agents in an agent-based simulation is shown. Implemented physical models describe the impact of process parameters on material structures. The tablet production with a pilot scale rotary press is experimentally characterized to provide calibration and validation data. Finally, the simulation results, predicting the final structures, are compared to the experimental data.

scholarly journals 13.D. Workshop: SDGs in the focus of environmental and health issues

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
◽  
Keyword(s):  
Public Health ◽  
Climate Change ◽  
Sustainable Development ◽  
Community Engagement ◽  
Health Professionals ◽  
Health Issues ◽  
The Sustainable Development ◽  
The World ◽  
Environmental And Health Issues ◽  
The Impact

Abstract This workshop is dedicated on SDGs in the focus of environmental and health issues, as very important and actual topic. One of the characteristics of today's societies is the significant availability of modern technologies. Over 5 billion (about 67%) people have a cellphone today. More than 4.5 billion people worldwide use the Internet, close to 60% of the total population. At the same time, one third of the people in the world does not have access to safe drinking water and half of the population does not have access to safe sanitation. The WHO at UN warns of severe inequalities in access to water and hygiene. Air, essential to life, is a leading risk due to ubiquitous pollution and contributes to the global disease burden (7 million deaths per year). Air pollution is a consequence of traffic and industry, but also of demographic trends and other human activities. Food availability reflects global inequality, famine eradication being one of the SDGs. The WHO warns of the urgency. As technology progresses, social inequality grows, the gap widens, and the environment continues to suffer. Furthermore, the social environment in societies is “ruffled” and does not appear to be beneficial toward well-being. New inequalities are emerging in the availability of technology, climate change, education. The achievement reports on the Sustainable Development Goals (SDGs), also point out to the need of reviewing individual indicators. According to the Sustainable Development Agenda, one of the goals is to reduce inequalities, and environmental health is faced by several specific goals. The Global Burden of Disease is the most comprehensive effort to date to measure epidemiological levels and trends worldwide. It is the product of a global research collaborative and quantifies the impact of hundreds of diseases, injuries, and risk factors in countries around the world. This workshop will also discuss Urban Health as a Complex System in the light of SDGs. Climate Change, Public Health impacts and the role of the new digital technologies is also important topic which is contributing to SDG3, improving health, to SDG4, allowing to provide distance health education at relatively low cost and to SDG 13, by reducing the CO2 footprint. Community Engagement can both empower vulnerable populations (so reducing inequalities) and identify the prior environmental issues to be addressed. The aim was to search for public health programs using Community Engagement tools in healthy environment building towards achievement of SDGs. Key messages Health professionals are involved in the overall process of transformation necessary to achieve the SDGs. Health professionals should be proactive and contribute to the transformation leading to better health for the environment, and thus for the human population.

scholarly journals Increased Emergency Calls during the COVID-19 Pandemic in Saudi Arabia: A National Retrospective Study

Healthcare ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
Ahmed Al-Wathinani ◽  
Attila J. Hertelendy ◽  
Sultana Alhurishi ◽  
Abdulmajeed Mobrad ◽  
Riyadh Alhazmi ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Retrospective Study ◽  
Emergency Medical Services ◽  
Indirect Effect ◽  
Mobile Application ◽  
Healthcare Systems ◽  
Emergency Calls ◽  
The World ◽  
Staffing Patterns ◽  
The Impact

The coronavirus 2019 (COVID-19) pandemic has a direct and indirect effect on the different healthcare systems around the world. In this study, we aim to describe the impact on the utilization of emergency medical services (EMS) in Saudi Arabia during the COVID-19 pandemic. We studied cumulative data from emergency calls collected from the SRCA. Data were separated into three periods: before COVID-19 (1 January–29 February 2020), during COVID-19 (1 March–23 April 2020), and during the Holy Month of Ramadan (24 April–23 May 2020). A marked increase of cases was handled during the COVID-19 period compared to the number before pandemic. Increases in all types of cases, except for those related to trauma, occurred during COVID-19, with all regions experiencing increased call volumes during COVID-19 compared with before pandemic. Demand for EMS significantly increased throughout Saudi Arabia during the pandemic period. Use of the mobile application ASAFNY to request an ambulance almost doubled during the pandemic but remained a small fraction of total calls. Altered weekly call patterns and increased call volume during the pandemic indicated not only a need for increased staff but an alteration in staffing patterns.

Modeling Consumer Decisions on Returning End-of-Use Products Considering Design Features and Consumer Interactions: An Agent Based Simulation Approach

2015 ◽  
Author(s):  
Ardeshir Raihanian Mashhadi ◽  
Behzad Esmaeilian ◽  
Sara Behdad
Keyword(s):  
Electronic Waste ◽  
Peer Pressure ◽  
Waste Stream ◽  
Contributing Factors ◽  
Agent Based Simulation ◽  
Design Features ◽  
Agent Based ◽  
Consumer Decisions ◽  
Take Back ◽  
The Impact

As electronic waste (e-waste) becomes one of the fastest growing environmental concerns, remanufacturing is considered as a promising solution. However, the profitability of take back systems is hampered by several factors including the lack of information on the quantity and timing of to-be-returned used products to a remanufacturing facility. Product design features, consumers’ awareness of recycling opportunities, socio-demographic information, peer pressure, and the tendency of customer to keep used items in storage are among contributing factors in increasing uncertainties in the waste stream. Predicting customer choice decisions on returning back used products, including both the time in which the customer will stop using the product and the end-of-use decisions (e.g. storage, resell, through away, and return to the waste stream) could help manufacturers have a better estimation of the return trend. The objective of this paper is to develop an Agent Based Simulation (ABS) model integrated with Discrete Choice Analysis (DCA) technique to predict consumer decisions on the End-of-Use (EOU) products. The proposed simulation tool aims at investigating the impact of design features, interaction among individual consumers and socio-demographic characteristics of end users on the number of returns. A numerical example of cellphone take-back system has been provided to show the application of the model.

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