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Within risk analysis and, more broadly, the decision behind the choice of which modeling technique to use to study the spread of disease, epidemics, fires, technology, rumors, or, more generally, spatial dynamics, is not well documented. While individual models are well defined and the modeling techniques are well understood by practitioners, there...
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Background:
RV144 is to date the only HIV vaccine trial to demonstrate efficacy, albeit rapidly waning over time. The HVTN 702 trial is currently evaluating in South Africa a similar vaccine formulation to that of RV144 for subtype C HIV with additional boosters (pox-protein regimen). Using a detailed stochastic individual-based network model of d...
Citations
... ABM is frequently used as a modeling method for recreating the operation of real-world complicated systems. ABMs are (continued) (Robertson, 2019). Dynamic models, on the other hand, are utilized to study the framework and actions of an entity and build effective system administration rules. ...
Purpose
The paper’s main goal is to examine the relationship between the video marketing of financial technologies (Fintechs) and their vulnerable website customers’ brand engagement in the ongoing coronavirus disease 2019 (COVID-19) crisis.
Design/methodology/approach
To extract the required outcomes, the authors gathered data from the five biggest Fintech websites and YouTube channels, performed multiple linear regression models and developed a hybrid (agent-based and dynamic) model to assess the performance connection between their video marketing analytics and vulnerable website customers’ brand engagement.
Findings
It has been found that video marketing analytics of Fintechs’ YouTube channels are a decisive factor in impacting their vulnerable website customers’ brand engagement and awareness.
Research limitations/implications
By enhancing video marketing analytics of their YouTube channels, Fintechs can achieve greater levels of vulnerable website customers’ engagement and awareness. Higher levels of vulnerable customers’ brand engagement and awareness tend to decrease their vulnerability by enhancing their financial knowledge and confidence.
Practical implications
Fintechs should aim to increase the number of total videos on their YouTube channels and provide videos that promote their customers’ knowledge of their services to increase their brand engagement and awareness, thus reducing their vulnerability. Moreover, Fintechs should be aware not to over-post videos because they will be in an unfavorable position against their competitors.
Originality/value
This research offers valuable insights regarding the importance of video marketing strategies for Fintechs in promoting their vulnerable website customers’ brand awareness during crisis periods.
... There are many ways to model the spatio-temporal spread of infectious diseases. Let me present the main contenders; see an interesting and more complete list in [161] or [147]. I do not detail reaction-diffusion equations, because, to the best of my knowledge, they have seen very little use in modelling the spatio-temporal spread of COVID-19; readers are referred to [158], for instance, for more details on deterministic aspects involving such systems. ...
Daily number of hospitalizations and deaths are key outcomes in quantifying the outbreak of infectious diseases. For the purposes of understanding the disease spread trend and the effect of observations from previous days, the time series approach for modeling the outcomes can be used to conduct a cointegration analysis to identify the long-run relationship between those multiple processes that are key to understanding trends such as hospitalization and death. As an alternative perspective, relationships between outcomes can be model through a shared latent stochastic error term; here, we propose a novel framework to study the underlying correlation between two time series processes through this method, called joint modeling. This framework is used for our Ontario Covid-19 study, where a cointegration analysis utilizes statistical tests to identify the long-run relationship between the daily number of new hospitalizations 6 days prior and the daily number of new deaths. Additionally, we show that a joint autoregressive model can provide a framework to model the underlying correlation between the processes.
... Examples of this vast literature are works such as Amini et al. (2012) and Stummer et al. (2015), in which ABM is used to simulate product diffusion, Utomo et al. (2018), on modeling agri-food supply chains, Barnes et al. (2010), Ayer et al. (2019), Barnes et al. (2020) on modeling disease transmission. Often, agent-based models are also part of hybrid simulations; we refer to the reviews of Brailsford et al. (2019), Robertson (2019), Currie et al. (2020) for additional details. Further discussion can also be found in Appendix 1. ...
Agent-based models (ABMs) are increasingly used in the management sciences. Though useful, ABMs are often critiqued: it is hard to discern why they produce the results they do and whether other assumptions would yield similar results. To help researchers address such critiques, we propose a systematic approach to conducting sensitivity analyses of ABMs. Our approach deals with a feature that can complicate sensitivity analyses: most ABMs include important non-parametric elements, while most sensitivity analysis methods are designed for parametric elements only. The approach moves from charting out the elements of an ABM through identifying the goal of the sensitivity analysis to specifying a method for the analysis. We focus on four common goals of sensitivity analysis: determining whether results are robust, which elements have the greatest impact on outcomes, how elements interact to shape outcomes, and which direction outcomes move when elements change. For the first three goals, we suggest a combination of randomized finite change indices calculation through a factorial design. For direction of change, we propose a modification of individual conditional expectation (ICE) plots to account for the stochastic nature of the ABM response. We illustrate our approach using the Garbage Can Model, a classic ABM that examines how organizations make decisions.
... Our work is motivated by one missing aspect in the related economic literature: the analysis of the economic impact of cross-region human mobility on pandemic mitigation. Human mobility across regions is an important factor behind the transmission of infectious diseases and has been modelled in some epidemiology literature (Sattenspiel and Dietz [1], Ferguson et al. [2], Hollingsworth et al. [3], Robertson [4], Lai et al. [5]). Cross-region human mobility, the movement of population from a severely-affected area to a mildly-affected one, increases the spread of disease during the outbreak of epidemics; for example, Sattenspiel and Dietz [1], Rvachev and Longini Jr [6], Wang and Zhao [7], Seno [8], Biswas and Mandal [9]. ...
This paper presents a model for finding optimal pandemic control policy considering cross-region human mobility. We extend the baseline susceptible-infectious-recovered (SIR) epidemiology model by including the net human mobility from a severely-impacted region to a mildly-affected region. The strategic optimal mitigation policy combining testing and lockdown in each region is then obtained with the goal of minimizing economic cost under the constraint of limited resources. We parametrize the model using the data of the COVID-19 pandemic and show that the optimal response strategy and mitigation outcome greatly rely on the mitigation duration, available resources, and cross-region human mobility. Furthermore, we discuss the economic impact of travel restriction policies through a quantitative analysis.
... Research teams worldwide are actively involved in supporting decision-makers with forecasts on alternative aspects of the COVID-19 pandemic ( Kaplan, 2020 ). A variety of modeling techniques, ranging from agent-based simulations to generalizations of the classical family of compartmental models are in use ( Currie et al., 2020;Robertson, 2019 ). Much attention is paid to models that support the assessment of the effectiveness of intervention measures ( Ferretti et al., 2020 ). ...
... Models used to support OR/MS investigations range from differential equations/system dynamics (e.g., Kaplan,Craft,& Wein,20 03;Rahmandad & Sterman,20 08 ) 1 , to agent-based simulation ( Pichler, Pangallo, del Rio-Chanona, Lafond, & Doyne Farmer, 2020 ), to network-based models (see the reviews of Dimitrov & Meyers, 2010;Robertson, 2019 ), to discrete event simulators, to hybrid simulators ( Currie et al., 2020 ). In several applications, forecasts from mathematical epidemiological models are fed into optimization models, playing the role of simulating infectious disease transmission dynamics. ...
Operations researchers worldwide rely extensively on quantitative simulations to model alternative aspects of the COVID-19 pandemic. Proper uncertainty quantification and sensitivity analysis are fundamental to enrich the modeling process and communicate correctly informed insights to decision-makers. We develop a methodology to obtain insights on key uncertainty drivers, trend analysis and interaction quantification through an innovative combination of probabilistic sensitivity techniques and machine learning tools. We illustrate the approach by applying it to a representative of the family of susceptible-infectious-recovered (SIR) models recently used in the context of the COVID-19 pandemic. We focus on data of the early pandemic progression in Italy and the United States (the U.S.). We perform the analysis for both cases of correlated and uncorrelated inputs. Results show that quarantine rate and intervention time are the key uncertainty drivers, have opposite effects on the number of total infected individuals and are involved in the most relevant interactions.
... At the community level, high susceptibility is the main driver (Robertson, 2019). The controlled measures including curbing population flows, access control, body temperature monitoring, use of health codes, reduction of unnecessary contact, implementing the combined intervention in quarantining infected individuals and their family members once transmission has been detected, regular disinfection, reinforce propaganda, and education could substantially reduce the number of COVID-19 infections (Li, Bi et al., 2020;Tay, Poh, Rénia, MacAry, & Ng, 2020;Zagmutt, Schoenbaum, & Hill, 2016). ...
The ongoing novel coronavirus (COVID‐19) epidemic has evolved into a full range of challenges that the world is facing. Health and economic threats caused governments to take preventive measures against the spread of the disease. This study aims to provide a correlation analysis of the response measures adopted by countries and epidemic trends since the COVID‐19 outbreak. This analysis picks 13 countries for quantitative assessment. We select a trusted model to fit the epidemic trend curves in segments and catch the characteristics based on which we explore the key factors of COVID‐19 spread. This review generates a score table of government response measures according to the Likert scale. We use the Delphi method to obtain expert judgments about the government response in the Likert scale. Furthermore, we find a significant negative correlation between the epidemic trend characteristics and the government response measure scores given by experts through correlation analysis. More stringent government response measures correlate with fewer infections and fewer waves in the infection curves. Stringent government response measures curb the spread of COVID‐19, limit the number of total infectious cases, and reduce the time to peak of total cases. The clusters of the results categorize the countries into two specific groups. This study will improve our understanding of the prevention of COVID‐19 spread and government response.
... Providing syntheses of evidence across models accounts for variability but how to interpret that variability requires expertise across disciplines. For example, an individual-based model may be an appropriate tool to enable understanding of early transmission dynamics, but compartmental models, network models, and contact matrix approaches may be preferred as the epidemic grows (Robertson, 2019). ...
The COVID-19 pandemic has seen infectious disease modelling at the forefront of government decision-making. Models have been widely used throughout the pandemic to estimate pathogen spread and explore the potential impact of different intervention strategies. Infectious disease modellers and policymakers have worked effectively together, but there are many avenues for progress on this interface. In this paper, we identify and discuss seven broad challenges on the interaction of models and policy for pandemic control. We then conclude with suggestions and recommendations for the future.
... There are many ways to model the spatio-temporal spread of infectious diseases. Let me present the main contenders; see an interesting and more complete list in [161] or [147]. I do not detail reaction-diffusion equations, because, to the best of my knowledge, they have seen very little use in modelling the spatio-temporal spread of COVID-19; readers are referred to [158], for instance, for more details on deterministic aspects involving such systems. ...
SARS-CoV-2 started propagating worldwide in January 2020 and has now reached virtually all communities on the planet. This short review provides evidence of this spread and documents modelling efforts undertaken to understand and forecast it, including a short section about the new variants that emerged in late 2020.
... At the community level, high susceptibility is the main driver (Robertson, 2019). The controlled measures including curbing population flows, access control, body temperature monitoring, use of health codes, reduction of unnecessary contact, implementing the combined intervention in quarantining infected individuals and their family members once transmission has been detected, regular disinfection, reinforce propaganda, and education could substantially reduce the number of COVID-19 infections (Li, Bi et al., 2020;Tay, Poh, Rénia, MacAry, & Ng, 2020;Zagmutt, Schoenbaum, & Hill, 2016). ...
... This innovative solution permits modelling the spread of seasonal influenza more efficiently. The SEIR (Prakash et al., 2017;Ozalp and Demirci, 2011;Robertson, 2019;Thompson, 2016), issued from mathematical epidemiology, uses nonlinear ordinary differential equations, while the transmission of the influenza virus occurs through person-to-person contact. The contact between susceptible and/or infected individuals takes the form of a network. ...
Accounting for about 290,000–650,000 deaths across the globe, seasonal influenza is estimated by the World Health Organization to be a major cause of mortality. Hence, there is a need for a reliable and robust epidemiological surveillance decision‐making system to understand and combat this epidemic disease. In a previous study, the authors proposed a decision support system to fight against seasonal influenza. This system is composed of three subsystems: (i) modeling and simulation, (ii) data warehousing, and (iii) analysis. The analysis subsystem relies on spatial online analytical processing (S‐OLAP) technology. Although the S‐OLAP technology is useful in analyzing multidimensional spatial data sets, it cannot take into account the inherent multicriteria nature of seasonal influenza risk assessment by itself. Therefore, the objective of this article is to extend the existing decision support system by adding advanced multicriteria analysis capabilities for enhanced seasonal influenza risk assessment and monitoring. Bearing in mind the characteristics of the decision problem considered in this article, a well‐known multicriteria classification method, the dominance‐based rough set approach (DRSA), was selected to boost the existing decision support system. Combining the S‐OLAP technology and the multicriteria classification method DRSA in the same decision support system will largely improve and extend the scope of analysis capabilities. The extended decision support system has been validated by its application to assess seasonal influenza risk in the northwest region of Algeria.
