Management Information Systems

Permanent URI for this collectionhttps://hdl.handle.net/10679/7976

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Browsing by Author "Eryarsoy, E."

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    Managing disease containment measures during a pandemic
    (Wiley, 2023-05) Shahmanzari, Masoud; Tanrisever, F.; Eryarsoy, E.; Şensoy, A.; Management Information Systems; SAYIN, Mesut
    Throughout the current COVID-19 pandemic, governments have implemented a variety of containment measures, ranging from hoping for herd immunity (which is essentially no containment) to mandating complete lockdown. On the one hand, containment measures reduce lives lost by limiting the disease spread and controlling the load on the healthcare system. On the other hand, such measures slow down economic activity, leading to lost jobs, economic stall, and societal disturbances, such as protests, civil disobedience, and increases in domestic violence. Hence, determining the right set of containment measures is a key social, economic, and political decision for policymakers. In this paper, we provide a model for dynamically managing the level of disease containment measures over the course of a pandemic. We determine the timing and level of containment measures to minimize the impact of a pandemic on economic activity and lives lost, subject to healthcare capacity and stochastic disease evolution dynamics. On the basis of practical evidence, we examine two common classes of containment policies—dynamic and static—and we find that dynamic policies are particularly valuable when the rate of disease spread is low, recovery takes longer, and the healthcare capacity is limited. Our work reveals a fundamental relationship between the structure of Pareto-efficient containment measures (in terms of lives lost and economic activity) and key disease and economic parameters such as disease infection rate, recovery rate, and healthcare capacity. We also analyze the impact of virus mutation and vaccination on containment decisions.
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    Models for government intervention during a pandemic
    (Elsevier, 2023-01-01) Eryarsoy, E.; Shahmanzari, Masoud; Tanrisever, F.; Management Information Systems; SAYIN, Mesut
    While intervention policies such as social distancing rules, lockdowns, and curfews may save lives during a pandemic, they impose substantial direct and indirect costs on societies. In this paper, we provide a mathematical model to assist governmental policymakers in managing the lost lives during a pandemic through controlling intervention levels. Our model is non-convex in decision variables, and we develop two heuristics to obtain fast and high-quality solutions. Our results indicate that when anticipated economic consequences are higher, healthcare overcapacity will emerge. When the projected economic costs of the pandemic are large and the illness severity is low, however, a no-intervention strategy may be preferable. As the severity of the infection rises, the cost of intervention climbs accordingly. The death toll also increases with the severity of both the economic consequences of interventions and the infection rate of the disease. Our models suggest earlier mitigation strategies that typically start before the saturation of the healthcare system when disease severity is high.
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    The role of sociodemographic factors during a pandemic outbreak: Aggravators and mitigators
    (Sociological Demography Press, 2021) Shahmanzari, Masoud; Eryarsoy, E.; Management Information Systems; SAYIN, Mesut
    Many macro-and micro-level factors affect the spread of an infectious disease. Among them are sociodemographic, socioeconomic, sociocultural, health care system infrastructure, use of alcohol or substances, level of life disruptions because of chronic illnesses. Because of accuracy and timeliness issues, officials are often forced to make one-size-fits-all decisions across all regions. This paper offers a framework to analyze and quantify the interrelationships between a wide set of sociodemographic factors and the transmission speed of the pandemic to facilitate custom-fitted regional containment measures. The purpose of this paper is to investigate the role of a comprehensive set of sociodemographic factors in the diffusion of COVID-19 analytically. Our findings suggest that diverse sets of sociodemographic factors drive the transmission during different stages of the pandemic. In specific, we show that variables such as gender, age groups, daily commuting distances, modes of employment, poverty and transportation means are found to be statistically significant in the transmission speed of COVID-19. Our results do not suggest a statistically significant relationship between transmission speed and migration-related variables. We also find that the importance levels for the statistically significant variables vary across different stages of the pandemic. Our results point out a variety of public policy insights and implications.