Managing disease containment measures during a pandemic

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.

Assessing IoT challenges in supply chain: A comparative study before and during- COVID-19 using interval valued neutrosophic analytical hierarchy process

Although the Internet of Things (IoT) has spawned a new breed of smart factories within supply chains, the latest pandemic has ushered in unparalleled supply chain disturbances. Following the challenges identified in the literature, we interview top experts to evaluate the significance of these challenges. We apply a multi-criteria decision analysis (MCDA) tool, analytical hierarchy process (AHP) in combination with interval-valued neutrosophic numbers (IVN). The critical part of this research is that we also perform a comparative analysis by focusing on before- and during- the pandemic periods individually to better assess the impact of the latest pandemic on the IoT challenges. Our study also includes a comprehensive, systematic literature review to bring the readers up-to-date.

Models for Government Intervention During a Pandemic

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.

A novel diffusion-based model for estimating cases, and fatalities in epidemics: The case of COVID-19.

While the COVID-19 pandemic is still ongoing in a majority of countries, a wealth of literature published in reputable journals attempted to model the spread of the disease. A vast majority of these studies dealt with compartmental models such as susceptible-infected-recovered (SIR) model. Although these models are rather simple, intuitive, and insightful, we argue that they do not necessarily provide a good enough fit to the reported data, which are usually reported in the form of daily fatalities and cases during pandemics. This study proposes an alternative analytics approach that relies on diffusion models to predict the number of cases and fatalities in epidemics. After evaluating several of the well-known and widely used diffusion models in business literature, including ADBUDG, Gompertz, and Bass models, we developed and used a modified/improved version of the original Bass diffusion model to address the shortcomings of the ordinary compartmental models such as SIR and demonstrated its applicability on the portrayal of the COVID-19 pandemic incident data. The proposed model differentiates itself from other similar models by fitting the data without the need for preprocessing, requiring no initial conditions and assumptions, not involving in heavy parameterization, and also properly addressing the pressing issues such as undocumented cases, length of infectious or recovery periods.

No Place Like Home: A Cross-National Assessment of the Efficacy of Social Distancing during the COVID-19 Pandemic

BACKGROUND: In the absence of a cure in the time of pandemics, social distancing measures seem to be the most effective intervention to slow down the spread of disease. Various simulation-based studies have been conducted in the past to investigate the effectiveness of such measures. While those studies unanimously confirm the mitigating effect of social distancing on the disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. A real transactional data, however, can reduce the uncertainty and provide a less noisy picture of social distancing effectiveness. OBJECTIVE: In this paper, we integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics data from ECDC) to study the role of social distancing policies in 26 countries wherein the transmission rate of the COVID-19 pandemic is analyzed over the course of five weeks. METHODS: Relying on the SIR model and official COVID-19 reports, we first calculated the weekly transmission rate (β) of the coronavirus disease in 26 countries for five consecutive weeks. Then we integrated that with the Google's and Apple's mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between mobility factors and β values. RESULTS: Gradient Boosted Trees (GBT) regression analysis showed that changes in mobility patterns, resulted from social distancing policies, explain around 47% of the variation in the disease transmission rate. CONCLUSIONS: Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing down the spread of the disease. Apart from providing less noisy and more generalizable support for the whole social distancing idea, we provide specific insights for public health policy-makers as to what locations should be given a higher priority for enforcing social distancing measures.