Herndon, Va. (May 20, 2021) - The international journal Risk Analysis has published a timely special issue for May 2021, "Global Systemic Risk and Resilience for Novel Coronavirus and COVID-19." Featuring 11 papers written for this issue over the past year, the collection represents a sampling of insights and viewpoints from scholars across risk sciences and resilience analytics to guide decision-making and operations related to the COVID-19 pandemic.
The 11 papers address the breadth of risk sciences represented by the Society for Risk Analysis (SRA), including risk perception, risk and resilience, human health and infection risk, and risk management strategies and economic evaluation. From a systems perspective, the multidisciplinary expertise of the SRA and its specialty groups can offer new tools for public health practitioners, infrastructure owners/operators, and policymakers to coordinate global and local, context-specific interventions, with expanded access to health information and services.
The introduction to this special issue is written by Special Issue Co-Editors, Desheng Dash Wu of the University of Chinese Academy of Sciences and Stockholm University; Jade Mitchell of Michigan State University; and James H. Lambert of the University of Virginia. The Special Issue Co-Editors wrote the Call for Papers in January of 2020, solicited reviewers from across the Society and elsewhere, and provided the Editor-in-Chief with recommendations for the submitted manuscripts.
Here are the original research papers and perspectives included in the issue:
- The Impact of Trust and Risk Perception on the Acceptance of Measures to Reduce COVID-19 Cases, by Michael Siegrist, Larissa Luchsinger, and Angela Bearth
Researchers conducted a survey in the German-speaking part of Switzerland at the peak of confirmed COVID-19 cases during the first wave of infections in that country (March-April 2020). Their results suggest that perceived risks are important drivers for the acceptance of the government's implemented measures to control COVID-19 and for more precautionary behavior.
- Analyzing the Risk to COVID-19 Infection Using Remote Sensing and GIS, by Shruti Kanga, Gowhar Meraj, Sudhanshu, Majid Farooq, M.S. Nathawa, and Suraj Kumar Singh
This study proposes a risk-based assessment framework for analyzing risk of high transmission and prevalence for COVID-19 in spatial areas -- using integrated hazard and vulnerability components associated with the pandemic to guide effective risk mitigation. Focused on a region of Maharashtra, the study aims to serve as a baseline study to be replicated in other parts of India or the world to help eradicate the increased threat of COVID-19 in at-risk populations.
- Reinventing Cloth Masks in the Face of Pandemics, by Stephen Salter
This study points out that countries could rapidly implement Effective Fiber Mask Programs (EFMPs) to use local resources to mass-produce effective and affordable cloth masks, and to engage the public in their correct use during the COVID-19 pandemic.
- Fast and Frugal: Information Processing Related to The Coronavirus Pandemic, by Jody Chin Sing Wong, Janet Zheng Yang, Zhuling Liu, David Lee, and Zhiying Yue
This paper establishes how two different information processing modes are influenced by individuals' responsibility attribution, discrete negative emotions, and risk perception. The results revealed that information processing styles seem to be determined by social judgment surrounding the coronavirus pandemic.
- A Novel Causal Risk-Based Decision-Making Methodology: The Case of Coronavirus, by Stavros K. Stavroglou and Bilal Ayyub
The authors propose a novel risk-based, decision-making methodology capable of unveiling causal relationships between pairs of variables. This methodology offers a basis for identifying potential scenarios and consequences of the ongoing 2020 pandemic by drawing on weather variables to examine the causal impact of changing weather on the trend of daily coronavirus cases.
- Optimal Investment in Prevention and Recovery for Mitigating Epidemic Risks, by C. Derrick Huang, Milad Baghersad, Ravi S. Behara, and Christopher W. Zobel
Researchers built a mathematical model to optimize investments into two types of measures for mitigating the risks of epidemic spread: prevention/containment and treatment/recovery.
- Exploring Risks of Human Challenge Trials for COVID-19, by David Manheim
This study introduces an interactive model for exploring some risks of a SARS-COV-2 dosing study (a prerequisite for COVID-19 challenge trials). The risk estimates they use are based on a Bayesian evidence synthesis model which can incorporate new data on infection fatality risks (IFRs) to patients, and infer rates of hospitalization.
- Challenges Associated with the Response to the Coronavirus Disease (COVID-19) Pandemic in Africa - An African Diaspora Perspective, by Andre M. N. Renzaho
This paper discusses how lessons learned during the 2014-2016 Ebola outbreak in West Africa help to mitigate the likelihood of a long-term devastating effect of the COVID-19 outbreak on the African continent.
- Action Levels for SARS-COV-2 in Air: Preliminary Approach, by Charles N. Haas
In this study, the dose-response curve for Coronavirus 229E is used to develop a preliminary risk-based exposure criteria for SARS-CoV-2 via the respiratory portals of entry.
- Do the Benefits of COVID-19 Policies Exceed the Costs? Exploring Uncertainties in the Age-VSL Relationship, by Lisa A. Robinson, Ryan Sullivan, and Jason F. Shogren
This study explores the implications of theory and empirical studies, which suggest that the relationship between age and value per statistical life is uncertain. The researchers found that, when applied to the U.S. age distribution of COVID-19 deaths, their approaches result in average value per statistical life estimates from $4.47 million to $10.63 million.
- On Ambiguity Reduction and the Role of Decision Analysis During the Pandemic, by David C. Rode and Paul S. Fischbeck
Experts propose that scarce testing resources should be diverted away from confirmatory analysis of symptomatic people - as lab diagnosis appears to have little decision value in treatment choice over clinical diagnosis in patients with symptoms. In contrast, the exploratory use of testing resources to reduce ambiguity in estimates of the base rate of infection appears to have significant value and great practical import for public policy purposes. (These findings highlight the important role of decision analysts in responding to the challenges of the COVID-19 pandemic.)