Search

Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

State governments in the U.S. have been facing difficult decisions involving tradeoffs between economic and health-related outcomes during the COVID-19 pandemic. Despite evidence of the effectiveness of government-mandated restrictions mitigating the spread of contagion, these orders are stigmatized due to undesirable economic consequences. This tradeoff resulted in state governments employing mandates at widely different ways. We compare the different policies states implemented during periods of restriction ("lockdown") and reopening with indicators of COVID-19 spread and consumer card spending at each state during the first "wave" of the pandemic in the U.S. between March and August 2020. We find that while some states enacted reopening decisions when the incidence rate of COVID-19 was minimal or sustained in its relative decline, other states relaxed socioeconomic restrictions near their highest incidence and prevalence rates experienced so far. Nevertheless, all states experienced similar trends in consumer card spending recovery, which was strongly correlated with reopening policies following the lockdowns and relatively independent from COVID-19 incidence rates at the time. Our findings suggest that consumer card spending patterns can be attributed to government mandates rather than COVID-19 incidence in the states. We estimate the recovery in states that reopened in late April was more than the recovery in states that did not reopen in the same period- 15% for consumer card spending and 18% for spending by high income households. This result highlights the important role of state policies in minimizing health impacts while promoting economic recovery and helps planning effective interventions in subsequent waves and immunization efforts., Competing Interests: The authors have declared that no competing interests exist.

The emergence of COVID-19 in the United States has overwhelmed local hospitals, produced shortages in critical protective supplies for medical staff, and created backlogs in burials and cremations. Because systemic disruptions occur most acutely at a local scale, facilitating resource coordination across a broad region can assist local responses to COVID-19 surges. This article describes a structured systems approach for coordinating COVID-19 resource distribution across the six New England states of the United States. The framework combines modeling tools to anticipate resource shortages in medical supplies, personnel needs, and fatality management for individual states. The approach allows decision makers to understand the magnitude of local outbreaks and equitably allocate resources within a region based on the present and future needs. This model contributed to determining material distribution in New England as the 2020 COVID-19 surges unfolded in the spring and fall seasons. Using a systems analysis, the model demonstrates the translation of anticipated COVID-19 cases into resource demands to enable regional coordination of scarce resources.

Purpose: Despite rapid success in bringing SARS-CoV-2 vaccines to distribution by multiple pharmaceutical corporations, supply chain failures in production and distribution can plague pandemic recovery. This review analyzes and addresses gaps in modeling supply chain resilience in general and specifically for vaccines in order to guide researchers and practitioners alike to improve critical function of vaccine supply chains in the face of inevitable disruptions. Design/methodology/approach: Systematic review of the literature on modeling supply chain resilience from 2007 to 2020 is analyzed in tandem with the vaccine supply chain manufacturing literature. These trends are then used to apply a novel matrix analysis to seven Securities and Exchange Commission (SEC) annual filings of pharmaceutical corporations involved in COVID-19 vaccine manufacture and distribution. Findings: Pharmaceutical corporations favor efficiency as they navigate regulatory, economic and other threats to their vaccine supply chains, neglecting resilience – absorption, adaptation and recovery from inevitable and unexpected disruptions. However, explicitly applying resilience analytics to the vaccine supply chain and further leveraging emerging network science tools found in the academic literature, such as artificial intelligence (AI), stress tests and digital twins, will help supply chain managers to better quantify efficiency/resilience tradeoffs across all associated networks/domains and support optimal system performance post disruption. Originality/value: This is the first review addressing resilience analytics in vaccine supply chains and subsequent extension to operational management through novel matrix analyses of SEC Filings. The authors provide analyses and recommendations that facilitate resilience quantification capabilities for vaccine supply chain managers, regulatory agencies and corporate stakeholders and are especially relevant for pandemic response, including application to the SARS-CoV-2 vaccines. [ABSTRACT FROM AUTHOR]

Many efforts to predict the impact of COVID-19 on hospitalization, intensive care unit (ICU) utilization, and mortality rely on age and comorbidities. These predictions are foundational to learning, policymaking, and planning for the pandemic, and therefore understanding the relationship between age, comorbidities, and health outcomes is critical to assessing and managing public health risks. From a US government database of 1.4 million patient records collected in May 2020, we extracted the relationships between age and number of comorbidities at the individual level to predict the likelihood of hospitalization, admission to intensive care, and death. We then applied the relationships to each US state and a selection of different countries in order to see whether they predicted observed outcome rates. We found that age and comorbidity data within these geographical regions do not explain much of the international or within-country variation in hospitalization, ICU admission, or death. Identifying alternative explanations for the limited predictive power of comorbidities and age at the population level should be considered for future research. [ABSTRACT FROM AUTHOR]

After implementing restrictions to curb the spread of coronavirus, governments in the United States and around the world are trying to identify the path to social and economic recovery. The White House and the Centers for Disease Control and Prevention have published guidelines to assist US states, counties, and territories in planning these efforts. As the impact of the coronavirus pandemic has not been uniform, these central guidelines need to be translated into practice in ways that recognize variation among jurisdictions. We present a core methodology to assist governments in this task, presenting a case for appropriate actions at each stage of recovery based on scientific data and analysis. Specifically, 3 types of data are needed: data on the spread of disease should be analyzed alongside data on the overall health of the population and data on infrastructure—for example, the capacity of health systems. Local circumstances will produce different needs and present different setbacks, and governments may need to reinstate as well as relax restrictions. Transparent, defensible analysis can assist in making these decisions and communicating them to the public. In the absence of a widely administered vaccine, analysis remains one of our most important tools in addressing the coronavirus pandemic. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID-19) has had an undeniable impact upon global societies, public health, and economies. Specifically, Ndiili notes that COVID is having and will continue to have a substantial impact upon international investment, economics, and trade - the implications of which are vast for the developing economies and industries across the diverse array of African nations. 10.1007/s10669-020-09780-1 10 Quigley M, Attanayake J, King A, Prideaux F. A multi-hazards earth science perspective on the COVID-19 pandemic: the potential for concurrent and cascading crises. [Extracted from the article]

Resilience and sustainability have each offered a path forward for post-COVID economic recovery and a post-Glasgow global financial order. Yet, the relationships between these two concepts are largely unexplored in economic policy and investment strategies. In light of emerging systemic risks and global demands for more resolute investments in resilience and sustainability, this perspective article took the position that policymakers must begin to draw greater conceptual, empirical, and practical linkages between sustainability and resilience. This perspective article provided a simplified framework for understanding the positively reinforcing, negatively conflicting, and neutral relationships between different types of resilience and sustainability consistent with two propositions. The Reinforcement Proposition argues (i) that various resilience processes may drive sustainable outcomes, and/or (ii) that an allocation of sustainable resources may reinforce resilience processes, as well as the transformative adaptation of markets. Conversely, the Conflict Proposition argues (i) that certain resilience processes may perpetuate stability features that may thwart an economic transition toward sustainability, and/or (ii) that certain sustainability outcomes associated with reorganized economic structures and relationships may undermine resources for resilience. This framework provides policymakers with an opportunity to evaluate the convergent and conflicting trade-offs of resilience processes and sustainable outcomes. [ABSTRACT FROM AUTHOR]