Reopening California: Seeking robust, non-dominated COVID-19 exit strategies

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

BACKGROUND

The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19).

METHODOLOGY

PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542.

FINDINGS

In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias.

INTERPRETATION

The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations

Throughout the COVID-19 pandemic, policymakers have proposed risk metrics, such as the CDC Community Levels, to guide local and state decision-making. However, risk metrics have not reliably predicted key outcomes and have often lacked transparency in terms of prioritization of false-positive versus false-negative signals. They have also struggled to maintain relevance over time due to slow and infrequent updates addressing new variants and shifts in vaccine- and infection-induced immunity. We make two contributions to address these weaknesses. We first present a framework to evaluate predictive accuracy based on policy targets related to severe disease and mortality, allowing for explicit preferences toward false-negative versus false-positive signals. This approach allows policymakers to optimize metrics for specific preferences and interventions. Second, we propose a method to update risk thresholds in real time. We show that this adaptive approach to designating areas as "high risk" improves performance over static metrics in predicting 3-wk-ahead mortality and intensive care usage at both state and county levels. We also demonstrate that with our approach, using only new hospital admissions to predict 3-wk-ahead mortality and intensive care usage has performed consistently as well as metrics that also include cases and inpatient bed usage. Our results highlight that a key challenge for COVID-19 risk prediction is the changing relationship between indicators and outcomes of policy interest. Adaptive metrics therefore have a unique advantage in a rapidly evolving pandemic context.

Phase-wise evaluation and optimization of non-pharmaceutical interventions to contain the COVID-19 pandemic in the U.S

Given that the effectiveness of COVID-19 vaccines and other therapies is greatly limited by the continuously emerging variants, non-pharmaceutical interventions have been adopted as primary control strategies in the global fight against the COVID-19 pandemic. However, implementing strict interventions over extended periods of time is inevitably hurting the economy. Many countries are faced with the dilemma of how to take appropriate policy actions for socio-economic recovery while curbing the further spread of COVID-19. With an aim to solve this multi-objective decision-making problem, we investigate the underlying temporal dynamics and associations between policies, mobility patterns, and virus transmission through vector autoregressive models and the Toda-Yamamoto Granger causality test. Our findings reveal the presence of temporal lagged effects and Granger causality relationships among various transmission and human mobility variables. We further assess the effectiveness of existing COVID-19 control measures and explore potential optimal strategies that strike a balance between public health and socio-economic recovery for individual states in the U.S. by employing the Pareto optimality and genetic algorithms. The results highlight the joint power of the state of emergency declaration, wearing face masks, and the closure of bars, and emphasize the necessity of pursuing tailor-made strategies for different states and phases of epidemiological transmission. Our framework enables policymakers to create more refined designs of COVID-19 strategies and can be extended to other countries regarding best practices in pandemic response.

Dealing With Uncertainty in Early Health Technology Assessment: An Exploration of Methods for Decision Making Under Deep Uncertainty

OBJECTIVES

In early stages, the consequences of innovations are often unknown or deeply uncertain, which complicates early health economic modeling (EHEM). The field of decision making under deep uncertainty uses exploratory modeling (EM) in situations when the system model, input probabilities/distributions, and consequences are unknown or debated. Our aim was to evaluate the use of EM for early evaluation of health technologies.

METHODS

We applied EM and EHEM to an early evaluation of minimally invasive endoscopy-guided surgery (MIS) for acute intracerebral hemorrhage and compared these models to derive differences, merits, and drawbacks of EM.

RESULTS

EHEM and EM differ fundamentally in how uncertainty is handled. Where in EHEM the focus is on the value of technology, while accounting for the uncertainty, EM focuses on the uncertainty. EM aims to find robust strategies, which give relatively good outcomes over a wide range of plausible futures. This was reflected in our case study. EHEM provided cost-effectiveness thresholds for MIS effectiveness, assuming fixed MIS costs. EM showed that a policy with a population in which most patients had severe intracerebral hemorrhage was most robust, regardless of MIS effectiveness, complications, and costs.

CONCLUSIONS

EHEM and EM were found to complement each other. EM seems most suited in the very early phases of innovation to explore existing uncertainty and many potential strategies. EHEM seems most useful to optimize promising strategies, yet EM methods are complex and might only add value when stakeholders are willing to consider multiple solutions to a problem and adopt flexible research and adoption strategies.

Optimal non-pharmaceutical pandemic response strategies depend critically on time horizons and costs

The COVID-19 pandemic has called for swift action from local governments, which have instated non-pharmaceutical interventions (NPIs) to curb the spread of the disease. The swift implementation of social distancing policies has raised questions about the costs and benefits of strategies that either aim to keep cases as low as possible (suppression) or aim to reach herd immunity quickly (mitigation) to tackle the COVID-19 pandemic. While curbing COVID-19 required blunt instruments, it is unclear whether a less-transmissible and less-deadly emerging pathogen would justify the same response. This paper illuminates this question using a parsimonious transmission model by formulating the social distancing lives vs. livelihoods dilemma as a boundary value problem using calculus of variations. In this setup, society balances the costs and benefits of social distancing contingent on the costs of reducing transmission relative to the burden imposed by the disease. We consider both single-objective and multi-objective formulations of the problem. To the best of our knowledge, our approach is distinct in the sense that strategies emerge from the problem structure rather than being imposed a priori. We find that the relative time-horizon of the pandemic (i.e., the time it takes to develop effective vaccines and treatments) and the relative cost of social distancing influence the choice of the optimal policy. Unsurprisingly, we find that the appropriate policy response depends on these two factors. We discuss the conditions under which each policy archetype (suppression vs. mitigation) appears to be the most appropriate.

Reopening Under Uncertainty: Stress-Testing California's COVID-19 Exit Strategy

The coronavirus disease 2019 pandemic required significant public health interventions from local governments. Early in the pandemic, RAND researchers developed a decision support tool to provide policymakers with insight into the trade-offs they might face when choosing among nonpharmaceutical intervention levels. Using an updated version of the model, the researchers performed a stress-test of a variety of alternative reopening plans, using California as an example. This article presents the general lessons learned from these experiments and discusses four characteristics of the best reopening strategies.

Too Good to Be True? Evaluation of Colonoscopy Sensitivity Assumptions Used in Policy Models

BACKGROUND

Models can help guide colorectal cancer (CRC) screening policy. While models are carefully calibrated and validated, there is less scrutiny of assumptions about test performance.

METHODS

We examined the validity of the CRC-SPIN model and colonoscopy sensitivity assumptions. Standard sensitivity assumptions, consistent with published decision analyses, assume sensitivity equal to 0.75 for diminutive adenomas (<6mm), 0.85 for small adenomas (6-10mm), 0.95 for large adenomas ( {greater than or equal to} 10mm), and 0.95 for preclinical cancer. We also selected adenoma sensitivity that resulted in more accurate predictions. Targets were drawn from the Wheat Bran Fiber study. We examined how well the model predicted outcomes measured over a three-year follow-up period, including: the number of adenomas detected, the size of the largest adenoma detected, and incident CRC.

RESULTS

Using standard sensitivity assumptions, the model predicted adenoma prevalence that was too low (42.5% versus 48.9% observed, with 95% confidence interval 45.3%-50.7%) and detection of too few large adenomas (5.1% versus 14.% observed, with 95% confidence interval 11.8%-17.4%). Predictions were close to targets when we set sensitivities to 0.20 for diminutive adenomas, 0.60 for small adenomas, 0.80 for 10-20mm adenomas, and 0.98 for adenomas 20mm and larger.

CONCLUSIONS

Colonoscopy may be less accurate than currently assumed, especially for diminutive adenomas. Alternatively, the CRC-SPIN model may not accurately simulate onset and progression of adenomas in higher-risk populations.

IMPACT

Misspecification of either colonoscopy sensitivity or disease progression in high-risk populations may impact the predicted effectiveness of CRC screening. When possible, decision analyses used to inform policy should address these uncertainties.