Emulator-based Bayesian calibration of the CISNET colorectal cancer models.

Purpose: To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET) 's SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets. Methods: We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANN) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets. Results: The optimal ANN for SimCRC had four hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had one hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 hours for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN. Conclusions: Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, like the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating three realistic CRC individual-level models using a Bayesian approach.

Robustness Analysis of Colorectal Cancer Colonoscopy Screening Strategies.

Colorectal Cancer (CRC) is a leading cause of cancer deaths in the United States. Despite significant overall declines in CRC incidence and mortality, there has been an alarming increase in CRC among people younger than 50. This study uses an established microsimulation model, CRC-SPIN, to perform a 'stress test' of colonoscopy screening strategies. First, we expand CRC-SPIN to include birth-cohort effects. Second, we estimate natural history model parameters via Incremental Mixture Approximate Bayesian Computation (IMABC) for two model versions to characterize uncertainty while accounting for increased early CRC onset. Third, we simulate 26 colonoscopy screening strategies across the posterior distribution of estimated model parameters, assuming four different colonoscopy sensitivities (104 total scenarios). We find that model projections of screening benefit are highly dependent on natural history and test sensitivity assumptions, but in this stress test, the policy recommendations are robust to the uncertainties considered.

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.

Unequal Recovery in Colorectal Cancer Screening Following the COVID-19 Pandemic: A Comparative Microsimulation Analysis.

The aftermath of the initial phase of the COVID-19 pandemic may contribute to the widening of disparities in access to colorectal cancer (CRC) screening due to differential disruptions to CRC screening. This comparative microsimulation analysis uses two CISNET CRC models to simulate the impact of ongoing screening disruptions induced by the COVID-19 pandemic on long-term CRC outcomes. We evaluate three channels through which screening was disrupted: delays in screening, regimen switching, and screening discontinuation. The impact of these disruptions on long-term colorectal cancer (CRC) outcomes was measured by the number of Life-years lost due to CRC screening disruptions compared to a scenario without any disruptions. While short-term delays in screening of 3-18 months are predicted to result in minor life-years loss, discontinuing screening could result in much more significant reductions in the expected benefits of screening. These results demonstrate that unequal recovery of screening following the pandemic can widen disparities in colorectal cancer outcomes and emphasize the importance of ensuring equitable recovery to screening following the pandemic.

Reopening California : Seeking Robust, Non-Dominated COVID-19 Exit Strategies.

Amid global scarcity of COVID-19 vaccines and the threat of new variant strains, California and other jurisdictions face the question of when and how to implement and relax COVID-19 Nonpharmaceutical Interventions (NPIs). While policymakers have attempted to balance the health and economic impacts of the pandemic, decentralized decision-making, deep uncertainty, and the lack of widespread use of comprehensive decision support methods can lead to the choice of fragile or inefficient strategies. This paper uses simulation models and the Robust Decision Making (RDM) approach to stress-test California's reopening strategy and other alternatives over a wide range of futures. We find that plans which respond aggressively to initial outbreaks are required to robustly control the pandemic. Further, the best plans adapt to changing circumstances, lowering their stringent requirements to reopen over time or as more constituents are vaccinated. While we use California as an example, our results are particularly relevant for jurisdictions where vaccination roll-out has been slower.

Modeling COVID-19 Nonpharmaceutical Interventions: Exploring periodic NPI strategies.

We developed a COVID-19 transmission model used as part of RAND's web-based COVID-19 decision support tool that compares the effects of nonpharmaceutical public health interventions (NPIs) on health and economic outcomes. An interdisciplinary approach informed the selection and use of multiple NPIs, combining quantitative modeling of the health/economic impacts of interventions with qualitative assessments of other important considerations (e.g., cost, ease of implementation, equity). This paper provides further details of our model, describes extensions, presents sensitivity analyses, and analyzes strategies that periodically switch between a base NPI level and a higher NPI level. We find that a periodic strategy, if implemented with perfect compliance, could have produced similar health outcomes as static strategies but might have produced better outcomes when considering other measures of social welfare. Our findings suggest that there are opportunities to shape the tradeoffs between economic and health outcomes by carefully evaluating a more comprehensive range of reopening policies.

Could periodic nonpharmaceutical intervention strategies produce better COVID-19 health and economic outcomes?

We developed a COVID-19 transmission model to compare the effects of nonpharmaceutical public health interventions (NPIs) on health and economic outcomes. An interdisciplinary approach informed the selection and use of multiple NPIs, combining quantitative modeling of the health and economic impacts of interventions with qualitative assessments of other important considerations (e.g., cost, ease of implementation, equity). We used our model to analyzed strategies that periodically switch between a base NPI and a high NPI level. We find that this systematic strategy could have produced similar health outcomes as static strategies but better social welfare and economic outcomes. Our findings suggest that there are opportunities to shape the tradeoffs between economic and health outcomes by carefully evaluating a more comprehensive range of reopening policies.

Desarrollamos un modelo de transmisión de COVID-19 para comparar los efectos de las intervenciones de salud pública (NPI) no farmacéuticas en los resultados económicos y de salud. Un enfoque interdisciplinario informó la selección y el uso de múltiples ISFL, combinando modelos cuantitativos de los impactos económicos y de salud de las intervenciones con evaluaciones cualitativas de otras consideraciones importantes (por ejemplo, costo, facilidad de implementación, equidad). Usamos nuestro modelo para analizar estrategias que cambian periódicamente entre un NPI base y un nivel alto de NPI. Encontramos que esta estrategia sistemática podría haber producido resultados de salud similares a los de las estrategias estáticas, pero mejores resultados económicos y de bienestar social. Nuestros hallazgos sugieren que existen oportunidades para dar forma a las compensaciones entre los resultados económicos y de salud al evaluar cuidadosamente una gama más completa de políticas de reapertura.