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Background: Mathematical models served a critical role in COVID-19 decision making throughout the pandemic. Model calibration is an essential, but often computationally burdensome, step in model development that provides estimates for difficult-to-measure parameters and establishes an up-to-date modeling platform for scenario analysis. In the evolving COVID-19 pandemic, frequent recalibration was necessary to provide ongoing support to decision makers. In this study, we address the computational challenges of frequent recalibration with a new calibration approach., Methods: We calibrated and recalibrated an age-stratified dynamic compartmental model of COVID-19 in Minnesota to statewide COVID-19 cumulative mortality and prevalent age-specific hospitalizations from March 22, 2020 through August 20, 2021. This period was divided into 10 calibration periods, reflecting significant changes in policies, messaging, and/or epidemiological conditions in Minnesota. When recalibrating the model from one period to the next, we employed a sequential calibration approach that leveraged calibration results from previous periods and adjusted only parameters most relevant to the calibration target data of the new calibration period to improve computational efficiency. We compared computational burden and performance of the sequential calibration approach to a more traditional calibration method, in which all parameters were readjusted with each recalibration., Results: Both calibration methods identified parameter sets closely reproducing prevalent hospitalizations and cumulative deaths over time. By the last calibration period, both approaches converged to similar parameter values. However, the sequential calibration approach identified parameter sets that more tightly fit calibration targets and required substantially less computation time than traditional calibration., Conclusions: Sequential calibration is an efficient approach to maintaining up-to-date models with evolving, time-varying parameters and potentially identifies better-fitting parameter sets than traditional calibration., Highlights: This study used a sequential calibration approach, which takes advantage of previous calibration results to reduce the number of parameters to be estimated in each round of calibration, improving computational efficiency and algorithm convergence to best-fitting parameter values.Both sequential and traditional calibration approaches were able to identify parameter sets that closely reproduced calibration targets. However, the sequential calibration approach generated parameter sets that yielded tighter fits and was less computationally burdensome.Sequential calibration is an efficient approach to maintaining up-to-date models with evolving, time-varying parameters., Competing Interests: The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The following authors are employed by the sponsor: ECS, ABS, PJM, SG. EAE received personal consulting fees from Janssen Pharmaceuticals for work unrelated to this study. ZL, SBM, SYZK, KMK have no conflicts of interest to report. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for this study was provided entirely by a contract from the Minnesota Department of Health. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report

Background. To support proactive decision making during the COVID-19 pandemic, mathematical models have been leveraged to identify surveillance indicator thresholds at which strengthening nonpharmaceutical interventions (NPIs) is necessary to protect health care capacity. Understanding tradeoffs between different adaptive COVID-19 response components is important when designing strategies that balance public preference and public health goals. Methods. We considered 3 components of an adaptive COVID-19 response: 1) the threshold at which to implement the NPI, 2) the time needed to implement the NPI, and 3) the effectiveness of the NPI. Using a compartmental model of SARS-CoV-2 transmission calibrated to Minnesota state data, we evaluated different adaptive policies in terms of the peak number of hospitalizations and the time spent with the NPI in force. Scenarios were compared with a reference strategy, in which an NPI with an 80% contact reduction was triggered when new weekly hospitalizations surpassed 8 per 100,000 population, with a 7-day implementation period. Assumptions were varied in sensitivity analysis. Results. All adaptive response scenarios substantially reduced peak hospitalizations relative to no response. Among adaptive response scenarios, slower NPI implementation resulted in somewhat higher peak hospitalization and a longer time spent under the NPIs than the reference scenario. A stronger NPI response resulted in slightly less time with the NPIs in place and smaller hospitalization peak. A higher trigger threshold resulted in greater peak hospitalizations with little reduction in the length of time under the NPIs. Conclusions. An adaptive NPI response can substantially reduce infection circulation and prevent health care capacity from being exceeded. However, population preferences as well as the feasibility and timeliness of compliance with reenacting NPIs should inform response design., Highlights: This study uses a mathematical model to compare different adaptive nonpharmaceutical intervention (NPI) strategies for COVID-19 management across 3 dimensions: threshold when the NPI should be implemented, time it takes to implement the NPI, and the effectiveness of the NPI.All adaptive NPI response scenarios considered substantially reduced peak hospitalizations compared with no response.Slower NPI implementation results in a somewhat higher peak hospitalization and longer time spent with the NPI in place but may make an adaptive strategy more feasible by allowing the population sufficient time to prepare for changing restrictions.A stronger, more effective NPI response results in a modest reduction in the time spent under the NPIs and slightly lower peak hospitalizations.A higher threshold for triggering the NPI delays the time at which the NPI starts but results in a higher peak hospitalization and does not substantially reduce the time the NPI remains in force., Competing Interests: The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The following authors are employed by the sponsor: ECS, PJM, ABS, SG. EAE received personal consulting fees from Janssen Pharmaceuticals for work unrelated to this study. ZL, SBM, SYZK, KMK have no conflicts of interest to report. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for this study was provided entirely by a contract from the Minnesota Department of Health. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report., (© The Author(s) 2023.)

Purpose. In 2018, the US Preventive Services Task Force (USPSTF) endorsed three strategies for cervical cancer screening in women ages 30 to 65: cytology every 3 years, testing for high-risk types of human papillomavirus (hrHPV) every 5 years, and cytology plus hrHPV testing (co-testing) every 5 years. It further recommended that women discuss with health care providers which testing strategy is best for them. To inform such discussions, we used decision analysis to estimate outcomes of screening strategies recommended for women at age 30. Methods. We constructed a Markov decision model using estimates of the natural history of HPV and cervical neoplasia. We evaluated the three USPSTF-endorsed strategies, hrHPV testing every 3 years and no screening. Outcomes included colposcopies with biopsy, false-positive testing (a colposcopy in which no cervical intraepithelial neoplasia grade 2 or worse was found), treatments, cancers, and cancer mortality expressed per 10,000 women over a shorter-than-lifetime horizon (15-year). Results. All strategies resulted in substantially lower cancer and cancer death rates compared with no screening. Strategies with the lowest likelihood of cancer and cancer death generally had higher likelihood of colposcopy and false-positive testing. Conclusions. The screening strategies we evaluated involved tradeoffs in terms of benefits and harms. Because individual women may place different weights on these projected outcomes, the optimal choice for each woman may best be discerned through shared decision making., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s), 2020.)

Objective: We developed a decision analytic model to evaluate the impact of a preoperative Staphylococcus aureus decolonization bundle on surgical site infections (SSIs), health-care-associated costs (HCACs), and deaths due to SSI., Methods: Our model population comprised US adults undergoing elective surgery. We evaluated 3 self-administered preoperative strategies: (1) the standard of care (SOC) consisting of 2 disinfectant soap showers; (2) the "test-and-treat" strategy consisting of the decolonization bundle including chlorhexidine gluconate (CHG) soap, CHG mouth rinse, and mupirocin nasal ointment for 5 days) if S. aureus was found at any of 4 screened sites (nasal, throat, axillary, perianal area), otherwise the SOC; and (3) the "treat-all" strategy consisting of the decolonization bundle for all patients, without S. aureus screening. Model parameters were derived primarily from a randomized controlled trial that measured the efficacy of the decolonization bundle for eradicating S. aureus., Results: Under base-case assumptions, the treat-all strategy yielded the fewest SSIs and the lowest HCACs, followed by the test-and-treat strategy. In contrast, the SOC yielded the most SSIs and the highest HCACs. Consequently, relative to the SOC, the average savings per operation was $217 for the treat-all strategy and $123 for the test-and-treat strategy, and the average savings per per SSI prevented was $21,929 for the treat-all strategy and $15,166 for the test-and-treat strategy. All strategies were sensitive to the probability of acquiring an SSI and the increased risk if SSI if the patient was colonized with SA., Conclusion: We predict that the treat-all strategy would be the most effective and cost-saving strategy for preventing SSIs. However, because this strategy might select more extensively for mupirocin-resistant S. aureus and cause more medication adverse effects than the test-and-treat approach or the SOC, additional studies are needed to define its comparative benefits and harms.