The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.

Purpose To evaluate the cost effectiveness of next-generation sequencing (NGS) panels for the diagnosis of colorectal cancer and polyposis (CRCP) syndromes in patients referred to cancer genetics clinics. Patients and Methods We developed a decision model to evaluate NGS panel testing compared with current standard of care in patients referred to a cancer genetics clinic. We obtained data on the prevalence of genetic variants from a large academic laboratory and calculated the costs and health benefits of identifying relatives with a pathogenic variant, in life-years and quality-adjusted life-years (QALYs). We classified the CRCP syndromes according to their type of inheritance and penetrance of colorectal cancer. One-way and probabilistic sensitivity analyses were conducted to assess uncertainty. Results Evaluation with an NGS panel that included Lynch syndrome genes and other genes associated with highly penetrant CRCP syndromes led to an average increase of 0.151 year of life, 0.128 QALY, and $4,650 per patient, resulting in an incremental cost-effectiveness ratio of $36,500 per QALY compared with standard care and a 99% probability that this panel was cost effective at a threshold of $100,000 per QALY. When compared with this panel, the addition of genes with low colorectal cancer penetrance resulted in an incremental cost-effectiveness ratio of $77,300 per QALY. Conclusion The use of an NGS panel that includes genes associated with highly penetrant CRCP syndromes in addition to Lynch syndrome genes as a first-line test is likely to provide meaningful clinical benefits in a cost-effective manner at a $100,000 per QALY threshold.

Purpose Te American College of Medical Genetics and Genomics (ACMG) recommended that clinical laboratories performing next-generation sequencing analyze and return pathogenic variants for 56 specific genes it considered medically actionable. Our objective was to evaluate the clinical and economic impact of returning these results. Methods We developed a decision-analytic policy model to project the quality-adjusted life-years and lifetime costs associated with returning ACMG-recommended incidental findings in three hypothetical cohorts of 10,000 patients. Results Returning incidental findings to cardiomyopathy patients, colorectal cancer patients, or healthy individuals would increase costs by $896,000, $2.9 million, and $3.9 million, respectively, and would increase quality-adjusted life-years by 20, 25.4, and 67 years, respectively, for incremental cost-effectiveness ratios of $44,800, $115,020, and $58,600, respectively. In probabilistic analyses, returning incidental findings cost less than $100,000/quality-adjusted life-year gained in 85, 28, and 91%, respectively, of simulations. Assuming next-generation sequencing costs $500, the incremental cost-effectiveness ratio for primary screening of healthy individuals was $133,400 (<$100,000/quality-adjusted life-year gained in 10% of simulations). Results were sensitive to the cohort age and assumptions about gene penetrance. Conclusion Returning incidental findings is likely cost-effective for certain patient populations. Screening of generally healthy individuals is likely not cost-effective based on current data, unless next-generation sequencing costs less than $500.