Optimization Modeling for Pandemic Vaccine Supply Chain Management: A Review and Future Research Opportunities.

During various stages of the COVID-19 pandemic, countries implemented diverse vaccine management approaches, influenced by variations in infrastructure and socio-economic conditions. This article provides a comprehensive overview of optimization models developed by the research community throughout the COVID-19 era, aimed at enhancing vaccine distribution and establishing a standardized framework for future pandemic preparedness. These models address critical issues such as site selection, inventory management, allocation strategies, distribution logistics, and route optimization encountered during the COVID-19 crisis. A unified framework is employed to describe the models, emphasizing their integration with epidemiological models to facilitate a holistic understanding. This article also summarizes evolving nature of literature, relevant research gaps, and authors' perspectives for model selection. Finally, future research scopes are detailed both in the context of modeling and solutions approaches.

An inaugural forum on epidemiological modeling for public health stakeholders in Arizona.

Epidemiological models-which help us understand and forecast the spread of infectious disease-can be valuable tools for public health. However, barriers exist that can make it difficult to employ epidemiological models routinely within the repertoire of public health planning. These barriers include technical challenges associated with constructing the models, challenges in obtaining appropriate data for model parameterization, and problems with clear communication of modeling outputs and uncertainty. To learn about the unique barriers and opportunities within the state of Arizona, we gathered a diverse set of 48 public health stakeholders for a day-and-a-half forum. Our research group was motivated specifically by our work building software for public health-relevant modeling and by our earnest desire to collaborate closely with stakeholders to ensure that our software tools are practical and useful in the face of evolving public health needs. Here we outline the planning and structure of the forum, and we highlight as a case study some of the lessons learned from breakout discussions. While unique barriers exist for implementing modeling for public health, there is also keen interest in doing so across diverse sectors of State and Local government, although issues of equal and fair access to modeling knowledge and technologies remain key issues for future development. We found this forum to be useful for building relationships and informing our software development, and we plan to continue such meetings annually to create a continual feedback loop between academic molders and public health practitioners.

Sentinel surveillance of SARS-CoV-2 rates and equity impacts using labor and delivery patients in Phoenix, Arizona.

Proactive management of SARS-CoV-2 requires timely and complete population data to track the evolution of the virus and identify at risk populations. However, many cases are asymptomatic and are not easily discovered through traditional testing efforts. Sentinel surveillance can be used to estimate the prevalence of infections for geographical areas but requires identification of sentinels who are representative of the larger population. Our goal is to evaluate applicability of a population of labor and delivery patients for sentinel surveillance system for monitoring the prevalence of SARS-CoV-2 infection. We tested 5307 labor and delivery patients from two hospitals in Phoenix, Arizona, finding 195 SARS-CoV-2 positive. Most positive cases were associated with people who were asymptomatic (79.44%), similar to statewide rates. Our results add to the growing body of evidence that SARS-CoV-2 disproportionately impacts people of color, with Black people having the highest positive rates (5.92%). People with private medical insurance had the lowest positive rates (2.53%), while Medicaid patients had a positive rate of 5.54% and people without insurance had the highest positive rates (6.12%). With diverse people reporting for care and being tested regardless of symptoms, labor and delivery patients may serve as ideal sentinels for asymptomatic detection of SARS-CoV-2 and monitoring impacts across a wide range of social and economic classes. A more robust system for infectious disease management requires the expanded participation of additional hospitals so that the sentinels are more representative of the population at large, reflecting geographic and neighborhood level patterns of infection and risk.

Public health impact of delaying second dose of BNT162b2 or mRNA-1273 covid-19 vaccine: simulation agent based modeling study.

OBJECTIVE: To estimate population health outcomes with delayed second dose versus standard schedule of SARS-CoV-2 mRNA vaccination. DESIGN: Simulation agent based modeling study. SETTING: Simulated population based on real world US county. PARTICIPANTS: The simulation included 100 000 agents, with a representative distribution of demographics and occupations. Networks of contacts were established to simulate potentially infectious interactions though occupation, household, and random interactions. INTERVENTIONS: Simulation of standard covid-19 vaccination versus delayed second dose vaccination prioritizing the first dose. The simulation runs were replicated 10 times. Sensitivity analyses included first dose vaccine efficacy of 50%, 60%, 70%, 80%, and 90% after day 12 post-vaccination; vaccination rate of 0.1%, 0.3%, and 1% of population per day; assuming the vaccine prevents only symptoms but not asymptomatic spread (that is, non-sterilizing vaccine); and an alternative vaccination strategy that implements delayed second dose for people under 65 years of age, but not until all those above this age have been vaccinated. MAIN OUTCOME MEASURES: Cumulative covid-19 mortality, cumulative SARS-CoV-2 infections, and cumulative hospital admissions due to covid-19 over 180 days. RESULTS: Over all simulation replications, the median cumulative mortality per 100 000 for standard dosing versus delayed second dose was 226 v 179, 233 v 207, and 235 v 236 for 90%, 80%, and 70% first dose efficacy, respectively. The delayed second dose strategy was optimal for vaccine efficacies at or above 80% and vaccination rates at or below 0.3% of the population per day, under both sterilizing and non-sterilizing vaccine assumptions, resulting in absolute cumulative mortality reductions between 26 and 47 per 100 000. The delayed second dose strategy for people under 65 performed consistently well under all vaccination rates tested. CONCLUSIONS: A delayed second dose vaccination strategy, at least for people aged under 65, could result in reduced cumulative mortality under certain conditions.

COVID-19 healthcare demand projections: Arizona.

Beginning in March 2020, the United States emerged as the global epicenter for COVID-19 cases with little to guide policy response in the absence of extensive data available for reliable epidemiological modeling in the early phases of the pandemic. In the ensuing weeks, American jurisdictions attempted to manage disease spread on a regional basis using non-pharmaceutical interventions (i.e., social distancing), as uneven disease burden across the expansive geography of the United States exerted different implications for policy management in different regions. While Arizona policymakers relied initially on state-by-state national modeling projections from different groups outside of the state, we sought to create a state-specific model using a mathematical framework that ties disease surveillance with the future burden on Arizona's healthcare system. Our framework uses a compartmental system dynamics model using a SEIRD framework that accounts for multiple types of disease manifestations for the COVID-19 infection, as well as the observed time delay in epidemiological findings following public policy enactments. We use a compartment initialization logic coupled with a fitting technique to construct projections for key metrics to guide public health policy, including exposures, infections, hospitalizations, and deaths under a variety of social reopening scenarios. Our approach makes use of X-factor fitting and backcasting methods to construct meaningful and reliable models with minimal available data in order to provide timely policy guidance in the early phases of a pandemic.

Using operative features to identify surgical complexity: a case in breast surgery practice.

Increasing workload is one of the main problems that surgical practices face. This increase is not only due to the increasing demand volume but also due to increasing case complexity. This raises the question on how to measure and predict the complexity to address this issue. Predicting surgical duration is critical to parametrize surgical complexity, improve surgeon satisfaction by avoiding unexpected overtime, and improve operation room utilization. Our objective is to utilize the historical data on surgical operations to obtain complexity groups and use this groups to improve practice.Our study first leverages expert opinion on the surgical complexity to identify surgical groups. Then, we use a tree-based method on a large retrospective dataset to identify similar complexity groups by utilizing the surgical features and using surgical duration as a response variable. After obtaining the surgical groups by using two methods, we statistically compare expert-based grouping with the data-based grouping. This comparison shows that a tree-based method can provide complexity groups similar to the ones generated by an expert by using features that are available at the time of surgical listing. These results suggest that one can take advantage of available data to provide surgical duration predictions that are data-driven, evidence-based, and practically relevant.

Correction: Performance of next-generation sequencing on small tumor specimens and/or low tumor content samples using a commercially available platform.

[This corrects the article DOI: 10.1371/journal.pone.0196556.].

Performance of next-generation sequencing on small tumor specimens and/or low tumor content samples using a commercially available platform.

BACKGROUND: Next generation sequencing tests (NGS) are usually performed on relatively small core biopsy or fine needle aspiration (FNA) samples. Data is limited on what amount of tumor by volume or minimum number of FNA passes are needed to yield sufficient material for running NGS. We sought to identify the amount of tumor for running the PCDx NGS platform. METHODS: 2,723 consecutive tumor tissues of all cancer types were queried and reviewed for inclusion. Information on tumor volume, success of performing NGS, and results of NGS were compiled. Assessment of sequence analysis, mutation calling and sensitivity, quality control, drug associations, and data aggregation and analysis were performed. RESULTS: 6.4% of samples were rejected from all testing due to insufficient tumor quantity. The number of genes with insufficient sensitivity make definitive mutation calls increased as the percentage of tumor decreased, reaching statistical significance below 5% tumor content. The number of drug associations also decreased with a lower percentage of tumor, but this difference only became significant between 1-3%. The number of drug associations did decrease with smaller tissue size as expected. Neither specimen size or percentage of tumor affected the ability to pass mRNA quality control. A tumor area of 10 mm2 provides a good margin of error for specimens to yield adequate drug association results. CONCLUSIONS: Specimen suitability remains a major obstacle to clinical NGS testing. We determined that PCR-based library creation methods allow the use of smaller specimens, and those with a lower percentage of tumor cells to be run on the PCDx NGS platform.

Two algorithms for biospecimen comparison and differentiation using SNP genotypes.

AIMS: Biobanks are frequently required to verify specimen relationships. We present two algorithms to compare SNP genotype patterns that provide an objective, high-throughput tool for verification. METHODS: The first algorithm allows for comparison of all holdings within a biobank, and is well suited to construct sample relationships de novo for comparison with assumed relationships. The second algorithm is tailored to oncology, and allows one to confirm that paired DNAs from malignant and normal tissues are from the same individual in the presence of copy number variations. To evaluate both algorithms, we used an internal training data set (n = 1504) and an external validation data set (n = 1457). RESULTS: In comparison with the results from manual review and a priori knowledge of patient relationships, we identified no errors in interpreting sample relationships within our validation data set. CONCLUSION: We provide an efficient and objective method of automated data analysis that is currently lacking for establishing and verifying specimen relationships in biobanks.