An iterative algorithm for optimizing COVID-19 vaccination strategies considering unknown supply.

BACKGROUND AND OBJECTIVE: The distribution of the newly developed vaccines presents a great challenge in the ongoing SARS-CoV-2 pandemic. Policy makers must decide which subgroups should be vaccinated first to minimize the negative consequences of the pandemic. These decisions must be made upfront and under uncertainty regarding the amount of vaccine doses available at a given time. The objective of the present work was to develop an iterative optimization algorithm, which provides a prioritization order of predefined subgroups. The results of this algorithm should be optimal but also robust with respect to potentially limited vaccine supply. METHODS: We present an optimization meta-heuristic which can be used in a classic simulation-optimization setting with a simulation model in a feedback loop. The meta-heuristic can be applied in combination with any epidemiological simulation model capable of depicting the effects of vaccine distribution to the modeled population, accepts a vaccine prioritization plan in a certain notation as input, and generates decision making relevant variables such as COVID-19 caused deaths or hospitalizations as output. We finally demonstrate the mechanics of the algorithm presenting the results of a case study performed with an epidemiological agent-based model. RESULTS: We show that the developed method generates a highly robust vaccination prioritization plan which is proven to fulfill an elegant supremacy criterion: the plan is equally optimal for any quantity of vaccine doses available. The algorithm was tested on a case study in the Austrian context and it generated a vaccination plan prioritization favoring individuals age 65+, followed by vulnerable groups, to minimize COVID-19 related burden. DISCUSSION: The results of the case study coincide with the international policy recommendations which strengthen the applicability of the approach. We conclude that the path-dependent optimum optimum provided by the algorithm is well suited for real world applications, in which decision makers need to develop strategies upfront under high levels of uncertainty.

Model based estimation of the SARS-CoV-2 immunization level in austria and consequences for herd immunity effects.

Several systemic factors indicate that worldwide herd immunity against COVID-19 will probably not be achieved in 2021. On the one hand, vaccination programs are limited by availability of doses and on the other hand, the number of people already infected is still too low to have a disease preventing impact and new emerging variants of the virus seem to partially neglect developed antibodies from previous infections. Nevertheless, by February 2021 after one year of observing high numbers of reported COVID-19 cases in most European countries, we might expect that the immunization level should have an impact on the spread of SARS-CoV-2. Here we present an approach for estimating the immunization of the Austrian population and discuss potential consequences on herd immunity effects. To estimate immunization we use a calibrated agent-based simulation model that reproduces the actual COVID-19 pandemic in Austria. From the resulting synthetic individual-based data we can extract the number of immunized persons. We then extrapolate the progression of the epidemic by varying the obtained level of immunization in simulations of an hypothetical uncontrolled epidemic wave indicating potential effects on the effective reproduction number. We compared our theoretical findings with results derived from a classic differential equation SIR-model. As of February 2021, [Formula: see text] of the Austrian population has been affected by a SARS-CoV-2 infection which causes a [Formula: see text] reduction of the effective reproduction number and a [Formula: see text] reduction of the prevalence peak compared to a fully susceptible population. This estimation is now recomputed on a regular basis to publish model based analysis of immunization level in Austria also including the fast growing effects of vaccination programs. This provides substantial information for decision makers to evaluate the necessity of non pharmaceutical intervention measures based on the estimated impact of natural and vaccinated immunization.

Contact Tracing for Disease Containment: a Network-Based Analysis.

Since the outbreak of the COVID-19 pandemic in spring 2020, the concept of test, trace, and isolate (TTI) was used as a non-pharmaceutical intervention against further spreading of the disease. Hereby, recent contact partners of newly confirmed SARS-CoV-2 infected persons were identified and isolated along with the originally detected case to avoid potential secondary infections. While the policy is, given the compliance of the traced persons, generally deemed efficient, not much is known about network-specific impact factors. In this work, we aim to evaluate the effectiveness of the TTI strategy when used (1) for diseases with different infectiousness levels and (2) on different contact networks. For the prior, we vary the infection probability per contact, for the latter, we analyse different clustering coefficients. Our goal is to test the validity of two hypotheses: First, we expect the policy to be more efficient if the infectiousness of the disease is small, since the time delay for isolating persons is crucial. Second, due to the implications of the friendship paradox, we expect the policy to be more effective if the clustering coefficient of the underlying contact network is high. We make use of an agent-based network model consisting of three intertwined model parts: an epidemiological SEIR model, a quarantine model and a contact-tracing model. To test the hypotheses, the disease parameters and the clustering coefficient of the underlying contact network are varied. The simulation results show that, indeed, tracing seems to have a slightly larger containment impact for networks with higher clustering, in particular for fast-spreading diseases. Yet, the effects are small compared to the impact of the infectiousness of the disease. Therefore, we find a significant decrease of the policy effectiveness the higher the transmission probability. The latter implies that the containment impact of tracing and isolating contacts becomes more efficient, if supported by additional measures that limit the infection probability or if applied in periods with low negative seasonality effects.

Evaluation of Contact-Tracing Policies against the Spread of SARS-CoV-2 in Austria: An Agent-Based Simulation.

BACKGROUND: Many countries have already gone through several infection waves and mostly managed to successfully stop the exponential spread of SARS-CoV-2 through bundles of restrictive measures. Still, the danger of further waves of infections is omnipresent, and it is apparent that every containment policy must be carefully evaluated and possibly replaced by a different, less restrictive policy before it can be lifted. Tracing of contacts and consequential breaking of infection chains is a promising strategy to help contain the disease, although its precise impact on the epidemic is unknown. OBJECTIVE: In this work, we aim to quantify the impact of tracing on the containment of the disease and investigate the dynamic effects involved. DESIGN: We developed an agent-based model that validly depicts the spread of the disease and allows for exploratory analysis of containment policies. We applied this model to quantify the impact of different approaches of contact tracing in Austria to derive general conclusions on contract tracing. RESULTS: The study displays that strict tracing complements other intervention strategies. For the containment of the disease, the number of secondary infections must be reduced by about 75%. Implementing the proposed tracing strategy supplements measures worth about 5%. Evaluation of the number of preventively quarantined persons shows that household quarantine is the most effective in terms of avoided cases per quarantined person. LIMITATIONS: The results are limited by the validity of the modeling assumptions, model parameter estimates, and the quality of the parametrization data. CONCLUSIONS: The study shows that tracing is indeed an efficient measure to keep case numbers low but comes at a high price if the disease is not well contained. Therefore, contact tracing must be executed strictly, and adherence within the population must be held up to prevent uncontrolled outbreaks of the disease.

Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities-An Agent-Based Modeling Evaluation.

(1) Background: The Austrian supply of COVID-19 vaccine is limited for now. We aim to provide evidence-based guidance to the authorities in order to minimize COVID-19-related hospitalizations and deaths in Austria. (2) Methods: We used a dynamic agent-based population model to compare different vaccination strategies targeted to the elderly (65 ≥ years), middle aged (45-64 years), younger (15-44 years), vulnerable (risk of severe disease due to comorbidities), and healthcare workers (HCW). First, outcomes were optimized for an initially available vaccine batch for 200,000 individuals. Second, stepwise optimization was performed deriving a prioritization sequence for 2.45 million individuals, maximizing the reduction in total hospitalizations and deaths compared to no vaccination. We considered sterilizing and non-sterilizing immunity, assuming a 70% effectiveness. (3) Results: Maximum reduction of hospitalizations and deaths was achieved by starting vaccination with the elderly and vulnerable followed by middle-aged, HCW, and younger individuals. Optimizations for vaccinating 2.45 million individuals yielded the same prioritization and avoided approximately one third of deaths and hospitalizations. Starting vaccination with HCW leads to slightly smaller reductions but maximizes occupational safety. (4) Conclusion: To minimize COVID-19-related hospitalizations and deaths, our study shows that elderly and vulnerable persons should be prioritized for vaccination until further vaccines are available.

Guidance for a causal comparative effectiveness analysis emulating a target trial based on big real world evidence: when to start statin treatment.

Aim: The aim of this project is to describe a causal (counterfactual) approach for analyzing when to start statin treatment to prevent cardiovascular disease using real-world evidence. Methods: We use directed acyclic graphs to operationalize and visualize the causal research question considering selection bias, potential time-independent and time-dependent confounding. We provide a study protocol following the 'target trial' approach and describe the data structure needed for the causal assessment. Conclusion: The study protocol can be applied to real-world data, in general. However, the structure and quality of the database play an essential role for the validity of the results, and database-specific potential for bias needs to be explicitly considered.

Alternative Conversion Methods for Transition Probabilities in State-Transition Models: Validity and Impact on Comparative Effectiveness and Cost-Effectiveness.

Background. In state-transition models (STMs), decision problems are conceptualized using health states and transitions among those health states after predefined time cycles. The naive, commonly applied method (C) for cycle length conversion transforms all transition probabilities separately. In STMs with more than 2 health states, this method is not accurate. Therefore, we aim to describe and compare the performance of method C with that of alternative matrix transformation methods. Design. We compare 2 alternative matrix transformation methods (Eigenvalue method [E], Schure-Padé method [SP]) to method C applied in an STM of 3 different treatment strategies for women with breast cancer. We convert the given annual transition matrix into a monthly-cycle matrix and evaluate induced transformation errors for the transition matrices and the long-term outcomes: life years, quality-adjusted life-years, costs and incremental cost-effectiveness ratios, and the performance related to the decisions. In addition, we applied these transformation methods to randomly generated annual transition matrices with 4, 7, 10, and 20 health states. Results. In theory, there is no generally applicable correct transformation method. Based on our simulations, SP resulted in the smallest transformation-induced discrepancies for generated annual transition matrices for 2 treatment strategies. E showed slightly smaller discrepancies than SP in the strategy, where one of the direct transitions between health states was excluded. For long-term outcomes, the largest discrepancy occurred for estimated costs applying method C. For higher dimensional models, E performs best. Conclusions. In our modeling examples, matrix transformations (E, SP) perform better than transforming all transition probabilities separately (C). Transition probabilities based on alternative conversion methods should therefore be applied in sensitivity analyses.

Health Care Atlases: Informing the General Public About the Situation of the Austrian Health Care System.

There is a great number of complex data concerning the Austrian Health Care System. The goal was to process this data and present it to the general public on an easily accessible information platform. The platform focuses on data about the burden of disease of the Austrian Population, the available medical care and the services provided by the physicians. Due to the vast differences in the underlying source data, the methods used for the data acquisition range from statistical linkage over web scraping to aggregating data on the reimbursed services. The results are published on a website and are mainly displayed with interactive graphics. Overall, these dynamic and interactive websites provide a good overview of the situation of the Austrian Health Care System and presents the information in an intuitive and comprehensible manner. Furthermore, the information given in the atlases can contribute to the health care planning in order to identify distinctive service provision in Austria.

The Long-Term Effectiveness and Cost Effectiveness of Organized versus Opportunistic Screening for Breast Cancer in Austria.

BACKGROUND: In 2014, Austrian health authorities implemented an organized breast cancer screening program. Until then, there has been a long-standing tradition of opportunistic screening. OBJECTIVES: To evaluate the cost-effectiveness of organized screening compared with opportunistic screening, as well as to identify factors influencing the clinical and economic outcomes. METHODS: We developed and validated an individual-level state-transition model and assessed the health outcomes and costs of organized and opportunistic screening for 40-year-old asymptomatic women. The base-case analysis compared a scenario involving organized biennial screening with a scenario reflecting opportunistic screening practice for an average-risk woman aged 45 to 69 years. We applied an annual discount rate of 3% and estimated the incremental cost-effectiveness ratio in terms of the cost (2012 euros) per life-year gained (LYG) from a health care perspective. Deterministic and probabilistic sensitivity analyses were performed to assess uncertainty. RESULTS: Compared with opportunistic screening, an organized program yielded on average additional 0.0118 undiscounted life-years (i.e., 4.3 days) and cost savings of €41 per woman. In the base-case analysis, the incremental cost-effectiveness ratio of organized screening was approximately €20,000 per LYG compared with no screening. Assuming a willingness-to-pay threshold of €50,000 per LYG, there was a 70% probability that organized screening would be considered cost-effective. The attendance rate, but not the test accuracy of mammography, was an influential factor for the cost-effectiveness. CONCLUSIONS: The decision to adopt organized screening is likely an efficient use of limited health care resources in Austria.

Statistical methods and modelling techniques for analysing hospital readmission of discharged psychiatric patients: a systematic literature review.

BACKGROUND: Psychiatric services have undergone profound changes over the last decades. CEPHOS-LINK is an EU-funded study project with the aim to compare readmission of patients discharged with psychiatric diagnoses using a registry-based observational record linkage study design and to analyse differences in the findings for five different countries. A range of different approaches is available for analysis of the available data. Although there are some studies that compare selected methods for evaluating questions on readmission, there are to our knowledge no published systematic literature reviews on commonly used methods and their comparison. This work shall therefore provide an overview of the methods in use, their evolution throughout history and new developments which can further improve the research quality in this area. METHODS: Based on systematic literature reviews realized in the course of the CEPHOS-LINK study, this work is a systematic evaluation of mathematical (statistical and modelling) methods used in studies examining psychiatric readmission. The starting point were 502 papers, of which 407 were analysed in detail; Methods used were assigned to one of five categories with subcategories and analysed accordingly. Our particular interest next to survival analysis and regression models is modelling and simulation. RESULTS: As population sizes and follow-up times in the included studies varied widely, a range of methods was applied. Studies with bigger sample sizes conducted survival and regression analysis more often than studies with fewer patients did. These latter relied more on classical statistical tests (e.g. t-tests and Student Newman Keuls). Statistical strategies were often insufficiently described, posing a major problem for the evaluation. Almost all cases failed to provide and explanation of the rationale behind using certain methods. CONCLUSION: There is a discernible trend from classical parametric/nonparametric tests in older studies towards regression and survival analyses in more recent ones. Modelling and simulation were under-represented despite their high usability, as has been identified in other health applications and comparable research areas.