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Evaluating vaccine allocation strategies using simulation-assisted causal modeling.

ABSTRACT: We develop a model to retrospectively evaluate age-dependent counterfactual vaccine allocation strategies against the coronavirus disease 2019 (COVID-19) pandemic. To estimate the effect of allocation on the expected severe-case incidence, we employ a simulation-assisted causal modeling approach that combines a compartmental infection-dynamics simulation, a coarse-grained causal model, and literature estimates for immunity waning. We compare Israel's strategy, implemented in 2021, with counterfactual strategies such as no prioritization, prioritization of younger age groups, or a strict risk-ranked approach; we find that Israel's implemented strategy was indeed highly effective. We also study the impact of increasing vaccine uptake for given age groups. Because of its modular structure, our model can easily be adapted to study future pandemics. We demonstrate this by simulating a pandemic with characteristics of the Spanish flu. Our approach helps evaluate vaccination strategies under the complex interplay of core epidemic factors, including age-dependent risk profiles, immunity waning, vaccine availability, and spreading rates.

SUBMITTER: Kekic A

PROVIDER: S-EPMC10155501 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Evaluating vaccine allocation strategies using simulation-assisted causal modeling.

Kekić Armin A Dehning Jonas J Gresele Luigi L von Kügelgen Julius J Priesemann Viola V Schölkopf Bernhard B

Patterns (New York, N.Y.) 20230503 6

We develop a model to retrospectively evaluate age-dependent counterfactual vaccine allocation strategies against the coronavirus disease 2019 (COVID-19) pandemic. To estimate the effect of allocation on the expected severe-case incidence, we employ a simulation-assisted causal modeling approach that combines a compartmental infection-dynamics simulation, a coarse-grained causal model, and literature estimates for immunity waning. We compare Israel's strategy, implemented in 2021, with counterfa ...[more]

PMID: 37304758

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Project description:BackgroundThe world is currently confronting the first influenza pandemic of the 21(st) century. Influenza vaccination is an effective preventive measure, but the unique epidemiological features of swine-origin influenza A (H1N1) (pH1N1) introduce uncertainty as to the best strategy for prioritization of vaccine allocation. We sought to determine optimal prioritization of vaccine distribution among different age and risk groups within the Canadian population, to minimize influenza-attributable morbidity and mortality.Methodology/principal findingsWe developed a deterministic, age-structured compartmental model of influenza transmission, with key parameter values estimated from data collected during the initial phase of the epidemic in Ontario, Canada. We examined the effect of different vaccination strategies on attack rates, hospitalizations, intensive care unit admissions, and mortality. In all scenarios, prioritization of high-risk individuals (those with underlying chronic conditions and pregnant women), regardless of age, markedly decreased the frequency of severe outcomes. When individuals with underlying medical conditions were not prioritized and an age group-based approach was used, preferential vaccination of age groups at increased risk of severe outcomes following infection generally resulted in decreased mortality compared to targeting vaccine to age groups with higher transmission, at a cost of higher population-level attack rates. All simulations were sensitive to the timing of the epidemic peak in relation to vaccine availability, with vaccination having the greatest impact when it was implemented well in advance of the epidemic peak.Conclusions/significanceOur model simulations suggest that vaccine should be allocated to high-risk groups, regardless of age, followed by age groups at increased risk of severe outcomes. Vaccination may significantly reduce influenza-attributable morbidity and mortality, but the benefits are dependent on epidemic dynamics, time for program roll-out, and vaccine uptake.

Project description:Prediction models learn patterns from available data (training) and are then validated on new data (testing). Prediction modeling is increasingly common in dental research. We aimed to evaluate how different model development and validation steps affect the predictive performance of tooth loss prediction models of patients with periodontitis. Two independent cohorts (627 patients, 11,651 teeth) were followed over a mean ± SD 18.2 ± 5.6 y (Kiel cohort) and 6.6 ± 2.9 y (Greifswald cohort). Tooth loss and 10 patient- and tooth-level predictors were recorded. The impact of different model development and validation steps was evaluated: 1) model complexity (logistic regression, recursive partitioning, random forest, extreme gradient boosting), 2) sample size (full data set or 10%, 25%, or 75% of cases dropped at random), 3) prediction periods (maximum 10, 15, or 20 y or uncensored), and 4) validation schemes (internal or external by centers/time). Tooth loss was generally a rare event (880 teeth were lost). All models showed limited sensitivity but high specificity. Patients' age and tooth loss at baseline as well as probing pocket depths showed high variable importance. More complex models (random forest, extreme gradient boosting) had no consistent advantages over simpler ones (logistic regression, recursive partitioning). Internal validation (in sample) overestimated the predictive power (area under the curve up to 0.90), while external validation (out of sample) found lower areas under the curve (range 0.62 to 0.82). Reducing the sample size decreased the predictive power, particularly for more complex models. Censoring the prediction period had only limited impact. When the model was trained in one period and tested in another, model outcomes were similar to the base case, indicating temporal validation as a valid option. No model showed higher accuracy than the no-information rate. In conclusion, none of the developed models would be useful in a clinical setting, despite high accuracy. During modeling, rigorous development and external validation should be applied and reported accordingly.

Project description:BackgroundScreening mammography guidelines do not explicitly consider racial differences in breast cancer epidemiology, treatment, and survival.ObjectiveTo compare tradeoffs of screening strategies in Black women versus White women under current guidelines.DesignAn established model from the Cancer Intervention and Surveillance Modeling Network simulated screening outcomes using race-specific inputs for subtype distribution; breast density; mammography performance; age-, stage-, and subtype-specific treatment effects; and non-breast cancer mortality.SettingUnited States.ParticipantsA 1980 U.S. birth cohort of Black and White women.InterventionScreening strategies until age 74 years with varying initiation ages and intervals.MeasurementsOutcomes included benefits (life-years gained [LYG], breast cancer deaths averted, and mortality reduction), harms (mammographies, false positives, and overdiagnoses), and benefit-harm ratios (tradeoffs) by race. Efficiency (benefits per unit resource), mortality disparity reduction, and equity in tradeoffs were evaluated. Equitable strategies for Black women were defined as those with tradeoffs closest to benchmark values for screening White women biennially from ages 50 to 74 years.ResultsBiennial screening from ages 45 to 74 years was most efficient for Black women, whereas biennial screening from ages 40 to 74 years was most equitable. Initiating screening 10 years earlier in Black versus White women reduced Black-White mortality disparities by 57% with similar LYG per mammogram for both populations. Selection of the most equitable strategy was sensitive to assumptions about disparities in real-world treatment effectiveness: The less effective treatment was for Black women, the more intensively Black women could be screened before tradeoffs fell short of those experienced by White women.LimitationSingle model.ConclusionInitiating biennial screening in Black women at age 40 years reduces breast cancer mortality disparities and yields benefit-harm ratios that are similar to tradeoffs of White women screened biennially from ages 50 to 74 years.Primary funding sourceNational Cancer Institute at the National Institutes of Health.

Project description:BackgroundThe development and widespread use of an effective SARS-CoV-2 vaccine could prevent substantial morbidity and mortality associated with COVID-19 and mitigate the secondary effects associated with non-pharmaceutical interventions.MethodsWe used an age-structured, expanded SEIR model with social contact matrices to assess age-specific vaccine allocation strategies in India. We used state-specific age structures and disease transmission coefficients estimated from confirmed incident cases of COVID-19 between 1 July and 31 August 2020. Simulations were used to investigate the relative reduction in mortality and morbidity of vaccine allocation strategies based on prioritizing different age groups, and the interactions of these strategies with concurrent non-pharmaceutical interventions. Given the uncertainty associated with COVID-19 vaccine development, we varied vaccine characteristics in the modelling simulations.ResultsPrioritizing COVID-19 vaccine allocation for older populations (i.e., >60 years) led to the greatest relative reduction in deaths, regardless of vaccine efficacy, control measures, rollout speed, or immunity dynamics. Preferential vaccination of this group often produced relatively higher total symptomatic infections and more pronounced estimates of peak incidence than other assessed strategies. Vaccine efficacy, immunity type, target coverage, and rollout speed significantly influenced overall strategy effectiveness, with the time taken to reach target coverage significantly affecting the relative mortality benefit comparative to no vaccination.ConclusionsOur findings support global recommendations to prioritize COVID-19 vaccine allocation for older age groups. Relative differences between allocation strategies were reduced as the speed of vaccine rollout was increased. Optimal vaccine allocation strategies will depend on vaccine characteristics, strength of concurrent non-pharmaceutical interventions, and region-specific goals.

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Evaluating vaccine allocation strategies using simulation-assisted causal modeling.

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Evaluating vaccine allocation strategies using simulation-assisted causal modeling.

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