Epidemiological impact of Neisseria gonorrhoeae and Chlamydia trachomatis screening in men having sex with men: a modelling study.

OBJECTIVES: The impact of the systematic screening of Neisseria gonorrhoeae (NG) and Chlamydia trachomatis (CT) in men having sex with men (MSM) on these pathogens' epidemiology remains unclear. We conducted a modelling study to analyse this impact in French MSM. METHODS: We modelled NG and CT transmission using a site-specific deterministic compartmental model. We calibrated NG and CT prevalence at baseline using results from MSM enrolled in the Dat'AIDS cohort. The baseline scenario was based on 1 million MSM, 40 000 of whom were tested every 90 days and 960 000 every 200 days. Incidence rate ratios (IRRs) at steady state were simulated for NG, CT, NG and/or CT infections, for different combinations of tested sites, testing frequency and numbers of frequently tested patients. RESULTS: The observed prevalence rate was 11.0%, 10.5% and 19.1% for NG, CT and NG and/or CT infections. The baseline incidence rate was estimated at 138.2 per year per 100 individuals (/100PY), 86.8/100PY and 225.0/100PY for NG, CT and NG and/or CT infections. Systematically testing anal, pharyngeal and urethral sites at the same time reduced incidence by 14%, 23% and 18% (IRR: 0.86, 0.77 and 0.82) for NG, CT and NG and/or CT infections. Reducing the screening interval to 60 days in frequently tested patients reduced incidence by 20%, 29% and 24% (IRR: 0.80, 0.71 and 0.76) for NG, CT and NG and/or CT infections. Increasing the number of frequently tested patients to 200 000 reduced incidence by 29%, 40% and 33% (IRR: 0.71, 0.60 and 0.67) for NG, CT and NG and/or CT infections. No realistic scenario could decrease pathogens' incidence by more than 50%. CONCLUSIONS: To curb the epidemic of NG and CT in MSM, it would not only be necessary to drastically increase screening, but also to add other combined interventions.

Disease X epidemic control using a stochastic model and a deterministic approximation: Performance comparison with and without parameter uncertainties.

BACKGROUND: The spread of infectious diseases can be modeled using deterministic or stochastic models. A deterministic approximation of a stochastic model can be appropriate under some conditions, but is unable to capture the discrete nature of populations. We look into the choice of a model from the perspective of decision making. METHOD: We consider an emerging disease (Disease X) in a closed population modeled by a stochastic SIR model or its deterministic approximation. The objective of the decision maker is to minimize the cumulative number of symptomatic infected-days over the course of the epidemic by picking a vaccination policy. We consider four decision making scenarios: based on the stochastic model or the deterministic model, and with or without parameter uncertainty. We also consider different sample sizes for uncertain parameter draws and stochastic model runs. We estimate the average performance of decision making in each scenario and for each sample size. RESULTS: The model used for decision making has an influence on the picked policies. The best achievable performance is obtained with the stochastic model, knowing parameter values, and for a large sample size. For small sample sizes, the deterministic model can outperform the stochastic model due to stochastic effects. Resolving uncertainties may bring more benefit than switching to the stochastic model in our example. CONCLUSION: This article illustrates the interplay between the choice of a type of model, parameter uncertainties, and sample sizes. It points to issues to be considered when optimizing a stochastic model.

Value of information dynamics in Disease X vaccine clinical trials.

BACKGROUND: Solutions have been proposed to accelerate the development and rollout of vaccines against a hypothetical disease with epidemic or pandemic potential called Disease X. This may involve resolving uncertainties regarding the disease and the new vaccine. However the value for public health of collecting this information will depend on the time needed to perform research, but also on the time needed to produce vaccine doses. We explore this interplay, and its effect on the decision on whether or not to perform research. METHOD: We simulate numerically the emergence and transmission of a disease in a population using a susceptible-infected-recovered (SIR) compartmental model with vaccination. Uncertainties regarding the disease and the vaccine are represented by parameter prior distributions. We vary the date at which vaccine doses are available, and the date at which information about parameters becomes available. We use the expected value of perfect information (EVPI) and the expected value of partially perfect information (EVPPI) to measure the value of information. RESULTS: As expected, information has less or no value if it comes too late, or (equivalently) if it can only be used too late. However we also find non trivial dynamics for shorter durations of vaccine development. In this parameter area, it can be optimal to implement vaccination without waiting for information depending on the respective durations of dose production and of clinical research. CONCLUSION: We illustrate the value of information dynamics in a Disease X outbreak scenario, and present a general approach to properly take into account uncertainties and transmission dynamics when planning clinical research in this scenario. Our method is based on numerical simulation and allows us to highlight non trivial effects that cannot otherwise be investigated.

Optimal Epidemic Control under Uncertainty: Tradeoffs between Information Collection and Other Actions.

BACKGROUND: Recent epidemics and measures taken to control them-through vaccination or other actions-have highlighted the role and importance of uncertainty in public health. There is generally a tradeoff between information collection and other uses of resources. Whether this tradeoff is solved explicitly or implicitly, the concept of value of information is central to inform policy makers in an uncertain environment. METHOD: We use a deterministic SIR (susceptible, infectious, recovered) disease emergence and transmission model with vaccination that can be administered as 1 or 2 doses. The disease parameters and vaccine characteristics are uncertain. We study the tradeoffs between information acquisition and 2 other measures: bringing vaccination forward and acquiring more vaccine doses. To do this, we quantify the expected value of perfect information (EVPI) under different constraints faced by public health authorities (i.e., the time of the vaccination campaign implementation and the number of vaccine doses available). RESULTS: We discuss the appropriateness of different responses under uncertainty. We show that, in some cases, vaccinating later or with less vaccine doses but more information about the epidemic, and the efficacy of control strategies may bring better results than vaccinating earlier or with more doses and less information, respectively. CONCLUSION: In the present methodological article, we show in an abstract setting how clearly defining and treating the tradeoff between information acquisition and the relaxation of constraints can improve public health decision making. HIGHLIGHTS: Uncertainties can seriously hinder epidemic control, but resolving them is costly. Thus, there are tradeoffs between information collection and alternative uses of resources.We use a generic SIR model with vaccination and a value-of-information framework to explore these tradeoffs.We show in which cases vaccinating later with more information about the epidemic and the efficacy of control measures may be better-or not-than vaccinating earlier with less information.We show in which cases vaccinating with fewer vaccine doses and more information about the epidemic and the efficacy of control measures may be better-or not-than vaccinating with more doses and less information.

Cost-Effectiveness of Respiratory Syncytial Virus Preventive Interventions in Children: A Model Comparison Study.

OBJECTIVES: Model-based cost-effectiveness analyses on maternal vaccine (MV) and monoclonal antibody (mAb) interventions against respiratory syncytial virus (RSV) use context-specific data and produce varied results. Through model comparison, we aim to characterize RSV cost-effectiveness models and examine drivers for their outputs. METHODS: We compared 3 static and 2 dynamic models using a common input parameter set for a hypothetical birth cohort of 100 000 infants. Year-round and seasonal programs were evaluated for MV and mAb interventions, using available evidence during the study period (eg, phase III MV and phase IIb mAb efficacy). RESULTS: Three static models estimated comparable medically attended (MA) cases averted versus no intervention (MV, 1019-1073; mAb, 5075-5487), with the year-round MV directly saving ∼€1 million medical and €0.3 million nonmedical costs, while gaining 4 to 5 discounted quality-adjusted life years (QALYs) annually in <1-year-olds, and mAb resulting in €4 million medical and €1.5 million nonmedical cost savings, and 21 to 25 discounted QALYs gained. In contrast, both dynamic models estimated fewer MA cases averted (MV, 402-752; mAb, 3362-4622); one showed an age shift of RSV cases, whereas the other one reported many non-MA symptomatic cases averted, especially by MV (2014). These differences can be explained by model types, assumptions on non-MA burden, and interventions' effectiveness over time. CONCLUSIONS: Our static and dynamic models produced overall similar hospitalization and death estimates, but also important differences, especially in non-MA cases averted. Despite the small QALY decrement per non-MA case, their larger number makes them influential for the costs per QALY gained of RSV interventions.

Hospital-wide surveillance-based antimicrobial treatments: A Monte-Carlo look-ahead method.

BACKGROUND AND OBJECTIVES: We present a heuristic solution method to the problem of choosing hospital-wide antimicrobial treatments that minimize the cumulative infected patient-days in the long run in a health care facility. METHODS: Our solution method is a rollout algorithm. We rely on the stochastic version of a compartmental model to describe the spread of an infecting organism in the health care facility and the emergence and spread of resistance to two drugs. We assume that the parameters of the model are known. Treatments are chosen at the beginning of each period based on the count of patients with each health status, and on stochastic simulations of the future emergence and spread of antimicrobial resistance. The same treatment is then administered to all patients, including uninfected patients, during the period and cannot be adjusted until the next period. RESULTS: In our simulations, our algorithm allows to reduce the average cumulative infected patient-days over two years by 47.0% compared to the best standard therapy, and by 32.2% compared to a similar heuristic algorithm not using surveillance data (significantly at the 95% threshold). CONCLUSION: Our heuristic solution method is simple yet flexible. We explain how it can be used either to perform online optimization, or to produce data for quantitative analysis. Its performance is illustrated using a relatively simple infectious disease transmission model, but it is compatible with more advanced epidemiological models.

Optimal dynamic empirical therapy in a health care facility: A Monte-Carlo look-ahead method.

BACKGROUND AND OBJECTIVES: Empirical antimicrobial prescription strategies have been proposed to counteract the selection of resistant pathogenic strains. The respective merits of such strategies have been debated. Rather than comparing a finite number of policies, we take an optimization approach and propose a solution to the problem of finding an empirical therapy policy in a health care facility that minimizes the cumulative infected patient-days over a given time horizon. METHODS: We assume that the parameters of the model are known and that when the policy is implemented, all patients receive the same treatment at a given time. We model the emergence and spread of antimicrobial resistance at the population level with the stochastic version of a compartmental model. The model features two drugs and the possibility of double resistance. Our solution method is a rollout algorithm. RESULTS: In our example, the optimal policy computed with this method allows to reduce the average cumulative infected patient-days over two years by 22% compared to the best standard therapy. Considering regularity constraints, we could derive a policy with a fixed period and a performance close to that of the optimal policy. The average cumulative infected patient-days over two years obtained with the optimal policy is 6% lower (significantly at the 95% threshold) than that obtained with the fixed period policy. CONCLUSION: Our results illustrate the performance of a highly flexible solution method that will contribute to the development of implementable empirical therapy policies.

Informed and uninformed empirical therapy policies.

We argue that a proper distinction must be made between informed and uninformed decision making when setting empirical therapy policies, as this allows one to estimate the value of gathering more information about the pathogens and their transmission and thus to set research priorities. We rely on the stochastic version of a compartmental model to describe the spread of an infecting organism in a health care facility and the emergence and spread of resistance to two drugs. We focus on information and uncertainty regarding the parameters of this model. We consider a family of adaptive empirical therapy policies. In the uninformed setting, the best adaptive policy allowsone to reduce the average cumulative infected patient days over 2 years by 39.3% (95% confidence interval (CI), 30.3-48.1%) compared to the combination therapy. Choosing empirical therapy policies while knowing the exact parameter values allows one to further decrease the cumulative infected patient days by 3.9% (95% CI, 2.1-5.8%) on average. In our setting, the benefit of perfect information might be offset by increased drug consumption.

Cost effectiveness and policy announcement: The case of measles mandatory vaccination.

In a vaccination game, individuals respond to an epidemic by engaging in preventive behaviors that, in turn, influence the course of the epidemic. Such feedback loops need to be considered in the cost effectiveness evaluations of public health policies. We elaborate on the example of mandatory measles vaccination and the role of its anticipation. Our framework is a SIR compartmental model with fully rational forward looking agents who can therefore anticipate on the effects of the mandatory vaccination policy. Before vaccination becomes mandatory, parents decide altruistically and freely whether to vaccinate their children. We model eager and reluctant vaccinationist parents. We provide numerical evidence suggesting that individual anticipatory behavior may lead to a transient increase in measles prevalence before steady state eradication. This would cause non negligible welfare transfers between generations. Ironically, in our scenario, reluctant vaccinationists are among those who benefit the most from mandatory vaccination.

Canonical modeling of anticipatory vaccination behavior and long term epidemic recurrence.

Vaccination is one of humanity's main tools to fight epidemics. In most countries and for most diseases, vaccination is offered on a voluntary basis. Hence, the spread of a disease can be described as two interacting opposite dynamic systems: contagion is determined by past vaccination, while individuals decide whether to vaccinate based on beliefs regarding future disease prevalence. In this study, we show how the interplay between such anticipating behavior and the otherwise biological dynamics of a disease may lead to the emergence of recurrent patterns. We provide simulation results for (i) a Measles-like outbreak, (ii) canonical fully rational and far-sighted individuals, (iii) waning vaccine efficacy and vital dynamics, and (iv) long periods of time, i.e. long enough to observe several vaccination peaks. For comparison, we conducted a similar analysis for individuals with adaptive behavior. As an extension, we investigated the case where part of the population has an anti-vaccination stance.