When Is Mass Prophylaxis Cost-Effective for Epidemic Control? A Comparison of Decision Approaches

Abstract

Background For certain communicable disease outbreaks, mass prophylaxis of uninfected individuals can curtail new infections. When an outbreak emerges, decision makers could benefit from methods to quickly determine whether mass prophylaxis is cost-effective. We consider 2 approaches: a simple decision model and machine learning meta-models. The motivating example is plague in Madagascar. Methods We use a susceptible-exposed-infectious-removed (SEIR) epidemic model to derive a decision rule based on the fraction of the population infected, effective reproduction ratio, infection fatality rate, quality-adjusted life-year loss associated with death, prophylaxis effectiveness and cost, time horizon, and willingness-to-pay threshold. We also develop machine learning meta-models of a detailed model of plague in Madagascar using logistic regression, random forest, and neural network models. In numerical experiments, we compare results using the decision rule and the meta-models to results obtained using the simulation model. We vary the initial fraction of the population infected, the effective reproduction ratio, the intervention start date and duration, and the cost of prophylaxis. Limitations We assume homogeneous mixing and no negative side effects due to antibiotic prophylaxis. Results The simple decision rule matched the SEIR model outcome in 85.4% of scenarios. Using data for a 2017 plague outbreak in Madagascar, the decision rule correctly indicated that mass prophylaxis was not cost-effective. The meta-models were significantly more accurate, with an accuracy of 92.8% for logistic regression, 95.8% for the neural network model, and 96.9% for the random forest model. Conclusions A simple decision rule using minimal information about an outbreak can accurately evaluate the cost-effectiveness of mass prophylaxis for outbreak mitigation. Meta-models of a complex disease simulation can achieve higher accuracy but with greater computational and data requirements and less interpretability. Highlights We use a susceptible-exposed-infectious-removed model and net monetary benefit to derive a simple decision rule to evaluate the cost-effectiveness of mass prophylaxis. We use the example of plague in Madagascar to compare the performance of the analytically derived decision rule to that of machine learning meta-models trained on a stochastic dynamic transmission model. We assess the accuracy of each approach for different combinations of disease dynamics and intervention scenarios. The machine learning meta-models are more accurate predictors of mass prophylaxis cost-effectiveness. However, the simple decision rule is also accurate and may be a preferred substitute in low-resource settings.

Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models

“…One approach to reducing the computational burden uses an emulator or metamodel that serves as a surrogate of the original complex simulation model (often referred to as a simulator) by mapping the relationship between the inputs and outputs of the simulator (7). An emulator is a proxy model that is less complex and faster than a simulator, can replace a simulator to predict outcomes, and is often used in other model-based analyses, such as sensitivity analysis (8), calibration procedures (9), policy optimization (7,10), cost-effectiveness analysis (11), and extrapolating findings to other settings (12). Using emulators for Bayesian calibration can reduce the computational time without losing the possibility of producing outcomes that match observed data (9,13).…”

Emulator-based Bayesian calibration of the CISNET colorectal cancer models

“…One approach to reducing the computational burden uses an emulator or metamodel that serves as a surrogate of the original complex simulation model (often referred to as a simulator) by mapping the relationship between the inputs and outputs of the simulator (11,12). An emulator is a proxy model that is less complex and faster than a simulator, can replace a simulator to predict outcomes, and is often used in other model-based analyses, such as calibration procedures (8), policy optimization (11,13), cost-effectiveness analysis (14), extrapolating findings to other settings (15) and probabilistic analysis (16). Using emulators for Bayesian calibration can reduce the computational time to obtain a sample from the model parameters posterior distribution, which has already been shown to work with a considerable reduction of time (8,17).…”