Responding rapidly to epidemic outbreaks presents significant challenges, due to resource, capacity and time limitations. Anticipatory Action (AA) is a newly emerging strategy in the field of humanitarian aid, designed to preemptively address potential crises. By taking impact-reducing actions before a disaster strikes, AA seeks to minimize human loss. However, AA frameworks currently use static prepared-in-advance plans. As a result, AA is not sufficiently able to deal with the uncertainty levels in the onset and spread of epidemics. Effective epidemic control requires plans that can adapt to a constantly changing environment and incoming information, such as the number and location of suspected cases, weather forecasts and population movement, while balancing flexibility with an effective management approach. We show how the (DAPP) framework for decisionmaking under deep uncertainty can be adapted to enhance the common anticipatory action approach with flexibility and effective management for epidemic control. More specifically, we show how DAPP allows to take into account newly available information and change the strategy to minimize human loss. We illustrate it with a case study of cholera in Cameroon, for which the French, Netherlands, and Cameroon Red Cross, supported by EHESP, are developing an early action protocol and a model that assesses the cost-effectiveness of actions for different risk levels and external shocks. Our results suggest that DAPP increase flexibility and coordination in anticipatory action for epidemics and helps optimizing early action strategies, which could have larger implications for global disease control.

Structural equation modeling (SEM) is frequently used in social sciences to analyze relations among observed and latent variables and test theoretical propositions regarding relations among these latent variables. Frequentist SEM relies on Maximum Likelihood Estimation, and although this method works well for many simple situations, its performance is unsatisfactory when dealing with complex models or small sample sizes. In search of a method that resolves those problems, Bayesian SEM has been developed recently. These models produce more accurate parameter estimates. The Bayesian approach to SEM offers the possibility of incorporating prior knowledge into SEM, allowing for model extension and improvement. In this research, the theory of both frequentist and Bayesian SEM is described. Subsequently, Bayesian SEM is illustrated with an application in educational sciences. A method is proposed to specify prior distributions that use correlation estimates found in previous research to reflect prior information and our confidence in that information. The results obtained by an informative prior model are analyzed and compared to the results of a noninformative, weakly informative, and frequentist model. It was found that the informative prior model produces more accurate estimates than the noninformative and weakly informative prior models, indicating the correctness of the specified priors.