Generation of a realistic virtual population. The concept of RVP has been developed between partner pharmacology university teams of Valparaiso and Lyon. The population model has been developed from observational Chilean data to modeling the risk of breast cancer death. This tool represents an instantaneous picture capturing the main features of a given population; pertinent to study a specific health problem each population must integrate pertinent characteristics associated to the occurrence of the disease.

Implementation of risk scores. The evolution of the disease of interest may be captured by the application of risk scores, allowing simulating new incident cases and the worsening of cases appeared using as indicators the probability of cases and levels of gravidity. The most classical example is the Framingham cardiovascular risk score59. It is key that the RVPs include the individual values for the variables included in the risk scores preserving the co-variation of these variables in the real populations they belong to. Added to the age and sex structure of that population, these co-distributions confer the realism to the virtual population and its evolution. In this case of use, a number of risk factors could be considered. The choice of scores to be implemented will depend on: 1) the validity of risk equations, ie, the performance of scores has been evaluated in populations that differ from the population used to derive the equations, 2) the availability of epidemiological data concerning the variables included in the scores, which are critical for their implementation in the RVPs.

Implementation of the effect model. The application of the effect model law60, issued from clinical trials meta-analyses allows simulating the impact of a given intervention on the incidence and/or the evolution of the disease. The effect model can be adapted to each individual profile whether the predictors of benefit have been identified61. Various interventions may be considered with or without interactions. The existence of interactions between cancer drugs can be extrapolated to the impact of interactions between pharmacological and non-pharmacological treatments. Sensitivity analyses allow evaluating the relevance of certain hypotheses: the weight of interventions will be determined according to their impact and level of evidence. Non-pharmacological therapies allowing preserving the autonomy and delay the institutionalization of patients are for instance, physical activity 62, sensorial deficits, psychosocial interventions 63 adjusted to the needs of patients and caregivers, cognitive rehabilitation 64 therapies. Pharmacological treatments integrate negative aspects since they may promote cognitive decline 65, or positive aspects with the addition of drugs which are able to prevent, delay the onset or the progression of the disease