During the 1st phase of all major crises, responses are needed despite the lack of realistic data that can’t be issued but from the field. Decision makers can’t wait the arrival of real field collected data. However, they need rational support to make decisions. Rational support should be systematic and perform close—to-real simulations.

Considering the COVID-19 global crisis and specifically the French case, decision makers are presently, (March 2020), focusing on two sets of populations: the set of infected individuals and the set of deceased individuals.

Regarding the infected population set, decision makers should assess the capacity of the medical infrastructure to handle the infected individuals (partially or totally) by medical treatment and by isolation. Both medical treatment and isolation require adequate infrastructures and resources. The adequacy between the required infrastructure/resources and the existing ones should be assessed as best as one can and in an evolutive (dynamic) manner. Financial consequences of bad assessments may endanger the whole economy of the state.

Regarding deceased individuals, decision makers need preparing adequate public communication patterns including explanations and perspectives. All inadequate responses may engender serious social unsatisfaction and irrational panics whose political consequences may seriously impact on the governance effectiveness.

Simplified mathematical models demanding few input data are of special interest during the earlier phases of a crisis management. They require small amount of resources, give immediate 1st order insight of the crisis and are interactive self-learning beings. Simplified models are thus very appreciated by decision makers.

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