Cholera is an infectious disease responsible for roughly 3–5 million morbidities and 100,000–120,000 mortalities every year at the global scale. Frequent cholera outbreaks in the recent history suggest unresolved inefficiency issues with regards to cholera prevention and intervention strategies. Guidelines of the World Health Organizations (WHO) advise country governments facing threats of cholera epidemics to prevent and control potential outbreaks by developing effective sanitation, proper waste management strategies and vaccination campaigns. These controls do not envision any focus on environmental determinants of cholera outbreaks. Failing to select the most appropriate prevention and intervention strategies at the health management scale based on public health, environmental, and social determinants is the fundamental cause for the low effectiveness of cholera outbreak containment strategies. This study targets this inefficiency via the creation of a model-based technology that detects the optimal combination of outbreak controls which minimize the number of cases at the system scale. As a case study we consider cholera but the model can be applied to any syndemic and/or complex diseases affected by natural and human systems. The technology is based on the integration of an epidemiology model that processes public health information and predicts population dynamics during the epidemic, an environmental model that predicts environmental fluxes (i.e., hydrologic fluxes) and a mobility model that predicts human fluxes. Results from the physical based model feeds a Portfolio Decision Model (PDM) that is composed by a Multi-Criteria Disease Analysis (MCDA) and a Pareto optimization model. The MCDA model is used for the static evaluation of the feasible controls at the smallest community scale; the Pareto optimization detects the most appropriate control strategy rather than one single control alternative. Preliminary applications of the model applied to the great Kolkata ecosystem shows an average 35% decrease in incidence for the portfolio versus the monocontrol scenario. Acknowledging spatial sensitivities in the epidemiological dynamics, PDM benefits public health management concerned with multiple populations with heterogeneous dependencies occurring simultaneously. PDM considers public health management scales and optimizes the distribution of economic resources for minimizing the risk of infection at the system scale. A major innovation is constituted by the explicit consideration of environmental dynamics, global sensitivity and uncertainty analysis, and MCDA that is particularly relevant for bringing together biophysical factors and stakeholder preferences in the decision making process. The model can be extended from one disease to syndemics linked together by common socio-environmental drivers or the structure of the natural-human systems responsible for their spreading.