Mapping and modelling the Brazilian food system and its impact on the syndemic of malnutrition, obesity and climate change

• Grant number: 23/16338-2
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: March 01, 2024
• End date: January 31, 2028
• Field of knowledge: Health Sciences - Collective Health - Public Health
• Principal Investigator: Aline Martins de Carvalho
• Grantee: Henrique Saldanha Melo
• Host Institution: Faculdade de Saúde Pública (FSP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 22/03091-6 - The complexity of Brazilian food systems and the global syndemic: simulations and proposals for action, AP.PNGP.PI

Abstract

The food systems encompass the entire range of actors, actions, and interactions related to the production, processing, distribution, consumption, and disposal of food. They have become a new way of understanding the globalized world with current epidemiological and nutritional conditions and are the main drivers of the global syndemic of malnutrition, obesity, and climate change. However, due to their complexity, involving numerous agents and interactions, there is a lack of evidence regarding the dynamics of food systems and their impacts on health and the environment, especially in the Global South. Thus, the objective of this project is to contribute to the development of the conceptual model of the Brazilian food system and to dynamically model it mathematically. The conceptual model will be represented by a set of static structure diagrams, with components obtained through a literature review. The conceptual model will identify the main agents, their relationships, and interactions that impact the syndemic of malnutrition, obesity, and climate change in different Brazilian regions. Mathematical models will be used based on the conceptual model to represent the behavior of agents, interaction rules, and system dynamics. Given its complexity, various types of models may be applied, such as linear models, methods for generating social networks and dynamic Bayesian networks, multicriteria causal models, among others. Libraries in Python (using available libraries such as NetworkX, NumPy, SciPy, Matplotlib, Pandas, and SymPy), Netlogo, R, or Matlab will be utilized. The mathematical model will also take into account previously available indicators related to food production and consumption to represent the food system in different Brazilian regions. Some indicators will come from metadata, while others will be derived from microdata available on government and research agency websites.