From bites to bytes: understanding how and why individual malaria risk varies using artificial intelligence and causal inference

Autor(es)

Ribeiro, Adele Helena ; Soler, Julia M. P. ; Corder, Rodrigo M. ; Ferreira, Marcelo U. ; Heider, Dominik

• Tipo de documento: Artigo Científico
• Fonte: FRONTIERS IN GENETICS; v. 16, p. 6-pg., 2025-05-16.

Resumo

With an estimated 263 million cases recorded worldwide in 2023, malaria remains a major global health challenge, particularly in tropical regions with limited healthcare access. Beyond its health impact, malaria disrupts education, economic development, and social equality. While traditional research has focused on biological factors underlying human-mosquito interactions, growing evidence highlights the complex interplay of environmental, behavioral, and socioeconomic factors, alongside mobility and both human and parasite genetics, in shaping transmission dynamics, recurrence patterns, and control effectiveness. This work shows how integrating Artificial Intelligence (AI), Machine Learning (ML), and Causal Inference can advance malaria research by identifying context-specific risk factors, uncovering causal mechanisms, and informing more effective, targeted interventions. Drawing on the M & acirc;ncio Lima cohort, a longitudinal, multimodal study of malaria risk in Brazil's main urban hotspot, and related studies in the Amazon, we highlight how rigorous, data-driven approaches can address the substantial variability in malaria risk across individuals and communities. AI-driven methods facilitate the integration of diverse high-dimensional datasets to uncover intricate patterns and improve individual risk stratification. Federated learning enables collaborative analysis across regions while preserving data privacy. Meanwhile, causal discovery and effect identification tools further strengthen these approaches by distinguishing genuine causal relationships from spurious associations. Together, these approaches offer a principled, scalable, and privacy-preserving framework that enables researchers to move beyond predictive modeling toward actionable causal insights. This shift supports precision public health strategies tailored to vulnerable populations, fostering more equitable and sustainable malaria control and contributing to the reduction of the global malaria burden. (AU)

• Processo FAPESP: 16/18740-9 - Bases científicas para a eliminação da malária residual na Amazônia Brasileira
• Beneficiário: Marcelo Urbano Ferreira
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 22/11963-3 - Variação individual no risco de malária: causas e consequências em populações amazônicas
• Beneficiário: Marcelo Urbano Ferreira
• Modalidade de apoio: Auxílio à Pesquisa - Temático