Dynamic modeling of antagonism between enteric infection and undernutritionin infancy

PROJECT SUMMARY The goal of this NIAID Mentored Research Scientist Development Award (K01) is to provide the candidate, Gwenyth O. Lee, with the training and protected time to build an independent, interdisciplinary research program. This program would bridge computational modeling methods and community-based research to address key questions in the field of enteric disease epidemiology. Stunted growth is a significant negative outcome of frequent enteric infection among children under five years in low and middle- income countries (LMICs). A major obstacle to the development of impactful interventions to prevent stunting has been methodological. Growth faltering in children is the cumulative result of complex interactions between infectious exposures and dietary inadequacies. Conventional statistical approaches are capable of, but not well-suited to, disentangling these interactions to identify those modifiable factors with the greatest potential impact. On the other hand, systems approaches are explicitly designed to capture complex interactions between multiple causal relationships. Therefore, the overall objective of this proposal is to develop a mechanistic model that captures patterns of feedback between infection, diet, and short-term growth dynamics and predicts how an individual infant might grow, given different patterns of infection experienced in the first two years of life. This model will be used to test whether feedback between infection and undernutrition has the potential to result in a ‘tipping point’, such that enteric infections impact stunting only when the negative effect of these infections outpaces the infant’s biological capacity for catch-up growth. The model will also be used to investigate mechanisms by which dietary inadequacies modify the relationship between enteric infections and growth and to compare enteropathogen- specific growth impacts. The model will be empirically based in 1) data from three Ecuadorian birth cohorts, sampled across rural-urban gradient with variable burdens of enteric exposures but relatively homogenous infant feeding practices and 2) data from six birth cohorts from multiple LMIC contexts (‘MAL-ED’ study), where enteric exposures but also dietary and social conditions are heterogeneous between cohorts. To match the proposed scientific work, the candidate will seek to integrate her experience in community-based enteric disease research with the following training areas: computational modeling, including 1) system dynamics and 2) agent-based modeling, and 3) the nutritional regulation of child growth. This training will be augmented with 4) career-building activities to acquire essential tools for leadership and professional growth. Experts have emphasized the need to better integrate computational modeling into applied epidemiology research, thereby creating models that can better predict contextually-tailored intervention strategies. The project builds upon the unique resources and mentoring available to the candidate through the University of Michigan to provide advanced methodological training, bolster applications for competitive funding, disseminate findings across the wider research community and attain research independence.