PraxisModus Institute for Advanced Studies emerged from a collaborative scientific effort that began during the COVID-19 lockdown period in 2022. The founding team brought together complementary expertise in infectious disease epidemiology, mathematical biology, systems modeling, computational analytics, and applied public-health research. The collaboration included Subashini Rajakaruna, PhD, an infectious disease and epidemiology researcher from Sri Lanka with doctoral training in biology in Canada; Han R., PhD, a mathematical biologist and systems biology modeler trained at the Centre for Mathematical Biology, University of Alberta, Canada, with research experience in mathematical medicine and biomedical and immunological systems modeling at the Battelle Center for Mathematical Medicine, Abigail Wexner Research Institute, Nationwide Children's Hospital, Ohio, USA; and Mevan Rajakaruna, a mathematical and computational modeler trained in mathematics, statistics, and computer science at the University of Toronto, Canada, with a key focus on mechanistic–AI/ML model hybridization. 

The group's first major collaborative modeling study focused on COVID-19 transmission dynamics, dynamical-systems modeling, and control-policy risk assessment in 2022. This work was published in Scientific Reports, an international open-access journal. Because the team believed that the work had humanitarian value and should remain freely accessible, the publication costs were supported personally by the founding collaborators. This early effort reflected the principle that continues to guide PraxisModus: scientifically rigorous modeling should be accessible, interpretable, and useful for public-health decision-making, especially in settings where institutional resources are limited.

Following this initial work, the collaboration evolved into a broader not-for-profit research initiative in Canada. PraxisModus Institute for Advanced Studies was formally established in 2025 to advance high-level scientific modeling, technological research, and computational decision-support systems. Its mission is to develop interpretable hybrid mechanistic–AI/ML modeling platforms for infectious and other disease forecasting, public-health preparedness, One Health risk assessment, and broader scientific applications. The organization is especially committed to strengthening modeling capacity for Sri Lanka and other resource-constrained countries and settings.

This mission is deeply personal. Given Sri Lanka's current economic constraints, national resources are understandably directed toward immediate public needs, essential services, and recovery priorities. As Sri Lankan-born scientists who benefited from Sri Lanka's free-education system before developing international scientific careers, we feel a strong responsibility to contribute back through the human capital, scientific training, and modeling expertise we have gained. PraxisModus was initially created in this spirit: to use our education and professional experience to support the world through voluntary research, international collaboration, and practical modeling tools that can strengthen disease preparedness and public-health decision-making.

We are currently conducting three volunteer-driven modeling research projects through Mevan focused on spatial and temporal disease spread, risk assessment, and early-warning prediction in Sri Lanka. These studies use hybrid mechanistic–AI/ML architectures that combine disease ecology, biological interpretation, mechanistic transmission modeling, and machine-learning-based predictive correction. Our ongoing work includes dengue forecasting and intervention-scenario modeling, canine anaplasmosis risk assessment, and leishmaniasis spatial–temporal modeling. These projects demonstrate our commitment to building interpretable, counterfactual, and policy-relevant predictive models and risk-warning systems rather than black-box forecasting tools alone. Manuscripts from these efforts are either submitted or in preparation.

More recently, our interests have expanded into omics-informed modeling for disease diagnostics, as well as the development of hybrid AI/ML–mechanistic systems-modeling frameworks and software tools for precision medicine and therapy optimization.