Molecular pathology diagnostics to subclassify diseases based on pathogenesis are increasingly common in clinical translational medicine. Molecular pathological epidemiology (MPE) is an integrative transdisciplinary science based on the unique disease principle and the disease continuum theory. While it has been most commonly applied to research on breast, lung, and colorectal cancers, MPE can investigate etiologic heterogeneity in non-neoplastic diseases, such as cardiovascular diseases, obesity, diabetes mellitus, drug toxicity, and immunity-related and infectious diseases. This science can enhance causal inference by linking putative etiologic factors to specific molecular biomarkers as outcomes. Technological advances increasingly enable analyses of various -omics, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, microbiome, immunomics, interactomics, etc. Challenges in MPE include sample size limitations (depending on availability of biospecimens or biomedical/radiological imaging), need for rigorous validation of molecular assays and study findings, and paucities of interdisciplinary experts, education programs, international forums, and standardized guidelines. To address these challenges, there are ongoing efforts such as multidisciplinary consortium pooling projects, the International Molecular Pathological Epidemiology Meeting Series, and the Strengthening the Reporting of Observational Studies in Epidemiology-MPE guideline project. Efforts should be made to build biorepository and biobank networks, and worldwide population-based MPE databases. These activities match with the purposes of the Big Data to Knowledge (BD2K), Genetic Associations and Mechanisms in Oncology (GAME-ON), and Precision Medicine Initiatives of the United States National Institute of Health. Given advances in biotechnology, bioinformatics, and computational/systems biology, there are wide open opportunities in MPE to contribute to public health.

Significance: It has been challenging to identify nonpharmaceutical intervention strategies that reconcile two conflicting aims: Reducing the spread of infections while maintaining economic activities amid the coronavirus disease 2019 (COVID-19) pandemic. Commonly implemented strategies such as lockdowns or stay-at-home orders involve a significant restriction of economic activities. Using agent-based simulations, we show that two network intervention strategies that divide or balance social groups can substantially reduce transmission while sustaining economic activities. When the two strategies are jointly implemented without any additional measures, they can keep the effective reproduction number of COVID-19 around 1.0 in most cases.