John Glasser

I studied biology at Princeton, population biology at Duke, and international health, biostatistics, and epidemiology at Harvard. After Epidemic Intelligence Service in the CDC’s Division of Reproductive Health, I returned to Harvard to study mathematical biology with the late Richard Levins. By comparing scenarios differing solely in phenomena of interest, realistic mathematical modeling is among the most reliable, if not only means of evaluating public health programs. Such evaluations can be prospective, involving hypothetical interventions being contemplated, or retrospective, involving alternatives to ongoing programs. Initially at the CDC and currently as a Health Policy Scholar at Emory, I have assisted in designing, or evaluating and occasionally improving public health policies. at home and abroad by modeling the transmission of multiple pathogens among human hosts, most causing vaccine-preventable respiratory illnesses. Working with the CDCs in China and Taiwan; the Ministries of Health in Bolivia, Costa Rica, Jamaica, Jujuy Province, Argentina, São Paulo State, Brazil, and Morelos State, Mexico; the Romanian Public Health Institute; Swedish Institute for Infectious Disease Control (now Public Health Agency of Sweden); and World Health Organization were most rewarding. But my colleagues and I also helped to formulate the US response were Variola major reintroduced by terrorists. And we explained why earlier care-seeking for symptoms that might herald SARS, together with increasingly accurate diagnoses and effective isolation, had far more impact than quarantine. As mixing is the essence of meta-population modeling, we generalized Annette Nold’s (Math Biosci 1980; 52:227-40) function to include preferential contacts between parents and children as well as contemporaries and age-independent contacts among co-workers. We also developed a method for estimating age-specific rates of infection from cross-sectional serological surveys when passively acquired maternal antibodies decay and active immunity wanes, allowing re-infection with clinical consequences that depend on residual immunity. Assuming that vaccination is responsible for secular changes in the epidemiology of pertussis throughout the developed world – by reducing the exposures to infectious children that used to boost immunity – we deduced the optimal number and timing of revaccinations in Sweden. We also explored the impact of heterogeneity in factors affecting effective reproduction numbers, and hence our ability to prevent or control outbreaks, and applied our insights to spatial heterogeneity in vaccine coverage due to personal-belief exemptions, together with preferential mixing among like-minded people. And, perhaps most significantly, we promoted gradients of effective reproduction numbers (partial derivatives with respect to control parameters) as means of identifying optimal outbreak prevention or control measures. Recently, we devised vaccination strategies for accelerating elimination of measles, rubella and other vaccine-preventable diseases from China, and compared strategies for mitigating COVID-19 disease and SARS-CoV-2 transmission in the US. Ongoing projects include comparing respiratory disease burden and mitigation measure impact assessments with surveillance-based ones, identifying means by which novel SARS-CoV-2 variants most likely emerged, and relaxing unrealistic assumptions in our gradient calculations. Finally, we have become concerned about fitting transmission model parameters to surveillance, as this is tantamount to assuming that models and observations are both correct. We consider our models to be hypotheses, compare their predictions to accurate independent observations, and remedy the cause(s) of any disparities before exploring contrafactual scenarios. But modelers trained in quantitative disciplines, few of which include philosophy of science, evidently are unaware that while fitting is necessary for descriptive models, it precludes testing mechanistic ones. Our efforts to remedy this problem, which is ethical – insofar as few health policymakers can evaluate mathematical models – as well as epistemological, include an essay (Math Biosci 2025; 383:109419), mini-symposium at this year’s Society for Mathematical Biology meeting, and planned workshop.