What we do

We build transmission models that project the future course of an infection — cases, hospitalizations, deaths — under scenarios you specify. This is the core of what we do, and it underpins most of the other work on this page.

• Seasonal and multi-year incidence forecasts
• Scenario analysis: new variants, waning immunity, changes in coverage or behavior
• Age-, region-, and risk-group–stratified burden estimates
• Counterfactuals: what would have happened without an intervention
• Sizing the population a product would serve, where that follows from the burden estimate

Most infectious disease modeling stops at the population level. We also work inside the host — how viral load rises and falls over an infection, how much that varies between people, and what shifts it. The modeling framework we introduced for estimating viral trajectories from longitudinal testing data is now widely used in the field.

• How kinetics differ across individuals, variants, and immune histories
• Whether a therapeutic measurably changes the trajectory of infection
• Linking viral load to infectiousness, and to what a diagnostic test will detect
• Viral load as a trial endpoint: what it can and cannot support

An event-driven trial in an infectious disease lives or dies on two things: whether the infection actually circulates where and when you enroll, and whether the endpoint you have chosen can detect the effect you expect. We simulate both before you commit to a protocol.

• Site selection informed by projected local incidence
• End-to-end trial simulation: the distribution of outcomes you should expect, not a single number
• Power and sample size under epidemiological, not just statistical, uncertainty
• Designing for and interpreting viral load endpoints, which can shorten a trial considerably
• Go / no-go timing for seasonal pathogens

We work directly with large administrative claims datasets to establish how medicines and vaccines are actually used in practice, and what drives the variation. Much of our published work in this area concerns outpatient prescribing.

• Prescribing and dispensing patterns in the outpatient setting, antibiotics in particular
• Vaccine and medication uptake across geography, age, and socioeconomic status
• What drives variation in use: care-seeking, comorbidity, provider behavior
• Measuring the effect of a guideline change, a stewardship program, or a product entering the market

Sometimes the model already exists and the question is whether to believe it. We provide an independent technical read, and we work as an ongoing advisor to teams that need epidemiological judgment on hand without hiring for it.

• Technical review of internal or vendor-built models
• Due diligence on epidemiological claims in a partnership or acquisition
• Sensitivity analysis: which assumptions actually drive the conclusion
• Retained advisory and standing analytic support