Dynamic Transmission Modeling for Economic Evaluation

Definition and scope

Models in which infection risk changes with the number and characteristics of infectious people, allowing an intervention to affect both recipients and others through transmission. The target population, use, and analysis remain protocol decisions; this card explains the choices and their consequences rather than approving a study.

Operational specification

Define infection states, mixing groups, population turnover, contact structure, susceptibility, infectiousness, duration, intervention uptake, waning, and the time horizon. Distinguish an individual treatment effect from direct and indirect population effects. Record calibration targets, external data, and which parameters are learned or assumed.

Analysis and diagnostics

Choose deterministic or stochastic, compartmental or individual-based structure from the decision and data. Check conservation and nonnegative states, transmission dynamics, calibration identifiability, and out-of-sample outcomes. Propagate parameter and structural uncertainty into costs and health effects; repeated stochastic epidemics under one parameter set are not parameter uncertainty.

When NOT to use and common errors

Do not add a fixed herd-protection multiplier to a static model without justification, assume homogeneous mixing is harmless, or infer one uniquely identified parameter set from a close fit to incidence alone.

Worked example and implementation boundary

For this discrete-time teaching example, each susceptible person has one-step infection probability p = beta*I/N = 0.02, holding the infectious fraction fixed during the step. Expected infections are S*p = 18. This convention requires probability between zero and one; beta*S*I/N is not automatically an exact finite-interval count under a continuous-time hazard. A synthetic one-step susceptible-infectious calculation has 900 susceptible people, 100 infectious people, population 1000, and an illustrative transmission coefficient 0.2 per step. Calculate expected new infections for that step only. The one-step expected infection flow is 18. This is an illustration, not a calibrated epidemic forecast. The code performs that explicitly stated calculation with synthetic inputs. It is not a complete fitted analysis and does not demonstrate the identification assumptions, estimate sampling uncertainty, or certify application in another population.

Data-source and reporting requirements

Preserve the provenance, dates, units, and observation rule for each input. Claims capture services and transactions under observable benefits; absence of a record is not proof that an event or resource use did not occur. EHR and registry measurements depend on the collection process and may miss care outside the system. Linked sources add reconciliation and selection questions. Primary collection can improve a missing domain but still requires valid measurement and follow-up. Report which of these limitations affects the stated use, which evidence supports the chosen inputs, and what remains uncertain.

Worked example

Scenario

A synthetic one-step susceptible-infectious calculation has 900 susceptible people, 100 infectious people, population 1000, and an illustrative transmission coefficient 0.2 per step. Calculate expected new infections for that step only. For this discrete-time teaching example, each susceptible person has one-step infection probability p = beta*I/N = 0.02, holding the infectious fraction fixed during the step. Expected infections are S*p = 18. This convention requires probability between zero and one; beta*S*I/N is not automatically an exact finite-interval count under a continuous-time hazard.

Dataset

Synthetic inputs with units defined in the scenario; no patient data.

Steps

Result

The one-step expected infection flow is 18. This is an illustration, not a calibrated epidemic forecast.

Trade-offs

Code example

beta = 0.2
susceptible = 900
infectious = 100
population = 1000
result = beta*susceptible*infectious/population
assert abs(result - 18.0) < 1e-9
print(result)

beta <- 0.2
susceptible <- 900
infectious <- 100
population <- 1000
result <- beta*susceptible*infectious/population
stopifnot(abs(result - 18.0) < 1e-9)
print(result)

Citations

1. [1]Pitman R, et al. Dynamic Transmission Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-5. Value in Health. 2012.
2. [2]Eddy DM, et al. Model Transparency and Validation: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7. Value in Health. 2012.