Mathematical Modeling for Public Health Practitioners and Researchers in Liberia

• Understanding dynamic systems

• Developing simple compartmental models

• Recognizing the value of dynamic modeling based on types of research/programmatic questions that can be addressed

• Specifying equations for simple compartmental models

• Writing user-defined functions in R

• Running a closed system SIR model in R

• Finding parameter values, both point estimates and ranges, using the literature

• Standardizing parameter values according to relevant and realistic time steps

• Finding parameter values, both point estimates and ranges, using the literature (cont.)

• Introducing uncertainty through distributions of parameter values

• Modifying a deterministic SIR model R script to include parameter uncertainty and store results from multiple simulations

• Specifying the Force of Infection equation for a SIR model with heterogeneous versus homogeneous mixing

• Recognizing the role of contact patterns in transmission

• Using data to develop a contact matrix for a stratified model structure

• Using case investigation data to create a contact matrix for informing the force of infection expression

• Coding equations in R for a SEIR model with infectiousness stratified between symptomatic and asymptomatic cases

• Breaking down the methods in a published mathematical modeling paper by identifying key terms and approaches

• Developing likelihood equations for comparing model output to observed data

• Coding decision rules for keeping versus rejecting parameter sets based on agreement with data

• Applying a while loop for assessing goodness of fit (ABC method as example)

• Developing a likelihood equation for comparing model output with observed data