Foundations of Dynamical Modeling

An introduction to the susceptible-infected-recovered compartmental model of infectious disease transmission. This video lecture includes a demonstration of how to build an SIR model using spreadsheet software (Google Sheets), including a capture of Jonathan's screen as he builds the model.<div><br></div><div><div>Latest slide set with video, MMED 2017:</div><div>- 'Dushoff-TheSIRModelFamily.pdf'</div><div>- 'Dushoff-Foundations of Dynamic Modelling.mp4'</div><div><br></div><div>Latest slide set, MMED 2018:</div><div>'DushoffFoundationsDynamicalModellingMMED2018.pdf'</div></div><div><br></div

Data wrangling I: Data management and cleaning

This lecture provides an introduction into the loading, cleaning, manipulation, and visualization of messy data, with a focus on epidemiological applications

Likelihood Fitting and Dynamic Models II

The second introductory lecture in a series on how to fit mechanistic disease transmission models to time series data. This lecture focuses on appropriate data transformations when fitting (log, logistic), the difference between observation and process error (noise), identifiability, and how to report uncertainty in results

Heterogeneity, Contact Patterns and Modeling Options

A lecture on how heterogeneity in infectivity, susceptibility, and contact patterns affect the transmission dynamics and control of infectious diseases

Model Assessment

An introduction to model assessment focusing on cross-validation, goodness of fit statistics, bias, and precision

Source code to replicate all results and figures

Unzip then read "README.md" for instruction

Additional file 1: of Partner age differences and associated sexual risk behaviours among adolescent girls and young women in a cash transfer programme for schooling in Malawi

Figure S1. Interaction plot based upon Fig. 3 from the manuscript. (PDF 267 kb

Prevalence sensitivities.

<p>Left panel shows the elasticities (unitless) of overall HIV prevalence to the three effective mixing rates (proportional change of prevalence for a given proportional change of , that is , with the prevalence). Right panel shows the sensitivities (absolute change of prevalence for a given absolute change of , that is . Units in years). See main text for interpretations.</p

Model diagram.

<p>The top panel describes all possible movements between compartments. The bottom panel shows the infection pathways for each group. The mixing pool is an abstract representation of where all extra-couple sexual contacts occur.</p

Discordant statistic and within-couple transmission contribution.

<p>The discordance statistic as a function of the contribution of within-couple transmission to the global incidence (). Our 10,000 simulations run with parameters sampled from realistic ranges (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082906#pone-0082906-t001" target="_blank">Table 1</a>) show a negative relationship, suggesting that for a given HIV prevalence in the whole population, the observed discordance (measured with ) may be a signature of the importance of within-couple transmission.</p