Given globalization and other social phenomena, controlling the spread of infectious diseases has become an imperative public health priority. A plethora of interventions which in theory can mitigate the spread of pathogens have been proposed and applied. Evaluating the effectiveness of such interventions is costly and in many circumstances unrealistic. Most importantly, the community effect (i.e. the ability of the intervention to minimize the spread of the pathogen from people who received the intervention to other community members) can rarely be evaluated. Here we propose a study design that can build and evaluate evidence in support of the community effect of an intervention. The approach exploits molecular evolutionary dynamics of pathogens in order to track new infections as having arisen from either a control or an intervention group. It enables us to evaluate whether an intervention reduces the number and length of new transmission chains in comparison to a control condition, and thus let us estimate the relative decrease in new infections in the community due to the intervention. We provide as an example one working scenario of how the approach can be applied with a simulation study and associated power calculations.
Bibliographical noteFunding Information:
Funding: This work was funded by NIH (NIDA) grant number DP1DA034989
© The Author(s) 2018.
- Transmission chain