Summary
We introduce two new plots for visualizing infectious disease outbreaks. Case tree plots depict the emergence and growth of clusters of zoonotic disease over time. Checkerboard plots also represent temporal case clusters, but do not construct transmission trees. These plots visualize outbreak dynamics and allow for analyses like case fatality risk stratified by generation.
Description
We present two new visualizations, case tree plots and checkerboard plots, for visualizing emerging zoonoses.
Zoonoses represent an estimated 58% of all human infectious diseases, and 73% of emerging infectious diseases. Recent examples of zoonotic outbreaks include H1N1, SARS and Middle East Respiratory Syndrome, which have caused thousands of deaths combined. The current toolkit for visualizing data from these emerging diseases is limited.
Case tree and checkerboard plots were developed to address that gap. The visualizations are best suited for diseases like SARS for which there are a limited number of cases, with data available on human to human transmission. They a) allow for easy estimation of epidemiological parameters like basic reproduction number b) indicate the frequency of introductory events, e.g. spillovers in the case of zoonoses c) represent patterns of case attributes like patient sex both by generation and over time.
Case tree plots depict the emergence and growth of clusters of disease over time. Each case is represented by a colored node. Nodes that share an epidemiological link are connected by an edge. The color of the node varies based on the node attribute; it could represent patient sex, health status (e.g. alive, dead), or any other categorical attribute. Node placement along the x-axis corresponds with the date of illness onset for the case.
A second visualization, the checkerboard plot, was developed to complement case tree plots. They can be used in conjunction with case tree plots, or in situations where representing a hypothetical network structure is inappropriate.
The plots are available in the open source package epipy, which is available on github. Detailed documentation and examples are also available. In addition to these visualizations, epipy includes functions for common epidemiology calculations like odds ratio and relative risk.