Abstract
The pathogenicity of a microbe is difficult to define in a comparative context: across microbes with varying virulence or across host genotypes with varying susceptibilities. Several different statistical approaches have been employed to investigate pathogenicity and susceptibility. Simple measures like proportional mortality or morbidity at a given time are attractive due to their simplicity but represent a single snapshot. Survival curve approaches, such as the Cox proportional hazards model and risk scores provide a more nuanced picture of the course of infection. More recently, Casadevall introduced the concept of pathogenic potential, a composite measure encompassing both host susceptibility and pathogen virulence, which focuses on the pathogenicity of a single pathogenic microbe, and later expanded to include additional nuances. Using Drosophila melanogaster, we conducted infection experiments with diverse species of Providencia that naturally vary in virulence. We also used several infectious doses to tune infections. We employed different host genotypes that vary in susceptibility to Providencia infection. Our analysis incorporates factors such as host genotype, pathogen type, inoculum load, symptomatic fraction, and mortality to compare host- and pathogen-based measures of pathogenicity. We discuss the advantages and limitations of each method, providing insights into their applicability in deciphering the intricacies of host-pathogen interactions and guiding the choice of analytical approaches in infectious disease research.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Data accessibility: Data and code is archived on GitHub (https://github.com/anjaligupta1210/Pathogen-Potential).