Considering humans as habitat reveals evidence of successional disease ecology among human pathogens

Abstract

The realization that ecological principles play an important role in infectious disease dynamics has led to a renaissance in epidemiological theory. Ideas from ecological succession theory have begun to inform an understanding of the relationship between the individual microbiome and health but have not yet been applied to investigate broader, population-level epidemiological dynamics. We consider human hosts as habitat and apply ideas from succession to immune memory and multi-pathogen dynamics in populations. We demonstrate that ecologically meaningful life history characteristics of pathogens and parasites, rather than epidemiological features alone, are likely to play a meaningful role in determining the age at which people have the greatest probability of being infected. Our results indicate the potential importance of microbiome succession in determining disease incidence and highlight the need to explore how pathogen life history traits and host ecology influence successional dynamics. We conclude by exploring some of the implications that inclusion of successional theory might have for understanding the ecology of diseases and their hosts.

Fig 1. Analyses of the correlation between Successional Score and Age of Greatest Prevalence.

In Panel A [F(1,28) = 21.85, p < 0.001 with an R² of 0.44], note the classification of Age of Greatest Prevalence into “Early” and “Late” by Successional Score alone (with a break-point of ≥2) (B) [Z-Score = −3.59, p < 0.001]. (B) Unfilled boxes represent sexually transmitted infections, except hepatitis B, represented by the striped box. The red box represents Ebola. Dashed horizontal lines provide mean maximum and minimum ages across pathogens in the “Early” and “Late” classification, respectively. The data underlying this figure can be found in S1 Data.

Fig 2. Correlation between Successional Score and Age of Greatest Prevalence.

After omitting sexually transmitted infections and Ebola from the analysis, the observed R² value increases [F (1, 22) = 46.49, p < 0.00001 with an R² of 0.68]. The data underlying this figure can be found in S1 Data.

Fig 3

Frequency distribution of permutation test regression slopes (Panel A) and R² values (Panel B) (see Methods). Note observed slope and R² value for all data (blue star and line) and for data without sexually transmitted diseases and Ebola (red star and line) fall well outside of permutation test distributions. The data underlying this figure can be found in S2 Data.