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The presence of heterogeneity in susceptibility, differences between hosts in their likelihood of becoming infected, can fundamentally alter disease dynamics and public health responses, for example, by changing the final epidemic size, the duration of an epidemic, and even the vaccination threshold required to achieve herd immunity. Yet, heterogeneity in susceptibility is notoriously difficult to detect and measure, especially early in an epidemic. Here we develop a method that can be used to detect and estimate heterogeneity in susceptibility given contact by using contact tracing data, which are typically collected early in the course of an outbreak. This approach provides the capability, given sufficient data, to estimate and account for the effects of this heterogeneity before they become apparent during an epidemic. It additionally provides the capability to analyze the wealth of contact tracing data available for previous epidemics and estimate heterogeneity in susceptibility for disease systems in which it has never been estimated previously. The premise of our approach is that highly susceptible individuals become infected more often than less susceptible individuals, and so individuals not infected after appearing in contact networks should be less susceptible than average. This change in susceptibility can be detected and quantified when individuals show up in a second contact network after not being infected in the first. To develop our method, we simulated contact tracing data from artificial populations with known levels of heterogeneity in susceptibility according to underlying discrete or continuous distributions of susceptibilities. We analyzed these data to determine the parameter space under which we are able to detect heterogeneity and the accuracy with which we are able to estimate it. We found that our power to detect heterogeneity increases with larger sample sizes, greater heterogeneity, and intermediate fractions of contacts becoming infected in the discrete case or greater fractions of contacts becoming infected in the continuous case. We also found that we are able to reliably estimate heterogeneity and disease dynamics. Ultimately, this means that contact tracing data alone are sufficient to detect and quantify heterogeneity in susceptibility. 

A common assumption in the evolution of virulence theory literature is that pathogens transmit better when they exploit their host more heavily, but by doing so, they impose a greater risk of killing their host, thus truncating infectious periods and reducing their own opportunities for transmission. Here, I derive an equation for the magnitude of this cost in terms of the infection fatality rate, and in doing so, I show that there are many cases where mortality costs are too small to plausibly constrain increases in host exploitation by pathogens. I propose that pathogen evolution may often be constrained by detection costs, whereby hosts alter their behaviour when infection is detectable, and thus reduce pathogen opportunities for onward transmission. I then derive an inequality to illustrate when mortality costs or detection costs impose stronger constraints on pathogen evolution, and I use empirical data from the literature to demonstrate that detection costs are frequently large in both human and animal populations. Finally, I give examples of how evolutionary predictions can change depending on whether costs of host exploitation are borne out through mortality or detection. 

A lack of tractable experimental systems in which to test hypotheses about the ecological and evolutionary drivers of disease spillover and emergence has limited our understanding of these processes. Here we introduce a promising system: Caenorhabditis hosts and Orsay virus, a positive-sense single-stranded RNA virus that naturally infects C. elegans . We assayed species across the Caenorhabditis tree and found Orsay virus susceptibility in 21 of 84 wild strains belonging to 14 of 44 species. Confirming patterns documented in other systems, we detected effects of host phylogeny on susceptibility. We then tested whether susceptible strains were capable of transmitting Orsay virus by transplanting exposed hosts and determining whether they transmitted infection to conspecifics during serial passage. We found no evidence of transmission in 10 strains (virus undetectable after passaging in all replicates), evidence of low-level transmission in 5 strains (virus lost between passage 1 and 5 in at least one replicate) and evidence of sustained transmission in 6 strains (including all three experimental C. elegans strains) in at least one replicate. Transmission was strongly associated with viral amplification in exposed populations. Variation in Orsay virus susceptibility and transmission among Caenorhabditis strains suggests that the system could be powerful for studying spillover and emergence. 

Intrinsically safe designs and a staged transparent development process will be essential 

Following the initiation of the unprecedented global vaccination campaign against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), attention has now turned to the potential impact of this large-scale intervention on the evolution of the virus. In this Essay, we summarize what is currently known about pathogen evolution in the context of immune priming (including vaccination) from research on other pathogen species, with an eye towards the future evolution of SARS-CoV-2.