Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies

Anderson, Thayer L.; Nande, Anjalika; Merenstein, Carter; Oommen, Anisha; Raynor, Brinkley; Levy, Michael Z.; Hill, Alison L.

doi:10.1016/j.epidem.2023.100710

“…Our method estimates heterogeneity in underlying susceptibility, and the remaining heterogeneity in contact rate can be determined from the contact network data. Anderson et al (2023) used household study data to estimate heterogeneity in susceptibility. While this method is suitable for use in real time, and can be applied to human infectious diseases, the method notably is designed to estimate heterogeneity within households, which is not the same as the population-level heterogeneity that drives population-level disease dynamics.…”

Section: Discussionmentioning

confidence: 99%

“…Gomes et al (2022) also used curve fitting with mortality data that could be implemented once at least four months of data were available, but their method is heavily dependent on the underlying model and assumptions. With the recent increased interest in real-time estimation, Anderson et al (2023) developed a method to estimate within-household heterogeneity in susceptibility, but this is not the same as the population-level heterogeneity that drives population-level disease dynamics. Here we develop a novel method to identify and quantify host heterogeneity in susceptibility using contact tracing data, which can be collected early in an epidemic.…”

Section: Introductionmentioning

confidence: 99%

Detecting and quantifying heterogeneity in susceptibility using contact tracing data

Tuschhoff,

Kennedy

2023

Preprint

2

0

View full text Add to dashboard Cite

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 is 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 this 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 is sufficient to detect and quantify heterogeneity in susceptibility.

show abstract

Prior exposure to pathogens augments host heterogeneity in susceptibility and has key epidemiological consequences

Hawley,

Pérez-Umphrey,

Adelman

et al. 2024

Preprint

0

View full text Add to dashboard Cite

Pathogen epidemics are key threats to human and wildlife health. Across systems, host protection from pathogens following initial exposure is often incomplete, resulting in recurrent epidemics through partially-immune hosts. Variation in population-level protection has important consequences for epidemic dynamics, but whether acquired protection influences host heterogeneity in susceptibility and its epidemiological consequences remains unexplored. We experimentally investigated whether prior exposure (none, low-dose, or high-dose) to a bacterial pathogen alters host heterogeneity in susceptibility among songbirds. Hosts with no prior pathogen exposure had little variation in protection, but heterogeneity in susceptibility was significantly augmented by prior pathogen exposure, with the highest variability detected in hosts given high-dose prior exposure. An epidemiological model parameterized with experimental data found that heterogeneity in susceptibility from prior exposure more than halved epidemic sizes compared with a homogeneous population with identical mean protection. However, because infection-induced mortality was also greatly reduced in hosts with prior pathogen exposure, reductions in epidemic size were smaller than expected in hosts with prior exposure. These results highlight the importance of variable protection from prior exposure and/or vaccination in driving host heterogeneity and epidemiological dynamics.

show abstract

Detecting and quantifying heterogeneity in susceptibility using contact tracing data

Tuschhoff,

Kennedy

2024

PLoS Comput Biol

0

View full text Add to dashboard Cite

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 is 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 this 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 is sufficient to detect and quantify heterogeneity in susceptibility.

show abstract

Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies

Cited by 4 publications

References 82 publications

Detecting and quantifying heterogeneity in susceptibility using contact tracing data

Detecting and quantifying heterogeneity in susceptibility using contact tracing data

Prior exposure to pathogens augments host heterogeneity in susceptibility and has key epidemiological consequences

Detecting and quantifying heterogeneity in susceptibility using contact tracing data

Contact Info

Product

Resources

About