Individual-Level Antibody Dynamics Reveal Potential Drivers of Influenza A Seasonality in Wild Pig Populations

Abstract

Swine are important in the ecology of influenza A virus (IAV) globally. Understanding the ecological role of wild pigs in IAV ecology has been limited because surveillance in wild pigs is often for antibodies (serosurveillance) rather than IAVs, as in humans and domestic swine. As IAV antibodies can persist long after an infection, serosurveillance data are not necessarily indicative of current infection risk. However, antibody responses to IAV infections cause a predictable antibody response, thus time of infection can be inferred from antibody levels in serological samples, enabling identification of risk factors of infection at estimated times of infection. Recent work demonstrates that these quantitative antibody methods (QAMs) can accurately recover infection dates, even when individual-level variation in antibody curves is moderately high. Also, the methodology can be implemented in a survival analysis (SA) framework to reduce bias from opportunistic sampling. Here we integrated QAMs and SA and applied this novel QAM–SA framework to understand the dynamics of IAV infection risk in wild pigs seasonally and spatially, and identify risk factors. We used national-scale IAV serosurveillance data from 15 US states. We found that infection risk was highest during January–March (54% of 61 estimated peaks), with 24% of estimated peaks occurring from May to July, and some low-level of infection risk occurring year-round. Time-varying IAV infection risk in wild pigs was positively correlated with humidity and IAV infection trends in domestic swine and humans, and did not show wave-like spatial spread of infection among states, nor more similar levels of infection risk among states with more similar meteorological conditions. Effects of host sex on IAV infection risk in wild pigs were generally not significant. Because most of the variation in infection risk was explained by state-level factors or infection risk at long-distances, our results suggested that predicting IAV infection risk in wild pigs is complicated by local ecological factors and potentially long-distance translocation of infection. In addition to revealing factors of IAV infection risk in wild pigs, our framework is broadly applicable for quantifying risk factors of disease transmission using opportunistic serosurveillance sampling, a common methodology in wildlife disease surveillance. Future research on the factors that determine individual-level antibody kinetics will facilitate the design of serosurveillance systems that can extract more accurate estimates of time-varying disease risk from quantitative antibody data.

Ambient hydrogen sulfide exposure increases the severity of influenza A virus infection in swine

“…Therefore, the large standard deviations observed in the data likely reflect individual biological differences between pigs. These data are consistent with a previous study that demonstrated IAV infection dynamics, lesions and clinical signs were not influenced by the sex of the pig [47].…”

Inferring time of infection from field data using dynamic models of antibody decay

“…This knowledge allows estimation of incidence (the number of new infections over time) and force of infection (the rate at which susceptible individuals become infected), quantities that are fundamental to understanding and modeling transmission dynamics (Heisey et al, 2006;Held et al, 2019;Hens et al, 2010) and developing mitigation strategies (Caley and Hone, 2004;Weitz et al, 2020). Knowledge of individual infection times is also relevant to a wide range of pathogen-related factors, including interpretation of the time course of clinical signs of disease (Hawley et al, 2011), vaccine efficacy (Antia et al, 2018), risk factors for infection (Borremans et al, 2011;Pepin et al, 2019), pathogen spillover (Smith et al, 2014), effects of disease on wildlife health and survival (Tersago et al, 2012), host immunity (Epstein et al, 2013) and tracing infection sources (Craft, 2015). However, even though a variety of data sources can theoretically be used to estimate infection time (e.g.…”

“…Knowledge of individual infection times is also relevant to a wide range of pathogen-related factors, including interpretation of the time course of clinical signs of disease (Hawley et al, 2011), vaccine efficacy (Antia et al, 2018), risk factors for infection (Borremans et al, 2011; Pepin et al, 2019), pathogen spillover (Smith et al, 2014), effects of disease on wildlife health and survival (Tersago et al, 2012), host immunity (Epstein et al, 2013) and tracing infection sources (Craft, 2015). However, even though a variety of data sources can theoretically be used to estimate infection time (e.g.…”

A framework for surveillance of emerging pathogens at the human-animal interface: Pigs and coronaviruses as a case study

“…Wild pigs contact a variety of wildlife species, including bats ( Wang et al, 2018 ), while also contacting humans through hunting ( Bevins et al, 2014 ), living in urban spaces ( Stillfried et al, 2017 ), and intense control programs ( Pepin et al, 2019b ) or with backyard domestic pigs ( Wyckoff et al, 2009 ; Wu et al, 2012 ). As such, pigs have been implicated in the emergence of novel influenza A viruses in humans ( Brown, 2001 ; Hass et al, 2011 ), and human influenza A prevalence is positively correlated with influenza A prevalence in wild pigs ( Pepin et al, 2019a ). Thus, relative to other animal species, pigs may be quite connected to other reservoir species and humans concurrently, while supporting pathogen transmission and evolution with an ample supply of susceptible hosts.…”

“…Applying survival analysis to surveillance data can help remove bias induced by temporally uneven sampling efforts. Survival analysis is widely applicable to commonly-collected disease surveillance data ( Pepin et al, 2019a ; Wilber et al, 2020 ), suggesting that functional epidemiological metrics can be gleaned from opportunistic sampling designs when the sampling design biases are accounted for. A second approach is to apply state-space models that infer epidemiological metrics while accounting for reporting biases ( Chen et al, 2012 ; Miller, 2017 ; Pepin et al, 2017a ; Baker et al, 2019 ; Tabak et al, 2019 ).…”