Next‐generation serology: integrating cross‐sectional and capture–recapture approaches to infer disease dynamics

Abstract

Two approaches have been classically used in disease ecology to estimate epidemiological parameters from field studies: cross-sectional sampling from unmarked individuals and longitudinal capture-recapture setups, which generally involve more limited numbers of marked individuals due to cost and logistical constraints. Although the benefits of longitudinal setups are increasingly acknowledged in the disease ecology community, cross-sectional data remain largely overrepresented in the literature, probably because of the inherent costs of longitudinal surveys. In this context, we used simulated data to compare the performances of cross-sectional and longitudinal designs to estimate the force of infection (i.e., the rate at which susceptible individuals become infected). Then, inspired from recent method developments in quantitative ecology, we explore the benefits of integrating both cross-sectional (seroprevalences) and longitudinal (individuals histories) data sets. In doing so, we investigate the effects of host species life history, antibody persistence, and degree of a priori knowledge and uncertainty on demographic and epidemiological parameters, as those are expected to affect in different ways the level of inference possible from the data. Our results highlight how those elements are important to consider in determining optimal sampling designs. In the case of long-lived species exposed to infectious agents resulting in persistent antibody responses, integrated designs are especially valuable as they benefit from the performances of longitudinal designs even with relatively small longitudinal sample sizes. As an illustration, we apply this approach to a combination of empirical and simulated data inspired from a case of bats exposed to a rabies virus. Overall, this work highlights that serology field studies could greatly benefit from the opportunity of integrating cross-sectional and longitudinal designs.

Percentage of reported Covid-19 cases in Colombia: Estimating the true scale of the pandemic

“…Therefore, we do not know the possible biases. Particularly, the Montería study [26] has a possible bias because tested people were chosen randomly in different neighbourhoods and it is known that cities have intrinsically heterogeneous mobility patterns [21], thus making any uniform random samples problematic [27, 28].…”

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

“…the signal to noise ratio, ( Gilbert et al, 2013 )). However, recent developments in quantitative antibody analyses have shown that the signal to noise ratio itself can be used to estimate when hosts were infected with a pathogen ( Borremans et al, 2016 ; Pepin et al, 2017b ) and have highlighted opportunities to use serological data to estimate functional epidemiological metrics through time ( Borremans et al, 2016 ; Pepin et al, 2017b ; Gamble et al, 2020 ; Hay et al, 2020 ). Moreover, integrating multiple streams of data, such as data on viral load, virus presence/absence, and host age, with quantitative antibody data can further improve inference on estimates ( Borremans et al, 2016 ; Wilber et al, 2020 ).…”

“…While longitudinal laboratory data on within-host infection dynamics facilitate inference of functional epidemiological metrics from serosurveillance data ( Pepin et al, 2017b ), they are not strict prerequisites ( Wilber et al, 2020 ). For example, Gamble et al (2020) showed that longitudinal data from the field in the form of mark-recapture data can be integrated with serosurveillance data to provide estimates of functional epidemiological metrics. Thus, for acute pathogens such as CoVs that may be difficult to find in wildlife hosts, serosurveillance data can be used to quantify functional epidemiological metrics which, compared to seroprevalence data, can improve seasonal predictions of when spillover risk might be greatest, helping further optimize the allocation of surveillance resources.…”

Towards an understanding of the avian virome

“…For example, individual-level information, (instead of pooling samples from different animals) is needed to make inferences about differential prevalence or abundance of viruses among birds in a population. Longitudinal sampling (of the same individuals, if possible) on a timescale determined by the lifespan and life history of the bird species in question will help to elucidate temporal virome changes and to distinguish transient from persistent viruses [17]. Further, sampling across different spatial scales and locations will allow us to test the impact of habitat and geographical distance on the sharing and structure of bird virus communities [18].…”