Next generation serology: integrating cross-sectional and capture-recapture approaches to infer disease dynamics.

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 constrains. Although the benefits of longitudinal setups are increasingly acknowledged in the disease ecology community, cross-sectional data remain largely over-represented 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) datasets. 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 to determine 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.

[1]  K. Lafferty,et al.  Evidence for the Role of Infectious Disease in Species Extinction and Endangerment , 2006, Conservation biology : the journal of the Society for Conservation Biology.

[2]  R. Delahay,et al.  Inference of the infection status of individuals using longitudinal testing data from cryptic populations: Towards a probabilistic approach to diagnosis , 2017, Scientific Reports.

[3]  Rémi Choquet,et al.  Exposure of black-legged kittiwakes to Lyme disease spirochetes: dynamics of the immune status of adult hosts and effects on their survival. , 2012, The Journal of animal ecology.

[4]  Byron J. T. Morgan,et al.  Exploring the consequences of reducing survey effort for detecting individual and temporal variability in survival , 2014 .

[5]  Graham C. Smith,et al.  Demographic buffering and compensatory recruitment promotes the persistence of disease in a wildlife population , 2016, Ecology letters.

[6]  O. Gimenez,et al.  Longitudinal survey of two serotine bat (Eptesicus serotinus) maternity colonies exposed to EBLV-1 (European Bat Lyssavirus type 1): Assessment of survival and serological status variations using capture-recapture models , 2017, PLoS neglected tropical diseases.

[7]  James D. Nichols,et al.  Monitoring of biological diversity in space and time , 2001 .

[8]  P Besbeas,et al.  Integrating Mark–Recapture–Recovery and Census Data to Estimate Animal Abundance and Demographic Parameters , 2002, Biometrics.

[9]  Evan G. Cooch,et al.  Multistate capture–recapture analysis under imperfect state observation: an application to disease models , 2009 .

[10]  W. Kendall,et al.  Seeking a second opinion: uncertainty in disease ecology. , 2010, Ecology letters.

[11]  Neff Walker,et al.  Mathematical models in the evaluation of health programmes , 2011, The Lancet.

[12]  David R. Anderson,et al.  Modeling Survival and Testing Biological Hypotheses Using Marked Animals: A Unified Approach with Case Studies , 1992 .

[13]  Kate E. Jones,et al.  Global trends in emerging infectious diseases , 2008, Nature.

[14]  Roger Pradel,et al.  Multievent: An Extension of Multistate Capture–Recapture Models to Uncertain States , 2005, Biometrics.

[15]  T. Boulinier,et al.  Long Antibody Persistence and Transgenerational Transfer of Immunity in a Long-Lived Vertebrate , 2014, The American Naturalist.

[16]  José J. Lahoz‐Monfort,et al.  Designing studies to detect differences in species occupancy: power analysis under imperfect detection , 2012 .

[17]  J. Rotella,et al.  Environmental extremes versus ecological extremes: impact of a massive iceberg on the population dynamics of a high-level Antarctic marine predator† , 2012, Proceedings of the Royal Society B: Biological Sciences.

[18]  P. Henry,et al.  Climate impacts on Mediterranean blue tit survival: an investigation across seasons and spatial scales , 2006 .

[19]  H. Leirs,et al.  Arenavirus infection correlates with lower survival of its natural rodent host in a long-term capture-mark-recapture study , 2018, Parasites & Vectors.

[20]  Shelly Lachish,et al.  The impact of disease on the survival and population growth rate of the Tasmanian devil. , 2007, The Journal of animal ecology.

[21]  T. Boulinier,et al.  Egg sampling as a possible alternative to blood sampling when monitoring the exposure of yellow-legged gulls (Larus michahellis) to avian influenza viruses , 2014, Avian pathology : journal of the W.V.P.A.

[22]  Perry J. Williams,et al.  Integrated population models: Model assumptions and inference , 2019, Methods in Ecology and Evolution.

[23]  Michael G. Buhnerkempe,et al.  Inferring infection hazard in wildlife populations by linking data across individual and population scales , 2017, Ecology letters.

[24]  José J Lahoz-Monfort,et al.  Statistical approaches to account for false‐positive errors in environmental DNA samples , 2016, Molecular ecology resources.

[25]  Dylan B. George,et al.  Model-guided fieldwork: practical guidelines for multidisciplinary research on wildlife ecological and epidemiological dynamics , 2012, Ecology letters.

[26]  Olivier Gimenez,et al.  An assessment of integrated population models: bias, accuracy, and violation of the assumption of independence. , 2010, Ecology.

[27]  R. Plowright,et al.  Sampling to elucidate the dynamics of infections in reservoir hosts , 2019, Philosophical Transactions of the Royal Society of London. Biological Sciences.

[28]  S. Cleaveland,et al.  Integrating serological and genetic data to quantify cross-species transmission: brucellosis as a case study , 2016, Parasitology.

[29]  Wilfried Thuiller,et al.  Sampling in ecology and evolution – bridging the gap between theory and practice , 2010 .

[30]  Benjamin Zuckerberg,et al.  A practical guide for combining data to model species distributions. , 2019, Ecology.

[31]  Roger Pradel,et al.  Estimating demographic parameters using hidden process dynamic models. , 2012, Theoretical population biology.

[32]  Thierry Chambert,et al.  Estimating transitions between states using measurements with imperfect detection: application to serological data. , 2013, Ecology.

[33]  M. Grüebler,et al.  IPM 2 : toward better understanding and forecasting of population dynamics , 2019, Ecological Monographs.

[34]  A. Shestopalov,et al.  Antibodies to Newcastle Disease Virus in Egg Yolks of Great Cormorant (Phalacrocorax carbo) at Qinghai Lake , 2014 .

[35]  M. Dubourg-Savage,et al.  Active surveillance of bat rabies in France: a 5-year study (2004-2009). , 2011, Veterinary microbiology.

[36]  H. Weimerskirch,et al.  Interpreting ELISA analyses from wild animal samples: some recurrent issues and solutions , 2017 .

[37]  Jeffrey S. Hall,et al.  The dynamics of avian influenza in Lesser Snow Geese: implications for annual and migratory infection patterns. , 2015, Ecological applications : a publication of the Ecological Society of America.

[38]  Darryl I. MacKenzie,et al.  Designing occupancy studies: general advice and allocating survey effort , 2005 .

[39]  R. Plowright,et al.  Deciphering Serology to Understand the Ecology of Infectious Diseases in Wildlife , 2013, EcoHealth.

[40]  O. Gimenez,et al.  Use of Integrated Modeling to Enhance Estimates of Population Dynamics Obtained from Limited Data , 2007, Conservation biology : the journal of the Society for Conservation Biology.

[41]  Jonas Reijniers,et al.  Estimating Time of Infection Using Prior Serological and Individual Information Can Greatly Improve Incidence Estimation of Human and Wildlife Infections , 2016, PLoS Comput. Biol..

[42]  Michael D. Samuel,et al.  Model-Based Evaluation of Highly and Low Pathogenic Avian Influenza Dynamics in Wild Birds , 2010, PloS one.

[43]  Lucile Marescot,et al.  Social status mediates the fitness costs of infection with canine distemper virus in Serengeti spotted hyenas , 2018, Functional ecology.

[44]  Olivier Gimenez,et al.  Designing cost-effective capture-recapture surveys for improving the monitoring of survival in bird populations , 2017 .

[45]  S. Gandon Local adaptation and the geometry of host–parasite coevolution , 2002 .

[46]  D. Pontier,et al.  Long-term monitoring of classical swine fever in wild boar (Sus scrofa sp.) using serological data. , 2005, Veterinary research.

[47]  Roger Pradel,et al.  Capture–recapture models with heterogeneity to study survival senescence in the wild , 2010 .

[48]  Ziv Shkedy,et al.  Modeling Infectious Disease Parameters Based on Serological and Social Contact Data , 2012 .

[49]  N. Hens,et al.  Mathematical models used to inform study design or surveillance systems in infectious diseases: a systematic review , 2017, BMC Infectious Diseases.

[50]  Juan Carlos Senar,et al.  State-specific detection probabilities and disease prevalence. , 2007, Ecological applications : a publication of the Ecological Society of America.

[51]  D. Oro,et al.  Exposure of yellow-legged gulls to Toxoplasma gondii along the Western Mediterranean coasts: Tales from a sentinel , 2019, International journal for parasitology. Parasites and wildlife.

[52]  H. Weimerskirch,et al.  Exposure of breeding albatrosses to the agent of avian cholera: dynamics of antibody levels and ecological implications , 2019, Oecologia.

[53]  COMPARISON OF METHODS TO DETECT PASTEURELLA MULTOCIDA IN CARRIER WATERFOWL , 2003, Journal of wildlife diseases.

[54]  T. Tveraa,et al.  Migration, Prospecting, Dispersal? What Host Movement Matters for Infectious Agent Circulation? , 2016, Integrative and comparative biology.

[55]  C Jessica E Metcalf,et al.  Use of serological surveys to generate key insights into the changing global landscape of infectious disease , 2016, The Lancet.

[56]  T. Tveraa,et al.  Interannual dynamics of antibody levels in naturally infected long-lived colonial birds. , 2007, Ecology.