Biology subjects

Dushoff, J.

Publications and source records attributed to Dushoff, J..

7 recordsLinked to original sources

bioRxiv2018

Male and female bees show large differences in floral preference

O_LIIntraspecific variation in foraging niche can drive food web dynamics and ecosystem processes. Field studies and theoretical analysis of plant-pollinator interaction networks typically focus on the partitioning of the floral community between pollinator species, with little attention paid to intraspecific variation among plants or foraging bees. In other systems, male and female animals exhibit different, cascading, impacts on interaction partners. Although the foraging ecology of male bees is little known, we expect foraging preferences to differ between male and female bees, which could strongly impact plant-pollinator interaction outcomes.\nC_LIO_LIWe designed an observational study to evaluate the strength and prevalence of sexually dimorphic foraging preferences in bees.\nC_LIO_LIWe observed bees visiting flowers in semi-natural meadows in New Jersey, USA. To detect differences in flower use against a shared background resource availability, we maximized the number of interactions observed within narrow spatio-temporal windows. To distinguish observed differences in bee use of flower species, which can reflect abundance patterns and sampling effects, from underlying differences in bee preferences, we analyzed our data with both a permutation-based null model and random effects models.\nC_LIO_LIWe found that the diets of male and female bees of the same species were often as dissimilar as the diets of bees of different species. Furthermore, we demonstrate differences in preference between male and female bees, and identify plant species that are particularly attractive to each sex. We show that intraspecific differences in preference can be robustly identified within interactions between hundreds of species, without precisely quantifying resource availability, and despite high phenological turnover.\nC_LIO_LIGiven the large differences in flower use and preference between male and female bees, ecological sex differences should be integrated into studies of bee demography, plant pollination, and coevolutionary relationships between flowers and insects.\nC_LI

ecology↗

bioRxiv2018

Equivalence of the Erlang SEIR epidemic model and the renewal equation

Most compartmental epidemic models can be represented using the Euler-Lotka renewal equation (RE). The value of the RE is not widely appreciated in the epidemiological modelling community, perhaps because its equivalence to standard models has not been presented rigorously in non-trivial cases. Here, we provide analytical expressions for the intrinsic generation interval distribution that must be used in the RE in order to yield epidemic dynamics that are identical to those of the susceptible-exposed-infectious-recovered (SEIR) compartmental model with Erlang-distributed latent and infectious periods. This class of models includes the standard (exponentially-distributed) SIR and SEIR models as special cases.

epidemiology↗

bioRxiv2018

Exploring how generation intervals link strength and speed of epidemics

Infectious-disease outbreaks are often characterized by the reproductive number and exponential rate of growth r. provides information about out-break control and predicted final size. Directly estimating is difficult, while r can often be estimated from incidence data. These quantities are linked by the generation interval - the time between when an individual is infected by an infector, and when that infector was infected. It is often infeasible to ob-tain the exact shape of a generation-interval distribution, and to understand how this shape affects estimates of . We show that estimating generation interval mean and variance provides insight into the relationship between and r. We use examples based on Ebola, rabies and measles to explore approximations based on gamma-distributed generation intervals, and find that use of these simple approximations are often sufficient to capture the r- relationship and provide robust estimates of .

epidemiology↗

bioRxiv2017

Quantifying ethical tradeoffs for vaccine efficacy trials during severe epidemics

BackgroundDuring emerging epidemics of highly fatal diseases, rapid development and testing of new vaccines may be critical to curbing transmission and saving lives. However, the design of vaccine efficacy trials in such contexts may face considerable logistical, epidemiological, or ethical impediments. Three different vaccine efficacy trials were conducted during the 2014-2016 Ebola virus epidemic in West Africa, each with different designs. At the time, there was vigorous debate on the tradeoff between a trials ability to yield information of scientific and societal value versus the perceived ethical dilemma of withholding potentially life-saving vaccines from control participants. Whereas the scientific value of a trial is often estimated in terms of statistical power, speed, and rigor, we lack similar metrics for the ethical costs of withholding interventions.\n\nMethods and FindingsHere, we introduce a conceptual framework that fills this gap and allows quantitative assessment of both the scientific value of a study and the risks incurred by trial participants. We show that even untested vaccines against severe diseases may be probabilistically beneficial--i.e. after accounting for realistic uncertainty in their safety and efficacy, trial participants are expected to be better off vaccinated than not. While accounting for this uncertainty, we estimate trial participant risk under a hypothetical, idealized vaccine rollout scenario and compare it to risk under various candidate trial designs, in order to elucidate specific quantitative tradeoffs between cumulative risk to trial participants and information gained. Through an illustrative simulation example, we highlight specific trial-design modifications that allow for conscientious balance between minimizing participant risk and acquiring information of societal value. These include modifications that affect the speed with which a trial would detect an efficacious vaccine (greater sample size or enrollment rate, interim analyses, or risk-prioritized vaccine rollout), which leads to earlier vaccination of control participants should the vaccine be efficacious, and those that systematically limit the risk \"spent\" by unvaccinated individuals (e.g., providing vaccination to controls after a delay, or presumptive vaccination of subjects above a risk threshold).\n\nConclusionWe advocate this conceptual approach as a means of clarifying the tensions between opposing viewpoints and facilitating transparent discussion to aid ethical and efficient responses to future emerging epidemics.

epidemiology↗

bioRxiv2017

Fitting mechanistic epidemic models to data: a comparison of simple Markov chain Monte Carlo approaches

BackgroundSimple mechanistic epidemic models are widely used for forecasting and parameter estimation of infectious diseases based on noisy case reporting data. Despite the widespread application of models to emerging infectious diseases, we know little about the comparative performance of standard computational-statistical frameworks in these contexts. Here we build a simple stochastic, discrete-time, discrete-state epidemic model with both process and observation error and use it to characterize the effectiveness of different flavours of Bayesian Markov chain Monte Carlo (MCMC) techniques. We use fits to simulated data, where parameters (and future behaviour) are known to explore the limitations of different platforms and quantify parameter estimation accuracy, forecasting accuracy, and computational efficiency across combinations of modeling decisions (e.g. discrete vs. continuous latent states, levels of stochasticity) and computational platforms (JAGS, NIMBLE, Stan).\n\nResultsModels incorporating at least one source of population-level variation (i.e., dispersion in either the transmission process or the observation process) provide reasonably good forecasts and parameter estimates, while models that incorporate only individual-level variation can lead to inaccurate (or overconfident) results. Models using continuous approximations to the transmission process showed improved computational efficiency without loss of accuracy.\n\nConclusionSimple models of disease transmission and observation can be fitted reliably to simple simulations, as long as population-level variation is taken into account. Continuous approximations can improve computational efficiency using more advanced MCMC techniques.

epidemiology↗

bioRxiv2016

Surveillance to Establish Elimination of Transmission and Freedom from Dog-mediated Rabies

BackgroundWith a global target set for zero human deaths from dog-mediated rabies by 2030 and some regional programmes close to eliminating canine rabies, there is an urgent need for enhanced surveillance strategies suitable for declaring freedom from disease and elimination of transmission with known confidence.\n\nMethodsUsing exhaustive contact tracing across settings in Tanzania we generated detailed data on rabies incidence, rabid dog biting behaviour and health-seeking behaviour of bite victims. Using these data we compared case detection of sampling-based and enhanced surveillance methodologies and investigated elimination verification procedures.\n\nFindingsWe demonstrate that patients presenting to clinics with bite injuries are sensitive sentinels for identifying dog rabies cases. Triage of patients based on bite history criteria and investigation of suspicious incidents can confirm >10% of dog rabies cases and is an affordable approach that will enable validation of disease freedom following two years without case detection. Approaches based on sampling the dog population without using bite-injury follow-up were found to be neither sensitive nor cost-effective.\n\nInterpretationThe low prevalence of rabies, and short window in which disease can be detected, preclude sampling-based surveillance. Instead, active case finding guided by bite-patient triage is needed as elimination is approached. Our proposed methodology is affordable, practical and supports the goal of eliminating human rabies deaths by improving administration of lifesaving post-exposure prophylaxis for genuinely exposed but untreated contacts. Moreover, joint investigations by public health and veterinary workers will strengthen intersectoral partnerships and capacity for control of emerging zoonoses.

epidemiology↗

bioRxiv2016

Eco-evolutionary theory and insect outbreaks

Eco-evolutionary theory argues that population cycles in consumer-resource interactions are partly driven by natural selection, such that changes in densities and changes in trait values are mutually reinforcing. Evidence that this theory explains cycles in nature, however, is almost nonexistent. Experimental tests of model predictions are almost always impossible because of the long time scales over which cycles occur, but for most organisms, even tests of model assumptions are logistically impractical. For insect baculoviruses in contrast, tests of model assumptions are straightforward, and baculoviruses often drive outbreaks of forest-defoliating insects, as in the gypsy moth that we study here. We therefore used field experiments with the gypsy moth baculovirus to test two key assumptions of eco-evolutionary models of host-pathogen population cycles, that reduced host infection risk is heritable and costly. Our experiments confirm the two assumptions, and inserting parameters estimated from our data into the models gives cycles closely resembling gypsy moth outbreak cycles in North America, whereas standard models predict unrealistic stable equilibria. Our work shows that eco-evolutionary models are useful for explaining outbreaks of forest insect defoliators, while widespread observations of intense selection imposed by natural enemies on defoliators, and frequent laboratory observations of heritable and costly resistance in defoliators, suggest that eco-evolutionary dynamics may play a general role in defoliator outbreaks.

ecology↗