Morrison, A. C. | Search bioRxiv

bioRxiv2018

Model-based assessment of public health impact and cost-effectiveness of dengue vaccination following screening for prior exposure

The tetravalent dengue vaccine CYD-TDV (Dengvaxia(R)) is the first licensed vaccine against dengue, but recent findings indicate an elevated risk of severe disease among vaccinees without prior dengue virus (DENV) exposure. The World Health Organization currently recommends CYD-TDV only for individuals with serological confirmation of past DENV exposure. Our objective was to evaluate the potential impact and cost-effectiveness of vaccination following serological screening. To do so, we used an agent-based model to simulate DENV transmission with and without vaccination over a 30-year timeframe. Under a range of values for the proportion of vaccinees with prior DENV exposure, we projected the proportion of symptomatic and hospitalized cases averted as a function of the sensitivity and specificity of serological screening. Scenarios about the cost-effectiveness of screening and vaccination were chosen to be representative of Brazil and the Philippines. We found that public health impact depended primarily on the sensitivity of serological screening in high-transmission settings and on a combination of sensitivity and specificity in low-transmission settings. Reducing risk from an individual perspective required high specificity, no less than 0.8. Cost-effectiveness could be achievable from the perspective of a public payer provided that screening sensitivity for prior DENV exposure and the value of a disability-adjusted life-year were both high. Cost-effectiveness was also achievable from an individual perspective, particularly with high screening specificity and low coverage. Whereas the results of this analysis offer general guidelines about CYD-TDV vaccination, decisions in specific contexts would benefit from additional, more context-specific modeling analyses. In conclusion, vaccination with CYD-TDV following serological screening could have a positive impact in certain epidemiological settings, provided that screening is highly specific, at least moderately sensitive, and inexpensive.

epidemiology↗

bioRxiv2017

Model-based analysis of experimental hut data elucidates multifaceted effects of a volatile chemical on Aedes aegypti mosquitoes

BackgroundInsecticides used against Aedes aegypti and other disease vectors can elicit a multitude of dose-dependent effects on behavioral and bionomic traits. Estimating the potential epidemiological impact of a product requires thorough understanding of these effects and their interplay at different dosages. Volatile spatial repellent (SR) products come with an additional layer of complexity due to the potential for movement of affected mosquitoes or volatile particles of the product beyond the treated house. Here, we propose a statistical inference framework for estimating these nuanced effects of volatile SRs.\n\nMethodsWe fitted a continuous-time Markov chain model in a Bayesian framework to mark-release-recapture (MRR) data from an experimental hut study conducted in Iquitos, Peru. We estimated the effects of two dosages of transfluthrin on Ae. aegypti behaviors associated with human-vector contact: repellency, exiting, and knockdown in the treated space and in \"downstream\" adjacent huts. We validated the framework using simulated data.\n\nResultsThe odds of a female Ae. aegypti being repelled from a treated hut (HT) increased at both dosages (low dosage: odds = 1.64, 95% highest density interval (HDI) = 1.30-2.09; high dosage: odds = 1.35, HDI = 1.04-1.67). The relative risk of exiting from the treated hut was reduced (low: RR = 0.70, HDI = 0.62-1.09; high: RR = 0.70, HDI = 0.40-1.06), with this effect carrying over to untreated spaces as far as two huts away from the treated hut (H2) (low: RR = 0.79, HDI = 0.59-1.01; high: RR = 0.66, HDI = 0.50-0.87). Knockdown rates were increased in both treated and downstream huts, particularly under high dosage (HT: RR = 8.37, HDI = 2.11-17.35; H1: RR = 1.39, HDI = 0.52-2.69; H2: RR = 2.22, HDI = 0.96-3.86).\n\nConclusionsOur statistical inference framework is effective at elucidating multiple effects of volatile chemicals used in SR products, as well as their downstream effects. This framework provides a powerful tool for early selection of candidate SR product formulations worth advancing to costlier epidemiological trials, which are ultimately necessary for proof of concept of public health value and subsequent formal endorsement by health authorities.

ecology↗