Variation in dengue virus plaque reduction neutralization testing: systematic review and pooled analysis.

BackgroundThe plaque reduction neutralization test (PRNT) remains the gold standard for the detection of serologic immune responses to dengue virus (DENV). While the basic concept of the PRNT remains constant, this test has evolved in multiple laboratories, introducing variation in materials and methods. Despite the importance of laboratory-to-laboratory comparability in DENV vaccine development, the effects of differing PRNT techniques on assay results, particularly the use of different dengue strains within a serotype, have not been fully characterized.MethodsWe conducted a systematic review and pooled analysis of published literature reporting individual-level PRNT titers to identify factors associated with heterogeneity in PRNT results and compared variation between strains within DENV serotypes and between articles using hierarchical models.ResultsThe literature search and selection criteria identified 8 vaccine trials and 25 natural exposure studies reporting 4,411 titers from 605 individuals using 4 different neutralization percentages, 3 cell lines, 12 virus concentrations and 51 strains. Of 1,057 titers from primary DENV exposure, titers to the exposure serotype were consistently higher than titers to non-exposure serotypes. In contrast, titers from secondary DENV exposures (n = 628) demonstrated high titers to exposure and non-exposure serotypes. Additionally, PRNT titers from different strains within a serotype varied substantially. A pooled analysis of 1,689 titers demonstrated strain choice accounted for 8.04% (90% credible interval [CrI]: 3.05%, 15.7%) of between-titer variation after adjusting for secondary exposure, time since DENV exposure, vaccination and neutralization percentage. Differences between articles (a proxy for inter-laboratory differences) accounted for 50.7% (90% CrI: 30.8%, 71.6%) of between-titer variance.ConclusionsAs promising vaccine candidates arise, the lack of standardized assays among diagnostic and research laboratories make unbiased inferences about vaccine-induced protection difficult. Clearly defined, widely accessible reference reagents, proficiency testing or algorithms to adjust for protocol differences would be a useful first step in improving dengue PRNT comparability and quality assurance

Opportunities for improved surveillance and control of dengue from age-specific case data.

One of the challenges faced by global disease surveillance efforts is the lack of comparability across systems. Reporting commonly focuses on overall incidence, despite differences in surveillance quality between and within countries. For most immunizing infections, the age distribution of incident cases provides a more robust picture of trends in transmission. We present a framework to estimate transmission intensity for dengue virus from age-specific incidence data, and apply it to 359 administrative units in Thailand, Colombia, Brazil and Mexico. Our estimates correlate well with those derived from seroprevalence data (the gold standard), capture the expected spatial heterogeneity in risk, and correlate with known environmental drivers of transmission. We show how this approach could be used to guide the implementation of control strategies such as vaccination. Since age-specific counts are routinely collected by masany surveillance systems, they represent a unique opportunity to further our understanding of disease burden and risk for many diseases

Measuring Spatial Dependence for Infectious Disease Epidemiology.

Global spatial clustering is the tendency of points, here cases of infectious disease, to occur closer together than expected by chance. The extent of global clustering can provide a window into the spatial scale of disease transmission, thereby providing insights into the mechanism of spread, and informing optimal surveillance and control. Here the authors present an interpretable measure of spatial clustering, τ, which can be understood as a measure of relative risk. When biological or temporal information can be used to identify sets of potentially linked and likely unlinked cases, this measure can be estimated without knowledge of the underlying population distribution. The greater our ability to distinguish closely related (i.e., separated by few generations of transmission) from more distantly related cases, the more closely τ will track the true scale of transmission. The authors illustrate this approach using examples from the analyses of HIV, dengue and measles, and provide an R package implementing the methods described. The statistic presented, and measures of global clustering in general, can be powerful tools for analysis of spatially resolved data on infectious diseases

Visualizing Clinical Evidence: Citation Networks for the Incubation Periods of Respiratory Viral Infections

Simply by repetition, medical facts can become enshrined as truth even when there is little empirical evidence supporting them. We present an intuitive and clear visual design for tracking the citation history of a particular scientific fact over time. We apply this method to data from a previously published literature review on the incubation period of nine respiratory viral infections. The resulting citation networks reveal that the conventional wisdom about the incubation period for these diseases was based on a small fraction of available data and in one case, on no retrievable empirical evidence. Overall, 50% of all incubation period statements did not provide a source for their estimate and 65% of original sources for incubation period data were not incorporated into subsequent publications. More standardized and widely available methods for visualizing these histories of medical evidence are needed to ensure that conventional wisdom cannot stray too far from empirically supported knowledge

Local variations in spatial synchrony of influenza epidemics

Background: Understanding the mechanism of influenza spread across multiple geographic scales is not complete. While the mechanism of dissemination across regions and states of the United States has been described, understanding the determinants of dissemination between counties has not been elucidated. The paucity of high resolution spatial-temporal influenza incidence data to evaluate disease structure is often not available. Methodology and Findings: We report on the underlying relationship between the spread of influenza and human movement between counties of one state. Significant synchrony in the timing of epidemics exists across the entire state and decay with distance (regional correlation = 62%). Synchrony as a function of population size display evidence of hierarchical spread with more synchronized epidemics occurring among the most populated counties. A gravity model describing movement between two populations is a stronger predictor of influenza spread than adult movement to and from workplaces suggesting that non-routine and leisure travel drive local epidemics. Conclusions: These findings highlight the complex nature of influenza spread across multiple geographic scales. © 2012 Stark et al

Differential mobility and local variation in infection attack rate.

Infectious disease transmission is an inherently spatial process in which a host's home location and their social mixing patterns are important, with the mixing of infectious individuals often different to that of susceptible individuals. Although incidence data for humans have traditionally been aggregated into low-resolution data sets, modern representative surveillance systems such as electronic hospital records generate high volume case data with precise home locations. Here, we use a gridded spatial transmission model of arbitrary resolution to investigate the theoretical relationship between population density, differential population movement and local variability in incidence. We show analytically that a uniform local attack rate is typically only possible for individual pixels in the grid if susceptible and infectious individuals move in the same way. Using a population in Guangdong, China, for which a robust quantitative description of movement is available (a travel kernel), and a natural history consistent with pandemic influenza; we show that local cumulative incidence is positively correlated with population density when susceptible individuals are more connected in space than infectious individuals. Conversely, under the less intuitively likely scenario, when infectious individuals are more connected, local cumulative incidence is negatively correlated with population density. The strength and direction of correlation changes sign for other kernel parameter values. We show that simulation models in which it is assumed implicitly that only infectious individuals move are assuming a slightly unusual specific correlation between population density and attack rate. However, we also show that this potential structural bias can be corrected by using the appropriate non-isotropic kernel that maps infectious-only code onto the isotropic dual-mobility kernel. These results describe a precise relationship between the spatio-social mixing of infectious and susceptible individuals and local variability in attack rates. More generally, these results suggest a genuine risk that mechanistic models of high-resolution attack rate data may reach spurious conclusions if the precise implications of spatial force-of-infection assumptions are not first fully characterized, prior to models being fit to data

Antibodies to Aedes spp. salivary proteins: a systematic review and pooled analysis

Aedes spp. mosquitos are responsible for transmitting several viruses that pose significant public health risks, including dengue, Zika, yellow fever, chikungunya, and West Nile viruses. However, quantifying the number of individuals at risk and their exposure to Aedes spp. mosquitos over time is challenging due to various factors. Even accurate estimation of mosquito numbers at the population level may not fully capture the fluctuations in human exposure based on factors that affect biting rates of mosquitoes. Measuring the antibody response of humans to mosquito salivary proteins (MSP) has been proposed as a method to assess human exposure to mosquito bites and predict disease risk. The presence of antibodies to MSP can be quantified using the enzyme-linked immunosorbent assay (ELISA). While there is known variability in laboratory methods, the consistency of MSP measurements across different research groups has not been quantitatively examined. Variation in laboratory protocols, antigens used, and the human populations sampled all may contribute to differences observed in measured anti-MSP responses. In this study, we conducted a systematic review of the published literature focusing on antibody responses to MSP in humans and other vertebrate hosts. Whenever possible, we extracted individual-level anti-MSP IgG data from these studies and performed a pooled analysis of quantitative outcomes obtained from ELISAs, specifically optical densities (OD). We analyzed the pooled data to quantify variation between studies and identify sample and study characteristics associated with OD scores. Our candidate list of characteristics included the type of antigen used, age of human subjects, mosquito species, population-level mosquito exposure, collection season, Köppen-Geiger climate classification, and OD reporting method. Our findings revealed that the type of antigen, population-level mosquito exposure, and Köppen-Geiger climate classification were significantly associated with ELISA values. Furthermore, we developed a classification algorithm based on OD scores, which successfully distinguished samples from individuals living in areas where a specific mosquito species was present from those where it was not, with a high degree of accuracy. The pooled analysis we conducted provides a harmonized assessment of ELISA testing, which can be utilized to refine the use of antibody responses as markers for mosquito exposure. In conclusion, our study contributes to the understanding of antibody responses to MSP and their utility as indicators of mosquito exposure. By identifying the factors associated with variations in ELISA values, we have provided valuable insights for future research and the refinement of antibody-based assessments of mosquito exposure

Multiphoton microfabrication of conducting polymer-based biomaterials

We report the application of multiphoton microfabrication to prepare conducting polymer (CP)-based biomaterials that were capable of drug delivery and interacting with brain tissue ex vivo, thereby highlighting the potential of multiphoton lithography to prepare electroactive biomaterials which may function as implantable neural biointerfaces (e.g. electrodes)