Utility of mosquito surveillance data for spatial prioritization of vector control against dengue viruses in three Brazilian cities

Background: Vector control remains the primary defense against dengue fever. Its success relies on the assumption that vector density is related to disease transmission. Two operational issues include the amount by which mosquito density should be reduced to minimize transmission and the spatio-temporal allotment of resources needed to reduce mosquito density in a cost-effective manner. Recently, a novel technology, MI-Dengue, was implemented city-wide in several Brazilian cities to provide real-time mosquito surveillance data for spatial prioritization of vector control resources. We sought to understand the role of city-wide mosquito density data in predicting disease incidence in order to provide guidance for prioritization of vector control work. Methods: We used hierarchical Bayesian regression modeling to examine the role of city-wide vector surveillance data in predicting human cases of dengue fever in space and time. We used four years of weekly surveillance data from Vitoria city, Brazil, to identify the best model structure. We tested effects of vector density, lagged case data and spatial connectivity. We investigated the generality of the best model using an additional year of data from Vitoria and two years of data from other Brazilian cities: Governador Valadares and Sete Lagoas. Results: We found that city-wide, neighborhood-level averages of household vector density were a poor predictor of dengue-fever cases in the absence of accounting for interactions with human cases. Effects of city-wide spatial patterns were stronger than within-neighborhood or nearest-neighborhood effects. Readily available proxies of spatial relationships between human cases, such as economic status, population density or between-neighborhood roadway distance, did not explain spatial patterns in cases better than unweighted global effects. Conclusions: For spatial prioritization of vector controls, city-wide spatial effects should be given more weight than within-neighborhood or nearest-neighborhood connections, in order to minimize city-wide cases of dengue fever. More research is needed to determine which data could best inform city-wide connectivity. Once these data become available, MI-dengue may be even more effective if vector control is spatially prioritized by considering city-wide connectivity between cases together with information on the location of mosquito density and infected mosquitos

Maternal antibody and the maintenance of a lyssavirus in populations of seasonally breeding African bats.

Pathogens causing acute disease and death or lasting immunity require specific spatial or temporal processes to persist in populations. Host traits, such as maternally-derived antibody (MDA) and seasonal birthing affect infection maintenance within populations. Our study objective is to understand how viral and host traits lead to population level infection persistence when the infection can be fatal. We collected data on African fruit bats and a rabies-related virus, Lagos bat virus (LBV), including through captive studies. We incorporate these data into a mechanistic model of LBV transmission to determine how host traits, including MDA and seasonal birthing, and viral traits, such as incubation periods, interact to allow fatal viruses to persist within bat populations. Captive bat studies supported MDA presence estimated from field data. Captive bat infection-derived antibody decayed more slowly than MDA, and while faster than estimates from the field, supports field data that suggest antibody persistence may be lifelong. Unobserved parameters were estimated by particle filtering and suggest only a small proportion of bats die of disease. Pathogen persistence in the population is sensitive to this proportion, along with MDA duration and incubation period. Our analyses suggest MDA produced bats and prolonged virus incubation periods allow viral maintenance in adverse conditions, such as a lethal pathogen or strongly seasonal resource availability for the pathogen in the form of seasonally pulsed birthing.Wellcome Trust, EU FP7, Royal Society, Alborada Trust

Sensitivity analysis results.

<p>Partial rank correlation coefficients (PRCC) for infection maintenance for 1000 simulations for 100 parameter sets for a 25 year time period for our stochastic <i>Eidolon helvum</i>–Lagos bat virus model. Positive PRCC indicate increasing a parameter increases infection maintenance. Parameters are: transmission rate <i>β</i>; adult mortality rate <i>μ</i>; juvenile mortality rate <i>δ</i>; disease induced mortality <i>α</i>; probability of becoming infectious <i>ρ</i>; incubation period <i>σ</i>; carrying capacity <i>K;</i> rate of seroconversion <i>τ</i>; rate of loss of maternally-derived immunity <i>ψ</i>; and annual birth synchrony <i>s</i>. Significance at α = 0.05 is demarcated by the red line. Parameters were varied according to those ranges in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198563#pone.0198563.t001" target="_blank">Table 1</a>.</p

Captive bat anti-Lagos bat virus antibody titers and daily changes in titer.

<p>The regression analysis of longitudinal captive bat anti-LBV antibody titers was adjusted for inclusion of multiple data points from the same individual. The ages are the ages at which the bats entered the study. All neonates were born in captivity. Time series with mixed effects model predictions (A) and mean decay rate (B, regression coefficients) with 95% confidence intervals are plotted. Note the different x-axes scales. Raw data are in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198563#pone.0198563.s002" target="_blank">S1 Dataset</a>.</p

Predicted Lagos bat virus metrics.

<p>(A) Prevalence (%), (B) seroprevalence (%), and (C) maintenance (proportion) in an <i>Eidolon helvum</i> population model with maternally-derived antibody, age structure (juvenile and adult), and a seasonal birth pulse. Parameters are as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198563#pone.0198563.t001" target="_blank">Table 1</a>, but with the carrying capacity parameter, <i>K</i> to control the population size for each age class. The mean prevalence (A) and seroprevalence (B) were estimated from the results of each of 1000 simulations at the end of 25 years of simulation, excluding the runs in which the infection did not persist (C).</p

Schematic of the model structure.

<p>The total bat population is <i>N;</i> susceptible <i>S;</i> exposed <i>E;</i> infectious <i>I;</i> immune <i>R;</i> the proportion becoming infectious <i>ρ</i>; exposed leading to infection <i>E</i>_<i>i;</i> exposed leading to recovery <i>E</i>_<i>r;</i> adults _<i>a</i> (top)_<i>;</i> juveniles _<i>j</i> (bottom, shaded). Juveniles age at rate <i>ε</i> (see text for <i>M</i> aging); maternally-derived antibody positive juveniles <i>M;</i> having waning immunity at rate <i>ψ</i>; and juveniles are born with a seasonal birth pulse <i>B(t)</i>. Lagos bat virus transmission was frequency dependent <i>β·S·I/N</i>. <i>E</i>_<i>i</i> become infectious at rate σ (1/incubation period). <i>E</i>_<i>r</i> seroconvert at rate τ (1/seroconversion period). Mortality is omitted for clarity.</p