Comparison of Two Detailed Models of Aedes aegypti Population Dynamics

The success of control programs for mosquito-­borne diseases can be enhanced by crucial information provided by models of the mosquito populations. Models, however, can differ in their structure, complexity, and biological assumptions, and these differences impact their predictions. Unfortunately, it is typically difficult to determine why two complex models make different predictions because we lack structured side-­by-­side comparisons of models using comparable parameterization. Here, we present a detailed comparison of two complex, spatially explicit, stochastic models of the population dynamics of Aedes aegypti, the main vector of dengue, yellow fever, chikungunya, and Zika viruses. Both models describe the mosquito?s biological and ecological characteristics, but differ in complexity and specific assumptions. We compare the predictions of these models in two selected climatic settings: a tropical and weakly seasonal climate in Iquitos, Peru, and a temperate and strongly seasonal climate in Buenos Aires, Argentina. Both models were calibrated to operate at identical average densities in unperturbedconditions in both settings, by adjusting parameters regulating densities in each model (number of larval development sites and amount of nutritional resources). We show that the models differ in their sensitivityto environmental conditions (temperature and rainfall) and trace differences to specific model assumptions.Temporal dynamics of the Ae. aegypti populations predicted by the two models differ more markedly under strongly seasonal Buenos Aires conditions. We use both models to simulate killing of larvae and/or adults with insecticides in selected areas. We show that predictions of population recovery by the models differ substantially, an effect likely related to model assumptions regarding larval development and (director delayed) density dependence. Our methodical comparison provides important guidance for model improvement by identifying key areas of Ae. aegypti ecology that substantially affect model predictions, and revealing the impact of model assumptions on population dynamics predictions in unperturbed and perturbed conditions.Fil: Legros, Mathieu. University of North Carolina; Estados UnidosFil: Otero, Marcelo Javier. Universidad de Buenos Aires; ArgentinaFil: Romeo Aznar, Victoria Teresa. Universidad de Buenos Aires; ArgentinaFil: Solari, Hernan Gustavo. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Gould, Fred. National Institutes of Health; Estados UnidosFil: Lloyd, Alun L.. National Institutes of Health; Estados Unido

Toward refined environmental scenarios for ecological risk assessment of down-the-drain chemicals in freshwater environments

Current regulatory practice for chemical risk assessment suffers from the lack of realism in conventional frameworks. Despite significant advances in exposure and ecological effect modeling, the implementation of novel approaches as high-tier options for prospective regulatory risk assessment remains limited, particularly among general chemicals such as down-the-drain ingredients. While reviewing the current state of the art in environmental exposure and ecological effect modeling, we propose a scenario-based framework that enables a better integration of exposure and effect assessments in a tiered approach. Global- to catchment-scale spatially explicit exposure models can be used to identify areas of higher exposure and to generate ecologically relevant exposure information for input into effect models. Numerous examples of mechanistic ecological effect models demonstrate that it is technically feasible to extrapolate from individual-level effects to effects at higher levels of biological organization and from laboratory to environmental conditions. However, the data required to parameterize effect models that can embrace the complexity of ecosystems are large and require a targeted approach. Experimental efforts should, therefore, focus on vulnerable species and/or traits and ecological conditions of relevance. We outline key research needs to address the challenges that currently hinder the practical application of advanced model-based approaches to risk assessment of down-the-drain chemicals

Gastrointestinal symptoms in low-dose aspirin users: a comparison between plain and buffered aspirin

Contains fulltext : 127588.pdf (publisher's version ) (Open Access)BACKGROUND: Aspirin is associated with gastrointestinal side effects such as gastric ulcers, gastric bleeding and dyspepsia. High-dose effervescent calcium carbasalate (ECC), a buffered formulation of aspirin, is associated with reduced gastric toxicity compared with plain aspirin in healthy volunteers, but at lower cardiovascular doses no beneficial effects were observed. AIM: To compare the prevalence of self-reported gastrointestinal symptoms between low-dose plain aspirin and ECC. METHODS: A total of 51,869 questionnaires were sent to a representative sample of the Dutch adult general population in December 2008. Questions about demographics, gastrointestinal symptoms in general and specific symptoms, comorbidity, and medication use including bioequivalent doses of ECC (100 mg) and plain aspirin (80 mg) were stated. We investigated the prevalence of self-reported gastrointestinal symptoms on ECC compared with plain aspirin using univariate and multivariate logistic regression analyses. RESULTS: A total of 16,715 questionnaires (32 %) were returned and eligible for analysis. Of these, 911 (5 %) respondents reported the use of plain aspirin, 633 (4 %) ECC and 15,171 reported using neither form of aspirin (91 %). The prevalence of self-reported gastrointestinal symptoms in general was higher in respondents using ECC (27.5 %) compared with plain aspirin (26.3 %), but did not differ significantly with either univariate (OR 1.06, 95 %CI 0.84-1.33), or multivariate analysis (aOR 1.08, 95 %CI 0.83-1.41). Also, none of the specific types of symptoms differed between the two aspirin formulations. CONCLUSIONS: In this large cohort representative of the general Dutch population, low-dose ECC is not associated with a reduction in self-reported gastrointestinal symptoms compared with plain aspirin

Dengue in Madeira Island

This is a preprint of a paper whose final and definite form will be published in the volume Mathematics of Planet Earth that initiates the book series CIM Series in Mathematical Sciences (CIM-MS) published by Springer. Submitted Oct/2013; Revised 16/July/2014 and 20/Sept/2014; Accepted 28/Sept/2014.Dengue is a vector-borne disease and 40% of world population is at risk. Dengue transcends international borders and can be found in tropical and subtropical regions around the world, predominantly in urban and semi-urban areas. A model for dengue disease transmission, composed by mutually-exclusive compartments representing the human and vector dynamics, is presented in this study. The data is from Madeira, a Portuguese island, where an unprecedented outbreak was detected on October 2012. The aim of this work is to simulate the repercussions of the control measures in the fight of the disease

Dengue epidemics and human mobility

In this work we explore the effects of human mobility on the dispersion of a vector borne disease. We combine an already presented stochastic model for dengue with a simple representation of the daily motion of humans on a schematic city of 20x20 blocks with 100 inhabitants in each block. The pattern of motion of the individuals is described in terms of complex networks in which links connect different blocks and the link length distribution is in accordance with recent findings on human mobility. It is shown that human mobility can turn out to be the main driving force of the disease dispersal.Comment: 24 pages, 13 figure

EarlyWarning System (EWS) for Dengue in Indonesia and Thailand

ABSTRACT Mohammad Juffrie, Dana A. Focks - Early Warning Systems (EWS) for Dengue in Indonesia and Thailand Background: Dengue virus infection is an acute febrile disease caused by 4 sero-type viruses. The transmission via mosquito vector Ae. Aegypti. The morbidity of dengue virus infection is quite high and the mortality below 5%. The most dangerous form is dengue shock syndrome, the mortality is very high. The effort to reduce morbidity and mortality is improvement of the clinical management and control of vector. Today, most dengue control efforts are based on suppression of Aedes aegypti (L.) and not eradication. EWS would provide significant utility where mitigation methods were available. EWSs were possible for three reasons, an extensive time series on the disease incidence the available, dengue being a vector-borne disease, is significantly influenced by weather, in many sub-regions of SE Asia, weather anomalies are significantly influenced by and lag behind several months, sea surface temperature (SST) anomalies. Methods: Analytic cross sectional study was conducted. The dependant variable in this analysis, Epi.yr. is dichotomous and indicates whether an epidemic occurred during a particular year. The two independent (predictor) variables are sea surface temperature anomalies as reported by the Japanese Meteorological Association (JMA) and previous cases. The monthly number of cases were dengue and DHF in Yogyakarta, Indonesia and the metropolitan area of Bangkok, Thailand. Results: Yogyakarta, many years were very near the epidemic cutoff of 278 cases, yet only one year, 1992 with 237 cases, was incorrectly labeled. The false positive in 1992, had a probability of 0.64 of epidemic and 0.36 of no epidemic. Bangkok, the best three-month prediction gave 6 false indication in 35 years, 5 false negatives, 1 false positive. For two month prediction, 3 errors in 35 years were made, 2 false negatives, 1 false positive. Conclusion: The results presented in this study is very use full for predicting the incidence of dengue virus infection using weather data. This method would only require a simple calculator, or preferably a PC using the derived equation. Key words: dengue -incidence -early warning -weather - probabilit

High potential risk of dengue transmission during the hot-dry season in Nha Trang City, Vietnam.

We visited houses and inspected water-holding containers to determine the potential risks of dengue transmission during different seasons. This survey was conducted in two neighbourhoods of Nha Trang City in July and December 2006, which correspond to the middle of the hot-dry season and the beginning of the cool-wet season, respectively. We inspected a total of 1438 wet containers in 196 premises during both survey periods; 20% of the containers were positive for Aedes aegypti larvae and 8% for A. aegypti pupae. Indoor water-holding containers, which were sparsely distributed, exhibited high pupal productivity and efficiency (pupal productivity of a type of container/prevalence of that type of container) in either the first survey conducted in July, or the second, conducted in December. Although rainfall may not influence the number and distribution of water-holding containers in the city, the high average temperature in the first survey period resulted in a higher potential risk of dengue transmission. Our analysis suggests that if intensive source reduction is conducted in summer and containers with high pupal productivity and efficiency are targeted, the risk of dengue transmission in the city could be effectively reduced

Optimal Timing of Insecticide Fogging to Minimize Dengue Cases: Modeling Dengue Transmission among Various Seasonalities and Transmission Intensities

Dengue virus infection is a serious infectious disease transmitted by Aedes mosquitoes in the tropics and sub-tropics. Disease control often involves the use of insecticide fogging against mosquito vectors. However, the effectiveness of this method for reducing dengue cases, in addition to appropriate application procedures, is still debated. The previous mathematical simulation study reported that insecticide fogging reduces dengue cases most effectively when applied soon after the epidemic peak; however, the model did not take into account seasonality and population immunity, which strongly affect the epidemic pattern of dengue infection. Considering these important factors, we used a mathematical simulation model to explore the most effective time for insecticide fogging and to evaluate its impact on reducing dengue cases. Simulations were conducted with various lengths of the wet season and population immunity levels. We found that insecticide fogging substantially reduces dengue cases if conducted at an appropriate time. In contrast to the previously suggested application time during the peak of disease prevalence, the optimal timing is relatively early: between the beginning of the dengue season and the prevalence peak

Protection of biodiversity as the ultimate goal of environmental safety assessment: how does chemical pollution affect biodiversity?

Biodiversity is unequivocally declining and chemical pollution is a major driver of its loss. Ecotoxicological studies report various effects of chemicals at different levels of biological organization, mostly individual and sub-organismal levels, while biodiversity is typically measured by taxonomic richness and abundance. This misalignment of metrics hampers building a causal link between chemical pollution and its effects on biodiversity. This review addresses the existing evidence (the obvious and the subtle ones) of the impact of chemicals on each sub-class of descriptors proposed by the list of Essential Biodiversity Variables (EBVs). For each biological level of organization, examples of ecotoxicological studies are reported that highlight strengths and weaknesses in describing the effects of chemical pollution on the specific biodiversity identifiers. In the last part, modelling is presented as one of the most powerful approaches to answer such a complex issue. Different modelling approaches are described according to their potential, for instance mechanistic models can simulate the effects of chemicals on organisms and populations while bayesian network models and Threshold Indicator Taxa Analysis (TITAN) can be used to predict the risk of chemicals to communities and ecosystems. Finally, biodiversity in the regulatory context and future perspectives are discussed, highlighting how crucial the collaboration between ecologists and ecotoxicologists is, as well as the integration of data from field and laboratory studies, and the development of new specific indicators to track progress towards biodiversity conservation.JRC.F.3 - Systems Toxicolog