A new approach to dengue vector surveillance based on permanent egg-collection using a modified ovitrap and Bacillus thuringiensis israelensis(Bti) was evaluated in different urban landscapes in Recife, Northeast Brazil. From April 2004 to April 2005, 13 egg-collection cycles of four weeks were carried out. Geo-referenced ovitraps containing grass infusion, Bti and three paddles were placed at fixed sampling stations distributed over five selected sites. Continuous egg-collections yielded more than four million eggs laid into 464 sentinel-ovitraps over one year. The overall positive ovitrap index was 98.5% (over 5,616 trap observations). The egg density index ranged from 100 to 2,500 eggs per trap-cycle, indicating a wide spread and high density of Aedes aegypti (Diptera: Culicidae) breeding populations in all sites. Fluctuations in population density over time were observed, particularly a marked increase from January on, or later, according to site. Massive egg-collection carried out at one of the sites prevented such a population outbreak. At intra-site level, egg counts made it possible to identify spots where the vector population is consistently concentrated over the time, pinpointing areas that should be considered high priority for control activities. The results indicate that these could be promising strategies for detecting and preventing Ae. aegypti population outbreaks.

This paper is concerned with building up methodological guidelines for modelling urban land‐use change through Geographical Information Systems, remote sensing imagery and Bayesian probabilistic methods. A medium‐sized town in the west of São Paulo State, Bauru, was adopted as a case study. Its urban structure was converted into a 100 m×100 m resolution grid and transition probabilities were calculated for each grid cell by means of the ‘weights of evidence’ statistical method and upon the basis of the information related to the technical infrastructure and socio‐economic aspects of the town. The probabilities obtained from there fed a cellular automaton simulation model—DINAMICA—developed by the Centre for Remote Sensing of the Federal University of Minas Gerais (CSR‐UFMG), based on stochastic transition algorithms. Different simulation outputs for the case study town in the period 1979–1988 were generated, and statistical validation tests were then conducted for the best results, employing a multiple resolution fitting procedure. This modelling experiment revealed the plausibility of adopting Bayesian empirical methods based on the available knowledge of technical infrastructure and socio‐economic status to simulate urban land‐use change. It indicates their possible further applicability for generating forecasts of growth trends both for Brazilian cities and cities world‐wide.

Dengue is a disease of great complexity, due to interactions between humans, mosquitoes and various virus serotypes as well as efficient vector survival strategies. Thus, understanding the factors influencing the persistence of the disease has been a challenge for scientists and policy makers. The aim of this study is to investigate the influence of various factors related to humans and vectors in the maintenance of viral transmission during extended periods. We developed a stochastic cellular automata model to simulate the spread of dengue fever in a dense community. Each cell can correspond to a built area, and human and mosquito populations are individually monitored during the simulations. Human mobility and renewal, as well as vector infestation, are taken into consideration. To investigate the factors influencing the maintenance of viral circulation, two sets of simulations were performed: (1^st) varying human renewal rates and human population sizes and (2^nd) varying the house index (fraction of infested buildings) and vector per human ratio. We found that viral transmission is inhibited with the combination of small human populations with low renewal rates. It is also shown that maintenance of viral circulation for extended periods is possible at low values of house index. Based on the results of the model and on a study conducted in the city of Recife, Brazil, which associates vector infestation with Aedes aegytpi egg counts, we question the current methodology used in calculating the house index, based on larval survey. This study contributed to a better understanding of the dynamics of dengue subsistence. Using basic concepts of metapopulations, we concluded that low infestation rates in a few neighborhoods ensure the persistence of dengue in large cities and suggested that better strategies should be implemented to obtain measures of house index values, in order to improve the dengue monitoring and control system. Dengue is the most rapidly spreading mosquito-borne viral disease in the world and approximately 2.5 billion people live in dengue endemic countries. In Brazil it is mainly transmitted by Aedes aegypti mosquitoes. The wide clinical spectrum ranges from asymptomatic infections or mild illness, to the more severe forms of infection such as dengue hemorrhagic fever or dengue shock syndrome. The spread and dramatic increase in the occurrence of dengue cases in tropical and subtropical countries has been blamed on uncontrolled urbanization, population growth and international traveling. Vaccines are under development and the only current disease control strategy is trying to keep the vector quantity at the lowest possible levels. Mathematical models have been developed to help understand the disease's epidemiology. These models aim not only to predict epidemics but also to expand the capacity of phenomena explanation. We developed a spatially explicit model to simulate the dengue transmission in a densely populated area. The model involves the dynamic interactions between humans and mosquitoes and takes into account human mobility as an important factor of disease spread. We investigated the importance of human population size, human renewal rate, household infestation and ratio of vectors per person in the maintenance of sustained viral circulation.

The Amazon is Brazil’s greatest natural resource and invaluable to the rest of the world as a buffer against climate change. The recent election of Brazil’s president brought disputes over development plans for the region back into the spotlight. Historically, the development model for the Amazon has focused on exploitation of natural resources, resulting in environmental degradation, particularly deforestation. Although considerable attention has focused on the long-term global cost of “losing the Amazon,” too little attention has focused on the emergence and reemergence of vector-borne diseases that directly impact the local population, with spillover effects to other neighboring areas. We discuss the impact of Amazon development models on human health, with a focus on vector-borne disease risk. We outline policy actions that could mitigate these negative impacts while creating opportunities for environmentally sensitive economic activities.