Background: In the last decades, attention to cholera epidemiology increased, as cholera epidemics became a worldwide health problem. Detailed investigation of V. cholerae interactions with its host and with other organisms in the environment suggests that cholera dynamics is much more complex than previously thought. Here, I formulate a mathematical model of cholera epidemiology that incorporates an environmental reservoir of V. cholerae. The objective is to explore the role of the aquatic reservoir on the persistence of endemic cholera as well as to define minimum conditions for the development of epidemic and endemic cholera.

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BackgroundRio de Janeiro, Brazil, experienced a severe dengue fever epidemic in 2008. This was the worst epidemic ever, characterized by a sharp increase in case-fatality rate, mainly among younger individuals. A combination of factors, such as climate, mosquito abundance, buildup of the susceptible population, or viral evolution, could explain the severity of this epidemic. The main objective of this study is to model the spatial patterns of dengue seroprevalence in three neighborhoods with different socioeconomic profiles in Rio de Janeiro. As blood sampling coincided with the peak of dengue transmission, we were also able to identify recent dengue infections and visually relate them to Aedes aegypti spatial distribution abundance. We analyzed individual and spatial factors associated with seroprevalence using Generalized Additive Model (GAM).Methodology/Principal FindingsThree neighborhoods were investigated: a central urban neighborhood, and two isolated areas characterized as a slum and a suburban area. Weekly mosquito collections started in September 2006 and continued until March 2008. In each study area, 40 adult traps and 40 egg traps were installed in a random sample of premises, and two infestation indexes calculated: mean adult density and mean egg density. Sera from individuals living in the three neighborhoods were collected before the 2008 epidemic (July through November 2007) and during the epidemic (February through April 2008). Sera were tested for DENV-reactive IgM, IgG, Nested RT-PCR, and Real Time RT-PCR. From the before–after epidemics paired data, we described seroprevalence, recent dengue infections (asymptomatic or not), and seroconversion. Recent dengue infection varied from 1.3% to 14.1% among study areas. The highest IgM seropositivity occurred in the slum, where mosquito abundance was the lowest, but household conditions were the best for promoting contact between hosts and vectors. By fitting spatial GAM we found dengue seroprevalence hotspots located at the entrances of the two isolated communities, which are commercial activity areas with high human movement. No association between recent dengue infection and household's high mosquito abundance was observed in this sample.Conclusions/SignificanceThis study contributes to better understanding the dynamics of dengue in Rio de Janeiro by assessing the relationship between dengue seroprevalence, recent dengue infection, and vector density. In conclusion, the variation in spatial seroprevalence patterns inside the neighborhoods, with significantly higher risk patches close to the areas with large human movement, suggests that humans may be responsible for virus inflow to small neighborhoods in Rio de Janeiro. Surveillance guidelines should be further discussed, considering these findings, particularly the spatial patterns for both human and mosquito populations.

Little follow-up data on malaria transmission in communities originating from frontier settlements in Amazonia are available. Here we describe a cohort study in a frontier settlement in Acre, Brazil, where 509 subjects contributed 489.7 person-years of follow-up. The association between malaria morbidity during the follow-up and individual, household, and spatial covariates was explored with mixed-effects logistic regression models and spatial analysis. Incidence rates for Plasmodium vivax and Plasmodium falciparum malaria were 30.0/100 and 16.3/100 person-years at risk, respectively. Malaria morbidity was strongly associated with land clearing and farming, and decreased after five years of residence in the area, suggesting that clinical immunity develops among subjects exposed to low malaria endemicity. Significant spatial clustering of malaria was observed in the areas of most recent occupation, indicating that the continuous influx of nonimmune settlers to forest-fringe areas perpetuates the cycle of environmental change and colonization that favors malaria transmission in rural Amazonia.

Dengue dynamics in Rio de Janeiro, Brazil, as in many dengue-endemic regions of the world, is seasonal, with peaks during the wet-hot months. This temporal pattern is generally attributed to the dynamics of its mosquito vector Aedes aegypti (L.). The objectives of this study were to characterize the temporal pattern of Ae. aegypti population dynamics in three neighborhoods of Rio de Janeiro and its association with local meteorological variables; and to compare positivity and density indices obtained with ovitraps and MosquiTraps. The three neighborhoods are distinct in vegetation coverage, sanitation, water supply, and urbanization. Mosquito sampling was carried out weekly, from September 2006 to March 2008, a period during which large dengue epidemics occurred in the city. Our results show peaks of oviposition in early summer 2007 and late summer 2008, detected by both traps. The ovitrap provided a more sensitive index than MosquiTrap. The MosquiTrap detection threshold showed high variation among areas, corresponding to a mean egg density of approximately 25-52 eggs per ovitrap. Both temperature and rainfall were significantly related to Ae. aegypti indices at a short (1 wk) time lag. Our results suggest that mean weekly temperature above 22-24 degrees C is strongly associated with high Ae. aegypti abundance and consequently with an increased risk of dengue transmission. Understanding the effects of meteorological variables on Ae. aegypti population dynamics will help to target control measures at the times when vector populations are greatest, contributing to the development of climate-based control and surveillance measures for dengue fever in a hyperendemic area.

Contribuição dos autores: Todos os autores contribuíram com a concepção, a redação, a revisão e a aprovação da versão final do manuscrito.

Daily survival rates, life expectancy, dispersal, and parity are important components of vectorial capacity of Aedes aegypti. These parameters were estimated for mosquito populations from a slum and a suburban district in Rio de Janeiro, during the wet and dry seasons in 2005. In each mark-release-recapture experiment, three cohorts of dust-marked Ae. aegypti females were released. Recaptures were carried out daily in randomly selected houses, using backpack aspirators, adult traps, and sticky ovitraps. Recapture varied between 6.81% and 14.26%. Daily survival was estimated by fitting two alternative models: exponential and nonlinear models with correction for the removal of individuals. Slum area presented higher survival and parity rates (68.5%). Dispersal rates were higher in the suburban area, where a maximum dispersal of 363 m was observed. Results suggest intense risk of dengue epidemic, particularly in the urban area.

We use the Box-Jenkins approach to fit an autoregressive integrated moving average (ARIMA) model to dengue incidence in Rio de Janeiro, Brazil, from 1997 to 2004. We find that the number of dengue cases in a month can be estimated by the number of dengue cases occurring one, two, and twelve months prior. We use our fitted model to predict dengue incidence for the year 2005 when two alternative approaches are used: 12-steps ahead versus 1-step ahead. Our calculations show that the 1-step ahead approach for predicting dengue incidence provides significantly more accurate predictions (P value=0.002, Wilcoxon signed-ranks test) than the 12-steps ahead approach. We also explore the predictive power of alternative ARIMA models incorporating climate variables as external regressors. Our findings indicate that ARIMA models are useful tools for monitoring dengue incidence in Rio de Janeiro. Furthermore, these models can be applied to surveillance data for predicting trends in dengue incidence.