Multiscale Analysis and Modelling of Aedes aegyti Population Spatial Dynamics (original) (raw)
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Multiscale analysis and modelling of Aedes aegypti population spatial dynamics
Population dynamic models requires the evaluation of the best scale of analysis. This work analyses three spatial scales in the context of the mosquito Aedes aegypti, main vector of dengue fever. One scale is the neighborhood, the others scales are the census tract and the lot. A geographical database was developed including point maps with trap locations, number of eggs collected per trap per week, polygons of census tracts, census data, among others. For simulation purposes, a layer of regular cells (10 x 10 meters) was created to store the model's inputs and outputs. A population dynamic model with temperature as input variable was parameterized and fitted to the neighborhood and census tract data. For the lot level, an allocation procedure was developed as the spatial resolution was higher than the data resolution. This procedure couples the population dynamic model with a kernel density map. Results indicate that at the neighborhood level, the population model captured well the overall pattern with lower mosquito density during the cold season and larger during the warm season. However, in the first warm season, two peaks did not fit well, suggesting the importance of investigating the role of other variables in the dynamics of Aedes aegypti. At the census tract level, we found no evidence of spatial clustering. At the lot level, the allocation model represented well the overall summer to winter variation in hotspot intensity. The cost of vector surveillance is high and the procedures proposed here can be used to design optimized control strategies and activities.
The importance of appropriate temporal and spatial scales for dengue fever control and management
Science of the Total Environment, 2012
It is important to have appropriate models for the surveillance and control of mosquito-borne diseases, such as dengue fever (DF). These models need to be based on appropriate temporal and spatial scales. The aim of this study was to illustrate the impact of different temporal and spatial scales on DF control decisions. We applied the Getis-Ord Gi* statistic at different temporal and spatial scales to examine the local level of spatial clusters at these scales in order to identify and visualize areas where numbers of adult female Aedes mosquitoes were extreme and geographically homogenous. The modeled hotspot areas were different, depending on whether they were modeled on weekly, monthly or yearly aggregated data. A similar result was found when using different spatial scales for modeling, with different scales giving different hotspot regions. For 2006, the highest risk areas (18 districts) were mostly identified in the central districts with a high rate of similarity (95%) compared to the highest risk areas (19) identified in the averaged five-year period model. Knowledge of appropriate temporal and spatial scales can provide an opportunity to specify the health burden of DF and its vector within the hotspots, as well as set a platform that can help to pursue further investigations into associated factors responsible for increased disease risk based on different temporal and spatial scales.
Parasites & Vectors
Background Larval indices such as the house index (HI), Breteau index (BI) and container index (CI) are widely used to interpret arbovirus vector density in surveillance programmes. However, the use of such data as an alarm signal is rarely considered consciously when planning programmes. The present study aims to investigate the spatial distribution pattern of the infestation of Aedes aegypti, considering the data available in the Ae. aegypti Infestation Index Rapid Survey (LIRAa) for the city of Campina Grande, Paraíba State in Brazil. Methods The global and local Moranʼs indices were used in spatial analysis to measure the effects of spatial dependencies between neighbourhoods, using secondary data related to HI and BI gathered from surveillance service. Results Our analysis shows that there is a predominance of high rates of mosquito infestation, placing Campina Grande at a near-constant risk of arbovirus outbreaks and epidemics. A highly significant Moranʼs index value (P < ...
A Stochastic Spatial Dynamical Model for Aedes Aegypti
Bulletin of Mathematical Biology, 2008
We develop a stochastic spatial model for Aedes aegypti populations based on the life cycle of the mosquito and its dispersal. Our validation corresponds to a monitoring study performed in Buenos Aires. Lacking information with regard to the number of breeding sites per block, the corresponding parameter (BS) was adjusted to the data. The model is able to produce numerical data in very good agreement with field results during most of the year, the exception being the fall season. Possible causes of the disagreement are discussed. We analyzed the mosquito dispersal as an advantageous strategy of persistence in the city and simulated the dispersal of females from a source to the surroundings along a 3-year period observing that several processes occur simultaneously: local extinctions, recolonization processes (resulting from flight and the oviposition performed by flyers), and colonization processes resulting from the persistence of eggs during the winter season. In view of this process, we suggest that eradication campaigns in temperate climates should be performed during the winter time for higher efficiency.
Stochastic Environmental Research and Risk Assessment, 2012
Identifying the impact of climatic factors on mosquito population dynamics is of great importance for dengue outbreak control. The purpose of this study is to develop an approach to predict spatial/temporal mosquito reproduction and disease outbreaks. The prediction of a dengue outbreak is only possible if the temporal relationship between mosquito replication and the weather is known. At present, this is unclear and needs to be examined. Moreover, because the development of mosquito density is a dynamic process in the course of time, it should be observed as closely as possible, in this study in a 1-day timeframe. This paper makes a thorough study of the situation in southern Taiwan and analyzes a large amount of data from 1999 to 2004 related to dengue cases and larval density. We first use the method, k-means, to conduct data clustering and derive representative larvae replication patterns. Then, we propose mathematical models to approximate the development of larval density, describe the expansion of mosquito activity areas, and construct a surveillance system to raise alerts based on real-time input of weather data and larval indices. Analysis of historic data reveals some new information on the spatial and temporal relationships between larval density and dengue outbreaks. In Taiwan, if the weather becomes or remains warm and humid for 6 days after a bout of rain, there can be a sharp increase in the larval mosquito population. About 7 days after the Breteau index begins to rise, larval density reaches its climax; and, about 12 days after the climax of larval density, cases of dengue may be reported. The system is tested using subsequent data from 2005 to 2009 and shows satisfactory accuracy. Numerous data support these findings, and this new knowledge is thus validated and can be used to assist public health professionals to take effective dengue control measures.
The Relation between Rainfall and Larval Density of Dengue Hemorrhagic Fever with Spatial Modeling
Media Kesehatan Masyarakat Indonesia
Dengue Hemorrhagic Fever (DHF) is a disease transmitted by the aedes aegypti mosquito. This study aims to determine the relationship between rainfall and larval density which consist of House Index (HI), Container Index (CI), Breteau Index (BI), and Larval Free Rate (LFR) on the incidence of dengue hemorrhagic fever using GIS modeling. The research method is quantitative with a spatial approach and Univariate and Bivariate analysis. The study population was all cases of DHF in all working areas of Lahat District Health Center, Lahat Regency in 2016-2019. The results of the statistical correlation test showed that there was a correlation between rainfall and the incidence of DHF with a value (p=0.003), while larval density showed a correlation between HI and the DHF incidence (p-value=0.007), CI (p-value=0.007), BI (p-value=0.007). ABJ (p-value=0.012). Spatially, it was found that the incidence of dengue fever was high in the working regions of Public Health Center with high HI (≥5%)...
Geospatial health, 2016
The main objective of this study was to obtain and analyse the space-time dynamics of Aedes aegypti breeding sites in Clorinda City, Formosa Province, Argentina coupled with landscape analysis using the maximum entropy approach in order to generate a dengue vector niche model. In urban areas, without vector control activities, 12 entomologic (larval) samplings were performed during three years (October 2011 to October 2014). The entomologic surveillance area represented 16,511 houses. Predictive models for Aedes distribution were developed using vector breeding abundance data, density analysis, clustering and geoprocessing techniques coupled with Earth observation satellite data. The spatial analysis showed a vector spatial distribution pattern with clusters of high density in the central region of Clorinda with a well-defined high-risk area in the western part of the city. It also showed a differential temporal behaviour among different areas, which could have implications for risk...
Modeling Mosquito-Borne Disease Spread in U.S. Urbanized Areas: The Case of Dengue in Miami
PLOS ONE, 2016
Expansion of mosquito-borne pathogens into more temperate regions of the world necessitates tools such as mathematical models for understanding the factors that contribute to the introduction and emergence of a disease in populations naïve to the disease. Often, these models are not developed and analyzed until after a pathogen is detected in a population. In this study, we develop a spatially explicit stochastic model parameterized with publicly available U.S. Census data for studying the potential for disease spread in Urbanized Areas of the United States. To illustrate the utility of the model, we specifically study the potential for introductions of dengue to lead to autochthonous transmission and outbreaks in a population representative of the Miami Urbanized Area, where introductions of dengue have occurred frequently in recent years. We describe seasonal fluctuations in mosquito populations by fitting a population model to trap data provided by the Miami-Dade Mosquito Control Division. We show that the timing and location of introduced cases could play an important role in determining both the probability that local transmission occurs as well as the total number of cases throughout the entire region following introduction. We show that at low rates of clinical presentation, small outbreaks of dengue could go completely undetected during a season, which may confound mitigation efforts that rely upon detection. We discuss the sensitivity of the model to several critical parameter values that are currently poorly characterized and motivate the collection of additional data to strengthen the predictive power of this and similar models. Finally, we emphasize the utility of the general structure of this model in studying mosquito-borne diseases such as chikungunya and Zika virus in other regions.
Spatial Patterns of Spread of Dengue with Human and Vector Mobility
2014
Background : Dengue is a vector borne disease transmitted to humans by Aedes Aegypti mosquitoes carrying Dengue virus of different serotypes. Primarily an urban epidemic, Dengue exhibits complex spatial and temporal dynamics, influenced by many biological, human and environmental factors. However, most of the existing models neglect the spatial factors influencing the spread of Dengue. This work sheds light on how Dengue parameters and human mobility changes the spatial spread of the infection and size of the epidemic. Methodology/Principal findings: We model the Dengue as a stochastic Cellular Automata (CA) process following Susceptible, Exposed, Infected, Recovered (SEIR) -Susceptible, Exposed, Infected (SEI)for human and vector dynamics respectively in each cell, and analyze the spatial and temporal spreading disease using parameters from field studies. We use the data on mosquito density from Ahmedabad city of India as input to our model to predict the dynamics of Dengue inciden...
Pathogens
Currently, DENV transmitted primarily by Aedes aegypti affects approximately one in three people annually. The spatio-temporal heterogeneity of vector infestation and the intensity of arbovirus transmission require surveillance capable of predicting an outbreak. In this work, we used data from 4 years of reported dengue cases and entomological indicators of adult Aedes collected from approximately 3500 traps installed in the city of Foz do Iguaçu, Brazil, to evaluate the spatial and temporal association between vector infestation and the occurrence of dengue cases. Entomological (TPI, ADI and MII) and entomo-virological (EVI) indexes were generated with the goal to provide local health managers with a transmission risk stratification that allows targeting areas for vector control activities. We observed a dynamic pattern in the evaluation; however, it was a low spatio-temporal correlation of Ae. aegypti and incidence of dengue. Independent temporal and spatial effects capture a sign...