Predictions of Malaria Vector Distribution in Belize Based on Multispectral Satellite Data

Roberts, Donald R.; Paris, J. F.; Manguin, Sylvie; Harbach, Ralph E.; Woodruff, Robert A.; Rejmánková, Eliška; Polanco, Jorge; Wullschleger, Bruce; Legters, Llewellyn J.

doi:10.4269/ajtmh.1996.54.304

Cited by 69 publications

(43 citation statements)

References 6 publications

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“…The abundance of water bodies and favorable temperatures, maize plantings, extensive deforestation, or farmland have been associated with increased larval or mosquito abundance and thus increased risk for malaria transmission in human populations. [31][32][33][34] Other studies have used geographic information systems and satellite imagery to investigate environmental factors that potentially drive the dynamics of malaria vector populations [34][35][36] and other vector-borne and zoonotic diseases such as dengue fever or hantavirus. [37][38][39] It has been shown that the efficacy of control measures such as intermittent preventive treatment (IPT) can be strongly dependent on the present malaria incidence, [40][41][42] and it can be assumed that direct and contextual effects increase with malaria risk after an intervention.…”

Section: Discussionmentioning

confidence: 99%

Modeling the Relationship between Precipitation and Malaria Incidence in Children from a Holoendemic Area in Ghana

Krefis¹,

Schwarz²,

Krüger³

et al. 2011

The American Society of Tropical Medicine and Hygiene

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Climatic factors influence the incidence of vector-borne diseases such as malaria. They modify the abundance of mosquito populations, the length of the extrinsic parasite cycle in the mosquito, the malarial dynamics, and the emergence of epidemics in areas of low endemicity. The objective of this study was to investigate temporal associations between weekly malaria incidence in 1,993 children < 15 years of age and weekly rainfall. A time series analysis was conducted by using cross-correlation function and autoregressive modeling. The regression model showed that the level of rainfall predicted the malaria incidence after a time lag of 9 weeks (mean = 60 days) and after a time lag between one and two weeks. The analyses provide evidence that high-resolution precipitation data can directly predict malaria incidence in a highly endemic area. Such models might enable the development of early warning systems and support intervention measures.

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Section: Discussionmentioning

confidence: 99%

Modeling the Relationship between Precipitation and Malaria Incidence in Children from a Holoendemic Area in Ghana

Krefis¹,

Schwarz²,

Krüger³

et al. 2011

The American Society of Tropical Medicine and Hygiene

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“…An. darlingi can fly up over 7 km from its release point (Charlwood & Alecrim 1989), numbers captured at 1 km distance from rivers are probably low (Roberts et al 1996). However, mark-release-recapture studies may not adequately predict behavior in a rural environment (Wernsdorfer & McGregor 1988).…”

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confidence: 99%

Man biting rate seasonal variation of malaria vectors in Roraima, Brazil

Barros

Honório

2007

Mem. Inst. Oswaldo Cruz

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Malaria control has been directed towards regional actions where more detailed knowledge of local determinants of transmission is of primary importance. This is a short report on range distribution and biting indices forThe most important local vector species have been determined to be An. darlingi Root and An. albitarsis sensu lato. As we have not taxonomically determined the species in the albitarsis complex ourselves, hereinafter the species will be simply referred as An. albitarsis. Nonetheless, previous studies determined the An. albitarsis population in Jardim das Copaíbas, our study area, as An. albitarsis species E (Póvoa et al. 2006). Studies using entomological inoculation rates and biting indices have implicated An. darlingi and An. albitarsis as the most important vectors in the area (Silva-Vasconcelos et al. 2002, Póvoa et al. 2006.In this communication we describe seasonal variations in the biting rate of two main local malaria vectors in Jardim das Copaíbas a riverside settlement in the Northern Amazon Basin during one year. An equation describing the distribution range of An. darlingi during the rainy season was also determined.The study area Jardim das Copaíbas is a rural settlement (02°45'28''N, 60°42' 18''W) by the Branco River 5 km south of Boa Vista, the capital of the state of Roraima (Fig. 1B). Jardim das Copaíbas can be characterized as a savanna/alluvial forest landscape area. This is because the settlement is located in a savanna in close contact (~ 100 m) to a dense alluvial rainforest that delineates the Branco River. The area has a long rainy season (April-November), a short dry season (DecemberMarch), in which the Branco River water levels increase (Póvoa et al. 2006, Fig. 2). During the rainy season, the alluvial forest becomes partly flooded. Average yearly rainfall is 1100-1400 mm/year and temperatures are permanently high (daily average 27.8ºC) with little yearlong variation. Relative humidity is also high (daily average 73.8%) and varies little over the year (Furley 1994).In . Rainfall data and river water levels were registered (Fig. 2).The 1n + 1 transformed numbers of mosquitoes captured during peak biting activity (the first 4 h after sunset) were used during the rainy season for deriving an exponential regression function for An. darlingi distribution range. Estimations of the percentages of the adult An. darlingi population that could reach over specified distances were given by the expression: y = 4.435 exp(x*-0.003) (p < 0.001, r = 0.98) where x is the distance from the larval habitat and y the percentage of the adult mosquitoes. Since An. albitarsis also has larval habitat dispersed in the open savanna fields, a distribution range function could not be derived from the data collected. It was not possible to determine all larval habitats for each species. The transect represents the closest distance a certain mosquito would have to fly to reach the capture stations.

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“…These epidemiologic data can be associated with entomologic and environmental data, some of the latter derived from satellite imagery. [11][12][13][14][15] These georeferenced data can be subject to spatial analysis to prioritize surveillance and control strategies. [16][17][18][19][20] The purpose of this study was to map and analyze the spatial and temporal distribution of all malaria cases in Trinidad since malaria eradication.…”

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confidence: 99%

Spatial and temporal patterns of imported malaria cases and local transmission in Trinidad.

Chadee¹,

Kitron²

1999

The American Journal of Tropical Medicine and Hygiene

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Abstract. Over a 30-year period 213 malaria cases in Trinidad were investigated by the Trinidad and Tobago Ministry of Health. Using a global positional system and a geographic information system, we mapped the precise location of all reported malaria cases, and associated them with breeding habitats of anopheline vectors. The majority of the cases (138, 63%) were individual imported cases around the big port cities. Plasmodium falciparum was the most common parasite, and Africa the most common source of imported cases. Two clusters of cases occurred: an introduced P. vivax outbreak associated with Anopheles aquasalis in 1990-1991, and an autochtonous focus of P. malariae associated with An. bellator and An. homunculus in 1994-1995. Application of a space-time statistic showed a significant clustering of P. malariae cases, and, to a lesser extent of P. vivax cases, but not of P. falciparum cases. Based on potential for occurrence of local transmission, we are developing risk maps to determine surveillance priorities, outbreak potential, and necessary degree and spatial range of control activities following case detections.Within the Americas malaria is a major cause of disease and death, with 21 countries reporting more than 1 million cases per year, and 280 million people at risk.

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Predictions of Malaria Vector Distribution in Belize Based on Multispectral Satellite Data

Cited by 69 publications

References 6 publications

Modeling the Relationship between Precipitation and Malaria Incidence in Children from a Holoendemic Area in Ghana

Modeling the Relationship between Precipitation and Malaria Incidence in Children from a Holoendemic Area in Ghana

Man biting rate seasonal variation of malaria vectors in Roraima, Brazil

Spatial and temporal patterns of imported malaria cases and local transmission in Trinidad.

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