Mapping Rift Valley fever and malaria risk over West Africa using climatic indicators

Caminade, Cyril; Ndione, Jacques André; Kébé, Cheikh Mouhamed Fadel; Jones, Anne E; Danuor, Sylvester K.; Tay, Samuel Crowther Kofi; Tourre, Yves M.; Lacaux, Jean‐Pierre; Vignolles, Cécile; Duchemin, Jean‐Bernard; Jeanne, Isabelle; Morse, Andrew P.

doi:10.1002/asl.296

Cited by 34 publications

(43 citation statements)

References 14 publications

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“…These factors are also important in the survival and transmission rate of mosquito borne pathogens; in particular, temperature affects their rate of multiplication in the insect [17]. On the other hand transmission can not occur if the development time of the pathogen exceeds the life span of the insect [31]. If water temperature rises, the larvae take a shorter time to mature and consequently there is a greater capacity to produce more offspring during the transmission period [4].The threshold for vector genesis and survival were adopted (Table 2) from the values available in the literature.…”

Section: Methodsmentioning

confidence: 99%

“…The values of Tmin, Tmax, Rmin, Rmax, Qmin and Qmax for temperature, humidity and rainfall (Table 2) have been adopted from standard data set [31], [6], [22], [29]. The condition of genesis for each day and for each meteorological variable is then evaluated over each district from daily mean values using data from NCEP Reanalysis [32].…”

Section: The Modelmentioning

confidence: 99%

“…While temperature, rainfall, humidity and other variables like wind and duration of day light are all known to be important for mosquito life cycle, the relative roles of these variables may depend on a given location. For example, the periodicity and the amplitude of mosquito population [abundance peaks] in northern Australia were found to strongly depend on frequency and the amplitude of tides [31]; a land-locked region, on the other hand, requires different considerations [32].…”

Section: Introductionmentioning

confidence: 99%

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A Model of Malaria Epidemiology Involving Weather, Exposure and Transmission Applied to North East India

et al. 2012

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BackgroundQuantitative relations between weather variables and malaria vector can enable pro-active control through meteorological monitoring. Such relations are also critical for reliable projections in a changing climate, especially since the vector abundance depends on a combination of weather variables, each in a given range. Further, such models need to be region-specific as vector population and exposure depend on regional characteristics.MethodsWe consider days of genesis based on daily temperature, rainfall and humidity in given ranges. We define a single model parameter based on estimates of exposure and transmission to calibrate the model; the model is applied to 12 districts of Arunachal Pradesh, a region endemic to malaria. The epidemiological data is taken as blood samples that test positive. The meteorological data is adopted from NCEP daily Reanalysis on a global grid; population data is used to estimate exposure and transmission coefficients.ResultsThe observed annual cycles (2006–2010) and the interannual variability (2002–2010) of epidemiology are well simulated for each of the 12 districts by the model. While no single weather variable like temperature can reproduce the observed epidemiology, a combination of temperature, rainfall and humidity provides an accurate description of the annual cycle as well as the inter annual variability over all the 12 districts.ConclusionInclusion of the three meteorological variables, along with the expressions for exposure and transmission, can quite accurately represent observed epidemiology over multiple locations and different years. The model is potentially useful for outbreak forecasts at short time scales through high resolution weather monitoring; however, validation with longer and independent epidemiological data is required for more robust estimation of realizable skill. While the model has been examined over a specific region, the basic algorithm is easily applicable to other regions; the model can account for shifting vulnerability due to regional climate change.

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Section: Methodsmentioning

confidence: 99%

Section: The Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Model of Malaria Epidemiology Involving Weather, Exposure and Transmission Applied to North East India

et al. 2012

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“…comm. ), for which promising shorter-term results already have been obtained (Cui et al 2009, Caminade et al 2011). Progress also is being made on water resource modeling for the Sudan-Sahel zone, as documented in Lebel et al (2009).…”

Section: Professional-development Context and Assessmentmentioning

confidence: 99%

Professional development for providers of seasonal climate prediction

Lamb¹,

Timmer²,

Lélé³

2011

Clim. Res.

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Provision of seasonal climate predictions is envisioned to be a major component of the intended expansion of climate services. This paper focuses on the research basis for seasonal climate prediction, with emphasis on the needed broad professional development. Use is made of a 3-step interdisciplinary framework that previously was proposed to maximize the societal value of seasonal climate prediction schemes. Those steps involve: (1) identification of the human activities most impacted by climate variability, (2) determination of how affected regional economies can adjust or change to capitalize on the availability of skillful climate predictions, and (3) use of results from the required interdisciplinary research to develop climate prediction schemes that have maximum societal value. Consideration of these steps stresses the need for daily meteorological data and appropriate sets of 'impacts' data (e.g. for agriculture, water resources, public health, energy), the importance of conceptualizing and modeling the management decisions involved in those sectors (especially coupling of economic and biological/physical process models), and the necessity for the climate research community to initiate and sustain collaboration with specialists from these other scientific areas. The strong El Niño control on US winter precipitation illustrates the potential for such impact-related guidance to maximize seasonal climate prediction value. This theme is developed further by emphasizing methodologies and re-assessing results for 2 recent strongly contrasting climate research projects, which documented the influence of (1) winter temperature on US residential natural gas consumption and (2) the Intertropical Front (ITF) latitude on rainfall in the West African Sudan-Sahel zone. For each of these regional climate situations, the above 3-step framework is used to assess the seasonal prediction potential and associated professional development needs.

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“…There are many malaria mapping projects that have been carried out in Africa with the use of satellite remote sensing instruments in combination with GIS ranging from low through medium to high spatial resolution datasets (Adjuik et al 1998, Diuk-Wasser et al 2004, Jacob et al 2008, Dambach et al 2009, Kelly-Hope and McKenzie 2009, Lyimo and Ferguson 2009, Machault et al 2010, Wayant et al 2010, Ashton et al 2011, Caminade et al 2011, Patil et al 2011 including the mapping malaria risk in Africa (MARA) project (Adjuik et al 1998). With notable exception of mapping with Landsat (Bogh et al 2007) and SPOT satellite sensors series (Dambach et al 2009), most of the malaria mapping initiatives was at a continental scale based on meteorological models that fell short of empirical evidence of environmental specific indicators of malaria distribution and were not able to provide an insight into malaria prevalence rates at a local scale.…”

Section: Introductionmentioning

confidence: 99%

Mapping malaria severity zones with Nigeriasat-1 incorporated into geographical information system

Ogunbadewa

2012

Geocarto International

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The aim of this study is to derive environmental factors that are likely to influence malarial distribution from Nigeriasat-1 in a geographical information systems (GIS) environment and relate it to the empirical evidence of reported malarial cases in the hospitals using discriminant analysis (DA) to characterize, identify and map malarial risk zones. It is found that using a stepwise DA, Nigeriasat-1 and GIS it is possible to classify the accurately the low malarial risk zone (100%), medium and high risk zones (83.33%), with an overall accuracy of 88.9% being achieved for the study area. The results obtained were in agreement with the ground validation exercise that was carried out and the cross validation method of ''leaving-one-out' in DA function. These findings indicate that Nigeriasat-1 and GIS combined with statistical technique of DA can be utilized as a decision support tool for a precise identification of the areas warranting mitigation efforts.

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Mapping Rift Valley fever and malaria risk over West Africa using climatic indicators

Cited by 34 publications

References 14 publications

A Model of Malaria Epidemiology Involving Weather, Exposure and Transmission Applied to North East India

A Model of Malaria Epidemiology Involving Weather, Exposure and Transmission Applied to North East India

Professional development for providers of seasonal climate prediction

Mapping malaria severity zones with Nigeriasat-1 incorporated into geographical information system

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