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Development of spatial statistical methods for geographical mapping of malaria transmission parameters derived from mathematical models of transmission

English title Development of spatial statistical methods for geographical mapping of malaria transmission parameters derived from mathematical models of transmission
Applicant Vounatsou Penelope
Number 102136
Funding scheme Project funding (Div. I-III)
Research institution Swiss Tropical and Public Health Institute
Institution of higher education University of Basel - BS
Main discipline Infectious Diseases
Start/End 01.10.2003 - 31.01.2007
Approved amount 197'000.00
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All Disciplines (2)

Infectious Diseases

Lay Summary (English)

Lay summary
Malaria remains the most important parasitic disease of humans. Mapping of the level of transmission in different areas and of the numbers of affected individuals is essential for accurate estimation of disease burden, for purposes of resource allocation and for assessing intervention programs. The most widely used measure of malaria transmission is the parasite prevalence estimated from human populations, by surveys carried out at various locations. However, the resulting maps ignore the age-prevalence relationship because the prevalence data are usually obtained in non-standardised age groups in the different sites and seasons. In addition maps of other important biological parameters related to malaria transmission from mosquito to humans (i.e. entomological inoculation rates, force of infection, incidence rates) or to events in the malaria vector (mosquito survival, infection probability, basic reproduction rate) cannot be produced due to lack of entomological data over large geographical areas. Mathematical models of malaria transmission, provide a way of estimating parameters that have not been observed from whatever malariological data are available at different locations. Spatial statistical models fitted on parameters of transmission models will enable us to produce smooth maps of the malariological indices of interest, to determine important environmental predictors of transmission and to assess the spatial effects of vector control interventions on malaria transmission. Modelling geostatistical data often relies on the assumption of stationarity which implies that the spatial correlation is a function of the distance and independent of location. This assumption cannot be justified when malariological indices are modelled since local characteristics related to human activities, landuse, environment and vector ecology influence spatial correlation differently at the different locations. Moreover, data on mosquito densities can be best fitted by negative binomial data and data on mosquito age categories or mosquitoe species are typically multinomially distributed. Geostatistical methods for modelling non-stationary as well as multinomial data have received very little attention.This project has (i) developed novel statistical methodologies in spatial analysis of non-stationary univariate and multivariate geostatistical data that have been implemented and on real data sets. (ii) generated age and seasonality adjusted maps of malaria transmission intensity in West and Central Africa. These maps provide estimates of burden of disease, replacing current estimates that assume uniformity in risk across wide areas, guide control programs and serve as baseline for estimating the effectiveness of national control plans. (iii) determined the spatial effects of bednet intervention on human-mosquito transmission and on mosquito survival. This is especially relevant for evaluating mosquito net programs. (iv) determined ecological predictors of the distribution of different malaria vectors and assessed the contribution of each species to malaria transmission which is important for effective vector control interventions
Direct link to Lay Summary Last update: 21.02.2013

Responsible applicant and co-applicants


Associated projects

Number Title Start Funding scheme
57165 Development of spatial statistical methods for modeling point- referenced data in malaria epidemiology. 01.12.1999 Project funding (Div. I-III)
118379 Development of spatial statistical methodology for the analysis of health demographic surveillance system (DSS) data 01.10.2007 Project funding (Div. I-III)