geostatistics; Demographic Health Surveys; mapping; mortality; Malaria Indicator Surveys; Gaussian process; Bayesian methods; Markov chain Monte Carlo; spatio-temporal process; Africa; Millennium Development Goals; malaria
Khagayi Sammy, Desai Meghna, Amek Nyaguara, Were Vincent, Onyango Eric Donald, Odero Christopher, Otieno Kephas, Bigogo Godfrey, Munga Stephen, Odhiambo Frank, Hamel Mary J., Kariuki Simon, Samuels Aaron M., Slutsker Laurence, Gimnig John, Vounatsou Penelope (2019), Modelling the relationship between malaria prevalence as a measure of transmission and mortality across age groups, in Malaria Journal
, 18(1), 247-247.
Nambuusi Betty B., Ssempiira Julius, Makumbi Fredrick E., Utzinger Jürg, Kasasa Simon, Vounatsou Penelope (2019), Geographical variations of the associations between health interventions and all-cause under-five mortality in Uganda, in BMC Public Health
, 19(1), 1330-1330.
Nambuusi Betty Bukenya, Ssempiira Julius, Makumbi Fredrick E., Kasasa Simon, Vounatsou Penelope (2019), The effects and contribution of childhood diseases on the geographical distribution of all-cause under-five mortality in Uganda, in Parasite Epidemiology and Control
Amek Nyaguara O., Van Eijk Annemieke, Lindblade Kim A., Hamel Mary, Bayoh Nabie, Gimnig John, Laserson Kayla F., Slutsker Laurence, Smith Thomas, Vounatsou Penelope (2018), Infant and child mortality in relation to malaria transmission in KEMRI/CDC HDSS, Western Kenya: validation of verbal autopsy, in Malaria Journal
, 17(1), 37-37.
Ssempiira Julius, Kasirye Ibrahim, Kissa John, Nambuusi Betty, Mukooyo Eddie, Opigo Jimmy, Makumbi Fredrick, Kasasa Simon, Vounatsou Penelope (2018), Measuring health facility readiness and its effects on severe malaria outcomes in Uganda, in Scientific Reports
, 8(1), 17928-17928.
Ssempiira Julius, Kissa John, Nambuusi Betty, Mukooyo Eddie, Opigo Jimmy, Makumbi Fredrick, Kasasa Simon, Vounatsou Penelope (2018), Interactions between climatic changes and intervention effects on malaria spatio-temporal dynamics in Uganda, in Parasite Epidemiology and Control
, 3(3), e00070-e00070.
Ssempiira J, Kissa J, Nambuusi B, Kyozira C, Rutazaana D, Mukooyo E, Opigo J, Makumbi F, Kasasa S, Vounatsou P (2018), The effect of case management and vector-control interventions on space–time patterns of malaria incidence in Uganda, in Malaria Journal
, 17:162, 1-11.
Massoda Tonye SG, Kouambeng C, Wounang R, Vounatsou P (2018), Challenges of DHS and MIS to capture the entire pattern of malaria parasite risk and intervention effects in countries with different ecological zones: the case of Cameroon, in Malaria Jounnal
, 17:156(1), 1-14.
Ssempiira Julius, Nambuusi Betty, Kissa John, Agaba Bosco, Makumbi Fredrick, Kasasa Simon, Vounatsou Penelope (2017), Geostatistical modelling of malaria indicator survey data to assess the effects of interventions on the geographical distribution of malaria prevalence in children less than 5 years in Uganda., in PloS one
, 12(4), 0174948-0174948.
Khagayi Sammy, Amek N, Bigogo G, Odhiambo F, Vounatsou Penelope (2017), Bayesian spatio-temporal modeling of mortality in relation to malaria incidence in Western Kenya, in PLoS One
, 12(7), 1-19.
Ssempiira J, Nambuusi B, Kissa J, Agaba B, Makumbi F, Kasasa S, Vounatsou P (2017), The contribution of malaria control interventions on spatio-temporal changes of parasitaemia risk in Uganda during 2009-2014, in Parasites and Vectors
, 10(1), 1-13.
Houngbedji Clarisse A, Chammartin Frédérique, Yapi Richard B, Hürlimann Eveline, N'Dri Prisca B, Silué Kigbafori D, Soro Gotianwa, Koudou Benjamin G, Assi Serge-Brice, N'Goran Eliézer K, Fantodji Agathe, Utzinger Jürg, Vounatsou Penelope, Raso Giovanna (2016), Spatial mapping and prediction of Plasmodium falciparum infection risk among school-aged children in Côte d'Ivoire., in Parasites & vectors
, 9(1), 494-494.
Diboulo Eric, Sié Ali, Vounatsou Penelope (2016), Assessing the effects of malaria interventions on the geographical distribution of parasitaemia risk in Burkina Faso., in Malaria journal
, 15, 228-228.
Stensgaard Anna-Sofie, Vounatsou Penelope, Onapa Ambrose W, Utzinger Jürg, Pedersen Erling M, Kristensen Thomas K, Simonsen Paul E (2016), Ecological Drivers of Mansonella perstans Infection in Uganda and Patterns of Co-endemicity with Lymphatic Filariasis and Malaria., in PLoS neglected tropical diseases
, 10(1), 0004319-0004319.
Adigun Abbas (2015), Malaria risk in Nigeria: Bayesian geostatistical modelling of 2010 malaria indicator survey data, in Malaria Journal
, (156), 1-8.
Diboulo Eric (2015), Bayesian variable selection in modelling geographical heterogeneity in malaria transmission from sparse data: an application to Nouna Health and Demographic Surveillance System (HDSS) data, Burkina Fa, in Parasites & Vectors
, 8(118), 1-14.
Giardina Federica, Kasasa Simon, Sié Ali, Utzinger Jürg, Tanner Marcel, Vounatsou Penelope (2014), Effects of vector-control interventions on changes in risk of malaria parasitaemia in sub-Saharan Africa: a spatial and temporal analysis, in Lancet Global Health
, 2(10), e601-e615.
Background: There is a strong commitment from most countries and donors to reach malaria targets set for the year 2015 in the Millennium Development Goals (MDG), the World Health Assembly (WHA), the Roll Back Malaria (RBM) Partnership and Global Malaria Action Plan (GMAP). In many countries, national surveys have been repeatedly conducted to monitor key health-related indicators such as malaria, child and maternal mortality and relevant interventions. However, despite these efforts and resources invested in data collection, the information extracted from the data by most countries is poor and it is rather limited to national averages, overlooking regional and smaller scale heterogeneities and disparities. Additionally, there is limited use of the data in evaluating on-going interventions because existing analyses do not relate variations in the health indicators, system performance and implementation of interventions at local scale. Bayesian geostatistical modelling is the state-of-the-art approach for estimating the disease burden or child mortality at local scales and for measuring the effects of interventions from national survey data. However, most analyses focus on the spatial distribution of the health outcomes without linking health intervention and system performance data. In addition, lack of advance statistical expertise in the control programmes and readily available software to perform routinely geostatistical analyses for surveillance limits the use of the methods to statisticians or specialised epidemiologists. Goal and specific objectives: The overarching goal of the project is to reduce malaria burden in Africa by developing and validating tools (methods, knowledge, software) to strengthen malaria surveillance for disease control and elimination. The project will pursue the following interrelated specific objectives: (i) assess spatio-temporal dynamics of malaria risk and measure effectiveness of related interventions at local scale in Africa; (ii) estimate the malaria-related mortality across all age groups in Africa; (iii) assess spatio-temporal dynamics and obtain up-todate high resolution estimates of infant and child mortality across Africa; (iv) estimate the contribution of health systems performance and interventions in the spatio-temporal dynamics of malaria and mortality (child and maternal) in Uganda (East Africa) and Burkina Faso (West Africa); (v) propose strategies to optimize health systems performance and interventions to reduce malaria and mortality burden and (vi) develop and disseminate software for malaria surveillance.Methods of investigation: We propose to accomplish these objectives by (a) employing and further developing Bayesian spatio-temporal methodology for mortality and malaria surveillance; (ii) analysing well known spatially structured databases from Africa (i.e. demographic health surveys (DHS), malaria indicator surveys (MIS), Malaria Indicator Cluster Surveys (MICS), malaria transmission intensity and mortality burden across Africa (MTIMBA)); and © compiling geo-referenced databases on interventions, health systems performance indicators and relevant epidemiological outcomes in Uganda and Burkina Faso. Additional data in the two study countries will be obtained from Health Demographic Surveillance Systems (HDSS), Health Information Systems (HIS), National Malaria Control Programmes (NMCP), health facilities and community surveys.Proposed time frame: January 2013 to December 2016.Significance: The spatio-temporal estimates will evaluate success in achieving malaria and mortality related MDGs and other global targets considering regional disparities rather than national averages. The project results will be translated to tools (e.g. maps, burden estimates at different geographical resolution, cost-effectiveness of different interventions, strategies to improve health system performance, surveillance software) that can directly assist efforts of the control programmes towards malaria elimination and reduction of child and maternal mortality. Additionally, these products can be used to influence policy makers to choose spatially targeted, cost-effective interventions and improve local health systems in Uganda and Burkina Faso. The up-to-date, geo-referenced estimates of infant and under five mortality can be considered as socio-economic proxies at high spatial resolution across Africa. They can be used to improve spatially explicit burden estimates of poverty-related neglected diseases in Africa. The malaria surveillance software would be easily installed in NMCPs other than Burkina Faso and Uganda. It can be also easily adapted for surveillance of other diseases.