1. www.uottawa.ca/imprimer www.uottawa.ca/print Imprimé par / printed by: Improving diagnosis of febrile illness – the role of malaria and arboviruses in fever prevalence in regions of Tanzania Esha Homenauth 1, Debora Kajeguka 2, Robert Kaaya 2, Natacha Protopopoff 3, Franklin W. Mosha 2, Rachelle E. Desrochers 4, and Manisha A. Kulkarni 1 1 School of Epidemiology, Public Health & Preventive Medicine, University of Ottawa, Canada; 2 Kilimanjaro Christian Medical University College, Tanzania; 3 London School of Hygiene & Tropical Medicine, UK; 4 HealthBridge, Canada Methods  Mosquito data Occurrence records of mosquito data including trap density and location of Anopheles, Culex and Aedes vectors were obtained per household from field collections carried out for a previous randomized control trial of malaria vector control interventions 1. This data was used to generate a vector density map via ordinary kriging.  Environment & Climate data A description of the remotely-sensed environmental and climatic data used in this analysis are provided in Table 1 and depicted in Figure 2.  Analysis Two buffer zones (with radii: 1.5km and 3km) were created around each household to assess for the effect of each environmental and climatic factor on vector distributions. These scales were chosen based on previous research that quantified the optimal dispersal distance of Anopheles vectors 7,8. The relationship between vector abundance and the variables identified in Figure 2 were determined using stepwise linear regression and models for the different buffer zones were compared based on their R 2. Preliminary Results Results from the stepwise linear regression indicates that mean annual temperature accounts for some variation in Anopheles abundance at both the household level and at the 1.5km buffer. The 1.5 km buffer had the higher R 2 (30%) compared to the household level (R 2 = 21%). At the household level, annual mean temperature and annual precipitation were the most important variables for predicting Anopheles abundance while at a scale of 1.5km, elevation, savannah land cover, population density and annual temperature best explained the variability of Anopheles abundance. Future Direction  Assess the relationship between Anopheles abundance at the 3.0km scale and generate a global model using important variables derived at each scale (household,1.5km, 3.0km)  Conduct a multiple logistic regression to assess the correlation between Anopheles vectors and the environmental and climatic factors identified in the global model  Assess for spatial autocorrelation and incorporate this information into a model using geographic weighted regression  Conduct spatial clustering analysis of human fever cases and characterize these malaria and non-malarial fever clusters using remotely sensed data (Figure 2) and entomological data  Develop a risk model for infection using principal component analysis Research Objective To explore spatial patterns of both Anopheles and Aedes vectors in Muleba, Tanzania, in order to predict vector density and distributions and draw inferences about high and low risk transmission regions Figure 1 shows the location of the study area, and the locations of the 64 rural villages sampled. Muleba is one of 6 districts in the Kagera region of Tanzania bordered to the east by Lake Victoria and sits at approximately 1237 m above sea level. There are on average 4.3 hamlets per village, 146 households per hamlet and 5.5 individuals per household. 1 Background Vector-borne diseases account for 17% of the estimated global disease burden of all infectious diseases with more than 50% of the world’s population currently at risk of infection 2. Malaria and arboviruses such as dengue and chikungunya pose a significant public health challenge in many parts of the world due to their associated disease burden documented in many countries, as well as the widespread expansion of their mosquito vectors: Anopheles spp and Aedes spp. The global distribution of these vectors have been previously studied where researchers incorporated a range of environmental and climatic factors to estimate and predict vector range and density 3–6. Understanding the spatial dynamics of malaria and arboviral vectors are crucial for the development of targeted interventions to allow for improved patient care and for prioritizing resources in regions where these vectors commonly persist. In this study, a spatial epidemiologic approach will be employed to assess the distribution of Anopheles and Aedes vectors in Muleba. Specifically, this study aims to: 1 Develop a vector-distribution map for Aedes and Anopheles vectors in the Muleba district 2 Use remotely-sensed environmental and climate data to characterize vector density The interpolated map seen in Figure 3 demonstrates spatial heterogeneity with respect to potential Anopheles vector abundance. This map indicates that households located in the southern part of Muleba have a higher estimated Anopheles abundance, while households located in the northern and central regions have a much lower likelihood of Anopheles density. Interpolation methods were not conducted for Aedes mosquitoes since the total count of this vector was marginal (~11 aedes in all households sampled). REFERENCES 1. West P a, Protopopoff N, Rowland M, et al. Malaria risk factors in North West Tanzania: the effect of spraying, nets and wealth. PLoS One. 2013;8(6):e65787. doi:10.1371/journal.pone.0065787. 2. WHO. Vector-Borne Diseases.; 2014. http://www.who.int/mediacentre/factsheets/fs387/en/. 3. Kraemer MUG, Sinka ME, Duda KA, et al. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. 2015:1-18. doi:10.7554/eLife.08347. 4. Bhatt S, Gething PW, Brady OJ, et al. The global distribution and burden of dengue. Nature. 2013;496(7446):504-507. doi:10.1038/nature12060. 5. Hay SI, Sinka ME, Okara RM, et al. Developing Global Maps of the Dominant Anopheles Vectors of Human Malaria. PLoS Med. 2010;7(2):e1000209. doi:10.1371/journal.pmed.1000209. 6.Sinka ME, Bangs MJ, Manguin S, et al. The dominant Anopheles vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and bionomic précis. Parasit Vectors. 2010;3(1):117. doi:10.1186/1756-3305-3-117. 7.. Kulkarni M a., Desrochers RE, Kerr JT. High resolution niche models of malaria vectors in Northern Tanzania: A new capacity to predict malaria risk? PLoS One. 2010;5(2). doi:10.1371/journal.pone.0009396. 8. Thomas CJ, Cross DE, Bøgh C. Landscape movements of Anopheles gambiae malaria vector mosquitoes in rural Gambia. PLoS One. 2013;8(7):e68679. doi:10.1371/journal.pone.0068679. Table 1. Description of land cover, elevation, population and bioclimatic variables included in analysis VariableDescriptionResolution Land cover Composed of 16 classes: water, evergreen needleleaf & broadleaf forest, deciduous needleleaf & broadleaf forest, mixed forest, closed & open shrublands, woody savannahs, Savannahs, grasslands, permanent wetlands, croplands, urban and built-up, cropland/natural vegetation mosaic, snow and ice, barren or sparsely vegetated 500m ElevationShuttle Radar Topography Emission90m Population LandScan data of Human population density (2011) - Oak Ridges National Laboratory 1 km BIOCLIM variables 19 BIOCLIM variables that represent annual trends (such as mean annual temperature), seasonality (such as annual range in temperature), and extreme or limiting environmental factors (such as temperature of the coldest month). 1 km