A Geographical Location Model for Targeted Implementation of Lure-and-Kill Strategies Against Disease-Transmitting Mosquitoes in Rural Areas

Outdoor devices for luring and killing disease-transmitting mosquitoes have been proposed as potential com- plementary interventions alongside existing intra-domiciliary methods namely insecticide treated nets and house spraying with residual insecticides. To enhance effectiveness of such outdoor interventions, it is essential to optimally locate them in such a way that they target most of the outdoor mosquitoes. Using odour-baited lure and kill stations (OBS) as an example, we describe a map model derived from: 1) com-munity participatory mapping conducted to identify mosquito breeding habitats, 2) entomological field studies conducted to estimate outdoor mosquito densities and to determine safe distances of the OBS from human dwellings, and 3) field surveys conducted to map households, roads, outdoor human aggregations and landmarks. The resulting data were combined in a Ge- ographical Information Systems (GIS) environment and analysed to determine optimal locations for the OBS. Separately, a GIS-interpolated map produced by asking community members to rank different zones of the study area and show where they expected to find most mosquitoes, was visually compared to another map interpolated from the entomological survey of outdoor mosquito densities. An easy-to-interpret suitability map showing optimal sites for placing OBS was produced, which clearly depicted areas least suitable and areas most suitable for locating the devices. Comparative visual interpretation of maps derived from interpolating the community knowledge and entomological data revealed major similarities between the two maps. Using distribution patterns of human and mosquito populations as well as characteristics of candidate outdoor interventions, it is possible to readily determine suitable areas for targeted positioning of the interventions, thus improve effectiveness. This study also highlights possibilities of relying on community knowledge to approximate areas where mosquitoes are most abundant and where to locate outdoor complementary interventions such as odour-baited lure and kill stations for controlling disease-transmitting mosquitoes.\u

Mapping hotspots of malaria transmission from pre-existing hydrology, geology and geomorphology data in the pre-elimination context of Zanzibar, United Republic of Tanzania

Background: Larval source management strategies can play an important role in malaria elimination programmes, especially for tackling outdoor biting species and for eliminating parasite and vector populations when they are most vulnerable during the dry season. Effective larval source management requires tools for identifying geographic foci of vector proliferation and malaria transmission where these efforts may be concentrated. Previous studies have relied on surface topographic wetness to indicate hydrological potential for vector breeding sites, but this is unsuitable for karst (limestone) landscapes such as Zanzibar where water flow, especially in the dry season, is subterranean and not controlled by surface topography. Methods: We examine the relationship between dry and wet season spatial patterns of diagnostic positivity rates of malaria infection amongst patients reporting to health facilities on Unguja, Zanzibar, with the physical geography of the island, including land cover, elevation, slope angle, hydrology, geology and geomorphology in order to identify transmission hot spots using Boosted Regression Trees (BRT) analysis. Results: The distribution of both wet and dry season malaria infection rates can be predicted using freely available static data, such as elevation and geology. Specifically, high infection rates in the central and southeast regions of the island coincide with outcrops of hard dense limestone which cause locally elevated water tables and the location of dolines (shallow depressions plugged with fine-grained material promoting the persistence of shallow water bodies). Conclusions: This analysis provides a tractable tool for the identification of malaria hotspots which incorporates subterranean hydrology, which can be used to target larval source management strategies

Climate Change and Highland Malaria: Fresh Air for a Hot Debate

In recent decades, malaria has become established in zones at the margin of its previous distribution, especially in the highlands of East Africa. Studies in this region have sparked a heated debate over the importance of climate change in the territorial expansion of malaria, where positions range from its neglect to the reification of correlations as causes. Here, we review studies supporting and rebutting the role of climatic change as a driving force for highland invasion by malaria. We assessed the conclusions from both sides of the argument and found that evidence for the role of climate in these dynamics is robust. However, we also argue that over-emphasizing the importance of climate is misleading for setting a research agenda, even one which attempts to understand climate change impacts on emerging malaria patterns. We review alternative drivers for the emergence of this disease and highlight the problems still calling for research if the multidimensional nature of malaria is to be adequately tackled. We also contextualize highland malaria as an ongoing evolutionary process. Finally, we present Schmalhausen's law, which explains the lack of resilience in stressed systems, as a biological principle that unifies the importance of climatic and other environmental factors in driving malaria patterns across different spatio-temporal scales

Earth observation and mosquito-borne diseases: assessing environmental risk factors for disease transmission via remote sensing data

Despite global intervention efforts, mosquito-borne diseases remain a major public health concern in many parts of the world. New strategies to target interventions rely on spatially explicit information about disease transmission risk. Because the transmission of mosquito borne diseases is influenced by environmental conditions, environmental data are often used to predict disease risk. However, the relationships between environmental conditions and such diseases are not homogeneous across different landscapes and requires a context-dependent understanding. The research presented in this dissertation consists of three case studies that used remote sensing data to identify environmental risk factors for mosquito-borne diseases in different geographic settings. In the first project, the distribution of malaria cases in two study areas in the Amhara region of Ethiopia was analyzed with the help of remote sensing data on land surface temperature, precipitation, spectral indices, as well as land cover and water availability. Environmental variables were derived from remote sensing data and their relationships with spatial and temporal patterns of malaria occurrence were investigated. Settlement structure played an important role in malaria occurrence in both study areas. Climatic factors were also important, with relative risk following a precipitation gradient in the area by lake Tana and a temperature gradient along the Blue Nile River escarpment. This research suggests that studies aiming to understand malaria-environmental relationships should be geographically context specific so they can account for such differences. Second, the spatial distribution of West Nile virus (WNV) risk in South Dakota was studied via different geospatial environmental datasets. We compared the effectiveness of 1) land cover and physiography data, 2) climate data, and 3) spectral data for mapping the risk of WNV transmission. The combination of all data sources resulted in the most accurate predictions. Elevation, late season (July/August) humidity, and early-season (May/June) surface moisture were the most important predictors of disease distribution. Indices that quantified inter-annual variability of climatic conditions and land surface moisture were better predictors than inter-annual means. These results suggest that combining measures of inter-annual environmental variability with static land cover and physiography variables can help to improve spatial predictions of arbovirus transmission risk. Third, mosquito populations in Norman, Oklahoma, were analyzed to investigate the influences of land cover and microclimate on the abundance of vector mosquitoes in a heterogeneous urban environment. Remotely-sensed variables, microclimate measurements, and weather station data were used to study patterns of mosquito abundances. Spatial distributions of the two vector species Ae. albopictus and Cx. quinquefasciatus were strongly associated with land cover variables. Impervious surface area positively affected the abundance of both species. Canopy cover was positively associated with the abundance of Cx. quinquefasciatus but negatively with Ae. albopictus abundance. Among all models based on time-varying environmental data, those based on remotely-sensed variables performed best in predicting species abundances. Abundances of both species were positively associated with high temperature and high relative humidity on the trap day, but negatively associated with precipitation two weeks prior to trapping. These results emphasize the great potential for including satellite imagery in habitat analyses of different vector mosquitoes. The results presented in this dissertation contribute to the understanding of how land cover and geographic context influence the transmission of mosquito-borne diseases. Particularly remote sensing variables capturing static land cover conditions and dynamic measures of vegetation greenness and moisture can explain spatial variation in disease transmission. as well as vector mosquito distribution. Whereas remotely sensed climatic variables like temperature and precipitation influenced gradients in malaria cases at a regional scale, they explained mostly seasonal variation in mosquito distribution at a city scale. Over-all, freely available remote sensing data can help us understand the environmental determinants of disease distribution and can be a valuable tool for predicting disease dynamics on a landscape scale

Climate Driven Changes to Malaria Transmission Patterns in Ethiopian Highlands

In the highlands of East Africa, the most populated regions in Africa, temperature is assumed to be intimately connected to the patterns of malaria both in time and space. A large section of the Ethiopian population in this region has historically been shielded from the disease mainly due to the altitudes in the highland regions that have remained free of the disease. However, the region has also seen a large part of its population being affected by malaria in epidemic outbreaks that seem to follow climatic anomalies, especially those of inter-annual increases in temperature. This project examines the inter-annual variability in the distribution of disease incidence over space and explores how changes in these distributions correlate to corresponding climate variability. By using extensive records of disease cases at high spatial resolution, it explores how population at the high end of the disease transmission range is affected by inter-annual climate variability. It further examines factors at play in the persistence of the disease in these low-transmission highland fringes to draw lessons for better targeting of interventions. With lessons learnt from the micro-scale investigations of associations between spread of the disease and generalizable factors, especially climatic factors, the project scales up to the national level to explore the risk of malaria transmission among the Ethiopian population, and how these risks have changed with the observed climatic factors in the last few decades. Finally, it quantifies the potential impacts of climate change on the spatial spread and intensity of malaria incidence and risks in a country whose population has doubled in the last 30 years. In chapter two, at a micro-scale and with high resolution disease and climate data, we examine the impact of inter-annual climate variation on the spatial distribution of malaria incidence over time. With the looming climate change in mind, we examine and infer what this could mean for the future. The impact of global warming on insect-borne diseases and on highland malaria in particular remains controversial. Temperature is known to influence transmission intensity through its effects on the population growth of the mosquito vector and on pathogen development within the vector. Spatio-temporal data at a regional scale in the highlands of Ethiopia provide an opportunity to examine how the spatial distribution of the disease changes with the interannual variability of temperature. We provide evidence for an increase in altitude of the malaria distribution in warmer years. This implies that climate change will, without mitigation, result in an increase of the malaria burden in the densely populated highlands of Africa and other regions with similar conditions. In chapter three, we stay at the same scale as in Paper II, and explore factors that explain the persistence of malaria in this low transmission epidemic prone region. A better understanding of malaria\u27s persistence in highly seasonal environments such as highlands and desert fringes requires identifying the factors behind the spatial and temporal reservoir of the pathogen in the low transmission season. In these \u27unstable\u27 malaria regions, such reservoirs play a critical role during the low transmission season by allowing persistence between seasonal outbreaks. In the highlands of East Africa, the most populated epidemic regions in Africa, temperature is expected to be intimately connected to spatial persistence because of pronounced altitudinal gradients. It is not clear, however, that variation in altitude is in itself sufficient to explain persistence of the disease during the low season, and that other environmental and demographic factors, in particular population density are not also major factors. We address this question with an extensive spatio-temporal data set of confirmed monthly Plasmodium falciparum cases from 1995 to 2005 that finely resolves space in an Ethiopian highland. Using a Bayesian approach for parameter estimation and a generalized linear mixed model that includes a spatially-structured random effect, we demonstrate that population density is important to disease persistence during the low transmission season. As malaria risk usually decreases in more urban environments with increased human densities, this counter-intuitive finding identifies novel control targets during the low transmission season in African highlands. It also underscores limitations of current coupled vector-host models of the population dynamics of the disease, which do not typically incorporate an explicit effect of population density. In chapter four, we scale-up to the national level and explore the use of climate factors to quantify spatially explicit malaria risk for Ethiopia. Climate suitability for malaria transmission has been used to account for Africa\u27s continental distribution of the disease and to estimate the potential effects of climate change. So far, the limited application of the standard suitability index on smaller spatial scales, and the coarse resolution of future climate scenarios, which can overestimate malaria suitability, have hampered adequate estimation of the regional impact of global warming in the highly populated African highlands. In this chapter we intend to validate the existing African malaria Suitability index for Ethiopian conditions in order to study potential shifts in the epidemiology of malaria with past and predicted warming. With a modified suitability index for Ethiopia, we estimate that since the 1970s 12% of the rural population has become exposed to the disease, and 7% of the rural population who live in areas above 1000m. shifted into the stable malaria category. These figures reflect less than 1 degrees of warming that have occurred between 1975 and 2010. With 2 and 3 degrees of additional warming possible in the 21th century, the proportion of Ethiopia\u27s population safe from malaria is likely to decrease to 10% and 5% respectively from 31% in the pre-1990 baseline assessment. At the same time, endemic stable malaria is predicted to affect 51% and 64% of the population respectively compared to 22% pre-1990s. With a shifting uphill burden of malaria, epidemic risk will occur in vulnerable populations without previous disease exposure. This risk will materialize in exceptionally warm years that can now be forecasted with reasonable accuracy; epidemic warning and timely intervention should be able to avoid severe morbidity and mortality. However, the populations that are shifted into the stable malaria category due to warming will have to rely on the continuation of assistance that has alleviated Ethiopia\u27s malaria burden in the last decade, and future scientific progress to improve malaria control and keep ahead of developing drug and insecticide resistance. Despite applying a 50% reduction in malaria caused mortality to account for the reported progress achieved in Africa since 2000, we conservatively estimate that 2521 children and 1646 adults (above 15 years of age) die in Ethiopia each year from warming that has occurred so far. At current levels of technology, control effort and population in Ethiopia, a 3 degree increase in temperature would result in an eight fold increase in these figures

Spatial-temporal Distribution of Mosquito Larval Hot Spots in Papoli, Uganda: A Community-Based Approach to Mosquito Control

Mosquito species of the Anopheles gambaie complex are the predominant vectors of malaria transmission throughout sub-Saharan Africa. These mosquitoes tend to be endophilic, as well as anthropophilic, making them prime candidates for disease transmission. Within the same region, related mosquito vectors play a significant role in the transmission of additional human and zoonotic diseases. Furthermore, mosquito nuisance biting is an immense issue that cannot be ignored in terms of its impact on African communities. Depending on the respective factors involved, mosquito control programs throughout the continent have attempted to tackle these issues in a multitude of ways. This research approached the issue by developing and integrating an American-style mosquito control district within the eastern Ugandan community of Papoli. The basic structure of such a district was blended with a community-based approach, employing local community members and leaders, thus ensuring an effective and sustainable program. A guide detailing all aspects and steps needed to properly develop and implement such a program is outlined

Climatic, environmental and socio-economic factors for malaria transmission modelling in KwaZulu-Natal, South Africa.

Doctoral Degree. University of KwaZulu-Natal, Durban.Sub-Saharan Africa (SSA) largely bears the burden of the global malaria disease, with the transmission and intensity influenced by the interaction of a variety of climatic, environmental, socio-economic, and human factors. Other factors include parasitic and vectoral factors. In South Africa (SA) in general and KwaZulu-Natal (KZN) in particular, the change of the malaria control intervention policy in 2000, may be responsible for the significant progress over the past two decades in reducing malaria case report to near zero. Currently, malaria incidence in KZN is less than 1 case per 1000 persons at risk placing the province in the malaria elimination stage. To meeting the elimination target, it is necessary to study the dynamics of malaria transmission in KZN employing various analytical/statistical models. Thus, the aim of this study was to explore the factors that influence malaria transmission by employing different analytical models and approaches in a setting with low malaria endemicity and transmission. This involves a sound appraisal of the existing literature on the contribution of remote sensing technology in understanding malaria transmission, evaluation of existing malaria control intervention; delineation of empirical map of malaria risk; provide information on the climatic, environmental and socio-economic factors that influences malaria risk and transmission; and formulation of a relevant malaria forecast and surveillance models. The investigator started with a systemic review of studies in chapter two. The studies were aimed at identifying significant remotely-sensed climatic and environmental determinants of malaria transmission for modelling malaria transmission and risk in SSA via a variety of statistical approaches. Normalised difference vegetation index (NDVI) was identified as the most significant remotely-sensed climatic/environmental determinants of malaria transmission in SSA. Majority of the studies employed the generalised linear modelling approach compared to the Bayesian modelling approach. In the third chapter, malaria cases from the endemic areas of KZN with remotely-sensed climatic and environmental data were used to model the climatic and environmental determinants of malaria transmission and develop a malaria risk map in KZN. The spatiotemporal zero inflated Poisson model formulated indicates that at 95% Bayesian credible interval (BCI) NDVI (0.91; 95% BCI = 0.71, -1.12), precipitation (0.11; 95% BCI = 0.08, 0.14), elevation (0.05; 95% BCI = 0.032, 0.07) and night temperature (0.04; 95% BCI = 0.03, 0.04) are significantly related to malaria transmission in KZN, SA. The area with the highest risk of malaria morbidity in KZN was identified as the north-eastern part of the province. The fourth chapter was to establish the socio-economic status (SES) that influence malaria transmission in the endemic areas of KZN, by employing a Bayesian inference approach. The obtained posterior samples revealed that, significant association existed between malaria disease and low SES such as illiteracy, unemployment, no toilet facilities and no electricity at 95% BCI Lack of toilet facilities (odds ration (OR) =12.54; 95% BCI = 0.63, 24.38) exhibited the strongest association with malaria and highest risk of malaria disease. This was followed by no education (OR =11.83; 95% BCI = 0.54, 24.27) and lack of electricity supply (OR =10.56; 95% BCI = 0.43, 23.92) respectively. In the fifth chapter, the seasonal autoregressive integrated moving average (SARIMA) intervention time series analysis (ITSA) was employed to model the effect of the malaria control intervention, dichlorodiphenyltrichloroethane (DDT) on confirmed monthly malaria cases. The result is an abrupt and permanent decline of monthly malaria cases (w0= −1174.781, p-value = 0.003) following the implementation of the intervention policy. Finally, the sixth chapter employed a SARIMA modelling approach to predict malaria cases in the endemic areas of KZN. Three plausible models were identified, and based on the goodness of fit statistics and parameter estimation, the SARIMA (0,1,1) (0,1,1)12 model was identified as the best fit model. The SARIMA (0,1,1)(0,1,1)12 model was used to forecast malaria cases during 2014, and it was observed to fit closely with the reported malaria cases during January to December 2014. The models generated in this study demonstrated the need for the KZN malaria program, relevant policy makers and stakeholders to further strengthen the KZN malaria elimination efforts. The required malaria elimination fortification are not limited to the implementation of additional sustainable developmental approach that combines both improved malaria intervention resources and socio-economic conditions, strengthening of existing community health workers, and strengthening of the already existing cross-border collaborations. However, more studies in the area of statistical modelling as well as practical applications of the generated models are encouraged. These can be accomplished by exploring new avenues via cross-sectional survey to understand the impact of community and social related structures in malaria burden; strengthening of existing community health workers; knowledge, attitude and practices in malaria control and intervention; and the likely effects of temporal/seasonal and spatial variations of malaria incidence in neighbouring endemic countries should be explored

On the dynamics of two efficient malaria vectors of the Afrotropical region: Anopheles gambiae s.s. and Anopheles arabiensis

Weather and climate are only some of the factors influencing the dynamics of malaria. With the ongoing debate on the consequences of climate change, there is a need for models which are designed to address these questions. Historically, models have focused on the theoretical principles of eradication, with less emphasis on a changing environment. To estimate the potential impact of climate change on malaria, we need new models which consider a wider range of environmental variables. In this thesis, we point at some factors which are important to robustly project the influence of climate and weather on malaria. These factors are described using a mathematical model which focus on the weather sensitive parts of malaria transmission; the mosquitoes and the parasites. Mosquitoes transmitting malaria belong to the genus Anopheles. There are about 460 known anophelines, where 41 are considered to be dominant vectors of malaria. Each of these species have its own life history, and consequently weather and climate influence each species differently. In Africa, the public health impact of malaria is devastating, despite variable transmission. The most efficient mosquitoes are found in this continent: among them Anopehels gambiae sensu stricto and Anopheles arabiensis, which are considered to be of major importance. In this thesis (Paper I) we describe a dynamical model which include these two species. Based on a literature review, we formulate a model which allows weather to influence each of the two species according to their life history. They compete over puddles, important for reproduction; An. gambiae s.s. mainly feed on humans opposed to An. arabiensis which feed on cattle and humans; they are allowed to disperse, meaning new ares can be occupied by the species; and as they become older, the daily probability of survival changes. Many of these factors are not important in a short time perspective. But, since climate change is slow process compared to the life of a singe mosquito, there is a need for additional complexity to study how a slowly changing environment influence the population dynamics of these malaria vectors. To have confidence the model is realistic in the current climate we validated the model in paper II. To date, we constructed the most extensive database on the occurrence of the two mosquitoes. These data were used to validate the model described in paper I. We concluded the mosquito model produced comparable or better results than existing predictions of the two species under current climate. An. arabiensis feed on humans and cattle. Since the density and distribution of those are not static, but are changing over time, and the distribution of An. arabiensis is highly dependent on the density of cattle, there is a need to; 1. Document historical changes; 2. Understand how they are influenced by the environment. In paper III we reconstruct the cattle distribution and density in the 1960s, and show how climate variability influence the national cattle holdings. While climate variability has a minor influence in many countries, we also find variations in the climate can explain more than 40% of the national cattle holdings in some countries. The data developed in this paper can be used in the model described in paper I, as well as other studies where cattle is an important part of the system. It has been claimed the optimal temperature for malaria transmission is between 30 to 32°C, with the potential increasing linearly from 20 to 32°C. With this claim, any warming in sub-Saharan Africa would potentially cause more malaria. Using the model developed in paper I, we show malaria transmission is most effective around 25◦C, with a decline in efficiency over end below this temperature (Paper IV). This disputes the theory claimed in previous papers. Any projections relating temperature and malaria should be interpreted with care. The influence of climate change on malaria transmission is still uncertain. With this thesis, we have come a step further in understanding how the environment can alter malaria transmission. However, the future occurrence of malaria is dependent on many other factors, including malaria control measures, access to and usage of treatment, city planning, and immunity

Metapopulation Modelling and Spatial Analysis for HEG Technology in the Control of Malaria

The success of any vector control strategy can be enhanced by onsite analysis and investigation. Combatting malaria, a global disease carried by the vector Anopheles gambiae, has led to the development of novel genetic technologies such as the use of HEG; homing endonuclease genes. This thesis explored the age and stage elements of the vector, building upon current biological understanding and using fitting algorithms with metapopulation matrices to create cohort orientated survival and transition. The environmental forces were analysed alongside this with emphasis on sub-model creation and tool design, employing an array of methods from RBF to satellite classification to couple the local environment and vector. When added, the four potential genetic strategies all demonstrated the ability to suppress a wild type population and even eradicate it, although reinvasion and hotspot population phenomena were reoccurring observations. The movement of the vector was an important factor in control efficiency, which was investigated as a series of different assumptions using wind driven movement and host attraction. Lastly, practical factors such as monitoring and resource distribution within a control project were assessed, which required routing solutions and landscape trapping assessments. This was explored within a framework of Mark-Release-Recapture experiment design that could provide critical information for efficient HEG release strategies.Open Acces

Identifying, characterizing, and targeting the reservoir of malaria transmission in Southern Tanzania.

Malaria continues to be a leading cause of morbidity and mortality in countries where it is endemic. While dramatic progress has been achieved globally, recent global malaria reports suggested that overall global progress has stalled since 2014. The plateau in improvement, particularly in high transmission settings of Africa, is associated with several factors, including inadequate coverage and use of the interventions, poor health service coverage, changes in vectors bionomics and insecticides resistance to malaria vectors. In addition, many high transmission countries have insufficient community based interventions to reduce malaria morbidity and mortality. Barriers to progress are associated with uncoordinated surveillance systems, low socioeconomic and living standards as well as inadequate adherence of the affected population to interventions. This hinders the efforts to achieve the overall goal of malaria elimination in many malaria endemic settings, highlighting the need for overall health system improvement to allow for innovative control and surveillance techniques. Furthermore, it necessitates a better understanding of malaria transmission dynamics. In order to meet this challenge, we must delve deeper into the underlying malaria transmission dynamics. The proposed PhD project was embedded within a tripartite pilot project between China-United Kingdom-Tanzania. The project was about Malaria control in Rufiji district, Tanzania, that started in September 2015. The overarching goal of the PhD project was to study the dynamics of malaria transmission and evaluate the impact of community-based malaria reactive case detection strategy in strengthening the transmission-reduction of human malaria infections in areas with high coverage of LLINs. To achieve the project's goal, four specific objectives were specified. This matches to the project chapters' conclusions in this thesis. In Chapter three of this thesis, the effectiveness of implementing a community-based testing and treatment plan to reduce the malaria burden in moderate to high transmission areas is analyzed. The "1-3-7" surveillance and response model developed in China, which prompted the development of this initiative and subsequent adoption of the 1,7-malaria Reactive Community-based Testing and Response (1,7mRCTR) approach, is a novel method for implementing the WHO-T3 and surveillance intervention to eliminate malaria. However, the 1-3-7 model is more effective in China, where the goal is to eradicate the disease, than in Tanzania, where the bulk of the population still has moderate to high transmission. The 1,7-mRCTR is locally-tailored for reporting malaria cases on day one and intervention on day seven, with community-based testing and treatment in high-burden areas stratified based on weekly data from health facilities. In the same district, control areas with comparable epidemiology (no 1,7-mRCTR) were selected and monitored for the duration of the project. After two years of implementing the 1.7 mRCTR, the prevalence of parasites in the target areas was reduced by 66 percent above and above the benefit provided by national measures already in place. Despite the fact that new technology and techniques may be required to eradicate malaria in stable transmission areas of Sub-Saharan Africa, this experiment proved that a locally tailored approach could help to expedite malaria control and elimination efforts. In addition, it highlight the opportunities of validating the results and possibilities of scaling up 1,7-mRCTR approach in other settings within Tanzania, and other African countries for accelerating malaria control and elimination across Africa. In chapter four of this dissertation, the household cross-sectional survey data gathered prior to 1,7-mRCTR intervention were used to describe and characterize the malaria prevalence and the associated exposures risk . In the context of the Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS) 2015-16, this study’s findings are discussed. The findings highlight the importance of national malaria monitoring, and its ramifications for present malaria management strategies. The prevalence of malaria varied by ward, ranging from 5.6 percent to 18 percent, with the average prevalence reported by this study (13 percent) being higher than the reported by the RDHS-MIS national (7.3 percent). Based on the findings of this chapter, t is important for the new malaria control plans to be effective in sustaining gains and accelerating progress towards the end goals in the fight against malaria will depend on clearing parasitaemia and ensuring that poverty is eleviated Importantly, programs intended to improve malaria interventions for the currently recognized vulnerable groups should be modified to include other groups observed with highest parasitaemia. Chapter five investigated and assessed one of the extremely sensitive epidemiological study of malaria transmission (host preference by malaria vectors). In addition to being a significant predictor of malaria transmission patterns, this indicator is essential for determining the appropriateness and efficacy of vector control interventions and for predicting malaria transmission patterns. Using the direct host-preference experiment, the host preference of the primary malaria vector species, Anopheles arabiensis and Anopheles gambiae sensu stricto, was examined in two distinct ecological contexts in Tanzania. In contrast to historical accounts, the data indicate that urban An. arabiensis showed a stronger preference for cattle than rural An. arabiensis, but An. gambiae showed no preference for either people or animals under the same conditions. To achieve malaria eradication, we must have a deeper understanding of the prevalent vectors, their feeding behavior in varied ecological situations, and their feeding preferences. This will allow us to more effectively design and implement locally-tailored, high-impact, integrated interventions. Anopheles mosquito species composition, abundance, and spatial-temporal variability must be thoroughly understood in order to optimally exploit high-resolution malaria vector control strategies. Community-based mapping of residual malaria vector densities to support malaria elimination efforts in southeastern Tanzania is discussed in Chapter six. The findings highligth the changing composition of vector species through time, as well as the presence of many malaria vector species at the village scale, which is characterized by a wide spectrum of ecological variation. Human biting rates (HBR) in the study wards ranged from 1.5 to 73 bites per person every night. Characterization of Anopheles vectors indicated disparities between villages and wards in the distribution of Anopheles gambiae s.l., Anopheles funestus, and Anopheles coustani. This study's findings give evidence-based information that is essential for planning and implementing vector control actions in a local setting, complementing the results of Chapter four. In addition, the findings highlight the significance of comprehending and incorporating vector bionomics data into surveillance and response systems in order to successfully implement the ongoing micro-stratification of malaria strata. Surveillance is acknowledged as an intervention and considered instrumental in accelerating global malaria elimination efforts. However, all available evidence to date supports the incorporation of surveillance as in intervention in low endemicity areas, with no evidence comes from moderate to high endemicity areas. Therefore, this PhD project is the first attempt to develop a surveillance and response strategy in moderate to high transmission setting. The findings aline the current Tanzania mid-term review of the national malaria control as well as with the goals Global Technical Strategy 2015-2030 (GTS) and the High Burden to High Impact (HBHI) initiative, which both reiterated the importance of tailoring intervention approaches to the sub-national local context in order to accelerate progress toward malaria reduction and elimination. Behaviour ecology matters and so does evolutionary biology in human-modified environment, the spatial-temporal variation findings in vector composition at a fine scale level of village and the reduced human-biting preference of the primary malaria vectors collected from two distinct characterized with different ecological features is an example illustrating why regular surveys of mosquito compositions and behaviour need to be incorporated in malaria surveillance. Furthermore, support that, in regions with a high malaria incidence, the convention interventions should be maintained, while prioritizing taiolored approach based on the local contex