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Liver flukes are very successful group of parasites that can infect many different hosts including humans. They can be found in different forms and sizes, and found almost all of the world. In Denmark, two species are found. The well-known "common liver fluke" has existed in Denmark since the Vikings, but is increasing again recently. Not so known is "the lancet fluke", which is also fascinating as it can make the ants behave like zombies.

A remarkable reduction of the total number of under-five deaths was achieved between 1990 and 2018 in the African setting, as pre-school mortality fell to 5.3 million deaths compared to 12.5 million in 1990. The bulk share of this reduction is attributed to the Millennium Development Goals (MDGs) era, during which time the under-five mortality rate has been declining with an annual rate of 3.8% across Africa. Despite these important achievements, the sub-Sahara African region did not meet the fourth target of the MDGs and still has an unacceptably high under-five mortality rate. Crucially, limiting the under-five mortality rate to a maximum of 25 deaths per 1,000 live births by 2030 lies at the heart of the Sustainable Development Goals (SDGs) and a recent report from the United Nations has warned that based on current trends, the African continent will not meet the SDG target for under-five mortality. Hence, providing useful insights from the associations between under-five mortality, the leading causes of disease and preventative or curative health interventions could make available valuable information to decision makers in order the African countries to achieve the SDGs on pre-school mortality. Malaria is a major contributor to under-five mortality in sub-Saharan Africa, accounting for 400,000 deaths, approximately 60% of which are in children below the age of five. At global scale, the disability-adjusted life-years for the malaria disease are 45 million. An important aspect of the disease is that infection by malaria parasites does not necessarily lead to mortality and it is rather conditions that follow infection or other comorbidities that produce severe forms of the disease with increased mortality risk. Apart from malaria, pneumonia and diarrhea account for the most frequent causes of pre-school deaths. An interesting feature of all these three leading causes of under-fives in Africa, i.e. pneumonia, diarrhea and malaria, is that they share febrile response as their main clinical manifestation. Against the leading causes of under-five mortality, preventative or curative health interventions have been widely adopted in Africa, with their spatial coverage being on a significant rise, particularly due to the so-called scaling-up of health interventions during the last five years of the MDGs. For instance, ownership of Insecticide-Treated nets against malaria rose from 50 to 80 percent between 2010 and 2015, while their utilization averted 663 million clinical malaria cases over the MDGs era. Yet, the coverage of health interventions and the subsequent reduction in under-five deaths has happened in an unequal way across sub- Saharan Africa, raising concerns about health inequities at sub-national level. The overall aim of the present PhD thesis is to develop, implement and interpret Bayesian geostatistical models with spatially varying coefficients in order to analyze approximately one million, cross-sectional mortality related-data in Africa and associate under-five mortality with malaria and health interventions. The point-by-point objectives of our work are as follows: 1. To develop a novel indicator for quantifying malaria-related mortality for children under the age of five in sub-Saharan Africa, namely the malaria-anemia comorbidity prevalence indicator (chapter 2); 2. To identify health inequities experienced by sub-national populations due to the geographical variation in the association between curative or preventive health interventions and under-five mortality in sub-Saharan Africa (chapter 3); 3. To assess the contribution of the leading causes of under-five mortality in sub- Saharan Africa on febrile response by associating the prevalence of malaria parasitaemia, diarrhoea and ARI with fever. (chapter 4); 4. To estimate the association between health interventions and under-five mortality on changes in mortality risk between two time points across Africa (chapter 5); 5. To compare Bayesian variable selection methods for spatially varying coefficient models, given that these approaches are at the forefront of analyzing geolocated mortality data in Africa (chapter 6). In chapter 2, we estimated the association of malaria parasitaemia, anemia, and malaria- anemia comorbidity with all-cause under-five mortality and evaluated the potential of malaria-anemia comorbidity prevalence to quantify malaria-related deaths in sub-Saharan Africa. Additionally, we estimated within-country variation of the association between comorbidity and under-5 mortality, using spatially varying coefficient models. We presented our results at high spatial resolution, including model-based risk maps of malaria, anemia, and malaria-anemia comorbidity. In chapter 3, we modeled the geographical variation in the association between health interventions and all-cause, under-five mortality in order to identify health inequities experienced by sub-national populations within a given country. To achieve that, we developed Bayesian geostatistical Weibull survival models with spatially varying coefficients for the effect of health interventions on mortality. Our approach allowed us to calculate the number of statistically important associations between interventions and mortality at regional level and hence to assess if health equity of interventions exists across the regions of a given country. In chapter 4, we assessed the contribution of the leading causes of under-five mortality in sub-Saharan Africa on febrile response by associating the prevalence of malaria parasitaemia, diarrhoea and ARI with fever. Our flexible Bayesian spatial modelling approach allowed evaluating the geographical distribution of disease-exposure effect on fever in space (Administrative level 1). We also calculated the Potential Attributable Fraction (PAF) in order to quantify the contribution of childhood diseases on fever. In chapter 5, we developed a novel methodology to statistically model the effect of health interventions on the changes in under-five mortality risk between two DHS survey time-points for 21 countries in Africa. We used a Bayesian geostatistical Weibull survival modeling approach and implemented rigorous Bayesian variable selection procedures in order to identify the most suitable set of health interventions for subsequent model fit. In chapter 6, we assessed the performance of stochastic search variable selection (SSVS) for the fixed effects of geostatistical models, we compared three different Bayesian variable selection (BVS) methods for conditionally autoregressive (CAR) structured spatially varying coefficient models and finally we assessed the sensitivity of SSVS for the fixed effects when is co-implemented with a BVS procedure. We conducted a simulation study and applied the methods to the Burundi DHS in order to assess the aforementioned selection procedures. The present PhD thesis has contributed to the scientific fields of Epidemiology and Statistics by committing to the spatio-temporal modelling of under-five mortality data in the African setting, using primarily routinely collected, cross-sectional, household-based survey data coming from the Demographic and Health surveys program. The key outcomes of the research conducted in this thesis are as follows: 1. Our work contributed to the development, proposal and validation of a novel indicator for quantifying malaria-mortality using survey data, i.e. the malaria-anemia comorbidity indicator. Our main conclusions were that malaria burden in sub-Saharan Africa is considerably underestimated when anemia in not taken into account and that the malaria-anemia comorbidity prevalence provides a useful measure of the malaria-related deaths; 2. We presented the first study to assess sub-national health inequities, across most countries in Africa, by employing a spatial statistical modelling approach and routinely collected survey data coming from the DHS and MIS. Our results demonstrated strong sub-national health inequities across various regions for 28 African countries; 3. Our estimates confirmed the strong contribution of diarrhoea and acute respiratory infection on febrile response and accounted only one out of five cases to malaria; 4. Our work concluded that the health interventions that are mostly associated with changes in all-cause, under-five mortality risk in sub-Saharan Africa were Bacillus Calmette–Guérin (BCG) immunization, vitamin A supplementation and deworming medication; 5. Our analysis showed that the SSVS method is able to accurately identify the statistically important predictors for the fixed effects of geostatistical models and that SSVS is not sensitive to co-implementation with a BVS procedure for CAR- structured, spatially varying coefficients. We also concluded that one of the three BVS methods for varying coefficients, namely the Global selection method, is able to identify true varying coefficients with 70% success rate.

Neglected tropical diseases (NTDs) are a group of communicable diseases affecting more than one billion of the world’s poorest population. Soil-transmitted helminth infections, schistosomiasis, and foodborne trematodiasis are among the most important NTDs. Soil-transmitted helminth infections are caused by a group of parasite nematode worms (i.e., Ascaris lumbricoides, Trichuris trichiura, and hookworm) through contact with parasite eggs or larvae which thrive in warm and moist soil. They are widely endemic in the tropics and sub-tropics and ranked on the top among all NTDs burden, contributing to the global disease burden with 5.2 million disability-adjusted life years (DALYs). Schistosomiasis is caused by trematode parasites of the genus Schistosoma. It is the second highest in terms of NTD burden and responsible for around 3.3 million DALYs worldwide. More than 90% of schistosomiasis cases occur in Africa. Clonorchiasis is one of the most important foodborne trematodiasis and it is caused by infection with the Chinese liver fluke, Clonorchis sinensis. China accounts for around 85% of the global infected people and most cases occur in the southern and the northeastern parts of the country. For all the three diseases, preventive chemotherapy is advocated by WHO as a key strategy for morbidity control. Furthermore, integrated approaches are highly recommended to achieve sustainable control and elimination. Such approaches may include preventive chemotherapy in combination with improvement of water, sanitation, and hygiene, as well as better information, education, and communication. To implement control strategies cost-effectively, high-resolution maps depicting the geographical distribution of disease risk are important. These maps provide useful information for spatial targeting of control measures and for long-term monitoring and surveillance. Geostatistical modeling is the most rigorous inferential approach for high-resolution risk mapping of NTDs. It is a data-driven approach, which relates georeferenced disease data (usually point-referenced) with potential predictors (e.g., environmental and socioeconomic factors) that are considered important for disease transmission. Location-specific random effects can explain geographical variation in the data, assuming that neighboring areas have similar infection status due to common disease exposures they receive. Geostatistical models are highly parameterized, however Bayesian model formulations provide a flexible inferential framework and powerful computational tools such as Markov chain Monte Carlo (MCMC) simulation or approximations (e.g., integrated nested Laplace approximation (INLA)) are applied for model fit. A good coverage and a fine amount of disease data are necessary to capture the spatial heterogeneity of the infection risk. Due to lack of large surveys covering the whole study region, this PhD thesis is based on historical survey data that are compiled via bibliometric searches. Publications however are either report the survey data as point-referenced (with geographical information at the survey location) or as areal, aggregated over several locations within an administrative level (e.g., county or district). The areal data can provide useful information especially when the spatial coverage of point-referenced data is low. Geostatistical model for jointly analysing point-level and areal survey data are not available. Furthermore, historical data are generated from studies with different designs between locations, including different population age-groups. Geostatistical models that align survey data across locations to a common age group do not exist in the field of NTDs. Ignoring the age-heterogeneity of the data can lead to biased estimation because models cannot distinguish whether risk differences between locations is due to differences in age or to exposures. Mathematical models can be used to age-align the surveys, but there is no model formulation allowing changes of the shape of the age-prevalence curve over space as a result of the varying endemicity. The overall goal of the thesis is to develop Bayesian geostatistical and mathematical models for analysing georeferenced NTD survey data and to provide tools and knowledge for disease control and prevention. In Chapter 2 surveys pertaining to soil-transmitted helminth infections in People’s Republic of China (P.R. China) were compiled. Bayesian geostatistical models were developed and used to estimate the disease risk throughout the country at high spatial resolution. Advanced Bayesian variable selection methods were employed to identify the most important predictors. Results indicate that the prevalence of soil-transmitted helminth infections in P.R. China considerably decreased from 2005 onwards. Yet, some 144 million people were estimated to be infected in 2010. High prevalence (>20%) was predicted in large areas of Guizhou and the southern part of Hubei and Sichuan provinces for Ascaris lumbricoides infection, in large areas of Hainan, the eastern part of Sichuan, and the southern part of Yunnan provinces for hookworm infection, as well as in a few small areas of south P.R. China for Trichuris trichiura infection. In Chapter 3 a systematic review was carried out to identify prevalence surveys to soil-transmitted helminth infections in South Asia. Bayesian geostatistical models were applied to identify important environmental and socioeconomic predictors, and to estimate infection risk at high spatial resolution across the study region. Results show that 397 million of South Asia population was infected with at least one species of soil-transmitted helminths in 2015. A. lumbricoides was the most common infection species. Moderate to high prevalence (>20%) of any soil-transmitted helminth infection was predicted in the northeastern part and some northern areas of the study region as well as the southern coastal-line areas of India. The annual treatment needs for the school-aged population requiring preventive chemotherapy was estimated at 187 million doses. The study highlights the need for up-to-date surveys to accurately evaluate the disease burden in the region. In Chapter 4 georeferenced survey data of C. sinensis infection were obtained via a systematic review and additional data were provided by the National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention. Bayesian geostatistical models were applied to quantify the relation between infection risk and important predictors, and to predict the risk of infection across P.R. China at high spatial resolution. The results show an increasing risk of C. sinensis infection over time, particularly from 2005 onwards, which urges the Chinese government to pay more attention on the public health importance of the diseases. Highly endemic areas (>20%) were concentrated in southern and northeastern parts of the country. The provinces with the highest risk of infection and the largest number of infected people were Guangdong, Guangxi and Heilongjiang. In Chapter 5 a systematic review was conducted to identify relevant surveys pertaining to prevalence of Schistosoma infection in sub-Saharan Africa. Bayesian geostatistical meta-analysis and rigorous variable selection were used to obtain up-to-date risk estimates of schistosomiasis at high spatial resolution, based on environmental and socioeconomic predictors. The literature search identified Schistosoma haematobium and Schistosoma mansoni surveys at 9,318 and 9,140 unique locations, respectively. Results show a decreased infection risk from 2000 onwards, yet suggesting that 163 million Africans were infected in 2012. Mozambique had the highest prevalence of Schistosoma infection among 44 countries of sub-Saharan Africa. Annualised treatment needs with praziquantel were estimated at 123 million doses for school-aged children and 247 million for the entire population. In Chapter 6 a Bayesian geostatistical modeling approach was developed to analyse jointly areal and point-referenced survey data. We assumed that the point-referenced data arise from a binomial distribution and that the aggregated area data follow a Poisson binomial distribution which was approximated by a two parameter shifted binomial distribution. Results from extensive simulations shows that our proposed model has better predictive ability and improves parameter estimation compared to models that treat area data as points, located at the centroid of the areas. We applied the new model to obtain high spatial resolution estimates of the infection risk of clonorchiasis in an endemic region of P.R. China. In Chapter 7 we integrated geostatistical and mathematical transmission models of schistosomiasis within a single model formulation to analyse age-heterogenous S. mansoni data from Côte d’Ivoire. A series of age-specific risk maps of S. mansoni infection in Côte d’Ivoire were produced at high geographical resolution, which allow us to identify the most important age groups of the population to treat at a given place. We predicted that the infection risk reached the peak at younger ages in high risk areas and at older ages in low risk areas. Moreover, a more rapid decline rate of infection risk was observed at older ages in high risk areas compared to that in moderate and low risk ones. In summary, this PhD thesis contributes to the fields of spatial statistics and of epidemiology of NTDs with (i) statistical methodology for modeling spatially-structured disease data, having heterogeneous geographical support (i.e., georeferenced at point or area level) across the study region and they are collected over different age groups between locations, (ii) applications on soil transmitted helminth infections, schistosomiasis, and clonorchiasis in sub-Saharan Africa, South Asia, and P.R. China, to obtain spatially explicit estimates of disease risk, number of infected people, and annual treatment needs for preventive chemotherapy at different administrative levels, and (iii) large amount of geo-referenced data on NTD surveys conducted at over 10,750 unique locations that are available via the open access Global Neglected Tropical Diseases Database (GNTD). The innovative statistical methodology for analysing historical survey data, heterogeneous in space can be readily applied to other disease survey data. The up-to-date, model-based, high-resolution risk maps and estimates of treatment needs provide useful tools and information for guiding disease control and interventions.