Novel Epidemiological tools to inform malaria control and elimination in Melanesia

Background: Malaria is a vector-borne parasitic disease that in 2017 was responsible for an estimated 219 million clinical cases of infection and an estimated 435000 deaths globally. Estimating the spatial distribution of malaria within endemic countries, and risk factors for transmission, is essential to the effective planning and allocation of malaria prevention interventions. The aims of this PhD thesis were to: 1) describe the epidemiology of malaria in Papua New Guinea (PNG) and summarise previous control strategies and outcomes in PNG over the last century; 2) compare the accuracy of multilevel generalised linear regression models (GLMs) with Bayesian decision network (BDN) models in the spatial prediction of prevalence of malaria in PNG; 3) predict the geographic niches of eight genotypes of Plasmodium falciparum in PNG to ascertain patterns of connectivity in the human population in terms of malaria transmission; and 4) examine the impact of human movement between high and low transmission intensity locations on malaria transmission using a mathematical model based on the example of two islands of Solomon Islands. Methods: Data for this research was obtained from published literature, a national malaria indicator survey conducted randomly selected villages in PNG in 2010 and 2011, and genotyped malaria indicator survey data collected in PNG and Solomon Islands between 2008 and 2009. Climate data at 1km resolution was obtained from the WorldClim and environmental remote sensing image data were obtained from Earthdata. Modelling approaches included: a comparison of GLMs with BDN models using point prevalence and ecological data to predict the spatial distribution of P. falciparum and P. vivax malaria in PNG; a Dirichlet regression model examining associations of P. falciparum genotype predominance with ecological covariates for the prediction of geographic niches of distinct parasite genotypes in PNG; and a Ross-Macdonald mathematical model using varying estimations of human migration rates and estimates of P. falciparum prevalence in two island of Solomon Islands for the estimation of the impact of human migration on malaria transmission. Results: In terms of P. falciparum and P. vivax spatial distribution in PNG, BDN models were found to have improved accuracy in spatial predictions when compared with generalised linear models. The predicted spatial distribution of P. falciparum and P. vivax based on BDN models followed a similar pattern to survey data with higher predicted prevalence on the islands to the East of PNG and northern coastline of the mainland, and lower predicted prevalence in the highlands and south coast. The results of the Dirichlet regression model identified geographic niches of eight distinct P. falciparum genotypes in PNG based on associations with population density, elevation, distance to the coastline, latitude and longitude, and their quadratic terms. The results of the mathematical model predicted that in the absence of sustained vector control post-elimination, resurgence of malaria may occur relatively quickly in low-transmission intensity locations where connectivity with high-transmission intensity locations exists due to human migration, such as in the islands of Solomon Islands. Conclusions: This PhD research provides a comprehensive review of literature on the control strategies for and challenges to, achieving goal of global malaria elimination, and a review of the current epidemiology of malaria, and major periods of malaria control in PNG. This thesis identifies novel epidemiological methods for improved prediction accuracy in the spatial distribution of malaria based on environmental and climate predictors, a method for inferring human connectivity in terms of malaria transmission in PNG using parasite genotype data and the application of a mathematical model to in examining the transmission dynamics of malaria transmission in two islands in Solomon Islands

Quantifying the impacts of variation in entomological and epidemiological determinants of malaria transmission

Malaria epidemiology is characterised by extensive heterogeneity that manifests across a range of spatial and temporal scales. This heterogeneity is driven by a diversity of factors spanning the human host, the parasite, the mosquito vector and the environment. Together, variation in these factors lead to marked differences in the epidemiology of malaria across different settings; in where malaria is concentrated, how malaria is transmitted and who is most at-risk. These differences have material consequences for the impact of control interventions aimed at combatting the disease, underscoring the crucial need to better understand and quantify the factors underlying heterogeneity in malaria epidemiology and transmission dynamics. In this thesis, I use a combination of statistical and mathematical modelling to further our understanding of how variation in the epidemiological and entomological determinants of malaria transmission drives heterogeneity in dynamics across settings and explore the implications of this variation for control efforts. Accurate ascertainment of malaria infections represents a crucial component of malaria surveillance and control. Previous work has revealed the often-substantial prevalence of infections with parasite densities lower than the threshold of detection by microscopy (so called “submicroscopic” infections). The drivers of these infections remain uncertain, despite their established relevance to onwards transmission. In Chapter 2, I carry out a systematic literature review and meta-analysis exploring the prevalence of submicroscopic malaria infections and how this varies between settings. My results highlight extensive variation between settings, with much of this driven by a combination of both historical and current levels of transmission. Crucially, these results highlight significant variation in the prevalence of submicroscopic infections even across settings characterised by similar current levels of transmission, with implications for the utility of control efforts specifically targeting this infected sub-group depending on the context. Within communities, the distribution of malaria infections is frequently characterised by extensive spatial heterogeneity, which can make identification and treatment of infections challenging. In Chapter 3, using a regression-based approach, I characterise the fine-scale spatial clustering of malaria infections at the household level across a diverse range of sub-Saharan African settings through systematic analysis of 57 Demographic and Health Surveys spanning 23 countries. My results highlight that malaria infections cluster within households, and that the extent of this clustering becomes significantly more pronounced as transmission declines – a factor which will affect the comparative impact of household-targeting or whole-community based control strategies and result in their appropriateness depending closely on the levels of transmission characterising a setting. In addition to this spatial heterogeneity, malaria transmission dynamics are also frequently characterised by extensive temporal heterogeneity, a phenomenon underpinned by the (often annual) temporal fluctuations in the size of the mosquito populations responsible for transmission. Many questions remain surrounding the drivers of these dynamics however, questions that are rarely answerable from individual entomological studies (focussed on only a single location or species). In Chapter 4 I carry out a systematic literature review to collate anopheline mosquito time-series data from across India and develop a statistical framework capable of characterising the dominant temporal patterns in this dataset. The results demonstrate extensive diversity in the timing and extent of seasonality across mosquito species, but also show that this diversity can be clustered into a small number of “dynamical archetypes”, each shaped and driven by a largely unique set of environmental factors including rainfall, temperature, proximity to water bodies and patterns of land use. In Chapter 5, I apply this framework to time-series data from across South Asia and the Middle East for the highly efficient vector Anopheles stephensi, to better understand the factors shaping its seasonal dynamics and the likely impact of its recent establishment in the Horn of Africa. My results reveal significant differences in the extent of seasonality across Anopheles stephensi populations, with dynamics frequently differing between rural and urban settings, suggesting structural differences in how these environments shape patterns of vector abundance and potentially warranting different vector control strategies depending on predominant patterns of land-use. Integrating these seasonal profiles into a mathematical model of malaria transmission highlights the crucial need for an understanding of the timing of seasonal peaks in vector density if control interventions like IRS are to be most effectively deployed. Overall, the results presented here highlight some of the drivers influencing spatial and temporal heterogeneity in malaria epidemiology, quantifies how they contribute to the diverse malaria dynamics observed across different settings, and explores the implication of this variation for effective control of the disease.Open Acces

Quantifying spatio-temporal variation in malaria transmission in near elimination settings using individual level surveillance data

As countries move towards malaria elimination, tracking progress through quantifying changes in transmission over space and time is key. This information is necessary to effectively target resources to remaining ‘hotspots’ (high-risk locations) and ‘hotpops’ (high-risk populations) where transmission remains, decide if and when it is appropriate to scale back interventions, and to evaluate the success of existing interventions. However, as countries approach zero cases, it becomes difficult to measure transmission. Traditional metrics, such as the prevalence of parasites in the population, are no longer appropriate due to small numbers and increasingly focal distributions of cases over space and time. In order to address this, this thesis developed Bayesian network inference approaches to utilise information about the time and location of cases showing symptoms of malaria to jointly infer the likelihood that a) each observed case was linked to another by transmission and b) that a case was infected by an external, unobserved source. This information was used to calculate individual reproduction numbers for each reported case, or how many new cases of malaria are expected to have resulted from each case. In elimination settings, quantifying the distribution of individual reproduction numbers provides useful information about how quickly a disease may die out, and how the introduction of new cases through importation may affect ongoing transmission. These estimates were incorporated into additive regression models as well as geostatistical models to map how malaria transmission varied over space and time as well as considering timelines to elimination and the likelihood of resurgence of transmission once zero cases is achieved. This approach was applied to previously unanalysed individual-level datasets of malaria cases from China and El Salvador.Open Acces

The spatial scale of immune gene variation within and among bottlenecked populations

The general aim of this thesis is to explore different spatial scales at which pathogen-mediated selection drives the evolution of immune genes across and within populations of Berthelot’s pipit (Anthus berthelotii), a historically bottlenecked passerine endemic to the oceanic islands of the Canary, Selvagens and Madeira archipelagos. I first investigated the evolution of key innate immune genes among the populations that the pipits inhabit. I found that while demographic history has played the major role in shaping patterns of among population variation at toll-like receptor loci, balancing selection (possibly pathogen-mediated) appears to have helped maintain functional variation at some specific loci. Second, I assessed the contribution of environmental factors to pathogen distribution and their subsequent effects on the major histocompatibility complex (MHC) class I genes of the acquired immune system within the population on Tenerife. I found a high prevalence of malaria in this population, the presence of which was correlated with climatic and anthropogenic variables: temperature, distance to poultry farms and distance to artificial water sources. Within the MHC I found evidence of trans-species polymorphism and gene conversion, and signatures of positive selection. Using landscape genetic analysis methods I found no evidence for overall within population patterns of structure at either neutral markers or the MHC. However, one MHC allele was associated to malaria infection risk and its distribution was (more strongly) associated with distance to poultry farms. These results suggest that demographic processes are the most important evolutionary force shaping variation at functional loci in isolated, bottlenecked populations. Nevertheless, selection can also shape patterns of variation at immunity loci, both at the coarser and the finer landscape scale, apparently in response to pathogens. This study therefore highlights the importance of considering different spatial scales when studying the evolutionary processes that shape functional genetic variation within populations

Advancing climate change health adaptation through implementation science

To date, there are few examples of implementation science studies that help guide climate-related health adaptation. Implementation science is the study of methods to promote the adoption and integration of evidence-based tools, interventions, and policies into practice to improve population health. These studies can provide the needed empirical evidence to prioritise and inform implementation of health adaptation efforts. This Personal View discusses five case studies that deployed disease early warning systems around the world. These cases studies illustrate challenges to deploying early warning systems and guide recommendations for implementation science approaches to enhance future research. We propose theory-informed approaches to understand multilevel barriers, design strategies to overcome those barriers, and analyse the ability of those strategies to advance the uptake and scale-up of climate-related health interventions. These findings build upon previous theoretical work by grounding implementation science recommendations and guidance in the context of real-world practice, as detailed in the case studies.Peer ReviewedArticle signat per 14 autors/es: Gila Neta, PhD; William Pan, DrPH; Prof Kristie Ebi, PhD; Daniel F Buss, PhD; Trisha Castranio, BS; Prof Rachel Lowe, PhD; Sadie J Ryan, PhD; Anna M Stewart-Ibarra, PhD; Limb K Hapairai, PhD; Meena Sehgal, MPH; Prof Michael C Wimberly, PhD; Leslie Rollock, DrPH; Prof Maureen Lichtveld, MD; John Balbus, MD"Postprint (published version

The Molecular, Spatial, and Genetic Epidemiology of Malaria in the Democratic Republic of the Congo

In 2018, the Democratic Republic of the Congo (DRC), accounted for nearly 12% of the global malaria case burden and 11% of the global malaria death toll. In my dissertation, I explore the molecular, spatial, and genetic epidemiology of malaria in the DRC and provide novel insights that will help inform malaria control policy in this high burden country. In the first aim of my dissertation, I investigate the recent finding that Plasmodium vivax transmission is occurring among Duffy-negative host in sub-Saharan Africa. Using data from approximately 18,000 adults, I found a 2.97% prevalence of P. vivax infections across the DRC. Nearly all infections were among Duffy-negative adults (486/489). Infections were not associated with typical risk-factors and were not geographically clustered. Mitochondrial genomes suggested that DRC P. vivax is an older clade with isolates from South America as its most recent common ancestor. Although P. vivax is more prevalent than previously expected, P. vivax in the DRC appears to be innocuous given its relatively flat distribution across space, lack of association with expected malaria risk factors, and potentially ancestral lineage. As a result, the first aim of my dissertation helps to provide public health officials with the information needed to form strategies for P. vivax in sub-Saharan Africa. In the second aim of my dissertation, I used 1,111 P. falciparum isolates genotyped at nearly 1,800 loci from across the DRC to analyze the decay of genetic and spatial relatedness across three measures of space: (1) greater-circle distance, (2) road distance, and (3) river distance. I found that road distance best explained the genetic relatedness in the DRC under a classic isolation by distance model. In addition, I found evidence that suggests that highly related pairs in the DRC are more frequently connected between urban and rural settings. These results suggest that human movement may be driving falciparum parasite dispersion across the DRC. Characterization of how P. falciparum parasites are migrating in the DRC can direct policymakers where antimalarial interventions may be most effective.Doctor of Philosoph

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

GIS and Health: Enhancing Disease Surveillance and Intervention through Spatial Epidemiology

The success of an evidence-based intervention depends on precise and accurate evaluation of available data and information. Here, the use of robust methods for evidence evaluation is important. Epidemiology, in its conventional form, relies on statistics and mathematics to draw inferences on disease dynamics in affected populations. Interestingly, most of the data used tend to have spatial aspects to them. However, most of these statistical and mathematical methods tend to either neglect these spatial aspects or consider them as artefacts, thereby biasing the resultant estimates. Thankfully, spatial methods allow for evidence evaluation and prediction in epidemiologic data while considering their inherent spatial characteristics. This, thus, promises more precise and accurate estimates.This thesis documents and illustrates the contribution spatial methods and spatial thinking makes to epidemiology through studies carried out in two countries with different heath-data quality realities, Uganda and Sweden. To be able to use spatial methods for epidemiology studies, proper spatial data need to be available, which is not the case in Uganda. Consequently, this study had two main aims: (1) It proposed and implemented a novel way of spatially-enabling patient registry systems in settings where the existing infrastructures do not allow for the collection of patient-level spatial details, prerequisites for fine-scale spatial analyses; (2) Where spatial data were available, spatial methods were used to study associative relationships between health outcomes and exposure factors. Spatial econometrics approaches, especially spatially autoregressive regression models were adopted. Also, consistent with location-specific epidemiologic intervention, the advantages of using spatial scan statistics, Geographically Weighted (Poisson) Regression and local entropy maps to distil model parameter estimates into their inherent spatial heterogeneities were illustrated. Our results illustrated that through the use of mobile and web technologies and leveraging on existing spatial data pools, systems that enable recording and storage of geospatially referenced patient records can be created. Also, spatial methods outperformed conventional statistical approaches, giving refined and more accurate parameter estimates. Finally, our study illustrates that the use of local spatial methods can inform policy and intervention better through the identification of areas with elevated disease burden or those areas worth additional scrutiny as illustrated by our study of HIV-TB coinfection areas in Uganda, the areas with high CVD-air pollution associations in Sweden, and areas with consistently high joint mortality burden for CVD and cancer among the Swedish elderly.Overall, the incorporation of spatial approaches and spatial thinking in epidemiology cannot be overemphasized. First, by enabling the capture of fine-scale personal-level spatial data, our study promises more robust analyses and seamless data integration. Secondly, associative analyses using spatial methods showed improved results. Thirdly, identification of the areas with elevated disease burden makes identifying the primary drivers of the observed local patterns more informed and focused. Ultimately, our results inform healthcare policy and strategic intervention as the most affected areas can easily be zoned out. Therefore, by illustrating these benefits, this study contributes to epidemiology, through spatial methods, especially in the aspects of disease surveillance, informing policy, and driving possible effective intervention

Spatial dependence of body mass index and exposure to night-time noise in the Geneva urban area

In this study, we calculated the night-noise mean (SonBase 2014, compatible with the EU Environmental Noise Directive) for the 5 classes obtained after computation of Local Indicators of Spatial Association (LISA; Anselin et al 1995) on the BMI of the participants in the Bus Santé study, a cohort managed by the Geneva University Hospitals (N=15’544; Guessous et al 2014). We expected the mean of dBs to be significantly higher in the group showing spatial dependence of high BMI values (high-high class). We ran an ANOVA and multiple T-tests to compare the dB means between LISA clusters. The approach was applied to the participants of the whole State Geneva cohort, and to a reduced set of individuals living in the urban environment of the municipality of Geneva only