Spatial prediction of malaria prevalence in Papua New Guinea: a comparison of Bayesian decision network and multivariate regression modelling approaches for improved accuracy in prevalence prediction

BACKGROUND: Considerable progress towards controlling malaria has been made in Papua New Guinea through the national malaria control programme's free distribution of long-lasting insecticidal nets, improved diagnosis with rapid diagnostic tests and improved access to artemisinin combination therapy. Predictive prevalence maps can help to inform targeted interventions and monitor changes in malaria epidemiology over time as control efforts continue. This study aims to compare the predictive performance of prevalence maps generated using Bayesian decision network (BDN) models and multilevel logistic regression models (a type of generalized linear model, GLM) in terms of malaria spatial risk prediction accuracy. METHODS: Multilevel logistic regression models and BDN models were developed using 2010/2011 malaria prevalence survey data collected from 77 randomly selected villages to determine associations of Plasmodium falciparum and Plasmodium vivax prevalence with precipitation, temperature, elevation, slope (terrain aspect), enhanced vegetation index and distance to the coast. Predictive performance of multilevel logistic regression and BDN models were compared by cross-validation methods. RESULTS: Prevalence of P. falciparum, based on results obtained from GLMs was significantly associated with precipitation during the 3 driest months of the year, June to August (β = 0.015; 95% CI = 0.01-0.03), whereas P. vivax infection was associated with elevation (β = - 0.26; 95% CI = - 0.38 to - 3.04), precipitation during the 3 driest months of the year (β = 0.01; 95% CI = - 0.01-0.02) and slope (β = 0.12; 95% CI = 0.05-0.19). Compared with GLM model performance, BDNs showed improved accuracy in prediction of the prevalence of P. falciparum (AUC = 0.49 versus 0.75, respectively) and P. vivax (AUC = 0.56 versus 0.74, respectively) on cross-validation. CONCLUSIONS: BDNs provide a more flexible modelling framework than GLMs and may have a better predictive performance when developing malaria prevalence maps due to the multiple interacting factors that drive malaria prevalence in different geographical areas. When developing malaria prevalence maps, BDNs may be particularly useful in predicting prevalence where spatial variation in climate and environmental drivers of malaria transmission exists, as is the case in Papua New Guinea

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

Modelling Spatial And Temporal Changes With Gis And Spatial And Dynamic Bayesian Networks

State-and-transition models (STMs) have been successfully combined with Dynamic Bayesian Networks (DBNs) to model temporal changes in managed ecosystems. Such models are useful for exploring when and how to intervene to achieve the desired management outcomes. However, knowing where to intervene is often equally critical. We describe an approach to extend state-and-transition dynamic Bayesian networks (ST-DBNs) - incorporating spatial context via GIS data and explicitly modelling spatial processes using spatial Bayesian networks (SBNs). Our approach uses object-oriented (OO) concepts and exploits the fact that ecological systems are hierarchically structured. This allows key phenomena and ecological processes to be represented by hierarchies of components that include similar, repetitive structures. We demonstrate the generality and power of our approach using two models - one developed for adaptive management of eucalypt woodland restoration in south-eastern Australia, and another developed to manage the encroachment of invasive willows into marsh ecosystems in east-central Florida

Razvoj modela za integrisano upravljanje izvorom mera prilagođavanja na klimatske promene na lokalnom nivou

In synergy with other socio-economic risks, the effects of climate change pose contemporary structural challenges that can not be considered only as an environmental issue. They affect the general development and therefore make the adaptive capacity of a population uncertain in the following decades. The subject of this dissertation comprises the development of a new decision support model for the selection of local level climate change adaptation measures. Considering the nature of management issues in climate policies, which involves decision-making under the conditions of uncertainty, the model employs adaptive management principles. It was designed to help decision-makers in selection of adequate adaptation measures, and to enable monitoring of the implementation process. The key objective of the research is fulfilled by developing a model for the selection of priority adaptation measures. The model is based on scenarios of the synergistic influence of diverse sets of measures on the observed system vulnerability. It takes into account climate projections and relevant biophysical and anthropogenic factors. The model relies on a combination of several methodological approaches. The scenario method was used for the selection of adaptation measures. It is based on the assessment of the simultaneous contribution of a group of measures to the reduction of vulnerability of the observed climate impact, by forming a conditional probability diagram using Bayesian networks. Through the analysis of the likelihood of diverse states of the observed group of criteria, it is possible to examine the effect of individual measures (or sets of measures) adaptation capacity, as a result of the joint probability distribution of all criteria in the network. The analytical hierarchical process (AHP) was used to quantify the distinct qualitative relationships between the risk criteria of the observed climate impact and the adaptation measures. A GIS is used to calculate the specific values of the criteria on the network, to profile the vulnerability, sensitivity, adaptation capacity and exposure index, as well as for data integration. The model can improve the decision-making in adaptation planning process. As the results are expressed as a probability distribution for each alternative, the model can help decision makers predict the chances of achieving desired effects of selected measures, and develop detailed programs at the local level to increase their efficiency. The model is also capable to transparently monitor the application process and facilitate the development of appropriate capacities for the purpose in local communities. In this respect, the developed model also provides a methodological contribution for improving the planning framework for the local adaptation project management