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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.

malaria journal

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