Longitudinal characterization of Plasmodium inter-species interactions during a period of increasing prevalence of Plasmodium ovale

Abstract

(296 Words) Background The epidemiology and severity of non-falciparum malaria in endemic settings has garnered limited attention. We aimed to characterize the prevalence, interaction, clinical risk factors and temporal trends of non-falciparum malaria in endemic settings of Kenya. Methods We diagnosed and analyzed infecting malaria species via PCR in 2027 clinical samples collected between 2008 and 2016. Descriptive statistics were used to describe the prevalence and distribution of Plasmodium species. A statistical model was designed and used for estimating the frequency of Plasmodium species and assessing inter-species interactions. Mixed effect linear regression models with random intercepts for each location was used to test for change in prevalence over time. Findings 72.5% of the samples were P. falciparum single species infections, 25.8% were mixed infections and only 1.7% occurred as single non-falciparum species infections. 23.1% were mixed infections containing P. ovale. A likelihood-based model calculation of the population frequency of each species estimated a significant within-host interference between P. falciparum and P. ovale curtisi. Mixed-effect logistic regression models identified a significant increase of P. ovale wallikeri and P. ovale curtisi species over time with reciprocal decrease in P. falciparum single species and P. malariae. The risk of P. falciparum infections presenting with fever was 0.43 times less likely if co-infected with P. malariae. Interpretation Findings show higher prevalence of non-falciparum malaria than expected. The proportion of infections that were positive for infection by P. ovale wallikeri and P. ovale curtisi was observed to significantly increase over the period of study which could be due to attenuated responsiveness to malaria drug treatment on these species. The increase in frequency of P. ovale spp in Kenya could threaten malaria control effort in Kenya and pose increased risk of malaria to travelers. Funding AFHSB and its GEIS Section Key words: increasing; Plasmodium ovale; Malari

The epidemiology of Plasmodium vivax among adults in the Democratic Republic of the Congo

“…In addition, we considered several alternative P. vivax risk factors that could not be estimated with a parametric approach due to model assumption violations or a lack of data. These additional risk factors included: (1) the putative Duffy phenotype (Supplementary Materials: Duffy-Genotyping); (2) within-host interactions of P. vivax and P. falciparum using a multinomial likelihood-based model that assumes independent infection acquisition (Supplementary Materials: P. falciparum-P. vivax Co-infection Model) 49 ; (3) interactions between nonhuman ape ranges and P. vivax cluster-level prevalences using permutation tests (Supplementary Materials: Overlap with Non-Human Ape Permutation Testing) 18,50 ; and (4) the association between P. vivax cluster prevalence and the proximity to airports, as a proxy for importation of P. vivax via airline travel. Proximity to airports was calculated as the minimum greater-circle distance from each cluster to an airport that was classified as "medium" or "large" (Supplementary Materials: Covariate Feature Engineering).…”