In silico characterisation of putative Plasmodium falciparum vaccine candidates in African malaria populations.

Genetic diversity of surface exposed and stage specific Plasmodium falciparum immunogenic proteins pose a major roadblock to developing an effective malaria vaccine with broad and long-lasting immunity. We conducted a prospective genetic analysis of candidate antigens (msp1, ama1, rh5, eba175, glurp, celtos, csp, lsa3, Pfsea, trap, conserved chrom3, hyp9, hyp10, phistb, surfin8.2, and surfin14.1) for malaria vaccine development on 2375 P. falciparum sequences from 16 African countries. We described signatures of balancing selection inferred from positive values of Tajima's D for all antigens across all populations except for glurp. This could be as a result of immune selection on these antigens as positive Tajima's D values mapped to regions with putative immune epitopes. A less diverse phistb antigen was characterised with a transmembrane domain, glycophosphatidyl anchors between the N and C- terminals, and surface epitopes that could be targets of immune recognition. This study demonstrates the value of population genetic and immunoinformatic analysis for identifying and characterising new putative vaccine candidates towards improving strain transcending immunity, and vaccine efficacy across all endemic populations.

Spatial epidemiology of tuberculosis in the high-burden counties of Kisumu and Siaya, Western Kenya, 2012-2015.

BACKGROUND: Effective management of tuberculosis (TB) and reduction of TB incidence relies on knowledge of where, when and to what degree the disease is present. METHODS: In a retrospective cross-sectional study, we analysed the spatial distribution of notified TB incidence from 1 January 2012 and 31 December 2015 in Siaya and Kisumu Counties, Western Kenya. TB data were obtained from the Division of Leprosy, Tuberculosis and Lung Disease, Nairobi, Kenya, as part of an approved TB case detection study. Cases were linked to their corresponding geographic location using physical address identifiers. Spatial analysis techniques were used to examine the spatial and temporal patterns of TB. Assessment of spatial clustering was carried out following Moran's I method of spatial autocorrelation and the Getis-Ord Gi* statistic. RESULTS: The notified TB incidence varied from 638.0 to 121.4 per 100 000 at the small area level. Spatial analysis identified 16 distinct geographic regions with high TB incidence clustering (GiZScore 2.58, P < 0.01). There was a positive correlation between population density and TB incidence that was statistically significant (rs = 0.5739, P = 0.0001). CONCLUSION: The present study presents an opportunity for targeted interventions in the identified subepidemics to supplement measures aimed at the general population.