Salmonella enterica serovar Typhi H58 clone has been endemic in Zimbabwe from 2012 to 2019.

BACKGROUND: Typhoid fever, caused by S. enterica ser. Typhi, continues to be a substantial health burden in developing countries. Little is known of the genotypic diversity of S. enterica ser. Typhi in Zimbabwe, but this is key for understanding the emergence and spread of this pathogen and devising interventions for its control. OBJECTIVES: To report the molecular epidemiology of S. enterica ser. Typhi outbreak strains circulating from 2012 to 2019 in Zimbabwe, using comparative genomics. METHODS: A review of typhoid cases records from 2012 to 2019 in Zimbabwe was performed. The phylogenetic relationship of outbreak isolates from 2012 to 2019 and emergence of antibiotic resistance was investigated by whole-genome sequence analysis. RESULTS: A total 22 479 suspected typhoid cases, 760 confirmed cases were reported from 2012 to 2019 and 29 isolates were sequenced. The majority of the sequenced isolates were predicted to confer resistance to aminoglycosides, ß-lactams, phenicols, sulphonamides, tetracycline and fluoroquinolones (including qnrS detection). The qnrS1 gene was associated with an IncN (subtype PST3) plasmid in 79% of the isolates. Whole-genome SNP analysis, SNP-based haplotyping and resistance determinant analysis showed that 93% of the isolates belonged to a single clade represented by multidrug-resistant H58 lineage I (4.3.1.1), with a maximum pair-wise distance of 22 SNPs. CONCLUSIONS: This study has provided detailed genotypic characterization of the outbreak strain, identified as S. Typhi 4.3.1.1 (H58). The strain has reduced susceptibility to ciprofloxacin due to qnrS carried by an IncN (subtype PST3) plasmid resulting from ongoing evolution to full resistance.

Inclusion of edaphic predictors for enhancement of models to determine distribution of soil-transmitted helminths: the case of Zimbabwe.

BACKGROUND: Reliable mapping of soil-transmitted helminth (STH) parasites requires rigorous statistical and machine learning algorithms capable of integrating the combined influence of several determinants to predict distributions. This study tested whether combining edaphic predictors with relevant environmental predictors improves model performance when predicting the distribution of STH, Ascaris lumbricoides and hookworms at a national scale in Zimbabwe. METHODS: Geo-referenced parasitological data obtained from a 2010/2011 national survey indicating a confirmed presence or absence of STH among school children aged 10-15 years was used to calibrate ten species distribution models (SDMs). The performance of SDMs calibrated with a set of environmental and edaphic variables was compared to that of SDMs calibrated with environmental variables only. Model performance was evaluated using the true skill statistic and receiver operating characteristic curve. RESULTS: Results show a significant improvement in model performance for both A. lumbricoides and hookworms for all ten SDMs after edaphic variables were combined with environmental variables in the modelling of the geographical distribution of the two STHs at national scale. Using the top three performing models, a consensus prediction was developed to generate the first continuous maps of the potential distribution of the two STHs in Zimbabwe. CONCLUSIONS: The findings from this study demonstrate significant model improvement if relevant edaphic variables are included in model calibration resulting in more accurate mapping of STH. The results also provide spatially-explicit information to aid targeted control of STHs in Zimbabwe and other countries with STH burden.

Spatio-temporal analysis of the role of climate in inter-annual variation of malaria incidence in Zimbabwe.

BACKGROUND: On the fringes of endemic zones climate is a major determinant of inter-annual variation in malaria incidence. Quantitative description of the space-time effect of this association has practical implications for the development of operational malaria early warning system (MEWS) and malaria control. We used Bayesian negative binomial models for spatio-temporal analysis of the relationship between annual malaria incidence and selected climatic covariates at a district level in Zimbabwe from 1988-1999. RESULTS: Considerable inter-annual variations were observed in the timing and intensity of malaria incidence. Annual mean values of average temperature, rainfall and vapour pressure were strong positive predictors of increased annual incidence whereas maximum and minimum temperature had the opposite effects. Our modelling approach adjusted for unmeasured space-time varying risk factors and showed that while year to year variation in malaria incidence is driven mainly by climate, the resultant spatial risk pattern may to large extent be influenced by other risk factors except during high and low risk years following the occurrence of extremely wet and dry conditions, respectively. CONCLUSION: Our model revealed a spatially varying risk pattern that is not attributable only to climate. We postulate that only years characterized by extreme climatic conditions may be important for developing climate based MEWS and for delineating areas prone to climate driven epidemics. However, the predictive value of climatic risk factors identified in this study still needs to be evaluated.