Determining Plasmodium falciparum Malaria Transmission Networks Through Sequenom And Capillary Electrophoresis Genotyping

Declining malaria transmission leads to infection hotspots which need to be targeted to eliminate and eradicate malaria. The degree of parasite mixing in and around transmission foci is likely to impact the effectiveness of targeted interventions and should be considered when developing control programmes. Few studies currently provide empiric evidence on parasite mixing over time and space, making it hard to predict the likely outcomes of targeted interventions. Here, spatio-temporal malaria transmission networks were inferred using genetic data. P. falciparum SNP data were analysed at micro-epidemiological scales in two sites in Kenya and one site in The Gambia, and in a subsequent study at macro-epidemiological scales in Western Kenya. Principal component analysis and linear regression were used to analyse population structure and genetic relatedness in time and space, respectively. Study sites were analysed for parasite genotype clusters, barriers to, and directionality in parasite movement. Parasite genetic relatedness was predicted by relatedness in time and space at micro-geographical scales, but no evidence of population structure was seen over larger areas. No barriers to parasite movement were detected at micro or macro-epidemiological scales, although directional movement was observed in two regions of Western Kenya. PfAMA1 and surf4.2 capillary sequence data from parasites collected between 1995 – 2014 in Kilifi county were used to validate SNP data results. Sequence data showed high parasite mixing, with no clustering of distinct haplotypes in time or space. Time and distance interacted antagonistically such that distance no longer predicted genetic variation for parasites collected more than 1 year apart. These findings show parasite populations that are well mixed in time and space, thus targeting hotspots is likely to benefit surrounding communities. However, this high parasite movement is likely to lead to re-introduction of infection from surrounding regions following “one-off” interventions, although repeated targeted interventions may be effective

Effect of transmission intensity on hotspots and micro-epidemiology of malaria in sub-Saharan Africa

Background: Malaria transmission intensity is heterogeneous, complicating the implementation of malaria control interventions. We provide a description of the spatial micro-epidemiology of symptomatic malaria and asymptomatic parasitaemia in multiple sites. Methods: We assembled data from 19 studies conducted between 1996 and 2015 in 7 countries of sub-Saharan Africa with homestead-level geospatial data. Data from each site were used to quantify spatial autocorrelation and examine the temporal stability of hotspots. Parameters from these analyses were examined to identify trends over varying transmission intensity. Results: Significant hotspots of malaria transmission were observed in most years and sites. The risk ratios of malaria within hotspots were highest at low malaria positive fractions (MPFs) and decreased with increasing MPF (p<0·001). However, statistical significance of hotspots was lowest at extremely low and extremely high MPFs, with a peak in statistical significance at an MPF of ~0·3. In four sites with longitudinal data we noted temporal instability and variable negative correlations between MPF and average age of symptomatic malaria across all sites, suggesting varying degrees of temporal stability. Conclusions: We observed geographical micro-variation in malaria transmission at sites with a variety of transmission intensities across sub-Saharan Africa. Hotspots are marked at lower transmission intensity, but it becomes difficult to show statistical significance when cases are sparse at very low transmission intensity. Given the predictability with which hotspots occur as transmission intensity falls, malaria control programmes should have a low threshold for responding to apparent clustering of cases

Author Correction: Investigating the drivers of the spatio-temporal patterns of genetic differences between Plasmodium falciparum malaria infections in Kilifi County, Kenya.

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Variation in the effectiveness of insecticide treated nets against malaria and outdoor biting by vectors in Kilifi, Kenya

Background: Insecticide treated nets (ITNs) protect humans against bites from the Anopheles mosquito vectors that transmit malaria, thereby reducing malaria morbidity and mortality. It has been noted that ITN use leads to a switch from indoor to outdoor feeding among these vectors. It might be expected that outdoor feeding would undermine the effectiveness of ITNs that target indoors vectors, but data are limited. Methods: We linked homestead level geospatial data to clinical surveillance data at a primary healthcare facility in Kilifi County in order to map geographical heterogeneity in ITN effectiveness and observed vector feeding behaviour using landing catches and CDC light traps in seven selected areas of high and low ITN effectiveness. Results: We observed 33% and 39% visits associated with positive malaria slides among ITN users and non-ITN-users, respectively; ITN use was associated with 22% protection from malaria (crude OR = 0.78, 95% CI: 0.72, 0.84). We obtained significant modification of ITN effectiveness by geographical area (p=0.022), and identified significant hotspots using the spatial scan statistic. Most biting occurred outdoors (62%) and was by An. funestus (76%), and appeared to be more frequent in low ITN effectiveness areas compared with high ITN effectiveness areas (69% vs. 26%, p&lt;0.001), but this was due to a single outlying area.  After excluding this outlying area, outdoor biting was similar in low vs. high ITN effectiveness area (69% vs. 75%, p=0.76). Conclusion: Our data therefore do not support the hypothesis that outdoor biting undermines the effectiveness of ITNs in our study area.</ns4:p

Evaluating the performance of malaria genomics for inferring changes in transmission intensity using transmission modelling

AbstractAdvances in genetic sequencing and accompanying methodological approaches have resulted in pathogen genetics being used in the control of infectious diseases. To utilise these methodologies for malaria we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment. Here we develop an individual-based transmission model to simulate malaria parasite genetics parameterised using estimated relationships between complexity of infection and age from 5 regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterise the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The best performing model successfully predicted malaria prevalence with mean absolute error of 0.055, suggesting genetic tools could be used for monitoring the impact of malaria interventions.</jats:p

Clinical epidemiology of COVID-19 among hospitalized children in rural western Kenya

The epidemiology of pediatric COVID-19 in sub-Saharan Africa and the role of fecal-oral transmission in SARS-CoV-2 are poorly understood. Among children and adolescents in Kenya, we identify correlates of COVID-19 infection, document the clinical outcomes of infection, and evaluate the prevalence and viability of SARS-CoV-2 in stool. We recruited a prospective cohort of hospitalized children aged two months to 15 years in western Kenya between March 1 and June 30 2021. Children with SARS-CoV-2 were followed monthly for 180-days after hospital discharge. Bivariable logistic regression analysis was used to identify the clinical and sociodemographics correlates of SARS-CoV-2 infection. We also calculated the prevalence of SARS-CoV-2 detection in stool of confirmed cases. Of 355 systematically tested children, 55 (15.5%) were positive and were included in the cohort. The commonest clinical features among COVID-19 cases were fever (42/55, 76%), cough (19/55, 35%), nausea and vomiting (19/55, 35%), and lethargy (19/55, 35%). There were no statistically significant difference in baseline sociodemographic and clinical characteristics between SARS-CoV-2 positive and negative participants. Among positive participants, 8/55 (14.5%, 95%CI: 5.3%-23.9%) died; seven during the inpatient period. Forty-nine children with COVID-19 had stool samples or rectal swabs available at baseline, 9 (17%) had PCR-positive stool or rectal swabs, but none had SARS-CoV-2 detected by culture. Syndromic identification of COVID-19 is particularly challenging among children as the presenting symptoms and signs mirror other common pediatric diseases. Mortality among children hospitalized with COVID-19 was high in this cohort but was comparable to mortality seen with other common illnesses in this setting. Among this small set of children with COVID-19 we detected SARS-CoV-2 DNA, but were not able to culture viable SARs-CoV-2 virus, in stool. This suggests that fecal transmission may not be a substantial risk in children recently diagnosed and hospitalized with COVID-19 infection

Evaluating the Performance of Malaria Genetics for Inferring Changes in Transmission Intensity Using Transmission Modeling.

Substantial progress has been made globally to control malaria, however there is a growing need for innovative new tools to ensure continued progress. One approach is to harness genetic sequencing and accompanying methodological approaches as have been used in the control of other infectious diseases. However, to utilize these methodologies for malaria, we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment, which all impact the level of genetic diversity and relatedness of malaria parasites. We develop an individual-based transmission model to simulate malaria parasite genetics parameterized using estimated relationships between complexity of infection and age from five regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterize the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The model predicted malaria prevalence with a mean absolute error of 0.055. Different assumptions about the availability of sample metadata were considered, with the most accurate predictions of malaria prevalence made when the clinical status and age of sampled individuals is known. Parasite genetics may provide a novel surveillance tool for estimating the prevalence of malaria in areas in which prevalence surveys are not feasible. However, the findings presented here reinforce the need for patient metadata to be recorded and made available within all future attempts to use parasite genetics for surveillance

A review of the frequencies of Plasmodium falciparum Kelch 13 artemisinin resistance mutations in Africa.

Artemisinin resistance (AR) emerged in South East Asia 13 years ago and the identification of the resistance conferring molecular marker, Plasmodium falciparum Kelch 13 (Pfk13), 7 years ago has provided an invaluable tool for monitoring AR in malaria endemic countries. Molecular Pfk13 surveillance revealed the resistance foci in the Greater Mekong Subregion, an independent emergence in Guyana, South America, and a low frequency of mutations in Africa. The recent identification of the R561H Pfk13 AR associated mutation in Tanzania, Uganda and in Rwanda, where it has been associated with delayed parasite clearance, should be a concern for the continent. In this review, we provide a summary of Pfk13 resistance associated propeller domain mutation frequencies across Africa from 2012 to 2020, to examine how many other countries have identified these mutations. Only four African countries reported a recent identification of the M476I, P553L, R561H, P574L, C580Y and A675V Pfk13 mutations at low frequencies and with no reports of clinical treatment failure, except for Rwanda. These mutations present a threat to malaria control across the continent, since the greatest burden of malaria remains in Africa. A rise in the frequency of these mutations and their spread would reverse the gains made in the reduction of malaria over the last 20 years, given the lack of new antimalarial treatments in the event artemisinin-based combination therapies fail. The review highlights the frequency of Pfk13 propeller domain mutations across Africa, providing an up-to-date perspective of Pfk13 mutations, and appeals for an urgent and concerted effort to monitoring antimalarial resistance markers in Africa and the efficacy of antimalarials by re-establishing sentinel surveillance systems

Genome sequence of the tsetse fly (Glossina morsitans):Vector of African trypanosomiasis

Tsetse flies are the sole vectors of human African trypanosomiasis throughout sub-Saharan Africa. Both sexes of adult tsetse feed exclusively on blood and contribute to disease transmission. Notable differences between tsetse and other disease vectors include obligate microbial symbioses, viviparous reproduction, and lactation. Here, we describe the sequence and annotation of the 366-megabase Glossina morsitans morsitans genome. Analysis of the genome and the 12,308 predicted protein-encoding genes led to multiple discoveries, including chromosomal integrations of bacterial (Wolbachia) genome sequences, a family of lactation-specific proteins, reduced complement of host pathogen recognition proteins, and reduced olfaction/chemosensory associated genes. These genome data provide a foundation for research into trypanosomiasis prevention and yield important insights with broad implications for multiple aspects of tsetse biology.IS

An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples.

MalariaGEN is a data-sharing network that enables groups around the world to work together on the genomic epidemiology of malaria. Here we describe a new release of curated genome variation data on 7,000 Plasmodium falciparum samples from MalariaGEN partner studies in 28 malaria-endemic countries. High-quality genotype calls on 3 million single nucleotide polymorphisms (SNPs) and short indels were produced using a standardised analysis pipeline. Copy number variants associated with drug resistance and structural variants that cause failure of rapid diagnostic tests were also analysed.  Almost all samples showed genetic evidence of resistance to at least one antimalarial drug, and some samples from Southeast Asia carried markers of resistance to six commonly-used drugs. Genes expressed during the mosquito stage of the parasite life-cycle are prominent among loci that show strong geographic differentiation. By continuing to enlarge this open data resource we aim to facilitate research into the evolutionary processes affecting malaria control and to accelerate development of the surveillance toolkit required for malaria elimination