Abstract:Background: Malaria elimination efforts can be undermined by imported malaria infections. Imported infections are classified based on travel history. Methods: A genetic strategy was applied to better understand the contribution of imported infections and to test for local transmission in the very low prevalence region of Richard Toll, Senegal. Results: Genetic relatedness analysis, based upon molecular barcode genotyping data derived from diagnostic material, provided evidence for both imported infections and … Show more
“…Parasite genotypes from the first infection were genetically distinct from the re-infected parasites (Fig. 4 ), and three of them (U5, U6 and U7) represented genotypes which had not been previously described, either in Thiès or in multiple regions of Senegal from 2006 to present 14 , 16 , 23 . Figure 4 Barcodes of initial and re-infected participant samples.…”
Molecular epidemiology using genomic data can help identify relationships between malaria parasite population structure, malaria transmission intensity, and ultimately help generate actionable data to assess the effectiveness of malaria control strategies. Genomic data, coupled with geographic information systems data, can further identify clusters or hotspots of malaria transmission, parasite genetic and spatial connectivity, and parasite movement by human or mosquito mobility over time and space. In this study, we performed longitudinal genomic surveillance in a cohort of 70 participants over four years from different neighborhoods and households in Thiès, Senegal—a region of exceptionally low malaria transmission (entomological inoculation rate less than 1). Genetic identity (identity by state, IBS) was established using a 24-single nucleotide polymorphism molecular barcode, identity by descent was calculated from whole genome sequence data, and a hierarchical Bayesian regression model was used to establish genetic and spatial relationships. Our results show clustering of genetically similar parasites within households and a decline in genetic similarity of parasites with increasing distance. One household showed extremely high diversity and warrants further investigation as to the source of these diverse genetic types. This study illustrates the utility of genomic data with traditional epidemiological approaches for surveillance and detection of trends and patterns in malaria transmission not only by neighborhood but also by household. This approach can be implemented regionally and countrywide to strengthen and support malaria control and elimination efforts.
“…Parasite genotypes from the first infection were genetically distinct from the re-infected parasites (Fig. 4 ), and three of them (U5, U6 and U7) represented genotypes which had not been previously described, either in Thiès or in multiple regions of Senegal from 2006 to present 14 , 16 , 23 . Figure 4 Barcodes of initial and re-infected participant samples.…”
Molecular epidemiology using genomic data can help identify relationships between malaria parasite population structure, malaria transmission intensity, and ultimately help generate actionable data to assess the effectiveness of malaria control strategies. Genomic data, coupled with geographic information systems data, can further identify clusters or hotspots of malaria transmission, parasite genetic and spatial connectivity, and parasite movement by human or mosquito mobility over time and space. In this study, we performed longitudinal genomic surveillance in a cohort of 70 participants over four years from different neighborhoods and households in Thiès, Senegal—a region of exceptionally low malaria transmission (entomological inoculation rate less than 1). Genetic identity (identity by state, IBS) was established using a 24-single nucleotide polymorphism molecular barcode, identity by descent was calculated from whole genome sequence data, and a hierarchical Bayesian regression model was used to establish genetic and spatial relationships. Our results show clustering of genetically similar parasites within households and a decline in genetic similarity of parasites with increasing distance. One household showed extremely high diversity and warrants further investigation as to the source of these diverse genetic types. This study illustrates the utility of genomic data with traditional epidemiological approaches for surveillance and detection of trends and patterns in malaria transmission not only by neighborhood but also by household. This approach can be implemented regionally and countrywide to strengthen and support malaria control and elimination efforts.
“…Malaria parasite molecular data have been utilized for a wide set of research and programmatic questions beyond drug and diagnostic resistance detection. Molecular data enables the identification of different malaria species [9, 10] providing especially helpful information in regions where malaria infections were either absent or considered completely imported [11]. An extensive library of parasite genetic samples can also help researchers and programmatic investigators during an out-break to help determine the geographic origin, rate of importation, and subsequent level of local transmission [12, 13, 11].…”
Section: Introductionmentioning
confidence: 99%
“…Molecular data enables the identification of different malaria species [9, 10] providing especially helpful information in regions where malaria infections were either absent or considered completely imported [11]. An extensive library of parasite genetic samples can also help researchers and programmatic investigators during an out-break to help determine the geographic origin, rate of importation, and subsequent level of local transmission [12, 13, 11]. More broadly, genetic similarity between samples has been used to identify the parasite connectivity between human populations [14, 15, 16, 17, 18, 19], which may provide a pragmatic and cost-effective tool to inform control programs as they plan local elimination efforts [20].…”
Molecular data and analysis outputs are being integrated into malaria surveillance efforts to provide valuable programmatic insights for national malaria control programs (NMCPs). A plethora of studies from diverse geographies have demonstrated that malaria parasite genetic data can be an important tool for drug resistance monitoring, species identification, outbreak analysis, and transmission characterization. Despite many successful research efforts, there are still important knowledge gaps hindering practical translation of each of these use cases for NMCPs. Here, we leverage epidemiological modeling and time series data of 2035 genetic sequences collected in Thi`es, Senegal from 2006-2018 to provide a quantitative and setting-specific assessment of the levels, trends, and connectivity of malaria transmission. We also identify the genetic features that are the most informative for inferring transmission in Thi`es, such as the fraction of the population with multiple infections and the persistence of parasite lineages across multiple transmission seasons. The model fitting and uncertainty quantification framework also reveals a significant decrease in the level of malaria transmission around 2013. This difference coincides with a large-scale drought and bed net campaign by the NMCP and USAID and is independently corroborated by geo-spatial models of incidence in Thi`es. We find that genetically identical samples are more likely to be geographically clustered even at the neighborhood scale; and moreover, these lineages propagate non-randomly around the city. Our approach and results provide quantitative guidance for the interpretation of malaria parasite genetic data from Thi`es, Senegal and indicates the value of increased malaria genomic surveillance for NMCPs.
“…Re-infected participants were all from the same household (MS). Parasite genotypes from the first infection were genetically distinct from the re-infected parasites (Figure 4), and three of them (U5, U6 and U7) represented genotypes which had not been previously described, either in Thiès or in multiple regions of Senegal from 2006-present 14, 16, 21 .…”
Section: Resultsmentioning
confidence: 73%
“…Having such different parasites in the same household could be the result of a hotspot of local intense transmission 17 , coupled with genetic recombination (outcrossing) within the Anopheles mosquito 26 , and the subsequent transmission of new genetic combinations 17 . An alternative could be the importation of diverse genotypes due to human or mosquito mobility 21, 27–30 . A similar study of malaria incidence and prevalence has demonstrated the existence of malaria transmission hotspots at the village level in Senegal 7, 8 .…”
Molecular epidemiology using genomic data can help identify relationships between malaria parasite population structure, malaria transmission intensity, and ultimately help generate actionable data to assess the effectiveness of malaria control strategies. Genomic data, coupled with geographic information systems data, can further identify clusters or hotspots of malaria transmission, parasite genetic and spatial connectivity, and parasite movement by human or mosquito mobility over time and space. In this study, we performed longitudinal genomic surveillance in a cohort of 70 participants over four years from different neighborhoods and households in Thiès, Senegal—a region of exceptionally low malaria transmission (entomological inoculation rate (EIR) less than 1). Genetic identity (identity by state) was established using a 24 single nucleotide polymorphism molecular barcode and a multivariable linear regression model was used to establish genetic and spatial relationships. Our results show clustering of genetically similar parasites within households and a decline in genetic similarity of parasites with increasing distance. One household showed extremely high diversity and warrants further investigation as to the source of these diverse genetic types. This study illustrates the utility of genomic data with traditional epidemiological approaches for surveillance and detection of trends and patterns in malaria transmission not only by neighborhood but also by household. This approach can be implemented regionally and countrywide to strengthen and support malaria control and elimination efforts.
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