Evaluating the performance of Plasmodium falciparum genetic metrics for inferring National Malaria Control Programme reported incidence in Senegal.

BACKGROUND: Genetic surveillance of the Plasmodium falciparum parasite shows great promise for helping National Malaria Control Programmes (NMCPs) assess parasite transmission. Genetic metrics such as the frequency of polygenomic (multiple strain) infections, genetic clones, and the complexity of infection (COI, number of strains per infection) are correlated with transmission intensity. However, despite these correlations, it is unclear whether genetic metrics alone are sufficient to estimate clinical incidence. METHODS: This study examined parasites from 3147 clinical infections sampled between the years 2012-2020 through passive case detection (PCD) across 16 clinic sites spread throughout Senegal. Samples were genotyped with a 24 single nucleotide polymorphism (SNP) molecular barcode that detects parasite strains, distinguishes polygenomic (multiple strain) from monogenomic (single strain) infections, and identifies clonal infections. To determine whether genetic signals can predict incidence, a series of Poisson generalized linear mixed-effects models were constructed to predict the incidence level at each clinical site from a set of genetic metrics designed to measure parasite clonality, superinfection, and co-transmission rates. RESULTS: Model-predicted incidence was compared with the reported standard incidence data determined by the NMCP for each clinic and found that parasite genetic metrics generally correlated with reported incidence, with departures from expected values at very low annual incidence (< 10/1000/annual [‰]). CONCLUSIONS: When transmission is greater than 10 cases per 1000 annual parasite incidence (annual incidence > 10‰), parasite genetics can be used to accurately infer incidence and is consistent with superinfection-based hypotheses of malaria transmission. When transmission was < 10‰, many of the correlations between parasite genetics and incidence were reversed, which may reflect the disproportionate impact of importation and focal transmission on parasite genetics when local transmission levels are low.

Investigating the etiologies of non-malarial febrile illness in Senegal using metagenomic sequencing.

The worldwide decline in malaria incidence is revealing the extensive burden of non-malarial febrile illness (NMFI), which remains poorly understood and difficult to diagnose. To characterize NMFI in Senegal, we collected venous blood and clinical metadata in a cross-sectional study of febrile patients and healthy controls in a low malaria burden area. Using 16S and untargeted sequencing, we detected viral, bacterial, or eukaryotic pathogens in 23% (38/163) of NMFI cases. Bacteria were the most common, with relapsing fever Borrelia and spotted fever Rickettsia found in 15.5% and 3.8% of cases, respectively. Four viral pathogens were found in a total of 7 febrile cases (3.5%). Sequencing also detected undiagnosed Plasmodium, including one putative P. ovale infection. We developed a logistic regression model that can distinguish Borrelia from NMFIs with similar presentation based on symptoms and vital signs (F1 score: 0.823). These results highlight the challenge and importance of improved diagnostics, especially for Borrelia, to support diagnosis and surveillance.

Evaluating the performance of Plasmodium falciparum genetics for inferring National Malaria Control Program reported incidence in Senegal.

Genetic surveillance of the Plasmodium falciparum parasite shows great promise for helping National Malaria Control Programs (NMCPs) assess parasite transmission. Genetic metrics such as the frequency of polygenomic (multiple strain) infections, genetic clones, and the complexity of infection (COI, number of strains per infection) are correlated with transmission intensity. However, despite these correlations, it is unclear whether genetic metrics alone are sufficient to estimate clinical incidence. Here, we examined parasites from 3,147 clinical infections sampled between the years 2012-2020 through passive case detection (PCD) across 16 clinic sites spread throughout Senegal. Samples were genotyped with a 24 single nucleotide polymorphism (SNP) molecular barcode that detects parasite strains, distinguishes polygenomic (multiple strain) from monogenomic (single strain) infections, and identifies clonal infections. To determine whether genetic signals can predict incidence, we constructed a series of Poisson generalized linear mixed-effects models to predict the incidence level at each clinical site from a set of genetic metrics designed to measure parasite clonality, superinfection, and co-transmission rates. We compared the model-predicted incidence with the reported standard incidence data determined by the NMCP for each clinic and found that parasite genetic metrics generally correlated with reported incidence, with departures from expected values at very low annual incidence (<10/1000/annual [‰]). When transmission is greater than 10 cases per 1000 annual parasite incidence (annual incidence >10 ‰), parasite genetics can be used to accurately infer incidence and is consistent with superinfection-based hypotheses of malaria transmission. When transmission was <10 ‰, we found that many of the correlations between parasite genetics and incidence were reversed, which we hypothesize reflects the disproportionate impact of importation and focal transmission on parasite genetics when local transmission levels are low.

Malaria surveillance reveals parasite relatedness, signatures of selection, and correlates of transmission across Senegal.

We here analyze data from the first year of an ongoing nationwide program of genetic surveillance of Plasmodium falciparum parasites in Senegal. The analysis is based on 1097 samples collected at health facilities during passive malaria case detection in 2019; it provides a baseline for analyzing parasite genetic metrics as they vary over time and geographic space. The study's goal was to identify genetic metrics that were informative about transmission intensity and other aspects of transmission dynamics, focusing on measures of genetic relatedness between parasites. We found the best genetic proxy for local malaria incidence to be the proportion of polygenomic infections (those with multiple genetically distinct parasites), although this relationship broke down at low incidence. The proportion of related parasites was less correlated with incidence while local genetic diversity was uninformative. The type of relatedness could discriminate local transmission patterns: two nearby areas had similarly high fractions of relatives, but one was dominated by clones and the other by outcrossed relatives. Throughout Senegal, 58% of related parasites belonged to a single network of relatives, within which parasites were enriched for shared haplotypes at known and suspected drug resistance loci and at one novel locus, reflective of ongoing selection pressure.

Improving diagnosis of non-malarial fevers in Senegal: Borrelia and the contribution of tick-borne bacteria.

The worldwide decline in malaria incidence is revealing the extensive burden of non-malarial febrile illness (NMFI), which remains poorly understood and difficult to diagnose. To characterize NMFI in Senegal, we collected venous blood and clinical metadata from febrile patients and healthy controls in a low malaria burden area. Using 16S and unbiased sequencing, we detected viral, bacterial, or eukaryotic pathogens in 29% of NMFI cases. Bacteria were the most common, with relapsing fever Borrelia and spotted fever Rickettsia found in 15% and 3.7% of cases, respectively. Four viral pathogens were found in a total of 7 febrile cases (3.5%). Sequencing also detected undiagnosed Plasmodium, including one putative P. ovale infection. We developed a logistic regression model to distinguish Borrelia from NMFIs with similar presentation based on symptoms and vital signs. These results highlight the challenge and importance of improved diagnostics, especially for Borrelia, to support diagnosis and surveillance.

Ex vivo RSA and pfkelch13 targeted-amplicon deep sequencing reveal parasites susceptibility to artemisinin in Senegal, 2017.

BACKGROUND: Malaria control is highly dependent on the effectiveness of artemisinin-based combination therapy (ACT), the current frontline malaria curative treatment. Unfortunately, the emergence and spread of parasites resistant to artemisinin (ART) derivatives in Southeast Asia and South America, and more recently in Rwanda and Uganda (East Africa), compromise their long-term use in sub-Saharan Africa, where most malaria deaths occur. METHODS: Here, ex vivo susceptibility to dihydroartemisinin (DHA) was evaluated from 38 Plasmodium falciparum isolates collected in 2017 in Thiès (Senegal) expressed in the Ring-stage Survival Assay (RSA). Both major and minor variants were explored in the three conserved-encoding domains of the pfkelch13 gene, the main determinant of ART resistance using a targeted-amplicon deep sequencing (TADS) approach. RESULTS: All samples tested in the ex vivo RSA were found to be susceptible to DHA (parasite survival rate < 1%). The non-synonymous mutations K189T and K248R in pfkelch13 were observed each in one isolate, as major (99%) or minor (5%) variants, respectively. CONCLUSION: The results suggest that ART is still fully effective in the Thiès region of Senegal in 2017. Investigations combining ex vivo RSA and TADS are a useful approach for monitoring ART resistance in Africa.

Malaria surveillance reveals parasite relatedness, signatures of selection, and correlates of transmission across Senegal.

Parasite genetic surveillance has the potential to play an important role in malaria control. We describe here an analysis of data from the first year of an ongoing, nationwide program of genetic surveillance of Plasmodium falciparum parasites in Senegal, intended to provide actionable information for malaria control efforts. Looking for a good proxy for local malaria incidence, we found that the best predictor was the proportion of polygenomic infections (those with multiple genetically distinct parasites), although that relationship broke down in very low incidence settings (r = 0.77 overall). The proportion of closely related parasites in a site was more weakly correlated ( r = -0.44) with incidence while the local genetic diversity was uninformative. Study of related parasites indicated their potential for discriminating local transmission patterns: two nearby study areas had similarly high fractions of relatives, but one area was dominated by clones and the other by outcrossed relatives. Throughout the country, 58% of related parasites proved to belong to a single network of relatives, within which parasites were enriched for shared haplotypes at known and suspected drug resistance loci as well as at one novel locus, reflective of ongoing selection pressure.

Ex vivo RSA and Pfkelch13 targeted-amplicon deep sequencing reveal parasites susceptibility to artemisinin in Senegal, 2017.

INTRODUCTION: Malaria control is highly dependent on the effectiveness of artemisinin-based combination therapies (ACTs), the current frontline malaria curative treatments. Unfortunately, the emergence and spread of parasites resistant to artemisinin (ART) derivatives in Southeast Asia and South America, and more recently in Rwanda and Uganda (East Africa), compromise their long-term use in Sub-Saharan Africa where most malaria deaths occur. METHODS: Here, we evaluated ex vivo susceptibility to dihydroartemisinin (DHA) from 38 P. falciparum isolates collected in 2017 in Thiès (Senegal) expressed with the Ring-stage Survival Assay (RSA). We explored major and minor variants in the full Pfkelch13 gene, the main determinant of ART resistance using a targeted-amplicon deep sequencing (TADS) approach. RESULTS: All samples tested in the ex vivo RSA were found to be susceptible to DHA. Both non-synonymous mutations K189T and K248R were observed each in one isolate, as major (99%) or minor (5%) variants, respectively. CONCLUSION: Altogether, investigations combining ex vivo RSA and TADS are a useful approach for monitoring ART resistance in Africa.

Genomic investigation of a dengue virus outbreak in Thiès, Senegal, in 2018.

Dengue virus is a major and rapidly growing public health concern in tropic and subtropic regions across the globe. In late 2018, Senegal experienced its largest dengue virus outbreak to date, covering several regions. However, little is known about the genetic diversity of dengue virus (DENV) in Senegal. Here we report complete viral genomes from 17 previously undetected DENV cases from the city of Thiès. In total we identified 19 cases of DENV in a cohort of 198 individuals with fever collected in October and November 2018. We detected 3 co-circulating serotypes; DENV 3 was the most frequent accounting for 11/17 sequences (65%), 4 (23%) were DENV2 and 2 (12%) were DENV1. Sequences were most similar to recent sequences from West Africa, suggesting ongoing local circulation of viral populations; however, detailed inference is limited by the scarcity of available genomic data. We did not find clear associations with reported clinical signs or symptoms, highlighting the importance of testing for diagnosing febrile diseases. Overall, these findings expand the known range of DENV in Senegal, and underscore the need for better genomic characterization of DENV in West Africa.

Quality control of malaria microscopy reveals misdiagnosed non-falciparum species and other microscopically detectable pathogens in Senegal.

BACKGROUND: In developing countries, malaria diagnosis relies on microscopy and rapid diagnostic tests. In Senegal, national malaria control program (NMCP) regularly conducts supervisory visits in health services where malaria microscopy is performed. In this study, expert microscopists assessed the performance of laboratory technicians in malaria microscopy. METHODS: The present external quality assessment (EQA) was conducted in three different areas of malaria transmission. Participants were laboratory technicians previously trained by NMCP on malaria microscopy. Stored read slides were randomly collected for blinded re-checking by expert microscopists. At the same time a set of 8 slides (3 positive P. falciparum and 5 negative slides) were submitted to participants for proficiency testing. Microscopists performance were evaluated on the basis of the errors rates on slide reading-high false positive (HFP), high false negative (HFN), low false positive (LFP) and low false negative (LFN)-and the calculation of their sensitivities and specificities relative to expert microscopy. Data were entered and analysed using Microsoft Excel software. RESULTS: A total of 450 stored slides were collected from 17 laboratories for re-checking. Eight laboratories scored 100% of correct reading. Only one major error was recorded (HFP). Six laboratories recorded LFN results: Borrelia, P. ovale, and low parasite densities (95 and 155 p/µl) were missed. Two P. falciparum slides were misidentified as P. malariae and one P. ovale slide as P. vivax. The overall sensitivities and specificities for all participants against expert microscopists were 97.8 and 98.2% respectively; Sensitivities and specificities of hospital microscopists (96.7 and 98.9%) were statistically similar to those of health centre microscopists (98.5 and 97.8% respectively) (p = 0.3993 and p = 0.9412 respectively). Overall, a very good agreement was noted with kappa value of 0.96 (CI95% 93.4-98.6%) relative to expert microscopy. Proficiency testing showed that among the 17 participants, 11 laboratories scored 100% of correct reading. Three LFN and four LFP results were recorded respectively. The P. falciparum slide with Maurer dots was misidentified as P. ovale in 1 centre and the same slide was misread as P. vivax in another centre; No major error (HFP or HFN) was noted. CONCLUSION: EQA of malaria microscopy showed an overall good performance especially regarding P. falciparum detection. However, efforts need to be made addressing the ability to detect non-falciparum species and others endemic blood pathogens such as Borrelia. The further NMCP training sessions and evaluations should consider those aspects to expect high quality-assured capacity for malaria microscopy.

Assessment of the utility of a symptom-based algorithm for identifying febrile patients for malaria diagnostic testing in Senegal.

BACKGROUND: Malaria rapid diagnostic tests (RDTs) enable point-of-care testing to be nearly as sensitive and specific as reference microscopy. The Senegal National Malaria Control Programme introduced RDTs in 2007, along with a case management algorithm for uncomplicated febrile illness, in which the first step stipulates that if a febrile patient of any age has symptoms indicative of febrile illness other than malaria (e.g., cough or rash), they would not be tested for malaria, but treated for the apparent illness and receive an RDT for malaria only if they returned in 48 h without improvement. METHODS: A year-long study in 16 health posts was conducted to determine the algorithm's capacity to identify patients with Plasmodium falciparum infection identifiable by RDT. Health post personnel enrolled patients of all ages with fever (≥37.5 °C) or history of fever in the previous 2 days. After clinical assessment, a nurse staffing the health post determined whether a patient should receive an RDT according to the diagnostic algorithm, but performed an RDT for all enrolled patients. RESULTS: Over 1 year, 6039 patients were enrolled and 58% (3483) were determined to require an RDT according to the algorithm. Overall, 23% (1373/6039) had a positive RDT, 34% (1130/3376) during rainy season and 9% (243/2661) during dry season. The first step of the algorithm identified only 78% of patients with a positive RDT, varying by transmission season (rainy 80%, dry 70%), malaria transmission zone (high 75%, low 95%), and age group (under 5 years 68%, 5 years and older 84%). CONCLUSIONS: In all but the lowest malaria transmission zone, use of the algorithm excludes an unacceptably large proportion of patients with malaria from receiving an RDT at their first visit, denying them timely diagnosis and treatment. While the algorithm was adopted within a context of malaria control and scarce resources, with the goal of treating patients with symptomatic malaria, Senegal has now adopted a policy of universal diagnosis of patients with fever or history of fever. In addition, in the current context of malaria elimination, the paradigm of case management needs to shift towards the identification and treatment of all patients with malaria infection.