Biology subjects

Donfack, O. T.

Publications and source records attributed to Donfack, O. T..

3 recordsLinked to original sources

bioRxiv2024

Regular Plasmodium falciparum importation onto Bioko Island, Equatorial Guinea, hampers malaria elimination from the island

The Bioko Island Malaria Elimination Project (BIMEP) has made significant progress in reducing the prevalence of Plasmodium falciparum on Bioko Island, Equatorial Guinea. However, like other malaria endemic islands like Sao Tome and Principe and Zanzibar, Tanzania, elimination efforts are hampered by imported infections. In an effort to understand the local transmission dynamics and the influence of importation on Bioko Islands P. falciparum population, whole-genome sequences were generated from field samples collected during the BIMEPs 2019 Malaria Indicator Survey (MIS). Within the sub-Saharan African context, we observed Bioko Island parasites did not significantly differentiate from nearby continental neighbors. Among Bioko infections, within-host diversity and the quantity of polyclonal infections appear similar to an area of moderate malaria transmission. However, we observed higher than expected genetic diversity among Bioko parasites, similar to high transmission areas, suggesting imported strains are contributing to transmission on the island. Among Biokos closest geographical neighbors, the flow of parasites with Bioko appeared more pronounced with the Gabonese parasite population, implying more importation may be coming from this region than others. Overall, despite significant investment in malaria control, results illustrate the challenges of eliminating malaria without both interrupting local transmission and accounting for importation from higher transmission areas, likely due to human migration. For there to be sustained progress towards elimination, the BIMEP needs, if feasible, to conduct targeted interventions of outgoing/incoming travelers, and ideally expand malaria control interventions to the continental region of Equatorial Guinea. ImportancePlasmodium falciparum accounts for the majority of malaria deaths globally, with over 500,000 estimated deaths in 2023, predominantly in sub-Saharan Africa, despite strong investment in control and elimination interventions. Incorporating sequencing technologies into malaria surveillance is viewed as a powerful tool to improve control strategies, including identifying parasite transmission pathways. Here, we provide the first genomic characterization of P. falciparum on Bioko Island and Equatorial Guinea since malaria control began in 2004, and use these data to better understand the contribution of neighboring regions to Biokos P. falciparum population. Results highlight the need to account for offisland contributors to transmission and to understand how unmitigated transmission in neighboring regions can hamper progress. This study furthers our understanding of how the flow of parasites between regions impacts infectious disease control and provides foundational genomic data in a previously undescribed region that can be used to inform malaria elimination efforts.

genomics↗

bioRxiv2021

Analysis of nucleic acids extracted from rapid diagnostic tests reveals a significant proportion of false positive test results associated with recent malaria treatment

Surveillance programs often use malaria rapid diagnostic tests (RDTs) to determine the proportion of the population carrying parasites in their peripheral blood to assess the malaria transmission intensity. Despite an increasing number of reports on false-negative and false-positive RDT results, there is a lack of systematic quality control activities for RDTs deployed in malaria surveillance programs. Our study provides a larger scale comparative evaluation of RDTs used in the 2018 Malaria Indicator Survey (MIS) conducted on Bioko Island, Equatorial Guinea. We conducted a molecular analysis by extraction of nucleic acids from 1,800 negative and 1,065 positive RDTs followed by qPCR analysis. These results were combined with a dataset collected in a comprehensive questionnaire from each MIS participant. Of the 2,865 RDTs that were collected in 2018 on Bioko Island and analysed in our study, 4.7% had a false-negative result. These false-negative RDT results were associated with low parasite density infections. In a substantial proportion of samples, we identified masked pfhrp2 and pfhrp3 gene deletions in which at least one P. falciparum strain carried a gene deletion. Among all positive RDTs analysed, 28.4% were tested negative by qPCR and therefore considered to be false-positive. Analysing the questionnaire data collected from the participants, this high proportion of false-positive RDT results could be explained by PfHRP2 antigen persistence after recent malaria treatment. We conclude that malaria surveillance depending solely on RDTs needs well-integrated quality control procedures assessing the extend and impact of reduced sensitivity and specificity of RDTs on malaria control programs.

microbiology↗

bioRxiv2020

Measuring the accuracy of gridded human population density surfaces: a case study in Bioko Island, Equatorial Guinea

Geospatial datasets of population are becoming more common in models used for health policy. Publicly-available maps of human population in sub-Saharan Africa make a consistent picture from inconsistent census data, and the techniques they use to impute data makes each population map unique. Each mapping model explains its methods, but it can be difficult to know which map is appropriate for which policy work. Gold-standard census datasets, where available, are a unique opportunity to characterize maps by comparing them with truth. We use census data from Bioko Island, in Equatorial Guinea, to evaluate LandScan (LS), WorldPop (WP), and the High-Resolution Settlement Layer (HRSL). Each layer is compared to the gold-standard using statistical measures to evaluate distribution, error, and bias. We investigated how map choice affects burden estimates from a malaria prevalence model. Specific population layers were able to match the gold-standard distribution at different population densities. LandScan was able to most accurately capture highly urban distribution, HRSL matched best at all other lower population densities and WorldPop performed poorly everywhere. Correctly capturing empty pixels is key, and smaller pixel sizes (100 m vs 1 km) improve this. Normalizing areas based on known district populations increased performance. The use of differing population layers in a malaria model showed a disparity in results around transition points between endemicity levels. The metrics in this paper, some of them novel in this context, characterize how these population maps differ from the gold standard census and from each other. We show that the metrics help understand the performance of a population map within a malaria model. The closest match to the census data would combine LandScan within urban areas and the HRSL for rural areas. Researchers should prefer particular maps if health calculations have a strong dependency on knowing where people are not, or if it is important to categorize variation in density within a city.

ecology↗