Evidence for a role of Anopheles stephensi in the spread of drug- and diagnosis-resistant malaria in Africa

Anopheles stephensi, an Asian malaria vector, continues to expand across Africa. The vector is now firmly established in urban settings in the Horn of Africa. Its presence in areas where malaria resurged suggested a possible role in causing malaria outbreaks. Here, using a prospective case–control design, we investigated the role of An. stephensi in transmission following a malaria outbreak in Dire Dawa, Ethiopia in April–July 2022. Screening contacts of patients with malaria and febrile controls revealed spatial clustering of Plasmodium falciparum infections around patients with malaria in strong association with the presence of An. stephensi in the household vicinity. Plasmodium sporozoites were detected in these mosquitoes. This outbreak involved clonal propagation of parasites with molecular signatures of artemisinin and diagnostic resistance. To our knowledge, this study provides the strongest evidence so far for a role of An. stephensi in driving an urban malaria outbreak in Africa, highlighting the major public health threat posed by this fast-spreading mosquito.

Methods
Data collection: Data on the socio-demographic, epidemiological, intervention, and travel history were collected verbally using pre-tested questionnaires which were uploaded to mobile tablets using REDCap tools. The entomological survey data and intervention availability were scored by the study data collectors. Malaria case incidence data (from January 2019 to May 2022) were collected from the records of both private and public health facilities (n=34).

Data processing:  Data collection tools (mobile application version 5.20.11) were prepared and managed using REDCap electronic data capture tools hosted at AHRI. CSV files exported from REDCap were analyzed using STATA 17 (StataCorp., TX, USA), RStudio v.2022.12.0.353 (Posit, 2023), QGIS v.3.22.16 (QGIS Development Team, 2023. QGIS Geographic Information System. Open Source Geospatial Foundation Project), and GraphPad Prism 5.03 (Graph Pad Software Inc., CA, USA). All statistical analyses were performed in RStudio with packages lme4 (generalized linear mixed models) and dcifer.

Bioinformatic analysis: FASTQ files from multiplexed amplicon sequencing of P. falciparum were subjected to filtering, demultiplexing and allele inference using a Nextflow-based pipeline (https://github.com/EPPIcenter/mad4hatter). We used cut adapt to demultiplex reads for each locus based on the locus primer sequences (no mismatches or indels allowed), filter reads by length (100 base pairs) and quality (default NextSeq quality trimming). We used dada2 to infer variants and remove chimeras. Reads with a PHRED quality score of less than 5 were truncated. The leftmost base was trimmed and trimmed reads of less than 75 base pairs were filtered out. Default values were used for all other parameters. We then aligned alleles to their reference sequence and filtered out reads with low alignment. We masked homopolymers and tandem repeats to avoid false positives.

Epidemiological analysis: We used standard Case-Control analyses to examine the association between risk factors and malaria infection. It calculates point estimates and confidence intervals for the OR along with the significance level based on the chi squared test. Continuous variables were presented as median and interquartile range (IQR). Tests of association between two categorical variables were performed using contingency tables. Mann-Kendall statistical test was used to test for monotonic (increasing or decreasing) trends of malaria cases using the secondary data obtained from the private and public health facilities at the city and DDU.