Skip to main content

Effects of indoor residual spraying and outdoor larval control on Anopheles coluzzii from São Tomé and Príncipe, two islands with pre-eliminated malaria

Abstract

Background

Vector control is a key component of malaria prevention. Two major vector control strategies have been implemented in São Tomé and Príncipe (STP), indoor residual spraying (IRS) and outdoor larval control using Bacillus thuringiensis israelensis (Bti). This study evaluated post-intervention effects of control strategies on vector population density, composition, and knockdown resistance mutation, and their implications for malaria epidemiology in STP.

Methods

Mosquitoes were collected by indoor and outdoor human landing catches and mosquito light traps in seven districts. Mosquito density was calculated by numbers of captured adult mosquitoes/house/working hour. Mitochondrial cytochrome c oxidase subunit I (COI) was PCR amplified and sequenced to understand the spatial–temporal population composition of malaria vector in STP. Knockdown resistance L1014F mutation was detected using allele-specific PCR. To estimate the malaria transmission risks in STP, a negative binomial regression model was constructed. The response variable was monthly incidence, and the explanatory variables were area, rainfall, entomological inoculation rate (EIR), and kdr mutation frequency.

Results

Malaria vector in STP is exophilic Anopheles coluzzii with significant population differentiation between Príncipe and São Tomé (mean FST = 0.16, p < 0.001). Both vector genetic diversity and knockdown resistance mutation were relatively low in Príncipe (mean of kdr frequency = 15.82%) compared to São Tomé (mean of kdr frequency = 44.77%). Annual malaria incidence rate in STP had been rapidly controlled from 37 to 2.1% by three rounds of country-wide IRS from 2004 to 2007. Long-term application of Bti since 2007 kept the mosquito density under 10 mosquitoes/house/hr/month, and malaria incidence rate under 5% after 2008, except for a rising that occurred in 2012 (incidence rate = 6.9%). Risk factors of area (São Tomé compared to Príncipe), rainfall, outdoor EIR, and kdr mutation frequency could significantly increase malaria incidence by 9.33–11.50, 1.25, 1.07, and 1.06 fold, respectively.

Conclusions

Indoor residual spraying could rapidly decrease Anopheles density and malaria incidence in STP. Outdoor larval control using Bti is a sustainable approach for controlling local vector with exophilic feature and insecticide resistance problem. Vector control interventions should be intensified especially at the north-eastern part of São Tomé to minimize impacts of outbreaks.

Background

Malaria, a disease caused by Plasmodium parasites transmitted by Anopheles species, was responsible for 219 million malaria cases and 435 thousand deaths globally in 2017 [1]; about 90% of malaria cases and deaths occurred in Africa. The dominant malaria vectors in Africa belong to the Anopheles gambiae complex or the Anopheles funestus complex, which have long lifespans and strong human-biting habits [2].

São Tomé and Príncipe (STP) is an island nation located in the Gulf of Guinea, Central West Africa. It mainly consists of two islands, São Tomé main island and Príncipe offshore island. The reported malaria vector in STP is Anopheles gambiae M form [3,4,5], also known as Anopheles coluzzii [6], a cryptic species mainly found in West Africa belonging to the An. gambiae complex [7]. Another Anopheles species that can be found in STP is Anopheles coustani [3], but it is not considered responsible for the transmission of local malaria due to its small population. Unlike the African continent which is inhabited by various vector species [2, 8], malaria vector species in STP is less diverse and more unique due to its geographic isolation. Previous studies used genetic markers such as mitochondrial NADH dehydrogenase subunit 5 (ND5), rDNA intergenic spacer (IGS) and internal transcribed spacer, microsatellite DNA, and transposable elements for understanding the origins and structures of An. coluzzii, and the possibilities of implementing transgenic technologies in STP [4, 5, 9, 10]. Recently, DNA barcoding by cytochrome c oxidase subunit I (COI) has gained increasing popularity due to its ease of amplification, high copy number, lack of recombination, and constant evolutionary rate [11, 12]. It can be used to identify species, estimate phylogenies among closely related taxa, and trace evolutionary history [13]. It is especially beneficial for identifying cryptic species that are difficult to distinguish by morphology. For example, COI was used to reconstruct the molecular phylogeny of cryptic species members in Anopheles hyrcanus group in Asia [14], and Anopheles albitarsis complex in South America [15]. Thus, COI has become a popular and basic marker for understanding the species composition in vector mosquitoes. This study aims to explore the vector population composition by comparing COI sequences in Anopheles mosquitoes within STP, and with those from other African continental countries.

In the early 1980s, a national control programme spraying DDT in STP failed to reduce malaria incidence and effective population size of vector [16, 17]. In response to the United Nations Millennium Development Goal 6 during 2000 to 2015, Taiwan Anti-Malaria Advisory Mission collaborated with Centro National de Endemias of STP to scale up the national malaria prevention programme since 2004 [18]. During 2004 to 2006, implementation of IRS by alpha-cypermethrin successfully decreased the prevalence of malaria parasitaemia in children under 9-years-old from 20.1 to 0.6% [19]. In 2007, integrated vector management (IVM) was also implemented. IVM takes ecological and epidemiological characteristics of vectors into consideration [20], and the programme did so by applying an alternative vector control strategy using the microorganism Bacillus thuringiensis israelensis (Bti) to kill mosquito larvae. Both early and late instars of Anopheles larvae immediately responded to Bti application and reached 100% mortality in the laboratory and field [21, 22]. Other control strategies implemented in STP are intermittent preventive treatment in pregnancy since 2004, distribution of long-lasting insecticidal nets (LLINs) and the use of artemisinin-based combination therapy (ACT) since 2005, and mass-screening by rapid diagnostic test since 2008 [18, 23]. Due to the success of these control strategies, STP is on track for a 20–40% reduction in incidence by 2020, which is one of the targets in World Health Organization (WHO) Global Technical Strategy for Malaria 2016–2030 [24]. Furthermore, Príncipe island has reached the criterion of malaria pre-elimination [25]. The path to eliminating malaria should be promising under the premise of continued long-term follow-ups. Longitudinal monitoring of vector dynamic changes post-interventions and challenges such as potential insecticide resistance would be addressed in this study.

Severe chemical insecticide resistance is a major public health issue in many countries. According to WHO’s Global Report on Insecticide Resistance in Malaria Vectors: 2010–2016 [26], 68 countries have reported resistance to at least one class of insecticide, 57 of which reported resistance to two or more. Pyrethroid is the most commonly used insecticide class in IRS and insecticide-treated nets (ITNs). The L1014F and L1014S replacements are the major knockdown resistance mutations in An. gambiae sensu lato (s.l.) [27]. Anopheles coluzzii is a species that originally carried low kdr L1014F mutation (6.3–40%) before 2006 in West Africa [28]. However, after the year of 2010, there have been several reports on the development of high frequency of the kdr L1014F mutation (over 80–90%) showing up in An. coluzzii from Central and West Africa [29,30,31,32,33]. Although alpha-cypermethrin has shown a significant prevention effect against malaria transmission in STP, excessive use of insecticide can result in serious insecticide resistance problem. Since the insecticide resistance data has not been systematically surveyed in STP, this study aimed to conduct a longitudinal and country-wide survey on kdr in the local malaria vector collected from 2010 to 2016 to clarify the pyrethroid resistance status and its impact on malaria transmission in STP.

Malaria control and elimination in geographically isolated islands are less complicated compared to continental areas [34]. It is important to understand the transmission patterns on this isolated island nation for eliminating malaria. This study focused on investigating the characteristics of malaria vector and its implications for malaria epidemiology in STP. Spatial and temporal variations of vector density, population genetics, knockdown resistance mutation, and their potential risks to malaria epidemics form the core of this study.

Methods

Ethics statement

The content and methods used in this study have been reviewed and approved by the Centro Nacional de Endemias of the Ministry of Health in STP (official No. OF0N019/P0CNE/2016) and the Research Ethics Committee of National Taiwan University Hospital (NTUH-REC No.: 201110023RD).

Study site

São Tomé and Príncipe islands are both located on the Cameroon Volcanic Line separated by 150 km of ocean. The total area is approximately 1001 km2 inhabited by nearly 200 thousand residents (source: Wikipedia- https://en.wikipedia.org/wiki/S%C3%A3o_Tom%C3%A9_and_Pr%C3%ADncipe). Príncipe is an autonomous region where only 5% of the total population resides. There are six administrative districts in São Tomé, Água Grande (AG), Mé-Zóchi (MZ), Lobata (LO), Cantagalo (CT), Lembá (LE), and Caué (CU). With volcanic mountains distributed in southwest of São Tomé from Lembá to Caué, population are concentrated in the plains along the east coast. The capital São Tomé is in the district of Água Grande. The climate is equatorial with average temperatures around 25–27 °C. Three seasons were categorized based on rainfall in this study (Source: World Weather Online- https://www.worldweatheronline.com/sao-tome-weather-averages/sao-tome/st.aspx), which are rainy season from January to May, dry season from June to September, and heavy rain season from October to December.

Malaria vector control methods in STP

The time chart of malaria interventions from 2003 to 2016 was shown in Additional file 1: Fig. S1. Most interventions were introduced or scaled-up after 2004. In the early stage, STP applied three rounds of country-wide IRS with alpha-cypermethrin at a dosage of 50 mg/m2 from 2004 to 2007 with population coverage of 93.8% [19]. From 2007 to 2013, the programme shifted to using Bti for outdoor larval control. Two kinds of Bti formulation were applied in STP. Granular Bti 200 (VectoBac® G, serotype H-14, Lot number 145-077-N8, 200 international toxic units (ITU)/mg, Valent Bioscience Corporation, Libertyville, USA) was used at the dosage of 1 g/m2. Each province hired three technicians who spread the granules outdoors every day, with one application per location per week. The formulation was temporarily changed to Bti 3000 (VectoBac® WG, strain AM65-52 fermentation, Lot number 60215-08-03, 3000 ITU/mg, Valent Bioscience Corporation, Libertyville, USA) in São Tomé during 2011–2012, and a 10-L hand-press bucket pump was used to spray. The locations where Bti was applied were large areas of shallow accumulated water, permanent and temporary breeding sites.

Mosquito collection

Female Anopheles mosquitoes were captured in a total of 16 sites from 2010 to 2016 (Fig. 1). Each district has two collection sites, except for Água Grande which has four collection sites. Mosquito samples were collected by human landing catches (HLCs) from 9 p.m. to 2 a.m. inside and outside the house during their blood feeding period, and mosquito light traps (MLTs) hanging indoor and outdoor overnight. The preventive rapid screening tests (SD BIOLINE Malaria Ag P.f/Pan test, Abbott Laboratories, Illinois, USA) and mefloquine prophylaxis (Apotex Inc., Ontario, Canada) [35, 36] were provided for diminishing malaria infection risks of local workers who have been trained and in charge of capturing mosquitoes. The procedure was approved by the Institutional Research Ethics Committee: NTUH-REC No.: 201110023RD. The collected mosquitoes were preserved in tubes with cotton flap and silica gel for laboratory analysis [37]. After morphological identification, samples were stored in room temperature for molecular analyses.

Fig. 1
figure1

Map of mosquito collection sites in STP. Female adult mosquitoes were collected in two to four sites per district from 2010 to 2016. The seven districts are Príncipe (PR), Lobata (LO), Água Grande (AG, the colored region), Mé-Zóchi (MZ), Cantagalo (CT), Caué (CU), and Lembá (LE). The collection sites from north to south, east to west are Rua Trabalhadores (RT), Porto Real (PoR), Micolo (Mi), Conde (Cd), Praia Gamboa (PG), Bairro da Liberdade (BL), Riboque (Rb), Madre Deus (MD), Praia Melão (PM), Trindade (Tr), Zandrigo (Za), Ribeira Afonso (RA), Angolares (An), Emolve (Ev), Neves (Nv), and Generosa (Ge). The framed locations are the seven major sentinel sites which had integrated records of mosquito density. This map was drawn manually using QGIS ver 2.18

Mosquito density calculation

Mosquito density was calculated in seven major sentinel sites (Fig. 1, framed locations) including Porto Real (PoR), Micolo (Mi), Praia Gamboa (PG), Trindade (Tr), Ribeira Afonso (RA), Angolares (An), and Neves (Nv). Density was calculated by numbers of human landing captured adult mosquitoes/house/working hour/month. Annual average mosquito density of indoor and outdoor HLCs from 2004 to 2016 was calculated and compared between Príncipe and São Tomé islands and different control strategies. In order to explore the detailed density trend during 2010 to 2016, monthly density were compared between different collection methods, districts, years, and seasons by one-way analysis of variance (ANOVA) and Tukey’s test using R version 3.5.1.

Mosquito DNA extraction

Genomic DNA was extracted from 1923 individual mosquitoes collected from 2010 to 2016. The SpeedMill PLUS instrument (Analytik Jena AG, Jena, Germany) was used to homogenize mosquitoes, and the Genomic DNA Mini Kit for Tissue (Geneaid Biotech Ltd., Taipei, Taiwan) was used to extract DNA. Each mosquito was homogenized in a 1.5 mL Eppendorf tube containing a 3 mm sterilized stainless steel bead. A volume of 400 μL lysis buffer was added to break the mosquito debris. Samples were then incubated and washed according to the protocol from Genomic DNA Mini Kit for Tissue (Geneaid Biotech Ltd., Taipei, Taiwan). A volume of 50–100 μL DNA was eluted from each mosquito sample.

Polymerase chain reaction (PCR) and sequencing

About 25–30 samples collected per year among districts were selected for IGS form-specific identification and COI sequencing (Additional file 2: Table S1). From 2010 to 2016, a total of 205 mosquitoes were pre-identified by form-specific IGS using PCR-restriction fragment length polymorphism (RFLP) method described by Fanello et al. [38]. Twenty PCR products of IGS were sequenced to confirm the form-specific RFLP results. Mitochondrial COI was amplified using three pairs of primers (Additional file 2: Table S2) for construction of a full length of 1506-bp sequence in the 205 samples [39, 40]. PCR analysis used 10 μL of 2X HotStarTaq Master Mix (Qiagen, Hilden, Germany), 10 μM of forward and reverse primers, 2 μL of DNA template, and 6 μL of RNAse free water to a total volume of 20 μL. After confirming PCR products by 2% agarose gel electrophoresis, the products were sequenced by Applied Biosystems 3730xl DNA Analyzer (Thermo Fisher Scientific, Waltham, USA). If any mixed nucleotide appeared in the direct sequence of PCR product, cloning system by PCR-4-TOPO and One Shot™ TOP10 Chemically Competent Escherichia coli (Thermo Fisher Invitrogen, Carlsbad, USA) would be performed. The plasmid of the colony would be sequenced using M13 primers to confirm the nucleotide polymorphism in the sequence.

COI sequence analysis

The 205 COI sequences were aligned using Lasergene SeqMan ver 7.1 (DNASTAR Inc., Madison, USA) and BioEdit ver 7.0 [41]. Phylogenetic analysis was carried out by MEGA7 [42]. Neighbor-joining (NJ) tree of COI sequences was bootstrapped for 1000 times using Kimura 2-parameter model [43]. DNA polymorphism, Tajima’s DT, Fu’s Fs, Fu & Li’s F* and D* neutrality tests were performed by Dnasp6 [44]. A statistical parsimony TCS network was depicted using PopART ver 1.7 [45] to infer the genealogical relationships among haplotypes. Pairwise fixation index (FST) and hierarchical analysis of molecular variance (AMOVA) were analysed by Arlequin ver 3.5 [46]. Gene flow (Nm, number of migrants) was calculated by (1 − FST)/4*FST [47].

Knockdown resistance genotyping and calculation of mutation frequency

The 1014F kdr mutation was screened in the DNA of 1923 mosquitoes by allele-specific PCR as described in Martinez-Torres et al. [48]. Three genotypes, susceptible homozygotes (SS), resistant homozygotes (RR), and heterozygotes (RS) were identified by gel electrophoresis after PCR. Four samples from each genotype were sequenced to confirm the results from allele-specific PCR. The N1575Y kdr mutation was also screened in 30 samples across the study period using PCR-sequencing [49]. Acetylcholinesterase-1 (ace-1) G119S target site mutation of carbamate and organophosphate was screened in 345 samples across the study period using PCR–RFLP method [50].

The kdr mutation frequency and 95% confidence interval (CI) were estimated by Genepop ver 4.2 using maximum likelihood estimation [51, 52]. Comparison of kdr mutation frequencies between different districts was assessed by one-way ANOVA and Tukey’s test. The kdr mutation frequency trend by year was assessed by Pearson correlation analysis.

Risk characterization of geo-environmental and vectorial factors to malaria incidence

To explain malaria incidence with geo-environmental and vectorial factors in STP, a negative binomial regression model was fitted. Malaria cases were diagnosed and confirmed by microscopic examination of blood slide and rapid diagnostic test. The diagnostic results would be imported into the electronic medical record system. Malaria case numbers of aged < 5 and 5, and slide positivity rate (SPR) of fever patients in the medical record system were calculated and organized by the Taiwan Anti-Malaria Advisory Mission. Case management was carried out for 28 days post-treatment to confirm whether patients were completely cured. Population estimates by district per year were referred from official documents at Instituto Nacional de Estatística, São Tomé and Príncipe (https://www.ine.st/index.php/publicacao/documentos/category/76-dados-localidade-projecoes). This study assumed that the monthly population per district was the same as the annual estimates. Monthly incidence was calculated by number of monthly malaria cases by district/annual population estimates by district.

The considered covariates were (i) area divided by human population density and geography (urban: AG, MZ, LO, CT; rural: LE, CU; offshore island: PR) (ii) rainfall for 1-month lag (data retrieved from World Weather Online: https://www.worldweatheronline.com/sao-tome-weather-averages/sao-tome/st.aspx) (iii) entomological inoculation rate calculated by mosquito density of indoor or outdoor HLCs multiplied a sporozoite rate of 0.5% [3] (assuming that the sporozoite rate was fixed under low transmission scenario) (iv) kdr mutation frequency by season (because the sample size of analysed mosquitoes per month was too small, kdr mutation frequency was calculated per season). All statistical analyses were performed by R version 3.5.1.

Results

Dynamic changes of the density of Anopheles mosquitoes

The annual average of Anopheles mosquito density by indoor and outdoor HLCs from 2004 to 2016 under different control strategies was shown in Fig. 2. First, mosquitoes captured by outdoor HLCs (density range: 0.58–51.69) was higher than indoor HLCs (density range: 0.04–3.39). Second, mosquito density of outdoor HLCs in São Tomé (range: 1.17–51.69) was mostly higher than that in Príncipe (range: 0.58–10.75). However, mosquito density of indoor HLCs was mostly higher in Príncipe than São Tomé. Third, mosquito density on both islands were at the highest at the beginning of IRS application, which outdoor density in São Tomé and Príncipe were 51.69 and 10.75, and indoor density were 3.39 and 1.24, respectively. After the first round of IRS programme completed at the end of 2004, the outdoor density in the next year substantially dropped by 8.57-fold in São Tomé and 2.09-fold in Príncipe, and the indoor density dropped by 17.84-fold in São Tomé and 1.19-fold in Príncipe. This result demonstrated that IRS by alpha-cypermethrin could rapidly decrease Anopheles density at an early stage. However, after the second and third round of IRS completed in 2005 and 2006, the density slightly increased during 2006 to 2007. The programme, therefore, shifted focus to outdoor larval control using Bti 200 starting from 2007. During the period of Bti control, the density was gradually declined except for a fluctuation in 2012–2013 when STP temporarily changed from Bti 200 to Bti 3000. Nevertheless, outdoor larval control by Bti showed long-term efficacy for controlling mosquito density below 10.

Fig. 2
figure2

Annual average density of Anopheles mosquitoes by outdoor and indoor human landing catch (HLC) in São Tomé and Príncipe from 2004 to 2016. The average indoor and outdoor mosquito density in Sao Tome (blue and red line, respectively) was calculated from six sentinel sites in six districts (Micolo, Praia Gamboa, Trindade, Ribeira Afonso, Angolares, and Neves). The indoor and outdoor mosquito density in Príncipe (green and purple line, respectively) was calculated at one collection site, Porto Real. The Y axis (density) was shown in a scale factor of two. Mosquito density was substantially decreased during IRS control period (2004–2006). Larval control by Bti had long-term efficacy to keep the outdoor mosquito density under 10 (the dashed line)

Monthly vector density from 2010 to 2016 in seven sentinel sites showed the mean density of outdoor HLCs and MLTs (1.87 and 1.78) were significantly higher than those by indoor HLCs and MLTs (0.13 and 0.55). Concluding density from four methods of mosquito collection, spatial variations showed urban districts, Água Grande (AG, mean = 2.18) and Lobata (LO, mean = 2.01), had significantly higher vector density compared to rural districts, Lembá (LE, mean = 0.49) and Caué (CU, mean = 0.46). Temporal comparison showed a significant lower density in 2014–2016 (mean = 0.34–0.56) compared to 2010–2013 (mean = 1.30–2.49). The lower mosquito density occurred during 2014 to 2016 (annual average = 1.17–1.59) was in accordance with the low malaria incidence rate (0.9–1%) at the same period (Fig. 3). The seasonal effect of mosquito density was shown in Additional file 2: Table S3. Mean density was significantly lower in dry season compared to heavy rain season (mean difference = − 0.64, p < 0.001) and rainy season (mean difference = − 0.67, p < 0.001). No significant difference was observed between heavy rain season and rainy season.

Fig. 3
figure3

Malaria incidence rate, mosquito density, and kdr mutation frequency in STP from 2010 to 2016. The histogram shows average mosquito density by four collection methods (HLCs = human landing catches; MLTs = mosquito light traps), and the bars show the standard deviation. Black line is the malaria incidence rate (case numbers/total population*100%). Green and blue lines are the kdr mutation frequency (10% per unit) in São Tomé (green) and Príncipe (blue). The bars on the estimated kdr mutation frequency are 95% confidence intervals

Sequence analysis of malaria vector

IGS molecular M form was confirmed in a total of 205 mosquitoes using PCR–RFLP (GenBank accession no. KT284724). There was only 1 IGS haplotype (350 bp) in STP. The IGS sequence revealed polymorphisms compared to An. gambiae in Benin (GenBank accession nos. AF470112–470116) with the identity of 98.57–99.71%.

There were 48 haplotypes (GenBank accession nos. MH025842–025880 and MK330882–330890) found in 205 COI sequences (Additional file 2: Table S1). The 205 COI sequences of An. coluzzii included 39 singleton variable sites and 11 parsimony informative sites. Estimated transition over transversion bias was 9.41, resulting in 45 synonymous changes, four amino acid changes, and one ambiguous amino acid.

Haplotype 1 and 5 (H1, H5) were the two major COI haplotypes in STP (Additional file 2: Table S1). H1 was dominant in Príncipe population (93%, 40 sequences out of 43), whereas H5 was only found in São Tomé population (21.6%, 35/162). The phylogenetic tree was constructed by NJ analysis (Fig. 4) enrolling the two major COI haplotypes in STP with other reference sequences including An. gambiae sibling species, An. funestus (another major malaria vector complex in Africa), and Anopheles christyi (non-malaria vector but closely related to An. gambiae complex). Gene genealogies was depicted by TCS network within An. gambiae sensu stricto (s.s.) in Fig. 5. Both phylogenetic tree and TCS network showed that An. coluzzii in STP formed a subgroup with the most similarity to the An. coluzzii in West and Central African country including Mali and Cameroon.

Fig. 4
figure4

Phylogenetic tree constructed by COI sequences (1506 bp) of African Anopheles species. Sequences are denoted by Species/Origin/Year/Accession number. NCBI RefSeq means the sequence is originated from the NCBI Reference Sequence Genome Project. Two major haplotypes (H1 and H5) in STP are shown in bold. The number on the branch is the bootstrap value with 1000 replicates. Bootstrap value which is under 70 is not shown in this figure

Fig. 5
figure5

TCS haplotype network of COI sequences (1506 bp). A total of 48 haplotypes in São Tomé and Príncipe are displayed in circles with gray and white color, respectively. The size of the circle is proportional to its frequency. One node indicates one nucleotide difference. Vector population in Príncipe harbors only four haplotypes which H1 is the dominant (93%, 40/43), while population in São Tomé owns much diverse haplotypes. Other colors are reference sequences of An. gambiae s.s. from West, Central, and East African countries

Tajima’s DT, Fu’s Fs, Fu & Li’s F* and D* neutrality tests all showed significant negative values, indicating an excess of rare alleles in COI, and suggesting either a recent selective sweep or population expansion (Table 1). Anopheles coluzzii on Príncipe offshore island displayed simplex genetic structure with very low haplotype and nucleotide diversity. AMOVA and pairwise differentiation index showed that the Príncipe population had significant genetic differentiation (mean FST = 0.16, p < 0.001) with the population from São Tomé (Table 2 and Additional file 2: Table S4) which may be due to geographic isolation. Although diverse COI haplotypes were identified in São Tomé, pairwise FST showed no significant differentiation between districts in São Tomé except for rural areas- Lembá and Caué.

Table 1 DNA polymorphism and neutrality tests of COI in An. coluzzii from STP
Table 2 Pairwise differentiation index (FST) and gene flow (Nm) of An. coluzzii population between districts of STP

Spatial and temporal variations of kdr mutation

The kdr L1014F mutation was confirmed (GenBank accession nos. KT284726–284728), but no L1014S mutation was detected in An. coluzzii from STP. The kdr 1014F allele frequency by district per year was shown in Additional file 2: Table S5. The means of kdr mutation frequencies of An. coluzzii were significantly different between the two islands (F = 9.31, p = 0.003) in which São Tomé (mean frequency = 44.77%) was higher compared to Príncipe (mean frequency = 15.82%). Within São Tomé island, means of kdr mutation frequencies were not significantly different according to one-way ANOVA test (F = 1.43, p = 0.23) among vector mosquitoes collected from six districts.

Annual trend of kdr mutation frequency from 2010 to 2016 was shown in Fig. 3. The homozygous resistant allele (RR) was only detected in one mosquito sample (1.75%, 1/57) in 2010, indicating that kdr L1014F mutation was not widely distributed in the vector population at that time. However, the total kdr mutation frequency in São Tomé had gradually increased, reaching highest frequency in 2013 (Pearson’s r = 0.80, p < 0.001), 1 year after the malaria outbreak in 2012, but significantly decreased afterwards (Pearson’s r = − 0.49, p < 0.001). Four collection sites from Príncipe, Lobata, and Caué (Porto Real, Conde, Angolares, Emolve) had no resistant homozygotes (RR) detected at the end of 2016. The total kdr mutation frequency in Príncipe was relatively low (0–31%) except for a slight increase from 2014 to 2015.

In addition, subsets of samples were selected from each collection site and year to detect other target site mutations. Results showed that no kdr N1575Y (0%, 0/30) and ace-1 G119S (0%, 0/345) mutations were detected throughout the whole study period.

Transmission risks of environmental and vectorial factors to the malaria incidence

The national malaria case numbers, incidence rate, and SPR from 2003 to 2016 in STP was shown in Fig. 6. Malaria case numbers and incidence rate had substantially decreased by 16.3 and 17.6-fold during IRS control period (2004–2007), respectively, and kept under incidence rate of 5% after shifting to Bti control since 2007, except for a small rise in 2012 (incidence rate = 6.9%). The recent incidence rate after 2014 persisted at the lowest point (0.9–1%). The case proportion of the high-risk group, children under age of five, have gradually decreased from 45.87% in 2003 to 5.80% in 2016. On the contrary, 75–95% of the malaria cases were found in age 5 since 2008. National SPR showed corresponding trend with incidence rate (correlation coefficient = 0.987), and reached under 5% (WHO pre-elimination criterion [53]) in 2008, 2014, 2015, and 2016 when the annual incidence rate was around 1% and the case number was under 2000.

Fig. 6
figure6

National malaria case numbers, incidence rate, and slide positivity rate (SPR) in STP from 2003 to 2016. *The year that reaches pre-elimination criteria: SPR < 5% (referred from WHO [53]), annual incidence rate 1%, and case numbers < 2000

Malaria case numbers and incidence rate in Príncipe offshore island was decreased by 13.5 and 14.2-fold, respectively, during IRS control period from 2004 to 2007 (Additional file 1: Fig. S2). After shifting IRS to larval control by Bti 200 from 2007, Príncipe have been showing lower case numbers (2–51 cases per year) and incidence rate (under 1%) since 2008, and reached less than one case per 1000 person (< 0.1%) since 2014. This result indicated that Príncipe island was on the progress from malaria pre-elimination to elimination phase (criterion: < 1 case/1000 population at risk) [51].

To link the geo-environmental data with local malaria incidence, human population density by area and rainfall were enrolled as important factors that could affect malaria transmission. The census by district was conducted in 2012 (Additional file 2: Table S6). Over 80% of population are concentrated in the eastern part of São Tomé (AG, MZ, LO, and CT). In mountainous regions LE and CU resided 8.2% and 3.4% of total population, respectively. Príncipe resided 4.1% of total nationals. The seasonality of malaria incidence could be affected by rainfall with a time lag of 1 month (Additional file 1: Fig. S3). Also, when the monthly incidence rate was higher than 0.25% (25 cases per 10,000 persons) at the end of the year, the epidemic could be extended to the upcoming year.

From Fig. 3, a fluctuation between mosquito density, kdr mutation frequency and malaria incidence was observed during 2010 to 2016. To further understand the impacts of these vector characteristics and geo-environmental factors to the malaria incidence, a negative binomial regression model was constructed. The response variable was monthly malaria incidence rate in the seven districts during 7 years, and therefore the number of observations was 12 × 7 × 7 = 588. Details of the covariates were described in Table 3. This best model was selected by the lowest Akaike information criterion (AIC) value (2949.6). Malaria incidence in urban and rural area were 11.50 (95% CI 5.54–23.89) and 9.33 (95% CI 4.44–19.58) fold higher than Príncipe, respectively. The incidence of previous month would affect the latter by a risk ratio of 16.39 (95% CI 12.17–22.07). Incidence could increase by 1.25-fold (95% CI 1.13–1.39) when the rainfall in previous month increased 100 mm. Entomological inoculation rate (EIR, %) which was transformed from mosquito density was not significant in indoor EIR, but significant in outdoor EIR which could increase the incidence by 1.07-fold (95% CI 1.04–1.11). On the other hand, incidence could increase by 1.06-fold (95% CI 1.02–1.10) when kdr mutation frequency was increased by 10%. In conclusion, the geo-environmental factors including area and rainfall, and mosquito characteristics including outdoor EIR (density) and kdr mutation were significant factors that could increase malaria infection risks in STP.

Table 3 Results of negative binomial regression model (no. of observations = 588)

Discussion

This longitudinal study described malaria vector characteristics in STP including density changes, population composition, insecticide resistance status, and the implications for malaria epidemics. Both IRS and outdoor larval control by Bti had decreased vector density and malaria incidence of which the former showed short-term but rapid controlling effect, and the latter showed long-term and sustainable efficacy. The population composition in vector mosquitoes from Príncipe and São Tomé islands were different of which the former showed lower genetic diversity and kdr mutation compared to the latter. Outdoor EIR transformed from mosquito density, and kdr mutation were significant factors which could increase malaria incidence by 1.06–1.07 of risk ratio. Therefore, eliminating outdoor resistant mosquitoes should be of great priority to reduce malaria transmission in STP. This study also used the epidemiological data to better define the pre-elimination criteria for STP: (a) annual SPR < 5% and incidence rate 1% (b) annual case numbers < 2000 (c) annual average outdoor HLCs density < 1.6 mosquitoes/house/hr/month.

The simplex genetic structure and fewer kdr mutation in vector population from Príncipe are likely consequences of geographic isolation and absence of strong selection by insecticide due to very few malaria cases on this island (2–42 cases per year from 2010 to 2016). Only a small proportion of mosquitoes were detected harboring kdr mutant allele (2.3–9.6% RR, and 12.5–20.5% RS) in Príncipe from 2014 to 2015. On the contrary, São Tomé as the main island with more frequent transportation, malaria transmission and interventions [23], the vector population showed a more diverse genetic background and higher selection pressure by insecticide compared to Príncipe. However, due to the small area of São Tomé island (859 km2), less extent of difference was observed among districts in São Tomé for either COI or kdr markers. Only Lembá and Caué which were slightly isolated by surrounding mountain areas showed little but significant differentiation in COI. The heterogeneity and isolated characteristics of vector population between the two islands, as well as islands with the continental Africa were also supported by previous studies using mitochondrial, ribosomal, and microsatellite DNA markers of An. gambiae [4, 5, 54]. Marshall et al. analysed ND5 and ITS sequences of An. gambiae s.s. and reported that the vector population in STP was possibly derived from West and Central African countries following human migration [5]. This could support our results that COI sequences of An. coluzzii in STP showed the most similarity to An. coluzzii from West and Central African countries. Although the nucleotide variations in COI was not high especially within An. gambiae s.l. [55], vector population in STP still formed its own subgroup in the phylogenetic analysis. Haplotype 1 which was the representative COI haplotype in Príncipe distributed on both islands across the whole sampling period. A single nucleotide polymorphism (SNP) in the loci of A1263G (H5) only appeared in samples from São Tomé but not those from Príncipe. This SNP was less seen but appeared in the COI sequences from An. gambiae in Cameroon [55], and Nigeria [40].

When examining the malaria epidemics under different vector control strategies, it is clear to see that IRS had short-term and rapid effectiveness in reducing both vector density and malaria epidemics. However, vector density was not well-controlled in the late stage of IRS application possibly due to the decreased susceptibility of insecticide in mosquitoes. Therefore, the alternative vector control method using Bti was then implemented at outdoor mosquito breeding sites in order to control mosquito larvae. Long-term application of Bti kept the outdoor vector density under a controlled level (under10 mosquitoes/house/hr/month), and successfully prevented malaria outbreaks after 2008, except for a fluctuation in 2012. This fluctuation may be due to the failure of shifting from Bti 200 to Bti 3000 in São Tomé for a period of time which decreased work efficiency since Bti 3000 application required workers to carry heavy spraying equipment. On the other hand, though the programme no longer emphasized IRS, in response to the WHO global malaria elimination policy, São Tomé continued to implement national or small-scale IRS with alpha-cypermethrin funded by Global Fund during 2009 to 2012, resulting in the detection of kdr mutation in our sampling period from 2010 to 2016. On the contrary, IRS was not accelerated in Príncipe where malaria incidence rate was under 1%. This may lead to a slightly higher indoor mosquito density observed in Príncipe than São Tomé, and a lower detection rate of kdr mutation in mosquitoes from Príncipe.

Since the malaria vector in STP is single species with limited gene flow, the kdr 1014F mutation may likely arisen independently due to the local insecticide selection. The mean of kdr 1014F mutation frequency in STP from 2010 to 2016 was 43.8% (annual range: 13.3–74.8%). This was relatively low compared to its neighboring country, the coastal area of Cameroon, which the kdr 1014F mutation frequency in An. coluzzii was 70–93% in 2015 [56]. Kdr mutation had a clear temporal trend which increased to the highest in 2013 and dropped afterwards in São Tomé. The anterior increase in kdr mutation may be contributed by the accelerated insecticide interventions during 2010 to 2013. Supportive data from WHO malaria reports showed the estimated IRS coverage in STP elevated from 40% in 2010 [57] to 84% in 2013 [58]. The modelled % of population with access to ITNs increased from 39% in 2010 [57] to 53% in 2013 [58]. After the breakpoint of 2013, kdr mutation had decreased and remained its frequency around 40%. This study suggested two possible explanations. First, it was correlated with the declining malaria incidence and IRS coverage during 2014 to 2016 [59]. Second, a new insecticide, Ficam was introduced for IRS usage in replacement of alpha-cypermethrin at the end of 2013 [60]. Ficam is a carbamate insecticide which has different mode of action to pyrethroids. The ace-1 G119S is the known target site mutation against carbamate and organophosphate insecticides in An. gambiae [61]. However, 345 samples were screened for ace-1 G119S and no mutation was detected by 2016, suggesting the vector may remain susceptible to carbamates. Another recent marker of target site mutation for pyrethroid is N1575Y which confers a significant additive benefit to L1014F [49]. However, 30 samples from 3 kdr genotypes (SS, RR, and RS) were pre-screened for this locus and none was detected mutant.

Early investigation reported the exophagy and exophily of malaria vectors in the STP [62, 63]. After long-term vector control interventions, An. coluzzii remained exophilic by evidence of more outdoor captures than indoors in our study. Entomological inoculation rate is an indicator of human exposure to infectious mosquitoes [64], and is determined by human biting rate which can be estimated using HLCs method [65], and sporozoite rate in vector mosquitoes. Previous entomological survey in STP showed the geometric mean of vector density was 0.5–44.3 per man per hour by outdoor HLCs, and a low sporozoite rate (0.3–0.6%) [3]. In this study, we used the arithmetic mean to calculate the average density. The monthly average mosquito density in outdoor HLCs ranged from 0 to 46 which was similar to previous findings (0.5–44.3) [3]. Since STP was under low malaria transmission scenario, we assumed that the sporozoite rate would remain low and less variable. Therefore, sporozoite rate of 0.5% referred from the aforementioned entomological survey was used for calculating EIR in our study [3]. The indoor EIR was not a significant risk factor in the model possibly due to the chemical and physical protections by IRS and ITNs. In contrast, the outdoor EIR was a significant risk factor owing to the exophagy of the local vector and the lack of protection in human hosts during outdoor activities.

The kdr L1014F amino acid substitution could alter the affinity of pyrethroids and DDT to bind on the sodium channel, resulting in the knockdown resistance [48]. A recent study conducted in Gambia examined the risk for kdr mutation but failed to provide conclusive findings due to the co-occurrence of An. gambiae s.s. and homozygous L1014F mutation [66]. In this study, kdr mutation was a significant risk factor to malaria incidence at a risk ratio of 1.06 (95% CI 1.02–1.10) for a 10% increase. The risk ratio was not high but similar to the results from the aforementioned study in Gambia where the odds ratio for a 10% increase in kdr mutation was 1.01–1.02 [66].

Malaria transmission in STP has strong seasonal effects mainly affected by rainfall. According to Teklehaimano’s report [67], malaria cases could appear 4–5 weeks following rainfall under hot environment (30 °C) when considering the lead time of mosquito life cycle (10 days), sporogony cycle (6 days), and incubation period (10–16 days) in human hosts. The transmission pattern in STP showed the peak incidence appeared 1 month after the end of rainy season. Therefore, this study used 1 month lag of rainfall to become the predictive value of malaria incidence. The temperature factor was not considered in this study due to low variability in STP. Human population density also have an impact on malaria transmission. Although several reports showed that area with higher population density has less malaria cases due to reduction of vector breeding sites and low EIR by urbanization [68, 69], it is not the case in STP since the highest malaria cases are found in the most densely populated district, Água Grande, and its neighboring districts Mé-Zóchi and Lobata are often close seconds. Lee et al. [23] pointed out the malaria outbreaks in STP may follow the main roads, suggesting that population distribution and migration play roles in the occurrence and spread of malaria epidemics. Knowing that the hot spot of malaria in STP is at the north-eastern part of São Tomé, malaria control strategies should be strengthened in this area.

Overall, this study exhibits the unique characteristics of vector population and malaria transmission under the island scenario. Geographic barriers by oceans, lakes, or mountains may result in limited gene flow among vectors and pathogens. Evidences from Bioko island, the nearby island to the STP, demonstrated the distance-determined genetic differentiation, exophagy and kdr 1014F mutation in An. coluzzii after long-term interventions [70] which were similar to the present findings in STP. Differentiation of vector population by mitochondrial DNA marker in the large lake area have also been found in the lacustrine islands located in the Lake Victoria which borders Kenya, Uganda and Tanzania [71]. Due to the small area and the relative simplicity of human mobility and vectorial-parasitological system on islands, it is much easier to eliminate malaria compared to the continental areas. Examples from East African islands such as the Comoros [72] and Zanzibar [73] showed opportunities to achieve malaria elimination by ACT administration and vector control interventions. Cape Verde, an archipelago of ten islands in West Africa, also achieved malaria pre-elimination according to the surveillance data from 2010 to 2016 [74]. However, imported infections from Africa continent, fluctuations of vector population, and sustainability of controlling interventions makes it challenging to ultimately eliminate or prevent recolonization of malaria on these islands [31]. It is essential to routinely monitor spatial–temporal vector dynamics, insecticide resistance in vectors, drug sensitivity in parasites, case detection and management, and to promote the health education in local residents, which all require financial and governmental support in order to maintain malaria control efficacy across these islands.

Although in parts of this study, secondary data or online resources were used due to the limited accessibility of direct data, this study still successfully explicated how vectorial and environmental factors would impact on the malaria epidemics in STP. During this longitudinal study, Taiwanese research team and Centro National de Endemias of STP collaborated to supervise and conduct routine inspections on the operation to ensure the accuracy and completeness of strategies implementation and data collection. Findings in this study focused on the geo-environmental and vectorial factors on malaria transmission. Future studies would be carried out to further investigate parasitological factors that could affect malaria control efficacy in STP.

Conclusions

Different vector characteristics were found between São Tomé and Príncipe islands. Anopheles mosquito population in São Tomé showed higher density, genetic diversity in COI, and kdr mutation frequency compared to the population in Príncipe. Temporal trend showed mosquito density and kdr mutation elevated to the highest followed by the outbreak during 2012 to 2013 period. Geo-environmental factors including area and rainfall, and vectorial factors including outdoor EIR transformed from density and kdr mutation were factors that could significantly affect local malaria transmission.

Vector control by IRS could rapidly decrease Anopheles density and malaria epidemics in STP. Outdoor larval control using Bti is a sustainable long-term approach for controlling local An. coluzzii with exophilic feature and insecticide resistance problem.

To pre-eliminate malaria in STP, the following criteria should be sustained: (a) annual SPR < 5% and incidence rate 1% (b) annual case numbers < 2000 (c) annual average outdoor HLCs density < 1.6 mosquitoes/house/hr/month. Malaria prevention by vector control strategies should be intensified at urban districts during the epidemic season from May to June in order to successfully reduce malaria disease burden in this island nation.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its additional files.

Abbreviations

STP:

São Tomé and Príncipe

IRS:

indoor residual spraying

Bti :

Bacillus thuringiensis israelensis

COI :

cytochrome c oxidase subunit I

kdr :

knockdown resistance

EIR:

entomological inoculation rate

IGS :

intergenic spacer

IVM:

integrated vector management

LLINs:

long-lasting insecticidal nets

WHO:

World Health Organization

ITNs:

insecticide-treated nets

AG:

Água Grande

MZ:

Mé-Zóchi

LO:

Lobata

CT:

Cantagalo

LE:

Lembá

CU:

Caué

PR:

Príncipe

ITU:

international toxic units

HLCs:

human landing catches

MLTs:

mosquito light traps

ANOVA:

analysis of variance

PCR:

polymerase chain reaction

RFLP:

restriction fragment length polymorphism

NJ:

Neighbor-joining

FST :

pairwise fixation index

AMOVA:

analysis of molecular variance

Nm:

number of migrants

SS:

kdr susceptible homozygotes (1014 L/L)

RR:

kdr resistant homozygotes (1014 F/F)

RS:

kdr heterozygotes (1014 L/F)

Ace-1 :

acetylcholinesterase-1

CI:

confidence interval

SPR:

slide positivity rate

References

  1. 1.

    WHO. World malaria report 2018. Geneva: World Health Organization; 2018.

    Google Scholar 

  2. 2.

    Sinka ME, Bangs MJ, Manguin S, Rubio-Palis Y, Chareonviriyaphap T, Coetzee M, et al. A global map of dominant malaria vectors. Parasit Vectors. 2012;5:69.

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Pinto J, Sousa CA, Gil V, Ferreira C, Goncalves L, Lopes D, et al. Malaria in São Tomé and Príncipe: parasite prevalences and vector densities. Acta Trop. 2000;76:185–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Pinto J, Donnelly MJ, Sousa CA, Malta-Vacas J, Gil V, Ferreira C, et al. An island within an island: genetic differentiation of Anopheles gambiae in São Tomé, West Africa, and its relevance to malaria vector control. Heredity (Edinb). 2003;91:407–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Marshall JC, Pinto J, Charlwood JD, Gentile G, Santolamazza F, Simard F, et al. Exploring the origin and degree of genetic isolation of Anopheles gambiae from the islands of São Tomé and Príncipe, potential sites for testing transgenic-based vector control. Evol Appl. 2008;1:631–44.

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Coetzee M, Hunt RH, Wilkerson R, Torre AD, Coulibaly MB, Besansky NJ. Anopheles coluzzii and Anopheles amharicus, new members of the Anopheles gambiae complex. Zootaxa. 2013;3619:246–74.

    Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Wiebe A, Longbottom J, Gleave K, Shearer FM, Sinka ME, Massey NC, et al. Geographical distributions of African malaria vector sibling species and evidence for insecticide resistance. Malar J. 2017;16:85.

    Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Gregory CL, Yoosook L. Speciation in Anopheles gambiae—the distribution of genetic polymorphism and patterns of reproductive isolation among natural populations. IntechOpen. 2013.

  9. 9.

    Salgueiro P, Moreno M, Simard F, O’Brochta D, Pinto J. New insights into the population structure of Anopheles gambiae s.s. in the Gulf of Guinea Islands revealed by Herves transposable elements. PLoS ONE. 2013;8:e62964.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Gentile G, Della Torre A, Maegga B, Powell JR, Caccone A. Genetic differentiation in the African malaria vector, Anopheles gambiae s.s., and the problem of taxonomic status. Genetics. 2002;161:1561–78.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Wang G, Li C, Zheng W, Song F, Guo X, Wu Z, et al. An evaluation of the suitability of COI and COII gene variation for reconstructing the phylogeny of, and identifying cryptic species in, anopheline mosquitoes (Diptera, Culicidae). Mitochondrial DNA Part A. 2017;28:769–77.

    Article  CAS  Google Scholar 

  12. 12.

    Roe AD, Sperling FA. Patterns of evolution of mitochondrial cytochrome c oxidase I and II DNA and implications for DNA barcoding. Mol Phylogenet Evol. 2007;44:325–45.

    Article  CAS  Google Scholar 

  13. 13.

    Koosha M, Oshaghi MA, Sedaghat MM, Vatandoost H, Azari-Hamidian S, Abai MR, et al. Sequence analysis of mtDNA COI barcode region revealed three haplotypes within Culex pipiens assemblage. Exp Parasitol. 2017;181:102–10.

    Article  CAS  Google Scholar 

  14. 14.

    Fang Y, Shi W-Q, Zhang Y. Molecular phylogeny of Anopheles hyrcanus group (Diptera: Culicidae) based on mtDNA COI. Infect Dis Poverty. 2017;6:61.

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Lehr MA, Kilpatrick CW, Wilkerson RC, Conn JE. Cryptic species in the Anopheles (Nyssorhynchus) albitarsis (Diptera: Culicidae) Complex: Incongruence between random amplified polymorphic DNA-polymerase chain eeaction identification and analysis of mitochondrial DNA COI gene sequences. Ann Entomol Soc Am. 2005;98:908–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Snow RW, Amratia P, Kabaria CW, Noor AM, Marsh K. The changing limits and incidence of malaria in Africa: 1939–2009. Adv Parasitol. 2012;78:169–262.

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Pinto J, Donnelly MJ, Sousa CA, Gil V, Ferreira C, Elissa N, et al. Genetic structure of Anopheles gambiae (Diptera: Culicidae) in São Tomé and Príncipe (West Africa): implications for malaria control. Mol Ecol. 2002;11:2183–7.

    Article  CAS  Google Scholar 

  18. 18.

    Teklehaimanot HD, Teklehaimanot A, Kiszewski A, Rampao HS, Sachs JD. Malaria in São Tomé and Príncipe: on the brink of elimination after three years of effective antimalarial measures. Am J Trop Med Hyg. 2009;80(1):133–40.

    Article  Google Scholar 

  19. 19.

    Tseng LF, Chang WC, Ferreira MC, Wu CH, Rampão HS, Lien JC. Rapid control of malaria by means of indoor residual spraying of alphacypermethrin in the Democratic Republic of São Tomé and Príncipe. Am J Trop Med Hyg. 2008;78:248–50.

    Article  Google Scholar 

  20. 20.

    Chanda E, Masaninga F, Coleman M, Sikaala C, Katebe C, Macdonald M, et al. Integrated vector management: the Zambian experience. Malar J. 2008;7:164.

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Mwangangi JM, Kahindi SC, Kibe LW, Nzovu JG, Luethy P, Githure JI, et al. Wide-scale application of Bti/Bs biolarvicide in different aquatic habitat types in urban and peri-urban Malindi, Kenya. Parasitol Res. 2011;108(6):1355–63.

    Article  Google Scholar 

  22. 22.

    Dambach P, Louis VR, Kaiser A, Ouedraogo S, Sie A, Sauerborn R, et al. Efficacy of Bacillus thuringiensis var. israelensis against malaria mosquitoes in northwestern Burkina Faso. Parasit Vectors. 2014;7:371.

    Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Lee PW, Liu CT, do Rosario VE, de Sousa B, Rampao HS, Shaio MF. Potential threat of malaria epidemics in a low transmission area, as exemplified by São Tomé and Príncipe. Malar J. 2010;9:264.

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    WHO. Global technical strategy for malaria 2016–2030. Geneva: World Health Organization; 2015.

    Google Scholar 

  25. 25.

    Lee PW, Liu CT, Rampao HS, do Rosario VE, Shaio MF. Pre-elimination of malaria on the island of Príncipe. Malar J. 2010;9:26.

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    WHO. Global report on insecticide resistance in malaria vectors: 2010–2016. Geneva: World Health Organization; 2018.

    Google Scholar 

  27. 27.

    Silva AP, Santos JM, Martins AJ. Mutations in the voltage-gated sodium channel gene of anophelines and their association with resistance to pyrethroids—a review. Parasit Vectors. 2014;7:450.

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Santolamazza F, Calzetta M, Etang J, Barrese E, Dia I, Caccone A, et al. Distribution of knock-down resistance mutations in Anopheles gambiae molecular forms in west and west-central Africa. Malar J. 2008;7:74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Temu EA, Maxwell C, Munyekenye G, Howard AFV, Munga S, Avicor SW, et al. Pyrethroid resistance in Anopheles gambiae, in Bomi County, Liberia, compromises malaria vector control. PLoS ONE. 2012;7:e44986.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Aizoun N, Aikpon R, Akogbeto M. Evidence of increasing L1014F kdr mutation frequency in Anopheles gambiae s.l. pyrethroid resistant following a nationwide distribution of LLINs by the Beninese National Malaria Control Programme. Asian Pac J Trop Biomed. 2014;4:239–43.

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Etang J, Mbida Mbida A, Ntonga Akono P, Binyang J, Eboumbou Moukoko CE, Lehman LG, et al. Anopheles coluzzii larval habitat and insecticide resistance in the island area of Manoka, Cameroon. BMC Infect Dis. 2016;16:217.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Chabi J, Baidoo PK, Datsomor AK, Okyere D, Ablorde A, Iddrisu A, et al. Insecticide susceptibility of natural populations of Anopheles coluzzii and Anopheles gambiae (sensu stricto) from Okyereko irrigation site, Ghana, West Africa. Parasit Vectors. 2016;9:182.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Tia E, Chouaibou M, Gbalegba CNG, Boby AMO, Kone M, Kadjo AK. Distribution of species and kdr gene frequency among Anopheles gambiae s.s. and Anopheles coluzzii populations in five agricultural sites in Cote d’Ivoire. Bull Soc Pathol Exot. 2017;110:130–4.

    Article  CAS  Google Scholar 

  34. 34.

    Hagmann R, Charlwood JD, Gil V, Ferreira C, do Rosario V, Smith TA. Malaria and its possible control on the island of Príncipe. Malar J. 2003;2:15.

    Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    CDC. CDC Yellow Book 2020- Health information for international travel. Atlanta: Centers for Disease Control and Prevention; 2019.

    Google Scholar 

  36. 36.

    Briet OJT, Huho BJ, Gimnig JE, Bayoh N, Seyoum A, Sikaala CH, et al. Applications and limitations of Centers for Disease Control and Prevention miniature light traps for measuring biting densities of African malaria vector populations: a pooled-analysis of 13 comparisons with human landing catches. Malar J. 2015;14:247.

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Mayagaya VS, Ntamatungiro AJ, Moore SJ, Wirtz RA, Dowell FE, Maia MF. Evaluating preservation methods for identifying Anopheles gambiae s.s. and Anopheles arabiensis complex mosquitoes species using near infra-red spectroscopy. Parasit Vectors. 2015;8:60.

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Fanello C, Santolamazza F, della Torre A. Simultaneous identification of species and molecular forms of the Anopheles gambiae complex by PCR-RFLP. Med Vet Entomol. 2002;16:461–4.

    Article  CAS  Google Scholar 

  39. 39.

    Folmer O, Black M, Hoeh W, Lutz R, Vrijenhoek R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol Mar Biol Biotechnol. 1994;3:294–9.

    CAS  PubMed  Google Scholar 

  40. 40.

    Matthews SD, Meehan LJ, Onyabe DY, Vineis J, Nock I, Ndams I, et al. Evidence for late Pleistocene population expansion of the malarial mosquitoes, Anopheles arabiensis and Anopheles gambiae in Nigeria. Med Vet Entomol. 2007;21:358–69.

    Article  CAS  Google Scholar 

  41. 41.

    Hall TA. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl Acids Symp Ser. 1999;41:95–8.

    CAS  Google Scholar 

  42. 42.

    Kumar S, Stecher G, Tamura K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol. 2016;33:1870–4.

    Article  CAS  Google Scholar 

  43. 43.

    Kimura M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J Mol Evol. 1980;16:111–20.

    Article  CAS  Google Scholar 

  44. 44.

    Rozas J, Ferrer-Mata A, Sánchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE, et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol Biol Evol. 2017;34:3299–302.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Clement M, Posada D, Crandall KA. TCS: a computer program to estimate gene genealogies. Mol Ecol. 2000;9:1657–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Excoffier L, Lischer HEL. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour. 2010;10:564–7.

    Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Slatkin M. Gene flow and the geographic structure of natural populations. Science. 1987;236:787–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Martinez-Torres D, Chandre F, Williamson MS, Darriet F, Berge JB, Devonshire AL, et al. Molecular characterization of pyrethroid knockdown resistance (kdr) in the major malaria vector Anopheles gambiae s.s. Insect Mol Biol. 1998;7:179–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Jones CM, Liyanapathirana M, Agossa FR, Weetman D, Ranson H, Donnelly MJ, et al. Footprints of positive selection associated with a mutation (N1575Y) in the voltage-gated sodium channel of Anopheles gambiae. Proc Natl Acad Sci USA. 2012;109:6614–9.

    Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Weill M, Malcolm C, Chandre F, Mogensen K, Berthomieu A, Marquine M, et al. The unique mutation in ace-1 giving high insecticide resistance is easily detectable in mosquito vectors. Insect Mol Biol. 2004;13:1–7.

    Article  CAS  Google Scholar 

  51. 51.

    Rousset F. genepop’007: a complete re-implementation of the genepop software for Windows and Linux. Mol Ecol Resour. 2008;8:103–6.

    Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Series B Stat Methodol. 1977;39:1–38.

    Google Scholar 

  53. 53.

    Mendis K, Rietveld A, Warsame M, Bosman A, Greenwood B, Wernsdorfer WH. From malaria control to eradication: the WHO perspective. Trop Med Int Health. 2009;14:802–9.

    Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Moreno M, Salgueiro P, Vicente JL, Cano J, Berzosa PJ, de Lucio A, et al. Genetic population structure of Anopheles gambiae in Equatorial Guinea. Malar J. 2007;6:137.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Hanemaaijer M, Houston P, Collier T, Norris L, Fofana A, Lanzaro G, et al. Mitochondrial genomes of Anopheles arabiensis, An. gambiae and An. coluzzii show no clear species division. F1000Research. 2019;7:347.

    Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Boussougou-Sambe ST, Eyisap WE, Tasse GCT, Mandeng SE, Mbakop LR, Enyong P, Etang J, Fokam EB, Awono-Ambene PH. Insecticide susceptibility status of Anopheles gambiae (s.l.) in South-West Cameroon four years after long-lasting insecticidal net mass distribution. Parasit Vectors. 2018;11:347.

    Article  CAS  Google Scholar 

  57. 57.

    WHO. World malaria report 2012. Geneva: World Health Organization; 2012.

    Google Scholar 

  58. 58.

    WHO. World malaria report 2014. Geneva: World Health Organization; 2014.

    Google Scholar 

  59. 59.

    WHO. World malaria report 2017. Geneva: World Health Organization; 2017.

    Google Scholar 

  60. 60.

    Global Fund. Standard concept note- Investing for impact against HIV, tuberculosis and malaria in São Tomé and Príncipe. Geneva: Global Fund; 2016.

    Google Scholar 

  61. 61.

    Essandoh J, Yawson AE, Weetman D. Acetylcholinesterase (Ace-1) target site mutation 119S is strongly diagnostic of carbamate and organophosphate resistance in Anopheles gambiae s.s. and Anopheles coluzzii across southern Ghana. Malar J. 2013;12:404.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Charlwood JD, Pinto J, Ferrara PR, Sousa CA, Ferreira C, Gil V, Do Rosário VE. Raised houses reduce mosquito bites. Malaria J. 2003;2:45.

    Article  Google Scholar 

  63. 63.

    Ribeiro HR, Ramos HC, Janz JG. The feeding and resting behaviour of Anopheles gambiae on the islands of São Tomé and Príncipe, West Africa. J Vector Ecol. 1992;171:25–7.

    Google Scholar 

  64. 64.

    Kilama M, Smith DL, Hutchinson R, Kigozi R, Yeka A, Lavoy G, et al. Estimating the annual entomological inoculation rate for Plasmodium falciparum transmitted by Anopheles gambiae s.l. using three sampling methods in three sites in Uganda. Malar J. 2014;13:111.

    Article  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Kenea O, Balkew M, Tekie H, Gebre-Michael T, Deressa W, Loha E, et al. Comparison of two adult mosquito sampling methods with human landing catches in south-central Ethiopia. Malar J. 2017;16:30.

    Article  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Wilson AL, Pinder M, Bradley J, Donnelly MJ, Hamid-Adiamoh M, Jarju LBS, et al. Emergence of knock-down resistance in the Anopheles gambiae complex in the Upper River Region, The Gambia, and its relationship with malaria infection in children. Malar J. 2018;17:205.

    Article  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Teklehaimanot HD, Lipsitch M, Teklehaimanot A, Schwartz J. Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia I. Patterns of lagged weather effects reflect biological mechanisms. Malar J. 2004;3:41.

    Article  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Smith DL, Dushoff J, McKenzie FE. The risk of a mosquito-borne infection in a heterogeneous environment. PLoS Biol. 2004;2:e368.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Hay SI, Guerra CA, Tatem AJ, Atkinson PM, Snow RW. Urbanization, malaria transmission and disease burden in Africa. Nat Rev Microbiol. 2005;3:81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Reimer LJ, Tripet F, Slotman M, Spielman A, Fondjo E, Lanzaro GC. An unusual distribution of the kdr gene among populations of Anopheles gambiae on the island of Bioko, Equatorial Guinea. Insect Mol Biol. 2005;14:683–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Lukindu M, Bergey CM, Wiltshire RM, Small ST, Bourke BP, Kayondo JK, et al. Spatio-temporal genetic structure of Anopheles gambiae in the Northwestern Lake Victoria Basin, Uganda: implications for genetic control trials in malaria endemic regions. Parasit Vectors. 2018;11:246.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Chakir I, Said AI, Affane B, Jambou R. Control of malaria in the Comoro Islands over the past century. Malar J. 2017;16:387.

    Article  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Björkman A, Shakely D, Ali AS, Morris U, Mkali H, Abbas AK, et al. From high to low malaria transmission in Zanzibar-challenges and opportunities to achieve elimination. BMC Med. 2019;17:14.

    Article  PubMed  PubMed Central  Google Scholar 

  74. 74.

    DePina A, Namountougou M, Leal S, Varela I, Monteiro D. Susceptibility of Anopheles gambiae s.l. to the insecticides in Praia, Cape Verde: a country in the pre-elimination of malaria. Vector Biol J. 2018;3:2.

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Taiwan Anti-Malaria Advisory Mission for their enthusiastic participation and professional assistance, especially Dr. Jih-Ching Lien (project leader) and Ms. Lien-Feng Tseng (coordinator). We are grateful to Dr. Maria de Jesus Trovoada dos Santos and Mr. João Viegas d´Abreu for their efforts into communications with the local participants and laboratory workers. The authors also would like to thank Ms. Wan-Jen Chen and Jo-Ting Tang for analyzing kdr samples from 2010 to 2012, and the graduate students from Institute of Epidemiology and Preventive Medicine, National Taiwan University for providing statistical consultation.

Funding

This study was financially supported by Ministry of Foreign Affairs, Republic of China (Taiwan) and by Grants from the Ministry of Science and Technology Council (103-2314-B-002-036-MY2).

Author information

Affiliations

Authors

Contributions

YAC, JCL and KHT: initiated study hypothesis. YAC undertook statistical analysis, and drafted the manuscript. JCL, LFT, CFC, KHT (Taiwan Anti-Malaria Advisory Mission members): conducted the programme of IRS and larvae control by Bti, collected mosquito samples and records of malaria cases. WYL: instruction of statistical analysis. HYW: instruction of population genetics. JCL and KHT: supervision and correspondence. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jih-Ching Lien or Kun-Hsien Tsai.

Ethics declarations

Ethics approval and consent to participate

The content and methods used in this study have been reviewed and approved by the Centro Nacional de Endemias of the Ministry of Health in STP (official No. OF0N019/P0CNE/2016) and the Research Ethics Committee of National Taiwan University Hospital (NTUH-REC No.: 201110023RD).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chen, Y., Lien, J., Tseng, L. et al. Effects of indoor residual spraying and outdoor larval control on Anopheles coluzzii from São Tomé and Príncipe, two islands with pre-eliminated malaria. Malar J 18, 405 (2019). https://doi.org/10.1186/s12936-019-3037-y

Download citation

Keywords

  • São Tomé and Príncipe
  • Anopheles coluzzii
  • Indoor residual spraying
  • Bacillus thuringiensis israelensis
  • Vector density
  • Cytochrome c oxidase subunit I
  • Knockdown resistance mutation