Developing the role of Earth observation in spatio-temporal mosquito modelling to identify malaria hot-spots

Anopheles mosquitoes are the vectors of human malaria, a disease responsible for a significant burden of global disease and over half a million deaths in 2020. Here, methods using a time series of cost-free Earth Observation (EO) data, 45,844 in situ mosquito monitoring captures, and the cloud processing platform Google Earth Engine are developed to identify the biogeographical variables driving the abundance and distribution of three malaria vectors—Anopheles gambiae s.l., An. funestus, and An. paludis—in two highly endemic areas in the Democratic Republic of the Congo. EO-derived topographical and time series land surface temperature and rainfall data sets are analysed using Random Forests (RFs) to identify their relative importance in relation to the abundance of the three mosquito species, and they show how spatial and temporal distributions vary by site, by mosquito species, and by month. The observed relationships differed between species and study areas, with the overall number of biogeographical variables identified as important in relation to species abundance, being 30 for An. gambiae s.l. and An. funestus and 26 for An. paludis. Results indicate rainfall and land surface temperature to consistently be the variables of highest importance, with higher rainfall resulting in greater mosquito abundance through the creation of pools acting as mosquito larval habitats; however, proportional coverage of forest and grassland, as well as proximity to forests, are also consistently identified as important. Predictive application of the RF models generated monthly abundance maps for each species, identifying both spatial and temporal hot-spots of high abundance and, by proxy, increased malaria infection risk. Results indicate greater temporal variability in An. gambiae s.l. and An. paludis abundances in response to seasonal rainfall, whereas An. funestus is generally more temporally stable, with maximum predicted abundances of 122 for An. gambiae s.l., 283 for An. funestus, and 120 for An. paludis. Model validation produced R2 values of 0.717 for An. gambiae s.l., 0.861 for An. funestus, and 0.448 for An. paludis. Monthly abundance values were extracted for 248,089 individual buildings, demonstrating how species abundance, and therefore biting pressure, varies spatially and seasonally on a building-to-building basis. These methods advance previous broader regional mosquito mapping and can provide a crucial tool for designing bespoke control programs and for improving the targeting of resource-constrained disease control activities to reduce malaria transmission and subsequent mortality in endemic regions, in line with the WHO’s ‘High Burden to High Impact’ initiative. The developed method was designed to be widely applicable to other areas, where suitable in situ mosquito monitoring data are available. Training materials were also made freely available in multiple languages, enabling wider uptake and implementation of the methods by users without requiring prior expertise in EO

Modelling spatiotemporal trends in the frequency of genetic mutations conferring insecticide target-site resistance in African mosquito malaria vector species

Background Resistance in malaria vectors to pyrethroids, the most widely used class of insecticides for malaria vector control, threatens the continued efficacy of vector control tools. Target-site resistance is an important genetic resistance mechanism caused by mutations in the voltage-gated sodium channel (Vgsc) gene that encodes the pyrethroid target-site. Understanding the geographic distribution of target-site resistance, and temporal trends across different vector species, can inform strategic deployment of vector control tools. Results We develop a Bayesian statistical spatiotemporal model to interpret species-specific trends in the frequency of the most common resistance mutations, Vgsc-995S and Vgsc- 995F, in three major malaria vector species Anopheles gambiae, An. coluzzii, and An. arabiensis over the period 2005-2017. The models are informed by 2418 observations of the frequency of each mutation in field sampled mosquitoes collected from 27 countries spanning western and eastern regions of Africa. For nine selected countries, we develop annual predictive maps which reveal geographically-structured patterns of spread of each mutation at regional and continental scales. The results show associations, as well as stark differences, in spread dynamics of the two mutations across the three vector species. The coverage of ITNs was an influential predictor of Vgsc allele frequencies, with modelled relationships between ITN coverage and allele frequencies varying across species and geographic regions. We found that our mapped Vgsc allele frequencies are a significant partial predictor of phenotypic resistance to the pyrethroid deltamethrin in An. gambiae complex populations. Conclusions Our predictive maps show how spatiotemporal trends in insecticide target-site resistance mechanisms in African An. gambiae vary across individual vector species and geographic regions. Molecular surveillance of resistance mechanisms will help to predict resistance phenotypes and track their spread

Novel Wolbachia strains in Anopheles malaria vectors from Sub-Saharan Africa

Background: Wolbachia , a common insect endosymbiotic bacterium that can influence pathogen transmission and manipulate host reproduction, has historically been considered absent from the Anopheles (An.) genera, but has recently been found in An. gambiae s.l. populations.  As there are numerous Anopheles species that have the capacity to transmit malaria, we analysed a range of species to determine Wolbachia prevalence rates, characterise novel Wolbachia strains and determine any correlation between the presence of Plasmodium , Wolbachia  and the competing endosymbiotic bacterium Asaia . Methods: Anopheles adult mosquitoes were collected from five malaria-endemic countries: Guinea, Democratic Republic of the Congo (DRC), Ghana, Uganda and Madagascar, between 2013 and 2017.  Molecular analysis of samples was undertaken using quantitative PCR, Sanger sequencing, Wolbachia multilocus sequence typing (MLST) and high-throughput amplicon sequencing of the bacterial 16S rRNA gene.  Results : Novel Wolbachia strains were discovered in five species: An. coluzzii , An. gambiae s.s., An. arabiensis , An. moucheti and An. species ‘A’, increasing the number of Anopheles species known to be naturally infected. Variable prevalence rates in different locations were observed and novel strains were phylogenetically diverse, clustering with Wolbachia supergroup B strains.  We also provide evidence for resident strain variants within An . species ‘A’.  Wolbachia is the dominant member of the microbiome in An. moucheti and An. species ‘A’, but present at lower densities in An. coluzzii .  Interestingly, no evidence of Wolbachia/Asaia co-infections was seen and Asaia infection densities were also shown to be variable and location dependent.  Conclusions: The important discovery of novel Wolbachia strains in Anopheles provides greater insight into the prevalence of resident Wolbachia strains in diverse malaria vectors.  Novel Wolbachia strains (particularly high-density strains) are ideal candidate strains for transinfection to create stable infections in other Anopheles mosquito species, which could be used for population replacement or suppression control strategies

Developing the Role of Earth Observation in Spatio-Temporal Mosquito Modelling to Identify Malaria Hot-Spots

Anopheles mosquitoes are the vectors of human malaria, a disease responsible for a significant burden of global disease and over half a million deaths in 2020. Here, methods using a time series of cost-free Earth Observation (EO) data, 45,844 in situ mosquito monitoring captures, and the cloud processing platform Google Earth Engine are developed to identify the biogeographical variables driving the abundance and distribution of three malaria vectors—Anopheles gambiae s.l., An. funestus, and An. paludis—in two highly endemic areas in the Democratic Republic of the Congo. EO-derived topographical and time series land surface temperature and rainfall data sets are analysed using Random Forests (RFs) to identify their relative importance in relation to the abundance of the three mosquito species, and they show how spatial and temporal distributions vary by site, by mosquito species, and by month. The observed relationships differed between species and study areas, with the overall number of biogeographical variables identified as important in relation to species abundance, being 30 for An. gambiae s.l. and An. funestus and 26 for An. paludis. Results indicate rainfall and land surface temperature to consistently be the variables of highest importance, with higher rainfall resulting in greater mosquito abundance through the creation of pools acting as mosquito larval habitats; however, proportional coverage of forest and grassland, as well as proximity to forests, are also consistently identified as important. Predictive application of the RF models generated monthly abundance maps for each species, identifying both spatial and temporal hot-spots of high abundance and, by proxy, increased malaria infection risk. Results indicate greater temporal variability in An. gambiae s.l. and An. paludis abundances in response to seasonal rainfall, whereas An. funestus is generally more temporally stable, with maximum predicted abundances of 122 for An. gambiae s.l., 283 for An. funestus, and 120 for An. paludis. Model validation produced R2 values of 0.717 for An. gambiae s.l., 0.861 for An. funestus, and 0.448 for An. paludis. Monthly abundance values were extracted for 248,089 individual buildings, demonstrating how species abundance, and therefore biting pressure, varies spatially and seasonally on a building-to-building basis. These methods advance previous broader regional mosquito mapping and can provide a crucial tool for designing bespoke control programs and for improving the targeting of resource-constrained disease control activities to reduce malaria transmission and subsequent mortality in endemic regions, in line with the WHO’s ‘High Burden to High Impact’ initiative. The developed method was designed to be widely applicable to other areas, where suitable in situ mosquito monitoring data are available. Training materials were also made freely available in multiple languages, enabling wider uptake and implementation of the methods by users without requiring prior expertise in EO

Nationwide insecticide resistance status and biting behaviour of malaria vector species in the Democratic Republic of Congo

Abstract Background Globally, the Democratic Republic of Congo (DRC) accounted for 9% of malaria cases and 10% of malaria deaths in 2015. As part of control efforts, more than 40 million long-lasting insecticidal nets (LLINs) were distributed between 2008 and 2013, resulting in 70% of households owning one or more LLINs in 2014. To optimize vector control efforts, it is critical to monitor vector behaviour and insecticide resistance trends. Entomological data was collected from eight sentinel sites throughout DRC between 2013 and 2016 in Kingasani, Mikalayi, Lodja, Kabondo, Katana, Kapolowe, Tshikaji and Kalemie. Mosquito species present, relative densities and biting times were monitored using human landing catches (HLC) conducted in eight houses, three times per year. HLC was conducted monthly in Lodja and Kapolowe during 2016 to assess seasonal dynamics. Laboratory data included resistance mechanism frequency and sporozoite rates. Insecticide susceptibility testing was conducted with commonly used insecticides including deltamethrin and permethrin. Synergist bioassays were conducted with PBO to determine the role of oxidases in permethrin resistance. Results In Lodja, monthly Anopheles gambiae s.l. biting rates were consistently high at > 10 bites/person/night indoors and outdoors. In Kapolowe, An. gambiae s.l. dominated during the rainy season, and Anopheles funestus s.l. during the dry season. In all sites, An. gambiae and An. funestus biting occurred mostly late at night. In Kapolowe, significant biting of both species started around 19:00, typically before householders use nets. Sporozoite rates were high, with a mean of 4.3% (95% CI 3.4–5.2) for An. gambiae and 3.3% (95% CI 1.3–5.3) for An. funestus. Anopheles gambiae were resistant to permethrin in six out of seven sites in 2016. In three sites, susceptibility to deltamethrin was observed despite high frequency permethrin resistance, indicating the presence of pyrethroid-specific resistance mechanisms. Pre-exposure to PBO increased absolute permethrin-associated mortality by 24%, indicating that resistance was partly due to metabolic mechanisms. The kdr-1014F mutation in An. gambiae was present at high frequency (> 70%) in three sites (Kabondo, Kingasani and Tshikaji), and lower frequency (< 20%) in two sites (Lodja and Kapolowe). Conclusion The finding of widespread resistance to permethrin in DRC is concerning and alternative insecticides should be evaluated

Additional file 1 of Determination of the discriminating concentration of chlorfenapyr (pyrrole) and Anopheles gambiae sensu lato susceptibility testing in preparation for distribution of Interceptor® G2 insecticide-treated nets

Supporting data showing the predominant species tested in the dose-ranging studies and resistance to pyrethroid insecticides in chlorfenapyr susceptibility tests

Additional file 2 of Determination of the discriminating concentration of chlorfenapyr (pyrrole) and Anopheles gambiae sensu lato susceptibility testing in preparation for distribution of Interceptor® G2 insecticide-treated nets

Raw bioassay results for: "Determination of the discriminating concentration of chlorfenapyr (pyrrole) and Anopheles gambiae sensu lato susceptibility testing in preparation for distribution of Interceptor® G2 insecticide-treated nets.

Additional file 3 of Determination of the discriminating concentration of chlorfenapyr (pyrrole) and Anopheles gambiae sensu lato susceptibility testing in preparation for distribution of Interceptor® G2 insecticide-treated nets

Raw bioassay results for: "Determination of the discriminating concentration of chlorfenapyr (pyrrole) and Anopheles gambiae sensu lato susceptibility testing in preparation for distribution of Interceptor® G2 insecticide-treated nets.

A Multidisciplinary Investigation of the First Chikungunya Virus Outbreak in Matadi in the Democratic Republic of the Congo

Early March 2019, health authorities of Matadi in the Democratic Republic of the Congo alerted a sudden increase in acute fever/arthralgia cases, prompting an outbreak investigation. We collected surveillance data, clinical data, and laboratory specimens from clinical suspects (for CHIKV-PCR/ELISA, malaria RDT), semi-structured interviews with patients/caregivers about perceptions and health seeking behavior, and mosquito sampling (adult/larvae) for CHIKV-PCR and estimation of infestation levels. The investigations confirmed a large CHIKV outbreak that lasted February–June 2019. The total caseload remained unknown due to a lack of systematic surveillance, but one of the two health zones of Matadi notified 2686 suspects. Of the clinical suspects we investigated (n = 220), 83.2% were CHIKV-PCR or IgM positive (acute infection). One patient had an isolated IgG-positive result (while PCR/IgM negative), suggestive of past infection. In total, 15% had acute CHIKV and malaria. Most adult mosquitoes and larvae (&gt;95%) were Aedes albopictus. High infestation levels were noted. CHIKV was detected in 6/11 adult mosquito pools, and in 2/15 of the larvae pools. This latter and the fact that 2/6 of the CHIKV-positive adult pools contained only males suggests transovarial transmission. Interviews revealed that healthcare seeking shifted quickly toward the informal sector and self-medication. Caregivers reported difficulties to differentiate CHIKV, malaria, and other infectious diseases resulting in polypharmacy and high out-of-pocket expenditure. We confirmed a first major CHIKV outbreak in Matadi, with main vector Aedes albopictus. The health sector was ill-prepared for the information, surveillance, and treatment needs for such an explosive outbreak in a CHIKV-naïve population. Better surveillance systems (national level/sentinel sites) and point-of-care diagnostics for arboviruses are needed