Malaria epidemiology is doubly dependent on the frequency and efficiency of contacts between human hosts and Anopheles mosquitoes, which link the number of mosquito infections caused by an infectious human host and the rate at which uninfected humans acquire infections. Describing the variability in the frequency of human sampling by malaria vectors is therefore essential to understand parasite transmission from and to humans. While at a local level vector density determines average mosquito exposure, even within the same locality individuals may not be equally likely to be bitten by Anopheles mosquitoes (Lindsay et al., 1993; Knols et al., 1995a; Carnevale et al., 1978; Muirhead-Thomson, 1951). Exposure to malaria vectors is influenced by host availability (i.e., amount of time an individual remains unprotected against mosquito bites in an environment where anopheline mosquitoes are present) and attractiveness to mosquitoes (Stone et al., 2015; Yakob, 2016). Availability determines when and where individuals might be sampled by mosquitoes: a multicentre study in Africa that collected entomological and human behavioural data estimated that more than three quarters of human exposure to anopheline mosquito bites occur when individuals are indoors (Huho et al., 2013). For individuals who are accessible to malaria vectors, age and body surface area (Carnevale et al., 1978; Port et al., 1980) are two major determinants of attractiveness to mosquitoes, although other factors also play a role (Knols et al., 1995a).

The multifactorial nature of mosquito exposure in malaria endemic areas indicates that, while experimental and quasi-experimental, for example involving modified tents and huts, entomological studies are valuable, they will not accurately capture inter-individual variation in actual exposure. Identifying transmission heterogeneities, especially extreme heterogeneities, is however critical to better inform infectious disease epidemiology and well-established theory has elucidated their implications for pathogen spread and control measures (Woolhouse et al., 1997; Lloyd-Smith et al., 2005). Here, we describe the variability in natural exposure to malaria vectors by linking, through DNA fingerprinting, blood meals of wild-caught mosquitoes to humans living in the households where they were collected. Previously we have shown that these mosquitoes fed more often on adults (Gonçalves et al., 2017). We now extend this analysis to assess the degree of heterogeneity in the distribution of mosquito bites in the population at different times during the transmission season. We also present the frequency of Anopheles species-specific mosquito bites, and potential parasite inoculations (i.e., sporozoite-positive mosquito bites) per individual.

In this study, we quantified natural exposure to Anopheles mosquitoes using blood meals linked to household occupants. We observed significant differences in the numbers of bloodfed mosquitoes matched to study participants that are consistent with or even exceeding the Pareto rule, with ~20% of individuals being the source of more than 80% of all singly-matched blood meals. This heterogeneity was also apparent within-household, where the individual who received the most bites often contributed more than 50% of anopheline meals. Exposure to potentially infective mosquito bites also followed an aggregated pattern: 5.1, 13.9% and 6.5% of the population experienced 80% of parasite inoculations at the start, peak and end of the transmission season, respectively. Taken together, our observations indicate that relatively few individuals at different timepoints disproportionally contribute to malaria transmission by being repeatedly sampled and infected by malaria vectors. These data provide further insights into the mechanisms that lead to heterogeneity in human malaria infection risk (Bejon et al., 2014, 2010; Mwakalinga et al., 2016; Kangoye et al., 2016).

Several previous studies have quantified inter-individual differences in attractiveness and exposure to mosquitoes using a range of methodologies. While studies involving experimental huts (Lindsay et al., 1993) and olfactometers (Mukabana et al., 2002a) demonstrated the influence of individual-level factors such as pregnancy (Lindsay et al., 2000) and infection status (Lacroix et al., 2005) on attractiveness to mosquitoes, community-wide assessments of wild-caught mosquitoes (Port et al., 1980; Soremekun et al., 2004; Vazquez-Prokopec et al., 2016) are necessary to quantify variation in exposure to malaria vectors over larger scales. A study in The Gambia (Port et al., 1980) in the 1980's used ABO group and haptoglobin typing to identify the sources of mosquito blood meals; the small number of variants in these markers (Mukabana et al., 2002b) limited the selection of households. In Tanzania (Soremekun et al., 2004), microsatellites were used to link blood meals to humans to assess the protection afforded by bed nets against mosquitoes; a high proportion of the ~250 analysed bloodfed mosquitoes were matched to individuals sleeping in the same room where they were collected, including in the village with bed nets, and 80% of matched blood meals came from less than 20% of the population. In our study, we matched 666 blood meals to individual study participants and observed that the distribution of mosquito bites was highly overdispersed in relation to a Poisson assumption, with 76.0–95.5% of singly matched blood meals originating from ~20% of the study population. These results corroborate the findings of the study in Tanzania and of a different study in western Kenya (Scott et al., 2006), where 16% of the study participants were matched to 58% of Anopheles blood meals. By examining mosquito exposure repeatedly in an area of pronounced seasonality, we demonstrate even more unequal exposure patterns across three timepoints in the season. Applying simple, well-established (Smith et al., 2007; Churcher et al., 2015) methods, we estimated that the observed degree of mosquito biting heterogeneity could be linked to a 3-fold or higher increase in malaria R₀ compared to a random-mixing system. This suggests that in settings with considerable variation in mosquito exposure, heterogeneity in biting is likely to be a major determinant of the coverage of vector control interventions required to reduce and interrupt transmission (Smith et al., 2007). These R₀-focused calculations however have limitations. Firstly, although there is a positive relationship between R₀ and variability in Anopheles biting, in finite populations increasing aggregation in exposure does not necessarily equate to increasing community-wide infection burden, since in areas with highly heterogeneous mosquito biting a high proportion of parasite inoculations will be on already-infected individuals. Additionally, the framework used here does not account for previously observed interactions between frequency of exposure to infected mosquitoes and rates of immunity acquisition (Bejon et al., 2014; Bejon et al., 2010; Clarke et al., 2002), which could influence blood stage parasite burden and potentially the production of transmission stages in highly exposed, highly immune individuals. Development of detailed mathematical models that explicitly incorporate immunity and spatial dependencies (see next paragraph) would allow more precise estimation of the long-term influence of the observed variation in mosquito exposure on malaria transmission dynamics.

The fact that the majority of human-mosquito encounters in each survey involved only a few individuals is a consequence of both between- and within-household variation in exposure. Indeed, at the peak of the transmission season, the household with the highest number of fed mosquitoes had ~150 times more than the household with the lowest number. Household characteristics such as construction material, number of windows, eaves as well as geographical proximity to mosquito breeding sites have all been previously associated with increased exposure to mosquitoes indoors and explain some of the between-household variation in mosquito abundance. To understand the implications of our current findings for malaria control, data on geographical and temporal clustering of Anopheles exposure, malaria infection and disease are needed. A study in Kenya (Bejon et al., 2014) showed that foci of clinical malaria are unstable and that one-month surveillance data, for example, might only have predictive value of higher-than-average transmission over short periods of time. This is consistent with our observation that the ranking of households according to the number of infected mosquito bites per individual varies over time (Figure 3) and poses considerable challenges to sustainably targeting high exposure households for maximum community benefit.

We also found that household heterogeneity is compounded by inter-individual variation in frequency of sampling by mosquitoes for people in the same house. At the peak of the transmission season, the maximum difference in numbers of matched meals for participants in the same house ranged from 1 to 51, that is a difference of up to ~10 mosquito bites per day. Age and body size can partially explain differences in attractiveness (Muirhead-Thomson, 1951) yet we often noted age-matched individuals in the same house with dissimilar mosquito exposure. Other individual-level characteristics such as odours (Mukabana et al., 2002a; Qiu et al., 2006), (effective) use of protective measures or behaviour will be relevant; in our study, bed net use and gametocyte carriage were not significantly associated with the number of mosquito bites an individual received though this study was not designed to assess the influence of these factors on mosquito exposure. Of note, the age distribution of our study population (Table 1) might not reflect the true demography of the region. Whilst our results are in agreement with previous studies, we cannot exclude that a difference in age composition related to the fact that only houses with at least one child were included in the study may have influenced our heterogeneity estimates. Furthermore, we observed that blood meals from 8.2% of tested mosquitoes did not amplify for human DNA. Although it is possible that some of these mosquitoes fed on domestic animals, since it is unlikely that non-human biting would divert bites from specific hosts, we do not expect this to bias our individual-level estimates.

A technical limitation of this study was that our analyses only used data from ~70% of all successfully typed mosquito meals. Of those not matched, 139/292 were single blood meals from individuals not in our study houses and 153/292 had evidence of multiple blood meals. The distribution of these bites, each representing at least two host-vector encounters, is likely to be an important determinant of Anopheles exposure, in particular if some individuals are over-represented in these mixed meals, for example due to frequent defensive behaviour that leads to interrupted mosquito feeding and multiple probing. However, in a conservative sensitivity analysis that assigned these meals to the individuals with lowest exposure in each household, most mosquito bites were still linked to a small proportion of the population. Those mosquitoes whose blood meals did not match residents of study houses are likely to have fed in neighbouring houses or outdoors before entering study houses. The genetic profiles of these unmatched blood meals also suggest a heterogeneous feeding pattern: in the three houses with more than 10 unmatched mosquitoes, 16/41, 8/13 and 31/33 blood meals had the same profile. The only study house where a resident did not provide blood sample for matching had 8/10 unmatched meals presumably from the same human source. Concurrent collection of fed mosquitoes indoors and outdoors would help to understand how biting behaviour influences overall exposure.

Broad differences in anthropophily between mosquito species are well recognised (Takken and Verhulst, 2013) however there are fewer data (Knols et al., 1995b) on whether different Anopheles species have different feeding preferences with regards to individual humans. In this study, we analysed blood meals in mosquitoes from three species: A. gambiae s. s., A. coluzzii and Anopheles arabiensis. Although seasonal differences in species abundance were evident, we observed aggregation in human biting irrespective of vector species and exposure to one species was positively associated with exposure to the others. Though we only collected endophillic mosquitoes, it suggests that where vectors are anthropophilic heterogeneity in exposure to anopheline mosquitoes is a common epidemiological phenomenon including in areas with mixed vector species. Additionally, we observed that the most numerous species in our study area, A. coluzzii and A. gambiae s. s., differed in two key parameters that influence the transmission potential of mosquitoes: the likelihood of feeding on multiple individuals and the prevalence of sporozoites. There is evidence from membrane feeding experiments performed in Senegal (Ndiath et al., 2011) that A. gambiae s. s. mosquitoes might be more susceptible to malaria infection, although this association was not observed in another transmission study (Gnémé et al., 2013). Another possible explanation for the observed species-related difference in sporozoite prevalence could be variation in mosquito survival and consequently age structure. The higher frequency of falciparum infection in A. gambiae s. s. mosquitoes confirms, together with its relatively high abundance, the prime importance of this species for malaria transmission in the study setting. The contributions of the different Anopheles species to the mosquito infectious reservoir also depend on the number of potential parasite inoculations per mosquito-time: mosquitoes with multiple blood meals are more likely to infect more than one individual on a single night compared to mosquitoes with single-source blood meals, assuming sufficient quantities of sporozoites are inoculated during the probing of the different hosts. The higher proportion of blood meals with multiple human DNA sources suggests that A. coluzzii mosquitoes also contribute significantly to the incidence of infection in humans. Importantly, as species composition of local vector populations varies in the course of the rainy season (Dao et al., 2014), these differences might impact the rate of infection propagation in human populations over the course of a single transmission season as relative abundances of the different vector species change.

We also determined how the frequency of host-vector contacts might influence malaria infection risk in human populations. In our study area, there was considerable variation in household-specific exposure to sporozoite-positive mosquitoes (Figure 3). At the individual level, a few individuals received multiple potentially infective bites while between 75% and 90% of the study participants were not matched to feeding by infected mosquitoes during the three study periods. Whilst this does not represent all infected mosquito bites these individuals receive during an entire transmission season, it does highlight the degree of heterogeneity in likely parasite inoculations and the limitations of using population- or region-wide entomological measures of transmission that do not capture this small-scale variation. These results also suggest that the use of blood meal genotyping with concurrent assessment of mosquito sporozoite carriage during epidemiological studies could improve our understanding of the heterogeneity in clinical malaria risk (Mwangi et al., 2008; Loucoubar et al., 2013; Ndungu et al., 2015). Furthermore, whilst we observed a positive association between the total number of mosquito bites and the number of bites from sporozoite infected mosquitoes at the individual level, our data suggest clustering of sporozoite-positive mosquito bites at the household level that was not related to local mosquito abundance. This phenomenon could be linked to (i) a correlation between prevalence of infection in mosquitoes and in humans in the same households assuming limited mixing of mosquitoes and humans (e.g. due to the presence of breeding sites near houses, reducing the distances travelled by mosquitoes between consecutive blood meals [Le Menach et al., 2005], or to feeding site-fidelity in Anopheles mosquitoes [McCall et al., 2001]) or possibly to (ii) factors which affect mosquito survival on a very local scale, since mosquito age is associated with cumulative risk of sporozoite infection (Lines et al., 1991).

In summary, although studies have assessed natural exposure to vectors of other infections, such as Aedes (Harrington et al., 2014; Liebman et al., 2014) and Culex (Michael et al., 2001), only limited data are available for Anopheles mosquitoes (Soremekun et al., 2004; Scott et al., 2006). In our field site, characterized by high malaria transmission intensity, we show significant heterogeneity both between and within households in terms of the number of mosquito blood meals and the distribution of potentially infectious mosquito feedings; these patterns are consistent with the 20/80 rule and support the design of interventions that aim to reduce transmission by targeting a small proportion of the population. Opportunities to specifically target high-exposure households depend on the operational feasibility to identify these households and the stability in exposure over time. A quantitative understanding of the processes leading to heterogeneity in mosquito exposure and its temporal variability would inform at which level such interventions may be targeted in different settings; this would require quantification of the relative contributions of household-level factors, differential attractiveness to mosquitoes and human behavioural factors.

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Author details

1. Wamdaogo M Guelbéogo

   Department of Biomedical Sciences, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso

   Contribution

   Investigation, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Bronner Pamplona Gonçalves

   Competing interests

   No competing interests declared

2. Bronner Pamplona Gonçalves

   Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Wamdaogo M Guelbéogo

   Competing interests

   No competing interests declared

3. Lynn Grignard

   Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

4. John Bradley

   MRC Tropical Epidemiology Group, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   Formal analysis, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9449-4608

5. Samuel S Serme

   Department of Biomedical Sciences, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso

   Contribution

   Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

6. Joel Hellewell

   MRC Centre for Outbreak Analysis & Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom

   Contribution

   Formal analysis, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

7. Kjerstin Lanke

   Department of Medical Microbiology, Radboud University Medical Center, Nijmegen, Netherlands

   Contribution

   Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

8. Soumanaba Zongo

   Department of Biomedical Sciences, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso

   Contribution

   Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

9. Nuno Sepúlveda

   1. Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, United Kingdom
   2. Centre of Statistics and Applications, University of Lisbon, Lisbon, Portugal

   Contribution

   Formal analysis, Writing—review and editing

   Competing interests

   No competing interests declared

10. Issiaka Soulama

    Department of Biomedical Sciences, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso

    Contribution

    Investigation, Writing—review and editing

    Competing interests

    No competing interests declared

11. Dimitri W Wangrawa

    Department of Biomedical Sciences, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso

    Contribution

    Investigation, Writing—review and editing

    Competing interests

    No competing interests declared

12. Laith Yakob

    Department of Disease Control, London School of Hygiene and Tropical Medicine, London, United Kingdom

    Contribution

    Formal analysis, Writing—review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8639-4511

13. N'Falé Sagnon

    Department of Biomedical Sciences, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso

    Contribution

    Investigation, Writing—review and editing

    Competing interests

    No competing interests declared

14. Teun Bousema

    1. Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, United Kingdom
    2. Department of Medical Microbiology, Radboud University Medical Center, Nijmegen, Netherlands

    Contribution

    Conceptualization, Funding acquisition, Investigation, Methodology, Writing—original draft, Project administration, Writing—review and editing

    For correspondence

    teun.bousema@radboudumc.nl

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2666-094X

15. Chris Drakeley

    Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, United Kingdom

    Contribution

    Conceptualization, Funding acquisition, Investigation, Methodology, Writing—original draft, Project administration, Writing—review and editing

    For correspondence

    Chris.Drakeley@lshtm.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4863-075X

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