Variation in the effectiveness of insecticide treated nets against malaria and outdoor biting by vectors in Kilifi, Kenya [version 1; peer review: 4 approved with reservations]

Background: Insecticide treated nets (ITNs) protect humans against bites from the Anopheles mosquito vectors that transmit malaria, thereby reducing malaria morbidity and mortality. It has been noted that ITN use leads to a switch from indoor to outdoor feeding among these vectors. It might be expected that outdoor feeding would undermine the effectiveness of ITNs that target indoors vectors, but data are limited. Methods: We linked


Introduction
Despite the recent scale-up effort to achieve control, malaria continues to cause morbidity and mortality, especially in sub-Saharan Africa. There are uncertainties in global estimates [1][2][3] ; however in 2015, the World Health Organization estimated global deaths due to malaria to be 438,000 (range: 236,000-635,000) and the burden of febrile illness at 214 million cases (range: 149-303 million) 4 . Modelling studies suggest that approximately 1.4 billion of the world's population live at risk of stable malaria and ~1.1 billion at risk of unstable malaria 5 .
The frontline tools for malaria control in sub-Saharan Africa, insecticide treated nets (ITNs) and indoor residual spray, are optimally effective if baseline transmission occurs indoors 6 . The major vectors of human malaria mostly feed indoors, and transmission can therefore be substantially reduced by these tools 6 . The proportion of the at risk population who have access to ITNs was modeled to have increased from 4% to 67% between 2004 and 2015 7 . ITNs operate in three ways: deterrence, excitorepellence and killing, thereby reducing the density, feeding frequency, feeding success, and survival of Anopheles mosquito vectors 6 . By reducing vector densities and vector survival, ITNs not only directly protect the individual ITN user, but also reduce the overall transmission intensity and protect the whole community when a particular threshold of bed net coverage is reached [8][9][10] .
The evidence base supports ITN use over a range of transmission intensities 11 and protective efficacy has been demonstrated against infection, clinical disease and mortality [12][13][14][15][16] . However, residual malaria transmission is well described even after optimal ITN use, which could be caused by changes in the behaviour of the mosquito vector that allows them to evade fatal contact with these frontline tools of intervention 17,18 . The most obvious behavioural change is the mosquito vector exhibiting exophagic tendencies -i.e. the vector feeds outdoors on humans.
Among malaria vectors in Africa, the two principal species complexes are: Anopheles gambiae sensu lato (s.l.) and Anopheles funestus group. Both species complexes feed primarily indoors; however both have exhibited behavioral shifts to outdoor biting or feeding in the early part of the evening following prolonged use of ITNs in some areas 6,19,20 . This behavioral change might have resulted from one of three processes: (i) selection, either for species that more readily engages in outdoor feeding, for instance in favour of An. arabiensis rather than An. gambiae sensu strictu (s.s.); (ii) by selecting for evolutionary change within a species; or (iii) a response to inability to feed during the night in the absence of genetic variation 21 . In Western Kenya and South-eastern Tanzania there have been reports of a reduction in indoor feeding by An. gambiae sensu stricto (s.s.) and an increase in the relative abundance of An. arabiensis. The latter has a broader range of feeding times and biting behavior, including outdoor feeding 8,[21][22][23] . In northern Tanzania, where ITNs have been used for several years, the mosquitoes are biting more frequently during the hours of the early evening and early morning when people are more likely to be awake and vulnerable outside of their nets 6,24 . The potential for ITNs to result in species switches was appreciated in earlier controlled trials 21,22,25 , and is now reported more widely as ITN use is scaled up in Western Kenya and on the East African coast 21,22 .
In Kilifi, Kenya, a switch in the most common vector, from An. gambiae sensu stricto (s.s.) to An. arabienses, occurred during the period of ITN scale-up 19 . The increased ability of An. arabiensis to feed outdoors might be expected to result in a decrease in ITN effectiveness. However, there is little data to support this contention, and some data and models that are available suggest that ITNs continue to be effective despite outdoor feeding 26,27 . The objectives of this study were (i) to examine whether there has been a shift in vector biting patterns and/or vector behaviour, during the period of intense ITN use along the Kenyan coast; (ii) to test for geographical heterogeneity in ITN effectiveness within the surveillance area of a primary healthcare facility in Kilifi County; and (iii) to assess whether outdoor vector biting is a potential explanation for the variation in ITN effectiveness.

Study area
The clinical surveillance study was conducted between January 2009 and December 2014 within a 6km radius of Pingilikani dispensary in Kilifi County on the Kenyan Coast ( Figure 1): within the Kilifi Health and Demographic Surveillance System (KHDSS). All children under 13 years presenting for medical assessment to Pingilikani dispensary (except those with trauma as their only concern) were assessed by research staff and had finger-prick blood samples examined for malaria parasites. Thick and thin blood smears were stained with 10% Giemsa and examined at 1000X magnification for asexual Plasmodium falciparum parasites. Before slides could be considered negative, 100 fields were examined. Children with malaria positive slides were treated with co-artemether.
Transmission of malaria peaks after the long rains from April to June and the short rains from October to November each year, although transmission has been declining 28-31 . The surveillance area was divided into 2.5x2.5 km regular polygons resulting in 21 geographical areas ( Figure 2). As part of KHDSS, four-monthly enumeration rounds were conducted to identify births, deaths and migration events. Each inhabitant was described by their family relationships and their homestead of residence, with geospatial coordinates, and assigned a unique personal identifier 32 . These details were used to link children visiting Pingilikani dispensary to geospatial coordinates for the homestead of residence. Data on ITN use was collected once yearly during cross-sectional surveys integrated into the regular KHDSS enumeration since 2008. Questionnaires were used to collect household data on ITN ownership and use on the night prior to enumeration. Seven geographical areas were selected for mosquito sampling out of 21 areas for which clinical effectiveness estimates were determined ( Figure 2). The basis of selecting the seven areas was (i) geographical areas with >60 homesteads available for randomization; (ii) areas representative of highest and lowest ITN effectiveness.

Mosquito sampling
Indoor and outdoor biting profiles of An. gambiae s.l. and the An. funestus group were estimated using human landing catches (HLC) and CDC-light traps (CDC-LT) by visiting randomly selected houses (random selection done by stratified sampling) between July and August 2016. For both indoor and outdoor  mosquito collection, HLC was conducted by two pairs of trained male volunteers (one pair was located indoors and the other pair outdoors, but at the same homestead), who sat with their legs exposed and caught mosquitoes that attempted to bite them using an aspirator. HLC was conducted between 18:00hours and 06:00hours for 45 minutes each hour, allowing 15 minutes break for rest. The catches for each hourly interval were stored in separate collection cups. CDC-light traps were also set indoor and outdoor between 18:00hours and 06:00hours. The HLC and the CDC-LT collections took place in different houses. In each geographical area, sampling was conducted for at least 3 days in at least 16 houses; 8 houses for HLC and 8 houses for CDC-LT. In total, 26 days of sampling were conducted across 115 houses in the seven selected geographical areas within the surveillance area.

Mosquito processing
The mosquito samples were morphologically separated for sex and identified for species 6,24 . The female Anopheles mosquitoes were tested for falciparum infection using a sandwich circumsporozoite protein (CSP) enzyme linked immunosorbent assay (ELISA) 33 (anti-CSP capture: Pf2A10-28 and conjugate : Pf2A10-CDC antibodies; KPL, Gaithersburg, MD, USA). Individual mosquitoes were stored at -20°C in micro-centrifuge tubes containing a small amount of desiccant (silica gel) separated from the mosquito by a thin layer of cotton prior to ELISA and molecular analysis for sibling species by polymerase chain reaction 6,34 .

Statistical analysis
Statistical analyses were performed using STATA v13.1 (StataCorp, College Station, TX, USA). To assess for geographical heterogeneity, we used the logistic regression model to analyze data on over 20,000 visits from children attending Pingilikani dispensary. The outcome of interest was presence of malaria by microscopy on presentation to the dispensary. The potential risk factors included: ITN use, age of the child, year of presentation to the dispensary and the geographical area, as defined by the 2.5×2.5 km regular polygons. We assessed whether the effect of ITN use on malaria was altered by geographical area by including an interaction term between geographical area and ITN use. We also assessed whether the effect of ITN use was altered by the age of the child and whether geographical areas altered the effect of age. To assess the nonlinear effect of age in the regression models, multiple fractional polynomial transformation was used. Given that the hospital malaria episodes were clustered within patients, we allowed for clustering by using a logistic regression model with robust standard errors. The robust standard errors were used to account for the clustering effect in the estimation of the standard errors. The ratio of malaria in the non-ITN users to that in the ITN users was expressed as an odds ratio (OR) as determined by logistic regression. ITN effectiveness was calculated as (1 -OR) × 100. Model fit was assessed by examining residuals against covariates. Spearman's rank correlation was used to assess the association between ITN effectiveness and prevalence of malaria. SaTScan software (version 9.4; https://www. satscan.org/), a spatial scan statistic developed by Kulldorf 35 , was used to detect potential spatial variations of ITN effectiveness by identifying statistically significant geographical clustering of ITN effectiveness.
In order to compare counts of female Anopheles captured, we determined the relative proportion of each mosquito species in each geographical area and ITN effectiveness levels (ITN effectiveness was divided into 2 levels based on the estimates obtained from the logistic regression above -i.e. high and low ITN effectiveness). Three areas with high ITN effectiveness and four areas with low ITN effectiveness were selected based on the findings of the scan statistic. We compared the proportion of vectors biting outdoors in each geographical area. We estimated the confidence intervals of these proportions using the binomial distributions, and tested for an association between biting preference and ITN effectiveness (at the level of geographical area). This did not appear to be the explanation for variation in effectiveness in this data (Supplementary Figure 1); the Spearman rho coefficient value for the association of ITN effectiveness and prevalence of malaria was 0.308, p=0.331.

Hotspots
Using the logistic regression model, we estimated ITN effectiveness for each individual homestead where there was sufficient data to calculate a point estimate (i.e. >30 observations from homestead aggregated at a 2.5 km smoothing). Using SaTScan software, we identified 6 significant hotspots of low ITN effectiveness: p=0.001 for 4 hotspots, p=0.002 and p=0.014 for a 5th and 6th hotspot ( Figure 4). We concluded that spatial variation in ITN effectiveness was not due to random noise based on the 95% confidence intervals obtained from the logistic regression   The species and proportion of mosquitoes collected in areas of high vs. low ITN effectiveness are summarised in Table 2 and Supplementary Figure 2. Overall, the proportion of outdoor biting was higher in the low ITN effectiveness areas (69% vs. 27%, p <0.001), but this apparent significance was due to a single area (labelled area 6), which was an outlier for indoor biting ( Figure 5). When we excluded area 6, the proportion of outdoor biting in the low vs. high ITN effectiveness areas was nonsignificant (69% vs. 75%, p=0.76). Moreover, when analysed by individual geographical area there was not a visually obvious trend associating increasing outdoor biting with decreasing ITN effectiveness in the seven geographical areas ( Figure 5). The Spearman rho coefficient value for the association of ITN effectiveness and proportion of mosquitoes collected outdoors was -0.464, p=0.302.

Discussion
Malaria is an important public health problem in sub-Saharan Africa, and many countries, including Kenya, have attempted to reduce this burden by increasing ITN ownership and usage 37,38 . However, previous reports have shown that prolonged ITN use leads to behavioral shifts in the mosquito vector from indoor to outdoor biting or feeding in the early part of the evening 6,19,24 . This shift in mosquito feeding behavior might be expected to result in a decrease in ITN effectiveness. We identified statistically significant geographical variation in the effectiveness of ITN and identified areas where ITN effectiveness was found to be consistent with the 50% estimate reported in the literature 11,39,40 , and other areas where ITNs were less effective ( Figure 3). This variation could conceivably have arisen as a result of variations in quality of ITNs, patterns of use, host resistance, insecticide resistance or other factors, including random variation. We investigated whether variations in outdoor vector biting was a potential explanation.
We found that An. funestus was more prevalent than An. gambiae s.l. species complex, consistent with a previous report 19 . We observed small-scale spatial variability in vector abundance (Table 1), which is consistent with previous reports on the Kenyan Coast 20,41 . We also observed a higher proportion of mosquito vectors collected outdoors than indoors, in areas of both high and low ITN effectiveness ( Figure 5). On first principles one would expect that outdoor biting would lead to ITNs becoming ineffective. However, despite seeing consistent outdoor biting throughout the study area this did not appear to be associated with an overall reduction in ITN effectiveness. We may have observed an apparently statistically significant increase in the prevalence of outdoor biting in areas of low ITN effectiveness. However, this was due to a single outlying geographical area and there was no variation in prevalence of outdoor biting after this area was excluded. This suggests the statistical significance of the initial comparison may have been due to ecological confounding, where a geographical area with high ITN effectiveness happened to have more indoor mosquitoes, but this relationship was not confirmed in other areas ( Figure 5).
How should we interpret the finding that outdoor feeding does not consistently lead to a reduction in ITN effectiveness? It is possible that the higher proportion of mosquitoes biting outdoors represents a behavioral response to unsuccessful feeding attempts made indoors during the night, and therefore it may simply be a marker of successful ITN use. This avoidance behavior may exert a cost on the vector, and so ITNs may in fact still be protective in areas where outdoor biting is observed, as has been suggested previously 27 .
Spatial heterogeneity in malaria exposure has been described at micro-epidemiological level at varying transmission settings 42 and is responsible for variations in disease risk within small geographical areas and is evidenced by local clustering of malaria infections. Within the 2.5 km squared geographical areas, ITN effectiveness appears to have been spatially heterogeneous ( Figure 4); however, we were unable to demonstrate a significant association between ITN effectiveness and outdoor biting at the level of seven small geographical areas. The observed geographical variation in ITN effectiveness therefore remains unexplained. Possibilities include insecticide resistance, or geographical variations in human behaviour in terms of ITN use.
Our study has some limitations. Data on ITN use may have been incorrectly reported, as we did not require each resident to be present to respond to the ITN ownership and use questions. We attempted to minimize this by instructing data collecting teams to interview only residents of the same homestead regarding ITN ownership and usage. There may have been some misclassification as we did not ascertain ITN use during hospital visitation but instead used the yearly ITN data collected by the KHDSS. The results may also be biased and confounded by other unmeasured factors (e.g., variation in the quality and type of ITN, urbanization, socio-economic status and mother's education). Therefore, the estimates obtained could be an overestimation or underestimation of the true effectiveness. It is likely that we underestimated the protection afforded by the use of high-quality ITN because we included all ITNs, regardless of quality. The vast majority of ITNs in the area are long-lasting insecticidal nets, hence we do not expect substantial variation in insecticidal efficacy. The accuracy of the human landing catches may be affected by the inter-individual differences in attracting mosquitoes. The size of our study limits power: with a sample size of 411, and the proportion of mosquitoes biting outdoors at 69% in low ITN effectiveness areas we therefore had >90% power to detect a reduction to 27% or lower in high ITN effectiveness areas. Our study was therefore powered to detect only a large difference in the proportion of vectors caught outdoors. However, we reasoned that reductions of ITN effectiveness to less than half of the previously documented efficacy of 50% would require a doubling of the proportion of mosquitoes feeding outdoors.
Hence our study was powered to detect large variations in the frequency of outdoor biting. Furthermore, since the proportion of vectors collected outdoors was high throughout the study area despite preserved ITN effectiveness in many areas, we conclude that the pattern of outdoor feeding identified in our site does not undermine ITN effectiveness.
In summary, we found no evidence that the currently observed switch from indoor to outdoor biting leads to reduced ITN effectiveness. The outdoor biting observed may therefore have been the result of high levels of ITN use leading to unsuccessful attempts at indoor feeding. It remains possible that selection pressures might lead to the emergence of populations of mosquitoes that are better adapted to outdoor feeding. Outdoor feeding is becoming more common in parts of Africa 43 and may represent evolutionary change in some areas, with a potential to undermine ITN effectiveness. Therefore, malaria control programs require monitoring to assess the impact of ITNs on vector populations and vector behavioral change as well as monitoring ITN effectiveness as vectors evolve 6,21,22,24,25 . Detailed studies of vector bionomics, continuous monitoring and malaria transmission dynamics are essential for predicting disease outbreaks and vector control in the region.

Ethical approval
This study was approved by the Kenya Medical Research Institute Scientific Ethics Review Unit (KEMRI/SERU/CGMR-C/024/3148). Written informed consent was obtained from the parents/guardians of the children attending the dispensary.

Data availability
Data that support the findings of this study (hospital surveillance, ITN community surveys and mosquito collection) are available from the KEMRI Institutional Data Access/Ethics Committee, for researchers who meet the criteria for access to confidential data. Details of the criteria can be found in the KEMRI-Wellcome data sharing guidelines. The data includes homestead level coordinates as an essential component and these are personally identifiable data. Access to data is provided via the KEMRI-Wellcome Data Governance Committee: Data_Governance_ Committee@kemriwellcome.org; Tel, +254708 587 210; Contact person, Marianne Munene (Secretary; Tel, +254709 983 436).
Author contributions AK oversaw field implementation of the study, analyzed and interpreted the data and drafted the manuscript. JMM, MKR and PB conceived the study, helped with the field implementation of the study, and reviewed and revised the manuscript. PM, IO, JM, and JAGS reviewed and revised the manuscript. All authors approved the final manuscript, as submitted.

Competing interests
The authors declare that they have no competing interests.

Grant information
This work was supported by the Wellcome Trust [104015].
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Supplementary material
Supplementary   Click here to access the data.

1.
No PCR speciation was reported. In this area there are a number of cryptic species that look the same but differ in both their behaviour and their ability to transmit malaria. No molecular techniques were used to test the mosquito species. So you could have a switch from An. gambiae s.s. that bites indoors and has high vectorial competence to An. arabiensis that bites outdoors and has lower vectorial competence. The same is true in the An. funestus complex that is comprised of a number of outdoor biting species like An. leesoni or An. rivulorum.

2.
The authors reported that PCR (polymerase chain reaction) was done on the mosquitoes yet I cannot find data in the paper reporting the outcome of the PCR. All data reports An. gambiae s.l. and An. funestus group.

3.
The paper explores changing mosquito behaviour with lowered effectiveness of nets but only used one month of vector collections two years after the clinical data was collected to test this link and the actual species present are not reported. I therefore find this a big stretch of the data. Vector density, composition and behaviour varies throughout the year and these collections were made for a short time. I therefore don't think the data are sufficient to accept or reject the hypothesis.
That being said the rest of the data is very useful and nicely presented. The data do demonstrate that there is substantial outdoor biting in June/July, and I should like to see the species composition in the area seeing as the authors report that the PCR was done. Outdoor biting may not increase malaria if the vectors doing the outdoor biting are not very competent for malaria.

Is the work clearly and accurately presented and does it cite the current literature? Partly
Is the study design appropriate and is the work technically sound? No

If applicable, is the statistical analysis and its interpretation appropriate? Yes
Are all the source data underlying the results available to ensure full reproducibility? No

Are the conclusions drawn adequately supported by the results? No
Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Medical entomology
I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
Author Response 06 Jan 2018

Alice Kamau
We are grateful for this review and the helpful comments and suggestions that have been made. We have included a point-by-point response (in bold) to the issues raised.  Discussion section "Lack of explicit molecular data for distinguishing sibling species and molecular forms within the An. funestus group introduces ambiguity into the interpretation of the results of the study." Q3) The authors reported that PCR (polymerase chain reaction) was done on the mosquitoes yet I cannot find data in the paper reporting the outcome of the PCR. All data reports An. gambiae s.l. and An. funestus group.
A3: We have addressed this comment as shown above.
Q4) The paper explores changing mosquito behaviour with lowered effectiveness of nets but only used one month of vector collections two years after the clinical data was collected to test this link and the actual species present are not reported. I therefore find this a big stretch of the data. Vector density, composition and behaviour varies throughout the year and these collections were made for a short time. I therefore don't think the data are sufficient to accept or reject the hypothesis.

A4:
We have updated the clinical surveillance data to December 2016 and updated the manuscript accordingly. We have included the limitation of the one month vector collection in the discussion section as shown below.
Discussion section "The size of our study limits power: with a sample size of 415, and the proportion of mosquitoes biting outdoors at 67% in low ITN effectiveness areas we therefore had >90% power to detect a reduction to 27% or lower in high ITN effectiveness areas. Our study was therefore powered to detect only a large difference in the proportion of vectors caught outdoors. However, we reasoned that reductions of ITN effectiveness to less than half of the previously documented efficacy of 50% would require a doubling of the proportion of mosquitoes feeding outdoors. Hence our study was powered to detect large variations in the frequency of outdoor biting. In addition, the accuracy of mosquito sampling data is limited as only one month of sampling was conducted in this study, we recommend sampling for a longer duration of time." Q5) That being said the rest of the data is very useful and nicely presented. The data do demonstrate that there is substantial outdoor biting in June/July, and I should like to see the species composition in the area seeing as the authors report that the PCR was done.
Outdoor biting may not increase malaria if the vectors doing the outdoor biting are not very competent for malaria.

A5:
We have addressed this comment as shown above.

Gerry F Killeen
Environmental Health and Ecological Sciences Thematic group, Ifakara Health Institute, Dar es Salaam, Tanzania Apart from some unfortunately important exceptions, the data for this study are meticulously collected and analysed. However, many of the most important results are either over-interpreted or misinterpreted so these exceptions are substantive. In fact, the correct interpretation may well be almost the exact opposite of that presented here: That LLINs are consistently effective across a landscape where transmission is dominated by a vector that primarily attacks people indoors at night while they are asleep.
The biggest single problem with this paper is that the indoor and outdoor biting rate 1.
estimates come from stationary, fully exposed human volunteers exhibiting artificial experimental behaviours, without adjusting them for normal human behaviours that mean most of us are indoors asleep during the peak biting hours of nocturnal African malaria vector mosquitoes. This is an understandable and common mistake, but a very important one. Like Anopheles funestus in most locations across Africa, the 55-45 distribution of biting location preference for this population is essentially indiscriminate, so it is the behaviour of humans that determines where exposure actually occurs. So unless everyone in coastal Kenya sleeps half indoors and half outdoors throughout the night, simply comparing indoor versus outdoor HLCs is misrepresentative and greatly exaggerates the contribution of outdoor biting to transmission by this species. Once adjusted for human behaviour patterns, >90% of human biting exposure to this key vector species is consistently estimated to occur indoors in the absence of some protective measures at locations scattered all across Africa [1]. Unless human behaviour on the coast of Kenya is far more exophilic (everyone sleeps outdoors?) than all the other human populations we have data for, there is nothing in the data presented that is unusual or that convince me this vector population behaves differently from Anopheles funestus elsewhere. The logical conclusion of this paper (albeit with some additional data and analyses to support it) is that, unsurprisingly, there is little difference in the effectiveness of nets across landscapes dominated by the same vector that primarily encounters people indoors at night while they are asleep and can use a net.
The most important data clearly missing from the characterization of the study scenario are (a) spoorozoite rates (mentioned in the methods but not the results) and EIR estimates, to confirm that Anopheles funestus group mosquitoes are the most important vectors of malaria in this area, (b) quantitative estimates of where and when humans are exposed to these two major vector taxa (not species unless PCR data are added) that weight the biting estimates by surveys of human behaviour [2][3][4][5]. These are increasingly common calculations applied to data from all over the tropics [6][7][8][9][10][11][12][13], and vitally important to conduct before making any quantitative statements about proportional contributions of outdoor biting exposure.

2.
There is no evidence of any "shift" in behaviours over time presented here, so the term "undermines" is unjustified and seems to create an argument that hasn't been made. Most behaviours that enable residual malaria transmission despite LLIN use are pre-existing, although plastic, and often it's just the vector population composition that shifts 14, so the term "limits" is more appropriate.

3.
While indeed there is no evidence here that outdoor transmission contributes to ongoing transmission, there is also no evidence that it does not. Such outdoor fractions of transmission can only be expected to become epidemiologically detectable once larger quantities of indoor transmission (which I'm convinced is the case here as explained above) have been tackled. So the phraseology of conclusions needs to be tempered using words like "yet", and explain how these currently minor fractions of transmission may emerge as important contributors to sustained endemicity once further progress has been made with indoor control [14,15].

4.
In any case, LLINs clearly fall a long way short of being 100% efficacious with 22% personal protection estimated here, so there clearly are considerable limitations to this technology that need explanation. To get a better handle on whether outdoor exposure does 5.
contribute to residual transmission, in our experience it's necessary to test as a function of individual human behavioural profiles weighted by activity patterns for the most dominant local vectors [13]. Indeed human behaviour is the primary driver of where and when exposure occurs [1] and is far more variable than the mosquito behaviours that matter within a single vector species [15].
In any case, for many of the surveyed locations, very few mosquitoes were caught (Supplementary Table 2) and CDC light traps catches indoors and outdoors are not comparable, so reporting these data as indicators of the degree of exophagy or endophagy is going too far and overstretching very little entomological data.

6.
The fact that these are not differentiated to species (again, though this is mentioned in the methods but no results are presented) also means that areas with apparently different mosquito behaviours are probably areas that simply have different relative abundances of primary vector, secondary vector and non-vector species within the Anopheles funestus group and within the Anopheles gambiae complex. For example, greater outdoor feeding at dawn and dusk is a known characteristic of Anopheles rivulorum and Anopheles parensis, originally discovered in this region on the basis of their distinctive behaviours and much weaker vectorial capacities.

7.
The term "species" is used very loosely and interchangeably with other taxonomic classification levels, resulting in some misleading over-interpretation. While Anopheles gambiae sensu lato is indeed a complex, Anopheles funestus sensu lato is a group (not a complex, as stated in the introduction) and neither can be described as a species, unless one is talking about unambiguously identified individual specimens of the nominate species, which are by far the most efficient species within each taxon.

8.
All of these most important limitations seem to be missing from the paragraph opening with the sentence "Our study has some limitations".

9.
What is called "effectiveness" here refers only to the relatively minor personal protection effect of bednets, and does not capture any variations in community-level impact. All fine but please explain this study limitation clearly.

10.
Correspondingly, doesn't capture how big a change this transmission picture is relative to the same setting 10 to 15 years ago when nominate Anopheles gambiae were still quite abundant. The explanations about the relative abundance of vector taxa (not species) is accurate but rather static and lacking in long term context, demonstrating the much bigger overall impact on vector populations and endemicity. This is a pity when this contemporary study has been conducted in an area with so much historical entomological literature, so please enrich the narrative.

11.
While I agree with the closing statement about enhancing entomological surveillance, in my experience many groups are under-interpreting or misinterpreting the data they already have, so perhaps that capacity limitation merits some emphasis as a priority.

If applicable, is the statistical analysis and its interpretation appropriate? No
Are all the source data underlying the results available to ensure full reproducibility? Partly

Are the conclusions drawn adequately supported by the results? No
Competing Interests: No competing interests were disclosed.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
Author Response 09 Jan 2018

Alice Kamau
We are grateful for this review and the helpful comments and suggestions that have been made. We have included a point-by-point response (in bold) to the issues raised.
Q1) The biggest single problem with this paper is that the indoor and outdoor biting rate estimates come from stationary, fully exposed human volunteers exhibiting artificial experimental behaviours, without adjusting them for normal human behaviours that mean most of us are indoors asleep during the peak biting hours of nocturnal African malaria vector mosquitoes. This is an understandable and common mistake, but a very important one. Like Anopheles funestus in most locations across Africa, the 55-45 distribution of biting location preference for this population is essentially indiscriminate, so it is the behaviour of humans that determines where exposure actually occurs. So unless everyone in coastal Kenya sleeps half indoors and half outdoors throughout the night, simply comparing indoor versus outdoor HLCs is misrepresentative and greatly exaggerates the contribution of outdoor biting to transmission by this species. Once adjusted for human behaviour patterns, >90% of human biting exposure to this key vector species is consistently estimated to occur indoors in the absence of some protective measures at locations scattered all across Africa [1]. Unless human behaviour on the coast of Kenya is far more exophilic (everyone sleeps outdoors?) than all the other human populations we have data for, there is nothing in the data presented that is unusual or that convince me this vector population behaves differently from Anopheles funestus elsewhere. The logical conclusion of this paper (albeit with some additional data and analyses to support it) is that, unsurprisingly, there is little difference in the effectiveness of nets across landscapes dominated by the same vector that primarily encounters people indoors at night while they are asleep and can use a net.

A1: We have made adjustments to the indoor and outdoor biting rate by human behavior as follows in the following sections:
Methods section "To determine the human-mosquito contact, we administered questionnaires to 304 randomly selected households in the six selected areas between September and October 2016. We asked the household head time when each household member went to sleep and the time they woke up. Data on human behaviour was used to make adjustments to the indoor and outdoor biting rate."

Statistical analysis section "Questionnaire data about the time household members went to sleep and at what time they woke up were combined with human landing catches measurements of hourly rates for indoor and outdoor biting. We estimated the proportion of human exposure to mosquito bites occurring indoors (π s ) by taking into consideration the movement pattern of people using the following method [1]: by weighting the mean indoor and outdoor biting rates throughout the night by the proportion of humans reporting to have gone to sleep at each hour of the night as follows; π s = Σ ( Bi,t S t ) / Σ ( Bi,t S t + B o,t ( 1 -S t )) (1) Where: = an estimate of human exposure to bites which occurs when residents are both indoors and sleeping S t = the proportion of humans indoors reporting to have gone to sleep at each hour of the night (t) B i,t = mean indoor biting rate at each hour of the night (t) B o,t = mean outdoor biting rates at each hour of the night (t) (1-S t ) = proportion of humans not yet asleep at each hour of the night."
Result section "Seventy three percent of children <5 years were reported to be asleep between 6 pm and 9 pm, these rose monotonically over the course of the night reaching 100% by 10 pm (Table 4 & Figure 6). Children aged between 6-14 years spent more time awake, only 45% were asleep before 9 pm (Figure 6 & Supplementary Table 3). Human landing catches are not sufficient in themselves to survey normal human exposure to mosquito bite. The timing of human activity and sleeping behaviour in particular modulates the effect of human-mosquito contact and the effectiveness of ITN. We quantified the interaction between mosquitoes and humans to evaluate whether outdoor vector biting is a potential explanation for the variation in ITN effectiveness. The peak biting activity for each mosquito vector is illustrated in Figure 7. Clearly higher indoor biting activity was observed for the An. funestus group. The overall propensity to feed at times when most people are indoor was high (Figure 8): the vast majority of the Anopheles mosquitoes were caught at times when most people are indoors (Figure 7). Estimates for the proportion of human-mosquito contact between the first and last hour when most humans were indoors was consistently high, ranging from 0.83 to 1.00. Therefore, the estimated proportion of exposure to Anopheles mosquito bites that occurred indoor was high."

Discussion section
It is possible that a higher proportion of mosquitoes caught outdoors represents a behavioral response to unsuccessful feeding attempts made indoors during the night, and therefore it may simply be a marker of successful ITN use. This avoidance behavior may exert a cost on the vector, and so ITNs may in fact still be protective in areas where outdoor biting is observed, as has been suggested previously [2]. Furthermore, outdoor biting exposure and the probability of successful feeding outdoors cannot be directly inferred from the human landing catches, since the landing catches are not in themselves sufficient to survey pattern of normal human exposure to mosquito bite. Once adjusted for human behaviour, most human-vector interaction in this study occurred indoors (Figure 8 & Supplementary Table 3). Outdoor biting is currently not a major factor influencing residual malaria transmission since 95% of the population are indoors at the peak biting period for malaria vector mosquitoes. Human behaviour is the primary driver of when and where exposure occurs and is far more variable than the mosquito behaviour that matter within a single vector species [3].
Q2) The most important data clearly missing from the characterization of the study scenario are (a) spoorozoite rates (mentioned in the methods but not the results) and EIR estimates, to confirm that Anopheles funestus group mosquitoes are the most important vectors of malaria in this area, (b) quantitative estimates of where and when humans are exposed to these two major vector taxa (not species unless PCR data are added) that weight the biting estimates by surveys of human behaviour [2][3][4][5]. These are increasingly common calculations applied to data from all over the tropics [6][7][8][9][10][11][12][13], and vitally important to conduct before making any quantitative statements about proportional contributions of outdoor biting exposure.
A2: We have included data obtained from the ELISA-CSP and molecular analysis. We have also added data on sporozoite rate as shown below in the result section. HLC and 143 by CDC-LT), representing a mean of 16 mosquitoes per night. 66% of mosquitoes were collected using HLC. Of the 415 mosquitoes morphologically identified, 311 (75%) were An. funestus group, 84 (20%) were An. gambiae s.l. and 20 (5%) were other Anopheles i.e. An. protoriensis, An. coustani, An. moucheti and An. squamosus ( Table 2). The An. funestus group was significantly greater than An. gambiae s.l (p<0.001). Out of the 84 amplified samples of An. gambiae s.l., 68 (81%) were An. Arabiensis and 16 (19%) were An. gambiae s.s. The proportion of Anopheles mosquitoes caught outdoors (60%; 95% CI: 55%, 65%) was significantly greater than the proportion caught indoors (p<0.001). There were more Anopheles mosquitoes collected outdoors in all geographical areas except area 6, where most of the mosquitoes were collected indoor ( Table 2). The frequencies of vectors collected in each geographical area are summarized in Supplementary Table 2. An. funestus group was the most prevalent vector in all areas. Of the 272 mosquitoes collected by HLC, 3.3% (9/272) tested positive for P. falciparum sporozoites. Higher sporozoite rate was observed among the An. funestus group (7/9). The rate of indoor and outdoor biting estimated by HLC was 19.8 and 25.5 bites per person per night, respectively." Q3) There is no evidence of any "shift" in behaviours over time presented here, so the term "undermines" is unjustified and seems to create an argument that hasn't been made. Most behaviours that enable residual malaria transmission despite LLIN use are pre-existing, although plastic, and often it's just the vector population composition that shifts 14, so the term "limits" is more appropriate.

A3: We have revised as proposed above in the abstract section.
Conclusion "Our data therefore do not support the hypothesis that outdoor biting limits the effectiveness of ITNs in our study area." Q4) While indeed there is no evidence here that outdoor transmission contributes to ongoing transmission, there is also no evidence that it does not. Such outdoor fractions of transmission can only be expected to become epidemiologically detectable once larger quantities of indoor transmission (which I'm convinced is the case here as explained above) have been tackled. So the phraseology of conclusions needs to be tempered using words like "yet", and explain how these currently minor fractions of transmission may emerge as important contributors to sustained endemicity once further progress has been made with indoor control [14,15]. A4: We have revised as proposed above in the discussion section.
Discussion section "In summary, our data do not support the hypothesis that outdoor biting limits the effectiveness of ITNs in our study area. The outdoor biting observed may therefore have been the result of high levels of ITN use leading to unsuccessful attempts at indoor feeding. However, it remains possible that continued selection pressures might lead to the emergence of populations of mosquitoes that are better adapted to outdoor feeding in the future. Outdoor feeding is becoming more common in parts of Africa [4] and may represent evolutionary change in some areas, with a potential to undermine ITN effectiveness. The outdoor fractions of transmission can be expected to be epidemiologically detectable once indoor transmission has been tackled. Therefore, malaria control programs require monitoring to assess the impact of ITNs on vector populations and vector behavioral change as well as monitoring ITN effectiveness as vectors evolve [5][6][7][8][9]. Continuous monitoring of vector bionomics, and malaria transmission dynamics are essential for predicting disease outbreaks and guiding vector control in the region. Furthermore, capacity needs to be built in interpreting and applying these data to malaria control policy." Q5) In any case, LLINs clearly fall a long way short of being 100% efficacious with 22% personal protection estimated here, so there clearly are considerable limitations to this technology that need explanation. To get a better handle on whether outdoor exposure does contribute to residual transmission, in our experience it's necessary to test as a function of individual human behavioural profiles weighted by activity patterns for the most dominant local vectors [13]. Indeed human behaviour is the primary driver of where and when exposure occurs [1] and is far more variable than the mosquito behaviours that matter within a single vector species [15]. A5: We have made adjustments to the indoor and outdoor biting rate by human behavior as shown above, and accordingly revised the discussion as above.
Q6) In any case, for many of the surveyed locations, very few mosquitoes were caught (Supplementary Table 2) and CDC light traps catches indoors and outdoors are not comparable, so reporting these data as indicators of the degree of exophagy or endophagy is going too far and overstretching very little entomological data.

A6: We have made revision in the result and discussion section as shown above.
Q7) The fact that these are not differentiated to species (again, though this is mentioned in the methods but no results are presented) also means that areas with apparently different mosquito behaviours are probably areas that simply have different relative abundances of primary vector, secondary vector and non-vector species within the Anopheles funestus group and within the Anopheles gambiae complex. For example, greater outdoor feeding at dawn and dusk is a known characteristic of Anopheles rivulorum and Anopheles parensis, originally discovered in this region on the basis of their distinctive behaviours and much weaker vectorial capacities.

A7: We have addressed the above comment as follows:
We have included Figure 7 which illustrates hourly biting pattern of Anopheles mosquitoes occurring both indoors (solid lines) and outdoors (dashed lines). The grey area represents the proportion of the children <5 years asleep at each hour of the night.
We do not have molecular data for the An. funestus group. We have included this as a limitation in the discussion section as shown below.
Discussion section "Lack of explicit molecular data for distinguishing sibling species and molecular forms within the An. funestus group introduces ambiguity into the interpretation of the results of the study." Q8) The term "species" is used very loosely and interchangeably with other taxonomic classification levels, resulting in some misleading over-interpretation. While Anopheles gambiae sensu lato is indeed a complex, Anopheles funestus sensu lato is a group (not a complex, as stated in the introduction) and neither can be described as a species, unless one is talking about unambiguously identified individual specimens of the nominate species, which are by far the most efficient species within each taxon.

A8: We have made revisions accordingly.
Q9) All of these most important limitations seem to be missing from the paragraph opening with the sentence "Our study has some limitations". A9: We have updated the manuscript with the adjustments to the indoor and outdoor biting rate by human behaviour as shown above. We have also updated the limitations of our study as shown under the discussion section.
Discussion section "Our study has a number of limitations. Data on ITN use may have been incorrectly reported, as we did not require each resident to be present during the survey. We attempted to minimize this by instructing data collecting teams to interview only residents of the same homestead regarding ITN ownership and usage. There may have been some misclassification as we did not ascertain ITN use during hospital presentation but instead used the yearly ITN data collected by the annual survey. The results may also be confounded by other unmeasured factors (e.g., variation in the quality and type of ITN, urbanization, socio-economic status and mother's education). It is likely that we underestimated the protection afforded by the use of high-quality ITN because we included all ITNs, regardless of quality, physical integrity or bioefficacy of the insecticidal compounds. The vast majority of ITNs in the area are long-lasting insecticidal nets, hence we do not expect substantial variation in insecticidal efficacy. The accuracy of the mosquito survey is limited by the practical challenges of maintaining consistently sensitive human landing catches throughout the night. Lack of explicit molecular data for distinguishing sibling species and molecular forms within the An. funestus group introduces ambiguity into the interpretation of the results of the study. In this study, we examined variations in the personal protection afforded by ITNs and did not examine variation in community level effect. The size of our study limits power: with a sample size of 415, and the proportion of mosquitoes biting outdoors at 67% in low ITN effectiveness areas we therefore had >90% power to detect a reduction to 27% or lower in high ITN effectiveness areas. Our study was therefore powered to detect only a large difference in the proportion of vectors caught outdoors. However, we reasoned that reductions of ITN effectiveness to less than half of the previously documented efficacy of 50% would require a doubling of the proportion of mosquitoes feeding outdoors. Hence our study was powered to detect large variations in the frequency of outdoor biting. In addition, the accuracy of mosquito sampling data is limited as only one month of sampling was conducted in this study, we recommend sampling for a longer duration of time." Q10) What is called "effectiveness" here refers only to the relatively minor personal protection effect of bednets, and does not capture any variations in community-level impact. All fine but please explain this study limitation clearly.

A10: We have made revision in the discussion section indicating this limitation as follows.
Discussion section In this study, we examined variations in the personal protection afforded by ITNs and did not examine variation in community level effect. Q11) Correspondingly, doesn't capture how big a change this transmission picture is relative to the same setting 10 to 15 years ago when nominate Anopheles gambiae were still quite abundant. The explanations about the relative abundance of vector taxa (not species) is accurate but rather static and lacking in long term context, demonstrating the much bigger overall impact on vector populations and endemicity. This is a pity when this contemporary study has been conducted in an area with so much historical entomological literature, so please enrich the narrative.

A11: We have added points as follows in the discussion section;
"Malaria transmission has reduced dramatically over the last 15 years in Kilifi, evidenced by falling rates of clinical malaria cases in hospital [10,11] in the community [12] and falling community prevalence of asymptomatic infection [13]. A recent resurgence has been noted with increasing cases among older children, and increasing prevalence of infection more widely around the coast [14]. The reductions have been temporally associated with marked reductions in the prevalence of the abundance of vectors [15] and with a pronounced shift away from Anopheles gambiae s.s, which was previously the dominant vector, and a shift away from Anopheles arabiensis." Q12) While I agree with the closing statement about enhancing entomological surveillance, in my experience many groups are under-interpreting or misinterpreting the data they already have, so perhaps that capacity limitation merits some emphasis as a priority.

Reviewer Report 02 May 2017
In the section of "Results" I think that the Supplementary Table 1 has to be presented in the main manuscript as it present malaria prevalence according to area and the level of ITNs use. Moreover the presentation of results must be more detailed and the effect of each risk factors cited in the part of "Statistical analysis" must be presented. I don't understand why authors said "ITN use was consistently >50% in all geographical areas", meaning that here we have no information about the difference of level use between areas. The authors have summarized too much the description of the results in this part. Authors presented in the part "Mosquito processing" laboratory works such ELISA-CSP and molecular analysis, however the results of these analysis have not been presented in this study. Regarding the result on vector abundance, authors have to present the results according to absolute densities and less on the proportion of species in the place of mosquito collection.
The relevance of the study will be more remarkable if authors greatly discuss in deep their outcomes by comparing with other studies. Additionally, the review of the literature has to be strengthened, "33 off the 43 references are more than 5 years old and some newer papers are missing.

Is the work clearly and accurately presented and does it cite the current literature? Partly
Is the study design appropriate and is the work technically sound? Yes

If applicable, is the statistical analysis and its interpretation appropriate? Partly
Are all the source data underlying the results available to ensure full reproducibility? Partly

Are the conclusions drawn adequately supported by the results? Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Entomology, immunology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
Author Response 06 Jan 2018

Alice Kamau
We are grateful for this review and the helpful comments and suggestions that have been made. We have included a point-by-point response (in bold) to the issues raised.
Q1) The manuscript reported the geographical heterogeneity of malaria prevalence according several parameters mainly including the ITN effectiveness and the feeding behaviour of Anopheles vectors. The design and method of the study are well presented in the section "Methods" as well as the statistical analysis. Clinical surveillance was analyzed in the study between January 2009 and December 2014 that covers a long period. Thus it will be interesting if authors add in their explanatory factors the dry and wet season. It will be also important to explain the discrepancy between the date of clinical surveillance data collection (January 2009 and December 2014) and the mosquito collection (July and August 2016). We have any information if the level of ITN use varied or is the same during both periods.

A1: We have made revision to address all the questions above as follows:
We have updated the clinical surveillance data to December 2016 and updated the manuscript accordingly. We have also included season as a covariate in both univariable and multivariable analysis, Supplementary Table 1 Q2) Additionally the main part of the subject underlines the effectiveness of the ITNs. However, authors should describe at first that the effectiveness of ITNs is monitoring taking into account the physical integrity of nets, bioefficacity and the insecticidal compounds even though they focused more their study on feeding place and malaria prevalence. It will be also more appropriate if authors interpreted their result according to level of ITNs use according to areas and discuss though their outcomes the effectiveness of ITN. For instance, in the abstract the expression of "high and low effectiveness" in the part of method is a hasty affirmation. A2: We have included the above comment as a limitation to our study as we did not have data on the physical integrity of nets or the bioefficacy of the insecticidal compounds. We've also revised the abstract session. We've revised the abstract session to indicate "varying ITN effectiveness" rather than "high and low ITN effectiveness" Abstract section "We linked homestead level geospatial data to clinical surveillance data at a primary healthcare facility in Kilifi County in order to map geographical heterogeneity in ITN effectiveness and observed vector feeding behaviour using landing catches and CDC light traps in six selected areas of varying ITN effectiveness." Discussion section "It is likely that we underestimated the protection afforded by the use of high-quality ITN because we included all ITNs, regardless of quality, physical integrity or bioefficacy of the insecticidal compounds." Q3) In the section of "Results" I think that the Supplementary Table 1 has to be presented in the main manuscript as it present malaria prevalence according to area and the level of ITNs use. Moreover, the presentation of results must be more detailed and the effect of each risk factors cited in the part of "Statistical analysis" must be presented. I don't understand why authors said "ITN use was consistently >50% in all geographical areas", meaning that here we have no information about the difference of level use between areas. The authors have summarized too much the description of the results in this part. Authors presented in the part "Mosquito processing" laboratory works such ELISA-CSP and molecular analysis, however the results of these analysis have not been presented in this study. Regarding the result on vector abundance, authors have to present the results according to absolute densities and less on the proportion of species in the place of mosquito collection.  Table 2). The An. funestus group was significantly greater than An. gambiae s.l (p<0.001). Out of the 84 amplified samples of An. gambiae s.l., 68 (81%) were An. Arabiensis and 16 (19%) were An. gambiae s.s. The proportion of Anopheles mosquitoes caught outdoors (60%; 95% CI: 55%, 65%) was significantly greater than the proportion caught indoors (p<0.001). There were more Anopheles mosquitoes collected outdoors in all geographical areas except area 6, where most of the mosquitoes were collected indoor ( Table 2). The frequencies of vectors collected in each geographical area are summarized in Supplementary Table 2. An. funestus group was the most prevalent vector in all areas. Of the 272 mosquitoes collected by HLC, 3.3% (9/272) tested positive for P. falciparum sporozoites. Higher sporozoite rate was observed among the An. funestus group (7/9). The rate of indoor and outdoor biting estimated by HLC was 19.8 and 25.5 bites per person per night, respectively." Q4) The relevance of the study will be more remarkable if authors greatly discuss in deep their outcomes by comparing with other studies. Additionally, the review of the literature has to be strengthened, "33 off the 43 references are more than 5 years old and some newer papers are missing. polynomial procedure used?).
The Kulldorf statistic was used, if I understand correctly, to identify clusters of high or low ITN effectiveness without taking malaria prevalence and ITN use into account. Is that true? This seems not correct to me but maybe I misunderstood the procedure.
(reference should be provided). I wonder why season (rainy / dry) was not considered. The full result of the model is not given, and I wonder whether the large number of interaction terms in the model gave in a meaningful result. The Supplementary Table 1 gives the ORs for ITN use by area which is difficult to follow since (i) the numbering of the areas does not give information on spatial distribution (ii) it is not easy to see from the table whether malaria prevalence and ITN use differs between areas (iii) the effect of the other covariables is unknown (is there some confounding? What is the effect of age? Was a full fractional polynomial procedure used?).

A1: We have made revisions to address the questions raised above as follows:
Statistical analysis section Wet vs. dry season was included as a covariate. We have included a reference for the multiple fractional polynomial transformation procedure [1,2]. We used the "mfp" command in STATA to assess the non-linear effect of age. We have also included a reference, which indicates what method was used for the robust standard error [3].
"The outcome of interest was presence of malaria by microscopy on presentation to the dispensary. The potential risk factors included: ITN use, age of the child, year of presentation to the dispensary, season (the wet season comprised of April, May, June, October and November) and the geographical area, as defined by the 2.5x2.5 km regular polygons.
To assess the non-linear effect of age in the regression models, multiple fractional polynomial transformation was used [1]. A list of fractional polynomial (FP) powers (-2, -1, -0.5, 0, 0.5, 1, 2, 3) were investigated for inclusion in the model using an algorithm that combines a backward elimination procedure with a search for an FP function that best predicts the outcome variable as previously described [2].
Given that the hospital malaria episodes were clustered within patients, we allowed for clustering by using a logistic regression model with robust standard errors [3]. We found the interaction terms to be significant and therefore retained them in the model.

Result section
Q2) The Kulldorf statistic was used, if I understand correctly, to identify clusters of high or low ITN effectiveness without taking malaria prevalence and ITN use into account. Is that true? This seems not correct to me but maybe I misunderstood the procedure. Statistical analysis section "SaTScan software (version 9.4; https://www.satscan.org/), a spatial scan statistic developed by Kulldorf [4], was used to detect potential spatial variations of ITN effectiveness (without smoothing) by identifying statistically significant geographical clustering of ITN effectiveness using the normal model. The space-time parameter of the spatial scan statistic places a cylindrical window on the coordinates grid for the locations studied and moves the center of the cylinder base over the grid so that the sets of geographic units covered by the window are constantly changing. Whenever the cylindrical window includes a new event, SaTScan calculates a likelihood function to test for elevated risk within the cylinder as compared with outside the cylinder. The observed test statistic is obtained by calculating the likelihood ratio maximized over the collection of zones in the alternative hypothesis. The p value for the detection of clusters is calculated by using the Monte Carlo hypothesis testing (where a number of random replications of the dataset under the appropriate null hypothesis are generated, their test statistics computed and then compared with the observed test statistic to obtain the p-value). The null hypothesis is that the risk of malaria inside and outside the scanning window is the same." Q3) The proportion of vectors biting outdoors was compared for the areas. This would mean ignoring the absolute biting frequency which differs largely between areas. Table 2 below: Result section "There were more Anopheles mosquitoes collected outdoors in all geographical areas except area 6, where most of the mosquitoes were collected indoor (