# Field evidence for manipulation of mosquito host selection by the human malaria parasite, Plasmodium falciparum

Amélie Vantaux, Franck Yao, Domonbabele Fds Hien, Edwige Guissou, Bienvenue K Yameogo, Louis-Clément Gouagna, Didier Fontenille, François Renaud, Frédéric Simard, Carlo Constantini, et al.

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## To cite this version:

Amélie Vantaux, Franck Yao, Domonbabele Fds Hien, Edwige Guissou, Bienvenue K Yameogo, et al. Field evidence for manipulation of mosquito host selection by the human malaria parasite, Plasmodium falciparum. Peer Community Journal, 2021, 1, pp.e13. 10.24072/pcjournal.13. hal-04349954

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RESEARCH ARTICLE

Published
2021-11-24

**Cite as**
Amélie Vantaux, Franck Yao, Domonbabele FdS Hien, Edwige Guissou, Bienvenue K. Yameogo, Louis-Clément Gouagna, Didier Fontenille, François Renaud, Frédéric Simard, Carlo Constantini, Fréderic Thomas, Karine Mouline, Benjamin Roche, Anna Cohuet, Kounbobr R Dabiré and Thierry Lefèvre (2021) *Field evidence for manipulation of mosquito host selection by the human malaria parasite*, Plasmodium falciparum, Peer Community Journal, 1: e13.

**Correspondence**
amelie.vantaux@gmail.com

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# Field evidence for manipulation of mosquito host selection by the human malaria parasite, *Plasmodium falciparum*

Amélie Vantaux<sup>D,1,2</sup>, Franck Yao<sup>1</sup>, Domonbabele FdS Hien<sup>1</sup>, Edwige Guissou<sup>1</sup>, Bienvenue K. Yameogo<sup>1</sup>, Louis-Clément Gouagna<sup>2</sup>, Didier Fontenille<sup>2</sup>, François Renaud<sup>2</sup>, Frédéric Simard<sup>2</sup>, Carlo Constantini<sup>2</sup>, Fréderic Thomas<sup>2</sup>, Karine Mouline<sup>1,3,2</sup>, Benjamin Roche<sup>2,4</sup>, Anna Cohuet<sup>2</sup>, Kounbobr R Dabiré<sup>1,3</sup>, and Thierry Lefèvre<sup>1,3,2</sup>

Volume 1 (2021), article e13

https://doi.org/10.24072/pcjournal.13

## Abstract

Whether the malaria parasite Plasmodium falciparum can manipulate mosquito host choice in ways that enhance parasite transmission toward humans is unknown. We assessed the influence of P. falciparum on the blood-feeding behaviour of three of its major vectors (Anopheles coluzzii, An. gambiae and An. arabiensis) in Burkina Faso. Host preference assays using odour-baited traps revealed no effect of infection on mosquito long-range anthropophily. However, the identification of the blood meal origin of mosquitoes showed that females carrying sporozoites, the mature transmissible stage of the parasite, displayed a 24% increase in anthropophagy compared to both females harbouring oocysts, the parasite immature stage, and uninfected individuals. Using a mathematical model, we further showed that this increased anthropophagy in infectious females resulted in a 250% increase in parasite transmission potential, everything else being equal. This important epidemiological consequence highlights the importance of vector control tools targeting infectious females.

<footnote n="1"><sup>1</sup>Institut de Recherche en Sciences de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso., <sup>2</sup>MIVEGEC, IRD, CNRS, University of Montpellier, Montpellier, France., <sup>3</sup>Laboratoire mixte international sur les vecteurs (LAMIVECT), Bobo Dioulasso, Burkina Faso., <sup>4</sup>Unité de Modélisation Mathématique et Informatique des Systèmes complexes (UMMISCO), UMI IRD/UPMC209, Bondy, France.</footnote>
## Introduction

There is mounting evidence that malaria parasites affect phenotypic traits of their vectors and hosts in ways that increase contacts between them, hence favouring parasite transmission (Hurd, 2003; Koella, 2005; Lefèvre & Thomas, 2008). In addition to increased vertebrate attractiveness to mosquito vectors (Batista et al., 2014; Busula et al., 2017; Cornet et al., 2013; De Moraes et al., 2014; Emami et al., 2017; Lacroix et al., 2005), another frequently reported parasite-induced change is the alteration of vector motivation and avidity to feed (L. J. Cator et al., 2012; Stanczyk et al., 2017). Mosquitoes infected with *Plasmodium* sporozoites (the mosquito to human transmission stage) can indeed display increased (i) responses to host odours (L. J. Cator et al., 2013; Rossignol et al., 1986), (ii) landing and biting activity (Anderson et al., 1999; Koella et al., 2002; Rossignol et al., 1984, 1986; Smallegange et al., 2013; Wekesa et al., 1992), (iii) number of feeds (Koella et al., 1998) and (iv) blood volume intake (Koella & Packer, 1996; Koella et al., 2002; Koella et al., 1998). In contrast, mosquitoes infected with oocysts (the immature non-transmissible stage of the parasite), are less likely to attempt to feed (Anderson et al., 1999; L. J. Cator et al., 2013; Koella et al., 2002). Since biting is risky (e.g., host defensive behaviours can kill the vector and its parasite), reduced feeding attempts would be beneficial to the parasite during the non-transmissible stage as this would reduce mortality before the parasite reaches maturity and is ready to be transmitted (Schwartz & Koella, 2001).

These "stage-dependent" behavioural alterations likely increase parasite transmission (L. Cator et al., 2014; Dobson, 1988), provided that mosquito feeds are taken on a suitable vertebrate host species for the parasite. While malaria vectors can usually feed on a range of different vertebrate species (Takken & Verhulst, 2013), the malaria parasites they transmit are often highly host-specific, infecting only one or a few vertebrate species (Perkins, 2014). For example *P. falciparum*, which causes the most severe form of human malaria, displays an extreme form of specificity and can develop and reproduce in hominids only (predominantly in humans and to a lesser extent in chimpanzees, bonobos, and gorillas) (Ngoubangoye et al., 2016; Prugnolle et al., 2011; Rayner et al., 2011), such that any mosquito bite on another vertebrate species would be a dead-end for the parasite. In contrast, the vectors of *P. falciparum* can feed on a wide range of vertebrate host species in the wild depending on the geographic area and the relative abundance of humans and other vertebrates (Costantini et al., 1999; Takken & Verhulst, 2013). Accordingly, *P. falciparum* could modify its vector choice in ways that enhance transmission toward humans and/or reduce mosquito attraction to other unsuitable host species (i.e. specific manipulation). A previous study testing this hypothesis found no effect of *P. falciparum* infection on host preference of three major vector species, *An. coluzzii*, *An. gambiae*, and *An. arabiensis* (Nguyen et al., 2017). However, this study examined the odour-mediated mosquito host preference in laboratory conditions using a dual-port olfactometer, not the final realised host choice which is of primary importance for parasite transmission.

Here, we assessed the influence of *P. falciparum* on *An. coluzzii*, *An. gambiae* and *An. arabiensis* blood-feeding behaviour in three villages in Burkina Faso. First, odour-baited traps, set side by side in a choice arrangement and releasing either human or calf odours were used to determine odour-mediated mosquito host preference (Experiment 1). Second, indoor-resting blood-fed mosquito females were collected and the origin of their blood meal was identified to determine mosquito host selection (Experiment 2). Third, we quantified the epidemiological consequences of variation in the patterns of host selection using a compartmental model for *Plasmodium* transmission between humans and mosquitoes.

## Material and methods

### Collection sites

The study was conducted in three villages in South-Western Burkina Faso: Soumousso (11°23'14"N, 4°24'42"W), Klesso (10°56'40.5"N, 3°59'09.9"W) and Samendeni (11°27'14.3"N, 4°27'37.6"W) (Figure supplement S1). The three villages are located in an area characterized by wooded savannah, where *Anopheles* females only have access to temporary, rain-filled puddles and quarries that permit larval development during the rainy season from June to November. The dry season extends from December to May. In these rural villages, domestic animals (including cattle, goats, sheep, pigs, chickens, donkeys, dogs)
are usually kept in compounds in open conditions but a few households use separate roofed shelters for
sheep, goats, pigs and chickens. Most houses are mud-walled with roofs of iron sheets or thatch, but a few
houses are made of bricks.

**Experiment 1: Mosquito host preference**

Two odour-baited entry traps (OBETs as in Costantini et al., 1996; Costantini et al., 1998; Lefèvre et al., 2009) and two odour-baited double net traps (BNTs as in Tangena et al., 2015) baited with calf and human odours were used to assess the host preference of field populations of mosquitoes in Samandeni and Klesso villages (Figure 1). The two OBETs were connected to a tent (Lxlxh: 250x150x150 cm) by air vent hoses (Scanpart®, DxL=10*300cm; Figure 1a). The odours of the two hosts were drawn by a 12-V fan from the tents and into the OBETs by the air vent hoses, coming out of the traps at a speed of 15cm/s (±2cm/s), as measured with a Testo 425-Compact Thermal Anemometer (Testo, Forbach, France) equipped with a hot wire probe [range: 0 to + 20m/s, accuracy: ± (0.03 m/s + 5% of mv)]. Host-seeking mosquitoes responding to the host cues flew up the odour-laden streams and entered one of the two traps. The two odour-baited double net traps (BNTs) consisted of an untreated bed net (Lxlxh: 300x250x185 cm) from which each corner was raised 20 cm above ground and a smaller untreated bed net (Lxlxh: 190x120x150 cm) protecting the human volunteer in the human baited trap (Figure 1b).

In both OBETs and BNTs, the human volunteers rested on a metal-framed bed (Lxl: 190x80 cm) and were protected from mosquito bites. OBETs and BNTs were operated from 19:00 to 05:30 hours, for 3 nights in June 2013, and 13 nights in September 2013 in Samendeni. The BDNTs only were set-up for 6 nights in September in Klesso. Different combinations of live calves and humans were used as odour sources on each testing day to obviate any individual effect. Calves of about similar size and weight as human volunteers were used to equalize the quantity of emitted odours. Trapped mosquitoes were retrieved in the morning using mouth aspirators. They were kept in a 20x20x20 cm cage with a humid towel on top and brought back to the laboratory for further processing (see below).

**Experiment 2: Mosquito blood-feeding pattern**

Indoor resting blood-fed mosquitoes were collected between 7 am and 9 am by insecticide spray catches as in Lefèvre et al. (2009) to determine the origin of their blood-meal. Briefly, white sheets were spread over the floor surface and the furniture inside houses. The houses were then sprayed with an insecticide (Kaltox®: allethrin 0.27%, tetramethrin 0.20 %, permethrin 0.17%, propoxur 0.68%) to knock down the mosquitoes. Fifteen minutes after spraying, blood-fed *An. gambiae s.l.* mosquitoes were collected from the white sheet using forceps and placed on moist filter paper inside labeled petri dishes.

In Samandeni and Klesso, mosquito collections were carried out in the rainy season only (4 days in June 2013, and 13 days in September 2013 in Samendeni, and 6 days in September 2015 in Klesso), whereas in Soumousso they were conducted in both the rainy and the dry season (26 days between January and November 2009). In Soumousso, human dwellings (from 10 neighbourhoods) only were sampled whereas animal sheds and unoccupied houses were also sampled in Samandeni and Klesso. A total of 27 human dwellings, 7 unoccupied houses and 20 animal sheds were sampled in Samendeni. A total of 7 human dwellings, 7 unoccupied houses and 9 animal sheds were sampled in Klesso. All mosquitoes were kept in a Petri dish with a humid paper towel to facilitate later dissection and brought back to the laboratory for further processing (see below).
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Traps baited with calf and human odours used to assess the host preference of field populations of mosquitoes in Samandeni and Klesso villages. a) Two odour-baited entry traps (OBETs) were connected to a tent by air vent hoses. b) Two odour-baited double net traps (BNTs).</figcaption></figure>

## Laboratory processing of samples

A total of 3447 blood-fed *Anopheles gambiae s.l.* collected indoors (Experiment 2) and 674 females collected in the choice traps (Experiment 1) were processed. In addition, a subset of 276 females collected indoors was used to determine parity (parous versus nulliparous) based on the condition of ovarian tracheoles in order to control for age. Similarly, a subset of 418 individuals was used to determine different species within the *Anopheles gambiae sensu stricto* complex (i.e. distinguishing *Anopheles arabiensis*, *Anopheles coluzzii* and *Anopheles gambiae*) using routine PCR-RFLP based on segregating SNP polymorphisms in the X-linked ribosomal DNA InterGenic Spacer region as described in Santolamazza *et al.* (2008).

*Anopheles gambiae s.l.* females were dissected in a drop of phosphate buffered saline (PBS) (pH 7.2). Blood-fed midguts were gently squeezed under a stereomicroscope (magnification 35x, Leica EZ4D, Wetzlar, Deutschland) to get the blood out, which was mixed with PBS, absorbed on a filter paper, and then kept at -20°C until identification by an enzyme-linked-immunosorbent assay (ELISA) for Soumousso and Samendeni samples (Beier et al., 1988) and by multiplex PCR for Klesso samples (Kent & Norris, 2005). Each blood meal was discriminated between human, cattle, goat/sheep, chicken, dog, pig, and horse/donkey origins. ELISA-based determination of mosquito blood meal origin was performed using anti-human IgG-, anti-bovine IgG-, anti-pig IgG, anti-chicken IgG-, anti-goat IgG-, anti-sheep IgG-, anti-dog IgG-, and anti-horse IgG-peroxidase conjugates (A8794, A5295, A5670, A9046, A5420, A3415, A6792, A6917, Sigma-Aldrich). PCR-based determination of the mosquito blood meal origin targeting the vertebrate host cytochrome B was performed as described by Kent and Norris (2005), with the following modifications: (i) Three additional primers were designed from available Genbank sequences to target the following potential hosts: chicken470F (Genbank accession number: AB044986.1), sheep695F (KY662385.1), donkey574F (FJ428520.1); (ii) for each individual, two multiplex reactions were performed to avoid cross-reactions between primers and to optimize the determination. In the multiplex reaction #1, UNREV1025, Chicken470F, Sheep695F, Goat894F and Donkey574F primers were used at an amplification temperature of 49.2 °C. In the multiplex reaction #2, UNREV1025, Dog368F, Human741F, Cow121F and Pig573F primers were used at an amplification temperature of 58°C. Blood meal origin diagnostic was based on the PCR products expected sizes as follow: donkey (460bp), sheep (340bp), chicken (290bp), goat (150bp), dog (680bp), cow (561bp), pig (453bp), human (334bp).

The extracted midguts were then stained with 1% Mercurochrome® solution to detect with a microscope (magnification 400x, Leica ICC50, Wetzlar, Deutschland) the presence and number of *Plasmodium* spp. oocysts. PCR on a subset of oocyst-infected individuals (20 midguts of a total of 118 oocyst-infected individuals) confirmed that these oocysts all belonged to *P. falciparum*. The head and thorax of individual mosquitoes were stored at -20°C in 1.5 mL Eppendorf tubes. Sporozoite infection with *P. falciparum* was determined by ELISA using peroxidase-conjugated *Plasmodium falciparum* circumsporozoite protein monoclonal antibody for the Soumousso samples (Wirtz et al., 1987) and by qPCR
for the samples from Samendeni and Klesso (Boissière et al., 2013). The quantification of *P. falciparum* sporozoites in salivary glands was determined by qPCR using 7500 Fast Real time PCR System (Applied Biosystems, Foster City CA, USA). The mosquito heads and thoraxes were crushed individually and DNA extracted as previously described (Morlais et al., 2004). For sporozoite quantification, we targeted the fragment of subunit 1 of the mitochondrial cytochrome c oxidase gene (cox 1) using the forward and reverse primer sequences, qPCR-PfF 5'-TTACATCAGGAATGTTATTGC-3' and qPCR-PfR 5'-ATATTGGATCTCCTGCAAAT-3, respectively. The reaction was conducted in a 10µL final volume containing: 1µL of DNA template, 1x HOT Pol EvaGreen qPCR Mix Plus ROX, and 600nM of each primer. Amplification was started by an initial activation step at 95°C for 15min and 40 cycles of denaturation at 95°C for 15s and annealing / extension at 58°C for 30s. Detection was conducted during the last step (Boissière et al., 2013). Quantification was based on a standard curve built from four serial dilutions (12%) of an asexual parasite culture. We made dilutions ranging from 60 to 60,000 genome/µL of DNAs from a standard culture. The first dilution (10⁻¹) was used as a positive control. The standard curve (y= -3.384X +35.874) was obtained by linear regression analysis of Ct values (Cycle threshold) versus log₁₀ genome copy number of parasite culture.

This protocol allowed us to gather the following information for each collected individual mosquito: immature *Plasmodium* infection status (presence of oocysts in the midgut); mature *P. falciparum* infection status (presence of sporozoites in salivary glands); source of blood meal or trap (calf/human) chosen; shelter type (human dwellings, unoccupied houses, animal sheds).

## Statistical analyses

*Experiment 1: Mosquito host preference* -The anthropophily index (AI) was expressed as the number of *Anopheles gambiae* s.l. caught in the human-baited trap over the total number of mosquitoes caught in both human- and calf- baited traps. We tested the effect of infection status (uninfected, infected with the oocyst immature stages and infected with the sporozoite transmissible stages), collection method (OBET vs. BNT), and their interaction on AI using a General Linear Model (GLM) with a binomial error structure.

*Experiment 2: Mosquito blood-feeding pattern* -The human blood index (HBI) was expressed as the number of *Anopheles gambiae* s.l. fed on humans including mixed human-animal blood meals over the total number of blood-fed *Anopheles gambiae* s.l.. We tested the effect of *Plasmodium* infection status (uninfected, oocyst-infected, sporozoite-infected individuals - 25 individuals with both oocysts and sporozoites were included in the sporozoite infected group and excluding these individuals from the analysis yielded similar results), village (Soumousso, Samendeni, Klesso), shelter type (human dwelling, unoccupied house, animal shed) and relevant two-way interactions (infection status by shelter type and infection status by village) on HBI using a GLM with a binomial error structure. The effect of species (*Anopheles gambiae*, *An. coluzzii* and *An. arabiensis*), infection status, shelter type, and their interactions on HBI was assessed using the subset of females identified to the molecular level using a GLM with a binomial error structure. The effect of parity (nulliparous vs. parous) on HBI was assessed on a subset of females using a GLM with a binomial error structure.

We also verified for both AI and HBI whether choice significantly differed from a random distribution between humans and animals or whether mosquitoes displayed a statistically significant attraction to one type of blood meal or trap.

For model selection, we used the stepwise removal of terms, followed by likelihood ratio tests (LRT). Term removals that significantly reduced explanatory power (P<0.05) were retained in the minimal adequate model (Crawley, 2007). All analyses were performed in R v.3.0.3.

## Mathematical model

In order to explore the epidemiological consequences of variation in HBI, we built a compartmental model for *Plasmodium* transmission between humans and mosquitoes (Keeling & Rohani, 2008):
$$ \frac{dS_m}{dt} = \mu N_m - ab \frac{S_m}{N_m} I_h \varepsilon_s - \mu S_m $$

$$ \frac{dE_m}{dt} = ab \frac{S_m}{N_m} I_h \varepsilon_s - (\mu + \gamma) E_m $$

$$ \frac{dI_m}{dt} = \gamma E_m - \mu I_m $$

$$ \frac{dS_h}{dt} = -ac \frac{S_h}{N_h} I_m \varepsilon_i + \delta I_h $$

$$ \frac{dI_h}{dt} = ac \frac{S_h}{N_h} I_m \varepsilon_i - \delta I_h $$

Susceptible mosquitoes ($S_m$) are born at rate $\mu$ and become exposed ($E_m$) according to their biting rate (a), their probability to get infected (b) and the HBI of susceptible mosquitoes ($\varepsilon_s$). Then, exposed mosquitoes become infectious ($I_m$) according to their extrinsic incubation period ($\gamma$). Mosquito population die at rate ($\mu$). $Nm$ is the number of mosquitoes. Susceptible humans ($S_h$) get infected according to mosquito biting rate, the probability to develop infection (c) and the HBI of infectious mosquitoes ($\varepsilon_i$). $Nh$ is the number of humans. Then, infectious humans remain infectious ($I_h$) during a period equals to $1/\delta$ on average. See parameter values in table supplement S1 (Roux et al., 2015; Vantaux et al., 2016). In our simulation we based the HBI of exposed mosquitoes ($\varepsilon_s$) on the confidence intervals of oocyst-infected mosquitoes that were experimentally measured in this study. Then we explored the impact of the HBI of infectious mosquitoes ($\varepsilon_i$ during the sporozoite stage) on the Entomological Inoculation Rate (EIR), representing the number of infectious bites received by a human during one year (D. Smith & Ellis McKenzie, 2004), as defined by:

$$ EIR = ma \frac{I_m}{N_m} $$

where $m$ is the ratio between mosquitoes and humans, and other parameters are as above. We kept an identical human population size of 100 individuals and only varied mosquito densities to assume different ratio values ($m$) between mosquitoes and humans (low: $m=1$, medium: $m=10$ and high: $m=100$) in order to explore the impact of different HBIs on the EIR in relation to mosquito densities. Then, the mathematical model was simulated for one season in order to estimate the proportion of infectious mosquitoes.

## Ethics

Ethical approval was obtained from the Centre Muraz Institutional Ethics Committee under agreement no. 0003-2009/CE-CM and A0003-2012/CE-CM.

## Results

### Experiment 1: Mosquito host preference

To assess the inherent mosquito host preference of field populations of mosquitoes, we used two odour-baited entry traps (OBETs) and two odour-baited double net traps (BNTs) releasing either calf or human odours. The anthropophily index (AI) was expressed as the number of Anopheles gambiae s.l. caught in the human-baited trap over the total number of mosquitoes caught in both human- and calf-baited traps. The infection status was successfully determined in 584 out of the 674 mosquitoes (86.6%).
collected in the OBETs (383 individuals) and BNTs (201 individuals). Uninfected, oocyst-infected and sporozoite-infected females displayed similar host preferences ($X_2^2 = 3.6, P = 0.17$, Figure supplement S2, Al uninfected females: 63.3 ± 4%, N=531, OR=0.58, 95% CI = 0.53-0.63, P <0.0001; Al oocyst-infected females: 55.2 ± 18%, N=29, OR=0.81, 95% CI = 0.56-1.18, P=0.58; Al sporozoite-infected females: 45.8 ± 20%; N=24, OR=1.18, 95% CI = 0.78-1.78, P=0.7). There was no effect of collection method on Al (OBETs: 64 ± 5%, BNTs: 59 ± 7%; $X_1^2 = 1.5, P = 0.21$), indicating that both methods are comparable to assess mosquito host preference. There was no interaction between mosquito infection and collection method ($X_2^2 = 0.26, P = 0.9$; Figure supplement S2).

## Experiment 2: Mosquito blood-feeding pattern

To assess the realized host selection of Anopheles gambiae s.l., the blood meal origins of indoor-resting females were identified. The human blood index (HBI) was expressed as the number of females fed on humans (including mixed human-animal blood meals) over the total number of blood-fed females. Of the 3447 blood-fed Anopheles gambiae s.l. collected indoors, the blood meal origin was successfully identified in 2627 samples (76%). Among these 2627 samples, infection status was successfully determined in 2328 mosquitoes (88.6%). The following analyses are restricted to these 2328 females. HBI was significantly affected by mosquito infection status ($X_2^2 = 13.007, P = 0.0015$; Figure 2) with a 24% increase in HBI in sporozoite-infected females compared to both their oocyst-infected and uninfected counterparts (sporozoite-infected: 77 ± 5.7%; N=209, deviation from random feeding: OR=0.3, 95% CI = 0.25-0.35, P <0.0001; oocyst-infected females: 63.6 ± 5.7%, N=118, OR=0.57, 95% CI = 0.47-0.69, P =0.004; uninfected females: 61.1 ± 2.1%; N=2001, OR=0.64, 95% CI = 0.61-0.66, P <0.0001). However, because sample size in the uninfected group (N=2001) was higher than that of both sporozoite-infected (N= 209) and oocyst-infected groups (N=118), we ran a second set of analyses using a subset of 150 randomly selected uninfected individuals. This approach normalizes statistical power to test for statistically significant differences in HBI across heterogeneous sample sets. The randomisation was repeated 100 times and the analysis confirmed a significantly higher anthropophagy in sporozoite-infected individuals compared to both oocyst-infected individuals and uninfected individuals in 100% of these randomisations (mean ($X_2^2$) = 12.7, CI ($X_2^2$) = (7.54-21.59), mean (P) = 0.0043, CI(P) = (0.00002-0.023); Tukey post-hoc tests: sporozoite-infected vs. oocyst-infected individuals, this pair-wise comparison was significantly different in 100 % of the randomisations: mean(P) = 0.02577, CI(P) = (0.02559-0.02591); sporozoite-infected vs. uninfected individuals, this pair-wise comparison was significantly different in 90% of the randomisations: mean (P) = 0.023, CI(P) = (5e-07 - 3e-01); oocyst-infected vs. uninfected individuals, this pair-wise comparison was significantly different in 0 % of the randomisations: mean (P) = 0.78, CI(P) = (0.07-0.99)).

The HBI of sporozoite-infected mosquitoes was higher than that of oocyst-infected and uninfected females regardless of the village considered (infection status: village interaction: X24 = 2.3, P = 0.68, Figure 2) or the shelter type in which mosquito females were collected (infection status: shelter type interaction: X24 = 0.7, P = 0.95, Figure supplement S3).

HBI was also significantly influenced by shelter type ($X_2^2 = 145.92, P < 0.0001$). Females collected in animal sheds were significantly less likely to have fed on human hosts (22.3 ± 4%) than females collected in unoccupied houses (40.9 ± 6.8%; Chi-square post-hoc test: X21 = 21.6, P < 0.0001) or in human dwellings (74.5 ± 2%; Chi-square post-hoc test: X21 = 385, P < 0.0001). Females collected in human dwellings were also significantly more likely to have fed on human hosts than females collected in unoccupied houses (Chi-square post-hoc test: X21 = 96, P < 0.0001). HBI was significantly affected by the village ($X_2^2 = 139.5, P < 0.0001$). However, in Soumousso only human dwellings were sampled confounding the effect of village and shelter type in this case. Therefore, we carried out an analysis on the human dwellings only to compare HBIs in the three villages. Mosquitoes were significantly less anthropophagic in Samendeni (56.5± 4%), compared to Soumousso (83.5±2.2%; Chi-square test: X21 =138.8, P < 0.0001) and Klesso (77.3±9%; Chi-square test: X21 = 12.7, P = 0.0004). HBIs in Soumousso and Klesso were not significantly different (83.5±2.2% vs. 77.3±9% respectively; Chi-square test: X21 = 1.8, P = 0.18).
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Effect of infection status on the human-blood index of *Anopheles gambiae* s. l. females expressed as the number of females fed on humans out of the total number of blood-fed females for the three sampled villages. Data show proportion ± 95% confidence intervals. Numbers in bars indicate the total numbers of mosquitoes. Different letters indicate differences between infection status (Chi-square post-hoc tests: sporozoite-infected vs. oocyst-infected females X²₁=6.1, P=0.013; sporozoite-infected vs. uninfected females X²₁=19.4, P<0.0001; oocyst-infected vs. uninfected females X²₁=0.18, P=0.67).</figcaption></figure>

A significant species variation in HBI was observed (X²₂ = 10.2, P = 0.006; Figure 3) with *Anopheles arabiensis* being significantly less anthropophagic (22.2 ± 15%, N=27, OR=3.5, 95% CI = 2.2-5.56, P = 0.007) than *An. gambiae* (54.8 ± 7.1%; N=186, OR=0.82, 95% CI = 0.71-0.95, P = 0.19) and *An. coluzzii* (55.1 ± 6.8%; N=205, OR=0.81, 95% CI = 0.71-0.94, P=0.14). Although HBI varied among mosquito species, sporozoite-infected individuals displayed the highest anthropophagy regardless of the species considered (infection status: species interaction: X²₄ = 4, P = 0.42; Figure 3 and supplementary material).
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Effect of infection status and mosquito species on the human-blood index expressed as the proportion of females fed on humans or humans and animals out of the total of blood-fed females. Data show proportion ± 95% confidence intervals. Numbers in bars indicate the total numbers of mosquitoes.</figcaption></figure>

Finally, HBI was not significantly affected by parity, a proxy used to estimate mosquito age (nulliparous females: 49.53 ± 9%, parous females: 45.6 ± 7.5%; X²₁ = 0.4, P = 0.52).

**Epidemiological consequences**

To investigate the epidemiological impact of a higher HBI in infectious females compared to oocyst-infected and uninfected females, we built a mathematical model based on the experimental values observed in this study. This model assessed the impact of different HBIs on the Entomological Inoculation Rate (EIR, number of infectious bites received by a person during one year) at different mosquito lifespans and densities. In order to consider the heterogeneity of HBI values on epidemiological consequences, the HBI of susceptible mosquitoes was based on the average value whereas the HBI of exposed mosquitoes were assumed to be uniformly distributed within the confidence intervals of the HBI of oocyst-infected mosquitoes that were experimentally measured in this study. Then, the impact of HBI variation in infectious (sporozoite-infected) mosquitoes on parasite transmission potential was explored fully (Figure 4). For an
average mosquito lifespan of 15 days (Figure 4a), an HBI of 0.62 in infectious mosquitoes (similar to that of susceptible mosquitoes) resulted in an EIR of 4 at a low ratio of 1 (1 mosquito per human), while an HBI of 0.77 (as observed here in infectious mosquitoes) resulted in an EIR of 14. In other words, a 24% increase in HBI resulted in a 250% increase in EIR, everything else being equal. Transmission consequences were even larger when the human-to-mosquito ratios were higher (EIR = 5 vs. EIR = 19 with a ratio of 10 or 100, i.e. a 280% increase in EIR) but the size of the increase in EIR for sporozoite-infected mosquitoes declined with increasing mosquito longevity (Figure 4c, 4d, and supplementary material).

<figure><img src="image_6.png" /><figcaption>**Figure 4.** Expected epidemiological consequences of HBI variation for different values of mosquito lifespan and mosquito/human ratio. The X axis represents the range of values considered for the HBI of infectious (sporozoite-infected) mosquitoes and the Y axis is the Entomological Inoculation Rate (EIR, number of infectious bites received by a person over one year) when the HBI of exposed mosquitoes corresponds to the confidence intervals of the HBI of oocyst-infected mosquitoes that were experimentally measured in this study. The ribbons represent the possible EIR values for different HBI of sporozoite-infected mosquitoes according to the confidence interval of HBI in oocyst-infected mosquitoes (63.6% ± 5.7%) and for different values of the mosquito to human ratio. The dashed lines represents the average value measured for susceptible mosquitoes (0.62) and for sporozoite-infected mosquitoes (0.77). Ratio=adult mosquito/human densities.</figcaption></figure>

Discussion

The mosquito host preference assays (experiment 1 using OBETs and BNTs,) showed that infected
mosquitoes displayed similar long-range attraction toward human odour as uninfected individuals
regardless of parasite developmental stages (oocyst vs. sporozoite), confirming previous laboratory results
(Nguyen et al., 2017). However, consistent with the hypothesis of specific manipulation, the patterns of
mosquito host selection (experiment 2 based on identification of mosquito blood-meal sources) showed
that sporozoite-infected *An. coluzzi*, *An. gambiae* and *An. arabiensis* females were more likely to have fed
on human than oocyst-infected and uninfected individuals. By distinguishing sporozoite and oocyst
infection, we ruled out the potential confounding effect of a mere intrinsic mosquito characteristic.
Infected mosquitoes may indeed exhibit increased anthropophagy not because of being infected but just
because of an innate preference for humans, thus making these mosquito individuals infected. Here,
individuals infected with sporozoites displayed different HBI than individuals infected with oocysts, thus
ruling out this possibility. Because *Plasmodium falciparum* takes about 10 to 18 days to complete its
development (depending on temperature, Nikolaev, 1935; Ohm et al., 2018; Shapiro et al., 2017) there is
an increased likelihood of sporozoite infection as mosquitoes become older. This means that mosquito age
could be a confounding factor of infection, with infected mosquitoes displaying increased HBI not because
they harbour sporozoites but because they are older. Such an age effect could be mediated by specific
physiological requirements in old mosquitoes or by a positive reinforcement (learning / memory) of feeding
on humans. Our data does not support an age effect as we did not find a significant effect of parity (a proxy
for age) on HBI (i.e. parous and nulliparous mosquito females displayed similar anthropophagy).

The precise mechanisms responsible for increased anthropophagy in sporozoite-infected mosquitoes
is not yet clear, but at least three hypotheses can be proposed. First, malaria parasites might manipulate
mosquito short-range behaviours only, whereas at longer range when mosquitoes rely mainly on CO2 and
other volatile odours (Cardé & Gibson, 2010; Gibson & Torr, 1999; Gillies, 1980; Mboera & Takken, 1997),
sporozoite-infected mosquitoes display similar preferences to uninfected and oocyst-infected individuals.
At short range, mosquitoes rely on other cues including visual stimuli, moisture, heat and skin emanations
/Cardé & Gibson, 2010; Gibson & Torr, 1999; Takken & Verhulst, 2013). These stimuli can be host specific,
and inform of host suitability for parasite development before the mosquito engages in selection and
eventually in feeding. In addition to a possible preferential short-range attraction of sporozoite-infected
mosquitoes toward host species suitable for parasite development, there could also be short-range
repellence by unsuitable host species.

Second, the parasite may induce changes in the vector such as an alteration of microhabitat choice to spatially match the habitat of the suitable host. This could be achieved through parasite manipulation of mosquito endophagic/philic behaviours resulting in a higher degree of indoor -feeding and -resting of sporozoite-infected females. For example, infectious mosquitoes may exhibit an enhanced tendency to enter (or a decreased tendency to exit) house interstices regardless of emitted odours.

Third, the parasite may induce changes in the vector such as an alteration of time activity in order to
temporally match the time of rest or activity of the suitable host. Mosquitoes exhibit circadian rhythms in
many activities such as flight, host-seeking, swarming, egg-laying, etc. (Rund et al., 2016). There is
mounting evidence that, following bed-net introduction, malaria vectors can display an increased tendency
to feed outdoors (Russell et al., 2011) or bite earlier in the evening or later in the morning (Moiroux et al.,
2012). Accordingly, *P. falciparum* could manipulate mosquito host-seeking rhythms in a way that increases
bites on unprotected people. Testing this hypothesis would require sampling mosquitoes at distinct periods
and comparing the proportion of uninfected, oocyst-infected and sporozoite-infected vectors among
samples.

Sporozoite-induced change in mosquito host selection occurred in three major and related mosquito
vectors, namely *An. coluzzii*, *An. gambiae* and *An. arabiensis*. This suggests that manipulation likely already
occurred in the common ancestor of these three species and that the parasites might exploit a physiological
pathway common to all three mosquito species to modify its vector host choice.

Transmission models generally assume that uninfected and infected vectors have similar preferences
for human (D. Smith & Ellis McKenzie, 2004; D. L. Smith et al., 2012). This study suggests that this
assumption may not be valid and that these models possibly underestimate transmission intensity. Our
modelling approach confirms that HBI increases in infectious mosquitoes can have a dramatic impact on
disease transmission. In particular, if we consider mosquito lifespans relevant to natural settings (i.e. 15 to
20 days; Charlwood et al., 1997; Gillies, 1961; Gillies & Wilkes, 1965; Killeen et al., 2000; Saul et al., 1990),
the transmission potential was almost multiplied by 3 when the HBI increased from 0.62 to 0.77 i.e. the
value observed for the infectious mosquitoes in this study. For many mosquito-Plasmodium associations
including *An. gambiae s.l.-P. falciparum*, the duration of the parasite's development within the mosquito
is as long as the insect vector's average lifespan (Charlwood et al., 1997; Gillies, 1961; Gillies & Wilkes,
1965; Killeen et al., 2000; Saul et al., 1990; World, 2014). This means that most mosquitoes do not live long
enough to transmit the disease, and hence that feeds taken by infectious mosquitoes on unsuitable host
species would have disastrous consequences for parasite fitness. The model suggests that the benefits of specific manipulation should be particularly high in vectorial systems in which transmission opportunities are rare (short vector lifespan, relatively long parasite development period, and diverse blood sources).

In conclusion, our results suggest that the human malaria parasite *P. falciparum* evolved the ability to enhance transmission toward humans, the appropriate host species, by increasing mosquito anthropophagy (or decreasing zoophagy) with potentially profound public health consequences. Future laboratory and field studies will be essential to confirm these results and to better understand the epidemiological, ecological and evolutionary consequences of parasite manipulation of vector behaviours.

## Acknowledgements

We would like to thank all volunteers for participating in this study as well as the local authorities for their support. We are very grateful to the IRSS staff in Burkina Faso for technical assistance. We thank Priscille Barreaux for discussion and comments. We are grateful to Alison Duncan, Ricardo S. Ramiro, Olivier Restif and an anonymous reviewer for comments and corrections which greatly improved the manuscript.

A previous version of this article has been reviewed and recommended by Peer Community In Evolutionary Biology (https://doi.org/10.24072/pci.evolbiol.100057).

## Data availability

Raw data are available on zenodo: https://doi.org/10.5281/zenodo.1296744. Statistical analyses are available as supplementary information.

## Competing interests

We have no competing interests.

## Funding

This study was supported by the ANR grant no. 11-PDOC-006-01 and the European Community's Seventh Framework Program (FP7/2007-2013) under grant agreements no. 242095 and no.223736. BR is supported by the ANR project PANIC.

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## Supplementary Material

### Supplementary Results

#### Species subset

HBI was significantly affected by mosquito infection status ($X^2_2 = 8.5, P = 0.014$) with sporozoite-infected females being significantly more anthropophagic (71.6 ± 9.4%) than oocyst-infected females (50 ± 11.9%; Chi-square post-hoc tests: $X^2_1=6.7, P = 0.0096$) and uninfected females (47.3 ± 6%; Chi-square post-hoc tests: $X^2_1 = 14.6, P < 0.0001$). There was no significant differences between oocyst-infected and uninfected females (Chi-square post-hoc test: $X^2_1 =0.07, P = 0.8$). HBI was significantly affected by the shelter type ($X^2_2 = 50.8, P < 0.0001$). In particular, the HBI in human dwelling females (73.7 ± 6.3%) was significantly higher than the HBI in unoccupied houses (34.7 ± 9.4%; Chi-square post-hoc tests: $X^2_1=39, P < 0.0001$) and animal sheds (37.3 ± 8.2%; $X^2_1 = 40.9, P < 0.0001$). The HBIs of unoccupied houses and animal sheds were not significantly different (Chi-square post-hoc test: $X^2_1=0.07, P = 0.8$). There was no significant interactions (infection status*shelter type: $X^2_4 = 2.4, P = 0.66$; shelter types*species: $X^2_4 = 2.3, P = 0.67$; three-way interaction: $X^2_5 = 8, P = 0.15$).

#### Epidemiological consequences

The impact of a larger HBI in infectious mosquitoes decreased with longer mosquito lifespan (20 days): the EIR increased by 54% at low mosquito density (EIR = 22 vs. EIR = 34), which was similar at larger densities, 51% increase in both cases (EIR = 33 vs. EIR = 50 for a ratio mosquito/human at 10 and EIR = 35 vs EIR = 53 for a ratio at 100). The pattern is similar with a mosquito lifespan of 25 days: an increase of 23% at a ratio of 1 (EIR =34 vs. EIR =42) and increases of 16% at a ratio of 10 (EIR =53 vs. EIR =62) or a ratio of 100 (EIR =56 vs. EIR =65). Pattern which is kept constant as well with a mosquito lifespan of 30 days: a 14% increase with a ratio of 1 (EIR = 41 vs. EIR =47), a 7% increase with a ratio of 10 (EIR =63 vs. EIR =68), and a 5% increase with a ratio 100 (EIR =67 vs. EIR =71).

### Supplementary Table

<table><caption>**Table S1:** Parameters used in the mathematical model.</caption><thead><tr><td>Parameter</td><td>Unit</td><td>Value</td></tr></thead><tbody><tr><td>a (biting frequency)</td><td>days.ind-1</td><td>4</td></tr><tr><td>b (mosquito probability to get infected)</td><td>%</td><td>0.5</td></tr><tr><td>e<sub>s</sub> (human biting rate of susceptible mosquitoes)</td><td>%</td><td>0.62</td></tr><tr><td>y (extrinsic incubation period)</td><td>days.ind-1</td><td>14</td></tr><tr><td>μ (mosquito population dying rate)</td><td>days.ind-1</td><td>variable</td></tr><tr><td>c (human probability to develop infection)</td><td>%</td><td>0.5</td></tr><tr><td>e<sub>i</sub> (human biting rate of infectious mosquitoes)</td><td>%</td><td>variable</td></tr></tbody></table>
<table><caption>Supplementary figures</caption><tr><td>1/δ (human infectious period)</td><td>day</td><td>30</td></tr></table>

<figure><img src="image_7.png" /></figure>

**Figure S1.** Study collection sites: Soumousso (11°23'14"N, 4°24'42"W), Klesso (10°56'40.5"N, 3°59'09.9"W), Samendeni (11°27'14.3"N, 4°27'37.6"W)
<figure><img src="image_8.png" /></figure>

**Figure S2.** Effect of infection status on the anthropophily index of *Anopheles gambiae* s. l. females expressed as the proportion of females caught in the human-baited traps out of the total number retrieved from both human- and calf-baited traps. Data show proportion ± 95% confidence interval. Numbers in bars indicate the total numbers of mosquitoes in both traps.
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

**Figure S3.** Effect of infection status on the human-blood index of *Anopheles gambiae* s. l. females expressed as the number of females fed on humans or human-animal mixed blood meals over the total number of blood-fed females in the different village samples in a) human dwellings, b) unoccupied houses and c) animal sheds. Data show proportion ± 95% confidence intervals. Numbers in bars indicate the total numbers of mosquitoes in both traps. Relative proportions of females according to their infection status in d) human dwellings, e) unoccupied houses and f) animal sheds.

**Supplementary material – Statistical analyses**

Analyses Experiment 1: Mosquito host preference

GLM mosquito infection by collection method
```r
> m1 = glm(choice2 ~ infection * collection, family = binomial)
> summary(m1)
```

Call:

`glm(formula = choice2 ~ infection * collection, family = binomial)`

Deviance Residuals:

Min 1Q Median 3Q Max
-1.4823 -0.9275 -0.9275 1.3517 1.4499

Coefficients:

<table><thead><tr><th></th><th>Estimate</th><th>Std. Error</th><th>z value</th><th>Pr(&gt;|z|)</th></tr></thead><tbody><tr><td>(Intercept)</td><td>-0.25131</td><td>0.50395</td><td>-0.499</td><td>0.618</td></tr><tr><td>infectionspz</td><td>0.25131</td><td>0.69007</td><td>0.364</td><td>0.716</td></tr><tr><td>infectionuninfected</td><td>-0.36961</td><td>0.51630</td><td>-0.716</td><td>0.474</td></tr><tr><td>collectiontente</td><td>0.09716</td><td>0.75066</td><td>0.129</td><td>0.897</td></tr><tr><td>infectionspz:collectiontente</td><td>0.59598</td><td>1.23924</td><td>0.481</td><td>0.631</td></tr><tr><td>infectionuninfected:collectiontente</td><td>0.12288</td><td>0.77393</td><td>0.159</td><td>0.874</td></tr></tbody></table>

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 774.72 on 583 degrees of freedom
Residual deviance: 769.34 on 578 degrees of freedom
AIC: 781.34

Number of Fisher Scoring iterations: 4

```r
> m2 = glm(choice2 ~ infection + collection, family = binomial)
> anova(m1, m2, test = "Chi")
Analysis of Deviance Table
```

Model 1: choice2 ~ infection * collection
Model 2: choice2 ~ infection + collection
Resid. Df Resid. Dev Df Deviance Pr(>Chi)

1 578 769.34
2 580 769.60 -2 -0.26068 0.8778

> summary(m2)

Call:

`glm(formula = choice2 ~ infection + collection, family = binomial)`

Deviance Residuals:

Min 1Q Median 3Q Max
-1.3246 -0.9263 -0.9263 1.3491 1.4513

Coefficients:

<table><thead><tr><th></th><th>Estimate</th><th>Std. Error</th><th>z value</th><th>Pr(&gt;|z|)</th></tr></thead><tbody><tr><td>(Intercept)</td><td>-0.3111</td><td>0.3828</td><td>-0.813</td><td>0.416</td></tr><tr><td>infectionspz</td><td>0.4213</td><td>0.5563</td><td>0.757</td><td>0.449</td></tr><tr><td>infectionuninfected</td><td>-0.3131</td><td>0.3851</td><td>-0.813</td><td>0.416</td></tr><tr><td>collectiontente</td><td>0.2293</td><td>0.1794</td><td>1.278</td><td>0.201</td></tr></tbody></table>

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 774.72 on 583 degrees of freedom
Residual deviance: 769.60 on 580 degrees of freedom
AIC: 777.6

Number of Fisher Scoring iterations: 4

```r
> m3 = glm(choice2 ~ collection, family = binomial)
> anova(m3, m2, test = "Chi")
Analysis of Deviance Table
```

Model 1: choice2 ~ collection
Model 2: choice2 ~ infection + collection
Resid. Df Resid. Dev Df Deviance Pr(>Chi)

1 582 773.18
2 580 769.60 2 3.5794 0.167

```r
> m4 = glm(choice2 ~ 1, family = binomial)
> anova(m3, m4, test = "Chi")
Analysis of Deviance Table
```

Model 1: choice2 ~ collection
Model 2: choice2 ~ 1
Resid. Df Resid. Dev Df Deviance Pr(>Chi)
1 582 773.18
2 583 774.72 -1 -1.5442 0.214
>

########## models with intercepts in each category of individuals ##########

> t=read.table("terrainangtraps.txt",header=T)
> attach(t)
> tooc=subset(t,infection=="oocyst")
> detach(t)
> attach(tooc)
> m=glm(choice2~1,family=binomial)
> summary(m)

Call:
glm(formula = choice2 ~ 1, family = binomial)

Deviance Residuals:

Min 1Q Median 3Q Max
-1.091 -1.091 -1.091 1.267 1.267

Coefficients:

Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2076 0.3734 -0.556 0.578

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 39.892 on 28 degrees of freedom
Residual deviance: 39.892 on 28 degrees of freedom
AIC: 41.892

Number of Fisher Scoring iterations: 3

> OR=exp(-0.2076)
> OR
[1] 0.812532
> CI1=exp(-0.2076-0.3734)
> CI1
[1] 0.5593387
> CI2=exp(-0.2076+0.3734)
> CI2
[1] 1.180337

> detach(tooc)
> attach(t)
> tspz=subset(t,infection=="spz")
> detach(t)
> attach(tspz)

> m=glm(choice2~1,family=binomial)
> summary(m)

Call:
glm(formula = choice2 ~ 1, family = binomial)

Deviance Residuals:

Min 1Q Median 3Q Max
-1.249 -1.249 1.107 1.107 1.107

Coefficients:

Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.1671 0.4097 0.408 0.683

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 33.104 on 23 degrees of freedom
Residual deviance: 33.104 on 23 degrees of freedom
AIC: 35.104

Number of Fisher Scoring iterations: 3

> OR=exp(0.1671)
> OR
[1] 1.181872
> CI1=exp(0.1671+0.4097)
> CI1
[1] 1.780332
> CI2=exp(0.1671-0.4097)
> CI2
[1] 0.7845853

> detach(tspz)
> attach(t)
> tun=subset(t,infection="uninfected")
> detach(t)
> attach(tun)

> m=glm(choice2~1,family=binomial)
> summary(m)

Call:
glm(formula = choice2 ~ 1, family = binomial)

Deviance Residuals:

Min 1Q Median 3Q Max
-0.9567 -0.9567 -0.9567 1.4155 1.4155

Coefficients:

Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.54411 0.09002 -6.044 1.5e-09 ***
---
Signif. codes: 0 '**' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 698.23 on 530 degrees of freedom
Residual deviance: 698.23 on 530 degrees of freedom
AIC: 700.23

Number of Fisher Scoring iterations: 4

> OR=exp(-0.54411)
> OR
[1] 0.5803581
> CI1=exp(-0.54411+0.09002)
> CI1
[1] 0.6350256
> CI2=exp(-0.54411-0.09002)
> CI2
[1] 0.5303967
>

######## Analyses Experiment 2: Mosquito blood-feeding pattern ########

> m1=glm(choice2~infection*origin+infection*village,family=binomial)
> summary(m1)

Call:
glm(formula = choice2 ~ infection * origin + infection * village,
      family = binomial)

Deviance Residuals:

Min 1Q Median 3Q Max
-1.8258 -0.6133 -0.6133 0.8630 2.1151

Coefficients:

Estimate Std. Error z value Pr(>|z|)
(Intercept)      1.34947   0.66654   2.025 0.042908 *
infectionspz   -1.31907   0.92452  -1.427 0.153650
infectionuninfected  -0.55363   0.68931  -0.803 0.421878
originMH         -2.13075   0.62341  -3.418 0.000631 ***
originMI         -1.10487   0.79012  -1.398 0.162006
villagesamandeni  -0.09770   0.61400  -0.159 0.873574
villagesoumouso  -0.80284   0.72406  -1.109 0.267516
infectionspz:originMH 0.57376 0.92201 0.622 0.533749
infectionuninfected:originMH 0.48010 0.64327 0.746 0.455463
infectionspz:originMI 0.60162 1.07679 0.559 0.576354
infectionuninfected:originMI 0.34986 0.81689 0.428 0.668446
infectionspz:villagesamandeni 0.72644 0.80107 0.907 0.364497
infectionuninfected:villagesamandeni 0.75924 0.63728 1.191 0.233506
infectionspz:villagesoumousso 0.20553 0.94357 0.218 0.827565
infectionuninfected:villagesoumousso 0.08233 0.75010 0.110 0.912597
---
Signif. codes: 0 '**' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 3075.1 on 2327 degrees of freedom
Residual deviance: 2502.3 on 2313 degrees of freedom
AIC: 2532.3

Number of Fisher Scoring iterations: 4

> m2=glm(choice2~origin+infection*village,family=binomial)
> anova(m1,m2,test="Chi")
Analysis of Deviance Table

Model 1: choice2 ~ infection * origin + infection * village
Model 2: choice2 ~ origin + infection * village
Resid. Df Resid. Dev Df Deviance Pr(>Chi)

1      2313      2502.3
2      2317      2503.0 -4 -0.72688      0.948
> summary(m2)

Call:

glm(formula = choice2 ~ origin + infection * village, family = binomial)

Deviance Residuals:

Min 1Q Median 3Q Max
-1.8336 -0.6133 -0.6133 0.8600 2.1151

Coefficients:

Estimate Std. Error z value Pr(>z|)
(Intercept) 1.0722 0.5150 2.082 0.0373 *
originMH -1.6781 0.1498 -11.204 < 2e-16 ***
originMI -0.7556 0.1921 -3.933 8.41e-05 ***
infectionspz -0.9040 0.6589 -1.372 0.1701
infectionuninfected -0.2680 0.5269 -0.509 0.6110
villagesamandeni -0.1348 0.5860 -0.230 0.8181
villagesoumousso -0.9782 0.6424 -1.523 0.1278
infectionspz:villagesamandeni 0.7649 0.7624 1.003 0.3158
infectionuninfected:villagesamandeni 0.8056 0.6085 1.324 0.1855
infectionspz:villagesoumousso 0.3642 0.8286 0.440 0.6602
infectionuninfected:villagesoumousso 0.2768 0.6601 0.419 0.6750
---
Signif. codes: 0 '**' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 3075.1 on 2327 degrees of freedom
Residual deviance: 2503.0 on 2317 degrees of freedom
AIC: 2525

Number of Fisher Scoring iterations: 4

> m3=glm(choice2~origin+infection+village,family=binomial)
> anova(m3,m2,test="Chi")
Analysis of Deviance Table

Model 1: choice2 ~ origin + infection + village
Model 2: choice2 ~ origin + infection * village
Resid. Df Resid. Dev Df Deviance Pr(>Chi)

1      2321      2505.3
2      2317      2503.0 4      2.29      0.6826
> summary(m3)

Call:

glm(formula = choice2 ~ origin + infection + village, family = binomial)

Deviance Residuals:

Min 1Q Median 3Q Max
-1.8244 -0.6175 -0.6175 0.8471 2.1436

Coefficients:

Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.5994 0.2629 2.280 0.0226 *
originMH -1.6762 0.1494 -11.221 < 2e-16 ***
originMI -0.7611 0.1916 -3.972 7.12e-05 ***
infectionspz -0.3902 0.2809 -1.389 0.1648
infectionuninfected 0.2408 0.2235 1.077 0.2814
villagesamandeni 0.6142 0.1562 3.933 8.40e-05 ***
villagesoumouss0 -0.7245 0.1801 -4.022 5.78e-05 ***
---
Signif. codes: 0 '**' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 3075.1 on 2327 degrees of freedom
Residual deviance: 2505.3 on 2321 degrees of freedom
AIC: 2519.3

Number of Fisher Scoring iterations: 4

> m4=glm(choice2~origin+village,family=binomial)
> anova(m3,m4,test="Chi")
Analysis of Deviance Table

Model 1: choice2 ~ origin + infection + village

Model 2: choice2 ~ origin + village
Resid. Df Resid. Dev Df Deviance Pr(>Chi)

1 2321 2505.3
2 2323 2518.3 -2 -13.007 0.001498 **

---

Signif. codes: 0 '**' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> m5=glm(choice2~infection+village,family=binomial)

> anova(m3,m5,test="Chi")
Analysis of Deviance Table

Model 1: choice2 ~ origin + infection + village

Model 2: choice2 ~ infection + village
Resid. Df Resid. Dev Df Deviance Pr(>Chi)

1 2321 2505.3
2 2323 2651.2 -2 -145.92 < 2.2e-16 ***

---

Signif. codes: 0 '**' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> m6=glm(choice2~origin+infection,family=binomial)

> anova(m3,m6,test="Chi")
Analysis of Deviance Table

Model 1: choice2 ~ origin + infection + village

Model 2: choice2 ~ origin + infection
Resid. Df Resid. Dev Df Deviance Pr(>Chi)

1 2321 2505.3
2 2323 2644.8 -2 -139.5 < 2.2e-16 ***

---

Signif. codes: 0 '**' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

> table(choice2,village)

village

choice2 klesso samandeni soumouss0

H 131 420 909
other 128 560 180

> ######## klesso-samandeni

> x=matrix(c(131,128,420,560),ncol=2)

> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data: x

X-squared = 4.639, df = 1, p-value = 0.03125

> ######## klesso-soumouss0

> x=matrix(c(131,128,909,180),ncol=2)

> chisq.test(x)
Pearson's Chi-squared test with Yates' continuity correction

data: x
X-squared = 126.5548, df = 1, p-value < 2.2e-16

> ######## samendeni-soumouso
> x=matrix(c(420,560,909,180),ncol=2)
> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data: x
X-squared = 368.5804, df = 1, p-value < 2.2e-16

> table(choice2,origin)
  origin
choice2 CA MH MI
H      89 1290  81
other  310  441 117

> ######## CA-MH
> x=matrix(c(89,310,1290,441),ncol=2)
> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data: x
X-squared = 385.0446, df = 1, p-value < 2.2e-16

> ######## CA-MI
> x=matrix(c(89,310,81,117),ncol=2)
> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data: x
X-squared = 21.5821, df = 1, p-value = 3.39e-06

> ######## MH-MI
> x=matrix(c(1290,441,81,117),ncol=2)
> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data: x
X-squared = 96.0216, df = 1, p-value < 2.2e-16

>

> ###### HBI ooc vs uninfected######
> x=matrix(c(75,43,1224,777),ncol=2)
> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data: x
X-squared = 0.177, df = 1, p-value = 0.674

> ###### HBI spz vs uninfected######
> x=matrix(c(161,48,1224,777),ncol=2)
> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data: x
X-squared = 19.6844, df = 1, p-value = 9.134e-06

> ###### HBI spz vs ooc######
> x=matrix(c(161,48,75,43),ncol=2)
```r
> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data:  x
X-squared = 6.1632, df = 1, p-value = 0.01304

########## intercept model by infectious status
```

```r
> t=read.table("terrainangmaisons.txt",header=T)
> attach(t)
> summary(t)
> tooc=subset(t,infection="oocyst")
> detach(t)
> attach(tooc)
> summary(tooc)
```

```r
> m=glm(choice2~1,family=binomial)
> summary(m)
```

```
Call:
  glm(formula = choice2 ~ 1, family = binomial)

Deviance Residuals:
 Min      1Q   Median     3Q      Max
-0.952 -0.952 -0.952  1.421  1.421
```

```
Coefficients:
              Estimate Std. Error z value Pr(>\|z\|)
(Intercept) -0.5563   0.1913  -2.908  0.00364 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
```

```r
Null deviance: 154.8 on 117 degrees of freedom
Residual deviance: 154.8 on 117 degrees of freedom
AIC: 156.8
```

```r
Number of Fisher Scoring iterations: 4
```

```r
> OR=exp(-0.5563)
> OR
[1] 0.5733265
> CI1=exp(-0.5563+0.1913)
> CI1
[1] 0.6941967
> CI2=exp(-0.5563-0.1913)
> CI2
[1] 0.4735016
```

```r
> detach(tooc)
> attach(t)
> tspz=subset(t,infection="spz")
> detach(t)
> attach(tspz)
> summary(tspz)
```

```r
> m=glm(choice2~1,family=binomial)
> summary(m)
```

```r
Call:
  glm(formula = choice2 ~ 1, family = binomial)
```

```
Deviance Residuals:
 Min      1Q   Median     3Q      Max
-0.7224 -0.7224 -0.7224 -0.7224  1.7153
```

```
Coefficients:
              Estimate Std. Error z value Pr(>\|z\|)
(Intercept) -1.2102   0.1645  -7.359  1.85e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
```
Null deviance: 225.25 on 208 degrees of freedom
Residual deviance: 225.25 on 208 degrees of freedom
AIC: 227.25

Number of Fisher Scoring iterations: 4

> OR=exp(-1.2102)

> OR

[1] 0.2981376

> CI1=exp(-1.2102+0.1645)

> CI1

[1] 0.3514457

> CI2=exp(-1.2102-0.1645)

> CI2

[1] 0.2529155

> detach(tspz)

> attach(t)

> tun=subset(t,infection="uninfected")

> detach(t)

> attach(tun)

> summary(tun)

> m=glm(choice2~1,family=binomial)

> summary(m)

Call:

glm(formula = choice2 ~ 1, family = binomial)

Deviance Residuals:

Min 1Q Median 3Q Max
-0.9915 -0.9915 -0.9915 1.3755 1.3755

Coefficients:

Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.45444 0.04587 -9.907 <2e-16 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 2673.3 on 2000 degrees of freedom

Residual deviance: 2673.3 on 2000 degrees of freedom

AIC: 2675.3

Number of Fisher Scoring iterations: 4

> OR=exp(-0.45444)

> OR

[1] 0.6348034

> CI1=exp(-0.45444+0.04587)

> CI1

[1] 0.6645999

> CI2=exp(-0.45444-0.04587)

> CI2

[1] 0.6063427

>

#### Analyses subset of 150 randomly selected uninfected individuals

install.packages("dplyr")

library(dplyr)

b = rbind(hbiam[which(hbiam$infection="oocyst"),],hbiam[which(hbiam$infection="spz"),],sample_n(hbiam[which(hbiam$infection="uninfected"),],150)))
> anova(mod1,test="Chi")
Analysis of Deviance Table

Model: binomial, link: logit

Response: choice2

Terms added sequentially (first to last)

Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL                 476       590.83
infection 2 11.567   474       579.26 0.003077 **
---

> summary glht mod

Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts

Fit: glm(formula = choice2 ~ infection, family = binomial)

Linear Hypotheses:

Estimate   Std. Error z value Pr(>z|)
spz - oocyst == 0      -0.65392   0.25226   -2.592   0.02570 *
uninfected - oocyst == 0  0.06674   0.25473    0.262   0.96283
uninfected - spz == 0   0.72066   0.23525    3.063   0.00614 **
---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)

######## Analyses of HBI in human dwellings only ########

> m1=glm(choice2~infection*village,family=binomial)
> summary(m1)

Call:
 glm(formula = choice2 ~ infection * village, family = binomial)

Deviance Residuals:

Min      1Q    Median      3Q     Max
-1.1072  -0.6133  -0.6133    1.2493  2.2974

Coefficients:

Estimate   Std. Error z value Pr(>z|)
(Intercept)                 -0.91629   0.83666   -1.095   0.273
infectionspz                -1.64866   1.33204   -1.238   0.216
infectionuninfected         -0.16252   0.88251   -0.184   0.854
villagesamandeni            0.06899   0.92668    0.074   0.941
villagesoumousso            -0.66783   0.92230   -0.724   0.469
infectionspz:villagesamandeni  1.72276   1.41925    1.214   0.225
infectionuninfected:villagesamandeni  0.84240   0.97272    0.866   0.386
infectionspz:villagesoumousso  1.10888   1.42364    0.779   0.436
infectionuninfected:villagesoumousso  0.17132   0.96797    0.177   0.860

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 1964.7 on 1730 degrees of freedom
Residual deviance: 1816.7 on 1722 degrees of freedom
AIC: 1834.7

Number of Fisher Scoring iterations: 4

> m2=glm(choice2~infection+village,family=binomial)
> anova(m1,m2,test="Chi")
Analysis of Deviance Table

Model 1: choice2 ~ infection * village

Model 2: choice2 ~ infection + village

Resid. Df Resid. Dev Df Deviance Pr(>Chi)
1      1722      1816.7
2      1726      1819.3 -4 -2.6054  0.6259
> summary(m2)
Call:
glm(formula = choice2 ~ infection + village, family = binomial)

Deviance Residuals:

Min 1Q Median 3Q Max
-1.1005 -0.6182 -0.6182 1.2563 2.1416

Coefficients:

Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.4252 0.3631 -3.925 8.69e-05 ***
infectionspz -0.3177 0.3414 -0.931 0.352112
infectionuninfected 0.3112 0.2785 1.117 0.263790
villagesamandeni 0.9303 0.2696 3.451 0.000559 ***
villagesoumouss0 -0.4440 0.2686 -1.653 0.098280 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 1964.7 on 1730 degrees of freedom
Residual deviance: 1819.3 on 1726 degrees of freedom
AIC: 1829.3

Number of Fisher Scoring iterations: 4

> m3=glm(choice2~village,family=binomial)

> anova(m3,m2,test="Chi")

Analysis of Deviance Table

Model 1: choice2 ~ village

Model 2: choice2 ~ infection + village
Resid. Df Resid. Dev Df Deviance Pr(>Chi)

1 1728 1829.4
2 1726 1819.3 2 10.099 0.006411 **

---

Signif. codes: 0 '**' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> m4=glm(choice2~infection,family=binomial)

> anova(m4,m2,test="Chi")

Analysis of Deviance Table

Model 1: choice2 ~ infection

Model 2: choice2 ~ infection + village
Resid. Df Resid. Dev Df Deviance Pr(>Chi)

1 1728 1956.6
2 1726 1819.3 2 137.27 < 2.2e-16 ***

---

Signif. codes: 0 '**' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

> table(choice2,village)
village

choice2 klesso samandeni soumouss0
H 68 313 909
other 20 241 180

> ######## klesso-samendeni

> x=matrix(c(68,20,313,241),ncol=2)

> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data: x

X-squared = 12.7365, df = 1, p-value = 0.0003586

> ######## klesso-soumouss0

> x=matrix(c(68,20,909,180),ncol=2)

> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data: x

X-squared = 1.8001, df = 1, p-value = 0.1797

> ######## samendeni-soumouss0

> x=matrix(c(313;241,909,180),ncol=2)

Error: unexpected ' ; ' in "x=matrix(c(313;"
> x=matrix(c(313,241,909,180),ncol=2)

> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data: x

X-squared = 138.7652, df = 1, p-value < 2.2e-16

> table(choice2,infection)

infection

choice2 oocyst spz uninfected

H 65 144 1081

other 19 30 392

> ######ooc-spz

> x=matrix(c(65,19,144,30),ncol=2)

> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data: x

X-squared = 0.744, df = 1, p-value = 0.3884

> ######ooc-uninf

> x=matrix(c(65,19,1081,392),ncol=2)

> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data: x

X-squared = 0.4629, df = 1, p-value = 0.4963

> ######spz-uninf

> x=matrix(c(144,30,1081,392),ncol=2)

> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data: x

X-squared = 6.6875, df = 1, p-value = 0.009709

>

########## Analyses by mosquito species ##########

> m1=glm(choice2~infection*species.mol*origin,family=binomial)

> summary(m1)

Call:

glm(formula = choice2 ~ infection * species.mol * origin, family = binomial)

Deviance Residuals:

Min 1Q Median 3Q Max

-2.0963 -0.9854 -0.4366 0.8733 2.1899

Coefficients: (3 not defined because of singularities)

<table><thead><tr><th></th><th>Estimate</th><th>Std. Error</th><th>z value</th></tr></thead><tbody><tr><td>(Intercept)</td><td>15.56607</td><td>1455.39751</td><td>0.011</td></tr><tr><td>infectionspz</td><td>-31.13214</td><td>2058.24292</td><td>-0.015</td></tr><tr><td>infectionuninfected</td><td>-13.48663</td><td>1455.39790</td><td>-0.009</td></tr><tr><td>species.molc</td><td>-14.55447</td><td>1455.39763</td><td>-0.010</td></tr><tr><td>species.molg</td><td>-13.95663</td><td>1455.39792</td><td>-0.010</td></tr><tr><td>originMH</td><td>-2.29066</td><td>2.03149</td><td>-1.128</td></tr><tr><td>originMI</td><td>-3.10159</td><td>2.03678</td><td>-1.523</td></tr><tr><td>infectionspz:species.molc</td><td>14.55447</td><td>2301.18562</td><td>0.006</td></tr><tr><td>infectionuninfected:species.molc</td><td>13.29601</td><td>1455.39806</td><td>0.009</td></tr><tr><td>infectionspz:species.molg</td><td>29.92816</td><td>2058.24342</td><td>0.015</td></tr><tr><td>infectionuninfected:species.molg</td><td>11.97897</td><td>1455.39833</td><td>0.008</td></tr><tr><td>infectionspz:originMH</td><td>-0.40547</td><td>1.97062</td><td>-0.206</td></tr><tr><td>infectionuninfected:originMH</td><td>0.61668</td><td>1.47262</td><td>0.419</td></tr><tr><td>infectionspz:originMI</td><td>34.23373</td><td>1782.49184</td><td>0.019</td></tr><tr><td>infectionuninfected:originMI</td><td>2.27491</td><td>1.54292</td><td>1.474</td></tr><tr><td>species.molc:originMH</td><td>0.43176</td><td>2.15095</td><td>0.201</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>species.molg:originMH</td><td>-0.01193</td><td>1.47545</td><td>-0.008</td></tr><tr><td>species.molc:originMI</td><td>2.78314</td><td>2.28897</td><td>1.216</td></tr><tr><td>species.molg:originMI</td><td>1.49215</td><td>1.39587</td><td>1.069</td></tr><tr><td>infectionspz:species.molc:originMH</td><td>16.52210</td><td>1029.12365</td><td>0.016</td></tr><tr><td>infectionuninfected:species.molc:originMH</td><td>0.01577</td><td>1.70392</td><td>0.009</td></tr><tr><td>infectionspz:species.molg:originMH</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>infectionuninfected:species.molg:originMH</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>infectionspz:species.molc:originMI</td><td>-18.81921</td><td>2058.24429</td><td>-0.009</td></tr><tr><td>infectionuninfected:species.molc:originMI</td><td>-1.67882</td><td>1.98388</td><td>-0.846</td></tr><tr><td>infectionspz:species.molg:originMI</td><td>-32.74207</td><td>1782.49162</td><td>-0.018</td></tr><tr><td>infectionuninfected:species.molg:originMI</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td></td><td>Pr(&gt;|z|)</td><td></td><td></td></tr><tr><td>(Intercept)</td><td>0.991</td><td></td><td></td></tr><tr><td>infectionspz</td><td>0.988</td><td></td><td></td></tr><tr><td>infectionuninfected</td><td>0.993</td><td></td><td></td></tr><tr><td>species.molc</td><td>0.992</td><td></td><td></td></tr><tr><td>species.molg</td><td>0.992</td><td></td><td></td></tr><tr><td>originMH</td><td>0.259</td><td></td><td></td></tr><tr><td>originMI</td><td>0.128</td><td></td><td></td></tr><tr><td>infectionspz:species.molc</td><td>0.995</td><td></td><td></td></tr><tr><td>infectionuninfected:species.molc</td><td>0.993</td><td></td><td></td></tr><tr><td>infectionspz:species.molg</td><td>0.988</td><td></td><td></td></tr><tr><td>infectionuninfected:species.molg</td><td>0.993</td><td></td><td></td></tr><tr><td>infectionspz:originMH</td><td>0.837</td><td></td><td></td></tr><tr><td>infectionuninfected:originMH</td><td>0.675</td><td></td><td></td></tr><tr><td>infectionspz:originMI</td><td>0.985</td><td></td><td></td></tr><tr><td>infectionuninfected:originMI</td><td>0.140</td><td></td><td></td></tr><tr><td>species.molc:originMH</td><td>0.841</td><td></td><td></td></tr><tr><td>species.molg:originMH</td><td>0.994</td><td></td><td></td></tr><tr><td>species.molc:originMI</td><td>0.224</td><td></td><td></td></tr><tr><td>species.molg:originMI</td><td>0.285</td><td></td><td></td></tr><tr><td>infectionspz:species.molc:originMH</td><td>0.987</td><td></td><td></td></tr><tr><td>infectionuninfected:species.molc:originMH</td><td>0.993</td><td></td><td></td></tr><tr><td>infectionspz:species.molg:originMH</td><td>NA</td><td></td><td></td></tr><tr><td>infectionuninfected:species.molg:originMH</td><td>NA</td><td></td><td></td></tr><tr><td>infectionspz:species.molc:originMI</td><td>0.993</td><td></td><td></td></tr><tr><td>infectionuninfected:species.molc:originMI</td><td>0.397</td><td></td><td></td></tr><tr><td>infectionspz:species.molg:originMI</td><td>0.985</td><td></td><td></td></tr><tr><td>infectionuninfected:species.molg:originMI</td><td>NA</td><td></td><td></td></tr></tbody></table>

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 578.09 on 417 degrees of freedom
Residual deviance: 483.69 on 394 degrees of freedom
AIC: 531.69

Number of Fisher Scoring iterations: 14

> m2=update(m1,~.-infection:origin:species.mol)

> anova(m1,m2,test="Chi")

Analysis of Deviance Table

Model 1: choice2 ~ infection * species.mol * origin

Model 2: choice2 ~ infection + species.mol + origin + infection:species.mol + infection:origin + species.mol:origin

<table><thead><tr><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>1</td><td>394</td><td>483.69</td><td></td><td></td></tr><tr><td>2</td><td>399</td><td>491.73</td><td>-5</td><td>-8.0469</td></tr></tbody></table>

> summary(m2)

Call:

glm(formula = choice2 ~ infection + species.mol + origin + infection:species.mol + infection:origin + species.mol:origin, family = binomial)

Deviance Residuals:

<table><thead><tr><th>Min</th><th>1Q</th><th>Median</th><th>3Q</th><th>Max</th></tr></thead><tbody><tr><td>-1.9258</td><td>-1.0285</td><td>-0.5927</td><td>0.9058</td><td>1.9110</td></tr></tbody></table>

Coefficients:

<table><thead><tr><th></th><th>Estimate</th><th>Std. Error</th><th>z value</th><th>Pr(&gt;|z|)</th></tr></thead><tbody><tr><td>(Intercept)</td><td>13.56607</td><td>535.41117</td><td>0.025</td><td>0.980</td></tr><tr><td>infectionspz</td><td>-13.19790</td><td>535.41330</td><td>-0.025</td><td>0.980</td></tr><tr><td>infectionuninfected</td><td>-12.02775</td><td>535.41182</td><td>-0.022</td><td>0.982</td></tr><tr><td>species.molc</td><td>-12.50596</td><td>535.41145</td><td>-0.023</td><td>0.981</td></tr><tr><td>species.molg</td><td>-12.11677</td><td>535.41173</td><td>-0.023</td><td>0.982</td></tr><tr><td>originMH</td><td>-1.85973</td><td>1.43990</td><td>-1.292</td><td>0.197</td></tr><tr><td>originMI</td><td>-1.11444</td><td>1.47179</td><td>-0.757</td><td>0.449</td></tr></tbody></table>
<table><tr><td>infectionspz:species.molc</td><td>11.02764</td><td>535.41375</td><td>0.021</td><td>0.984</td></tr><tr><td>infectionuninfected:species.molc</td><td>11.70660</td><td>535.41217</td><td>0.022</td><td>0.983</td></tr><tr><td>infectionspz:species.molg</td><td>11.27033</td><td>535.41388</td><td>0.021</td><td>0.983</td></tr><tr><td>infectionuninfected:species.molg</td><td>10.76388</td><td>535.41238</td><td>0.020</td><td>0.984</td></tr><tr><td>infectionspz:originMH</td><td>1.50382</td><td>1.13567</td><td>1.324</td><td>0.185</td></tr><tr><td>infectionuninfected:originMH</td><td>0.72687</td><td>0.73823</td><td>0.985</td><td>0.325</td></tr><tr><td>infectionspz:originMI</td><td>1.61505</td><td>1.25226</td><td>1.290</td><td>0.197</td></tr><tr><td>infectionuninfected:originMI</td><td>1.26021</td><td>0.94513</td><td>1.333</td><td>0.182</td></tr><tr><td>species.molc:originMH</td><td>0.03303</td><td>1.31626</td><td>0.025</td><td>0.980</td></tr><tr><td>species.molg:originMH</td><td>-0.70292</td><td>1.31078</td><td>-0.536</td><td>0.592</td></tr><tr><td>species.molc:originMI</td><td>0.28388</td><td>1.27592</td><td>0.222</td><td>0.824</td></tr><tr><td>species.molg:originMI</td><td>0.34763</td><td>1.20305</td><td>0.289</td><td>0.773</td></tr></table>

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 578.09 on 417 degrees of freedom
Residual deviance: 491.73 on 399 degrees of freedom
AIC: 529.73

Number of Fisher Scoring iterations: 12

> m3=update(m2,~.-infection:species.mol)

> anova(m3,m2,test="Chi")

Analysis of Deviance Table

Model 1: choice2 ~ infection + species.mol + origin + infection:origin + species.mol:origin

Model 2: choice2 ~ infection + species.mol + origin + infection:species.mol + infection:origin + species.mol:origin

<table><tr><td>1</td><td>403</td><td>495.66</td></tr><tr><td>2</td><td>399</td><td>491.73</td></tr></table>

Call:

glm(formula = choice2 ~ infection + species.mol + origin + infection:origin + species.mol:origin, family = binomial)

Deviance Residuals:

<table><tr><td>Min</td><td>1Q</td><td>Median</td><td>3Q</td><td>Max</td></tr><tr><td>-1.9344</td><td>-0.9708</td><td>-0.5987</td><td>0.9278</td><td>2.1687</td></tr></table>

Coefficients:

<table><thead><tr><th></th><th>Estimate</th><th>Std. Error</th><th>z value</th><th>Pr(&gt;|z|)</th><th></th></tr></thead><tbody><tr><td>(Intercept)</td><td>2.26031</td><td>0.95659</td><td>2.363</td><td>0.0181 *</td><td></td></tr><tr><td>infectionspz</td><td>-1.71217</td><td>0.92071</td><td>-1.860</td><td>0.0629 .</td><td></td></tr><tr><td>infectionuninfected</td><td>-0.68194</td><td>0.56434</td><td>-1.208</td><td>0.2269</td><td></td></tr><tr><td>species.molc</td><td>-0.95819</td><td>0.85517</td><td>-1.120</td><td>0.2625</td><td></td></tr><tr><td>species.molg</td><td>-1.33102</td><td>0.83125</td><td>-1.601</td><td>0.1093</td><td></td></tr><tr><td>originMH</td><td>-2.03824</td><td>1.40025</td><td>-1.456</td><td>0.1455</td><td></td></tr><tr><td>originMI</td><td>-0.96302</td><td>1.42391</td><td>-0.676</td><td>0.4988</td><td></td></tr><tr><td>infectionspz:originMH</td><td>1.08865</td><td>1.04636</td><td>1.040</td><td>0.2981</td><td></td></tr><tr><td>infectionuninfected:originMH</td><td>0.86533</td><td>0.72854</td><td>1.188</td><td>0.2349</td><td></td></tr><tr><td>infectionspz:originMI</td><td>1.22837</td><td>1.20557</td><td>1.019</td><td>0.3082</td><td></td></tr><tr><td>infectionuninfected:originMI</td><td>1.08847</td><td>0.91567</td><td>1.189</td><td>0.2346</td><td></td></tr><tr><td>species.molc:originMH</td><td>0.04529</td><td>1.28774</td><td>0.035</td><td>0.9719</td><td></td></tr><tr><td>species.molg:originMH</td><td>-0.51918</td><td>1.28081</td><td>-0.405</td><td>0.6852</td><td></td></tr><tr><td>species.molc:originMI</td><td>0.07551</td><td>1.22780</td><td>0.061</td><td>0.9510</td><td></td></tr><tr><td>species.molg:originMI</td><td>0.42551</td><td>1.18714</td><td>0.358</td><td>0.7200</td><td></td></tr></tbody></table>

---
Signif. codes: 0 \*\*\*\* 0.001 \*\*\* 0.01 \*\* 0.05 \* . 0.1 \* \*\* 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 578.09 on 417 degrees of freedom
Residual deviance: 495.66 on 403 degrees of freedom
AIC: 525.66

Number of Fisher Scoring iterations: 4

> m4=update(m3,~.-origin:species.mol)

> anova(m3,m4,test="Chi")

Analysis of Deviance Table

Model 1: choice2 ~ infection + species.mol + origin + infection:origin + species.mol:origin

Model 2: choice2 ~ infection + species.mol + origin + infection:origin
Resid. Df Resid. Dev Df Deviance Pr(>Chi)
1 403 495.66
2 407 498.01 -4 -2.3455 0.6725
> summary(m4)

Call:
glm(formula = choice2 ~ infection + species.mol + origin + infection:origin, family = binomial)

Deviance Residuals:
Min 1Q Median 3Q Max
-2.0593 -0.8993 -0.6727 0.9159 2.0021

Coefficients:
             Estimate Std. Error z value Pr(>\|z\|)
(Intercept)      2.1941    0.7129    3.078 0.002086 **
infectionspz    -1.6689    0.9124    -1.829 0.067389 .
infectionuninfected -0.6457    0.5567    -1.160 0.246073
species.molc     -0.8519    0.5244    -1.625 0.104254
species.molg     -1.3407    0.5139    -2.609 0.009086 **
originMH         -2.0885    0.6299    -3.315 0.000915 ***
originMI         -0.7358    0.8279    -0.889 0.374120
infectionspz:originMH  1.0445    1.0376    1.007 0.314094
infectionuninfected:originMH  0.6956    0.7003    0.993 0.320581
infectionspz:originMI  1.1446    1.2011    0.953 0.340614
infectionuninfected:originMI  1.1801    0.8964    1.317 0.188006
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 578.09 on 417 degrees of freedom
Residual deviance: 498.01 on 407 degrees of freedom
AIC: 520.01

Number of Fisher Scoring iterations: 4

> m5=update(m4,~-origin:infection)

> anova(m5,m4,test="Chi")

Analysis of Deviance Table

Model 1: choice2 ~ infection + species.mol + origin

Model 2: choice2 ~ infection + species.mol + origin + infection:origin
Resid. Df Resid. Dev Df Deviance Pr(>Chi)

1 411 500.41
2 407 498.01 4 2.3997 0.6627
> summary(m5)

Call:
glm(formula = choice2 ~ infection + species.mol + origin, family = binomial)

Deviance Residuals:
Min 1Q Median 3Q Max
-2.0031 -0.9315 -0.6338 0.9649 2.0625

Coefficients:
             Estimate Std. Error z value Pr(>\|z\|)
(Intercept)      1.69014    0.58389    2.895 0.00380 **
infectionspz    -0.89306    0.36979    -2.415 0.01573 *
infectionuninfected -0.07748    0.30825    -0.251 0.80154
species.molc     -0.84224    0.51873    -1.624 0.10445
species.molg     -1.33892    0.50965    -2.627 0.00861 **
originMH         -1.45808    0.26040    -5.599 2.15e-08 ***
originMI         0.24905    0.28962    0.860 0.38982
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 578.09 on 417 degrees of freedom
Residual deviance: 500.41 on 411 degrees of freedom
AIC: 514.41

Number of Fisher Scoring iterations: 4

> m6=update(m5,~-species.mol)

> anova(m6,m5,test="Chi")
Analysis of Deviance Table

Model 1: choice2 ~ infection + origin
Model 2: choice2 ~ infection + species.mol + origin
Resid. Df Resid. Dev Df Deviance Pr(>Chi)

1 413 510.60

2 411 500.41 2 10.19 0.006128 **

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> m7=update(m5,~.origin)

> anova(m7,m5,test="Chi")

Analysis of Deviance Table

Model 1: choice2 ~ infection + species.mol
Model 2: choice2 ~ infection + species.mol + origin
Resid. Df Resid. Dev Df Deviance Pr(>Chi)

1 413 551.22

2 411 500.41 2 50.805 9.284e-12 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> m8=update(m5,~.infection)

> anova(m8,m5,test="Chi")

Analysis of Deviance Table

Model 1: choice2 ~ species.mol + origin
Model 2: choice2 ~ infection + species.mol + origin
Resid. Df Resid. Dev Df Deviance Pr(>Chi)

1 413 508.95

2 411 500.41 2 8.5437 0.01396 *

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> table(choice2,species.mol)
  species.mol

choice2 a c g

H 6 113 102

other 21 92 84

> table(choice2,origin)
  origin

choice2 CA MH MI

H 50 137 34

other 84 49 64

>

> ######## MH vs CA

> x=matrix(c(50,84,137,49),ncol=2)

> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data: x

X-squared = 40.8718, df = 1, p-value = 1.625e-10

> ######## MI vs CA

> x=matrix(c(50,84,34,64),ncol=2)

> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data: x

X-squared = 0.0739, df = 1, p-value = 0.7858

> ######## MI vs MH

> x=matrix(c(137,49,34,64),ncol=2)

> chisq.test(x)

Pearson's Chi-squared test with Yates' continuity correction

data: x

X-squared = 39.0592, df = 1, p-value = 4.111e-10

########## modele intercept par especes

> m=glm(choice2~1,family=binomial)

> summary(m)
Call:
glm(formula = choice2 ~ 1, family = binomial)

Deviance Residuals:

Min 1Q Median 3Q Max
-1.734 0.709 0.709 0.709 0.709

Coefficients:

Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.2528 0.4629 2.706 0.0068 **
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 28.604 on 26 degrees of freedom
Residual deviance: 28.604 on 26 degrees of freedom
AIC: 30.604

Number of Fisher Scoring iterations: 4

> OR=exp(1.2528)

> OR

[1] 3.50013
> CI1=exp(1.2528+0.4629)

> CI1

[1] 5.560567
> CI2=exp(1.2528-0.4629)

> CI2

[1] 2.203176

> detach.ta)

> attach(t)

> tc=subset(t,species.mol="c")

> detach(t)

> attach.tc)

> summary.tc)

> m=glm(choice2~1,family=binomial)

> summary(m)

Call:
glm(formula = choice2 ~ 1, family = binomial)

Deviance Residuals:

Min 1Q Median 3Q Max
-1.091 -1.091 -1.091 1.266 1.266

Coefficients:

Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2056 0.1404 -1.464 0.143
(Dispersion parameter for binomial family taken to be 1)

Null deviance: 282.04 on 204 degrees of freedom
Residual deviance: 282.04 on 204 degrees of freedom
AIC: 284.04

Number of Fisher Scoring iterations: 3

> OR=exp(-0.2056)

> OR

[1] 0.8141587
> CI1=exp(-0.2056+0.1404)

> CI1

[1] 0.9368801
> CI2=exp(-0.2056-0.1404)

> CI2

[1] 0.7075125

> detach.tc)

> attach(t)

> tg=subset(t,species.mol="g")

> detach(t)
> attach(tg)
> summary(tg)

> m=glm(choice2~1,family=binomial)
> summary(m)

Call:
glm(formula = choice2 ~ 1, family = binomial)

Deviance Residuals:
Min      1Q Median      3Q     Max
-1.096   -1.096  -1.096   1.261  1.261

Coefficients:
              Estimate Std. Error z value Pr(>\|z\|)
(Intercept)   -0.1942     0.1473   -1.318     0.188

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 256.11 on 185 degrees of freedom
Residual deviance: 256.11 on 185 degrees of freedom
AIC: 258.11

Number of Fisher Scoring iterations: 3

> OR=exp(-0.1942)
> OR
[1] 0.8234932
> CI1=exp(-0.1942+0.1473)
> CI1
[1] 0.9541828
> CI2=exp(-0.1942-0.1473)
> CI2
[1] 0.7107035
>

########## Analysis parity ##########

> m1=glm(choice2~parity,family=binomial)
> summary(m1)

Call:
glm(formula = choice2 ~ parity, family = binomial)

Deviance Residuals:
Min      1Q Median      3Q     Max
-1.254   -1.254  1.103   1.103  1.169

Coefficients:
              Estimate Std. Error z value Pr(>\|z\|)
(Intercept)   0.01869     0.19336     0.097     0.923
parityP       0.15929     0.24747     0.644     0.520

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 381.69 on 275 degrees of freedom
Residual deviance: 381.27 on 274 degrees of freedom
AIC: 385.27

Number of Fisher Scoring iterations: 3

> m2=glm(choice2~1,family=binomial)
> anova(m1,m2,test="Chi")
Analysis of Deviance Table

Model 1: choice2 ~ parity
Model 2: choice2 ~ 1
Resid. Df Resid. Dev Df Deviance Pr(>Chi)
1      274      381.27
2      275      381.69 -1 -0.4144  0.5197
> table(choice2,parity)
parity
choice2 N P
H 53 77
other 54 92