Impact of increased ventilation on indoor temperature and malaria mosquito density: an experimental study in The Gambia

Abstract

In sub-Saharan Africa, cooler houses would increase the coverage of insecticide-treated bednets, the primary malaria control tool. We examined whether improved ventilation, using windows screened with netting, cools houses at night and reduces malaria mosquito house entry in The Gambia. Identical houses were constructed, with badly fitting doors the only mosquito entry points. Two men slept in each house and mosquitoes captured using light traps. First, temperature and mosquito density were compared in four houses with 0, 1, 2 and 3 screened windows. Second, carbon dioxide (CO 2 ), a major mosquito attractant, was measured in houses with (i) no windows, (ii) screened windows and (iii) screened windows and screened doors. Computational fluid dynamic modelling captured the spatial movement of CO 2 . Increasing ventilation made houses cooler, more comfortable and reduced malaria mosquito house entry; with three windows reducing mosquito densities by 95% (95%CI = 90–98%). Screened windows and doors reduced the indoor temperature by 0.6°C (95%CI = 0.5–0.7°C), indoor CO 2 concentrations by 31% between 21.00 and 00.00 h and malaria mosquito entry by 76% (95%CI = 69–82%). Modelling shows screening reduces CO 2 plumes from houses. Under our experimental conditions, cross-ventilation not only reduced indoor temperature, but reduced the density of house-entering malaria mosquitoes, by weakening CO 2 plumes emanating from houses.

Full text

royalsocietypublishing.org/journal/rsif
Research
Cite this article: Jatta E et al. 2021 Impact of
increased ventilation on indoor temperature
and malaria mosquito density: an experimental
study in The Gambia. J. R. Soc. Interface 18:
20201030.
https://doi.org/10.1098/rsif.2020.1030
Received: 21 December 2020
Accepted: 21 April 2021
Subject Category:
Life Sciences–Engineering interface
Subject Areas:
biomedical engineering, biometeorology,
environmental science
Keywords:
housing, malaria, ventilation, carbon dioxide,
human comfort, sub-Saharan Africa
Author for correspondence:
Steve W. Lindsay
e-mail: s.w.lindsay@durham.ac.uk
†
These authors contributed equally to the
study.
Electronic supplementary material is available
online at https://doi.org/10.6084/m9.figshare.
c.5416035.
Impact of increased ventilation on indoor
temperature and malaria mosquito
density: an experimental study
in The Gambia
Ebrima Jatta1,†, Majo Carrasco-Tenezaca2,†, Musa Jawara3, John Bradley4,
Sainey Ceesay3, Umberto D’Alessandro3, David Jeffries3, Balla Kandeh1,
Daniel Sang-Hoon Lee5, Margaret Pinder2,3, Anne L. Wilson2,6,
Jakob Knudsen5and Steve W. Lindsay2,4
1
National Malaria Control Programme, Banjul, The Gambia
2
Department of Biosciences, Durham University, Durham, UK
3
Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine,
Banjul, The Gambia
4
London School of Hygiene & Tropical Medicine, London, UK
5
Royal Danish Academy - Architecture, Design, Conservation, Copenhagen, Denmark
6
Liverpool School of Tropical Medicine, Liverpool, UK
JK, 0000-0002-5348-8439; SWL, 0000-0002-3461-9050
In sub-Saharan Africa, cooler houses would increase the coverage of insecti-
cide-treated bednets, the primary malaria control tool. We examined
whether improved ventilation, using windows screened with netting, cools
houses at night and reduces malaria mosquito house entry in The Gambia.
Identical houses were constructed, with badly fitting doors the only mosquito
entry points. Two men slept in each house and mosquitoes captured using
light traps. First, temperature and mosquito density were compared in four
houses with 0, 1, 2 and 3 screened windows. Second, carbon dioxide (CO
2
),
a major mosquito attractant, was measured in houses with (i) no windows,
(ii) screened windows and (iii) screened windows and screened doors.
Computational fluid dynamic modelling captured the spatial movement of
CO
2
. Increasing ventilation made houses cooler, more comfortable and
reduced malaria mosquito house entry; with three windows reducing mos-
quito densities by 95% (95%CI = 90–98%). Screened windows and doors
reduced the indoor temperature by 0.6°C (95%CI = 0.5–0.7°C), indoor CO
2
concentrations by 31% between 21.00 and 00.00 h and malaria mosquito
entry by 76% (95%CI = 69–82%). Modelling shows screening reduces CO
2
plumes from houses. Under our experimental conditions, cross-ventilation not
only reduced indoor temperature, but reduced the density of house-entering
malaria mosquitoes, by weakening CO
2
plumes emanating from houses.
1. Introduction
Between 2000 and 2015, massive deployment of malaria control interventions in
sub-Saharan Africa reduced malaria prevalence by half and clinical malaria by
40% [1]. Of the interventions used to achieve this remarkable level of control,
insecticide-treated nets (ITNs) were widely used and most effective, contribut-
ing to 68% of the reduction in malaria infection prevalence. The World Health
Organization’s target of universal coverage with ITNs requires the distribution of
one bednet for two people at risk of malaria, repeated every three years [2].
In 2019, although 68% of households in sub-Saharan Africa had at least one
net, only 46% of people reported using a net [3], mainly because it was too
© 2021 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution
License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original
author and source are credited.

hot to sleep under a net [4]. Cooling a house at night, by
improving ventilation, could increase net use and further
reduce the malaria burden.
Anopheles gambiae s.l., the principal vectors of malaria in
sub-Saharan Africa, bite predominantly indoors at night [5].
These mosquitoes locate a blood meal using a range of chemical
cues generated by people, particularly carbon dioxide (CO
2
)[6].
This gas is a major component of exhaled breath and stimulates
take-off, extends flight duration [7] and is a long-range attrac-
tant for An. gambiae and other mosquitoes [6,8]. Thus to
understand the role of ventilation in a building, one needs to
understand how this gas accumulates in inhabited rooms and
leaks out of buildings, since the concentration and shape of
the odour plumes will affect how readily mosquitoes are able
to locate people within a house.
One simple way to ventilate a bedroom, and cool the room
at night, is to install mosquito screening on doors and win-
dows [9]. We designed a series of experiments to explore the
effect of house screening on indoor temperature and
mosquito house entry and used computational fluid dynamic
(CFD) modelling [10] to visualize the distribution of the CO
2
and to provide realistic approximations of CO
2
concentrations
generated by people sleeping in single-roomed houses with
and without mosquito screening. Our findings are relevant
to those interested in designing and constructing buildings
that protect people from malaria in sub-Saharan Africa.
2. Methods
2.1. Study design
A detailed description of the experiments is provided in the elec-
tronic supplementary material. Briefly, in year 1, four square
single-roomed experimental houses constructed with mud walls,
metal roofs and closed eaves, each sleeping two men, were used
to examine the effect of insecticide-free screened windows on
indoor climate and mosquito house entry (figure 1). In all exper-
iments, each house had narrow slits above and below two metal
doors, which constituted the main entry point for mosquitoes,
and represented badly fitting doors, commonly found in local vil-
lages. We carried out three experiments in year 1. Experiment 1
compared houses with badly fitting screens (control) to houses
with one small-screened window, two small-screened windows,
one small-screened window and one medium-screened window.
Experiment 2 maintained the same control compared to houses
with one, two or three large-screened windows. Experiment 3
compared unscreened houses with narrow slits above and below
the doors with those with slits above or below the door. In each
house, temperature and relative humidity were measured using
a data logger and mosquitoes captured using a CDC light trap.
Collections were made over 25 nights and typologies rotated
between houses each week, so that at the end of each experiment,
each typology had been tested in each house.
We found that increasing ventilation, by increasing the number
and size of windows, had a small cooling effect, but, surprisingly,
also markedly reduced the numbers of malaria mosquitoes enter-
ing the house. We formulated three hypotheses to explain why
screened windows reduced mosquito house entry through the
slits above and below the doors. These were that screening:
(i) acts as a site of attraction, with mosquitoes attracted to host
odours leaking from the screening, accumulating on the outside
of the window screening; (ii) reduces indoor concentrations of
host odours by a combination of increased ventilation and a
cooler house reducing sweating and the production of host
odours; and (iii) changes the shape of odour plumes emanating
from a house, attracting fewer mosquitoes.
To test these hypotheses, we carried out a further series of
field studies using two experimental houses in year 2, repeat-
ing the basic study design from year 1, but, in addition to
routine monitoring, also measured indoor CO
2
concentrations.
In experiment 4, one house with solid doors was compared
with one with screened doors, both doors having narrow
slits top and bottom. If hypothesis 1 was correct, more mosqui-
toes would be collected in the house with screened doors than
solid doors, since mosquitoes accumulating outside the
screened door, attracted to odours from the door, would
enter the house through the door gaps in larger numbers
than houses with solid doors. In experiment 5, both houses
had solid doors, one had screened windows and one had no
windows. This experiment allowed us to confirm the findings
of experiments 1 and 2, but in addition allowed us to measure
indoor CO
2
concentrations to test hypotheses two and three.
Typologies were rotated between houses weekly and each
experiment lasted 20 nights.
2.2. Study area
In year 1, in 2017, the study took place outside Wellingara
village (N 13°33.3650, W 14°55.4610), adjacent to a large area
of irrigated rice, and, in year 2, in 2019, it took place approxi-
mately 2 km to the north, in the grounds of the Medical
Research Council’s field station at Wali Kunda (N 13°34.250,
W14° 55.280). Both sites are situated on the south bank of the
River Gambia, in the Central River Region. The study was
done during the rainy season, from June to October, when
malaria transmission is high and people are most likely to
sleep under an ITN [11].
2.3. Experimental houses
In year 1, experiments were conducted from 2 July to 23 October
2017. Construction of the experimental houses has been described
in detail elsewhere [12]. Briefly, four square single-roomed mud-
block houses were constructed on the western edge of Wellingara
village and two in Wali Kunda field station. Each house was the
average size of single-roomed houses in the Upper River Region
and was built along a line, 10 m apart. Each house was 4.20 m
by 4.20 m in floor area, with walls 2.20 m high and a saddle-
shaped roof, made of corrugate sheeting, with closed eaves. Each
house had two doors (1.75 m high × 0.75 m wide), built on oppo-
site sides of the house. In all experiments (apart from experiment
3), screened and unscreened doors had narrow slits, 0.02 m high
and 0.75 m wide, above and below the door to simulate badly
fitted doors common in local villages (electronic supplementary
material, figure S1). In experiments 1 and 2, the reference house
was a metal-roofed house which had narrow slits above and
below the solid metal doors and windows (figure 1). In experiment
3, the reference house had no windows, only badly fitting doors.
Polyester netting (2 × 2 mm mesh) screens mounted on metal
frames were used for screening windows.
In year 2, field experiments were conducted during the rainy
season, from 28 August to 11 November 2019. Experiments were
conducted using two experimental houses of a similar design to
those used in year 1, apart from the saddle roof apex in year 2
houses faced the front and back facades, rather than the sides,
i.e. the roof was rotated 90° and the roofs had been painted
red in year 2, rather than bare metal in year 1. We compared mos-
quito entry in screened and unscreened houses. As described
earlier, two men slept in separate beds, under ITNs during the
night and mosquitoes collected using light traps. We measured
indoor and outdoor climate as before and also measured CO
2
concentrations in the houses, to inform the modelling of CO
2
movement within and outside the houses. At the start of each
experiment, each typology was randomly allocated to one
house position. Each house was arranged with two single beds
royalsocietypublishing.org/journal/rsif J. R. Soc. Interface 18: 20201030
2

located parallel to one another on opposite walls to the doors. Two
adult men slept on separate beds in each house under an ITN
(Olyset, Sumitomo Chemical, Japan) from 21.00 h to 06.00 h.
The men remained in the house in the same position throughout
each experiment, so that the relative attractiveness of the house
positions to mosquitoes was combined with that of the pair of slee-
pers. For each experiment, CDC light traps were used to collect
mosquitoes indoors for five weeks, each week for five nights.
Each house typology was rotated weekly between houses using
a replicated Latin rectangle design.
2.4. Procedures
In both years, mosquitoes were collected from each house using a
CDC light trap (Model 512, John W. Hock Co., Gainesville, USA),
sampled nightly for five nights each week for five weeks. Traps
were suspended from the roof with the light 1 m above the
ground in the centre of the room between the two sleepers at
the foot end of the bed and operated from 21.00 h to 06.00 h.
Mosquitoes were collected from each house at 06.00 h and
killed by freezing. Mosquitoes were identified morphologically
and female An. gambiae s.l. identified to species by PCR [13,14].
Indoor temperature and relative humidity were measured
every 30 min using data loggers (Tiny tag, TGU 4500, Gemini
Data Loggers, Chichester, UK) suspended 1 m from the floor in
the centre of the room. In year 2, CO
2
concentrations were recorded
outdoors with the logger situated on a tripod at a height of 1.3 m,
midway between the two experimental houses in Wali Kunda
during the rainy season, from 2 July to 25 July 2017. Recordings
were made on 12 nights from 21.00 to 06.59 h.
2.5. Outcomes
Primary outcomes were the mean indoor temperature, and the
mean number of An. gambiae s.l./light trap/night for each
house typology. Night-time temperature was analysed from
21.00 to 23.59 h, when adults in the study area go to bed and
make a decision to sleep under a bednet or not [15], and from
00.00 to 06.59 h, when most are asleep.
2.6. Data analyses
Sample size calculations were based on mosquito counts since
these are considerably more variable than the environmental
recordings made. In year 1, the sample size was estimated via
simulation based on data from a similar trial conducted in the
study area, [16] where the mean number of An. gambiae s.l. col-
lected indoors was 10.8/hut/night (standard deviation, s.d. =
8.7). The study was powered to detect an effect size of 50% at
the 5% level of significance and 80% power and required 25
nights of observation. In year 2, we based our calculations on a
study conducted in 2017 in nearby Wellingara village [12]. In
this study, there was a mean of 6.2 An. gambiae/night (s.d. =
5.1). To detect 66% fewer mosquitoes indoors at the 5% level of
significance, with 80% power, required 20 nights of observation.
experiment 1: small screened windows
experiment 2: large screened windows
experiment 3: door gap height
experiment 5: no windows versus screened windowsexperiment 4: solid doors versus screened doors
second year first year
Figure 1. Summary of experiments. The reference house in each experiment is shown in the first column of each row. In the experiments, local, badly fitting doors
and windows were mimicked by adding narrow gaps above and below the doors and some windows. Screened windows are shown as translucent squares and
rectangles.
royalsocietypublishing.org/journal/rsif J. R. Soc. Interface 18: 20201030
3

Table 1. Indoor temperatures in year 1. CI, confidence intervals. General linearized modelling results, adjusting for house position and night. Temperatures of test houses are statistically significant from the reference house for both
experiments and time periods ( p< 0.001). Data are means (95% CIs). Larger drawings of the house typologies are shown in figure 1.
typology
period
21.00 to 23.59 h (n= 25) 00.00 to 06.00 h (n= 25)
mean temperature (°C) temperature difference (°C) pmean temperature (°C) temperature difference (°C) p
experiment 1: small-screened windows
two badly fitting small windows (reference) 31.7 (31.6 to 31.8) ——(29.4 to 29.6) ——
one small-screened window 31.9 (31.8 to 32.0) 0.19 (0.05 to 0.33) <0.001 29.7 (29.6 to 29.8) 0.17 (0.05 to 0.28) <0.001
two small-screened windows 31.9 (31.8 to 32.0) 0.21 (0.07 to 0.35) <0.001 29.7 (29.6 to 29.8) 0.18 (0.07 to 0.29) <0.001
one small-screened window and one medium-screened window 31.9 (31.8 to 32.0) 0.21 (0.07 to 0.35) <0.001 29.7 (29.6 to 29.8) 0.17 (0.06 to 0.29) <0.001
experiment 2: large-screened windows
two badly fitting large windows (reference) 31.7 (31.6 to 31.8) ——29.6 (29.5 to 29.6) ——
one large-screened window 31.6 (31.5 to 31.7) −0.11 (−0.20 to −0.02) <0.001 29.5 (29.4 to 29.5) −0.008 (−0.16 to −0.01) <0.001
two large-screened windows 31.3 (31.2 to 31.4) −0.41 (−0.50 to −0.32) <0.001 29.1 (29.1 to 29.2) −0.41 (−0.49 to −0.32) <0.001
three large-screened windows 31.1 (31.1 to 31.2) −0.55 (−0.64 to −0.46) <0.001 29.0 (28.9 to 29.1) −0.55 (−0.63 to −0.46) <0.001
royalsocietypublishing.org/journal/rsif J. R. Soc. Interface 18: 20201030
4

The effect of house typology on indoor climate and mosquito
house entry was assessed using generalized linear modelling,
using a negative binomial model with a log link function for
mosquito count data, while comparisons of indoor temperatures
and CO
2
were made using linear regression. In addition to house
typology, house position and day were included in the model as
fixed effects. For analysis of temperature and CO
2
we calculated
mean values for two separate periods, from 21.00 h to 23.30 h
and 00.00 h to 07.00 h, for each house each night.
LadyBug software (LadyBug Products, Athol, ID, USA)
was used to estimate the percentage of time occupants of
various house typologies spent in the ‘comfort zone’[17]. The
human comfort index is widely used by those working in
the built environment to assess how comfortable a building is
and is based on experiments with hundreds of volunteers of
different genders, ages and ethnicity wearing different amounts
of clothing [18]. The index is used in the ANSI/ASHRAE
Standard 55: Thermal Environmental Conditions for Human
Occupancy. It is an American National Standard published by
ASHRAE that establishes the ranges of indoor environmental
conditions to achieve acceptable thermal comfort for occupants
of buildings [19].
χ
2
-test was used to look for trends in human comfort with
increasing numbers of large-screened windows and for compari-
sons between typologies. Analyses, apart from psychrometric
analysis, were done using Stata version 16 (StataCorp, College
Station, TX, USA), except for chi-square calculations which were
done with Epi Info v. 3.01 (CDC, Atlanta, USA).
CFD modelling was used to simulate CO
2
concentrations using
Ansys
®
Fluent (v. 19). Model assumptions and set-up configur-
ations were as follows: (i) the house was based on the structure
of an experimental house and was assumed air-tight, except for
21.00–24.00 00.00–06.00
reference
one screened window
two screened windows
three screened windows
1421
727
440
246
dry bulb temperature
humidity ratio
dry bulb temperature
humidity ratio
dry bulb temperature
humidity ratio
dry bulb temperature
humidity ratio
dry bulb temperature
humidity ratio
dry bulb temperature
humidity ratio
dry bulb temperature
humidity ratio
dry bulb temperature
humidity ratio
clothing rate: 0.3
metabolic rate: sitting
clothing rate: 0.3
metabolic rate: reclining
(a)(b)
Figure 2. Psychrometric charts showing the human comfort index of adults in houses with and without large-screened windows. (a) Readings, shown as coloured
polygons, made from 21.00 to 23.59 h. (b) Readings made from 00.00–06.00 h. Each data point represents a combination of temperature and relative humidity at
different times of the night. Data points that fall within the black polygons represent values that are known to be comfortable for lightly dressed adults sitting or
reclining. Values in red, percentage of readings comfortable.
royalsocietypublishing.org/journal/rsif J. R. Soc. Interface 18: 20201030
5

gaps at the top and bottom of the doors, and the screened doors
and windows, (ii) two men were modelled as geometrically simpli-
fied mannequins with rectilinear body shapes, (iii) exhalation
velocity of the mannequins was 0.77 m s
−1
upwards, with
40 000 ppm of CO
2
, (iv) the temperature of exhaled breath was
32.85°C and body temperature 35.85°C, (v) from field data, back-
ground CO
2
ppm was 410 ppm, and outdoor air temperature
25.5°C, (vi) outdoor night wind speed was 0.44 m s
−1
and wind
direction 54.3°, (vii) bednets and screens reduced air flow by
64%, (viii) air is incompressible, and air flow is steady turbulent
flow, (ix) we used realizable k–ϵturbulent models with enhanced
wall treatments since they best matched field data and (x) the
entire model had 7.5 million polyhedral cells, with 1 mm cells
for the mouths and 10 mm for the nets.
CFD simulations were verified: (i) against detailed field data
collected in Tanzania during the rainy season (D. Sang-Hoon Lee
et al. in preparation) and (ii) using CO
2
data logger recordings
made in The Gambia. These simulations provide an accurate
approximation of the site conditions at Wali Kunda with a
9% discrepancy.
Table 2. Effect of screened windows and door gaps on house entry by An. gambiae s.l. General linearized modelling results, adjusting for house position and
night. Data are means (95% CI). Larger drawings of the house typologies are shown in figure 1.
screened window
area (m
2
)(n=25)
no. of An. gambiae s.l.
per night (n= 25)
adjusted estimates,
mean ratio p-value
experiment 1: small-screened windows
two badly fitting small windows
(reference)
—6.16 (4.07 to 8.25) 1.0 —
one small-screened window 0.09 6.44 (4.04 to 8.84) 0.91 (0.65 to 1.28) 0.586
two small-screened windows 0.18 3.80 (1.15 to 6.45) 0.60 (0.40 to 0.91) 0.015
one small-screened window and
one medium-screened window
0.27 2.60 (1.22 to 3.98) 0.37 (0.22 to 0.64) <0.001
experiment 2: large-screened windows
two badly fitting large windows
(reference)
—11.04 (7.72 to 14.36) 1.0 —
one large-screened window 0.50 4.72 (3.16 to 6.28) 0.43 (0.28 to 0.64) <0.001
two large-screened windows 1.00 2.64 (1.55 to 3.73) 0.21 (0.14 to 0.32) <0.001
three large-screened windows 1.50 0.56 (0.15 to 0.97) 0.05 (0.02 to 0.10) <0.001
experiment 3: door gaps
gap above and below the doors, no
windows (reference)
—2.04 (1.45 to 3.35) 1.0 —
gaps above the doors, no windows —3.96 (2.43 to 5.49) 1.28 (0.83 to 1.97) 0.259
gap below the doors, no windows —3.28 (2.13 to 4.43) 1.30 (0.81 to 2.09) 0.285
gaps above and below doors, with
two small-screened windows
—2.92 (1.52 to 4.32) 1.05 (0.63 to 1.76) 0.844
–40
–20
0
20
40
60
80
100
0.5 1.0 1.5
area of screened window (m2)
reduction in An. gambiae s.l. house entry (%)
1W
2W
3W 1W
2W
3W
0
Figure 3. Relationship between the area of window screening and reduction
in An. gambiae s.l. numbers indoors. Blue data points, small windows (exper-
iment 1). Red data points, large windows (experiment 2). Reductions are
relative to the reference house in each experiment. Error bars represent
95% confidence intervals. W refers to the number of windows in each house.
royalsocietypublishing.org/journal/rsif J. R. Soc. Interface 18: 20201030
6

2.7. Role of the funding source
The funders had no role in study design, data collection,
data analysis, data interpretation or writing of the report. All
authors had full access to all data in the studies and had final
responsibility for the decision to submit for publication.
3. Results
3.1. Experimental houses
In year 1, there were roughly equal numbers of An. arabiensis
(50.7%) and An. coluzzii (49.3%), while in year 2, An. coluzzii
(69.0%) was more common than An. arabiensis (30.5%; electronic
supplementary material, table S1). There were no differences in
how each species responded to the different housing typologies.
In experiment 1, installing small-screened windows did not
cool houses and were associated with an increased mean temp-
erature of 0.2°C, both before and after midnight, compared to
the reference house with badly fitting small windows
(table 1, p< 0.001). By contrast, in experiment 2, installing
two large-screened windows lowered indoor temperature by
0.4 to 0.5°C, and three large-screened windows by 0.6°C (p<
0.001) compared with the reference house with badly fitting
large windows. The psychrometric analysis of large-screened
windows in experiment 2 shows that from 21.00 to 23.59 h,
human comfort increased with the increasing number of
screened windows ( p= 0.009), while from 00.00 to 05.59 h
human comfort decreased with increasing numbers of win-
dows since it became too cold ( p= 0.0007, figure 2). Human
comfort, however, was statistically different from the reference
house only when there were two or more large-screened win-
dows on opposite walls (21.00–23.59 h, χ
2
=3.9, p= 0.048;
00.00–05.59 h, p= 0.009).
As the number of small- or large-screened windows
increased, the number of An. gambiae s.l., and other mosquito
species (electronic supplementary material, table S1), collected
indoors decreased (table 2). A summary figure combining the
results from experiment 1 and 2 shows the percentage of
An.gambiae s.l. collected indoors declines with increasing
number and area of the screened window, with two or three
windows providing best protection (figure 3).
In experiment 3, reducing the number of gaps around the
door from two to one or installing two small-screened
windows into the house did not reduce the numbers of
An. gambiae s.l. collected indoors compared with the reference
house (table 2). Numbers of Mansonia spp. and Culex spp.,
however, were lower in houses where there was no gap at
the bottom of the door, compared to the reference house
with gaps both at the top and bottom of the door (electronic
supplementary material, table S1).
In experiment 4, there were 76% fewer An. gambiae (95%
CI 69–82%, p< 0.001) in houses with screened doors
and screened windows than those with solid doors and
screened windows. Temperatures in houses with screened
doors were 0.6°C less than houses with screened doors
before midnight (95% CI 0.4–0.7°C, p< 0.001) and after mid-
night (95% CI 0.5–0.7°C, p< 0.001). Indoor concentrations of
CO
2
rose above background levels shortly after two men
entered each house at 21.00 h, to a maximum roughly 1 h
later, before declining gradually through the night, before a
small rise around 05.00 h, one hour before the men left the
houses (figure 4). Indoor CO
2
concentrations were 152 ppm
(95% CI 109–195 ppm, p< 0.001) lower in screened door
houses than those with solid doors from 19.00 to 23.59 h
(p≤0.001) and 120 ppm lower (95% CI 81–159 ppm,
p≤0.001) from 00.00 to 05.59 h.
In experiment 5, there were 38% (95% CI 23–50%, p< 0.001)
fewer An. gambiae entering houses with two screened windows
compared to control houses without windows. Temperatures
in houses with two large-screened windows were 0.2°C
cooler before midnight (95% CI 0.0–0.3°C, p= 0.019) and after
midnight (95% CI 0.1–0.3°C, p< 0.001) than the reference
mean temperature (ºC) mean temperature (ºC)
time
time
time
time
24
25
26
27
28
29
30
23
21.00 22.00 23.00 00.00 01.00 02.00 03.00 04.00 05.00 06.00 07.00
450
550
650
750
850
950
1050
350
21.00 22.00 23.00 00.00 01.00 02.00 03.00 04.00 05.00 06.00
450
550
650
750
850
950
1050
350
21.00 22.00 23.00 00.00 01.00 02.00 03.00 04.00 05.00 06.00
24
25
26
27
28
29
30
23
21.00 22.00 23.00 00.00 01.00 02.00 03.00 04.00 05.00 06.00 07.00
mean carbon dioxide (ppm) mean carbon dioxide (ppm)
(a)(b)
(c)(d)
Figure 4. Nocturnal indoor and outdoor temperature (a,c) and indoor CO
2
concentrations (b,d) in screened and unscreened houses. Magenta lines, houses with
screened windows and solid doors. Green lines, houses with screened windows and doors. Blue solid lines, houses with no windows and solid doors. Black broken
lines, outdoor temperature. Red broken lines, outdoor CO
2
concentration. All measurements were made in year 2, apart from outdoor CO
2
, which was made in year 1.
royalsocietypublishing.org/journal/rsif J. R. Soc. Interface 18: 20201030
7

house. In houses with screened windows, indoor CO
2
concen-
trations were 81 ppm (95% CI 11–150 ppm, p= 0.027) lower
than those without windows from 19.00 to 23.59 h and
83 ppm less (95% CI = −4 to 170 ppm, p= 0.060) from 00.00 to
06.00 h.
Also, in experiment 5, although CO
2
concentrations
did not rise with increasing temperature in houses with
screened windows, they did in houses without. In houses
with screened doors and windows, there were similar CO
2
concentrations before midnight (45 ppm, 95% CI −2to
91 ppm, p= 0.059) and after midnight (9 ppm, 95% CI −30
to 48 ppm, p= 0.626). Similarly, CO
2
concentrations in
houses with screened windows did not vary before (9 ppm,
95% CI −22 to 40 ppm, p= 0.555) and after midnight
(6 ppm, 95% CI −31 to 43 ppm, p= 0.751). In marked contrast,
for a 1°C rise in indoor temperature in unventilated houses,
there was a 60 ppm increase in CO
2
before midnight (95%
CI 24–96 ppm, p= 0.004) and 73 ppm (95% CI 43–104 ppm,
p< 0.001) after midnight.
3.2. Computational fluid dynamic modelling
The exchange of CO
2
between the indoor and outdoor
environment of a house is shown through sections made
through the doors and windows of the experimental houses
(figure 5). The flow of CO
2
out of a house differs according
to whether doors and windows are screened and whether
there is no wind (figure 6) or light wind (figure 7). With no
doors
windows
section through doors
section through windows
0 1.500
0.750 2.250
3.000 (m)
(a)
(b)
Figure 5. (a) Structure of an experimental house showing (b) sections through windows and doors. Here, two men sleep under a bednet in a house with two badly
fitting doors and two screened windows. The illustration represents the house typology common in experiments 4 and 5 (figure 1). The sections taken here are used
to show CO
2
concentrations in figures 6 and 7.
royalsocietypublishing.org/journal/rsif J. R. Soc. Interface 18: 20201030
8

wind, unscreened houses produce horizontal jets of CO
2
from
gaps below and above the doors reaching more than 4 m
from a house. When screened windows are installed, little
CO
2
is streamed from the doors, with most leaking from
the windows, rising vertically into space. In houses with
screened doors and windows, less CO
2
accumulates indoors
and low levels are projected from the doors and windows,
rising upwards. On windy nights, screening reduces the con-
centration of CO
2
indoors (figure 7). On the windward side of
the house, little or no CO
2
escapes from all house typologies.
In marked contrast, on the leeward side, high concentrations
of CO
2
leak from above and below the door of unscreened
houses. Screening results in markedly less CO
2
leaking
from the screened doors and windows, presenting a diffuse
source of the gas.
4. Conclusion
Well-ventilated houses are not only cooler at night and more
comfortable to live in, they also have markedly fewer malaria
mosquitoes than poorly ventilated houses. Installing two or
more large-screened windows, each 0.5 m
2
in area, lowered
the indoor temperature at night by 0.2–0.6°C, depending on
outdoor wind speed. Adding two screened doors to a house
with screened windows further reduced the temperature.
Cross-ventilation of the room, where windows are positioned
on opposite walls, without any internal obstruction, is essential
to encourage airflow and reduce the indoor temperature.
Increased ventilation makes it more comfortable for the occu-
pants before midnight, making it more likely they will use an
ITN. By contrast, after midnight, houses with screened win-
dows or doors cool down further and become uncomfortably
cool. Being too cool at night, however, is easily resolved by
sleeping under a sheet, while being too hot, is impossible to
resolve without fans, air conditioning or sleeping under a
wet towel or sheet. The cooling effect of screening is also sup-
ported by our previous findings in The Gambia and elsewhere
[9]. Nonetheless, the cooling effect of cross-ventilation is lim-
ited in the hot humid tropics, especially where using a
bednet will reduce airflow by an average of 64% [20].
Surprisingly, we discovered that increasing both the
number and size of screened windows in a Gambian house
reduced house entry of An. gambiae s.l. mosquitoes. With
(a)
(b)
(c)
(i) (ii)
(i) (ii)
(i) (ii)
CO2 (ppm)
CO2 (ppm)
CO2 (ppm)
Figure 6. CFD simulations of CO
2
produced from two sleeping adults in unscreened and screened houses with two badly fitting doors in windless conditions: (a)
houses without screened doors or windows; (b) screened windows; (c) both screened windows and doors. (i) and (ii) indicate the section is through door or
windows, respectively (figure 5). Note that CO
2
is released from narrow gaps above and below all doors and windows, and through screening. In (a)ii, (b)ii
and (c)ii the section through the house includes a sleeping person generating CO
2
from the head. Note that the legend limits were chosen to enable comparisons
between the different typologies and that the maximum value of 557 ppm of CO
2
in the legend was exceeded in simulations (a) and (b), with maximum values of
over 1000 ppm; typically around the mouths of the sleepers.
royalsocietypublishing.org/journal/rsif J. R. Soc. Interface 18: 20201030
9

three screened windows and a total surface area of 1.5 m
2
,
there was a 95% reduction in the number of An. gambiae s.l.
found indoors. This level of protection against malaria vec-
tors is equivalent to that seen with some ITNs in the same
area [21], but in our case, no insecticides were used on the
screening or doors. The only source of insecticide was on
the ITNs. Similar findings were observed with other taxa of
mosquitoes, suggesting that the addition of screening can
reduce the house entry of many species of African mosqui-
toes. While it is well known that a screened house provides
a physical barrier against mosquitoes, this study is the first
to show screening reduces mosquito entry in houses which
are not perfectly closed and are therefore more representative
of typical village houses.
We explored three hypotheses to explain the indirect pro-
tective effect of screening. First, our findings suggest that
mosquitoes are not attracted in large numbers to external
faces of screening, since houses with slits above and below
screened doors had markedly fewer, not more, mosquitoes
than those with solid doors and slits. Second, and more
plausibly, the mechanism of protection results from screening
reducing the concentration of CO
2
dispersing from a house.
CO
2
concentrations were approximately 80 ppm less in
houses with screened windows compared to houses without
windows and adding two screened doors to a house with
screened windows resulted in a further 120–152 ppm
reduction. Houses with screened doors will leak CO
2
more
rapidly than the door gaps common in most rural houses.
The reductions in CO
2
levels are of biological significance
since female mosquitoes can detect differences in CO
2
con-
centration as small as 40 ppm [22]. Interestingly, in poorly
ventilated houses, CO
2
concentration increased with increas-
ing indoor temperature. This is probably related to higher
rates of metabolism associated with sweating and keeping
the body cool. Our third hypothesis about the structure of
the odour plume emanating from the house is also likely to
be important. Computer simulations show that jets of CO
2
project from unscreened houses from above and below the
doors, both accessible entry points for malaria mosquitoes.
In marked contrast, in screened houses, the odour plume is
(a)
(b)
(c)
(i) (ii)
(i) (ii)
(i) (ii)
CO2 (ppm)
CO2 (ppm)
CO2 (ppm)
Figure 7. CFD simulations of CO
2
produced from two sleeping adults in unscreened and screened houses with two badly fitting doors in field conditions where the
average wind speed is 0.44 m s
−1
:(a) houses without screens; (b) screened windows; (c) both screened windows and doors; (i) and (ii) indicate the section is
through door or windows, respectively (figure 5). Note that CO
2
is released from narrow gaps above and below all doors and windows, and through screening. In
(a)ii, (b)ii and (c)ii, the section through the house includes a sleeping person generating CO
2
from the head. Note that the legend limits were chosen to enable
comparisons between the different typologies and that the maximum value of 557 ppm of CO
2
in the legend was exceeded in simulations (a), with maximum
values of over 1000 ppm; typically around the mouths of the sleepers.
royalsocietypublishing.org/journal/rsif J. R. Soc. Interface 18: 20201030
10

weaker and rises upwards. Thus blood-seeking mosquitoes
are likely to detect an unscreened house from greater dis-
tances than a screened house, potentially explaining why
more mosquitoes find their way into an unscreened house
compared to a screened one. In nature, the picture is more
complicated, since a blood-seeking mosquito finds itself in
an environment where there is a continuous plume of CO
2
from a house, when there is no wind, and turbulent pulses
of CO
2
, when the wind blows and eddies around the
house. Anopheles gambiae is, however, able to orientate readily
along such turbulent odour plumes, moving towards the
odour source.
There are four main limitations to this study. First, the
experiment was carried out without considering the variability
in ‘real world’human behaviour. The sleepers went to bed ear-
lier than many adults, the doors remained closed during the
night, unlike those in the villages which are frequently
opened and closed until midnight [11], and there was no
wood smoke or bright lights indoors. Second, while our
focus is on CO
2
as a long-distance attractant, there are other
volatiles produced by humans that are also strongly attractive
to An. gambiae s.l. [23,24]. We do not know how these short-dis-
tance attractants affect mosquito movement in our study.
Thirdly, there were only two men in each house, fewer individ-
uals than most village houses. Increasing the number of people
sleeping indoors is likely to increase CO
2
concentrations, and
hence indoor mosquito collections [25]. Fourth, our study
used single-roomed houses, and the effects reported here are
likely to vary according to different house typologies including
those with multiple rooms, ceilings, porosity of structure, and
presence and proximity of adjacent buildings.
Findings from the present study suggest that screening is
not only an effective barrier against mosquitoes, it also
increases ventilation, cools the house and reduces CO
2
concen-
tration indoors. In houses with screening, the CO
2
-odour
plumes releasedfromthe house are likely toattract mosquitoes
from shorter distances, reducing the number that enter the
house, compared to unscreened houses with badly fitting
doors. The World Health Organization recommends house
improvements, such as installing window screens, for reducing
malaria [26,27]. There has never been a better time for develop-
ing new ways of screening houses and facilitating the scale-up
of this intervention, since it coincides with a period when
Africa’s housing stock is modernizing [28], and more housing
is urgently needed to accommodate the more than one billion
new Africans that will need homes in rural areas by 2050
[29]. Building healthy homes that reduce the threat from
malaria and other mosquito-borne diseases, that prevent
over-crowding, increase airflow, reduce indoor air pollution,
and keep the occupants clean, secure and comfortable during
the day and night is an imperative and a basic human right.
Ethics. The study was approved by the Gambia Government/Medical
Research Council’s joint ethics committee (9 and 17 February 2017
and 11 July 2019) and the Department of Biosciences ethics commit-
tee, Durham University, UK (16 March 2017 and 28 August 2019). All
volunteer sleepers provided written informed consent at the time of
enrolment.
Data accessibility. All data used in this paper are available as electronic
supplementary material.
Authors’contributions. S.W.L. is the principal investigator, who coordi-
nated the study. S.W.L., E.J., M.C.T., J.B., D.J. and J.K. designed the
study. E.J., M.C.T., B.K., M.J., M.P. and U.D. coordinated the field
studies. E.J. and M.C.T. collected data. S.C. identified mosquitoes
by PCR. S.W.L., E.J., M.C.T., J.B., D.J., M.J., D.S.H.L., M.P., A.W.
and J.K. contributed to the data analysis and interpretation. E.J.
and M.C.T. wrote the draft report. All authors critically reviewed
the manuscript and approved the final version.
Competing interests. All authors declare no competing interests.
Funding. This work was supported by the Halley Stewart Trust and
Global Health Trials funded by the MRC-DfID Wellcome Trust
(MR/M007383/1); S.W.L. and A.L.W. are supported by the Global
Challenges Research Fund for Networks in Vector Borne Disease
Research which is co-funded by BBSRC, MRC and NERC (BB/
R00532X/1), and M.C.T. is supported by the Durham Global Chal-
lenges Centre for Doctoral Training. J.B. was supported by grants
from the MRC and DfID under the MRC/DFID Concordat
(K012126/1) and is also part of the EDCTP2 programme supported
by the European Union.
Acknowledgements. We are grateful for the cooperation of the study
subjects and the MRCG field staff for their support.
References
1. Bhatt S et al. 2015 The effect of malaria control on
Plasmodium falciparum in Africa between 2000 and
2015. Nature 526, 207–211. (doi:10.1038/
nature15535)
2. WHO. 2019 Guidelines for malaria vector control.
Geneva, Switzerland: WHO. See https://www.who.
int/malaria/publications/atoz/9789241550499/en/.
3. WHO. 2020 World malaria report 2020. Geneva,
Switzerland: WHO. See https://www.who.int/
publications/i/item/9789240015791.
4. Pulford J, Hetzel MW, Bryant M, Siba PM, Mueller I.
2011 Reported reasons for not using a mosquito net
when one is available: a review of the published
literature. Malaria J. 10, e83. (doi:10.1186/1475-
2875-10-8)
5. Huho B et al. 2013 Consistently high estimates for the
proportion of human exposure to malaria vector
populations occurring indoors in rural Africa.
Int. J. Epidemiol. 42, 235–247. (doi:10.1093/ije/dys214)
6. Gillies MT. 1980 The role of carbon dioxide in host
finding by mosquitoes (Diptera: Culicidae): a review.
Bull. Entomol. Res. 70, 525–532. (doi:10.1017/
S0007485300007811)
7. Geier M, Bosch OJ, Boeckh J. 1999 Influence of
odour plume structure on upwind flight of
mosquitoes towards hosts. J. Exp. Biol. 202,
1639–1648. (doi:10.1371/journal.pone.0226815.
g003)
8. Takken W, Knols BGJ. 1999 Odor-mediated behavior
of Afrotropical malaria mosquitoes. Ann. Rev.
Entomol. 44, 131–157. (doi:10.1146/annurev.ento.
44.1.131)
9. Knudsen J, Von Seidlein L. 2014 Healthy homes in
tropical zones: improving rural housing in Asia and
Africa. London, UK: Axel Menges.
10. Chen Q, Zhang Z, Zuo W. 2007 Computational fluid
dynamics for indoor environment modeling: past,
present, and future. In Proc. of XXV Congresso
Nazionale UIT sulla Trasmissione del Calore (25th UIT
National Heat Transfer Conference),Trieste, Italy,
18–20 June 2007, pp. 3–8. See http://citeseerx.ist.
psu.edu/viewdoc/download?doi=10.1.1.536.
3493&rep=rep1&type=pdf.
11. Mwesigwa J et al. 2017 Residual malaria transmission
dynamics varies across The Gambia despite high
coverage of control interventions. PLoS ONE 12,
e0187059. (doi:10.1371/journal.pone.0187059)
12. Jatta E et al. 2018 How house design affects malaria
mosquito density, temperature, and relative
humidity: an experimental study in The Gambia.
Lancet Planet. Hlth. 2, e498–e508. (doi:10.1016/
S2542-5196(18)30234-1)
13. Scott JA, Brogdon WG, Collins FH. 1993
Identification of single specimens of the Anopheles
gambiae complex by the polymerase chain reaction.
Am. J. Trop. Med. Hyg. 49, 520–529. (doi:10.4269/
ajtmh.1993.49.520)
royalsocietypublishing.org/journal/rsif J. R. Soc. Interface 18: 20201030
11

14. Fanello C, Santolamazza F, della Torre A. 2002
Simultaneous identification of species and
molecular forms of the Anopheles gambiae
complex by PCR-RFLP. Med. Vet. Entomol.
16, 461–464. (doi:10.1046/j.1365-2915.2002.
00393.x)
15. Jawara M et al. 2018 New prototype screened doors
and windows for excluding mosquitoes from
houses: a pilot study in rural Gambia. Am. J. Trop.
Med. Hyg. 99, 1475–1484. (doi:10.4269%2Fajtmh.
18-0660)
16. Lindsay SW, Jawara M, Paine K, Pinder M, Walraven
GE, Emerson PM. 2003 Changes in house design
reduce exposure to malaria mosquitoes. Trop. Med.
Int. Health 8, 512–517. (doi:10.1046/j.1365-3156.
2003.01059.x)
17. Roudsari MS, Pak M, Smith A. 2013 LadyBug: a
parametric environmental plugin for grasshopper
to help designers create an environmentally-
conscious design. In Int. IBPSA Conf.,Lyon, France.
See http://www.ibpsa.org/proceedings/bs2013/p_
2499.pdf.
18. Fanger PO. 1970 Thermal comfort. Analysis and
applications in environmental engineering.
New York, NY: McGraw Hill.
19. ASHRAE. 2017 Standard 55–2010. Thermal
environmental conditions for human occupancy.
Atlanta, GA: ASHRAE.
20. von Seidlein L, Ikonomidis K, Bruun R, Jawara M,
Pinder M, Knols BGJ, Knudsen JB. 2012 Airflow
attenuation and bed net utilization: observations
from Africa and Asia. Malaria J. 11, 200. (doi:10.
1186/1475-2875-11-200)
21. Lindsay SW, Adiamah JH, Miller JE, Armstrong JR.
1991 Pyrethroid-treated bednet effects on
mosquitoes of the Anopheles gambiae complex in
The Gambia. Med. Vet. Entomol. 5, 477–483.
(doi:10.1111/j.1365-2915.1991.tb00576.x)
22. Grant A, O’Connell R. 1996 Electrophysiological
responses from receptor neurons in mosquito
maxillary palp sensilla. In Olfaction in mosquito–
host interactions (eds GR Bock, G Cardew),
pp. 233–253. Chichester, UK: Wiley.
23. van Loon JJA, Smallegange RC, Bukovinszkiné-Kiss
G, Jakobs F, De Rijk M, Mukabana WR, Verhulst NO,
Menger DJ, Takken W. 2015 Mosquito attraction:
crucial role of carbon dioxide in formulation of a
five-component blend of human-derived volatiles.
J. Chem. Ecol. 41, 567–573. (doi:10.1007/s10886-
015-0587-5)
24. Spitzen J, Smallegange RC, Takken W. 2008 Effect of
human odours and positioning of CO
2
release point on
trap catches of the malaria mosquito Anopheles gambiae
sensu stricto in an olfactometer. Physiol. Entomol. 33,
116–122. (doi:10.1111/j.1365-3032.2008.00612.x)
25. Kaindoa EW, Mkandawile G, Ligamba G, Kelly-Hope
LA, Okumu FO. 2016 Correlations between household
occupancy and malaria vector biting risk in rural
Tanzanian villages: implications for high-resolution
spatial targeting of control interventions. Malaria J.
15, 199. (doi:10.1186/s12936-016-1268-8)
26. WHO. 2017 Global vector control response 2017–
2030. Geneva, Switzerland: WHO. See https://www.
who.int/vector-control/publications/global-control-
response/en/.
27. WHO. 2017 Keeping the vector out: housing
improvements for vector control and sustainable
development. Geneva, Switzerland: WHO. See
https://apps.who.int/iris/handle/10665/259404.
28. Tusting LS et al. 2019 Mapping changes in housing
in sub-Saharan Africa from 2000 to 2015. Nature
568, 391–394. (doi:10.1038/s41586-019-1050-5)
29. United Nations. 2018 World urbanization prospects
2018. New York, NY: UN. See https://population.un.
org/wup/.
royalsocietypublishing.org/journal/rsif J. R. Soc. Interface 18: 20201030
12