1
Title: Household-Level Risk Factors for Aedes aegypti Pupal Density in Guayaquil, 1
Ecuador 2
Authors and Affiliations: Thien-An Ha1, Tomás M. León1, Karina Lalangui2, Patricio Ponce2, 3
John M. Marshall1, Varsovia Cevallos2 4
1. School of Public Health, University of California, Berkeley, USA 5 2. Centro de Investigación en Vectores Artrópodos, Instituto Nacional de Investigación en Salud Pública "Dr. 6
Leopoldo Izquieta Pérez", Quito, Ecuador 7
Corresponding Authors: Thien-An Ha ([email protected]) 8
Abstract: 9
Background 10
Vector-borne diseases are a major cause of disease burden in Guayaquil, Ecuador, especially 11
arboviruses spread by Aedes aegypti mosquitoes. Understanding which household characteristics 12
and risk factors lead to higher Ae. aegypti densities and consequent disease risk can help inform 13
and optimize vector control programs. 14
Methods 15
Cross-sectional entomological surveys were conducted in Guayaquil between 2013 and 2016, 16
covering household demographics, municipal services, potential breeding containers, presence of 17
Ae. aegypti larvae and pupae, and history of using mosquito control methods. A zero-truncated 18
negative binomial regression model was fitted to data for estimating the household pupal index. 19
An additional model assessed the factors of the most productive breeding sites across all of the 20
households. 21
Results 22
Of surveyed households, 610 satisfied inclusion criteria. The final household-level model found 23
that collection of large solid items (e.g., furniture and tires) and rainfall the week of and 2 weeks 24
before collection were negatively correlated with average pupae per container, while bed canopy 25
use, unemployment, container water volume, and the interaction between large solid collection 26
and rainfall 2 weeks before the sampling event were positively correlated. Selection of these 27
variables across other top candidate models with ∆AICc < 1 was robust, with the strongest 28
effects from large solid collection and bed canopy use. The final container-level model 29
explaining the characteristics of breeding sites found that contaminated water is positively 30
correlated with Ae. aegypti pupae counts while breeding sites composed of car parts, furniture, 31
sewerage parts, vases, ceramic material, glass material, metal material, and plastic material were 32
all negatively correlated. 33
Conclusion 34
Having access to municipal services like bulky item pickup was effective at reducing mosquito 35
proliferation in households. Association of bed canopy use with higher mosquito densities is 36
unexpected, and may be a consequence of large local mosquito populations or due to limited use 37
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or effectiveness of other vector control methods. The impact of rainfall on mosquito density is 38
multifaceted, as it may both create new habitat and “wash out” existing habitat. Providing 39
services and social/technical interventions focused on monitoring and eliminating productive 40
breeding sites is important for reducing aquatic-stage mosquito densities in households at risk for 41
Ae. aegypti-transmitted diseases. 42
43
Keywords: aedes aegypti, mosquito, household risk factors, arbovirus, collection services, 44
precipitation, predictive modeling 45
46
Background 47
Vector-borne febrile illnesses such as dengue, chikungunya, and Zika virus are of pressing public 48
health concern in Latin America and the Caribbean [1]. The mosquito Aedes aegypti is the 49
region’s primary vector of these arboviruses, which co-circulate in populations in the tropics and 50
subtropics [1,2]. The burden of these diseases weighs heavily on susceptible populations in low 51
and middle-income countries such as Ecuador [1]. 52
Between 2010 and 2014, over 70,000 cases of dengue were reported in Ecuador, with the highest 53
incidence clustered in urbanized coastal areas like the city of Guayaquil [1,3]. Dengue infection 54
can be asymptomatic or present as a moderate febrile illness, with some symptoms advancing to 55
hemorrhage, shock, and death [4]. Without an effective dengue vaccine, community and 56
household-level vector control of Ae. aegypti remains the primary means of preventing and 57
controlling dengue outbreaks [2]. In Ecuador, each household currently employs, on average, 58
five different mosquito control methods, including sprays, aerosols, repellents, mosquito coils, 59
screens, and bed nets [1]. 60
Ae. aegypti population management is an ongoing public health challenge for countries with 61
limited resources that must efficiently plan and utilize targeted control. Ae. aegypti is a mosquito 62
species that primarily amplifies epidemics among urban populations [5]. The species is an 63
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effective vector for dengue because it is highly adapted to urban environments, where it lays 64
eggs in artificial containers of water near human dwellings and preferentially feeds on humans 65
[6]. Adult Ae. aegypti lay eggs in such habitats, and larvae develop in both natural water-66
retaining structures and in domestic water containers [7]. Examples of outdoor breeding sites for 67
Ae. aegypti include large tires, flower vases, and plastic gallon containers [8]. Understanding the 68
local characteristics of Ae. aegypti habitats can be used to inform vector control efforts [9]. 69
Previous studies done in Machala, Ecuador, found that local socio-ecological conditions such as 70
proximity to abandoned properties, interruptions in the piped water supply, and a highly shaded 71
patio were risk factors for Ae. aegypti proliferation and the presence of dengue [2]. Further 72
investigation into household factors, in conjunction with the evaluation of vector control efforts, 73
is necessary to reduce and prevent dengue incidence by reducing Ae. aegypti habitat and 74
population. 75
Our study describes potential household-level risk factors for Ae. aegypti pupal proliferation in 76
the city of Guayaquil, Ecuador’s largest and most populous city, its most important commercial 77
port, and the historical epicenter of yellow fever and dengue in the country[1]. Guayaquil (2016 78
Instituto Nacional de Investigación en Salud Pública projected population: 2,482,789), is located 79
on the west bank of the Guayas River (Fig. 1), which flows into the Pacific Ocean. The urban 80
core of Guayaquil is surrounded by low-income neighborhoods with limited basic services and 81
high rates of migration. Guayaquil has had the greatest number of dengue cases in Ecuador since 82
1988, with all 4 serotypes circulating since then. Seasonally, the highest incidence occurs in the 83
rainy season because of favorable environmental conditions for transmission. The first four 84
months of the year have abundant rain in this coastal region, with over 17 average days of rain 85
totalling more than 200 mm each month; in the province of Guayas, the days are hot and humid, 86
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with average high temperatures between 29°C and 32°C and high humidity (NOAA ASOS 87
Data). 88
Methods 89
Household Data Collection 90
Household cross-sectional surveys were conducted every month in Guayaquil from January 2013 91
to August 2016 to investigate factors correlated with mosquito pupal counts per container. Each 92
month, one random subcircuit (an administrative unit covering ~10,000 inhabitants) was 93
randomly selected in the North, Center, and South of Guayaquil, resulting in three sampling 94
events (Fig. 1A). Guayaquil subcircuits were also classified into low, medium, and high 95
socioeconomic status derived from three indicators: illiteracy, overcrowding, and unemployment 96
(Fig. 1B). Each of these variables were averaged across the subcircuit, where illiteracy and 97
unemployment were represented as percentages and overcrowding was the average number of 98
people per household. 99
Fig 1. Subcircuits and zones of Guayaquil showing sampling zones (A) and socioeconomic 100
status (B) by subcircuit. 101
102
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103
Households were defined as in-use residential units. Household addresses were obtained prior to 104
the survey. Containers were defined as in-use breeding sites near or inside the household. Each 105
container was examined for immature mosquito stages in the water, material, and type. Each 106
house was visited only once in order to maximize the geographical area covered. Each visit 107
assessed mosquito presence and conducted sampling in artificial containers of water. During 108
each house visit, questions were asked to any household member over the age of 18 after 109
verifying their residency. These questions focused on Ae. aegypti risk factors, including Ministry 110
of Health vector control efforts and mosquito control practices which were used as candidate 111
predictors in our model (Table 1, Table S1). 112
Water interruptions were characterized by whether or not the household experienced a water 113
service interruption in the last 24 hours. Collection of large solids are the municipal service of 114
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collecting large furniture or disposed tires. Adult mosquito fumigation, Abate (temephos), and 115
biolarvicide are Ministry of Health vector control efforts that were implemented between 2013 116
and 2016. These vector control efforts were self-reported by the household member for previous 117
weeks up to a month. Adult mosquito fumigation refers to the spraying of deltamethrin, an 118
insecticide that is applied inside the house every four months [10]. The Ministry of Health 119
decided upon which houses to fumigate based on historical locations of high numbers of dengue 120
cases. Abate, the commercial name for temephos, is an organophosphate larvicide which is 121
applied following World Health Organization (WHO) guidelines of 20g of granular product per 122
189 liters [10]. This product is applied to water containers 3 to 4 times a year [10], and is 123
commonly used as a dengue vector control method [11]. The Ministry of Health distribution of 124
abate was chosen based on previous larvae indices. Biolarvicide refers to the use of organisms 125
such as bacteria to target the larval stage of a mosquito. In Guayaquil, the Ministry of Health 126
applied and distributed the biolarvicide Bacillus thuringiensis israelensis. Protective mesh refers 127
to whether the household has mesh around windows and doors. 128
Precipitation measurements at week 0, week 1 lag, and week 2 lag were also included. Week 0 129
indicates the amount of rainfall the week that the sampling event occurred. The lag variables, 130
week 1 lag and week 2 lag, indicate precipitation one week previous to the sampling event and 131
precipitation two weeks previous to the sampling event, respectively. Only variables that were 132
most relevant to mosquito ecology based on literature review and entomologist consultation were 133
included for the models. 134
Entomological Data 135
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Three field technicians conducted each sampling event, which took place across 250 households 136
over a period of five days each time. In each house, technicians searched for immature 137
mosquitoes in containers both inside and outside of the household. Each container carrying 138
immatures was recorded for its container type and material type. These live immatures were 139
transported to an insectary in Quito, where the lab at INSPI recorded the numbers and stage of 140
development. Immatures were reared until adult stage for full species identification. 141
Statistical Analysis 142
All analyses were performed in R version 3.5.3. We used zero-truncated negative binomial 143
models to assess the appropriate household-level and container-level predictors of Ae. aegypti 144
pupal population. Zero-truncated models were used because the data was only recorded for 145
containers with present immature mosquitoes. Our outcome for the household-level statistical 146
models was average pupal counts per container (APC), or pupal index, and was calculated as the 147
total number of household pupae divided by the number of containers carrying these immature 148
stage mosquitoes. Our outcome for the secondary analysis on container-level data was the sum of 149
the pupae in each artificial breeding site. We determined that outliers for each outcome were 150
those beyond Q3 (75th percentile) + 1.5 * IQR (Q3 - Q1) and omitted them [12]. 151
We performed a chi-squared test for the Poisson model assumption that conditional variance 152
equals conditional mean in our data set. We rejected the null hypothesis that the Poisson model 153
best fits our data (p < 0.01) and fitted a negative binomial model with a dispersion parameter of 154
1.4103 and a standard error of 0.0881. We fitted a full model using all variables for the 155
household model (Table S1) and the container model (Table S2) and used the ‘dredge’ function 156
(R package MuMIn v1.43.17) to find all possible models through the best subset selection 157
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technique [13]. Best subset selection exhaustively searches all combinations of candidate 158
variables and ranks models using specific selection criteria. In this study, we used the Akaike 159
information criterion corrected for small sample sizes (AICc) to compare all the candidate 160
models (Fig S1). AICc has a penalty term for small sample sizes: as sample size increases, AIC 161
is approximated and therefore, AICc is preferred over AIC [14]. Effect sizes were considered 162
significant if 95% confidence intervals for corresponding explanatory variables did not overlap 163
zero. We evaluated model performances using 100 simulations of 10-fold cross-validation as our 164
model evaluation method. 165
Results 166
In the surveys, 830 households of the total 990 households surveyed were found to have Ae. 167
aegypti mosquito pupae. Of the 830 households, 220 had missing or erroneous location data 168
(e.g., coordinates indicated a household was not in Guayaquil) and were omitted, yielding 610 169
households for our analysis. The mean pupal index for the subset of included households was 170
11.08, with a standard deviation of 10.26, and a maximum pupal index of 42. 171
About 47% of the 610 households used bed canopies as a method for preventing mosquito biting 172
(Table 1). Approximately 25% of households had water service interruptions. Only 22% of 173
households did not have large solid collection services. About 15% of households had protective 174
mesh around their windows and doors. 175
Table 1. Summary statistics for habitat and vector control effort variables included in the 176
analysis. 177
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178
Influence of household-related factors on Ae. aegypti pupal abundance 179
The model with the smallest (best) AICc value included the variables: canopy use, large solid 180
service, unemployment, water volume, precipitation at week 0, precipitation at week 2 lag, and 181
Variables of Interest Number of
Households
(n = 610)
Variable Description
Number of Children Number of children (<18 years) residing in the household
0 172 (28%)
1-3 373 (61%)
4-7 61 (10%)
>7 4 (1%)
Number of Adults Number of adults ( ≥ 18 years) residing in the household
0-3 323 (53%)
4-7 258 (42%)
>7 29 (5%)
Water Interruption Whether or not the household experience a water service interruption in the last 24
hours
Yes 153 (25%)
No 457 (75%)
Trash Service Per Week Number of times garbage collections occurs at the household per week
0-3 529 (87%)
4-7 81 (13%)
Large Solid Collection Municipal service of collecting large furniture, tires or other items
Yes 478 (78%)
No 132 (22%)
Sewer Connection Whether or not there is a sewer connection to the household’s waste system
Yes 550 (90%)
No 60 (10%)
Fumigation Spraying of deltamethrin, an insecticide applied inside the house every four months
Yes 190 (31%)
No 420 (69%)
Abate Also known as temephos, an organophosphate larvicide which is applied by 20 grams
of granular product per 189 liters
Yes 151 (25%)
No 459 (75%)
Bio-larvicide Bacillus thuringiensis used to target the larval stage of a mosquito
Yes 322 (53%)
No 288 (47%)
Canopy Use The usage of a canopy over a bed
Yes 287 (47%)
No 323 (53%)
Protective Mesh Mesh present around doors and windows
Yes 90 (15%)
No 520 (85%)
Avg. Water Volume (L) Total water volume in all household breeding containers divided by the number of
breeding sites
1-25 435 (71%)
26-50 21 (4%)
>50 154 (25%)
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the interaction of large solid service and precipitation at week 2 lag (Table 2). The predictors 182
with statistically significant associations were consistently selected into our top models ∆AICc < 183
1 (Table 2). The explanatory variable estimates for the model with the smallest AICc value, our 184
top model, indicated that canopy use, unemployment, average container water volume, and the 185
interaction between large solid service and precipitation (with 2-week lag) all had a statistically 186
significant positive relationship with Ae. aegypti pupal abundance and large solid service had a 187
significant negative relationship with Ae. aegypti pupal abundance (Table 3). 188
Table 2. Variables included in models with the smallest (best) AICc values. Only models that lie 189
within a ∆AICc of 1 of the smallest AICc value are shown. The response variable was the total 190
number of household Ae. aegypti pupae over the total number of household breeding sites. Error 191
indicates the cross-validation prediction error off of the mean pupal index of 11.08. 192
Precipitation0 indicates rainfall from the week of sampling and Precipitation2 indicates rainfall 193
with 2-week lag (i.e., 2 weeks before sampling). 194
Model df LogLik ΔAICc Error
Canopy Use + Large Solid Service + Unemployment + Water Volume + Precipitation0 +
Precipitation2 + Large Solid Service * Precipitation2
9 -2075.162 0 10.08
Canopy Use + Water Interruption + Large Solid Service + Unemployment + Water Volume
+ Precipitation0 + Precipitation2 + Water Interruption * Precipitation0 + Large Solid Service
* Precipitation2
11 -2073.210 0.2374 10.12
Canopy Use + Large Solid Service + Unemployment + Water Volume + Precipitation2 +
Large Solid Service * Precipitation2
8 -2076.444 0.5036 10.10
Biolarvicide + Canopy Use + Large Solid Service + Unemployment + Water Volume +
Precipitation0 + Precipitation2 + Large Solid Service * Precipitation2
10 -2074.407 0.5566 10.09
Biolarvicide + Canopy Use + Water Interruption + Large Solid Service + Unemployment +
Water Volume + Precipitation0 + Precipitation2 + Water Interruption * Precipitation0 +
Large Solid Service * Precipitation2
12 -2072.431 0.7603 10.13
Table 3. Explanatory variable estimates for the model with the smallest AICc value. The 195
response variable is the average pupae per container in a household. 196
197
198
199
Variable Log Estimate Estimate 95% CI
Intercept 1.790 6.00 (3.68, 9.79)
Canopy Use 0.240 1.271* (1.06, 1.52)
Large Solid Service -0.280 0.756* (0.610, 0.932)
Unemployment 0.0641 1.0662* (1.02, 1.114)
Water Volume 0.00166 1.0016* (1.0003, 1.003)
Precipitation0 -0.00600 0.994 (0.986, 1.0013)
Precipitation2 -0.0103 0.990 (0.970, 1.0118)
Large Solid Service * Precipitation2 0.0214 1.0216* (1.00, 1.043)
*p < 0.05
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The average prediction error over 100 simulations of 10-fold cross-validation was 10 pupae off 200
of the true value where the mean pupal index was 11.08. Fig. 2 shows the distribution of pupal 201
index measurements in households and prediction error mapped across Guayaquil (also see Fig. 202
S3 for corresponding heatmaps and Fig. S4 for relative error). Because of the tightly clustered 203
sampling of some neighborhoods and the contribution of local environmental effects, there was 204
significant spatial autocorrelation (Moran’s I test, p < 0.01). The socioeconomic variables (such 205
as unemployment) captured some of these effects, but there were likely other unmeasured 206
exposures contributing to the spatial pattern. An assessment of multicollinearity, excluding 207
interaction terms, revealed that in our top model, none of the variables tested had variance 208
inflation factor scores above 2, indicating that there is little collinearity between predictors 209
(Table S3). 210
211
212
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213
Fig. 2. Pupal index measurements (A) and prediction error map (B) based on the final model. 214
Each dot indicates a household included in the final analysis. Error is the difference between the 215
data and the model for each household’s characteristics. 216
Influence of container-related factors on Ae. aegypti pupal abundance 217
We used data from 924 containers to conduct our analysis. The mean pupal sum for the included 218
containers was 15.46, with a standard deviation of 21.63, and a maximum pupal count of 253. 219
We observed that the top container-level models (∆AICc < 1) consistently selected contaminated 220
water, sewer parts, vases, ceramic material, glass material as appropriate predictors of pupal sum 221
(Table 4, Table S4). 222
223
Table 4. Variables included in container-level models with the smallest (best) AICc values. Only 224
models that lie within a ∆AICc of 1 of the smallest AICc value are shown. The response variable 225
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was the total number of container-level Ae. aegypti pupae in each breeding site. Top 5 of all 226
models with ∆AICc < 1 displayed. 227
Model df LogLik ΔAICc
Car Parts + Contaminated Water + Furniture + Ceramic Material + Glass Material + Metal
Material + Plastic Material + Sewer + Vase
11 -3459.19 0
Car Parts + Contaminated Water + Ceramic Material + Glass Material + Metal Material +
Plastic Material + Sewer + Vase
10 -3460.23 0.03
Bamboo + Car Parts + Contaminated Water + Furniture + Ceramic Material + Glass
Material + Metal Material + Plastic Material + Sewer + Vase
12 -3458.20 0.08
Bamboo + Car Parts + Contaminated Water + Ceramic Material + Glass Material + Metal
Material + Plastic Material + Sewer + Vase
11 -3459.24 0.11
Bucket Part + Car Parts + Contaminated Water + Ceramic Material + Glass Material +
Metal Material + Plastic Material + Sewer + Tub + Vase
12 -3458.28 0.24
The average prediction error over 100 simulations of 10-fold cross validation for the container 228
models was 21.54 pupae off of the true mean value of 15.46 pupae. The variables car parts, 229
sewer, vase, ceramic material, glass material, metal material and plastic material were all found 230
to have a significant negative association with the outcome, pupal sum (Table 5). Contaminated 231
water was the singular predictor found to have a significant positive association with pupal sum 232
(Table 5). 233
Table 5. Explanatory variable estimates for the container model with the smallest AICc value. 234
Response variable is pupal sum per container. 235
236
237
238
239
240
241
242
Variable Log Estimate Estimate 95% CI
Intercept 3.129 22.844 (17.74, 29.98)
Car Parts -0.666 0.513* (0.35, 0.74)
Contaminated Water 0.247 1.280* (1.10, 1.49)
Furniture -0.484 0.616 (0.35, 1.21)
Ceramic Material -0.942 0.390* (0.23, 0.68)
Glass Material -0.900 0.407* (0.23, 0.74)
Metal Material -0.425 0.654* (0.47, 0.90)
Plastic Material -0.477 0.620* (0.47, 0.81)
Sewer -0.617 0.540* (0.31, 1.02)
Vase -0.414 0.661* (0.46, 0.97)
*p < 0.05
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Discussion 243
The burden of arboviruses transmitted by Ae. aegypti in Guayaquil has increased significantly 244
since the 1980s, and targeted interventions are necessary to halt the spread of such diseases [3]. 245
The findings of this study provide evidence that Ae. aegypti proliferation is influenced by 246
specific household risk factors. The household models that performed best as determined by 247
AICc contained these variables: canopy use, large solid collection services, unemployment, 248
water volume, water interruptions, biolarvicide, precipitation at week 0, precipitation at week 2 249
lag, and the interactions: precipitation at week 0 * water interruption and large solid collection 250
services * week 2 lag. 251
Canopy use has a significant positive association with Ae. aegypti abundance, which is 252
counterintuitive and may be attributed to other vector control practices being limited when bed 253
canopies are in use. It is likely that those with higher mosquito populations inside the house are 254
more prone to using bed canopies. The usage of these canopies may result in a false sense of 255
security, or may be an indication of other unmeasured household risk factors that allow for high 256
Ae. aegypti densities. The previously mentioned study from Machala, Ecuador also cites an 257
unclear relationship between dengue infections in a city near to Guayaquil, and bed canopy 258
usage [2]. Furthermore, Ae. aegypti are daytime feeders so these nets would only affect mosquito 259
feeding if household members are napping during the day. 260
In our analysis, large solid collection services have a significant negative association with pupal 261
abundance, which may be because there remain fewer untouched breeding sites available for Ae. 262
aegypti. Regular bulky item pickup removes tires and other potential mosquito habitats where 263
water could pool. This is corroborated by previous studies which have found that Ae. aegypti 264
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positive containers were, among the most common, to be trash and flower pots [16]. These 265
containers may be more regularly eliminated and maintained in higher socioeconomic areas 266
through large solid collection services. In alignment with this finding, it was also found that 267
unemployment had a significant positive relationship with pupal abundance. This corresponds 268
with the comparison of the socioeconomic status map from Fig. 1, and the pupal index map from 269
Fig. 2, where we find that there is a higher density of Ae. aegypti positive households in lower 270
socioeconomic areas. There is also a higher prediction error for these areas as seen in Fig. 2 and 271
Fig. S3, suggesting that areas with higher unemployment need emphasis on research to better 272
understand the specific household risk factors attributed to Ae. aegypti pupal density. 273
Our study also found that average water volume had a significant positive relationship with pupal 274
abundance. A 2012 study from the Tri Nguyen village in Vietnam found that containers where 275
the water volume increased relative to the previous survey had a significantly higher count of Ae. 276
aegypti pupae [17]. The study also found that the greatest increase in pupal abundance occurred 277
after a rainfall event. This corresponds to our study’s findings in which both precipitation during 278
week 0 and increasing water volume results in higher APC. Heavy rainfall is known to flush out 279
existing containers, which could explain the negative (not statistically significant) association 280
between rainfall the week of sampling and APC. A negative association (although again not 281
statistically significant) between rainfall with a two-week lag and APC could conceivably result 282
from the same flushing phenomenon, adjusting for the 8-12 days for Ae. aegypti eggs to develop 283
into pupae [18,19]. The interaction between large solid collection services and precipitation two 284
weeks prior was significantly positively associated with Ae. aegypti pupal counts, which could 285
be due to rainfall providing habitat for eggs to be laid which then develop into pupae 8-12 days 286
later. The meaning of the interaction is somewhat unclear; however, wealthier neighborhoods 287
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16
have increased access to large solid collection services, so there is greater creation and 288
destruction of mosquito habitat compared with poorer neighborhoods. Fig. 2 shows that 289
wealthier areas of Guayaquil have a lower number of high density Ae. aegypti households. With 290
fewer breeding habitats in wealthier neighborhoods, precipitation may have a larger and 291
differential effect on pupal density and therefore, any marginal effect may be picked up by the 292
model. 293
This differentiation is further explained in the water storage practices and distribution of houses. 294
In neighborhoods with higher employment rates and lower illiteracy, there are an increased 295
number of natural areas where precipitation may collect, especially since houses are spread 296
further apart. In less developed neighborhoods, there are different relationships with standing 297
containers. During the rainy season, there are not as many water-holding containers because the 298
water is constantly replenished by the rain. However, in the dry season, there are more standing 299
containers because water is scarcer and needs to be stored for the households. 300
When the interaction term is included in the final model, precipitation at a week 2 lag has a 301
negative correlation with the outcome. When the interaction term is not being controlled for, 14 302
days after a precipitation event correlates with higher pupal density. This suggests that there is a 303
specific relationship between large solid collection services and precipitation at a week 2 lag on 304
our outcome, pupal density. However, since large solid services may serve as a proxy for 305
socioeconomic status, this may suggest that there is a dynamic effect across socioeconomic 306
statuses. These nuances are difficult to account for within the model context. For vector control 307
efforts to be effective, it may require a more thorough understanding of the relationship between 308
rainfall and socioeconomic factors that influence pupal density. 309
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Ae. aegypti are highly adaptable mosquitoes that were historically found in forested areas using 310
tree holes for breeding but have since adapted to breeding in tires, vases, and other objects found 311
in proximity to human habitations [20]. Their resilience and adaptability pose difficulties when 312
searching for effective control methods, especially for outdoor areas [20]. However, in light of 313
our analyses, certain types and materials of containers may be more or less productive than 314
others. 315
In our study, vase-type containers were found to be a significant predictor and were correlated 316
with lower pupae counts. Glass material composition was selected as an appropriate explanatory 317
predictor and is correlated with lower pupae counts as well. Vases, other glass-type containers, 318
metal material and ceramic material containers, may have more variable water temperature that 319
impedes Ae. aegypti development. Contaminated water was found to be a significant predictor 320
that was correlated with higher pupae counts. The survey had field technicians qualitatively 321
assess if water was contaminated, so it was not quantifiably measured. Research suggests that 322
Ae. aegypti prefer “clean water,” but this is a relative designation, as some nutrients in the water 323
may support mosquito populations [7]. Contaminated water may have organic components 324
within the container that promote algal growth and support mosquito proliferation. Water that is 325
contaminated is likely to be untouched and stagnant, allowing Ae. aegypti to lay eggs and 326
develop as opposed to cleaner water which may be flushed more often [7]. This finding is 327
corroborated by previous studies that have noted that poor sanitation and water storing habits 328
provide viable habitats for Ae. aegypti[3]. 329
These results suggest that trash collection services targeting large solids, and monitoring of 330
containers that could serve as juvenile mosquito habitat contribute to suppressing Ae. aegypti 331
pupal proliferation and consequent adult mosquito densities. These predictive models provide 332
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household factors of interest that could be included in future surveys to test hypotheses or 333
assessed in rigorous causal models. 334
Limitations and future directions 335
For the top household model, the mean error was high (10 pupae off of the true value) relative to 336
the mean pupal index (11.0836). However, the standard deviation of the data is 10.263 indicating 337
that the high error is due to the relatively high variance of the data, and the maximum pupal 338
count is 253. Overdispersion and high variance are common in insect count data, therefore these 339
results remain valid [15]. 340
There were months without sampling in each of the years for each of the three parts of 341
Guayaquil; however, they did not share the same months missing in each area, so it was not 342
possible to address this through a time-series analysis to account for the repeated measurements 343
on households. Predictive modeling has limitations. Best subset selection assesses 2p models, 344
where p indicates the number of parameters, making the implementation of every interaction 345
computationally infeasible when the number of parameters is large. Using previous literature, we 346
assessed the most pertinent interactions and limited our model variable subset selection to 221 347
models (Fig. S1). This study could be improved with the inclusion of zero APC households to 348
differentiate between containers and households that have zero mosquito pupae compared with 349
those that have positive counts. Additionally, a longitudinal study, as opposed to the cross-350
sectional study design here, could track temporal dynamics in pupae populations. With a 351
longitudinal study, a time series analysis would be able to assess changing exposures to vector 352
control methods and the environment and any subsequent changes in mosquito populations. 353
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Future studies could correlate pupae counts with household demographics such as age and sex of 354
inhabitants. Noting behavioral differences across these characteristics could also inform efforts 355
for reducing mosquito proliferation and arbovirus spread. Additionally, further studies should 356
compare our estimates of household factors in Guayaquil to those in more rural settings. 357
Household risk factors such as water service interruption and temephos use may have a larger 358
impact in more rural areas, where water interruptions may be more frequent. A similar study 359
placed on an urban-to-rural gradient may help capture these effects. Additionally, dengue 360
serological data could be incorporated to assess correlation between household risk factors and 361
past exposure to dengue, which would be closer to the health endpoint and valuable for the 362
public health sector. Random-effects modeling may further assess our covariates and outcomes 363
with contextual understanding of variable distributions between and within households. Lastly, 364
an understanding of competing dynamics between Ae. aegypti and other species of mosquitoes 365
for habitat, breeding, and feeding would provide further context for targeted interventions in 366
areas where multiple species co-exist. 367
Conclusion 368
The results of this study indicate that household factors influenced Ae. aegypti pupae 369
proliferation from 2013 to 2016 in Guayaquil, Ecuador. The most notable household-level risk 370
factors for pupae proliferation were the use of bed canopies, unemployment, and water volume in 371
artificial containers, as well as precipitation with two-week lag in conjunction with large solid 372
collection. The positive association of use of bed canopies may indicate areas with high 373
mosquito populations of several species including Ae. aegypti. Having access to municipal 374
services like bulky item pickup was protective against mosquito proliferation in households 375
while higher levels of unemployment had the opposite effect indicating that lower 376
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20
socioeconomic neighborhoods have a distinct relationship with Ae. aegypti pupal proliferation 377
that requires further exploration. The positive relationship between bed canopy usage and pupal 378
density is counterintuitive, and may be related to a false sense of security or other factors that put 379
households at risk for high mosquito densities. The impact of rainfall on mosquito density is 380
multifaceted as it may both create new mosquito habitat and “wash out” existing habitat. 381
Providing services and social/technical interventions focused on monitoring and eliminating 382
breeding sites may be important for reducing aquatic-stage mosquito densities in households at 383
risk for Ae. aegypti-transmitted diseases. The development of Ae. aegypti prediction models 384
contributes to public health efforts in Ecuador by providing information to optimize interventions 385
for reducing mosquito densities and preventing dengue outbreaks. 386
Declarations: 387
Ethics approval & Consent to participate 388
This research is IRB exempt since it involves non-human and animal subjects. 389
Consent for publication 390
Not applicable. 391
Availability of data & materials 392
The datasets generated and analyzed during the current study are not publicly available as they 393
are the property of the Centro de Investigación en Vectores Artrópodos, Instituto Nacional de 394
Investigación en Salud Pública "Dr. Leopoldo Izquieta Pérez", Quito, Ecuador. Data are however 395
available from the corresponding author on reasonable request. 396
Competing Interests 397
The authors declare that they have no competing interests. 398
Funding 399
The data collection for this work was supported by the Secretaria de Educación Superior, 400
Ciencia, Tecnología e Innovación (SENESCYT), Proyecto PIC-12-INHMT-002 (Grant PIC-12-401
INH-002). This research was supported by a 2019 Seed Fund award from Tecnológico de 402
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21
Monterrey and CITRIS and the Banatao Institute at the University of California and the Center 403
for Global Public Health (CGPH) at University of California, Berkeley. 404
Authors’ contributions 405
TH developed the hypothesis, analyzed and interpreted the entomological data and authored 406
most of the manuscript. TM assisted in the analysis and conception of the research idea, created 407
the geographical figures and was a main contributor in writing the manuscript. KL performed the 408
geographical analyses to utilize for the geospatial figures. PP and VC guided the main data 409
collection for this project and assisted in the conception of research idea as well as provided 410
guidance on interpretation of the results for the paper. JM provided statistical guidance and was 411
also a contributor in writing the manuscript. All authors read and approved the final manuscript. 412
Acknowledgements 413
We thank Dr. Hsiang-Yu Yuan for helpful comments on the statistical aspects of the manuscript. 414
415
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22
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478
479
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Supplementary Material 480
Table S1. Full list of household candidate variables used to find the best model by AICc. 481
482
483
484
485
486
Full List of Possible Model Variables Variable Description
Illiteracy A relative subcircuit index on illiteracy
Unemployment A relative subcircuit index on unemployment
Overcrowding A relative subcircuit index on overcrowding
Number of Children Number of children (<18 years) residing in the household
Number of Adults Number of adults ( ≥ 18 years) residing in the household
Water Interruption Whether or not the household experience a water service
interruption in the last 24 hours
Duration of Water Interruption Duration of the water service interruption
Trash Collection Service Number of times garbage collections occurs at the household per
week
Large Solid Collection Service Municipal service of collecting large furniture, tires or other
items
Sewer Connection Whether or not there is a sewer connection to the household’s
waste system
Fumigation in Past Weeks Spraying of deltamethrin, an insecticide applied inside the house
in the last 1-4 weeks
Abate in Past Weeks Also known as temephos, an organophosphate larvicide which is
applied by 20 grams of granular product per 189 liters
Biolarvicide in Past Weeks Bacillus thuringiensis used to target the larval stage of a
mosquito
Bed Canopy Use The usage of a canopy over a bed
Protective Window/Door Mesh Mesh present around doors and windows
Volume of Breeding Site Average dimensions of household breeding sites
Water Volume of Breeding Site Total water volume in all household breeding containers divided
by the number of breeding sites
Current Dengue Case Household case of dengue at time of interview
Dengue in the Last Month Household case of dengue within the last month at time of
interview
Precipitation at week 0 Rainfall during the week of the household interview
Week 1 Precipitation Lag Rainfall during the week prior to the household interview
Week 2 Precipitation Lag Rainfall two weeks prior to the household interview
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25
Table S2. Full list of container-level candidate variables used to find the best model by AICc. 487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
Full List of Possible Model
Variables
Variable Description
Outside Location The breeding site is within the walls of the house or outside the walls of
the house
Turbid Water Cloudy water or water containing particulates
Sewer Part Of or relating to a sewer, waterway, storm drain or manhole
Bucket Part Of or relating to a bucket or bucket cap
Bamboo Natural material bamboo
Trash Of or relating to trash (i.e. bag, Styrofoam box, cistern)
Toilet Of or relating to toilets (i.e. indoor toilet, temporary toilet)
Small Liquid Container Smaller artificial containers (i.e. flask, bottle, cup, jar, bowl, pot)
Tank A tank used for storage or part of household utility system
Vase A flower vase
Furniture Household furniture items (i.e. table, drawer, chair, refrigerator)
Car Part A car fender or a wheel rim
Sink Any appliance used as a sink (i.e. Outside sink, washbasin, laundry sink)
Pool Large recreational area for swimming
Tub Bathing tub
Barrel Often used for water storage practices
Plastic Material
Metal Material
Cement Material
Rubber Material
Ceramic Material
Glass Material
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26
Table S3. Variance Inflation Factors (VIF) assess multicollinearity found that (outside of 504
interaction terms) there are no VIF scores >2. 505
Variable VIF
Large Solid Collection 1.519
Unemployment 1. 047
Water Volume 1.250
Canopy Use 1.519
Precipitation at Week 0 1.355
Precipitation at Week 2 Lag 14.225
Large Solid Collection * Week 2 Lag 14.612
506
507
508
509
510
511
512
513
514
515
516
517
518
519
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27
Table S4. Full list of top candidate models for artificial breeding sites. Top models included have 520
∆AICc < 1. 521
Model df LogLik ΔAICc
Car Parts + Contaminated Water + Furniture + Ceramic Material + Glass Material + Metal Material + Plastic Material +
Sewer + Vase
11 -3459.19 0
Car Parts + Contaminated Water + Ceramic Material + Glass Material + Metal Material + Plastic Material + Sewer + Vase 10 -3460.23 0.03
Bamboo + Car Parts + Contaminated Water + Furniture + Ceramic Material + Glass Material + Metal Material + Plastic
Material + Sewer + Vase
12 -3458.20 0.08
Bamboo + Car Parts + Contaminated Water + Ceramic Material + Glass Material + Metal Material + Plastic Material +
Sewer + Vase
11 -3459.24 0.11
Bucket Part + Car Parts + Contaminated Water + Ceramic Material + Glass Material + Metal Material + Plastic Material +
Sewer + Tub + Vase
12 -3458.28 0.24
Bucket Part + Car Parts + Contaminated Water + Ceramic Material + Glass Material + Metal Material + Plastic Material +
Sewer + Vase
11 -3459.33 0.27
Bamboo + Bucket Part + Car Parts + Contaminated Water + Ceramic Material + Glass Material + Metal Material + Plastic
Material + Tub + Vase
13 -3457.31 0.35
Bamboo + Bucket Part + Car Parts + Contaminated Water + Ceramic Material + Glass Material + Metal Material + Plastic
Material + Vase
12 -3458.35 0/37
Bucket Part + Car Parts + Contaminated Water + Furniture + Ceramic Material + Glass Material + Metal Material + Plastic
Material + Vase
12 -3458.39 0.45
Bamboo + Bucket Part + Car Parts + Contaminated Water + Furniture + Ceramic Material + Glass Material + Metal Material
+ Plastic Material + Vase
13 -3457.41 0.55
Car Part + Dirty Water + Ceramic Material + Glass Material + Metal Material + Plastic Material + Sewer + Tub + Vase 11 -3459.49 0.60
Bucket Part + Car Part + Dirty Water + Furniture + Ceramic Material + Glass Material + Metal Material + Plastic Material +
Sewer + Tub + Vase
13 -3457.43 0.60
Bamboo + Car Part + Dirty Water + Ceramic Material + Glass Material + Metal Material + Plastic Material + Sewer + Tub +
Vase
12 -3458.51 0.69
Car Part + Dirty Water + Furniture + Ceramic Material + Glass Material + Metal Material + Plastic Material + Sewer + Tub
+ Vase
12 -3458.51 0.69
Bamboo + Bucket Parts + Car Part + Contaminated Water + Furniture + Ceramic Material + Glass Material + Metal Material
+ Plastic Material + Sewer + Tub + Vase
14 -3456.46 0.71
Contaminated Water + Cement Material + Ceramic Material + Glass Material + Sewer + Vase 8 -3462.64
0.77
Bamboo + Car Part + Contaminated Water + Furniture + Ceramic Material + Glass Material + Metal Material + Plastic
Material + Sewer + Tub + Vase
13 -3457.52 0.78
Car Part + Contaminated Water + Furniture + Cement Material + Ceramic Material + Glass Material + Sewer + Vase 10
-3460.61 0.79
Contaminated Water + Furniture + Cement Material + Ceramic Material + Glass Material + Sewer + Vase 9 -3461.67 0.86
Car Part + Contaminated Water + Cement Material + Ceramic Material + Glass Material + Sewer + Vase 9 -3461.67 0.87
Bamboo + Car Part + Contaminated Water + Furniture + Cement Material + Ceramic Material + Glass Material + Metal
Material + Plastic Material + Sewer + Vase
13 -3457.58 0.88
Bamboo + Car Part + Contaminated Water + Cement Material + Ceramic Material + Glass Material + Metal Material +
Plastic Material + Sewer + Vase
12 -3458.62 0.91
Bucket Part + Contaminated Water + Cement Material + Ceramic Material + Glass Material + Sewer + Vase 9 -3461.73 1.00
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28
Fig. S1. Model selection table where the top axis are all the candidate variables for pupal index 522
(Table S1) and the y-axis represents the frequency the variables were selected in the top 523
candidate models with ∆AICc < 2 (31 models shown). 524
525
526
527
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Fig S2. Model selection table where the top axis are all the candidate variables for pupal sum in 528
containers (Table S2) and the y-axis represents the frequency the variables were selected in the 529
top candidate models with ∆AICc < 1 (23 models total shown). 530
531
532
533
534
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Fig S3. Pupal index measurements (A) and prediction error heatmaps (B) based on the final 535
model. Weighted by value with a bandwidth of 1 km. Error is the difference between the data 536
and the model for each household’s characteristics. 537
538
539
540
541
542
543
544
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Fig S4. Relative error map for pupal index prediction based on the final model. Each dot 545
indicates the relative prediction error of that household, i.e. the difference between data and 546
model for each household’s characteristics divided by the data. 547
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.CC-BY-NC-ND 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.23.391938doi: bioRxiv preprint