Household-Level Risk Factors for Aedes aegypti Pupal Density in Guayaquil… · 2020. 11. 23. · 77 the city of Guayaquil, Ecuador’s largest and most populous city, its most important

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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

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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


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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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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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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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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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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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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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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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


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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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

548

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Household-Level Risk Factors for Aedes aegypti Pupal Density in Guayaquil… · 2020. 11. 23. · 77 the city of Guayaquil, Ecuador’s largest and most populous city, its most important