Content uploaded by José Guillermo Juárez
Author content
All content in this area was uploaded by José Guillermo Juárez on May 17, 2021
Content may be subject to copyright.
Content uploaded by José Guillermo Juárez
Author content
All content in this area was uploaded by José Guillermo Juárez on May 08, 2021
Content may be subject to copyright.
RESEARCH ARTICLE
Global patterns of aegyptism without
arbovirus
Mark F. Olson
1
, Jose G. JuarezID
1
, Moritz U. G. Kraemer
2
, Jane P. MessinaID
3
, Gabriel
L. HamerID
1
*
1Department of Entomology, Texas A&M University, College Station, Texas, United States of America,
2Department of Zoology, University of Oxford, Oxford, United Kingdom, 3School of Geography and the
Environment, and Oxford School of Global and Area Studies, University of Oxford, Oxford, United Kingdom
*ghamer@tamu.edu
Abstract
The world’s most important mosquito vector of viruses, Aedes aegypti, is found around the
world in tropical, subtropical and even some temperate locations. While climate change may
limit populations of Ae.aegypti in some regions, increasing temperatures will likely expand
its territory thus increasing risk of human exposure to arboviruses in places like Europe,
Northern Australia and North America, among many others. Most studies of Ae.aegypti biol-
ogy and virus transmission focus on locations with high endemicity or severe outbreaks of
human amplified urban arboviruses, such as dengue, Zika, and chikungunya viruses, but
rarely on areas at the margins of endemicity. The objective in this study is to explore previ-
ously published global patterns in the environmental suitability for Ae.aegypti and dengue
virus to reveal deviations in the probability of the vector and human disease occurring. We
developed a map showing one end of the gradient being higher suitability of Ae.aegypti with
low suitability of dengue and the other end of the spectrum being equal and higher environ-
mental suitability for both Ae.aegypti and dengue. The regions of the world with Ae.aegypti
environmental suitability and no endemic dengue transmission exhibits a phenomenon we
term ‘aegyptism without arbovirus’. We then tested what environmental and socioeconomic
variables influence this deviation map revealing a significant association with human popula-
tion density, suggesting that locations with lower human population density were more likely
to have a higher probability of aegyptism without arbovirus. Characterizing regions of the
world with established populations of Ae.aegypti but little to no autochthonous transmission
of human-amplified arboviruses is an important step in understanding and achieving aegyp-
tism without arbovirus.
Author summary
The preeminent vector of arboviruses, Aedes aegypti, is distributed globally and capable of
transmitting deadly pathogens to over half the world’s population. While most studies
focus on areas where Ae.aegypti and human-amplified urban arboviruses such as dengue
and Zika viruses are locally established, our study explores the margins of endemicity
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0009397 May 5, 2021 1 / 12
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Olson MF, Juarez JG, Kraemer MUG,
Messina JP, Hamer GL (2021) Global patterns of
aegyptism without arbovirus. PLoS Negl Trop Dis
15(5): e0009397. https://doi.org/10.1371/journal.
pntd.0009397
Editor: Mariangela Bonizzoni, Universita degli Studi
di Pavia, ITALY
Received: August 28, 2020
Accepted: April 19, 2021
Published: May 5, 2021
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pntd.0009397
Copyright: This is an open access article, free of all
copyright, and may be freely reproduced,
distributed, transmitted, modified, built upon, or
otherwise used by anyone for any lawful purpose.
The work is made available under the Creative
Commons CC0 public domain dedication.
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
Information files.
Funding: This publication was supported by
Cooperative Agreement Number U01CK000512,
where Aedes aegypti can be found, but arboviral illness is rare. These areas where we find
environmental suitability for the vector but an absence of established arboviral transmis-
sion we term ‘aegyptism without arbovirus’. This builds on the long-held observation of
‘anophelism without malaria’ in which some regions having Plasmodium-competent
Anopheles spp. mosquitoes but not the associated human malaria. This study uses previ-
ously published maps to reveal locations with higher suitability of Ae.aegypti and low suit-
ability of dengue. Additionally, we analyzed the resulting map of deviations between Ae.
aegypti and dengue and found significant associations with human population density,
infant mortality rate, temperature, and precipitation. To the best of our knowledge, this is
the first study to characterize places around the world that exhibit ‘aegyptism without
arbovirus’ which is an important first step in our ongoing battle with human-amplified
urban arboviruses.
Introduction
Over half the world’s population lives in areas at risk of human-amplified urban arboviruses
transmitted by Aedes aegytpi mosquitoes [1]. In addition to chikungunya, yellow fever, and
Zika viruses, Ae.aegypti is the primary vector for dengue virus which infects an estimated 390
million individuals each year [2], with 100 million of those being symptomatic [3]. While great
strides have been made in vector surveillance and control through conventional, biological,
and genetic approaches, and vaccine development is ongoing [see 4], dengue transmission is
expected to persist and in some regions expand while other regions contract [1].
Many studies have identified environmental, meteorological, and demographic factors
related to vector populations and arboviral transmission such as human population density,
climate, normalized difference vegetation index (NDVI), and gross domestic product (GDP)
[5,6]. More recent research has considered the impact of socio-economic status [7] and urban-
ization including urban heat islands [8] on risk of increased dengue transmission [9,10].
Understandably, studies tend to be conducted in locations of high endemicity for arboviral dis-
ease transmission or where recent outbreaks have occurred. Rarely have studies evaluated
landscape influences of Ae.aegypti populations or arbovirus transmission in locations repre-
senting the margins of endemicity. We recently conducted a study in South Texas where large
populations of Ae.aegypti occur yet local transmission of human-amplified urban arboviruses
is rare, and we discovered high rates of non-human feeding by Ae.aegypti [11]. These wasted
bites on non-amplification hosts likely reduced R
0
for ZIKV limiting local transmission to 10
human cases between 2016–2017. In contrast, Tamaulipas, the Mexican state across the bor-
der, reported 16,835 cases in the same period. The lower availability of humans to Ae.aegypti
and associated utilization of non-human hosts is one of several mechanisms for a phenomenon
we term ‘aegyptism without arbovirus’; defined as the occurrence of established Ae.aegypti
populations without endemic human-amplified urban arboviruses. This context is similar to
the long-held observation of ‘anophelism without malaria’ [12,13], where researchers in the
1920s started to notice and understand the mechanisms of some regions having Plasmodium-
competent Anopheles spp. mosquitoes but not the associated human malaria. The objective of
this study is to explore the global patterns of environmental suitability for Ae.aegypti and den-
gue to characterize the deviations in these predictions. We addressed this objective by develop-
ing a map predicting a gradient ranging from higher environmental suitability for Ae.aegypti
but low suitability for dengue to the other end of the spectrum where areas have similar and
higher suitability for both Ae.aegypti and dengue. Our analysis is based upon previously
PLOS NEGLECTED TROPICAL DISEASES
Global patterns of aegyptism without arbovirus
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0009397 May 5, 2021 2 / 12
funded by the Centers for Disease Control and
Prevention (G.L.H.; www.cdc.gov). Its contents are
solely the responsibility of the authors and do not
necessarily represent the official views of the
Centers for Disease Control and Prevention or the
Department of Health and Human Services.
Additional support came from NIH K01AI128005
(G.L.H.; www.nih.gov). The funders had no role in
study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
published data estimating global environmental suitability for Ae.aegypti [14] and dengue [1].
We used these suitability maps projected to 5 km
2
grids to then further calculate deviations in
Aedes aegypti and dengue suitability. We then identify environmental, meteorological, and
demographic factors associated with this gradient in the deviation between Ae.aegypti and
dengue suitability to explore the social-ecological factors driving aegyptism without arbovirus.
Materials and methods
Deviation between the probability of occurrence of Aedes aegypti and
dengue
This study utilized the 2015 global probability of occurrence for Ae.aegypti based on a mos-
quito database and environmental variables predicting their global distribution [14]. We also
used the 2015 global probability of dengue occurrence which was based on an ecological niche
model of human cases to predict environmental suitability [1]. It is important to note that
these maps are predictions of environmental suitability, not occurrence of Aedes aegypti or
dengue. To compare the global pattern of Ae.aegypti and dengue suitability we performed ras-
ter calculations in QGIS (version 3.10.1-A Coruña). Both the Ae.aegypti environmental suit-
ability map and the global probability of dengue suitability are at 5 km
2
resolution. We
removed all cells where either Ae.aegypti or dengue suitability were <0.1 to filter out locations
where environmental suitability for Ae.aegypti or dengue virus is extremely low (e.g. Green-
land and Arctic locations). To create a map that illustrates where Ae.aegypti and dengue devi-
ate spatially, we generated an initial raster that calculated “Ae.aegypti” minus “dengue”. This
procedure removed all pixels where an interaction between Ae.aegypti and dengue did not
occur. This resulting Ae.aegypti minus dengue raster (‘Uncorrected deviation layer’) produced
one end of the spectrum with a suitable environment for Ae.aegypti but low suitability for den-
gue and the other end of the spectrum included an equal suitability for both Ae.aegypti and
dengue. The problem with this later end was that areas of the world with near zero suitability
for both Ae.aegypti and dengue were indifferent from areas with high suitability for Ae.aegypti
and dengue. To account for this, we created seven unique raster’s that would incrementally
remove areas with lower dengue environmental suitability according to the gradient levels in
Table 1. These rasters were merged to develop an image that encompasses the deviation
between Ae.aegypti probability of occurrence and dengue environmental suitability. Briefly, to
create Level 1, we performed the raster calculation: (“Uncorrected deviation layer” -0.5)
AND (“Dengue 2015 filtered” 0.5). This level represents areas with similar and high
Table 1. Correction to the deviation between Ae.aegypti and dengue map by clipping out respective areas with a lower probability of dengue environmental
suitability.
Level Uncorrected deviation in Ae.
aegypti and dengue
Clip areas these values for the
probability of dengue suitability
Corrected deviation in Ae.aegypti
and dengue (range)
Description
1-0.5 0.8 - 0.5–0.19 Remaining cells only have higher
dengue suitability
2-0.35 0.75 -0.35–0.24
3-0.2 0.7 -0.20–0.27
4-0.05 0.65 -0.05–0.32 Remaining cells have medium-
higher dengue suitability
50.1 0.6 0–0.36
60.25 0.55 0–0.41
70.4 0.5 0–0.48 Remaining cells have lower-higher
dengue suitability
https://doi.org/10.1371/journal.pntd.0009397.t001
PLOS NEGLECTED TROPICAL DISEASES
Global patterns of aegyptism without arbovirus
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0009397 May 5, 2021 3 / 12
environmental suitability of both Ae.aegypti and dengue. This same procedure was used to
create the remaining levels in Table 1. Because our focus is on aegyptism without arbovirus,
we filtered the deviation raster to only include values 0 (to exclude areas where dengue envi-
ronmental suitability was greater than Ae.aegypti) (Fig 1).
Socio-ecological patterns in the deviation between Ae.aegypti and
arbovirus
To identify environmental, meteorological, and demographic factors relating to the deviations
between Ae.aegypti probability of occurrence and dengue environmental suitability we gath-
ered several global datasets. Human population density maps and subnational infant mortality
rates, both of 2015, were obtained through NASA’s SEDAC website [15]. Infant mortality rate
is defined as the number of children who die before their first birthday per 1000 live births.
Infant mortality rate (IMR) is often used as an indicator for poverty [16] and dengue infection
during pregnancy has been linked to increased risk of infant mortality, among other adverse
health outcomes [17]. IMR data was available from 234 countries, with 143 of those countries
reporting subnational units at the 30 arc-second (approximately 1 km
2
) resolution [18]. A
global map of total gross domestic product (GDP) per capita data at 30 arc-sec resolution for
2015 was obtained from Kummu et al. [19]. Total GDP per 5 x 5 km
2
cell was estimated by
multiplying per capita GDP by gridded human population data [19]. Global precipitation and
temperature rasters at 30 arc-sec spatial resolution were obtained from worldclim.org [20].
These rasters represent average monthly data from 1970 to 2000 and are separated by month.
We combined the 12 monthly rasters to create one annual mean temperature raster, and a
cumulative annual precipitation raster. While combining the seasonal data into an annual met-
ric loses the opportunity to investigate temporal heterogeneity, we did this to facilitate this
exploratory analysis on a global scale. We hypothesize that temperature and precipitation will
Fig 1. Deviation between Ae.aegypti and dengue environmental suitability. Green indicates areas where Ae.aegypti is likely to be found, but the environment is not
considered suitable for dengue transmission (e.g. Southern United States, Northern Argentina, Northern Australia). White indicates areas where the environmental
suitability of Ae.aegypti and dengue is similar and higher. Inset histogram provides distribution of the corrected deviation values. The map was created by the author
using QGIS 3.10 (https://qgis.org/en/site/) with public domain map data from Natural Earth (https://www.naturalearthdata.com/downloads/50m-physical-vectors/) and
U.S. Geological Survey (https://woodshole.er.usgs.gov/pubs/of2005-1071/data/background/us_bnds/state_boundsmeta.htm).
https://doi.org/10.1371/journal.pntd.0009397.g001
PLOS NEGLECTED TROPICAL DISEASES
Global patterns of aegyptism without arbovirus
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0009397 May 5, 2021 4 / 12
have an inverse relationship, where higher annual average temperatures and higher cumulative
rainfall will be correlated with a lower deviation value on the scale of equal and greater proba-
bility of Ae.aegypti occurrence without dengue environmental suitability. We also hypothesize
that elevation will be positively correlated with aegyptism without arbovirus. Cells with miss-
ing values were removed across all layers before performing the analysis.
We used a gradient boosting machine (GBM) approach with a Gaussian distribution to
evaluate how the corrected deviation probability of Ae.aegypti and dengue is influenced by
human population density, temperature, precipitation, IMR, GDP and elevation. Regression
trees were fitted using a learning rate of 0.001, 5-fold cross validation and 10,000 trees to mini-
mize the mean squared error (MSE) loss function [21]. Each tree was iteratively improved
using a stepwise manner reducing the variation in the response variable. Models were gener-
ated using the “gbm” package in R [22]. Subsequently, we used generalized additive models
(GAM) for count data (Poisson) to determine which effect variables (human population den-
sity, temperature, precipitation, IMR, GDP and elevation) best explain the variation of the cor-
rected deviation probability. Smoothing terms were evaluated based on their estimated
degrees of freedom (edf). We used the adjusted R
2
value to determine the best fit model struc-
ture. All statistical analyses were conducted in R version 3.5.1 [23] using RStudio version
1.1.456 [24]. To import and analyze rasters in R, we utilized the packages of ‘raster’, ‘dplyr’,
‘mgcv’, and ‘ggplot2’ [25].
Results
Deviation between the probability of occurrence of Aedes aegypti and
dengue environmental suitability
A map was generated showing global deviations between the probability of Ae.aegypti and
dengue habitat suitability (Fig 1). Values range from 0 (equal and higher environmental suit-
ability for both Ae.aegypti and dengue) to 0.48 (higher suitability of Ae.aegypti with low suit-
ability of dengue) with a mean of 0.07 (residual standard error: 0.09). For example, a 5 x 5 km
2
area that has a 0.78 probability of occurrence for Ae.aegypti but only a 0.3 environmental suit-
ability for dengue would have a deviation value of 0.48. The mean deviation value for South
Africa, United States, and Australia is 0.18, 0.16, and 0.13, respectively. Locations with a lower
probability of aegyptism without arbovirus include Mexico, Thailand and Guatemala which
have a mean deviation value of 0.04, 0.02 and 0.01, respectively. We report the mean deviation
values for each country in S2 Table which range from 0 to 0.27.
Socio-ecological patterns in the deviation between Ae.aegypti and dengue
The gradient boosting machine (GBM) full model resulted in an MSE of 0.084. The indepen-
dent variable contributing the most to explaining the variation in the dependent variable was
human population density (38.475) and the variable with least relative influence was elevation
(0.067). After removing the elevation data, GBM was conducted again on the remaining vari-
ables in a stepwise fashion (Table 2). A generalized additive model revealed model 3 to have
the best-fit with an R
2
value of 0.152 (Table 3). Statistically significant effects were found
between the corrected deviance raster and human population density, IMR, temperature, and
precipitation as the smooth terms. The human population density layer had a range of 0 to
119,921 persons per km
2
and a mean of 135.93 (residual standard error: 0.09) persons per km
2
.
(Table 4).
The subnational IMR ranged from a low of 0.24 to a high of 142.93 and a mean of 35.63
(infant deaths per 1,000 live births) (residual standard error: 0.09). Using human population
PLOS NEGLECTED TROPICAL DISEASES
Global patterns of aegyptism without arbovirus
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0009397 May 5, 2021 5 / 12
density, temperature and precipitation as smoothing terms, the parametric coefficient for IMR
was 3.002e-04 (±3.067e-06 SE; p <0.001). Mean annual temperatures ranged from 6.98˚C to
31.21˚C throughout the range covered by the deviation raster, with a mean of 25.31˚C (resid-
ual standard error: 0.09 on 1,207,056 degrees of freedom). The parametric coefficient for tem-
perature was 1.347e-03 (±4.233e-05 SE; pr (>|t|) = <2e-16). Precipitation had a range of 4 to
9,083 mm rainfall and a global mean of 1,550.18 mm (residual standard error: 0.09 on
1,207,195 degrees of freedom). The parametric coefficient for precipitation, using human pop-
ulation density, temperature and IMR as smoothing terms, was -1.530e-05 (±1.127e-07 SE; pr
(>|t|) = <2e-16).
Discussion
Aedes aegypti has proliferated in urban areas around the globe in the last century. While ubiq-
uitous in many tropical and subtropical urban areas, some locations infested with Ae.aegypti
do not exhibit high levels of human-amplified urban arboviral transmission as in other areas.
This study built on previous studies mapping the global suitability of Ae.aegytpi and dengue to
generate a map of deviation values including the observation of aegyptism without arbovirus.
We produced a global map showing this gradient from high suitability for Ae.aegypti but low
suitability for dengue to the other end of the spectrum where areas have similar and higher
suitability for both Ae.aegypti and dengue. We show that some countries on the margins of
endemicity of human-amplified arboviruses have a higher deviation value compared to highly
endemic countries. For example, the U.S. and Argentina, both countries with occasional
autochthonous transmission of dengue virus [26–28] have mean deviation values of 0.16 and
0.18, respectively (S2 and S3 Figs). These higher values along this spectrum are more represen-
tative of aegyptism without arbovirus. This is also corroborated by empirical data showing that
even in areas with high abundances of Ae.aegypti, low human feeding diminishes the risk of
Zika virus transmission [11,29]. Likewise, two major urban centers of Kenya exhibit higher
values of aegyptism without arbovirus while Mombasa, a coastal city in the same country has
frequent dengue epidemics (S4 Fig) [29]. Countries highly endemic for dengue, such as Hon-
duras and Thailand, have mean deviation values of 0.038 and 0.023, respectively, which are val-
ues representing the regions with higher suitability for both Ae.aegypti and dengue. We
Table 2. Gradient Boosting Machine (GBM) to determine best-fit model. Abbreviated variable names include human population density (pop), gross domestic product
(gdp), infant mortality rate (imr), annual mean temperature (temp), annual cumulative precipitation (prec), elevation (elev).
Dependent variable Independent variables Greatest relative influence (value) Least relative influence (value)
amd pop, gdp, imr, temp, prec, elev pop (38.475) elev (0.067)
amd pop, gdp, imr, temp, prec pop (38.460) gdp (2.276)
amd pop, imr, temp, prec temp (44.382) imr (10.905)
amd pop, temp, prec temp (48.580) prec (22.376)
amd pop, temp temp (68.744) pop (31.256)
https://doi.org/10.1371/journal.pntd.0009397.t002
Table 3. Results of Generalized Additive Model (GAM). Family: gaussian; link function: identity.
Model Formula Adjusted R
2
Deviance explained
1amd ~ s(pop) + s(gdp) + s(imr) + s(temp) + s(prec) + s(elev) 0.115 11.5%
2amd ~ s(pop) + s(gdp) + s(imr) + s(temp) + s(prec) 0.107 10.7%
3amd ~ s(pop) + s(imr) + s(temp) + s(prec) 0.152 15.2%
4amd ~ s(pop) + s(temp) + s(prec) 0.138 13.8%
5amd ~ s(pop) + s(temp) 0.113 11.3%
https://doi.org/10.1371/journal.pntd.0009397.t003
PLOS NEGLECTED TROPICAL DISEASES
Global patterns of aegyptism without arbovirus
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0009397 May 5, 2021 6 / 12
identified a significant association between human population density and the deviation in
environmental suitability of Ae.aegypti and dengue. Locations with higher deviation values
had lower human population densities. This means that regions of the world with aegyptism
without arbovirus are more likely to be lower human population densities compared to regions
with more equal and higher probabilities of Ae.aegypti and dengue. It was surprising to see
that GDP did not have a significant effect on the deviation values. Åstro¨m et al. modeled vari-
ous scenarios of dengue distribution according to climate and socioeconomic change, finding
a beneficial, protective effect from increasing GDP [30]. Locations with higher GDP would
presumably have better access to piped water, screened windows and possibly air conditioning,
factors which could reduce arboviral transmission [31]. In addition to GDP, Kummu et al. also
mapped a human development index (HDI) which is composed of the achievement of several
key development indicators, and this may be a better predictor of deviation. Interestingly, the
deviation values for aegyptism without arbovirus were positively correlated to infant mortality
rates. We expected to see higher deviation values representing aegyptism without arbovirus in
places with lower IMR, but this wasn’t the case. One potential explanation is reporting bias
with some low-income areas having higher dengue burdens than what are reported. For exam-
ple, Africa has a wide variety of common febrile illnesses with varying etiology, thus a case of
dengue fever could be inadvertently misdiagnosed as malaria, especially in places where testing
is less than rigorous or non-existent [32]. Regions with notoriously high IMR, but where den-
gue is underreported could therefore appear to have higher presence of aegypti without
arbovirus.
Recent studies suggest that climate change, while limiting expansion of Ae.aegypti in some
locations, will likely increase the risk of human exposure in other areas like North America,
Australia and Europe [33,34]. Certainly, temperature plays an important role in its propaga-
tion [35]. Interestingly, our study found a significant relationship between temperature and
aegyptism without arbovirus, where higher average annual temperatures were associated with
higher suitability for Ae.aegypti and lower suitability of dengue. This pattern is based on aver-
age yearly temperatures and seasonality and diurnal temperature fluctuations were not consid-
ered. Carrington et al. found greater potential for dengue virus transmission in Ae.aegypti
exposed to large diurnal fluctuations at lower mean temperatures [36]. Further study on the
effects of temperature on aegyptism without arbovirus is needed. Precipitation is also a main
driver of Ae.aegypti populations as a water source is necessary for oviposition. We observed a
significant effect on deviation where lower average precipitation was associated with higher
Table 4. Results of Generalized Additive Model (GAM) for Model 3. Family: gaussian; link function: identity. (Formula: amd_r ~ s(pop_r) + s(imr_r) + s(temp_r) + s
(prec_r); n = 1,190,702).
Parametric coefficients:
Estimate Standard Error t-value pr (>|t|)
(intercept) 7.223e-02 8.197e-05 881.15 <2e-16
Approximate significance of smooth terms:
edf F p-value
s(pop) 9.000 5888 <2e-16
s(imr) 8.998 1981 <2e-16
s(temp) 8.988 10640 <2e-16
s(prec) 8.997 2597 <2e-16
<0.001
https://doi.org/10.1371/journal.pntd.0009397.t004
PLOS NEGLECTED TROPICAL DISEASES
Global patterns of aegyptism without arbovirus
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0009397 May 5, 2021 7 / 12
probability of Ae.aegypti without arbovirus disease. It is interesting to note, however, that
many locations with less than 100 mm per year in rainfall were still considered highly suitable
for Ae.aegypti. Perhaps places with little to no rainfall such as Phoenix, Arizona, are still capa-
ble of maintaining high populations of Ae.aegypti due to prolific use of water in the urban
landscape and abundant container habitat [37].
The complex nature of dengue transmission requires competent mosquito vectors and vire-
mic and susceptible humans to initiate and sustain transmission. This current study does not
explore additional factors that could influence the abundance of Ae.aegypti and the probability
of local transmission of dengue virus. For example, the endophilic behavior and propensity to
feed on humans of Ae.aegypti is known to vary [38,39]. Some dengue endemic settings have
high abundance of indoor populations [40] but in other less endemic areas, outdoor Ae.
aegypti populations are larger than indoor populations [41]. Also, this study does not consider
heterogeneity in virus importation by humans or human herd immunity. Some regions with
abundant Ae.aegypti have frequent importation of viremic humans helping to initiate local
transmission [42]. Heterogeneity in human herd immunity to dengue serotypes is also a factor
informing probability of dengue transmission [43], a factor that we have not considered.
While we are pointing out regions of the world with higher suitability of Ae.aegypti and lower
suitability of dengue virus we also acknowledge Ae.aegypti is not the only vector for dengue
virus. Multiple studies have documented dengue virus transmission in the absence of Ae.
aegypti and instead incriminate the Asian tiger mosquito, Ae.albopictus as a secondary vector
[44–47]. A future study could take a similar approach to identifying global patterns of dengue
disease in the absence of Ae.aegypti to help provide more evidence of transmission by other
vector species.
Our analysis is built upon predictions of environmental suitability of Ae.aegypti [14] and
dengue [1], which introduces sources of error and uncertainty. For example, Messina et al. [1]
global predictions of dengue includes high risk in regions such as Arkansas, USA, with values
around 0.87 (range of 0–1). There is no documented autochthonous transmission of any
human-amplified arbovirus in Arkansas in the last two centuries [48,49]. As a result of this
model’s prediction, our deviation map includes values in Arkansas from 0–0.15, that would
falsely indicate that this region has both similar and high levels of Ae.aegypti and dengue.
These anomalies likely occur elsewhere in the world with these deviation value predictions,
especially in developing countries where differential diagnosis of febrile illness in humans is
less common. At the global scale, our deviation map identifies regions around the world on
the margins of arboviral endemicity but where the environment is suitable for Ae.aegypti.
However, at a finer resolution (e.g. at the county or city level), one can find deviation values
that don’t reflect updated data documenting Ae.aegypti and human dengue cases. In the
future, this same analysis could be done with improved Ae.aegypti and human case data for
dengue or other arboviral diseases at a finer spatial scale.
In conclusion, our study identified several focal points around the globe which appear to
exhibit this phenomenon of aegyptism without arbovirus. Parts of South America, Africa,
South Europe, and North Australia appear to exhibit this same phenomenon that we find in
the United States. While Ae.aegypti is found in all of these locations and even expanding in
many areas, vector presence does not unequivocally translate to the transmission of human-
amplified urban arboviruses such as dengue. A suite of factors such as Ae.aegypti vector com-
petence, utilization of humans as hosts, and human social practices reducing contact with
mosquitoes are likely to influence the risk of arbovirus transmission. Further research to eluci-
date the underlying mechanisms which facilitate aegyptism without arbovirus is warranted.
The knowledge gained from this research will help guide scientists, public health officials and
policy makers in our ongoing battle against mosquito-borne viruses.
PLOS NEGLECTED TROPICAL DISEASES
Global patterns of aegyptism without arbovirus
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0009397 May 5, 2021 8 / 12
Supporting information
S1 Fig. Deviation between Ae.aegypti probability of occurrence and dengue environmental
suitability, zoomed in on North America, South America, South Europe and North Africa,
Africa, and Australia. The map was created by the author using QGIS 3.10 (https://qgis.org/
en/site/) with public domain map data from Natural Earth (https://www.naturalearthdata.
com/downloads/50m-physical-vectors/) and U.S. Geological Survey (https://woodshole.er.
usgs.gov/pubs/of2005-1071/data/background/us_bnds/state_boundsmeta.htm).
(TIF)
S2 Fig. Deviation between Ae.aegypti probability of occurrence and dengue environmental
suitability for the Southern United States. The map was created by the author using QGIS
3.10 (https://qgis.org/en/site/) with public domain map data from Natural Earth (https://www.
naturalearthdata.com/downloads/50m-physical-vectors/) and U.S. Geological Survey (https://
woodshole.er.usgs.gov/pubs/of2005-1071/data/background/us_bnds/state_boundsmeta.htm).
(TIF)
S3 Fig. Deviation between Ae.aegypti probability of occurrence and dengue environmental
suitability for Northern Argentina, Paraguay, and Southern Brazil. The map was created by
the author using QGIS 3.10 (https://qgis.org/en/site/) with public domain map data from Nat-
ural Earth (https://www.naturalearthdata.com/downloads/50m-physical-vectors/) and U.S.
Geological Survey (https://woodshole.er.usgs.gov/pubs/of2005-1071/data/background/us_
bnds/state_boundsmeta.htm).
(TIF)
S4 Fig. Deviation between Ae.aegypti probability of occurrence and dengue environmental
suitability for Kisumu, Nairobi and Mombasa, Kenya. The map was created by the author
using QGIS 3.10 (https://qgis.org/en/site/) with public domain map data from Natural Earth
(https://www.naturalearthdata.com/downloads/50m-physical-vectors/) and U.S. Geological
Survey (https://woodshole.er.usgs.gov/pubs/of2005-1071/data/background/us_bnds/state_
boundsmeta.htm).
(TIF)
S1 Table. Data sources for the global rasters used in this paper.
(DOCX)
S2 Table. Statistical summary of Ae.aegypti minus dengue deviation, by country. White
indicates the lower end of the spectrum, where Ae.aegypti occurrence and risk of dengue is
nearly equal and high, and green represents the other end of the spectrum where Ae.aegypti
can be found without dengue. Countries with 5 or fewer cells (5 km
2
) were removed from the
table for brevity.
(DOCX)
Acknowledgments
We thank Sarah Hamer and Micky Eubanks for providing input that helped improve the anal-
ysis and interpretation.
Author Contributions
Conceptualization: Gabriel L. Hamer.
Data curation: Mark F. Olson, Moritz U. G. Kraemer, Jane P. Messina.
PLOS NEGLECTED TROPICAL DISEASES
Global patterns of aegyptism without arbovirus
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0009397 May 5, 2021 9 / 12
Formal analysis: Mark F. Olson, Jose G. Juarez.
Methodology: Mark F. Olson, Jose G. Juarez, Jane P. Messina, Gabriel L. Hamer.
Project administration: Gabriel L. Hamer.
Resources: Moritz U. G. Kraemer, Jane P. Messina.
Supervision: Gabriel L. Hamer.
Visualization: Jose G. Juarez.
Writing – original draft: Mark F. Olson.
Writing – review & editing: Jose G. Juarez, Moritz U. G. Kraemer, Jane P. Messina, Gabriel L.
Hamer.
References
1. Messina JP, Brady OJ, Golding N, Kraemer MU, Wint GW, Ray SE, et al. The current and future global
distribution and population at risk of dengue. Nature microbiology. 2019:1. https://doi.org/10.1038/
s41564-018-0331-3 PMID: 30546101
2. Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and
burden of dengue. Nature. 2013; 496(7446):504–7. https://doi.org/10.1038/nature12060 PMID:
23563266
3. Liu Y, Lillepold K, Semenza JC, Tozan Y, Quam MB, Rocklo
¨v J. Reviewing estimates of the basic repro-
duction number for dengue, Zika and chikungunya across global climate zones. Environmental
Research. 2020:109114.
4. Durbin AP, Gubler DJ. What is the prospect of a safe and effective dengue vaccine for travellers? Jour-
nal of travel medicine. 2019. https://doi.org/10.1093/jtm/tay153 PMID: 30657935
5. Sumanasinghe N, Mikler A, Tiwari C, Muthukudage J. Geo-statistical dengue risk model using GIS tech-
niques to identify the risk prone areas by linking rainfall and population density factors in Sri Lanka. Cey-
lon Journal of Science. 2016; 45(3).
6. Cao Z, Liu T, Li X, Wang J, Lin H, Chen L, et al. Individual and interactive effects of socio-ecological fac-
tors on dengue fever at fine spatial scale: A geographical detector-based analysis. International journal
of environmental research and public health. 2017; 14(7):795. https://doi.org/10.3390/ijerph14070795
PMID: 28714925
7. Farinelli EC, Baquero OS, Stephan C, Chiaravalloti-Neto F. Low socioeconomic condition and the risk
of dengue fever: a direct relationship. Acta tropica. 2018; 180:47–57. https://doi.org/10.1016/j.
actatropica.2018.01.005 PMID: 29352990
8. Araujo RV, Albertini MR, Costa-da-Silva AL, Suesdek L, Franceschi NCS, Bastos NM, et al. São Paulo
urban heat islands have a higher incidence of dengue than other urban areas. The Brazilian Journal of
Infectious Diseases. 2015; 19(2):146–55. https://doi.org/10.1016/j.bjid.2014.10.004 PMID: 25523076
9. Teurlai M, Menkès CE, Cavarero V, Degallier N, Descloux E, Grangeon J-P, et al. Socio-economic and
climate factors associated with dengue fever spatial heterogeneity: a worked example in New Caledo-
nia. PLoS neglected tropical diseases. 2015; 9(12). https://doi.org/10.1371/journal.pntd.0004211
PMID: 26624008
10. Wijayanti SP, Porphyre T, Chase-Topping M, Rainey SM, McFarlane M, Schnettler E, et al. The impor-
tance of socio-economic versus environmental risk factors for reported dengue cases in Java, Indone-
sia. PLoS neglected tropical diseases. 2016; 10(9). https://doi.org/10.1371/journal.pntd.0004964 PMID:
27603137
11. Olson MF, Ndeffo-Mbah ML, Juarez JG, Garcia-Luna S, Martin E, Borucki MK, et al. High Rate of Non-
Human Feeding by Aedes aegypti Reduces Zika Virus Transmission in South Texas. Viruses. 2020; 12
(4):453. https://doi.org/10.3390/v12040453 PMID: 32316394
12. Jetten TH, Takken W. Anophelism without malaria in Europe. A review of the ecology and distribution of
the genus Anopheles in Europe: Unknown Publisher; 1994.
13. De Buck A, Swellengrebel N, Schoute E. Studies on anophelism without malaria in the vicinity of
Amsterdam. Bulletin of Entomological Research. 1927; 17(4):351–71.
14. Kraemer MU, Sinka ME, Duda KA, Mylne AQ, Shearer FM, Barker CM, et al. The global distribution of
the arbovirus vectors Aedes aegypti and Ae.albopictus. elife. 2015; 4:e08347. https://doi.org/10.7554/
eLife.08347 PMID: 26126267
PLOS NEGLECTED TROPICAL DISEASES
Global patterns of aegyptism without arbovirus
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0009397 May 5, 2021 10 / 12
15. CIESN. Gridded Population of the World, Version 4 (GPWv4): Population Density Adjusted to Match
2015 Revision UN WPP Country Totals 2016 [cited 2019 13 September 2019]. Available from: http://
sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-adjusted-to-2015-unwpp-country-
totals/metadata.
16. Center for International Earth Science Information Network—CIESIN—Columbia University. Documen-
tation for the Global Subnational Infant Mortality Rates, Version 2.2018 May 18, 2020. Available from:
https://doi.org/10.7927/H44J0C25.
17. Feitoza HAC, Koifman S, Koifman RJ, Saraceni V. Dengue infection during pregnancy and adverse
maternal, fetal, and infant health outcomes in Rio Branco, Acre State, Brazil, 2007–2012. Cadernos de
saude publica. 2017; 33:e00178915. https://doi.org/10.1590/0102-311X00178915 PMID: 28614453
18. Center for International Earth Science Information Network—CIESIN—Columbia University. Global
Subnational Infant Mortality Rates, Version 2. Palisades, NY: NASA Socioeconomic Data and Applica-
tions Center (SEDAC); 2019.
19. Kummu M, Taka M, Guillaume JH. Gridded global datasets for gross domestic product and Human
Development Index over 1990–2015. Scientific data. 2018; 5:180004. https://doi.org/10.1038/sdata.
2018.4 PMID: 29406518
20. Fick SE, Hijmans RJ. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas.
International journal of climatology. 2017; 37(12):4302–15.
21. Boehmke B. Gradient Boosting Machines Cincinatti, Ohio2018 [cited 2020 May 20, 2020]. Available
from: http://uc-r.github.io/gbm_regression.
22. Greenwell B, Boehmke B, Cunningham J, Developers G, Greenwell MB. Package ‘gbm’. R package
version. 2019; 2(5).
23. Team RC. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for
Statistical Computing; 2018.
24. Team R. RStudio: integrated development for R. RStudio, Inc, Boston, MA. URL: http://
wwwrstudiocom. 2015;42:14.
25. Wickham H. ggplot2: elegant graphics for data analysis: Springer; 2016.
26. Rivera A, Adams LE, Sharp TM, Lehman JA, Waterman SH, Paz-Bailey G. Travel-Associated and
Locally Acquired Dengue Cases—United States, 2010–2017. Morbidity and Mortality Weekly Report.
2020; 69(6):149. https://doi.org/10.15585/mmwr.mm6906a1 PMID: 32053577
27. Porcasi X, Rotela CH, Introini MV, Frutos N, Lanfri S, Peralta G, et al. An operative dengue risk stratifi-
cation system in Argentina based on geospatial technology. Geospatial health. 2012:S31–S42. https://
doi.org/10.4081/gh.2012.120 PMID: 23032281
28. Carbajo AE, Schweigmann N, Curto SI, De Garı
´n A, Bejara
´n R. Dengue transmission risk maps of
Argentina. Tropical Medicine & International Health. 2001; 6(3):170–83. https://doi.org/10.1046/j.1365-
3156.2001.00693.x PMID: 11299033
29. Agha SB, Tchouassi DP, Turell MJ, Bastos AD, Sang R. Entomological assessment of dengue virus
transmission risk in three urban areas of Kenya. PLoS neglected tropical diseases. 2019; 13(8):
e0007686. https://doi.org/10.1371/journal.pntd.0007686 PMID: 31442223
30. Åstro
¨m C, Rocklo
¨v J, Hales S, Be
´guin A, Louis V, Sauerborn R. Potential distribution of dengue fever
under scenarios of climate change and economic development. Ecohealth. 2012; 9(4):448–54. https://
doi.org/10.1007/s10393-012-0808-0 PMID: 23408100
31. Reiter P, Lathrop S, Bunning M, Biggerstaff B, Singer D, Tiwari T, et al. Texas lifestyle limits transmis-
sion of dengue virus. Emerging infectious diseases. 2003; 9(1):86. https://doi.org/10.3201/eid0901.
020220 PMID: 12533286
32. Jaenisch T, Junghanss T, Wills B, Brady OJ, Eckerle I, Farlow A, et al. Dengue expansion in Africa—
not recognized or not happening? Emerging infectious diseases. 2014; 20(10). https://doi.org/10.3201/
eid2010.140487 PMID: 25271370
33. Monaghan AJ, Sampson K, Steinhoff D, Ernst KC, Ebi K, Jones B, et al. The potential impacts of 21st
century climatic and population changes on human exposure to the virus vector mosquito Aedes
aegypti. Climatic change. 2018; 146(3–4):487–500. https://doi.org/10.1007/s10584-016-1679-0 PMID:
29610543
34. Ryan SJ, Carlson CJ, Mordecai EA, Johnson LR. Global expansion and redistribution of Aedes-borne
virus transmission risk with climate change. PLoS neglected tropical diseases. 2019; 13(3). https://doi.
org/10.1371/journal.pntd.0007213 PMID: 30921321
35. Brady OJ, Golding N, Pigott DM, Kraemer MUG, Messina JP, Reiner RC Jr, et al. Global temperature
constraints on Aedes aegypti and Ae. albopictus persistence and competence for dengue virus trans-
mission. Parasites & Vectors. 2014; 7(1):338. https://doi.org/10.1186/1756-3305-7-338 PMID:
25052008
PLOS NEGLECTED TROPICAL DISEASES
Global patterns of aegyptism without arbovirus
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0009397 May 5, 2021 11 / 12
36. Carrington LB, Armijos MV, Lambrechts L, Scott TW. Fluctuations at a low mean temperature acceler-
ate dengue virus transmission by Aedes aegypti. PLoS neglected tropical diseases. 2013; 7(4).
37. Monaghan AJ, Morin CW, Steinhoff DF, Wilhelmi O, Hayden M, Quattrochi DA, et al. On the seasonal
occurrence and abundance of the Zika virus vector mosquito Aedes aegypti in the contiguous United
States. PLoS currents. 2016; 8. https://doi.org/10.1371/currents.outbreaks.
50dfc7f46798675fc63e7d7da563da76 PMID: 27066299
38. Ndenga BA, Mutuku FM, Ngugi HN, Mbakaya JO, Aswani P, Musunzaji PS, et al. Characteristics of
Aedes aegypti adult mosquitoes in rural and urban areas of western and coastal Kenya. PloS one.
2017; 12(12):e0189971. https://doi.org/10.1371/journal.pone.0189971 PMID: 29261766
39. Estrada-Franco JG, Ferna
´ndez-Santos NA, Adebiyi AA, Lo
´pez-Lo
´pez MdJ, Aguilar-Dura
´n JA, Herna
´n-
dez-Triana LM, et al. Vertebrate-Aedes aegypti and Culex quinquefasciatus (Diptera)-arbovirus trans-
mission networks: Non-human feeding revealed by meta-barcoding and next-generation sequencing.
PLOS Neglected Tropical Diseases. 2020; 14(12):e0008867. https://doi.org/10.1371/journal.pntd.
0008867 PMID: 33382725
40. Ponlawat A, Harrington LC. Blood feeding patterns of Aedes aegypti and Aedes albopictus in Thailand.
Journal of medical entomology. 2005; 42(5):844–9. https://doi.org/10.1093/jmedent/42.5.844 PMID:
16363170
41. Martin E, Medeiros MC, Carbajal E, Valdez E, Juarez JG, Garcia-Luna S, et al. Surveillance of Aedes
aegypti indoors and outdoors using Autocidal Gravid Ovitraps in South Texas during local transmission
of Zika virus, 2016 to 2018. Acta tropica. 2019; 192:129–37. https://doi.org/10.1016/j.actatropica.2019.
02.006 PMID: 30763563
42. Wilder-Smith A, Gubler DJ. Geographic expansion of dengue: the impact of international travel. Medical
Clinics of North America. 2008; 92(6):1377–90. https://doi.org/10.1016/j.mcna.2008.07.002 PMID:
19061757
43. Ribeiro GS, Hamer GL, Diallo M, Kitron U, Ko AI, Weaver SC. Influence of herd immunity in the cyclical
nature of arboviruses. Current opinion in virology. 2020; 40:1–10. https://doi.org/10.1016/j.coviro.2020.
02.004 PMID: 32193135
44. Qin C, Shi P. Dengue in China: not a passing problem. Science China Life Sciences. 2014; 57
(12):1230–1. https://doi.org/10.1007/s11427-014-4783-2 PMID: 25502399
45. Arima Y, Matsui T, Shimada T, Ishikane M, Kawabata K, Sunagawa T, et al. Ongoing local transmission
of dengue in Japan, August to September 2014. Western Pacific surveillance and response journal:
WPSAR. 2014; 5(4):27. https://doi.org/10.5365/WPSAR.2014.5.3.007 PMID: 25685602
46. Gossner CM, Ducheyne E, Schaffner F. Increased risk for autochthonous vector-borne infections trans-
mitted by Aedes albopictus in continental Europe. Eurosurveillance. 2018; 23(24):1800268. https://doi.
org/10.2807/1560-7917.ES.2018.23.24.1800268 PMID: 29921345
47. Hasty JM, Felix GE, Amador M, Barrera R, Santiago GS, Nakasone L, et al. Entomological investigation
detects dengue virus type 1 in Aedes (Stegomyia) albopictus (Skuse) during the 2015–16 outbreak in
Hawaii. The American journal of tropical medicine and hygiene. 2020; 102(4):869–75. https://doi.org/
10.4269/ajtmh.19-0732 PMID: 32043443
48. Beaumier C, Garcia MN, Murray KO. The history of dengue in the United States and its recent emer-
gence. Current Tropical Medicine Reports. 2014; 1(1):32–5.
49. Gubler D. Dengue and dengue hemorrhagic fever in the Americas. Monograph on dengue/dengue hem-
orrhagic fever WHO regional publication SEARO. 1993;(22):9–22. PMID: 8012739
PLOS NEGLECTED TROPICAL DISEASES
Global patterns of aegyptism without arbovirus
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0009397 May 5, 2021 12 / 12
