## Abstract

Zika virus is a mosquito-borne pathogen that is rapidly spreading across the
Americas. Due to associations between Zika virus infection and a range of fetal
maladies^{1,2}, the epidemic trajectory of this viral
infection poses a significant concern for the nearly 15 million children
born in the Americas each year. Ascertaining the portion of this population that
is truly at risk is an important priority. One recent estimate^{3} suggested that
5.42 million childbearing women live in areas of the Americas that are
suitable for Zika occurrence. To improve on that estimate, which did not take
into account the protective effects of herd immunity, we developed a new
approach that combines classic results from epidemiological theory with
seroprevalence data and highly spatially resolved data about drivers of
transmission to make location-specific projections of epidemic attack rates. Our
results suggest that 1.65 (1.45–2.06) million childbearing women and 93.4
(81.6–117.1) million people in total could become infected before the
first wave of the epidemic concludes. Based on current estimates of rates of
adverse fetal outcomes among infected women^{2,4,5}, these results suggest that tens of
thousands of pregnancies could be negatively impacted by the first wave of the
epidemic. These projections constitute a revised upper limit of populations at
risk in the current Zika epidemic, and our approach offers a new way to make
rapid assessments of the threat posed by emerging infectious diseases more
generally.

On 1 February 2016, the World Health Organization (WHO) designated the ongoing Zika virus
epidemic in the Americas as a Public Health Emergency of International Concern (PHEIC),
defined as an ‘extraordinary event’ that ‘potentially require[s] a
coordinated international response’^{6}. This declaration acknowledges the high potential for Zika to
establish across the Americas given that its dominant vector, the *Aedes
aegypti* mosquito, is endophilic and occupies an exceptionally broad
geographical range^{7}. Concern underlying
this rare WHO declaration also stems from an association between Zika virus infection in
pregnant women and a range of adverse fetal outcomes^{2}, most notably congenital microcephaly^{1}. As of 30 June 2016, there were 1,674 confirmed
cases of microcephaly associated with Zika virus infection in five countries^{8}, and there is widespread concern that
these numbers could increase further as the virus continues to spread across the
Americas^{9}.

A number of uncertainties surround the future of the Zika epidemic in the Americas,
particularly questions about how many women may be at risk of having children with
congenital microcephaly and other adverse outcomes associated with Zika virus
infection^{10}. Of women who
become infected with Zika virus during a vulnerable stage of their pregnancy, evidence
is emerging that 1–13% may go on to develop congenital
microcephaly^{2,4,5}. However,
the number of women who become infected with Zika virus during that timeframe is
difficult to ascertain. One recent study^{3} estimated that 5.42 million births occurred in 2015 in
regions of the Americas with ‘suitability’ for Zika
‘occurrence’. Such estimates come with many caveats though, as they rely
on a relatively limited number of reported cases and apply a method based on equilibrium
assumptions to a situation involving active range expansion^{11}. Most importantly, the estimate of
5.42 million births^{3} reflects
the total population within a demarcated area and does not take into account that large
fractions of the populations in those areas may remain uninfected due to herd immunity
generated over the course of the first wave of the epidemic^{12,13}.

To quantify the potential magnitude of the ongoing Zika epidemic in terms of people who
realistically might become infected, we formulated and applied a method for projecting
location-specific epidemic attack rates on highly spatially resolved human demographic
projections^{14}. The central
concept behind our approach is that of the ‘first-wave’ epidemic. Zika and
other mosquito-borne viruses have been known to exhibit explosive outbreaks, infecting
as much as 75% of a population in a single year^{15}. Classical epidemiological theory predicts that
some proportion of a population will remain uninfected during an epidemic, because herd
immunity eventually causes the epidemic to burn out^{12}. A related prediction of this theory is that the proportion
infected before epidemic burnout (that is, the epidemic attack rate) has a one-to-one
relationship with the basic reproduction number, *R*_{0} (ref.
13). The latter quantity has a well-known
mechanistic formulation for mosquito-borne pathogens^{16} that accommodates the effects of environmental drivers on
transmission^{17,18}. For example, the incubation periods of dengue
viruses in the *Ae. aegypti* mosquitoes that transmit Zika virus have an
empirically derived relationship with temperature^{18}, which can in turn be used to inform calculations of
*R*_{0}. Together with similar relationships for other
transmission parameters, it is possible to characterize *R*_{0},
a fundamental measure of transmission potential, as a function of local environmental
conditions.

We leveraged these classic results from epidemiological theory to first perform highly
spatially resolved calculations of *R*_{0} and then to translate
those calculations into location-specific projections of first-wave epidemic attack
rates (Fig. 1). Because Zika-specific values of
transmission parameters are largely unknown at present but may be well approximated by
dengue-specific values^{19}, we used some
parameter values for dengue virus in our *R*_{0} calculations. We
also calibrated our attack rate projections to match empirically estimated attack rates
from 12 chikungunya epidemics and one Zika epidemic in naive populations (Supplementary Table 1). This step
afforded us the flexibility to enhance the realism of the model with respect to firmly
established but poorly quantified associations between human–mosquito contact and
economic prosperity^{20}. In doing so,
one departure from the classic relationship between *R*_{0} and
attack rate that we made was to rescale *R*_{0} by an exponent
*α* ∈ (0, 1] to allow for better
correspondence with observed attack rates. Although there is no theoretical
justification for this or any other particular scaling relationship, it is consistent
with theoretical expectations^{21} that
attack rates should be lower in populations with equal *R*_{0}
values but more heterogeneous contact patterns, which are typical for transmission by
*Ae. aegypti*^{22}. To
provide a point of reference for our model-based approach, we also fitted a statistical
description of the 13 seroprevalence estimates as a function of the environmental
drivers that we considered. For both approaches, we applied their respective
location-specific attack rate projections to demographic projections on a 5
km × 5 km grid across Latin America and the Caribbean to
obtain the expected numbers of infections in the overall population and among
childbearing women in particular (Fig. 2a). All
such calculations were performed for 1,000 Monte Carlo samples of model parameters.

In total, our median projection suggests that as many as 93.4 (range: 81.6–117.1)
million people in Latin America and the Caribbean could become infected during the first
wave of the epidemic (Table 1). To place this
number into context, we refer to an estimate^{23} that 53.8 (40.0**–**71.8) million dengue
infections occurred in this region in 2010 alone. Our projections of nearly double this
number for Zika are not surprising, given that there is extensive immunity to dengue but
not Zika in this region and given that it would probably take longer than a year for the
first wave of the epidemic to conclude in all locations within this region. At the
country level, we project that Brazil will have the largest total number of infections
by more than double that of any other country, due to a combination of its size and
suitability for transmission. Island countries in the Caribbean are projected to
experience the highest nationally averaged attack rates, with seven of the highest ten
values projected for countries including Aruba, Haiti and Cuba. This projection is
consistent with a frequent history of arbovirus outbreaks on islands^{24} and may be due to the uniformity of
environmental conditions on the portions of islands where people tend to live. In more
heterogeneous regions, the 5 km × 5 km spatial resolution of
our maps allows for nuanced projections for areas of interest to local stakeholders
(Fig. 2b,c). To facilitate the use of these
local projections, we have made 5 km × 5 km minimum, median
and maximum projections of attack rates, total infections and infections among
childbearing women publically available online (http://github.com/TAlexPerkins/Zika_nmicrobiol_2016).

Among childbearing women, our median projection suggests that there could be as many as
1.65 (range: 1.45**–**2.06) million infections in Latin America and the
Caribbean before the first wave of the epidemic concludes (Table 1). Assuming that birth rates are temporally constant,
our projections are robust to uncertainty about the timing of local epidemics and the
timeframe of the first wave of the epidemic, because they are based on cumulative
proportions infected. These projections can also be used to postulate numbers at risk of
microcephaly by multiplying them by the fraction of a year in which a pregnant woman is
susceptible to developing microcephaly (for example, multiply by 1/4 in the case of
first-trimester susceptibility). We also note that there were some discrepancies in our
projections in terms of the rank order of countries experiencing the most infections
among childbearing women versus the population as a whole. In particular, Cuba was fifth
in terms of projected infections in the overall population but twelfth in terms of
infections among childbearing women due to its low birth rate compared to other
countries in the Americas^{25}. Such
discrepancies are also likely to exist subnationally^{26}, and their elucidation should be a priority for future
work.

By accounting for uncertainty distributions for each of the key drivers of our model (Fig. 3a–e), we found that uncertainty distributions for infections across the region as a whole and by country were often multimodal (Fig. 3f–o) due to uncertainty in the shape of the relationship between mosquito–human contact and the local economic index that we considered (Fig. 3d). Summing our projections across Latin America and the Caribbean revealed variation that was modest, in the sense that none of our 1,000 Monte Carlo samples resulted in fewer than 81 million infections overall and 1.4 million among childbearing women (Fig. 3f,k). There are many reasons that even these numbers could be overestimates though. Our projections are conditioned on a local epidemic taking place in each 5 km × 5 km grid cell in the region, which is unlikely to happen given dispersal limitation, stochastic fadeout, geographic mismatches in seasonality and other factors. Therefore, it is most appropriate to interpret our projections as either a plausible worst-case scenario or an expectation of local epidemic size conditional on there being a local epidemic in the first place.

Although our approach was very much rooted in mechanistic models from epidemiological
theory, two critical steps in our method involved fitting curves to describe
theoretically motivated but heretofore unknown relationships: an association between
mosquito–human contact and economic prosperity (Fig. 3d) and a scaling relationship between *R*_{0}
and attack rates (Fig. 3e). Allowing these
relationships to be informed by local seroprevalence estimates (Supplementary Table 1) left open
the question of the extent to which our projections were informed by the mechanistic
assumptions of the model versus statistical fits to the seroprevalence estimates that we
used. On the one hand, an alternative statistical approach accounted for much more
variation in seroprevalence estimates
(*R*^{2} = 0.89) than did the model-based
approach (*R*^{2} = 0.32). On the other
hand, the statistical approach offered a dichotomous set of projections about numbers of
infections outside the context of the data to which it was fitted: either everyone will
become infected or very few people will (Fig. 4).
Relationships between attack rates and predictor variables inferred by the statistical
approach (Fig. 5d–i) were also implausible:
a narrow temperature range in which attack rates increase sharply towards 100%
(Fig. 5d–h) and a reversal of economic
effects whereby wealthy populations experience higher attack rates than poor populations
when mosquito occurrence probabilities are high (Fig.
5f,i). By contrast, the model-based approach yielded more moderate attack
rate projections overall (Figs 3f versus 4a) in which temperature, economic prosperity and
mosquito occurrence probability all had plausible relationships with attack rates (Fig. 5a–c).

In conclusion, our model-based approach offers a unique way to leverage a variety of
spatially detailed data products^{7,14,27,28} to make *a
priori* projections of attack rates and infections that could be experienced
in the first wave of the ongoing Zika epidemic. Projections such as these have an
important role to play in the early stages of an epidemic, when planning for
surveillance and outbreak response is actively under way both internationally and
locally^{9}. At the same time, it
is important for consumers of this information to be aware of uncertainties in these and
other projections, which often exceed the amount of uncertainty that can be identified
*a priori*^{29}.
Similarly, following up on these projections in the aftermath of the epidemic—by
comparing against projections made with alternative models and additional serological
surveys^{30}—will provide
an exceptional opportunity to enhance capabilities to anticipate the severity of future
epidemic threats.

## Methods

## Data sources and processing

*Human demography*. To estimate the annual numbers of pregnancies
per 1 km × 1 km grid cell in 2015, methods developed
by the WorldPop project (www.worldpop.org)^{25,31} were adapted for the Americas region. High-resolution
estimates of population counts per 100 m × 100 m
grid cell for 2015 were recently constructed for Latin American, Asian and
African countries^{14,32}. With consistent subnational data on sex
and age structures, as well as subnational age-specific fertility rate data
across the Americas currently unavailable for fully replicating the approaches
of Tatem and colleagues^{31},
national-level adjustments were made to construct pregnancy and birth counts.
Data on estimated total numbers of births^{33} and pregnancies^{31} occurring annually in 2012 were assembled for all Latin
American study countries, as well as births in 2015 (ref. 33). As no 2015 pregnancy estimates existed at the
time of writing, the ratios of births to pregnancies for each country in the
Americas were calculated using 2011 and 2012 estimates, and these were then
applied to the 2015 birth numbers to obtain 2015 estimates of annual pregnancy
numbers per country. This made the assumption that per-country
births-to-pregnancies ratios remained the same in 2015 as they were in 2011 and
2012. The 100 m × 100 m gridded population totals
were aggregated to 1 km × 1 km spatial resolution
and the per-country totals were linearly adjusted to match the 2015 pregnancy
estimates.

### Temperature

We used interpolated meteorological station temperature data from the
1950–2000 period at 5 km × 5 km spatial
resolution, processed to create climatological monthly averages that
represent ‘typical’ conditions (www.worldclim.org)^{27}.

*Ae. aegypti* occurrence probability

To predict the likely distribution of *Ae. aegypti*
mosquitoes, Kraemer *et al*.^{7} generated high-resolution occurrence
probability surfaces based on a species distribution modelling
approach^{11}. More
specifically, a boosted regression tree model was applied using a
comprehensive set of known occurrences
(*n* = 19,930) of *Ae.
aegypti* and a set of environmental predictors known to
influence the distribution of the species^{7}. Covariates included a temperature suitability
index^{17},
contemporary mean and range maps of the Enhanced Vegetation Index and
precipitation^{34},
and an urbanization index from the Global Rural Urban Mapping Project. We
used a set of 100 spatial layers sampled from the posterior distribution
estimated by Kraemer *et al.*^{7}

### Economic index

To account for socio-economic differences among populations residing in
different regions, we used one-degree-resolution gridded estimates of
purchasing power parity (PPP) in US$ from 2005 adjusted for inflation
(G-Econ)^{28}. When
we encountered missing values, we imputed values in one of two ways. Grid
cells in small island countries with data missing for the entire country
were uniformly filled with population-adjusted PPP figures obtained from the
US CIA World Factbook^{35}.
Missing values in continental grid cells were imputed with the mean of the
surrounding eight grid cell values. Once we obtained a complete PPP grid
layer at one-degree resolution, we resampled the layer to a resolution of 5
km × 5 km to match the resolution of gridded
layers for human demography, temperature and *Ae. aegypti*
occurrence probability.

### Seroprevalence estimates

To calibrate our model, we identified published estimates of seroprevlance
that were relevant to the context of our study (Supplementary Table
1). Specifically, we sought estimates of seroprevalence to either
Zika or chikungunya viruses in populations that were presumably naive before
an outbreak. Thus, we excluded some seroprevalence estimates that were
obtained from endemic populations. We also excluded estimates from small
islands—namely, Reunion and Grande Comore—for which it was
clear that gridded temperature data were unrealistically low due to steep
elevational gradients and other features of island geography. Although the
focus of our analysis was on Latin America and the Caribbean, we were not
able to exclude locations on the basis of location given that only 2 of 13
came from the focal region. Appropriately, however, a number of the
seroprevalence estimates we obtained pertained specifically to pregnant
women, although there did not appear to be differences in the seroprevalence
of pregnant women and the population at large, at least in the context of a
naive population following an outbreak^{36}.

## Calculation of derived quantities

*Mosquito abundance*. Occurrence probabilities can be translated
into proxies for abundance provided that an assumption is made about how
abundance is distributed as a random variable^{37}. Assuming that mosquito abundance is
distributed as a Poisson random variable, the probability that there is at least
one mosquito present in a given location is
1 – exp(–*λ*), where
*λ* is the expected abundance of mosquitoes. Inverting
this relationship, we obtained an estimate
*λ* = –ln(1 – occurrence
probability) of expected mosquito abundance under the Poisson model and used
this as a proxy for mosquito abundance in our calculations.

### Mosquito–human ratios

The estimates of mosquito occurrence probability that we used incorporated a
number of environmental variables^{7}. They did not account for factors that modulate
contact between mosquitoes and humans, however. Due in part to economic
differences, factors such as air conditioning and piped water can
drastically limit mosquito–human contact and virus transmission, even
when mosquitoes are abundant^{20}. We accounted for the effect of economic differences
between locations by multiplying our proxy for mosquito abundance
*λ* by a multiplication factor, which we specified
as a function of the aforementioned economic index. We specified the
relationship between the economic index and the multiplication factor by
using a shape constrained additive model (SCAM^{38}). This allowed for flexibility in
the shape of this relationship but constrained it such that the
multiplication factor (and thus, presumed mosquito–human contact)
could only have a monotonically decreasing relationship with increasing
values of the economic index. The specific shape of this relationship was
determined by fitting it to values of the multiplication factors that would
be necessary for modelled attack rates to perfectly match published
seroprevalence estimates.

### Basic reproduction number *R*_{0}

We calculated the basic reproduction number *R*_{0}
according to its classic Ross–Macdonald formulation and as a function
of temperature *T*, $$\begin{array}{}\text{(1)}& {R}_{0}(T)=\frac{mbc{a}^{2}{e}^{-\mu (T)n(T)}}{\mu (T)r}\end{array}$$with adult mosquito mortality
*µ* and extrinsic incubation period
*n* specified as functions of temperature. Because
temperature values were available for each location on a monthly basis, we
computed monthly values of *R*_{0} for each location
and then used the mean of the highest six monthly values of
*R*_{0} as a singular estimate of
*R*_{0} for each location. This approach was
broadly consistent with the way in which a temperature suitability index was
used to inform mosquito occurrence probabilities by Kraemer and
co-authors^{7}.

For mosquito mortality, we used the temperature- and age-dependent model of
Brady and colleagues^{39}, to
which we added an additional force of extrinsic mortality
(0.025 d^{−1}) to match an overall daily mortality
value of 0.115 estimated in a mark–release–recapture
experiment carried out under temperatures ranging from 20 to
34 °C (ref. 40). We
then computed the mean of the age- and temperature-dependent lifespan
distribution as a function of temperature to inform
*μ*(*T*). For the relationship
between temperature and mean duration of the extrinsic incubation period, we
used the temperature-dependent exponential rate estimated by Chan and
Johansson^{18}. The
ratio of mosquitoes to humans, *m*, was quantified using a
combination of occurrence probabilities and the gross cell product economic
index, as described in the previous two sections. Parameters that did not
depend on temperature were set at the following values according to
published estimates for *Ae. aegypti* and dengue virus:
mosquito-to-human transmission probability,
*b* = 0.4 (ref. 41); human-to-mosquito transmission probability
times number of days of human infectiousness,
*c*/*r* = 3.5 (ref.
42); mosquito biting rate,
*a* = 0.67 (ref. 43). Although there is uncertainty around these
parameter values, any such uncertainty was effectively subsumed by fitting
*m* to seroprevalence data given that
*bca*^{2}/*r* entered
*R*_{0} as a constant.

### Attack rates under a model-based formulation

Under a susceptible–infected–recovered (SIR) transmission
model, there is a one-to-one relationship between
*R*_{0} and final epidemic size, which is
equivalent to the attack rate over the course of an epidemic^{13}. Intuitively, the final
epidemic size is reached once herd immunity is sufficient to limit contacts
between infectious and susceptible individuals to the extent necessary to
reduce the pathogen's force of infection to zero. There is no
explicit solution for final epidemic size as a function of model parameters,
but it can be calculated numerically by obtaining an implicit solution of
*S*_{∞} = *e*^{−R0(1−S∞)}
for *S*_{∞}, which is the proportion remaining
susceptible after the epidemic has burned out^{13}. Under the assumptions of the SIR
model, the attack rate over the course of an epidemic is
*AR* = 1 – *S*_{∞}.

To apply this theoretical insight to Zika or other mosquito-borne pathogens,
several limiting assumptions of the SIR model must first be reconciled. One
such assumption is that individuals become infectious immediately upon
becoming infected and remain infectious for an exponentially distributed
period of time^{44};
mosquito-borne pathogens such as Zika virus are instead characterized by a
distinct lag between human and mosquito infection^{45}. Despite this discrepancy between
assumptions of the SIR model and the reality of many pathogen systems,
mathematical analyses^{46}
have shown that final epidemic size is insensitive to details about the
shape of the distribution that characterizes the time period between
successive cases (that is, the generation interval).

Another limiting assumption of the SIR model is that of homogeneous
encounters between people and mosquitoes^{44}, which are understood to be extensive for
mosquito-borne diseases^{22}.
Mathematical analyses^{21} in
this case show that a seemingly infinite complexity of relationships between
*R*_{0} and final epidemic size are possible in a
heterogeneous system. As a general rule, however, final epidemic size in a
system with contact heterogeneity and proportional mixing is expected to be
strictly less than the final epidemic size in an otherwise equivalent system
with homogeneous contacts^{21}. How the ratio of these final epidemic sizes scales as
a function of *R*_{0} depends entirely on the details
of a given system and would therefore be extremely difficult to generalize
without copious data on mosquito–human contact and further
investigation, which is beyond the scope of our study.

To capture the potentially very strong effects of heterogeneity in reducing
final epidemic size in populations subject to Zika epidemics, we scaled the
final epidemic size by substituting ${R}_{0}^{\alpha}$ for *R*_{0} in the
SIR-based final epidemic size formula given some constant
*α* ∈ (0, 1].
Although there is no theoretical justification for this or any other choice
of how to scale *R*_{0} and *AR* in
the presence of contact heterogeneity, the choice we made has the following
desirable properties: (1) it implies that
*AR* → 1 as
*R*_{0} → ∞; (2) it
leads to the function *AR*(*R*_{0})
having a more gradual slope and thereby allows for intermediate attack rates
to be more common than they would be otherwise; (3) it preserves the
property that *AR* = 0 for
*R*_{0} < 1. At the same
time, this and possible alternative formulations are limited by a general
lack of understanding about the relationship between
*R*_{0} and *AR* in heterogeneous
systems, relationships that may furthermore be heterogeneous themselves
across different areas^{47}.

To estimate *α*, we performed the following procedure
for candidate values of *α* between 0.01 and 1 in
increments of 0.01: (1) calculate *R*_{0} according
to equation (1) and assume
*m* = *λ* for
each of the 13 sites from which seroprevalence estimates were derived; (2)
use those *R*_{0} values to calculate
*AR* values for each of those sites based on the classic
SIR formulation; (3) calculate what multiplication factor of
*R*_{0} would be necessary for
*AR* to match the empirical seroprevalence estimate; (4)
fit a SCAM model of the economic index to the multiplication factors; and
(5) use the fitted SCAM values to recalculate *R*_{0}
and then *AR* for each site. Next, we calculated the sum of
squares between the final predicted *AR* values associated
with each *α* and the empirical seroprevalence
estimates and we then selected the value of *α* that
minimized the sum of squares. Supplementary Fig. 1 illustrates this process given mean
estimates of *λ* from *Ae. aegypti*
occurrence probabilities, *μ*(*T*) and
*n*(*T*).

### Attack rates under a statistical formulation

As an alternative to our model-based characterization of attack rates, we
also considered a purely statistical approach that modelled
probit-transformed seroprevalence observations as functions of averaged
monthly temperatures, *Ae. aegypti* occurrence probabilities
and the economic index. We considered all combinations of linear, quadratic
and pairwise interaction terms of these variables, comparing them on the
basis of the Akaike Information Criterion using the lm and step functions in
R (ref. 48). Although additional
functional forms would have been of interest, this suite of models was as
complex as the limited set of 13 seroprevalence observations would
support.

### Quantifying uncertainty around attack rate projections

To quantify uncertainty associated with our projections, we generated 1,000
Monte Carlo samples from the uncertainty distributions of each model
parameter as described in each of the references^{7,17,18} in which
those parameters were originally described. For
*μ*(*T*) and
*n*(*T*), we took random draws of their
parameters consistent with published descriptions of uncertainty in the
parameters of those functions from their original sources^{17,18}. For *Ae. aegypti* occurrence
probabilities, we drew randomly with replacement from 100 sample layers from
the posterior distribution^{7}. For the relationship involving the economic index and the
*R*_{0} scaling factor *α*,
we used best-fit SCAM models and *α* values
corresponding to each set of random draws of the parameters of
*μ*(*T*),
*n*(*T*) and *λ*
from the *Ae. aegypti* layers. For each of the 1,000 Monte
Carlo samples of the statistical model, we performed resampling with
replacement among the 13 seroprevalence values, performed the same model
fitting and model selection procedure described in the previous section, and
took a multivariate normal random sample of the parameter values of the
best-fit model based on the model's best-fit parameters and
variance–covariance matrix.

## Projecting attack rates and numbers of infections

To obtain estimates of the numbers of infections in total and among childbearing women for the model-based and statistical approaches, we multiplied their respective attack rate projections applied to 5 km × 5 km grids across Latin America and the Caribbean by human demographic layers for total population and births in 2015. For both the model-based and statistical approaches, we performed these calculations and summed at the country level once for each of the 1,000 Monte Carlo samples that we produced. High-resolution spatial projections of attack rates and numbers of infected childbearing women under the model-based approach are presented in Supplementary Figs 2–10. Most projections based on the statistical approach resulted in attack rates of 100% in nearly all locations throughout Latin America and the Caribbean.

## Code availability

Code in the R language for reproducing all analyses is available at http://github.com/TAlexPerkins/Zika_nmicrobiol_2016.

## References

- 1.
Mlakar, J.

*et al.*Zika virus associated with microcephaly.*New Engl. J. Med.***374**, 951–958 (2016). - 2.
Brasil, P.

*et al.*Zika virus infection in pregnant women in Rio de Janeiro—preliminary report.*New Engl. J. Med.*http://dx.doi.org/10.1056/NEJMoa1602412 (2016). - 3.
Messina, J. P.

*et al.*Mapping global environmental suitability for Zika virus.*eLife***5**, 15272 (2016). - 4.
Cauchemez, S.

*et al.*Association between Zika virus infection and microcephaly in French Polynesia, 2013–2015: a retrospective study.*Lancet***387**, 2125–2132 (2016). - 5.
Johansson, M. A.

*et al.*Risk estimates for microcephaly related to Zika virus infection—from French Polynesia to Bahia, Brazil. Preprint at http://dx.doi.org/10.1101/051060 (2016). - 6.
*WHO Statement on the First Meeting of the International Health Regulations (2005) (IHR 2005) Emergency Committee on Zika Virus and Observed Increase in Neurological Disorders and Neonatal Malformations*(WHO, 2016); http://www.who.int/mediacentre/news/statements/2016/1st-emergency-committee-zika/en/ - 7.
Kraemer, M. U. G.

*et al.*The global distribution of the arbovirus vectors*Aedes aegypti*and*Ae*.*albopictus. eLife***4**, e08347 (2015). - 8.
*Zika—Epidemiological Update*(Pan American Health Organization, 2016). - 9.
Lucey, D. R. & Gostin, L. O. The emerging Zika pandemic: enhancing preparedness.

*J. Am. Med. Assoc.***315**, 865–866 (2016). - 10.
The next steps on Zika.

*Nature***530**, 5 (2016). - 11.
Elith, J. & Leathwick, J. Species distribution models: ecological explanation and prediction across space and time.

*Annu. Rev. Ecol. Evol. System.***40**, 677–697 (2009). - 12.
Kermack, W. & McKendrick, A. A contribution to the mathematical theory of epidemics.

*Proc. R. Soc. A***115**, 700–721 (1927). - 13.
Keeling, M. & Rohani, P.

*Modeling Infectious Diseases in Humans and Animals*(Princeton Univ. Press, 2007). - 14.
Sorichetta, A.

*et al.*High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020.*Sci. Data***2**, 150045 (2015). - 15.
Sergon, K.

*et al.*Seroprevalence of chikungunya virus (CHIKV) infection on Lamu Island, Kenya, October 2004.*Am. J. Trop. Med. Hyg.***78**, 333–337 (2008). - 16.
Smith, D. L.

*et al.*Ross, Macdonald, and a theory for the dynamics and control of mosquito-transmitted pathogens.*PLOS Pathogens***8**, e1002588 (2012). - 17.
Brady, O. J.

*et al.*Global temperature constraints on*Aedes aegypti*and*Ae*.*albopictus*persistence and competence for dengue virus transmission.*Parasites Vector.***7**, 338 (2014). - 18.
Chan, M. & Johansson, M. A. The incubation periods of dengue viruses.

*PLoS ONE***7**, e50972 (2012). - 19.
Funk, S.

*et al.*Comparative analysis of dengue and Zika outbreaks reveals differences by setting and virus. Preprint at http://dx.doi.org/10.1101/043265 (2016). - 20.
Reiter, P.

*et al.*Texas lifestyle limits transmission of dengue virus.*Emerg. Infect. Dis.***9**, 86–89 (2003). - 21.
Andreasen, V. The final size of an epidemic and its relation to the basic reproduction number.

*Bull. Math. Biol.***73**, 2305–2321 (2011). - 22.
Liebman, K. A.

*et al.*Determinants of heterogeneous blood feeding patterns by*Aedes aegypti*in Iquitos, Peru.*PLoS Negl. Trop. Dis.***8**, e2702 (2014). - 23.
Bhatt, S.

*et al.*The global distribution and burden of dengue.*Nature***496**, 504–507 (2013). - 24.
Feldstein, L., Brownstein, J., Brady, O. J., Hay, S. I. & Johansson, M. A. Dengue on islands: a Bayesian approach to understanding the global ecology of dengue viruses.

*Trans. R. Soc. Trop. Med. Hyg.***109**, 303–312 (2015). - 25.
*The State of the World's Midwifery 2014*(UNFPA, 2014). - 26.
Tatem, A. J.

*et al.*Millenium development health metrics: where do Africa's children and women of childbearing age live?*Popul. Health Metrics***11**, 11 (2013). - 27.
Hijmans, R.

*et al.*Very high resolution interpolated climate surfaces for global land areas.*Int. J. Climatol.***25**, 1965–1978 (2005). - 28.
Nordhaus, W. Geography and macroeconomics: new data and new findings.

*Proc. Natl Acad. Sci. USA***103**, 3510–3517 (2006). - 29.
Taylor, B. P.

*et al.*Stochasticity and the limits to confidence when estimating*R*_{0}of Ebola and other emerging infectious diseases. Preprint at http://arxiv.org/abs/1601.06829 (2016). - 30.
Metcalf, C. J. E.

*et al.*Use of serological surveys to generate key insights into the changing global landscape of infectious disease.*Lancet*http://dx.doi.org/10.1016/S0140-6736(16)30164-7 (2016). - 31.
Tatem, A. J.

*et al.*Mapping for maternal and newborn health: the distributions of women of childbearing age, pregnancies and births.*Int. J. Health Geogr.***13**, 2 (2013). - 32.
Stevens, F. R.

*et al.*Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data.*PLoS ONE***10**, e0107042 (2015). - 33.
*World Population Prospects: The 2015 Revision*(UN, 2015). - 34.
Weiss, D.

*et al.*An effective approach for gap-filling continental scale remotely sensed time-series.*ISPRS J. Photogr. Remote Sens.***98**, 106–118 (2014). - 35.
*The World Factbook*(US Central Intelligence Agency, 2016); https://www.cia.gov/library/publications/the-world-factbook/geos/xx.html - 36.
Gerardin, P.

*et al.*Estimating chikungunya prevalence in Reunion Island outbreak by serosurveys: two methods for two critical times of the epidemic.*BMC Infect. Dis.***8**, 99 (2008). - 37.
Wright, D. Correlations between incidence and abundance are expected by chance.

*J. Biogeogr.***18**, 463–466 (1991). - 38.
Pya, N. & Wood, S. Shape constrained additive models.

*Statist. Comput.***25**, 543–559 (2015). - 39.
Brady, O. J.

*et al.*Modelling adult*Aedes aegypti*and*Aedes albopictus*survival at different temperatures in laboratory and field settings.*Parasites Vector.***6**, 351–362 (2013). - 40.
Muir, L. & Kay, B.

*Aedes aegypti*survival and dispersal estimated by mark–release–recapture in northern Australia.*Am. J. Trop. Med. Hyg.***58**, 277–282 (1998). - 41.
Nishiura, H. & Halstead, S. Natural history of dengue virus (DENV)-1 and DENV-4 infections: reanalysis of classic studies.

*J. Infect. Dis.***195**, 1007–1013 (2007). - 42.
Scott, T. W.

*et al.*Longitudinal studies of*Aedes aegypti*(Diptera: Culicidae) in Thailand and Puerto Rico: blood feeding frequency.*J. Med. Entomol.***37**, 89–101 (2000). - 43.
Reiner, R. C.

*et al.*A systematic review of mathematical models of mosquito-borne pathogen transmission: 1970–2010.*J. R. Soc. Interface***10**, 20120921 (2013). - 44.
Lessler, J.

*et al.*Times to key events in the course of Zika infection and their implications for surveillance: a systematic review and pooled analysis. Preprint at http://dx.doi.org/10.1101/041913 (2016). - 45.
Ma, J. & Earn, D. J. Generality of the final size formula for an epidemic of a newly invading infectious disease.

*Bull. Math. Biol.***68**, 679–702 (2006). - 46.
Kraemer, M. U. G.

*et al.*Big city, small world: density, contact rates, and transmission of dengue across Pakistan.*J. R. Soc. Interface***12**, 20150468 (2015). - 47.
R Core Team

*R: A Language for Statistical Computing**(R Foundation for Statistical Computing*, 2014); http://www.R-project.org/ - 48.
Duffy, M.

*et al.*Zika virus outbreak on Yap Island, Federated States of Micronesia.*New Engl. J. Med.***360**, 2536–2543 (2009). - 49.
Schwarz, N.

*et al.*Seroprevalence of antibodies against chikungunya, dengue, and Rift Valley fever viruses after febrile illness outbreak, Madagascar.*Emerg. Infect. Dis.***18**, 1780–1786 (2012). - 50.
Sissoko, D.

*et al.*Outbreak of chikungunya fever in Mayotte, Comoros archipelago, 2005–2006.*Trans. R. Soc. Trop. Med. Hyg.***102**, 780–786 (2008). - 51.
Dwibedi, B.

*et al.*Rapid spread of chikungunya virus infection in Orissa, India.*Indian J. Med. Res.***133**, 316–321 (2011). - 52.
Gay, N.

*et al.*Seroprevalence and asymptomatic rates of Asian lineage chikungunya virus infection on Saint Martin, Caribbean.*Am. J. Trop. Med. Hyg.***94**, 393–396 (2015). - 53.
Moro, M.

*et al.*Chikungunya virus in north-eastern Italy: a seroprevalence study.*Am. J. Trop. Med. Hyg.***82**, 508–511 (2010). - 54.
Balmaseda, A.

*et al.*Clinical attack rate of chikungunya in a cohort of Nicaraguan children.*Am. J. Trop. Med. Hyg.***94**, 397–399 (2016).

## Acknowledgements

The authors thank three anonymous reviewers, as well as J. Ashander, C.M. Barker, M.A. Johansson, R.C. Reiner, S.T. Stoddard, J.C. Miller and members of the Perkins Lab for discussions. The authors thank O.J. Brady for sharing code for calculating mosquito mortality as a function of temperature. T.A.P., A.S.S. and A.J.T. are supported by funding from the National Science Foundation (DEB 1641130). C.W.R. is supported by funding through the University of Southampton's Economic and Social Research Council's Doctoral Training Centre. M.U.G.K. receives funding from the International Research Consortium on Dengue Risk Assessment Management and Surveillance (IDAMS; European Commission 7th Framework Programme, 21893). A.J.T. is supported by funding from NIH/NIAID (U19AI089674), the BMGF (OPP1106427, 1032350), NORAD and a Wellcome Trust Sustaining Health Grant (106866/Z/15/Z). A.J.T. and C.W.R. acknowledge the support of the WorldPop (www.worldpop.org) and Flowminder Foundation (www.flowminder.org) teams in demographic data set production, and T.A.P. and A.S.S. acknowledge support from the Notre Dame Center for Research Computing.

## Author information

## Affiliations

### Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, 100 Galvin Hall, Notre Dame, Indiana 46556, USA

- T. Alex Perkins
- & Amir S. Siraj

### WorldPop Project, Department of Geography and Environment, University of Southampton, Southampton SO17 1BJ, UK

- Corrine W. Ruktanonchai
- & Andrew J. Tatem

### Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford OX1 3PS, UK

- Moritz U. G. Kraemer

### Flowminder Foundation, SE-11355 Stockholm, Sweden

- Andrew J. Tatem

## Authors

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## Contributions

T.A.P. conceived the research, designed the analysis and wrote the first draft of the manuscript. A.S.S. assembled data, performed calculations and contributed to writing. C.W.R. assembled data, produced map visuals and contributed to writing. M.U.G.K. assembled data and contributed to writing. A.J.T. contributed to the analysis, map visuals and writing.

## Competing interests

The authors declare no competing financial interests.

## Corresponding author

Correspondence to T. Alex Perkins.

## Supplementary information

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### Supplementary information

Supplementary Table 1, Supplementary Figures 1–10