Reported suspected and confirmed Zika cases in the Americas.

<p>Monthly distribution of reported (A) Zika cases and (B) Zika incidence rates for 15 countries and territories during the 2015-2016 epidemic (listed in descending order of incidence rates). (C) The number of countries and territories reporting local Zika cases per month. (D) The total number of Zika cases reported in the Americas per month and cumulatively during the epidemic.</p

International air passenger travel used to construct the Zika virus epidemic model network.

<p>Passenger air travel from regions that reported Zika virus transmission was used to construct a network for potential international virus spread from the epidemic origin, Brazil (pink). The monthly travel volumes were normalized to fit between 0 and 1 and summed across the 18 month study period. The color gradient represents the relative arrival volumes at each destination. The weighted lines represent the travel volumes along each route with the thicker end pointing towards the destination. The travel routes connect region centroids, not specific air ports. We only used departing flights from Brazil (pink). The maps were generated using open source shape files from Natural Earth (<a href="http://www.naturalearthdata.com/" target="_blank">http://www.naturalearthdata.com/</a>).</p

Temporal and regional risks for Zika virus introductions and transmission.

<p>(A) The temporal probabilities for Zika virus introductions via infected travelers and establishment of local transmssion are shown for 10 links, listed as origin-destination (rank). Relative risk profiles for temporal and regional Zika virus (B) exportations and (C) introductions were estimated using the network-level transmission probabilities (top 5 ranked regions shown for each). (D) Geographic variation in relative Zika virus importation risk for June, 2016. All Zika virus transmission data can be found in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006194#pntd.0006194.s001" target="_blank">S1 Data</a>. The maps were generated using open source shape files from Natural Earth (<a href="http://www.naturalearthdata.com/" target="_blank">http://www.naturalearthdata.com/</a>).</p

Regional variability in socioeconomic and human population data in the Americas.

<p>Regional values for (A) gross domestic product (GDP) purchasing power parity rates per capita and (B) human population densities (people per sq. km of land area) used in the models. The boxes show zoomed in views of the Caribbean Islands. The maps were generated using open source shape files from Natural Earth (<a href="http://www.naturalearthdata.com/" target="_blank">http://www.naturalearthdata.com/</a>).</p

Sensitivity analyses accounting for delays in reporting Zika virus transmission.

<p>The model was re-run by shifting the reported Zika virus cases by three and six months for each region to account for delays in outbreak detection.</p

Inferring the risk factors behind the geographical spread and transmission of Zika in the Americas

<div><p>Background</p><p>An unprecedented Zika virus epidemic occurred in the Americas during 2015-2016. The size of the epidemic in conjunction with newly recognized health risks associated with the virus attracted significant attention across the research community. Our study complements several recent studies which have mapped epidemiological elements of Zika, by introducing a newly proposed methodology to simultaneously estimate the contribution of various risk factors for geographic spread resulting in local transmission and to compute the risk of spread (or re-introductions) between each pair of regions. The focus of our analysis is on the Americas, where the set of regions includes all countries, overseas territories, and the states of the US.</p><p>Methodology/Principal findings</p><p>We present a novel application of the Generalized Inverse Infection Model (GIIM). The GIIM model uses real observations from the outbreak and seeks to estimate the risk factors driving transmission. The observations are derived from the dates of reported local transmission of Zika virus in each region, the network structure is defined by the passenger air travel movements between all pairs of regions, and the risk factors considered include regional socioeconomic factors, vector habitat suitability, travel volumes, and epidemiological data. The GIIM relies on a multi-agent based optimization method to estimate the parameters, and utilizes a data driven stochastic-dynamic epidemic model for evaluation. As expected, we found that mosquito abundance, incidence rate at the origin region, and human population density are risk factors for Zika virus transmission and spread. Surprisingly, air passenger volume was less impactful, and the most significant factor was (a negative relationship with) the regional gross domestic product (GDP) per capita.</p><p>Conclusions/Significance</p><p>Our model generates country level exportation and importation risk profiles over the course of the epidemic and provides quantitative estimates for the likelihood of introduced Zika virus resulting in local transmission, between all origin-destination travel pairs in the Americas. Our findings indicate that local vector control, rather than travel restrictions, will be more effective at reducing the risks of Zika virus transmission and establishment. Moreover, the inverse relationship between Zika virus transmission and GDP suggests that Zika cases are more likely to occur in regions where people cannot afford to protect themselves from mosquitoes. The modeling framework is not specific for Zika virus, and could easily be employed for other vector-borne pathogens with sufficient epidemiological and entomological data.</p></div

Estimated risk factors for Zika virus transmission and spread.

<p>(A) The mean and standard deviation of the model coefficients estimated across 20 runs. The estimated coefficients represent the relative contributions of each risk factor. (B) The model performance mean and standard deviation values for each month of the observations period.</p

Random variables associated with components of the transmission cycle (top) and their successive sums (bottom).

<p>On the top, the intrinsic incubation (IIP), human-to-mosquito transmission period (HMTP), extrinsic incubation period (EIP), and mosquito-to-human transmission period (MHTP) are shown from left to right, with the latter two parameterized for a temperature of 30°C. On the bottom, random variables for the elapsed time between inoculation of the primary infection and each event in the transmission cycle is shown in successive order from left to right.</p

Epidemic growth under an exponential model with values of the epidemic growth rate <i>r</i> ranging from 0.01 to 0.05 for a duration of 180 days.

<p>Epidemic growth under an exponential model with values of the epidemic growth rate <i>r</i> ranging from 0.01 to 0.05 for a duration of 180 days.</p

Relative contributions of the generation interval (blue) and the basic reproduction number <i>R</i>₀ (orange) to temperature-driven changes in epidemic growth rate <i>r</i>.

<p>Temperature changes are considered in 0.1°C increments and assume λ = 0.2. See <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005797#pntd.0005797.s003" target="_blank">S1 Fig</a> for consideration of alternative λ value.</p