Schematic of all incoming and outgoing flow at node k.

<p>Schematic of all incoming and outgoing flow at node k.</p

Map illustrating top 100 stopover risk airports identified by the model.

<p>Map illustrating top 100 stopover risk airports identified by the model.</p

Map illustrating the Species Distribution Models for (a) <i>Ae. aegyptii</i> and (b) <i>Ae. Albopictus</i>[19].

<p>The numbers on the scale are the predicted probabilities of presence.</p

Airports in List of Top 100 Stopover Risk located in non-endemic regions.

<p>Airports in List of Top 100 Stopover Risk located in non-endemic regions.</p

Schematic of a route with origin <i>i</i>, destination <i>j</i>, and stopover <i>k.</i>

<p>Schematic of a route with origin <i>i</i>, destination <i>j</i>, and stopover <i>k.</i></p

Map illustrating top 100 destination risk airports identified by the model.

<p>Map illustrating top 100 destination risk airports identified by the model.</p

Map of all airports included in risk model.

<p>Map of all airports included in risk model.</p

Vector status of <i>Aedes</i> species determines geographical risk of autochthonous Zika virus establishment

<div><p>Background</p><p>The 2015-16 Zika virus pandemic originating in Latin America led to predictions of a catastrophic global spread of the disease. Since the current outbreak began in Brazil in May 2015 local transmission of Zika has been reported in over 60 countries and territories, with over 750 thousand confirmed and suspected cases. As a result of its range expansion attention has focused on possible modes of transmission, of which the arthropod vector-based disease spread cycle involving <i>Aedes</i> species is believed to be the most important. Additional causes of concern are the emerging new links between Zika disease and Guillain-Barre Syndrome (GBS), and a once rare congenital disease, microcephaly.</p><p>Methodology/principal findings</p><p>Like dengue and chikungunya, the geographic establishment of Zika is thought to be limited by the occurrence of its principal vector mosquito species, <i>Ae. aegypti</i> and, possibly, <i>Ae. albopictus</i>. While <i>Ae. albopictus</i> populations are more widely established than those of <i>Ae. aegypti</i>, the relative competence of these species as a Zika vector is unknown. The analysis reported here presents a global risk model that considers the role of each vector species independently, and quantifies the potential spreading risk of Zika into new regions. Six scenarios are evaluated which vary in the weight assigned to <i>Ae. albopictus</i> as a possible spreading vector. The scenarios are bounded by the extreme assumptions that spread is driven by air travel and <i>Ae. aegypti</i> presence alone and spread driven equally by both species. For each scenario destination cities at highest risk of Zika outbreaks are prioritized, as are source cities in affected regions. Finally, intercontinental air travel routes that pose the highest risk for Zika spread are also ranked. The results are compared between scenarios.</p><p>Conclusions/significance</p><p>Results from the analysis reveal that if <i>Ae. aegypti</i> is the only competent Zika vector, then risk is geographically limited; in North America mainly to Florida and Texas. However, if <i>Ae. albopictus</i> proves to be a competent vector of Zika, which does not yet appear to be the case, then there is risk of local establishment in all American regions including Canada and Chile, much of Western Europe, Australia, New Zealand, as well as South and East Asia, with a substantial increase in risk to Asia due to the more recent local establishment of Zika in Singapore.</p></div

Influenza A H5N1 and H7N9 in China: A spatial risk analysis

<div><p>Background</p><p>Zoonotic avian influenza poses a major risk to China, and other parts of the world. H5N1 has remained endemic in China and globally for nearly two decades, and in 2013, a novel zoonotic influenza A subtype H7N9 emerged in China. This study aimed to improve upon our current understanding of the spreading mechanisms of H7N9 and H5N1 by generating spatial risk profiles for each of the two virus subtypes across mainland China.</p><p>Methods and findings</p><p>In this study, we (i) developed a refined data set of H5N1 and H7N9 locations with consideration of animal/animal environment case data, as well as spatial accuracy and precision; (ii) used this data set along with environmental variables to build species distribution models (SDMs) for each virus subtype in high resolution spatial units of 1km² cells using Maxent; (iii) developed a risk modelling framework which integrated the results from the SDMs with human and chicken population variables, which was done to quantify the risk of zoonotic transmission; and (iv) identified areas at high risk of H5N1 and H7N9 transmission. We produced high performing SDMs (6 of 8 models with AUC > 0.9) for both H5N1 and H7N9. In all our SDMs, H7N9 consistently showed higher AUC results compared to H5N1, suggesting H7N9 suitability could be better explained by environmental variables. For both subtypes, high risk areas were primarily located in south-eastern China, with H5N1 distributions found to be more diffuse and extending more inland compared to H7N9.</p><p>Conclusions</p><p>We provide projections of our risk models to public health policy makers so that specific high risk areas can be targeted for control measures. We recommend comparing H5N1 and H7N9 prevalence rates and survivability in the natural environment to better understand the role of animal and environmental transmission in human infections.</p></div