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Background Zika virus (ZIKV) spread rapidly in the Americas in 2015. Targeting effective public health interventions for inhabitants of, and travellers to and from, affected countries depends on understanding the risk of ZIKV emergence (and re-emergence) at the local scale. We explore the extent to which environmental, social and neighbourhood disease intensity variables influenced emergence dynamics. Our objective was to characterise population vulnerability given the potential for sustained autochthonous ZIKV transmission and the timing of emergence. Logistic regression models estimated the probability of reporting at least one case of ZIKV in a given municipality over the course of the study period as an indicator for sustained transmission; while accelerated failure time (AFT) survival models estimated the time to a first reported case of ZIKV in week t for a given municipality as an indicator for timing of emergence. Results Sustained autochthonous ZIKV transmission was best described at the temporal scale of the study period (almost one year), such that high levels of study period precipitation and low mean study period temperature reduced the probability. Timing of ZIKV emergence was best described at the weekly scale for precipitation in that high precipitation in the current week delayed reporting. Both modelling approaches detected an effect of high poverty on reducing/slowing case detection, especially when inter-municipal road connectivity was low. We also found that proximity to municipalities reporting ZIKV had an effect to reduce timing of emergence when located, on average, less than 100 km away. Conclusions The different modelling approaches help distinguish between large temporal scale factors driving vector habitat suitability and short temporal scale factors affecting the speed of spread. We find evidence for inter-municipal movements of infected people as a local-scale driver of spatial spread. The negative association with poverty suggests reduced case reporting in poorer areas. Overall, relatively simplistic models may be able to predict the vulnerability of populations to autochthonous ZIKV transmission at the local scale. Electronic supplementary material The online version of this article (10.1186/s13071-018-2867-8) contains supplementary material, which is available to authorized users.

Parasite transmission between captive and wild fish is mediated by spatial, abiotic, biotic, and management factors. More effective population management and conservation strategies can result from multivariable assessments of factors associated with spatial dynamics of parasite spillover. Our study characterised spatial patterns of sea lice (Lepeophtheirus salmonis, Caligus clemensi) infection on out-migrating chum (Oncorhynchus keta) and pink (O. gorbuscha) salmon in an area with Atlantic salmon (Salmo salar) farming. A multivariable statistical model for sea louse parasitism of out-migrating chum and pink salmon was developed from 166,316 wild salmon sampled in the Broughton Archipelago, British Columbia, Canada from 2003 to 2012. We assessed for factors hypothesized to influence sea lice infection levels, at the non-motile life stage, including spatial scales of infection sources. Fish length, sampling year and method were strong explanatory factors. Infection was greatest in higher salinity water. Farmed and wild juvenile salmon infection levels were correlated, on average, within 30 km. Except for 2004, sea lice infection on farms were typically well below the regulatory level (3 motiles per fish). Average intensity of non-motile infections observed on the wild fish were 6.36 (SD = 9.98) in 2004 compared to 1.66 (SD = 1.25) for the other years. Accuracy of future model estimates will benefit by including hydrodynamic data accounting for anisotropic spread of sea lice from sources. Multivariable statistical modelling over long time series data strengthens understanding of factors impacting wild juvenile salmon infection levels and informs spatial patterns of aquatic epidemiology.

Zika virus (ZIKV) infection is emerging globally, currently causing outbreaks in the Caribbean, and Central and South America, and putting travellers to affected countries at risk. Model-based estimates for the basic reproduction number (R 0 ) of ZIKV in affected Caribbean and Central and South American countries, obtained from 2015 to 2016 human case surveillance data, were compared by logistic regression and Receiver-Operating Characteristic (ROC), with the prevalence of ZIKV-positive test results in Canadians who travelled to them. Estimates of R 0 for each country were a good predictor of the ZIKV test result (ROC area under the curve = 0.83) and the odds of testing positive was 11-fold greater for travellers visiting countries with estimated R 0 ≥ 2.76, compared to those visiting countries with R 0