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Zika Virus Infection in Patient with No Known Risk Factors, Utah, USA, 2016.

ABSTRACT: In 2016, Zika virus disease developed in a man (patient A) who had no known risk factors beyond caring for a relative who died of this disease (index patient). We investigated the source of infection for patient A by surveying other family contacts, healthcare personnel, and community members, and testing samples for Zika virus. We identified 19 family contacts who had similar exposures to the index patient; 86 healthcare personnel had contact with the index patient, including 57 (66%) who had contact with body fluids. Of 218 community members interviewed, 28 (13%) reported signs/symptoms and 132 (61%) provided a sample. Except for patient A, no other persons tested had laboratory evidence of recent Zika virus infection. Of 5,875 mosquitoes collected, none were known vectors of Zika virus and all were negative for Zika virus. The mechanism of transmission to patient A remains unknown but was likely person-to-person contact with the index patient.

SUBMITTER: Krow-Lucal ER

PROVIDER: S-EPMC5547791 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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Project description:BACKGROUND:Several Zika virus (ZIKV) outbreaks have occurred since October 2015. Because there is no effective treatment for ZIKV infection, developing an effective surveillance and warning system is currently a high priority to prevent ZIKV infection. Despite Aedes mosquitos having been known to spread ZIKV, the calculation approach is diverse, and only applied to local areas. This study used meteorological measurements to monitor ZIKV infection due to the high correlation between climate change and Aedes mosquitos and the convenience to obtain meteorological data from weather monitoring stations. METHODS:This study applied the Bayesian structured additive regression modeling approach to include spatial interactive terms with meteorological factors and a geospatial function in a zero-inflated Poisson model. The study area contained 32 administrative departments in Colombia from October 2015 to December 2017. Weekly ZIKV infection cases and daily meteorological measurements were collected. Mapping techniques were adopted to visualize spatial findings. A series of model selections determined the best combinations of meteorological factors in the same model. RESULTS:When multiple meteorological factors are considered in the same model, both total rainfall and average temperature can best assess the geographic disparities of ZIKV infection. Meanwhile, a 1-in. increase in rainfall is associated with an increase in the logarithm of relative risk (logRR) of ZIKV infection of at most 1.66 (95% credible interval [CI]?=?1.09, 2.15) as well as a 1?°F increase in average temperature is significantly associated with at most 0.79 (95% CI?=?0.12, 1.22) increase in the logRR of ZIKV. Moreover, after controlling rainfall and average temperature, an independent geospatial function in the model results in two departments with an excessive ZIKV risk which may be explained by unobserved factors other than total rainfall and average temperature. CONCLUSION:Our study found that meteorological factors are significantly associated with ZIKV infection across departments. The study determined both total rainfall and average temperature as the best meteorological factors to identify high risk departments of ZIKV infection. These findings can help governmental agencies monitor at risk areas according to meteorological measurements, and develop preventions in those at risk areas in priority.

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Zika Virus Infection in Patient with No Known Risk Factors, Utah, USA, 2016.

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