Travel Surveillance and Genomics Uncover a Hidden Zika Outbreak during the Waning Epidemic.

The Zika epidemic in the Americas has challenged surveillance and control. As the epidemic appears to be waning, it is unclear whether transmission is still ongoing, which is exacerbated by discrepancies in reporting. To uncover locations with lingering outbreaks, we investigated travel-associated Zika cases to identify transmission not captured by reporting. We uncovered an unreported outbreak in Cuba during 2017, a year after peak transmission in neighboring islands. By sequencing Zika virus, we show that the establishment of the virus was delayed by a year and that the ensuing outbreak was sparked by long-lived lineages of Zika virus from other Caribbean islands. Our data suggest that, although mosquito control in Cuba may initially have been effective at mitigating Zika virus transmission, such measures need to be maintained to be effective. Our study highlights how Zika virus may still be "silently" spreading and provides a framework for understanding outbreak dynamics. VIDEO ABSTRACT.

An outbreak vector-host epidemic model with spatial structure: the 2015-2016 Zika outbreak in Rio De Janeiro.

BACKGROUND: A deterministic model is developed for the spatial spread of an epidemic disease in a geographical setting. The disease is borne by vectors to susceptible hosts through criss-cross dynamics. The model is focused on an outbreak that arises from a small number of infected hosts imported into a subregion of the geographical setting. The goal is to understand how spatial heterogeneity of the vector and host populations influences the dynamics of the outbreak, in both the geographical spread and the final size of the epidemic. METHODS: Partial differential equations are formulated to describe the spatial interaction of the hosts and vectors. The partial differential equations have reaction-diffusion terms to describe the criss-cross interactions of hosts and vectors. The partial differential equations of the model are analyzed and proven to be well-posed. A local basic reproduction number for the epidemic is analyzed. RESULTS: The epidemic outcomes of the model are correlated to the spatially dependent parameters and initial conditions of the model. The partial differential equations of the model are adapted to seasonality of the vector population, and applied to the 2015-2016 Zika seasonal outbreak in Rio de Janeiro Municipality in Brazil. CONCLUSIONS: The results for the model simulations of the 2015-2016 Zika seasonal outbreak in Rio de Janeiro Municipality indicate that the spatial distribution and final size of the epidemic at the end of the season are strongly dependent on the location and magnitude of local outbreaks at the beginning of the season. The application of the model to the Rio de Janeiro Municipality Zika 2015-2016 outbreak is limited by incompleteness of the epidemic data and by uncertainties in the parametric assumptions of the model.

Inequality, Zika epidemics, and the lack of reproductive rights in Latin America.

It is well-documented that structural economic inequalities in Latin America are expressed through and reinforce existing gender gaps. This article aims to look at the relationship between structural inequalities and reproductive health in the case of the Zika epidemic. The consequences of the epidemic will continue to affect the same women whose access to comprehensive reproductive health services, including safe abortion, is restricted at best.

Manda pra um, joga pra outro: a emergência sanitária da zika nas margens do Estado / Send it to one, throw it to another: the Zika health emergency on the margins of the State

Resumo Distantes temporalmente da declaração da emergência em saúde pública de importância internacional (ESPII) e emergência em saúde pública de importância nacional (ESPIN) provocada pela epidemia de zika, no ano de 2015, enunciamos a herança da emergência humanitária. Com base em uma pesquisa qualitativa, por meio de grupos focais realizados com profissionais de saúde e familiares das crianças afetadas epidemia de zika em Natal e Feira de Santana, buscamos discutir esse fenômeno de saúde pública pelas lentes da Antropologia do Estado. Concluímos que o não reconhecimento do Estado como uma instância encarnada no cotidiano das práticas por parte dos seus agentes locais leva à reprodução de práticas discriminatórias esvaziadas de sentido político e do reconhecimento de moralidades que permeiam as ausências nas ações de promoção de saúde e estratégias de reconhecimento e busca por estratégias para a garantia do direito à saúde.

Abstract Temporarily distant from the declaration of the Public Health Emergency of International Importance (ESPII) and Public Health Emergency of National Importance (ESPIN) caused by the Zika epidemic, in 2015, we enunciate the legacy of the humanitarian emergency. Based on qualitative research, through focus groups with health professionals and families of children affected by the Zika epidemic in Natal and Feira de Santana, we seek to discuss this public health phenomenon through the lens of State Anthropology. We conclude that the non-recognition of the State as an instance embodied in the daily practices of its local agents leads to the reproduction of discriminatory practices emptied of political sense and the recognition of moralities that permeate the absences in health promotion actions and recognition strategies, and search for methods to guarantee the right to health.

Dynamics of Health Agency Response and Public Engagement in Public Health Emergency: A Case Study of CDC Tweeting Patterns During the 2016 Zika Epidemic.

BACKGROUND: Social media have been increasingly adopted by health agencies to disseminate information, interact with the public, and understand public opinion. Among them, the Centers for Disease Control and Prevention (CDC) is one of the first US government health agencies to adopt social media during health emergencies and crisis. It had been active on Twitter during the 2016 Zika epidemic that caused 5168 domestic noncongenital cases in the United States. OBJECTIVE: The aim of this study was to quantify the temporal variabilities in CDC's tweeting activities throughout the Zika epidemic, public engagement defined as retweeting and replying, and Zika case counts. It then compares the patterns of these 3 datasets to identify possible discrepancy among domestic Zika case counts, CDC's response on Twitter, and public engagement in this topic. METHODS: All of the CDC-initiated tweets published in 2016 with corresponding retweets and replies were collected from 67 CDC-associated Twitter accounts. Both univariate and multivariate time series analyses were performed in each quarter of 2016 for domestic Zika case counts, CDC tweeting activities, and public engagement in the CDC-initiated tweets. RESULTS: CDC sent out >84.0% (5130/6104) of its Zika tweets in the first quarter of 2016 when Zika case counts were low in the 50 US states and territories (only 560/5168, 10.8% cases and 662/38,885, 1.70% cases, respectively). While Zika case counts increased dramatically in the second and third quarters, CDC efforts on Twitter substantially decreased. The time series of public engagement in the CDC-initiated tweets generally differed among quarters and from that of original CDC tweets based on autoregressive integrated moving average model results. Both original CDC tweets and public engagement had the highest mutual information with Zika case counts in the second quarter. Furthermore, public engagement in the original CDC tweets was substantially correlated with and preceded actual Zika case counts. CONCLUSIONS: Considerable discrepancies existed among CDC's original tweets regarding Zika, public engagement in these tweets, and actual Zika epidemic. The patterns of these discrepancies also varied between different quarters in 2016. CDC was much more active in the early warning of Zika, especially in the first quarter of 2016. Public engagement in CDC's original tweets served as a more prominent predictor of actual Zika epidemic than the number of CDC's original tweets later in the year.