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Background: To characterize neurodevelopmental abnormalities in children up to 36 months of age with congenital Zika virus exposure. Methods: From the U.S. Zika Pregnancy and Infant Registry, a national surveillance system to monitor pregnancies with laboratory evidence of Zika virus infection, pregnancy outcomes and presence of Zika associated birth defects (ZBD) were reported among infants with available information. Neurologic sequelae and developmental delay were reported among children with ≥ 1 follow-up exam after 14 days of age or with ≥ 1 visit with development reported, respectively. Results: Among 2,248 infants, 9.9% were born preterm, and 10.0% were small-for-gestational age. Overall, 122 (5.4%) had any ZBD; 91.8% of infants had brain abnormalities or microcephaly, 23.0% had eye abnormalities, and 14.8% had both. Of 1,881 children ≥ 1 follow-up exam reported, neurologic sequelae were more common among children with ZBD (44.6%) vs. without ZBD (1.5%). Of children with ≥ 1 visit with development reported, 46.8% (51/109) of children with ZBD and 7.4% (129/1739) of children without ZBD had confirmed or possible developmental delay. Conclusion: Understanding the prevalence of developmental delays and healthcare needs these children with congenital Zika virus exposure can inform health systems and planning to ensure services are available for affected families.

This paper discusses a model that predicts trap counts of Culex tarsalis, a female mosquito that is responsible for West Nile Virus (WNV) using machine-learning algorithms. Culex mosquitoes are the main transmission vectors for WNV infections. In this research, a Partial Least Square Regression (PLSR) has been deployed to predict mosquito trap counts of Culex tarsalis using historical meteorological and trap count data from 2005–2015. The associations between 10 years of mosquito capture data and the time lagged environmental quantities trap counts, rainfall, temperature, precipitation, and relative humidity were used to generate a predictive model for the population dynamics of this vector species. Statistical measure of Mean Absolute Error (MAE) is compared with other existing actual collected trap counts to analyze accuracy the predictive models. The paper also details the development of a user-friendly web-interface containing interactive web pages that allow users to visualize the North Dakota mosquito population, weather pattern, and WNV incidence data. The interface utilizes multi-layered Google Maps developed through Google Fusion Tables. An understanding of historical data and weather variables is essential for providing sufficient lead time to predict WNV occurrence, and for implementing disease control and prevention strategies such as spray period and hiring of seasonal mosquito workers. Further, an approach similar to the proposed approach of this paper, which involves the integration of data mining and data visualization techniques, brings novelty to vector control initiatives.