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Background: Vaccine-derived poliovirus (VDPV) remains a significant barrier to global poliomyelitis eradication. Following 18 years of polio-free status, an epidemic of circulating VDPV type 1 (cVDPV1) occurred in Papua New Guinea (PNG) in 2018. We describe the epidemiology of the 2018 cVDPV1 epidemic in PNG, and identify risk factors which may aid future policy and control efforts.Methods: Data pertaining to the 2018 PNG epidemic were extracted from EPIWATCH and supplemented with data from other sources, such as the World Health Organization (WHO), and published literature. Descriptive analyses were undertaken, and key risk factors identified.Results: 26 cases of cVDPV1 were confirmed throughout the duration of the epidemic (April to October 2018) in nine provinces. Of the 26 cases, 19 (73%) were males and 7 (27%) were females, and most of the cases (73%) occurred in children under the age of five. Population immunization coverage of three doses of oral polio vaccine (OPV3) was found to fluctuate between 60-80% between 2000 and 2018. Nonpolio Acute Flaccid Paralysis (NPAFP) surveillance rates were also found to be suboptimal over this period.Conclusions: A combination of low routine immunization coverage, lacking supplementary immunization activities, and ineffective surveillance systems, in the context of a struggling health system, culminated in this epidemic. To prevent future poliomyelitis epidemics in PNG, emphasis must be placed on supporting the health system to maintain high vaccination coverage, in conjunction with robust and effective surveillance systems.

Hierarchical neural networks are often used to model inherent structures within dialogues. For goal-oriented dialogues, these models miss a mechanism adhering to the goals and neglect the distinct conversational patterns between two interlocutors. In this work, we propose Goal-Embedded Dual Hierarchical Attentional Encoder-Decoder (G-DuHA) able to center around goals and capture interlocutorlevel disparity while modeling goal-oriented dialogues. Experiments on dialogue generation, response generation, and human evaluations demonstrate that the proposed model successfully generates higher-quality, more diverse and goal-centric dialogues. Moreover, we apply data augmentation via goal-oriented dialogue generation for task-oriented dialog systems with better performance achieved.