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Background Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to severe dengue shock syndrome. However, estimating the risk of severe outcomes using clinical presentation or laboratory test results for rapid patient triage remains a challenge. Here, we aimed to develop prognostic models for severe dengue using machine learning, according to demographic information and clinical laboratory data of patients with dengue. Methodology/Principal findings Out of 1,581 patients in the National Cheng Kung University Hospital with suspected dengue infections and subjected to NS1 antigen, IgM and IgG, and qRT-PCR tests, 798 patients including 138 severe cases were enrolled in the study. The primary target outcome was severe dengue. Machine learning models were trained and tested using the patient dataset that included demographic information and qualitative laboratory test results collected on day 1 when they sought medical advice. To develop prognostic models, we applied various machine learning methods, including logistic regression, random forest, gradient boosting machine, support vector classifier, and artificial neural network, and compared the performance of the methods. The artificial neural network showed the highest average discrimination area under the receiver operating characteristic curve (0.8324 ± 0.0268) and balance accuracy (0.7523 ± 0.0273). According to the model explainer that analyzed the contributions/co-contributions of the different factors, patient age and dengue NS1 antigenemia were the two most important risk factors associated with severe dengue. Additionally, co-existence of anti-dengue IgM and IgG in patients with dengue increased the probability of severe dengue. Conclusions/Significance We developed prognostic models for the prediction of dengue severity in patients, using machine learning. The discriminative ability of the artificial neural network exhibited good performance for severe dengue prognosis. This model could help clinicians obtain a rapid prognosis during dengue outbreaks. However, the model requires further validation using external cohorts in future studies.

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Background: Influenza A viruses cause epidemics/severe pandemics that pose a great global health threat. Among eight viral RNA segments, the multiple functions of nucleoprotein (NP) play important roles in viral replication and transcription. Methods: To understand how NP contributes to the virus evolution, we analyzed the NP gene of H3N2 viruses in Taiwan and 14,220 NP sequences collected from Influenza Research Database. The identified genetic variations were further analyzed by mini-genome assay, virus growth assay, viral RNA and protein expression as well as ferret model to analyze their impacts on viral replication properties.Results: The NP genetic analysis by Taiwan and global sequences showed similar evolution pattern that the NP backbones changed through time accompanied with specific residue substitutions from 1999 to 2018. Other than the conserved residues, fifteen sporadic substitutions were observed in which the 31R, 377G and 450S showed higher frequency. We found 31R and 450S decreased polymerase activity while the dominant residues (31 K and 450G) had higher activity. The 31 K and 450G showed better viral translation and replication in vitro and in vivo.Conclusions: These findings indicated variations identified in evolution have roles in modulating viral replication in vitro and in vivo. This study demonstrates that the interaction between variations of NP during virus evolution deserves future attention.