Covid-19 Pandemic是一个持续的全球大流行，这导致了公共卫生部门和全球经济中的前所未有的中断。病毒，SARS-COV-2负责冠状病毒病的快速传播。由于其传染性，病毒可以容易地感染不受保护和暴露的个体，从轻度到严重症状。对怀孕母亲和新生儿的病毒效应的研究现在是平民和公共卫生工作者在全球范围内的关于病毒如何影响母亲和新生儿健康的问题。本文旨在制定一种预测模型，以估算基于记录的症状的携带型患者死亡的可能性：呼吸困难，咳嗽，鼻子，关节痛和肺炎的诊断。我们研究中使用的机器学习模型是支持向量机，决策树，随机林，渐变升压和人工神经网络。该模型提供了令人印象深刻的结果，可以准确地预测给定输入的怀孕母亲的死亡率。3型号（ANN，渐变升压，随机林）的精度率为100％，最高精度得分（梯度提升，ANN）是95 ％，最高召回（支持向量机）为92.75％，最高F1得分（梯度提升，ANN）为94.66％。由于模型的准确性，怀孕的母亲可以基于其由于病毒而导致的可能性即时治疗。全球卫生工人可以利用该模型列出急诊患者，最终可以降低Covid-19诊断患者的死亡率。

There has been a recent explosion of impressive generative models that can produce high quality images (or videos) conditioned on text descriptions. However, all such approaches rely on conditional sentences that contain unambiguous descriptions of scenes and main actors in them. Therefore employing such models for more complex task of story visualization, where naturally references and co-references exist, and one requires to reason about when to maintain consistency of actors and backgrounds across frames/scenes, and when not to, based on story progression, remains a challenge. In this work, we address the aforementioned challenges and propose a novel autoregressive diffusion-based framework with a visual memory module that implicitly captures the actor and background context across the generated frames. Sentence-conditioned soft attention over the memories enables effective reference resolution and learns to maintain scene and actor consistency when needed. To validate the effectiveness of our approach, we extend the MUGEN dataset and introduce additional characters, backgrounds and referencing in multi-sentence storylines. Our experiments for story generation on the MUGEN, the PororoSV and the FlintstonesSV dataset show that our method not only outperforms prior state-of-the-art in generating frames with high visual quality, which are consistent with the story, but also models appropriate correspondences between the characters and the background.