COVID-19的狂热影响:六和七的分析研究

Q3 Mathematics
Madhu Raina
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引用次数: 0

摘要

这个新年的警钟提醒我们,COVID-19是德谟克利斯之剑,一种流行病变成了大流行。似乎每天都在达到一个新的可怕的里程碑,全球各国政府都在努力控制其传播。疫情正在加速蔓延,信息每小时都在更新和变化。迄今为止,已向世界卫生组织报告了全球范围内令人震惊的伤亡人数。尽管如此,世界正在紧迫而坚定地应对这一新的敌人。挑战是巨大的,但反应是巨大的。这种新型病原体的记录特征和多个序列正在全球平台上共享,从而开发出许多诊断方法。目前没有针对COVID-19的有效治疗方法,迫切需要国际社会团结一致,寻找有价值的治疗方法。本文简要介绍了致命病毒取得的一些里程碑,从而对医学科学提出了挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Zealous clout of COVID-19: analytical research at sixes and sevens
Abstract This New Year’s wake-up call warned us of Democles’ sword in the form of COVID-19, an epidemic turned pandemic. Seeming to reach a novel and awful landmark every day, governments across globe are fighting on toes to contain its spread. The pandemic is accelerating and information is being updated and changing by the hour. Till date shattering causalities across globe have been reported to World Health Organization. Nevertheless, the world is responding to this novel enemy with urgency and purpose. The challenge is great, but the response has been massive. Record characterisation and multiple sequences of this novel pathogen are being shared on global platform leading to a lot of diagnostics to get developed. Currently no treatment is effective against COVID-19 and there is a desperate need for international solidarity for valuable therapeutics. Present article briefs some milestones achieved by the killer virus thereby posing a challenge to medical science.
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来源期刊
Epidemiologic Methods
Epidemiologic Methods Mathematics-Applied Mathematics
CiteScore
2.10
自引率
0.00%
发文量
7
期刊介绍: Epidemiologic Methods (EM) seeks contributions comparable to those of the leading epidemiologic journals, but also invites papers that may be more technical or of greater length than what has traditionally been allowed by journals in epidemiology. Applications and examples with real data to illustrate methodology are strongly encouraged but not required. Topics. genetic epidemiology, infectious disease, pharmaco-epidemiology, ecologic studies, environmental exposures, screening, surveillance, social networks, comparative effectiveness, statistical modeling, causal inference, measurement error, study design, meta-analysis
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