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Covid-19 Disease Simulation using GAMA platform

机译:使用GAMA平台进行Covid-19疾病模拟

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In less than three months after its emergence in China, the Covid-19 pandemic has spread to at least 180 countries. In the absence of previous experience with this new disease, public health authorities have implemented many experiments in a short period and, in a mostly uninformed way, various combinations of interventions at different scales. These include a ban on large gatherings, closure of borders— individual and collective containment, monitoring of population movements, social tracing, social distancing, etc. However, as the pandemic is progressing, data are collected from various sources. On the one hand, authorities allow to make informed adjustments to the current and planned interventions and reveal them. On the other hand, an urgent need for tools and methodologies that enable fast analysis, understanding, comparison, and forecasting of the effectiveness of the responses against COVID-19 across different communities and contexts. In this perspective, computational modeling appears as invaluable leverage as it allows us to explore in silico a range of intervention strategies before the potential phase of field implementation.
机译:在中国出现后不到三个月的时间里,Covid-19大流行已经蔓延到至少180个国家。在没有这种新疾病的经验的情况下,公共卫生当局在短期内进行了许多实验,并且以一种几乎不为人所知的方式实施了不同规模的干预措施的各种组合。这些措施包括禁止大型聚会,封闭边界(个人和集体收容),监视人口流动,社会追踪,社会疏远等。然而,随着大流行的进行,数据来自各种来源。一方面,当局允许对当前和计划中的干预措施进行知情调整,并予以披露。另一方面,迫切需要能够在不同社区和环境中快速分析,理解,比较和预测针对COVID-19的响应的有效性的工具和方法。从这个角度来看,计算模型似乎具有不可估量的作用,因为它使我们能够在野外实施的潜在阶段之前,以计算机方式探索一系列干预策略。

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