Prioritizing COVID-19 Vaccine Delivery for the Indian Population

M. Singh, S. Modak, D. Sarkar
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Abstract

As India has successfully developed vaccine to fight against the Covid-19 pandemic, the government has started its immunization program to vaccinate the population. Initially with the limited availability in vaccines a prioritized roadmap is required to suggest public health strategies and target priority groups on the basis of population demographics, health survey information, city/region density, cold storage facilities, vaccine availability and epidemiologic settings. In this paper, a machine learning based predictive model is presented to help the government make informed decisions/insights around epidemiological and vaccine supply circumstances by predicting India's more critical segments that need to be catered with vaccine deliveries as prior as possible. Public data were scraped to create the dataset, exploratory data analysis was performed on the dataset to extract important features on which clustering and ranking algorithms were performed to figure out the importance and urgency of vaccine deliveries in each region.
优先为印度人口提供COVID-19疫苗
随着印度成功开发出对抗新冠肺炎大流行的疫苗,政府已经启动了为人口接种疫苗的免疫计划。在最初疫苗供应有限的情况下,需要制定优先路线图,根据人口统计、健康调查信息、城市/区域密度、冷藏设施、疫苗供应和流行病学环境,建议公共卫生战略和目标优先群体。在本文中,提出了一个基于机器学习的预测模型,通过预测印度需要尽可能提前提供疫苗的更关键部分,帮助政府就流行病学和疫苗供应情况做出明智的决策/见解。收集公共数据创建数据集,对数据集进行探索性数据分析,提取重要特征,并对其进行聚类和排序算法,以确定每个地区疫苗交付的重要性和紧迫性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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