COVID-19 in India: Statewise Analysis and Prediction
COVID-19 in India: Statewise Analysis and Prediction
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DOI：
10.2196/20341
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发表时间：
2020-07-01
影响因子：
8.5
通讯作者：
Chakraborty, Bibhas
中科院分区：
文献类型：
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作者：
Ghosh, Palash;Ghosh, Rik;Chakraborty, Bibhas
背景资料：高传染性冠状病毒病（COVID-19）于二零一九年十二月首次在中国武汉发现，随后蔓延至全球212个国家及地区，感染数百万人。在印度这个约13亿人口的大国，2020年1月30日，在一名从武汉返回的学生身上首次发现了这种疾病。截至2020年5月3日，印度的确诊感染总数超过37，000人，目前正在快速增长。目的：大多数先前的研究和媒体报道都集中在整个国家的感染人数上。然而，考虑到印度的面积和多样性，必须分别研究疾病在每个邦的传播情况，因为每个邦的情况都大不相同。在本文中，我们的目标是分析印度每个州的感染人数数据（仅限于那些有足够数据进行预测的州），并预测未来30天内该州的感染人数。我们希望这些州的预测能帮助州政府更好地利用有限的医疗资源。方法：由于任何一个模型的预测都有可能产生误导，我们考虑了三个增长模型，即逻辑模型、指数模型和易感-感染-易感模型，最后，使用无模型最大日感染率（ESTA）函数，从逻辑模型和指数模型中开发了一个数据驱动的预测集合在过去的两周内（衡量最近的趋势）作为权重。该指数被用来衡量全国封锁的成功与否。我们共同解释的结果，从所有的模型沿着与最近的平均值为每个国家和分类的状态为严重，中度，或controlled.Results：我们发现，7个国家，即马哈拉施特拉邦，德里，古吉拉特邦，中央邦，安得拉邦，北方邦，和西孟加拉是在严重的类别。在其余的邦中，泰米尔纳德邦、拉贾斯坦邦、旁遮普和比哈尔邦属于中等类别，而喀拉拉邦、哈里亚纳邦、查谟和克什米尔、卡纳塔克邦和特兰加纳属于受控类别。我们还列出了每个州的各种模型的实际预测数字。与logistic和指数模型对应的所有R-2值均大于0.90，表明拟合优度合理。我们还提供了一个网络应用程序，可以查看基于定期更新的最新数据的预测。结论：具有非下降趋势的国家需要立即加强预防措施，以抗击COVID-19大流行。另一方面，疫情下降的州可以保持相同的状态，看到疫情连续14天缓慢变为零或负值，从而能够宣布疫情结束。
Background: The highly infectious coronavirus disease (COVID-19) was first detected in Wuhan, China in December 2019 and subsequently spread to 212 countries and territories around the world, infecting millions of people. In India, a large country of about 1.3 billion people, the disease was first detected on January 30, 2020, in a student returning from Wuhan. The total number of confirmed infections in India as of May 3, 2020, is more than 37,000 and is currently growing fast.Objective: Most of the prior research and media coverage focused on the number of infections in the entire country. However, given the size and diversity of India, it is important to look at the spread of the disease in each state separately, wherein the situations are quite different. In this paper, we aim to analyze data on the number of infected people in each Indian state (restricted to only those states with enough data for prediction) and predict the number of infections for that state in the next 30 days. We hope that such statewise predictions would help the state governments better channelize their limited health care resources.Methods: Since predictions from any one model can potentially be misleading, we considered three growth models, namely, the logistic, the exponential, and the susceptible-infectious-susceptible models, and finally developed a data-driven ensemble of predictions from the logistic and the exponential models using functions of the model-free maximum daily infection rate (DIR) over the last 2 weeks (a measure of recent trend) as weights. The DIR is used to measure the success of the nationwide lockdown. We jointly interpreted the results from all models along with the recent DIR values for each state and categorized the states as severe, moderate, or controlled.Results: We found that 7 states, namely, Maharashtra, Delhi, Gujarat, Madhya Pradesh, Andhra Pradesh, Uttar Pradesh, and West Bengal are in the severe category. Among the remaining states, Tamil Nadu, Rajasthan, Punjab, and Bihar are in the moderate category, whereas Kerala, Haryana, Jammu and Kashmir, Karnataka, and Telangana are in the controlled category. We also tabulated actual predicted numbers from various models for each state. All the R-2 values corresponding to the logistic and the exponential models are above 0.90, indicating a reasonable goodness of fit. We also provide a web application to see the forecast based on recent data that is updated regularly.Conclusions: States with nondecreasing DIR values need to immediately ramp up the preventive measures to combat the COVID-19 pandemic. On the other hand, the states with decreasing DIR can maintain the same status to see the DIR slowly become zero or negative for a consecutive 14 days to be able to declare the end of the pandemic.