Ravinder R, Singh Sourabh, Bishnoi Suresh, Jan Amreen, Sharma Amit, Kodamana Hariprasad, Krishnan N M Anoop
Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
Molecular Medicine Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Road, New Delhi, 110 067, India.
Heliyon. 2020 Dec 14;6(12):e05722. doi: 10.1016/j.heliyon.2020.e05722. eCollection 2020 Dec.
The SARS-CoV-2 driven disease COVID-19 is pandemic with increasing human and monetary costs. COVID-19 has put an unexpected and inordinate degree of pressure on healthcare systems of strong and fragile countries alike. To launch both containment and mitigation measures, each country requires estimates of COVID-19 incidence as such preparedness allows agencies to plan efficient resource allocation and to design control strategies. Here, we have developed a new adaptive, interacting, and cluster-based mathematical model to predict the granular trajectory of COVID-19. We have analyzed incidence data from three currently afflicted countries of Italy, the United States of America, and India. We show that our approach predicts state-wise COVID-19 spread for each country with reasonable accuracy. We show that R as the effective reproduction number, exhibits significant spatial variations in these countries. However, by accounting for the spatial variation of R in an adaptive fashion, the predictive model provides estimates of the possible asymptomatic and undetected COVID-19 cases, both of which are key contributors in COVID-19 transmission. We have applied our methodology to make detailed predictions for COVID19 incidences at the district and state level in India. Finally, to make the models available to the public at large, we have developed a web-based dashboard, namely "Predictions and Assessment of Corona Infections and Transmission in India" (PRACRITI, see http://pracriti.iitd.ac.in), which provides the detailed R values and a three-week forecast of COVID cases.
由严重急性呼吸综合征冠状病毒2（SARS-CoV-2）引发的疾病2019冠状病毒病（COVID-19）正在全球大流行，造成的人员和经济损失不断增加。COVID-19给强国和弱国的医疗系统都带来了意想不到的巨大压力。为了实施遏制和缓解措施，每个国家都需要对COVID-19发病率进行估算，因为这样的准备工作能让各机构规划有效的资源分配并设计控制策略。在此，我们开发了一种新的基于自适应、交互和聚类的数学模型，以预测COVID-19的详细传播轨迹。我们分析了来自意大利、美利坚合众国和印度这三个当前受灾国家的发病率数据。我们表明，我们的方法能够以合理的准确性预测每个国家COVID-19按州的传播情况。我们表明，作为有效繁殖数的R在这些国家呈现出显著的空间差异。然而，通过以自适应方式考虑R的空间差异，该预测模型能够估算出可能的无症状和未被检测到的COVID-19病例数，这两者都是COVID-19传播的关键因素。我们已应用我们的方法对印度各邦和地区的COVID-19发病率进行详细预测。最后，为了让广大公众能够使用这些模型，我们开发了一个基于网络的仪表板，即“印度冠状病毒感染与传播的预测与评估”（PRACRITI，见http://pracriti.iitd.ac.in），它提供详细的R值以及COVID病例的三周预测。
