Gupta Mousumi, Nirola Madhab, Sharma Arpan, Dhungel Prasanna, Singh Harpreet, Gupta Amlan
Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, 737136, India.
Division of Biomedical Informatics, Indian Council of Medical Research, Delhi, 110029, India.
Lancet Reg Health Southeast Asia. 2024 Jul 19;28:100451. doi: 10.1016/j.lansea.2024.100451. eCollection 2024 Sep.
During the initial phase of the COVID-19 pandemic, the Government of India implemented a nationwide lockdown, sealing borders across states and districts. The northeastern region of India, surrounded by three international borders and connected to mainland India by a narrow passage, faced particular isolation. This isolation resulted in these states forming a relatively closed population. Consequently, the availability of population-based data from Indian Council of Medical Research, tracked through national identification cards, offered a distinctive opportunity to understand the spread of the virus among non-vaccinated and non-exposed populations. This research leverages this dataset to comprehend the repercussions within isolated populations.
The inter-district variability was visualized using geospatial analysis. The patterns do not follow any established grounded theories on disease spread. Out of 7.1 million total data weekly 0.35 million COVID-19-positive northeast data was taken from April 2020 to February 2021 including "date, test result, population density, area, latitude, longitude, district, and state" to identify the spread pattern using a modified reaction-diffusion model (MRD-Model) and Geographic Information System.
The analysis of the closed population group revealed an initial uneven yet rapidly expanding geographical spread characterized by a high diffusion rate α approximately 0.4503 and a lower reaction rate β approximately 0.0256, which indicated a slower growth trajectory of case numbers rather than exponential escalation. In the latter stages, COVID-19 incidence reached zero in numerous districts, while in others, the reported cases did not exceed 100.
The MRD-Model effectively captured the disease transmission dynamics in the abovementioned setting. This enhanced understanding of COVID-19 spread in remote, isolated regions provided by the MRD modelling framework can guide targeted public health strategies for similar isolated areas.
This study is Funded by Indian Council of Medical Research (ICMR).
在新冠疫情的初始阶段，印度政府实施了全国范围的封锁，封锁了各邦和地区的边界。印度东北地区被三条国际边界环绕，仅通过一条狭窄通道与印度大陆相连，面临着特殊的隔离状态。这种隔离导致这些邦形成了一个相对封闭的人口群体。因此，通过国家身份证追踪到的印度医学研究理事会提供的基于人群的数据，为了解病毒在未接种疫苗和未接触过病毒人群中的传播情况提供了独特的机会。本研究利用该数据集来理解隔离人群中的影响。
使用地理空间分析可视化地区间的变异性。这些模式并不遵循任何关于疾病传播的既定基础理论。在2020年4月至2021年2月期间，从每周710万条数据中选取了35万条新冠阳性的东北地区数据，包括“日期、检测结果、人口密度、面积、纬度、经度、地区和邦”，以使用改进的反应扩散模型（MRD模型）和地理信息系统来识别传播模式。
对封闭人群组的分析显示，最初地理传播不均衡但迅速扩大，其特征是扩散率α约为0.4503较高，反应率β约为0.0256较低，这表明病例数的增长轨迹较为缓慢，而非指数级增长。在后期阶段，许多地区的新冠发病率降至零，而在其他地区，报告病例数不超过100例。
MRD模型有效地捕捉了上述情况下的疾病传播动态。MRD建模框架对新冠病毒在偏远、隔离地区传播的这种增强理解，可以为类似隔离地区的针对性公共卫生策略提供指导。
本研究由印度医学研究理事会（ICMR）资助。
