Geospatial analysis of contagious infection growth and cross-boundary transmission in non-vaccinated districts of North-East Indian states during the COVID-19 pandemic

IF 5 Q1 HEALTH CARE SCIENCES & SERVICES The Lancet regional health. Southeast Asia Pub Date : 2024-07-19 DOI:10.1016/j.lansea.2024.100451
Mousumi Gupta , Madhab Nirola , Arpan Sharma , Prasanna Dhungel , Harpreet Singh , Amlan Gupta
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Abstract

Background

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.

Methods

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.

Findings

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.

Interpretation

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.

Funding

This study is Funded by Indian Council of Medical Research (ICMR).

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COVID-19 大流行期间印度东北部各邦未接种疫苗地区传染性感染增长和跨境传播的地理空间分析
背景在 COVID-19 大流行的初期阶段,印度政府在全国范围内实施了封锁,封闭了各邦和各区的边界。印度东北部地区被三条国际边界所包围,与印度大陆之间只有一条狭窄的通道相连,因此面临着特别的隔离。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,印度医学研究委员会通过国民身份证追踪的人口数据为了解病毒在未接种疫苗和未暴露人群中的传播情况提供了一个独特的机会。本研究利用这一数据集来了解病毒在孤立人群中的反响。这些模式并不遵循任何既定的疾病传播基础理论。从 2020 年 4 月至 2021 年 2 月的 710 万条总数据中,每周抽取 35 万条 COVID-19 阳性的东北地区数据,包括 "日期、检测结果、人口密度、地区、经纬度、区和州",利用改进的反应-扩散模型(MRD-模型)和地理信息系统确定传播模式。研究结果对封闭人群的分析表明,最初的地理分布不均衡,但迅速扩大,其特点是扩散率高,α 约为 0.4503,反应率较低,β 约为 0.0256,这表明病例数的增长轨迹较慢,而不是指数式增长。在后期阶段,许多地区的 COVID-19 发病率为零,而在其他地区,报告病例不超过 100 例。MRD 模型框架增强了人们对 COVID-19 在偏远、孤立地区传播情况的了解,可为类似孤立地区制定有针对性的公共卫生策略提供指导。
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