The coronavirus has a high basic reproduction number ($R0$) and has caused the global COVID-19 pandemic. Governments are implementing lockdowns that are leading to economic fallout in many countries. Policy makers can take better decisions if provided with the indicators connected with the disease spread. This study is aimed to cluster the countries using social, economic, health and environmental related metrics affecting the disease spread so as to implement the policies to control the widespread of disease. Thus, countries with similar factors can take proactive steps to fight against the pandemic. The data is acquired for 79 countries and 18 different feature variables (the factors that are associated with COVID-19 spread) are selected. Pearson Product Moment Correlation Analysis is performed between all the feature variables with cumulative death cases and cumulative confirmed cases individually to get an insight of relation of these factors with the spread of COVID-19. Unsupervised k-means algorithm is used and the feature set includes economic, environmental indicators and disease prevalence along with COVID-19 variables. The learning model is able to group the countries into 4 clusters on the basis of relation with all 18 feature variables. We also present an analysis of correlation between the selected feature variables, and COVID-19 confirmed cases and deaths. Prevalence of underlying diseases shows strong correlation with COVID-19 whereas environmental health indicators are weakly correlated with COVID-19.

冠状病毒具有很高的基本繁殖数（$R0$) 并导致了全球 COVID-19 大流行。各国政府正在实施封锁，导致许多国家的经济受到影响。如果提供与疾病传播相关的指标，决策者可以做出更好的决策。本研究旨在使用影响疾病传播的社会、经济、健康和环境相关指标对国家进行聚类，以实施控制疾病传播的政策。因此，具有类似因素的国家可以采取积极措施抗击疫情。获取了 79 个国家/地区的数据，并选择了 18 个不同的特征变量（与 COVID-19 传播相关的因素）。Pearson Product Moment Correlation Analysis 分别在所有具有累计死亡病例和累计确诊病例的特征变量之间进行，以深入了解这些因素与 COVID-19 传播的关系。使用无监督的 k-means 算法，特征集包括经济、环境指标和疾病流行率以及 COVID-19 变量。学习模型能够根据与所有 18 个特征变量的关系将国家分为 4 个集群。我们还分析了所选特征变量与 COVID-19 确诊病例和死亡之间的相关性。基础疾病的患病率与 COVID-19 有很强的相关性，而环境卫生指标与 COVID-19 的相关性较弱。