The rapid transmission of highly contagious infectious diseases within communities can yield potential hotspots or clusters across geographies. For COVID-19, the impact of population density on transmission models demonstrates mixed findings. This study aims to determine the correlations between population density, clusters, and COVID-19 incidence across districts and regions in Malaysia. This countrywide ecological study was conducted between 22 January 2021 and 4 February 2021 involving 51,476 active COVID-19 cases during Malaysia’s third wave of the pandemic, prior to the reimplementation of lockdowns. Population data from multiple sources was aggregated and spatial analytics were performed to visualize distributional choropleths of COVID-19 cases in relation to population density. Hierarchical cluster analysis was used to synthesize dendrograms to demarcate potential clusters against population density. Region-wise correlations and simple linear regression models were deduced to observe the strength of the correlations and the propagation effects of COVID-19 infections relative to population density. Distributional heats in choropleths and cluster analysis showed that districts with a high number of inhabitants and a high population density had a greater number of cases in proportion to the population in that area. The Central region had the strongest correlation between COVID-19 cases and population density (r = 0.912; 95% CI 0.911, 0.913; p < 0.001). The propagation effect and the spread of disease was greater in urbanized districts or cities. Population density is an important factor for the spread of COVID-19 in Malaysia.

中文翻译：

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人口密度对马来西亚 COVID-19 传播的影响：一项生态研究

社区内高度传染性疾病的快速传播可能会产生跨地域的潜在热点或集群。对于 COVID-19，人口密度对传播模型的影响显示出不同的发现。本研究旨在确定马来西亚各地区和地区的人口密度、集群和 COVID-19 发病率之间的相关性。这项全国性生态研究于 2021 年 1 月 22 日至 2021 年 2 月 4 日期间进行，涉及马来西亚第三波大流行期间的 51,476 例活跃的 COVID-19 病例，然后重新实施封锁。汇总了来自多个来源的人口数据，并进行了空间分析，以可视化与人口密度相关的 COVID-19 病例的分布区域。分层聚类分析用于合成树状图，以根据人口密度划分潜在的聚类。推导出区域相关性和简单的线性回归模型，以观察相关性的强度和 COVID-19 感染相对于人口密度的传播效应。等值区分布热度和聚类分析表明，居民人数多、人口密度高的地区的病例数与该地区的人口比例有关。中部地区 COVID-19 病例与人口密度之间的相关性最强（推导出区域相关性和简单的线性回归模型，以观察相关性的强度和 COVID-19 感染相对于人口密度的传播效应。等值区分布热度和聚类分析表明，居民人数多、人口密度高的地区的病例数与该地区的人口比例有关。中部地区 COVID-19 病例与人口密度之间的相关性最强（推导出区域相关性和简单的线性回归模型，以观察相关性的强度和 COVID-19 感染相对于人口密度的传播效应。等值区分布热度和聚类分析表明，居民人数多、人口密度高的地区的病例数与该地区的人口比例有关。中部地区 COVID-19 病例与人口密度之间的相关性最强（r = 0.912；95% 置信区间 0.911、0.913；p < 0.001)。城市化地区或城市的传播效应和疾病传播更大。人口密度是 COVID-19 在马来西亚传播的重要因素。