Spatial and Spatio-Temporal Epidemiology最新文献

Association between urban green space and transmission of COVID-19 in Oslo, Norway: A Bayesian SIR modeling approach 挪威奥斯陆城市绿地与COVID-19传播的关系：贝叶斯SIR建模方法

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2024-11-26 DOI: 10.1016/j.sste.2024.100699

Halvor Kjellesvig , Suleman Atique , Lars Böcker , Geir Aamodt

Background:

Access to green spaces can provide opportunities for physical activities and social interactions in urban areas during times with strict social distancing. In particular COVID-19 transmission is reduced in ventilated areas. During several waves of the pandemic, this study explores the association between access to urban green spaces and COVID-19 transmission at the district level in Norway’s capital, Oslo.

Methods:

We used daily numbers of confirmed laboratory PCR tests on district levels reported from the second to the fifth wave of the COVID-19 pandemic, from October 15, 2020 to April 15, 2022 in Oslo. We included the population’s access to urban green spaces using two objective measurements: percentage of green area (%Ga) and vegetation cover (NDVI) using 300 and 1000 m buffers. The socio-demographic variables percentage of low-income population, average life expectancy and population density were also included. A Bayesian Susceptible–Infected–Removed (SIR) model was used to take advantage of the daily updated data on COVID-19 incidence and account for spatial and temporal dependencies in the statistical analysis.

Results:

We found that low income as well as population density were significantly associated with incidence of COVID-19, but for the second and third waves only. For the second wave, a one percent increase in the proportion with low income at district level increased the risk of COVID-19 by 7 % (95 % CI: 3 % - 11 %) We did not find associations between access to green space and incidence rate for any of the buffer sizes. The second and third waves were more governed by socio-demographic factors than the fourth and fifth wave.

Conclusions:

Incidence rate of COVID-19 was not associated with access to green space, but to the socio-demographic variables; income, population density, and life expectancy. Access to green space is equally distributed among districts in Oslo which may explain our findings.

背景：在严格保持社交距离的时期，城市地区的绿地可以为体育活动和社会互动提供机会。特别是在通风区域，COVID-19的传播减少了。在几波大流行期间，本研究探讨了挪威首都奥斯陆地区城市绿地使用与COVID-19传播之间的关系。方法：利用2020年10月15日至2022年4月15日在奥斯陆报告的第二波至第五波COVID-19大流行期间每天的实验室PCR确认检测数。我们使用两种客观的测量方法：使用300米和1000米缓冲区的绿地面积百分比（%Ga）和植被覆盖（NDVI）来纳入人口对城市绿地的访问。社会人口变量包括低收入人口百分比、平均预期寿命和人口密度。采用贝叶斯易感-感染-去除（SIR）模型，利用每日更新的COVID-19发病率数据，并在统计分析中考虑时空依赖性。结果：我们发现低收入和人口密度与COVID-19发病率显著相关，但仅适用于第二和第三波。在第二波浪潮中，地区一级低收入人口比例每增加1%，COVID-19的风险就会增加7% （95% CI: 3% - 11%）。我们没有发现任何缓冲面积的绿地使用与发病率之间存在关联。第二次和第三次浪潮比第四次和第五次浪潮更受社会人口因素的支配。结论：2019冠状病毒病发病率与绿地可及性无关，而与社会人口学变量相关；收入，人口密度和预期寿命。在奥斯陆，绿地在各区之间分布均匀，这可以解释我们的研究结果。

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引用次数: 0

Employment industry and opioid overdose risk: A pre- and post-COVID-19 comparison in Kentucky and Massachusetts 2018–2021 就业行业和阿片类药物过量风险：2018-2021年肯塔基州和马萨诸塞州covid -19前后的比较

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2024-11-23 DOI: 10.1016/j.sste.2024.100701

Sumeeta Srinivasan , Shikhar Shrestha , Daniel R. Harris , Olivia Lewis , Peter Rock , Anita Silwal , Jennifer Pustz , Sehun Oh , Gia Barboza-Salerno , Thomas J. Stopka

The COVID-19 pandemic has exacerbated the risk of opioid-related harm, and previous studies suggest a connection between opioid overdose risk and industry of employment. We used descriptive and spatial-statistical tests with opioid overdose data from the vital records offices of Kentucky and Massachusetts to examine opioid overdose rates by employment industry before and after COVID-19 emergency declarations. Both states had consistently high rates of opioid-related overdose mortality for individuals employed in the construction and arts, recreation, food services, and accommodation service industries. Additionally in both states, census tracts with a high percentage of renters and non-Hispanic Black residents were more likely to be located in fatal opioid-related overdose hotspots following the initial surge of COVID-19 cases. In Kentucky, census tracts with higher percentages of employment in the transportation and other services were more likely to be located in an overdose hotspot before and after the COVID-19 emergency declaration, while in Massachusetts the same was true for census tracts with high employment in manufacturing, agriculture, forest, and fisheries, and hunting.

2019冠状病毒病大流行加剧了阿片类药物相关伤害的风险，之前的研究表明，阿片类药物过量风险与就业行业之间存在联系。我们对肯塔基州和马萨诸塞州生命记录办公室的阿片类药物过量数据进行了描述性和空间统计检验，以检查COVID-19紧急声明前后就业行业的阿片类药物过量率。这两个州在建筑和艺术、娱乐、食品服务和住宿服务行业就业的个人中，与阿片类药物相关的过量死亡率一直很高。此外，在这两个州，在COVID-19病例最初激增之后，租房者和非西班牙裔黑人居民比例较高的人口普查区更有可能位于致命的阿片类药物过量热点地区。在肯塔基州，交通和其他服务业就业比例较高的人口普查区更有可能位于COVID-19紧急声明前后的过量热点地区，而在马萨诸塞州，制造业、农业、森林、渔业和狩猎等就业比例较高的人口普查区也是如此。

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引用次数: 0

Investigating interaction effects of social risk factors and exposure to air pollution on pediatric lymphoma cancer in Georgia, United States 调查社会风险因素和暴露于空气污染对美国佐治亚州小儿淋巴瘤癌症的交互影响

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2024-11-01 DOI: 10.1016/j.sste.2024.100698

Theresa Unseld , Katja Ickstadt , Kevin Ward , Jeffrey M. Switchenko , Howard H. Chang , Anke Hüls

Childhood cancer constitutes a major cause of death in children. In a recent study of the Georgia Cancer Registry, joint exposures to environmental and social/behavioral stressors were associated with spatial clustering of lymphomas and reticuloendothelial neoplasms among the 159 counties in Georgia, USA. The present study aims to further investigate these associations on a more granular level. Bayesian Poisson and zero-inflated Poisson regression models with spatial and non-spatial variance structures were utilized to investigate whether county-specific cancer patterns may be explained by single or combinations of social stressors and ambient air pollution while adjusting for confounding and accounting for overfitting using differences in expected log predictive densities. While we did not find associations between lymphoma rates and social variables, air pollution, or their interactions, our proposed analysis workflow can serve as a blueprint for future studies investigating dependencies in regression models that feature combinations of unobserved and observed dependency structures.

儿童癌症是儿童死亡的主要原因。在最近对佐治亚癌症登记处进行的一项研究中，在美国佐治亚州的 159 个县中，环境和社会/行为压力因素的共同暴露与淋巴瘤和网状内皮肿瘤的空间聚集有关。本研究旨在从更细的层面进一步研究这些关联。我们利用具有空间和非空间方差结构的贝叶斯泊松回归模型和零膨胀泊松回归模型来研究县域特定癌症模式是否可由社会压力因素和环境空气污染的单一或组合来解释，同时利用预期对数预测密度的差异来调整混杂因素并考虑过度拟合。虽然我们没有发现淋巴瘤发病率与社会变量、空气污染或它们之间的相互作用有关联，但我们提出的分析工作流程可作为未来研究的蓝图，用于调查回归模型中的依赖关系，这些回归模型具有未观察到的和观察到的依赖结构组合。

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引用次数: 0

Spatial pattern of all cause excess mortality in Swiss districts during the pandemic years 1890, 1918 and 2020 1890 年、1918 年和 2020 年大流行期间瑞士各地区各种原因超额死亡率的空间模式

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2024-11-01 DOI: 10.1016/j.sste.2024.100697

Katarina L Matthes , Joël Floris , Aziza Merzouki , Christoph Junker , Rolf Weitkunat , Frank Rühli , Olivia Keiser , Kaspar Staub

Every pandemic is embedded in specific spatial and temporal context. However, spatial patterns have almost always only been considered in the context of one individual pandemic. Until now, there has been limited consideration of spatial similarities or differences between pandemics. In this study, Bayesian spatial models for disease mapping were used to estimate excess mortality for the pandemics of 1890, 1918 and 2020. A robust linear regression was used to assess the association between ecological determinants and excess mortality. Spatial variations of excess mortality across Switzerland were observed in each pandemic, but the spatial patterns differ between the pandemics. Different determinants contribute to excess mortality, and these factors vary between COVID-19 and the previous pandemics. Spatial excess mortality from COVID-19 is most likely due to cultural and SEP differences, whereas in historical pandemics, mobility, pre-existing tuberculosis or remote mountain living likely contributed to spatial excess mortality.

每一种大流行病都有其特定的时空背景。然而，空间模式几乎总是只在单个大流行的背景下被考虑。到目前为止，对不同大流行之间空间相似性或差异性的考虑还很有限。本研究采用贝叶斯疾病绘图空间模型来估算 1890 年、1918 年和 2020 年大流行的超额死亡率。采用稳健线性回归评估生态决定因素与超额死亡率之间的关联。在每一次大流行中，都观察到瑞士各地超额死亡率的空间变化，但不同大流行的空间模式有所不同。不同的决定因素导致了超额死亡率，而这些因素在 COVID-19 和之前的大流行中各不相同。COVID-19 造成的空间死亡率过高很可能是由于文化和公共教育部的差异造成的，而在以往的大流行中，流动性、原有肺结核或偏远山区生活很可能是造成空间死亡率过高的原因。

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引用次数: 0

Multiple “spaces”: Using wildlife surveillance, climatic variables, and spatial statistics to identify and map a climatic niche for endemic plague in California, U.S.A. 多重 "空间"：利用野生动物监测、气候变量和空间统计来识别和绘制美国加利福尼亚州地方性鼠疫的气候生态位。

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2024-11-01 DOI: 10.1016/j.sste.2024.100696

Ian D. Buller , Gregory M. Hacker , Mark G. Novak , James R. Tucker , A. Townsend Peterson , Lance A. Waller

Regional climatic features in endemic areas can help inform surveillance for plague, a bacterial disease typically transmitted by fleas and maintained in mammals. We use 7,954 coyotes (Canis latrans), a sentinel species for plague, screened for plague exposure by the California Department of Public Health - Vector-Borne Disease Section (CDPH-VBDS; 1983-2015) to identify and map plague-suitable local climates within California to empirically inform ongoing sampling and surveillance plans. Using spatial point processes, we compare the distributions of seropositive and seronegative coyotes within the “space” defined by the first two principal components of PRISM Climate Group 30-year average climate variables (primarily temperature and moisture). The approach identifies both regions consistent with CDPH-VBDS mapping of plague-positive rodent and other carnivore samples over the same period and additional plague-suitable areas with climate profiles similar to seropositive samples elsewhere but little or no historical sampling, providing new data-informed insight for prioritizing limited surveillance resources.

鼠疫是一种通常由跳蚤传播并在哺乳动物体内存留的细菌性疾病，鼠疫流行地区的区域气候特征有助于为鼠疫监测提供信息。我们利用加利福尼亚州公共卫生部病媒传染病科（CDPH-VBDS，1983-2015 年）筛查出的 7954 只郊狼（Canis latrans）（鼠疫的哨兵物种）来识别和绘制加利福尼亚州适合鼠疫的当地气候，从而为正在进行的采样和监测计划提供经验信息。利用空间点过程，我们比较了血清阳性和血清阴性郊狼在 PRISM 气候组 30 年平均气候变量（主要是温度和湿度）的前两个主成分所定义的 "空间 "内的分布情况。这种方法既能确定与同期鼠疫阳性啮齿动物和其他食肉动物样本的 CDPH-VBDS 图谱一致的区域，也能确定与其他地方血清阳性样本相似但历史采样很少或没有采样的其他鼠疫适宜区，从而为有限监测资源的优先排序提供新的数据信息。

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引用次数: 0

Road traffic deaths caused by at-fault drivers and drinking-driving in China: A spatiotemporal analysis of the 2017–2020 period 中国过失司机和酒驾导致的道路交通死亡：2017-2020 年期间的时空分析

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2024-10-19 DOI: 10.1016/j.sste.2024.100695

Feng Li , Yu Cheng Hsu , Yunyu Xiao , Paul S.F. Yip , Feng Yang

In China, the role that alcohol plays in road traffic deaths (RTDs) is poorly understood. In this study, RTD rates caused by at-fault drivers and drinking-driving by cases per 100,000 people were calculated at the city and provincial levels in China during 2017–2020. Spatial lag modeling was applied to measure the influence of drinking-driving RTD rates on at-fault RTD rates. In addition, the influence of seven geographic regions, six city tiers, three ethnicities, and six socioeconomic factors on drinking-driving and at-fault RTD rates was assessed. Drinking-driving RTD rates were positively associated with at-fault RTD rates. GDP per capita was negatively associated with drinking-driving RTD rates, but unemployment rates were positively associated. This study highlights the influence of drinking-driving on overall at-fault behavior. The reinforcement of traffic regulations against drinking-driving and general awareness could reduce RTD rates.

在中国，人们对酒精在道路交通死亡（RTD）中所起的作用知之甚少。本研究计算了 2017-2020 年期间中国省市两级每 10 万人因过失驾驶和酒后驾驶导致的道路交通死亡率。应用空间滞后模型测算了酒驾RTD率对过失驾驶RTD率的影响。此外，还评估了七个地理区域、六个城市级别、三个民族和六个社会经济因素对酒后驾驶和过失致残率的影响。结果表明，酒后驾车的实时交通事故发生率与过失实时交通事故发生率呈正相关。人均 GDP 与酒后驾车 RTD 率呈负相关，但失业率呈正相关。这项研究强调了酒后驾车对总体过失行为的影响。加强禁止酒后驾车的交通法规和普遍意识可以降低酒后驾车肇事率。

引用次数: 0

Spatio-temporal modeling to identify factors associated with stunting in Indonesia using a Modified Generalized Lasso 利用改良广义套索建立时空模型，确定印度尼西亚发育迟缓的相关因素

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2024-09-27 DOI: 10.1016/j.sste.2024.100694

Septian Rahardiantoro , Alfidhia Rahman Nasa Juhanda , Anang Kurnia , Aswi Aswi , Bagus Sartono , Dian Handayani , Agus Mohamad Soleh , Yusma Yanti , Susanna Cramb

This study investigates the factors associated with stunting prevalence in Indonesia, utilizing a generalized lasso framework with modified penalty matrices to accommodate spatio-temporal data structures. Novel approaches are introduced to construct the penalty matrices, with particular focus on defining neighborhood structures. The proposed method is applied to data from 34 Indonesian provinces, covering the years 2019 to 2023. The primary outcome is stunting prevalence, modeled against nine predictor variables: poverty, exclusive breastfeeding, low birth weight (LBW), high school completion, access to proper sanitation, unmet health service needs, Gross Domestic Product (GDP), calorie consumption, and protein consumption. A total of nine spatio-temporal models were compared, including a modified generalized lasso with three distinct penalty matrices for each two tuning selection methods and a generalized ridge regression with three penalty matrices. Results indicate that the generalized lasso model with a 3-nearest neighbor adjacency matrix outperformed the alternatives. Temporal variations were observed in the effects of exclusive breastfeeding, LBW, high school completion, and unmet health service needs. Positive associations with stunting prevalence were identified for poverty, exclusive breastfeeding, LBW, and unmet health service needs, while negative associations were found for high school completion rates, access to proper sanitation, GDP, calorie intake, and protein consumption. The strongest associations were observed in parts of Sumatra, Sulawesi, and Jakarta. These findings suggest that government interventions aimed at improving education, healthcare access, and poverty reduction may help alleviate stunting in Indonesia, particularly in regions with the greatest need.

本研究利用广义拉索框架和修改后的惩罚矩阵，对印度尼西亚发育迟缓患病率的相关因素进行了调查，以适应时空数据结构。研究介绍了构建惩罚矩阵的新方法，尤其侧重于定义邻域结构。所提出的方法适用于印度尼西亚 34 个省的数据，时间跨度为 2019 年至 2023 年。主要结果是发育迟缓发生率，并根据以下九个预测变量建立模型：贫困、纯母乳喂养、出生体重过轻（LBW）、高中毕业、获得适当卫生设施、未满足的医疗服务需求、国内生产总值（GDP）、卡路里消耗量和蛋白质消耗量。共对九个时空模型进行了比较，其中包括针对每两种调谐选择方法设置了三个不同惩罚矩阵的修正广义拉索模型和设置了三个惩罚矩阵的广义脊回归模型。结果表明，带有 3 个最近邻邻接矩阵的广义拉索模型优于其他模型。纯母乳喂养、低体重儿、高中毕业和未满足的医疗服务需求的影响存在时间上的差异。贫困、纯母乳喂养、低体重儿和未满足的医疗服务需求与发育迟缓发生率呈正相关，而高中毕业率、获得适当的卫生设施、国内生产总值、卡路里摄入量和蛋白质消耗量则呈负相关。苏门答腊岛、苏拉威西岛和雅加达部分地区的相关性最强。这些研究结果表明，旨在改善教育、医疗保健和减贫的政府干预措施可能有助于缓解印度尼西亚的发育迟缓问题，尤其是在需求最大的地区。

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引用次数: 0

Multivariate skew-normal distribution for modelling skewed spatial data 为倾斜空间数据建模的多元倾斜正态分布

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2024-09-27 DOI: 10.1016/j.sste.2024.100692

Kassahun Abere Ayalew , Samuel Manda , Bo Cai

Multivariate spatial data are commonly modelled using the shared spatial component and multivariate intrinsic conditional autoregressive (MICAR) models where the spatial random variables are assumed to be normally distributed. However, the normality assumption may not be always right as the spatially structured component may show non-normal distributions. We present, multivariate skew-normal spatial distribution in the modelling of multivariate conditional autoregressive models. Simulations and an application to estimate district HIV rates in South Africa are used for illustrating the capabilities of the proposed multivariate skewed spatial model. The estimation is done in a Bayesian framework. A comparison between our suggested approach and the common MICAR model is made using conditional predictive ordinate (CPO). The CPO values indicate that our suggested approach is better than the MICAR model for predicting the outcome variables of both the simulated and HIV data.

多变量空间数据通常使用共享空间分量和多变量内在条件自回归（MICAR）模型来建模，其中空间随机变量被假定为正态分布。然而，正态性假设并不总是正确的，因为空间结构成分可能呈现非正态分布。我们介绍了多元条件自回归模型建模中的多元倾斜正态空间分布。我们利用模拟和应用来估算南非的地区艾滋病毒感染率，以说明所提出的多元倾斜空间模型的能力。估计是在贝叶斯框架下进行的。使用条件预测序数（CPO）对我们建议的方法和常见的 MICAR 模型进行了比较。CPO 值表明，在预测模拟数据和艾滋病毒数据的结果变量方面，我们建议的方法优于 MICAR 模型。

引用次数: 0

Spatial pattern of congenital toxoplasmosis incidence and its relationship with vulnerability and national health indicators in Brazil 巴西先天性弓形虫病发病率的空间模式及其与易感性和国家健康指标的关系

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2024-09-24 DOI: 10.1016/j.sste.2024.100693

Matheus Santos Melo , Lúcia Rolim Santana de Freitas , Francisco Edilson Ferreira Lima-Júnior , Alexander Vargas , Júlio dos Santos Pereira , Pedro de Alcântara Brito-Júnior , Renata Carla de Oliveira , Janaína de Sousa Menezes , Tarcilla Corrente Borghesan , Josivânia Arrais de Figueiredo , Rosalynd Vinicios da Rocha Moreira , Alda Maria da Cruz , Ana Ribeiro , Tainá Raiol , Shirley Verônica Melo Almeida Lima , Márcio Bezerra-Santos , Allan Dantas dos Santos , Caíque Jordan Nunes Ribeiro , Vitor Vieira Vasconcelos

There is a gap in evidence regarding spatial clusters of the congenital toxoplasmosis (CT) and its association with social and health indicators in the Brazilian territory. Thus, we aimed herein to identify CT risk areas in Brazil and its association with social vulnerability and health indicators. An ecological and population-based study was conducted. The CT incidence coefficient was calculated and smoothed using the Local Empirical Bayesian method. Global regression models and local spatial regression model were applied. High-incidence clusters of the disease were identified throughout the country. Additionally, a positive association was observed between the incidence of congenital toxoplasmosis and the Social Vulnerability Index, coverage of community health agents, and the percentage of prenatal consultations. This association was stronger the further south in the country. Herewith, the implementation and strengthening of public strategies, with focus on priority intersectoral actions for prevention, early diagnosis, and prompt treatment, is urgently required for the effective control of CT in Brazilian municipalities.

关于先天性弓形虫病（CT）的空间集群及其与巴西境内的社会和健康指标之间的联系，目前还缺乏相关证据。因此，我们在此旨在确定巴西的先天性弓形虫病风险地区及其与社会脆弱性和健康指标的关联。我们开展了一项基于生态和人口的研究。我们使用地方经验贝叶斯方法计算并平滑了 CT 发病率系数。应用了全球回归模型和局部空间回归模型。在全国范围内发现了该疾病的高发集群。此外，还观察到先天性弓形虫病的发病率与社会脆弱性指数、社区卫生代理覆盖率和产前咨询比例之间存在正相关。在该国越往南，这种关联性越强。因此，要想在巴西各市有效控制先天性弓形虫病，迫切需要实施和加强公共战略，重点关注预防、早期诊断和及时治疗方面的优先跨部门行动。

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引用次数: 0

Identifying points of interest (POIs) as sentinels for infectious disease surveillance: A COVID-19 study 确定兴趣点 (POI) 作为传染病监测的哨兵：COVID-19 研究

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2024-09-20 DOI: 10.1016/j.sste.2024.100691

Fangye Du , Liang Mao

Traditional surveillance relies on medical facilities, such as clinics and laboratories, as sentinels to monitor disease activities. Few studies have investigated the feasibility of using Point of Interests (POIs) as sentinels for disease surveillance. POIs, such as restaurants, retail stores, and churches, are places where people often interact with one another and thus play a critical role in transmission of infectious diseases like influenza and COVID-19. To fill this gap, we proposed a method to estimate people's potential crowdedness at POIs and explored its utility as an early indicator to signal local disease outbreaks. In a case study in Florida, USA, we utilized weekly foot traffic data at 0.3 million POIs to calculate their weekly crowdedness, and tested local correlations between the crowdedness of each POI and its surrounding COVID-19 incidences with different time lags. We identified 261 POIs as potential sentinels that could signal the risk one to three weeks ahead of disease outbreaks. Most of these sentinel POIs provided food/drink services, ambulatory healthcare and religious/civic services. They were characterized by a relatively large group of customers and a stable patronization over time. This research provides new insights into improving current disease surveillance systems by incorporating more diverse and widely distributed POIs.

传统的监测依靠诊所和实验室等医疗设施作为哨点来监测疾病活动。很少有研究调查过利用兴趣点（POIs）作为疾病监测哨兵的可行性。餐馆、零售店和教堂等兴趣点是人们经常相互交流的地方，因此在流感和 COVID-19 等传染病的传播中起着至关重要的作用。为了填补这一空白，我们提出了一种估算人们在主要公共场所潜在拥挤程度的方法，并探讨了该方法作为地方疾病爆发信号早期指标的实用性。在美国佛罗里达州的一项案例研究中，我们利用 30 万个 POI 点的每周人流量数据来计算其每周的拥挤度，并测试了每个 POI 点的拥挤度与其周围 COVID-19 发病情况之间的相关性。我们确定了 261 个 POI 作为潜在的哨点，可在疾病爆发前一到三周发出风险信号。这些哨点 POI 大部分提供食品/饮料服务、非住院医疗保健和宗教/民事服务。它们的特点是拥有相对庞大的客户群体和长期稳定的客流量。这项研究为通过纳入更多样化和分布更广的 POI 来改进当前的疾病监测系统提供了新的见解。

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引用次数: 0