The Urban Sector Planning and Services Management Unit, Lahore, Pakistan; College of Earth and Environmental Sciences, University of Punjab, Lahore, Pakistan.
The Urban Sector Planning and Services Management Unit, Lahore, Pakistan.
Public Health. 2021 Jan;190:42-51. doi: 10.1016/j.puhe.2020.10.026. Epub 2020 Nov 10.
Real-time COVID-19 spread mapping and monitoring to identify lockdown and semi-lockdown areas using hotspot analysis and geographic information systems and also near future prediction modeling for risk of COVID-19 in Punjab, Pakistan.
Data for all COVID-19 cases were collected until 20 October 2020 in Punjab Province.
The methodology included geotagging COVID-19 cases to understand the trans-mobility areas for COVID-19 and characterize risk. The hotspot analysis technique was used to identify the number of areas in danger zones and the number of people affected by COVID-19. The complete lockdown areas were marked down geographically to be selected by the government of Pakistan based on increased numbers of cases.
As per predictive model estimates, almost 9.2 million people are COVID-19 infected by 20 October 2020 in Punjab Province. The compound growth rate of COVID-19 decreased to 0.012% per day and doubling rate increased to 364.5 days in Punjab Province. Based on Pueyo model predictions from past temporal data, it is more likely that Punjab and Pakistan entered into peak around the first week of July 2020, and the decline of growth rate (and doubling rate) of reported cases started afterward. Hospital load was also measured through the Pueyo model, and mostly, people in the 60+ years age group are expected to dominate the hospitalized population.
Pakistan is experiencing a high number of COVID-19 cases, with the maximum share from Punjab, Pakistan. Statistical modeling and compound growth estimation formulation were done through the Pueyo model, which was applied in Pakistan to identify the compound growth of COVID-19 patients and predicting numbers of patients shortly by slightly modifying it as per the local context.
利用热点分析和地理信息系统实时绘制和监测 COVID-19 的传播情况，以识别封锁和半封锁区域，并对巴基斯坦旁遮普邦 COVID-19 的未来风险进行预测建模。
收集了截至 2020 年 10 月 20 日旁遮普省所有 COVID-19 病例的数据。
该方法包括对 COVID-19 病例进行地理标记，以了解 COVID-19 的传播区域并确定风险。热点分析技术用于确定处于危险区域的区域数量和受 COVID-19 影响的人数。完全封锁的区域在地理上被标记出来，以便巴基斯坦政府根据病例增加的情况进行选择。
根据预测模型估计，截至 2020 年 10 月 20 日，旁遮普省约有 920 万人感染 COVID-19。COVID-19 的复合增长率降至每天 0.012%，倍增时间增加到 364.5 天。根据过去时间数据的 Pueyo 模型预测，旁遮普省和巴基斯坦更有可能在 2020 年 7 月的第一周左右达到峰值，此后报告病例的增长率（和倍增时间）开始下降。还通过 Pueyo 模型测量了医院的负荷，预计大多数 60 岁以上的人将成为住院人口的主要群体。
巴基斯坦 COVID-19 病例数量居高不下，其中旁遮普省所占比例最大。通过 Pueyo 模型进行了统计建模和复合增长率估计，该模型应用于巴基斯坦，通过对其进行轻微修改以适应当地情况，确定 COVID-19 患者的复合增长率并预测短期内患者数量。
