Identification of potential lock-down area during COVID-19 transmission in Punjab, Pakistan

Abstract Objectives 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. Study Design Data for all COVID-19 cases were collected till 20 October 2020 in Punjab province. Methods The methodology includes 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 markdown geographically to be selected by the government of Pakistan based on increased numbers of cases. Results The overall result is that 9.2 million people are COVID-19 infected by 20 October 2020 in Punjab Province. The compound growth of COVID-19 decreased by 0.012% and doubling in 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 in the 60+ age group, people are expected to dominate the hospitalized population. Conclusions Pakistan is experiencing a high number of COVID-19 cases, with the maximum share from Punjab, Pakistan. Statistical modelling and compound growth estimation formulation were done through the Pueyo model, for 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.