RESUMEN
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 until 20 October 2020 in Punjab Province. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
