key: cord-0869523-oicz8g0c authors: Saeed, Urooj; Sherdil, Khalid; Ashraf, Uzma; Mohey-ud-din, Ghulam; Younas, Imtiyaz; Butt, Hira Jannat; Rashid, Sajid title: Identification of potential lock-down area during COVID-19 transmission in Punjab, Pakistan date: 2020-11-10 journal: Public Health DOI: 10.1016/j.puhe.2020.10.026 sha: c8b0c1504f7df2d839d7c2f6b0dfd89b163a3162 doc_id: 869523 cord_uid: oicz8g0c 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. and rapidly growing literature, various models for forecasting COVID-19 are being used to inform public health interventions and preparedness. Pueyo et al. (2020) proposed a simple yet reliable model to predict the true case rates of the COVID-19 cases based on data from China 13 . COVID-19 is defined as a category B infectious disease that was declared as a class A infection by the Chinese government which is quite dangerous. Factors such as air pollution, adverse environmental conditions, and smoking may contribute to the severity and spread rate of COVID-19 [14] [15] [16] . Air pollution could exacerbate health outcomes of COVID-19 patients. Wang et al., 2020 17 argued that environmental conditions such as temperature and humidity could interrupt transmission of COVID-19 compared to other pandemic viruses, suggesting a possible decline in disease, although these conclusions have been disputed 17 . The geographic information system is an important tool with which to analyze spatial variation and distribution of this disease which can support the process of monitoring and controlling its progress [18] [19] [20] . In recent studies, critical spatial models have been used successfully to investigate statistically the geographic relationship between disease outbreak and explanatory variables [21] [22] [23] [24] . For instance, they have examined autoregressive spatial models and predictive models to assess COVID-19 in the United States of America based on demography, topography, and environmental factors, to provide intervention strategies to policymakers. For the present study, many people came into Pakistan from Iran, China, UK, USA, and other countries [25] [26] . Pakistan becomes reconciled with severe effects of COVID-19, with 323,452 patients suffering from the virus until 20 October 2020 and with a growth rate of 0.20% 27 . The objective of this study is real-time monitoring of COVID-19 cases in Punjab Province, Pakistan. The real-time complete lockdown areas are also identified through hotspot analysis. J o u r n a l P r e -p r o o f Predictive models were created to predict numbers of cases of COVID-19 in the near future in Punjab, Pakistan. To analyze COVID-19 spread in Punjab, all positive and negative cases were considered up to 20 October 2020. Punjab has the highest number of COVID-19 cases compared to other provinces of Pakistan 28 . The number of confirmed cases in Punjab are 101,652 out of 323,452 total in Pakistan which makes 31.4% of the total confirmed patients, according to the Government of Pakistan federal portal. It is situated in the center of Pakistan, with seven major airports, and having the most economic activity [29] [30] . Detailed information was collected, such as travel history, gender, residential address (where infected), age, contact number, lab result (negative/positive), etc. Raw data were processed and cleaned after collection through careful analysis of each case shown in (Figure 1a Figure 1d ). Based on the travel history of each patient, the data were divided into three categories including; Pilgrims i.e., coming from religious events in Iran, religious center's patients i.e. infected during a religious event in Lahore, and local spread. We have also collected J o u r n a l P r e -p r o o f information on the recovery of the COVID-19 patients in Punjab by capturing data from the follow-up of each case. Each case was geotagged to obtain the spatial distribution of COVID-19 cases by converting their addresses into real-time locations (Figure 1b) . The exact location was used to monitor the current trans-mobility areas for COVID-19 to identify the risk in neighboring areas 31 . Hotspot analysis depending upon the number of patients in a 25 km radius vicinity was performed in ArcGIS 10.6 to identify vulnerable clusters across Punjab 32 . This hotspot analysis utilizes the Gi* statistics technique 33 which is calculated as: where, Gi* defines the spatial autocorrelation statistics of any event denoted with i over n events, the equation x j shows the magnitude of the variable x at any event occurs at j over n, the overall weight is determined by the weight value between the I and j variables that identify the interrelationship. The hotspot analysis and Gi* statistics considered the magnitude of all features in the dataset of neighboring values. The overall sum was compared to the local sum of all the neighboring values. If a significant difference is found between the local and expected local sum, and that the difference is large then the significant z-score is counted as the final result 34 . The hotspot analysis calculated the z-score and P score for each COVID-19 patient location, which helped to indicate hotspots and cold spots of different kinds of events. The statistical significance of clustering was identified through z-score for a specified distance, whereas P represents the probability of the unified spatial pattern. Transmissibility of patients was monitored across 25km radius of each location of active COVID-19 cases in Punjab. However, at the city level, J o u r n a l P r e -p r o o f census blocks were used to micro analyze the patterns spatially. The red zones are the highly risky areas where the chances of being infected are relatively high in 25 km of radius as shown in ( Figure 3a ). We The present study attempts to apply the Pueyo et al., 2020 Model in Pakistan by slightly modifying it to local contexts. More than 101,652 cases have been recorded in Punjab Province which is 31.3% out of 323,452 overall patients in Pakistan (21 March to 20 October 2020). Figure 4 shows the cases through 30 May 2020 and the rate of increase doubled from 30 May 2020 to 11 June 2020 and 20 October 2020. In Punjab Province, the Lahore District is the most vulnerable area, with 50,111 cases as of 5 October 2020. Rawalpindi District was identified as the second most affected area, with 8093 cases and a compound growth rate of 0.16% shown in (Table 1) . Each district and Tehsil of Punjab is having confirmed cases ranging from 20% to 90% (Figure 2a, 2b) . The top ten cities, ranked from most affected to less affected are Lahore (50,111 cases), For traveling monitoring, it shows that more than 800+ people traveled from United Kingdom, France, Spain, US into Punjab, as of March 2020, and so on in Figure 5 . Many universities, hospitals, and restaurants were selected by the Government of Punjab for quarantine shown in (Figure 3b ). The ten major infected cities are compared in terms of identifying the number of days to double COVID-19 (Table 1) . Lahore is on the top of the list, where 50,111 cases doubling in 694.2 days shown in (Table 1b) . The compound growth was lower in Sargodha at only 0.23% and doubling in 296.7 days (Table 1b) . Lahore has the highest number of cases reported. Rawalpindi is marked as second. Khushab has the lowest number of cases reported only 304 as shown in (Table 1a) . In Pakistan, on 10 March 2020, 19 cases were reported for the first time, and 2 deaths were Our doubling rates are drawn from the actual reported data before and after lockdown, which shows that pre-lockdown average growth of reported cases is higher than the post-lockdown J o u r n a l P r e -p r o o f average growth rate of reported cases. The results of the model applied in Punjab which shows that true cases projected as per model (9, 261, 348) are 92 times higher than actual reported cases (101,014) on 13 October 2020 ( Figure 8 ); if these calculations are true, definitely most of these cases are asymptomatic. The graphs (Figure 8) show the slope of the predicted model is almost closer to the actual but the only difference is in the quantum of numbers. This huge gap is attributed to a higher number of asymptomatic cases and low testing capacity and affordability. In Pakistan, currently, the facilities of testing are much lower than the requirement. Pakistan has a testing capacity of 9,878 tests per million 37 . India is conducting 18,478 tests per million and US is doing 202,508 tests per million 37 . Based on the model estimations and pattern of actual reported cases it is more likely that Punjab and Pakistan entered into peak around the first week of July 2020 and a decline of growth rate (and doubling rate) of reported cases of COVID have been observed afterward (Figure 9 & 10) . Table 1 shows growth of COVID cases with doubling rates in the top 10 cities of Punjab. Lahore is the topmost city in terms of the number of cases reported with a growth rate of 0.10% and a doubling rate of 694.2 days. Whereas Faisalabad is on 4 th number of reported cases but has a low growth rate of 0.06% and a doubling rate of 1136 days. Similarly, the top 10 cities in terms of the 7-days average compound growth rate are shown in Table 1b . The confirmed ratio of patients varies by age; from age 1 to 15, most are recovering at home as the case fatality rate in percentage shows the hospital load and home load scenarios as of 14 October shown in (Table 2 ). But as for now in this study, the actual prediction was carried out based on previous and current data and concluded that the actual number of COVID-19 patients are 9.2 million till 14 th J o u r n a l P r e -p r o o f October 2020 (Figure 8 ). This gap is mostly attributed to a higher number of asymptomatic cases and very low testing capacity and affordability. Currently, the facilities of testing are much lower than the requirement. Pakistan has a testing capacity of 9,878 tests per million 37 . 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