Epidemiological characteristics and initial spatiotemporal visualisation of COVID-19 in a major city in the Middle East

Abstract

Background The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) emerged initially in China in December 2019 causing the COVID-19 disease, which quickly spread worldwide. Iran was one of the first countries outside China to be affected in a major way and is now under the spell of a fourth wave. This study aims to investigate the epidemiological characteristics of COVID-19 cases in north-eastern Iran through mapping the spatiotemporal trend of the disease. Methods The study comprises data of 4000 patients diagnosed by laboratory assays or clinical investigation from the beginning of the disease on Feb 14, 2020, until May 11, 2020. Epidemiological features and spatiotemporal trends of the disease in the study area were explored by classical statistical approaches and Geographic Information Systems. Results Most common symptoms were dyspnoea (69.4%), cough (59.4%), fever (54.4%) and weakness (19.5%). Approximately 82% of those who did not survive suffered from dyspnoea. The highest Case Fatality Rate (CFR) was related to those with cardiovascular disease (27.9%) and/or diabetes (18.1%). Old age (≥60 years) was associated with an almost five-fold increased CFR. Odds Ratio (OR) showed malignancy (3.8), nervous diseases (2.2), and respiratory diseases (2.2) to be significantly associated with increased CFR with developments, such as hospitalization at the ICU (2.9) and LOS (1.1) also having high correlations. Furthermore, spatial analyses revealed a geographical pattern in terms of both incidence and mortality rates, with COVID-19 first being observed in suburban areas from where the disease swiftly spread into downtown reaching a peak between 25 February to 06 March (4 incidences per km2). Mortality peaked 3 weeks later after which the infection gradually decreased. Out of patients investigated by the spatiotemporal approach (n = 727), 205 (28.2%) did not survive and 66.8% of them were men. Conclusions Older adults and people with severe co-morbidities were at higher risk for developing serious complications due to COVID-19. Applying spatiotemporal methods to identify the transmission trends and high-risk areas can rapidly be documented, thereby assisting policymakers in designing and implementing tailored interventions to control and prevent not only COVID-19 but also other rapidly spreading epidemics/pandemics.

Measuring COVID-19 vaccination coverage: an enhanced age-adjusted two-step floating catchment area model

“…With respect to COVID-19 vaccination, it is vital to prioritise the elderly population as it well known that higher age increases the risk of mortality [ 37 , 38 ]. In this study, we measured the PSA to vaccination centres by developing a modified version of the E2SFCA model using a weighted population classified by age structure in each geographical area, as this enhancement should generate more realistic results for healthcare decision-making.…”

A spatial-epidemiological dataset of subjects infected by SARS-CoV-2 during the first wave of the pandemic in Mashhad, second-most populous city in Iran

“…The spatial patterns of COVID-19 incidence were investigated by census level with addressing the epidemiological features, during the first wave of the pandemic in Mashhad, Iran [ 10 ]. In that study, hotspots and high/low-risk areas were detected by using the Getis-Ord Gi* and Local Moran’s I statistic [ 11 , 12 ].…”

Spatiotemporal Pattern of COVID-19–Related Mortality during the First Year of the Pandemic in Brazil: A Population-based Study in a Region of High Social Vulnerability

“…6 In this context, several studies using spatiotemporal analysis tools have demonstrated the impact of morbidity, mortality, and geographic spread of COVID-19 worldwide. [7][8][9][10] Importantly, a study carried out in the United States identified COVID-19 spatiotemporal clusters, which were classified as priority areas for resource allocation and implementation of diseasecontrol measures. 11 Likewise, studies assessing the spatiotemporal patterns of COVID-19 have been developed extensively in Brazil.…”