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

BMC Public Health

Mahsa Olia¹  Ayoub Tavakolian²  Alireza Mohammadi³  Elahe Pishgar⁴  Behzad Kiani⁵  Shahab MohammadEbrahimi⁶  Robert Bergquist⁷  Fatemeh Dolatkhah⁸ 

[1] Department of Anaesthesiology, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran;Department of Emergency Medicine, Faculty of Medicine, Sabzevar University of Medical Sciences, Sabzevar, Iran;Department of Geography and Urban Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran;Department of Human Geography and Logistics, Faculty of Earth Science, Shahid Beheshti University, Tehran, Iran;Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran;Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran;Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran;Ingerod, Brastad, Sweden;(Formerly with the UNICEF/UNDP/World Bank/WHO Special Program for Research and Training in Tropical Diseases, World Health Organization), Geneva, Switzerland;Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran;Department of Microbiology and Virology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran;

关键词: Coronavirus;    COVID-19;    Disease mapping;    Epidemiology;    Geographical information systems;    SARS-CoV-2;    Spatiotemporal mapping;   

DOI  :  10.1186/s12889-021-11326-2

来源: Springer

【 摘 要 】

BackgroundThe 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.MethodsThe 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.ResultsMost 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.ConclusionsOlder 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.

【 授权许可】

CC BY