COVID-19 re-infection in Shahroud, Iran: a follow-up study.
Fariba ZareMaryam TeimouriAhmad KhosraviMarzieh Rohani-RasafReza ChamanAli HosseinzadehHozhabr Jamali AtergelehEhsan BineshMohammad Hassan EmamianPublished in: Epidemiology and infection (2021)
Although many people became infected and recovered during the COVID-19 epidemic, the immunity duration and re-infection in recovered patients have recently attracted many researchers. The aim of this study was to evaluate the recurrence of the infection in recovered individuals over a 9-month period after the onset of the COVID-19 epidemic. In this study, data related to COVID-19 patients in Shahroud city were collected using the electronic system for registering suspicious patients and also by checking patients' hospital records. In this study, from 20 March 2020 to 20 November 2020 (9 months), a total of 8734 suspected patients with respiratory symptoms were observed and followed up. RT-PCR was positive for 4039 patients. During this period, out of the total number of positive cases of COVID-19, 10 cases became re-infected after complete recovery. The risk of re-infection was 2.5 per thousand (0.95 CI 1.2-4.5). The mean time interval between the first infection and re-infection was 134.4 ± 64.5 days (range 41-234 days). The risk of re-infection between male and females was not statistically different (1.98 per 1000 women and 2.96 per 1000 men). Exposure to COVID-19 may not establish long-term protective immunity to all patients and may predispose them to re-infection. This fact can be reminded that the use of masks, social distancing and other preventive measures are very important in recovered patients and should be emphasised especially in health care personnel who are more exposed to the virus.
Keyphrases
- end stage renal disease
- healthcare
- ejection fraction
- sars cov
- coronavirus disease
- newly diagnosed
- prognostic factors
- peritoneal dialysis
- type diabetes
- emergency department
- machine learning
- patient reported outcomes
- pregnant women
- mental health
- social media
- health insurance
- electronic health record
- respiratory syndrome coronavirus
- artificial intelligence
- deep learning
- depressive symptoms
- ultrasound guided
- big data