Abstract

Background:
 Implementation of strict measures to ensure the safety of cancer patients during the coronavirus disease (COVID-19) pandemic includes modification of treatment plans, strict physical distancing measures between healthcare workers (HCWs) and patients alike, and early detection of suspected cases. Serological testing can identify immunological responses, i.e., seroconversion, in HCWs presenting with subclinical symptoms. The detection of immunoglobulin (Ig) M specific antibodies demonstrates active disease, while (Ig) G specific antibodies indicate previous exposure to SARS-CoV-2.
 
 Methods:
 Among HCWs in a tertiary cancer center in Jordan, a cross-sectional study was conducted to detect those who had positive serology albeit previous negative diagnosis with COVID-19. We sent an invitation e-mail, and those who met the criteria were invited to a privately designated room to sign an informed consent form and obtain a blood sample for analysis. Results and demographic data were analyzed using SAS version 9.4.
 
 Findings:
 We recruited 583 participants between December 2020 and January 2021, with an equal distribution between genders and a mean age of 34.04 years (±9.29). The majority of participants were from the nursing department (n=390, 66.89%). A history of an upper respiratory tract infection was reported by 144 individuals (24.7%) with varying symptoms. Positive exposure was reported in 441 participants (75.6%). IgG seroconversion was detected in 41 participants (7%), while IgM seroconversion was only detected in three (0.5%).
 
 Interpretation:
 There was no correlation between positive IgG seroconversion and history of upper respiratory tract infection, exposure to infected patients, or profession. Therefore, subclinical COVID-19 is hard to detect, facilitating transmission of infection. Alongside polymerase chain reaction testing, strict physical distancing, and prompt vaccination among HCWs to mitigate disease spread, frequent serological surveillance can offer a way to understand the number of infections at different times and locations within different populations.

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