Network Analysis of MERS Coronavirus within Households, Communities, and Hospitals to Identify Most Centralized and Super-Spreading in the Arabian Peninsula, 2012 to 2016

Abstract

Contact history is crucial during an infectious disease outbreak and vital when seeking to understand and predict the spread of infectious diseases in human populations. The transmission connectivity networks of people infected with highly contagious Middle East respiratory syndrome coronavirus (MERS-CoV) in Saudi Arabia were assessed to identify super-spreading events among the infected patients between 2012 and 2016. Of the 1379 MERS cases recorded during the study period, 321 (23.3%) cases were linked to hospital infection, out of which 203 (14.7%) cases occurred among healthcare workers. There were 1113 isolated cases while the number of recorded contacts per MERS patient is between 1 (n=210) and 17 (n=1), with a mean of 0.27 (SD = 0.76). Five super-important nodes were identified based on their high number of connected contacts worthy of prioritization (at least degree of 5). The number of secondary cases in each SSE varies (range, 5–17). The eigenvector centrality was significantly (p < 0.05) associated with place of exposure, with hospitals having on average significantly higher eigenvector centrality than other places of exposure. Results suggested that being a healthcare worker has a higher eigenvector centrality score on average than being nonhealthcare workers. Pathogenic droplets are easily transmitted within a confined area of hospitals; therefore, control measures should be put in place to curtail the number of hospital visitors and movements of nonessential staff within the healthcare facility with MERS cases.

Individual and network characteristic associated with hospital-acquired Middle East Respiratory Syndrome coronavirus

“…Many studies indicate that healthcare workers are at greater risk of MERS infection [10,[12][13][14]33]. However, we found healthcare workers who were at the receiving end of MERS infections to act as epidemic attenuator.…”

“…Forty-five day survival rate was lowest in patients older than 65 years (44.86%) [11]. Also, healthcare workers (HCW) are regularly exposed to MERS due to their regular contacts with MERS patients and are at greater risk of being infected; however, they are less likely to die of the disease [10,[12][13][14].…”

Pragmatic Recommendations for Infection Prevention and Control Practices for Healthcare Facilities in Low- and Middle-Income Countries during the COVID-19 Pandemic

“…63,64 These were part of infection control measures that were effective in halting nosocomial outbreaks of MERS, SARS, and influenza H1N1. 46,52,[65][66][67][68][69][70][71][72][73] In Canada during the 2003 SARS epidemic, there was a Ministry of Health ban on all nonessential staff members and visitors, except on compassionate grounds (e.g., a parent visiting an ill child). 34 Visitor logs were used when visitors were allowed to facilitate contact tracing if a transmission occurred.…”

Analyzing the social context of health information and misinformation during the COVID-19 pandemic: a case of emerging inequities in Lebanon

“…In these social networks, individuals’ perceptions of what behaviors are accepted or not by a particular group will influence the adoption of these behaviors, which can be either health protective or health damaging ( 16 ). A network analysis of Middle East Respiratory Syndrome has identified the patients who were the most influential in spreading the infection, and used the location of these patients in predicting individuals who will most likely be infected ( 17 ). Social networks are used to disseminate health information through trusted sources, such as a local radio station in Nepal that improved contraception adoption within social networks ( 18 ), and community networks have had a role in disseminating HIV-related messages and improving the adoption of preventive measures ( 19 , 20 ).…”