Understanding the progress of COVID-19 transmission in a rural district: a social network approach

Abstract

Background Social interactions within and between communities influenced the spread of COVID-19. By using social network analysis (SNA), we aimed to understand the effect of social interaction on the spread of disease in a rural district. Method A retrospective record review study using positive COVID-19 cases and contact-tracing data from an area in Malaysia was performed and analysed using the SNA method through R software and visualised by Gephi software. The justification for utilizing SNA is its capability to pinpoint the individuals with the highest impact and accountability for the transmission of COVID-19 within the area, as determined through SNA. Result Analysis revealed 76 (4.5%) people tested positive for COVID-19 from 1,683 people, with 51 (67.1%) of the positive ones being male. Outdegrees for 38 positive people were between 1 and 12, while 41 people had 1–13 indegree. Older males have a higher outdegree, while younger females have a higher outdegree than other age groups among same-sex groups. Betweenness was between 0.09 and 34.5 for 15 people. We identified 15 people as super-spreaders from the 42 communities detected. Conclusion Women play a major role in bridging COVID-19 transmission, while older men may transmit COVID-19 through direct connections. Thus, health education on face mask usage and hand hygiene is important for both groups. Working women should be given priority for the work-from-home policy compared to others. A large gathering should not be allowed to operate, or if needed, with strict adherence to specific standard operating procedures, as it contributes to the spread of COVID-19 in the district. The SNA allows the identification of key personnel within the network. Therefore, SNA can help healthcare authorities recognise evolving clusters and identify potential super-spreaders; hence, precise and timely action can be taken to prevent further spread of the disease.

Keywords

• Social network analysis (SNA)
• COVID-19
• Super-spreader
• SARS-CoV-2
• Social network