We used social network analysis (SNA) to study the novel coronavirus (COVID-19) outbreak in Karnataka, India, and to assess the potential of SNA as a tool for outbreak monitoring and control. We analysed contact tracing data of 1147 COVID-19 positive cases (mean age 34.91 years, 61.99% aged 11-40, 742 males), anonymised and made public by the Karnataka government. Software tools, Cytoscape and Gephi, were used to create SNA graphics and determine network attributes of nodes (cases) and edges (directed links from source to target patients). Outdegree was 1-47 for 199 (17.35%) nodes, and betweenness, 0.5-87 for 89 (7.76%) nodes. Men had higher mean outdegree and women, higher mean betweenness. Delhi was the exogenous source of 17.44% cases. Bangalore city had the highest caseload in the state (229, 20%), but comparatively low cluster formation. Thirty-four (2.96%) 'super-spreaders' (outdegree ⩾ 5) caused 60% of the transmissions. Real-time social network visualisation can allow healthcare administrators to flag evolving hotspots and pinpoint key actors in transmission. Prioritising these areas and individuals for rigorous containment could help minimise resource outlay and potentially achieve a significant reduction in COVID-19 transmission.

No abstract

Eunuchs in India, commonly known as 'Hirjas', are under-represented and marginalized. They rarely access conventional health-provider systems due to mistrust and fear of censure. This is the story of Asha, who was brave enough to approach a community medicine resident in a municipal hospital for medical aid and counselling, and how she brought home the message that every patient deserves dignity and respect.

We used social network analysis (SNA) to study the novel coronavirus (COVID-19) outbreak in Karnataka, India, and assess the potential of SNA as a tool for outbreak monitoring and control. We analyzed contact tracing data of 1147 Covid-19 positive cases (mean age 34.91 years, 61.99% aged 11−40, 742 males), anonymized and made public by the government. We used software tools Cytoscape and Gephi to create SNA graphics and determine network attributes of nodes (cases) and edges (directed links, determined by contact tracing, from source to target patients). Outdegree was 1−47 for 199 (17.35%) nodes, and betweenness 0.5−87 for 89 (7.76%) nodes. Men had higher mean outdegree and women, higher betweenness. Delhi was the exogenous source of 17.44% cases. Bangalore city had the highest caseload in the state (229, 20%), but comparatively low cluster formation. Thirty-four (2.96%) super-spreaders (outdegree≥5) caused 60% of the transmissions. Real-time social network visualization can allow healthcare administrators to flag evolving hotspots and pinpoint key actors in transmission. Prioritizing these areas and individuals for rigorous containment could help minimize resource outlay and potentially achieve a significant reduction in COVID-19 transmission.

Background and Purpose: Various neurological complications have been reported in association with COVID-19. We report our experience of COVID-19 with stroke at a single center over a period of eight months spanning 1 March to 31 October 2020. Methods: We recruited all patients admitted to Internal Medicine with an acute stroke, who also tested positive for COVID-19 on RTPCR. We included all stroke cases in our analysis for prediction of in-hospital mortality, and separately analyzed arterial infarcts for vascular territory of ischemic strokes. Results: There were 62 stroke cases among 3923 COVID-19 admissions (incidence 1.6%). Data was available for 58 patients {mean age 52.6 years; age range 17 − 91; F/M=20/38; 24% (14/58) aged ≤ 40; 51% (30/58) hypertensive; 36% (21/58) diabetic; 41% (24/58) with O2 saturation <95% at admission; 32/58 (55.17 %) in-hospital mortality}. Among 58 strokes, there were 44 arterial infarcts, seven bleeds, three arterial infarcts with associated cerebral venous sinus thrombosis, two combined infarct and bleed, and two of indeterminate type. Among the total 49 infarcts, Carotid territory was the commonest affected (36/49; 73.5%), followed by vertebrobasilar (7/49; 14.3%) and both (6/49; 12.2%). Concordant arterial block was seen in 61% (19 of 31 infarcts with angiography done). 'Early stroke' (within 48 hours of respiratory symptoms) was seen in 82.7% (48/58) patients. Patients with poor saturation at admission were older (58 vs 49 years) and had more comorbidities and higher mortality (79% vs 38%). Mortality was similar in young strokes and older patients, although the latter required more intense respiratory support. Logistic regression analysis showed that low GCS and requirement for increasing intensity of respiratory support predicted in-hospital mortality. Conclusions: We had a 1.6% incidence of COVID-19 related stroke of which the majority were carotid territory infarcts. In-hospital mortality was 55.17%, predicted by low GCS at admission.