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Background and aims Health care workers (HCWs) are at increased risk of getting infected with Coronavirus disease 2019 (COVID-19) and suboptimal preventive practices have been identified as an important risk factor in this regard. This study was done to evaluate the preventive practices being followed by health care workers and identify reasons for suboptimal compliance. Methods A cross-sectional survey was done in HCWs belonging to various occupational roles and socio-cultural backgrounds across India through online platforms and telephonic interviews from July 30, 2020 to August 30, 2020. A scientifically designed and pre-validated questionnaire with good validity (CVR = 0.87, S-CVI/Av = 0.978) and internal consistency (Cronbach's alpha coefficient = 0.85) was used. Results The responses of 956 participants were analysed. Various suboptimal practices like touching outer surface of masks, lack of social distancing in cafeteria and duty rooms, inability to wash hands for adequate duration and properly follow steps of hand hygiene, inability to don and doff PPE properly, carrying PPE to duty rooms before completely doffing, use of personal mobile phones during duty and improper sleep were identified. Lack of knowledge, long duty hours, shortage of PPE, high patient workload, and casual attitude regarding own safety were identified as important barriers. Resident doctors and paramedical staff in the age group 18–30 years reported lower adherence. Conclusions Suboptimal compliance in preventive practices like handling PPE, distancing in cafeteria/duty rooms and hand hygiene is not uncommon in HCWs. Certain barriers are identified which should be addressed to ensure adequate safety of HCWs against COVID-19.

Chikungunya virus (CHIKV) is a mosquito-borne alphavirus which presents with symptoms of fever, rash, arthralgia, and occasional neurologic disease. While outbreaks have been earlier reported from India and other parts of the world, the recent outbreak in India witnessed more than 1000 cases. Various systemic and rarely neurological complications have been reported with CHIKV. We report two cases of Guillain-Barré syndrome (GBS) with CHIKV. GBS is a rare neurological complication which may occur after subsidence of fever and constitutional symptoms by several neurotropic viruses. We describe two cases of severe GBS which presented with rapidly progressive flaccid quadriparesis progressing to difficulty in swallowing and breathing. Both required mechanical ventilation and improved partly with plasmapharesis. The cases emphasize on (1) description of the rare complication in a setting of outbreak with CHIKV, (2) acute axonal as well as demyelinating neuropathy may occur with CHIKV, (3) accurate identification of this entity during outbreaks with dengue, both of which are vector borne and may present with similar complications.

In this paper, we propose DyPerm, the first dynamic community detection method which optimizes a novel community scoring metric, called permanence. DyPerm incrementally modifies the community structure by updating those communities where the editing of nodes and edges has been performed, keeping the rest of the network unchanged. We present strong theoretical guarantees to show how/why mere updates on the existing community structure leads to permanence maximization in dynamic networks, which in turn decreases the computational complexity drastically. Experiments on both synthetic and six real-world networks with given ground-truth community structure show that DyPerm achieves (on average) 35% gain in accuracy (based on NMI) compared to the best method among four baseline methods. DyPerm also turns out to be 15 times faster than its static counterpart. 1 final communities [16,23]. In this paper, we propose DyPerm, the first dynamic community detection method that adopts an effective community goodness metric, called "permanence" [12,13,9] and optimizes it to incrementally detect the community structure. The benefits of adopting permanence as an optimization function are two-fold: (i) Permanence, being a local vertex-centric metric (as opposed to the global network-centric metrics such as modularity, conductance), allows us to reassign communities to only those nodes whose associated topological structure has changed, and guarantees that the remaining nodes do not affect the optimization. This leads to very low computing complexity in updating the community structure when the network changes dynamically. (ii) Incremental changes in the local portion of the community structure guarantee that the resultant communities are highly correlated with that in the previous time-stamp. We present theoretical justifications why/how mere changes in the community structure lead to maximize permanence.We experiment with both synthetic and six real-world dynamic networks with known ground-truth community structure. A thorough comparative evaluation with four state-of-the-art baseline methods shows that DyPerm significantly outperforms all the baselines across different networks -DyPerm achieves up to 35% improvement in terms of Normalized Mutual Information (NMI) w.r.t. the best baseline method. Moreover, DyPerm tunrs out to be extremely fast, achieving up to 15 times speedup w.r.t. its static counterpart. In short, DyPerm is a fast and accurate dynamic community detection method.