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Purpose Seroprevalence surveys from different countries have reported SARS CoV-2 antibodies below 20% even in the most adversely affected areas and herd immunity cannot be predicted till more than half of the population gets the disease. The purpose of this survey was to estimate the magnitude of community-based spread of the infection, associated immunity, and the future prospects and proximity to a 'herd community'. Methods The study was undertaken as a cluster randomized, cross-sectional countrywide survey. This largest communitybased seroprevalence data of SARS-CoV-2 were collected between 15th and 31st July, 2020 from seven randomly selected cities belonging to the three most populous provinces of Pakistan. The FDA approved kit of ROCHE was used for detection of SARS-CoV-2 antibodies. Results Serum samples of 15,390 participants were tested for SARS CoV-2 antibodies with an overall seroprevalence of 42.4%. The seroprevalence ranged from 31.1% to 48.1% in different cities with the highest in Punjab province (44.5%). In univariable analysis, the odds of seropositivity was higher in men compared to women (OR: 1.10, 95% CI: 1.01-1.19, P < 0.05). In multivariable analysis, the risk of being seropositive was lower (OR 0.72, 95% CI: 0.60-0.87, P < 0.01) in younger group (≤ 20 years) than in those aged above 60 years. ConclusionThe study concluded that despite a reasonable seroprevalence, the country is yet to reach the base minimum of estimations for herd immunity. The durability of immunity though debated at the moment, has shown an evidenced informed shift towards longer side.

Automatic key concept extraction from text is the main challenging task in information extraction, information retrieval and digital libraries, ontology learning, and text analysis. The statistical frequency and topical graph-based ranking are the two kinds of potentially powerful and leading unsupervised approaches in this area, devised to address the problem. To utilize the potential of these approaches and improve key concept identification, a comprehensive performance analysis of these approaches on datasets from different domains is needed. The objective of the study presented in this paper is to perform a comprehensive empirical analysis of selected frequency and topical graph-based algorithms for key concept extraction on three different datasets, to identify the major sources of error in these approaches. For experimental analysis, we have selected TF-IDF, KP-Miner and TopicRank. Three major sources of error, i.e., frequency errors, syntactical errors and semantical errors, and the factors that contribute to these errors are identified. Analysis of the results reveals that performance of the selected approaches is significantly degraded by these errors. These findings can help us develop an intelligent solution for key concept extraction in the future.

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