YK
Y. Kalia
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Monitoring of SARS-CoV-2 variants is crucial to efforts in combating the COVID-19 pandemic. Lineage level abundance estimates for SARS-CoV-2 can be obtained from viral material present in domestic wastewater. The abundance predictions can be made at different levels of granularity-individual lineage level(high granularity) or variant level(low granularity). The question this paper answers is to what extent abundance predictions are more accurate at lower granularity. Here we show that when wastewater samples contain only one lineage low granularity predictions are in general more accurate than high granularity for all lineages across Alpha, Delta and Mu variants. No variant level overestimation was observed for this experiment, which was thought to be something that could have made low granularity predictions less accurate than those at high granularity. When lineages of a variant were combined into a wastewater sample, the prediction error rose because of the smaller relative abundances of the genome sequences. Overestimation due to predictions of all lineages being pooled into one lineage was observed here with the overestimated high granularity lineage being more accurate than the low granularity predictions. If samples are expected to contain a very small amount of lineages then it is better to make predictions at low granularity. On the other hand, as the relative abundances of lineages decrease in a sample due to a large number of lineages, the chances of lineage level predictions having a smaller relative prediction error rate increases- making high granularity the better choice for more accurate predictions.
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Monitoring of SARS-CoV-2 variants is crucial to efforts in combating the COVID-19 pandemic. Lineage level abundance estimates for SARS-CoV-2 can be obtained from viral material present in domestic wastewater. The abundance predictions can be made at different levels of granularity-individual lineage level(high granularity) or variant level(low granularity). The question this paper answers is to what extent abundance predictions are more accurate at lower granularity. Here we show that when wastewater samples contain only one lineage low granularity predictions are in general more accurate than high granularity for all lineages across Alpha, Delta and Mu variants. No variant level overestimation was observed for this experiment, which was thought to be something that could have made low granularity predictions less accurate than those at high granularity. When lineages of a variant were combined into a wastewater sample, the prediction error rose because of the smaller relative abundances of the genome sequences. Overestimation due to predictions of all lineages being pooled into one lineage was observed here with the overestimated high granularity lineage being more accurate than the low granularity predictions. If samples are expected to contain a very small amount of lineages then it is better to make predictions at low granularity. On the other hand, as the relative abundances of lineages decrease in a sample due to a large number of lineages, the chances of lineage level predictions having a smaller relative prediction error rate increases- making high granularity the better choice for more accurate predictions.
Search engine Entity Cards(ECs) display conciseinformation from the web about a topic or subjectin response to a user query. The topic or subjectcan be a person, an organization etc. and is referredto as an “Entity”. The specific topic under researchis how to determine which entity is most relevantfor the query in terms of helping the user find theinformation he/she is looking for. and what infor-mation about the chosen entity to display to answerthe query. The information can be in the form ofbut it not limited to text, images and hyperlinks.Research into the concepts of EC focuses on differ-ent components of the EC widget for example en-tity linking, tagging, extraction and fact summarygeneration. In the developed “EC algorithm” theseconcepts are combined into an implementation ofan Entity Card widget and then evaluated. The ECalgorithm utilizes tools such as DBPedia, DBPediaSpotlight and the Bing Web Search API to gener-ate an entity ranking for a query. The results ofevaluating the top ranked entity imply that the ECalgorithm retrieve on average a slightly to moder-ately relevant entity to the user. The fact retrievalalgorithm had predictably worse results given thecomplexity of finding truly relevant facts about en-tities.
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Search engine Entity Cards(ECs) display conciseinformation from the web about a topic or subjectin response to a user query. The topic or subjectcan be a person, an organization etc. and is referredto as an “Entity”. The specific topic under researchis how to determine which entity is most relevantfor the query in terms of helping the user find theinformation he/she is looking for. and what infor-mation about the chosen entity to display to answerthe query. The information can be in the form ofbut it not limited to text, images and hyperlinks.Research into the concepts of EC focuses on differ-ent components of the EC widget for example en-tity linking, tagging, extraction and fact summarygeneration. In the developed “EC algorithm” theseconcepts are combined into an implementation ofan Entity Card widget and then evaluated. The ECalgorithm utilizes tools such as DBPedia, DBPediaSpotlight and the Bing Web Search API to gener-ate an entity ranking for a query. The results ofevaluating the top ranked entity imply that the ECalgorithm retrieve on average a slightly to moder-ately relevant entity to the user. The fact retrievalalgorithm had predictably worse results given thecomplexity of finding truly relevant facts about en-tities.