{"title":"Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic.","authors":"Ian Cooper, Argha Mondal, Chris G Antonopoulos","doi":"10.1016/j.chaos.2020.110298","DOIUrl":"https://doi.org/10.1016/j.chaos.2020.110298","url":null,"abstract":"<p><p>In this paper, a susceptible-infected-removed (SIR) model has been used to track the evolution of the spread of COVID-19 in four countries of interest. In particular, the epidemic model, that depends on some basic characteristics, has been applied to model the evolution of the disease in Italy, India, South Korea and Iran. The economic, social and health consequences of the spread of the virus have been cataclysmic. Hence, it is imperative that mathematical models can be developed and used to compare published datasets with model predictions. The predictions estimated from the presented methodology can be used in both the qualitative and quantitative analysis of the spread. They give an insight into the spread of the virus that the published data alone cannot, by updating them and the model on a daily basis. We show that by doing so, it is possible to detect the early onset of secondary spikes in infections or the development of secondary waves. We considered data from March to August, 2020, when different communities were affected severely and demonstrate predictions depending on the model's parameters related to the spread of COVID-19 until the end of December, 2020. By comparing the published data with model results, we conclude that in this way, it may be possible to reflect better the success or failure of the adequate measures implemented by governments and authorities to mitigate and control the current pandemic.</p>","PeriodicalId":520585,"journal":{"name":"Chaos, solitons, and fractals","volume":" ","pages":"110298"},"PeriodicalIF":7.8,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.chaos.2020.110298","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38425232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bibliometric method for mapping the state of the art of scientific production in Covid-19.","authors":"Mohamed El Mohadab, Belaid Bouikhalene, Said Safi","doi":"10.1016/j.chaos.2020.110052","DOIUrl":"https://doi.org/10.1016/j.chaos.2020.110052","url":null,"abstract":"<p><p>Global scientific production around the Covid-19 pandemic, in the various disciplines on the various international scientific bibliographic databases, has grown exponentially. The latter builds a source of scientific enrichment and an important lever for most researchers around the world, each of its field and its position with an ultimate aim of overcoming this pandemic. In this direction, bibliometric data constitute a fundamental source in the process of evaluation of scientific production in the academic world; bibliometrics provides researchers and institutions with crucial strategic information for the enhancement of their research results with the local and international scientific community, especially in this international pandemic.</p>","PeriodicalId":520585,"journal":{"name":"Chaos, solitons, and fractals","volume":" ","pages":"110052"},"PeriodicalIF":7.8,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.chaos.2020.110052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38301398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}