{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T13:47:01Z","timestamp":1777902421782,"version":"3.51.4"},"reference-count":19,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2018,3,9]],"date-time":"2018-03-09T00:00:00Z","timestamp":1520553600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["SIMULATION"],"published-print":{"date-parts":[[2019,3]]},"abstract":"<jats:p>In order to prevent and control the transmission risk of the Ebola virus, an Ebola virus disease SEIQR model was built. The free-Ebola-virus-disease equilibrium state and the Ebola-virus-disease equilibrium state of the SEIQR model are asymptotically stable. On the basis of the SEIQR model, the numerical simulation was formulated. From the simulation curves, it is found that if the production speed of the medicine cannot keep pace with demand, the transmission risk of Ebola will become out of control. Assuming that the medicine demand point and the medicine supply point constitute a logistics system in a certain planar range, the center-of-gravity method was used to obtain the optimal location for medicine delivery. By using this discrete-time virus model\u2019s optimal control rate to control the serious transmission risk of Ebola virus in the crowd, the effective strategy for transmission was added to the model, and then the control sheet was obtained. From the simulation results on the basis of the SEIQR model, it is concluded that the theory of the optimal control rate has a negative effect on the transmission risk of Ebola.<\/jats:p>","DOI":"10.1177\/0037549718757652","type":"journal-article","created":{"date-parts":[[2018,3,9]],"date-time":"2018-03-09T05:14:32Z","timestamp":1520572472000},"page":"195-208","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Study on modeling simulation and optimal control method for the transmission risk of the Ebola virus"],"prefix":"10.1177","volume":"95","author":[{"given":"Shihao","family":"Liu","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hainan University, Haikou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2018,3,9]]},"reference":[{"key":"bibr1-0037549718757652","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1202753"},{"key":"bibr2-0037549718757652","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2017.09.022"},{"issue":"2","key":"bibr3-0037549718757652","first-page":"23","volume":"7","author":"Durojaye MO","year":"2017","journal-title":"J Appl Math"},{"key":"bibr4-0037549718757652","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2015.06.049"},{"issue":"9","key":"bibr5-0037549718757652","first-page":"16","volume":"48","author":"Salem D","year":"2016","journal-title":"Simulation"},{"issue":"8","key":"bibr6-0037549718757652","first-page":"621","volume":"38","author":"Thomson R.","year":"2014","journal-title":"Progress in Pharmaceutical Sci"},{"key":"bibr7-0037549718757652","first-page":"2244","volume-title":"25th control and decision conference (CCDC 2013)","author":"Ge ST"},{"key":"bibr8-0037549718757652","doi-asserted-by":"publisher","DOI":"10.1177\/0037549716640877"},{"key":"bibr9-0037549718757652","doi-asserted-by":"publisher","DOI":"10.1177\/0037549713495752"},{"key":"bibr10-0037549718757652","doi-asserted-by":"publisher","DOI":"10.1016\/j.apm.2009.06.011"},{"key":"bibr11-0037549718757652","unstructured":"WHO. 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