用Facebook先知法预测Bontang市COVID-19数据时间序列

Kurnia Kasturi, M. I. A. Putera, S. R. Natasia
{"title":"用Facebook先知法预测Bontang市COVID-19数据时间序列","authors":"Kurnia Kasturi, M. I. A. Putera, S. R. Natasia","doi":"10.1109/ISRITI54043.2021.9702874","DOIUrl":null,"url":null,"abstract":"The increasing trend of COVID-19 cases in Bontang makes it the first order of the highest incident rate in East Kalimantan, with a value of 1161.78 cases per 100 thousand inhabitants. The purpose of this study was to predict the increase in COVID-19 cases in Bontang City with a data set of positive confirmed cases, recovered and died of COVID-19 in Bontang city. The data set used starts from March 24, 2020 - March 1, 2021, using the Facebook Prophet method, the Jupyter Notebook application, and the Python programming language. The research process consists of the data collection stage, prediction implementation stage (data preprocessing, processing, performance evaluation, dashboard creation), and analysis of the result. The prediction was performed for up to 92 days until May 5, 2021. The result shows a trend of increasing cases of covid reaching the highest positive value, the highest recovery, and highest death, respectively, of 8695, 6099, and 156 people. According to the model, the average positive predictive error (MAE) and the average positive predictive accuracy value (MAPE) are 0.17 and 17.4%, indicating the positive prediction of contracting covid has good accuracy criteria. The next evaluation for the death prediction is accounted as reasonable accuracy criteria in which MAE and MAPE are 0.27 and 27%, respectively. Lastly, the recovery prediction has MAE of 0.17 and MAPE of 17.4%, implying good accuracy criteria. The study also provides recommendations to the COVID-19 Task Force to prepare the minimum number of PCR Tests by 870 tests and increase the hospitalization occupancy by 294 to control the spreading of the Coronavirus.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of Bontang City COVID-19 Data Time Series Using the Facebook Prophet Method\",\"authors\":\"Kurnia Kasturi, M. I. A. Putera, S. R. Natasia\",\"doi\":\"10.1109/ISRITI54043.2021.9702874\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The increasing trend of COVID-19 cases in Bontang makes it the first order of the highest incident rate in East Kalimantan, with a value of 1161.78 cases per 100 thousand inhabitants. The purpose of this study was to predict the increase in COVID-19 cases in Bontang City with a data set of positive confirmed cases, recovered and died of COVID-19 in Bontang city. The data set used starts from March 24, 2020 - March 1, 2021, using the Facebook Prophet method, the Jupyter Notebook application, and the Python programming language. The research process consists of the data collection stage, prediction implementation stage (data preprocessing, processing, performance evaluation, dashboard creation), and analysis of the result. The prediction was performed for up to 92 days until May 5, 2021. The result shows a trend of increasing cases of covid reaching the highest positive value, the highest recovery, and highest death, respectively, of 8695, 6099, and 156 people. According to the model, the average positive predictive error (MAE) and the average positive predictive accuracy value (MAPE) are 0.17 and 17.4%, indicating the positive prediction of contracting covid has good accuracy criteria. The next evaluation for the death prediction is accounted as reasonable accuracy criteria in which MAE and MAPE are 0.27 and 27%, respectively. Lastly, the recovery prediction has MAE of 0.17 and MAPE of 17.4%, implying good accuracy criteria. The study also provides recommendations to the COVID-19 Task Force to prepare the minimum number of PCR Tests by 870 tests and increase the hospitalization occupancy by 294 to control the spreading of the Coronavirus.\",\"PeriodicalId\":156265,\"journal\":{\"name\":\"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISRITI54043.2021.9702874\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISRITI54043.2021.9702874","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

邦唐省新冠肺炎病例呈上升趋势,是东加里曼丹省发病率最高的地区之一,每10万居民中有1161.78例病例。本研究的目的是利用Bontang市新冠肺炎阳性确诊病例、康复病例和死亡病例数据集预测Bontang市新冠肺炎病例的增加情况。使用的数据集始于2020年3月24日至2021年3月1日,使用Facebook Prophet方法,Jupyter Notebook应用程序和Python编程语言。研究过程包括数据收集阶段、预测实施阶段(数据预处理、处理、绩效评估、仪表板创建)和结果分析。该预测持续了92天,直到2021年5月5日。结果显示,新冠肺炎确诊病例呈增加趋势,阳性病例8695人,康复病例6099人,死亡病例156人,分别达到最高、最高水平。模型的平均阳性预测误差(MAE)和平均阳性预测精度值(MAPE)分别为0.17和17.4%,表明阳性预测具有较好的准确性标准。死亡预测的下一个评价作为合理准确率标准,其中MAE和MAPE分别为0.27和27%。最后,回收率预测MAE为0.17,MAPE为17.4%,具有较好的准确度标准。为了控制新冠肺炎的扩散,该研究还向新冠肺炎特别工作组提出了将PCR检测次数减少到870次,并将住院人数增加到294人的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Bontang City COVID-19 Data Time Series Using the Facebook Prophet Method
The increasing trend of COVID-19 cases in Bontang makes it the first order of the highest incident rate in East Kalimantan, with a value of 1161.78 cases per 100 thousand inhabitants. The purpose of this study was to predict the increase in COVID-19 cases in Bontang City with a data set of positive confirmed cases, recovered and died of COVID-19 in Bontang city. The data set used starts from March 24, 2020 - March 1, 2021, using the Facebook Prophet method, the Jupyter Notebook application, and the Python programming language. The research process consists of the data collection stage, prediction implementation stage (data preprocessing, processing, performance evaluation, dashboard creation), and analysis of the result. The prediction was performed for up to 92 days until May 5, 2021. The result shows a trend of increasing cases of covid reaching the highest positive value, the highest recovery, and highest death, respectively, of 8695, 6099, and 156 people. According to the model, the average positive predictive error (MAE) and the average positive predictive accuracy value (MAPE) are 0.17 and 17.4%, indicating the positive prediction of contracting covid has good accuracy criteria. The next evaluation for the death prediction is accounted as reasonable accuracy criteria in which MAE and MAPE are 0.27 and 27%, respectively. Lastly, the recovery prediction has MAE of 0.17 and MAPE of 17.4%, implying good accuracy criteria. The study also provides recommendations to the COVID-19 Task Force to prepare the minimum number of PCR Tests by 870 tests and increase the hospitalization occupancy by 294 to control the spreading of the Coronavirus.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信