Forecasting COVID-19 in Pakistan.
Forecasting COVID-19 in Pakistan.
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DOI：
10.1371/journal.pone.0242762
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发表时间：
2020
期刊：
影响因子：
3.7
通讯作者：
Khan Z
中科院分区：
文献类型：
--
作者：
Ali M;Khan DM;Aamir M;Khalil U;Khan Z
预测像COVID-19这样的流行病至关重要，它不仅有助于政府，也有助于医生了解未来的传播轨迹，这可能有助于他们采取最好的治疗，预防措施和保护措施。在这项研究中，流行的自回归综合移动平均（ARIMA）将用于预测2020年6月25日至2020年7月4日（提前预测10天）期间，巴基斯坦确诊、康复病例的累计数量和死亡人数。为了达到预期目标，本研究的数据取自巴基斯坦国家卫生服务部网站，时间为2020年2月27日至2020年6月24日。将使用两种不同的ARIMA模型来获得未来10天的累计确诊病例、治愈病例和死亡病例的提前点和95%区间预测。数据分析使用了RStudio统计软件，包括“预测”、“ggplot 2”、“tseries”和“季节性”软件包。预测截至2020年7月4日的累计确诊病例、治愈病例和死亡病例数为231239例，95%预测区间为（219648，242832）、111616（预测区间为（101063，122168））和5043（95%预测区间为（4791，5295））。统计测量，即均方根误差（RMSE）和平均绝对误差（MAE）用于模型精度。从分析结果可以看出，ARIMA和季节性ARIMA模型在预测精度方面优于其他时间序列模型，因此推荐用于COVID-19等疫情的预测。从这项研究中得出的结论是，ARIMA模型在RMSE和MAE方面的预测准确性优于其他时间序列模型，因此可以被认为是预测当前COVID-19爆发的传播，恢复和死亡的良好预测工具。此外，这项研究还可以帮助决策者根据目前的疾病发生数量制定短期策略，直到开发出适当的药物。
Forecasting epidemics like COVID-19 is of crucial importance, it will not only help the governments but also, the medical practitioners to know the future trajectory of the spread, which might help them with the best possible treatments, precautionary measures and protections. In this study, the popular autoregressive integrated moving average (ARIMA) will be used to forecast the cumulative number of confirmed, recovered cases, and the number of deaths in Pakistan from COVID-19 spanning June 25, 2020 to July 04, 2020 (10 days ahead forecast). To meet the desire objectives, data for this study have been taken from the Ministry of National Health Service of Pakistan’s website from February 27, 2020 to June 24, 2020. Two different ARIMA models will be used to obtain the next 10 days ahead point and 95% interval forecast of the cumulative confirmed cases, recovered cases, and deaths. Statistical software, RStudio, with “forecast”, “ggplot2”, “tseries”, and “seasonal” packages have been used for data analysis. The forecasted cumulative confirmed cases, recovered, and the number of deaths up to July 04, 2020 are 231239 with a 95% prediction interval of (219648, 242832), 111616 with a prediction interval of (101063, 122168), and 5043 with a 95% prediction interval of (4791, 5295) respectively. Statistical measures i.e. root mean square error (RMSE) and mean absolute error (MAE) are used for model accuracy. It is evident from the analysis results that the ARIMA and seasonal ARIMA model is better than the other time series models in terms of forecasting accuracy and hence recommended to be used for forecasting epidemics like COVID-19. It is concluded from this study that the forecasting accuracy of ARIMA models in terms of RMSE, and MAE are better than the other time series models, and therefore could be considered a good forecasting tool in forecasting the spread, recoveries, and deaths from the current outbreak of COVID-19. Besides, this study can also help the decision-makers in developing short-term strategies with regards to the current number of disease occurrences until an appropriate medication is developed.
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影响因子：
7.9
作者：
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