The role of information systems (IS) were widely discoursed during the spread of the COVID-19 outbreak. We have focused on developing a decision support systems (DSS) based on a combined prediction model, that can essentially be used at the start of any pandemic. Convalescent plasma therapy is generally applied during the spread of a pandemic as a therapy method that transfuses blood plasma from the people, who have recovered from an illness to treat critical cases. We observe, analyse, and predict the risks associated with the treatment effects of convalescent plasma therapy on COVID-19 patients. Based on the secondary data, we build a prediction model to evaluate and predict the trends in the clinical characteristics and laboratory findings for critically ill patients infected with COVID-19 and treated with convalescent plasma. Here, we use a combined prediction model utilizing three models; the grey prediction model (GM (1, 1)), the residual prediction model (residual GM (1, 1)), and a back propagation artificial neural network (BP-ANN) based residual sign prediction model. Also, a validation of the results of the study has been presented at two levels. On analysis of the results from the prediction model, it is observed that the convalescent plasma therapy can show progressive signs on COVID-19 infected patients. Health practitioners can understand, analyze, and predict the potential risks of convalescent plasma therapy based on the proposed model.

在 COVID-19 爆发的传播期间，信息系统 （IS） 的作用被广泛讨论。我们专注于开发基于组合预测模型的决策支持系统 （DSS），该系统基本上可以在任何大流行开始时使用。恢复期血浆疗法通常在大流行蔓延期间作为一种治疗方法，为从疾病中康复的人输注血浆以治疗危重病例。我们观察、分析和预测与恢复期血浆疗法对 COVID-19 患者的治疗效果相关的风险。基于二手数据，我们建立了一个预测模型，以评估和预测感染 COVID-19 并接受恢复期血浆治疗的危重患者的临床特征和实验室检查结果的趋势。在这里，我们使用了一个利用三个模型的组合预测模型;灰色预测模型 （GM （1， 1））、残差预测模型 （残差 GM （1， 1）） 和基于反向传播人工神经网络 （BP-ANN） 的残差符号预测模型。此外，还在两个层面上展示了对研究结果的验证。在分析预测模型的结果时，观察到恢复期血浆疗法可以在 COVID-19 感染患者身上显示进行性迹象。健康从业者可以根据所提出的模型理解、分析和预测恢复期血浆疗法的潜在风险。