Around the world, scientists are racing hard to understand how the COVID-19 epidemic is spreading and growing, thus trying to find ways to prevent it before medications are available. Many different models have been proposed so far correlating different factors. Some of them are too localized to indicate a general trend of the pandemic while some others have established transient correlations only. Hence, in this study, taking Bangladesh as a case, a 4P model has been proposed based on four probabilities (4P) which have been found to be true for all affected countries. Efficiency scores have been estimated from survey analysis not only for governing authorities on managing the situation (P(G)) but also for the compliance of the citizens ((P(P)). Since immune responses to a specific pathogen can vary from person to person, the probability of a person getting infected ((P(I)) after being exposed has also been estimated. And the vital one is the probability of test positivity ((P(T)) which is a strong indicator of how effectively the infected people are diagnosed and isolated from the rest of the group that affects the rate of growth. All the four parameters have been fitted in a non-linear exponential model that partly updates itself periodically with everyday facts. Along with the model, all the four probabilistic parameters are engaged to train a recurrent neural network using long short-term memory neural network and the followed trial confirmed a ruling functionality of the 4Ps.

在世界各地，科学家们正在努力了解 COVID-19 流行病是如何传播和发展的，从而试图在药物可用之前找到预防方法。迄今为止，已经提出了许多与不同因素相关的不同模型。其中一些过于局部化，无法表明大流行的总体趋势，而另一些则仅建立了短暂的相关性。因此，在本研究中，以孟加拉国为例，基于四种概率（4P）提出了 4P 模型，该模型已被发现适用于所有受影响国家。效率分数是根据调查分析估算出来的，不仅适用于管理当局管理情况的效率分数 ( P ( G ))，也适用于公民的依从性 (( P ( P ))。由于对特定病原体的免疫反应可能因人而异对于人来说，一个人在接触后被感染的概率（( P ( I )）也已被估计。其中最重要的是检测呈阳性的概率（（ P ( T )），它是衡量有效程度的有力指标。感染者被诊断出来，并与影响增长率的其他人隔离开来。所有四个参数都已拟合到非线性指数模型中，该模型会根据日常事实进行部分更新。使用长短期记忆神经网络使用四个概率参数来训练循环神经网络，随后的试验证实了 4P 的主导功能。