论文标题
Covid 19，一种现实的饱和，生长和衰变的模型
COVID 19, a realistic model for saturation, growth and decay of the India specific disease
论文作者
论文摘要
这项工作提出了一种简单而现实的方法，可以处理印度Covid-19患者的可用数据并预测情况。提出的模型基于可用的事实，例如锁定的发作（正如政府在第25天宣布的，τ0和恢复模式由平均恢复时间τ1（通常据说约为14天）。感染的Covid-19患者的数据从3月2日到2020年4月16日，到2020年4月16日的增长已被用来适合1次恢复和恢复的距离。发现几乎是线性上升的数字。锁定后R0的活动时间跨度是辩论的主题，并提供了引入触发因素更改这些模型的可能性。
This work presents a simple and realistic approach to handle the available data of COVID-19 patients in India and to forecast the scenario. The model proposed is based on the available facts like the onset of lockdown (as announced by the Government on 25th day, τ0 and the recovery pattern dictated by a mean life recovery time of τ1 ( normally said to be around 14 days). The data of infected COVID-19 patients from March 2, to April 16, 2020 has been used to fit the evolution of infected, recovery and death counts. A slow rising exponential growth, with R0 close to 1/6, is found to represent the infected counts indicating almost a linear rise. The rest of growth, saturation and decay of data is comprehensibly modelled by incorporating lockdown time controlled R0, having a normal error function like behaviour decaying to zero in some time frame of τ2 . The recovery mean life time τ1 dictates the peak and decay. The results predicted for coming days are interesting and optimistic. The introduced time constants based on experimental data for both the recovery rate as well as for determining the time span of activity of R0 after the lockdown are subject of debate and provide possibility to introduce trigger factors to alter these to be more suited to the model. The model can be extended to other communities with their own R0 and recovery time parameters.
