Objective

The Coronavirus Disease 2019 (COVID-19) has currently ravaged through the world, resulting in over thirteen million confirmed cases and over five hundred thousand deaths, a complete change in daily life as we know it, worldwide lockdowns, travel restrictions, as well as heightened hygiene measures and physical distancing. Being able to analyse and predict the spread of this epidemic-causing disease is hence of utmost importance now, especially as it would help in the reasoning behind important decisions drastically affecting countries and their people, as well as in ensuring efficient resource and utility management. However, the needs of the people and specific conditions of the spread are varying widely from country to country. Hence, this article has two fold objectives: (i) conduct an in-depth statistical analysis of COVID-19 affected patients in India, (ii) propose a mathematical model for the prediction of spread of COVID-19 cases in India.

Materials and method

There has been limited research in modeling and predicting the spread of COVID-19 in India, owing both to the ongoing nature of the pandemic and limited availability of data. Currently famous SIR and non-SIR based Gauss-error-function and Monte Carlo simulation models do not perform well in the context of COVID-19 spread in India. We propose a ‘change-factor’ or ‘rate-of-change’ based mathematical model to predict the spread of the pandemic in India, with data drawn from hundreds of sources.

Results

Average age of affected patients was found to be 38.54 years, with 66.76% males, and 33.24% females. Most patients were in the age range of 18–40 years. Optimal parameter values of the prediction model are identified (α = 1.35, N = 3 and T = 10) by extensive experiments. Over the entire course of time since the outbreak started in India, the model has been 90.36% accurate in predicting the total number of cases the next day, correctly predicting the range in 150 out of the 166 days looked at.

Conclusion

The proposed system showed an accuracy of 90.36% for prediction since the first COVID-19 case in India, and 96.67% accuracy over the month of April. Predicted number of cases for the next day is found to be a function of the numbers over the last 3 days, but with an ‘increase’ factor influenced by the last 10 days. It is noticed that males are affected more than females. It is also noticed that in India, the number of people in each age bucket is steadily decreasing, with the largest number of adults infected being the youngest ones—a departure from the world trend. The model is self-correcting as it improves its predictions every day, by incorporating the previous day's data into the trend-line for the following days. This model can thus be used dynamically not only to predict the spread of COVID-19 in India, but also to check the effect of various government measures in a short span of time after they are implemented.

中文翻译：

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带有印度COVID-19初始传播分析的预测模型。

目的

2019年冠状病毒病（COVID-19）目前在世界各地肆虐，已确诊的病例超过1300万，超过50万死亡，我们所知的日常生活的全面变化，全球范围内的封锁，旅行限制以及加强的卫生措施和身体疏远。因此，现在能够最重要的是能够分析和预测这种流行病的传播，特别是因为它有助于做出重大决定对国家及其人民产生重大影响的推理，并有助于确保有效的资源和公用事业管理。但是，各国的人民需求和传播的具体条件差异很大。因此，本文有两个目标：（i）对印度受COVID-19感染的患者进行深入的统计分析，（ii）提出一个数学模型来预测COVID-19在印度的传播情况。

材料与方法

由于大流行的持续性和数据的可获得性，在建模和预测COVID-19在印度的传播方面研究有限。当前著名的基于SIR和非基于SIR的高斯误差函数以及蒙特卡洛模拟模型在印度传播的COVID-19情况下表现不佳。我们提出了一个基于“变化因子”或“变化率”的数学模型来预测印度大流行的蔓延，其数据来自数百个来源。

结果

发现受影响患者的平均年龄为38.54岁，男性为66.76％，女性为33.24％。大多数患者的年龄在18-40岁之间。 通过广泛的实验确定了预测模型的最佳参数值（α  = 1.35，N  = 3和T = 10）。自印度爆发疫情以来的整个时间段内，该模型在预测第二天的病例总数时准确率为90.36％，可以正确预测所观察到的166天中的150天。

结论

自印度首例COVID-19病例以来，拟议系统显示的预测准确度为90.36％，4月份则为96.67％。发现第二天的预计病例数与最近三天的病例数有关，但是受最近十天的影响是“增加”因子。值得注意的是，男性比女性受到的影响更大。还应注意，在印度，每个年龄段的人数都在稳步减少，被感染的成年人数量最多的是最小的成年人，这与世界趋势背道而驰。该模型通过将前一天的数据合并到接下来几天的趋势线中来，每天改进其预测，因此可以进行自我校正。因此，该模型不仅可以动态地用于预测COVID-19在印度的传播，