论文标题
印度共同19日大流行的动力学
Dynamics of the COVID-19 pandemic in India
论文作者
论文摘要
了解COVID-19大流行的动态对于改善控制和社会疏远策略至关重要。为此，我们采用了易感性暴露于疾病的模型，通过来自印度喀拉拉邦，德里，马哈拉施特拉邦和西孟加拉邦的接触跟踪和住院数据，以及整体印度。我们已经进行了灵敏度分析以识别最关键的输入参数，并且我们校准了模型以尽可能地描述数据。短期预测表明，对于整个四个省和整个印度，Covid-19案件的日益增加和令人担忧的趋势，而长期预测也揭示了振荡动态的可能性。因此，我们的研究使得Covid-19可能成为季节性发生的选择使我们的选择开放。我们还模拟并讨论了媒体对COVID-19大流行动力学的影响。
Understanding the dynamics of the COVID-19 pandemic is crucial for improved control and social distancing strategies. To that effect, we have employed the susceptible-exposed-infectious-recovered model, refined by contact tracing and hospitalization data from Indian provinces Kerala, Delhi, Maharashtra, and West Bengal, as well as from overall India. We have performed a sensitivity analysis to identify the most crucial input parameters, and we have calibrated the model to describe the data as best as possible. Short-term predictions reveal an increasing and worrying trend of COVID-19 cases for all four provinces and India as a whole, while long-term predictions also reveal the possibility of oscillatory dynamics. Our research thus leaves the option open that COVID-19 might become a seasonal occurrence. We also simulate and discuss the impact of media on the dynamics of the COVID-19 pandemic.
