Amrit Campus, Tribhuvan University, Kathmandu, Nepal.
Ratna Rajya Laxmi Campus, Tribhuvan University, Kathmandu, Nepal.
J Theor Biol. 2021 Jul 21;521:110680. doi: 10.1016/j.jtbi.2021.110680. Epub 2021 Mar 24.
While most of the countries around the globe are combating the pandemic of COVID-19, the level of its impact is quite variable among different countries. In particular, the data from Nepal, a developing country having an open border provision with highly COVID-19 affected country India, has shown a biphasic pattern of epidemic, a controlled phase (until July 21, 2020) followed by an outgrown phase (after July 21, 2020). To uncover the effective strategies implemented during the controlled phase, we develop a mathematical model that is able to describe the data from both phases of COVID-19 dynamics in Nepal. Using our best parameter estimates with 95% confidence interval, we found that during the controlled phase most of the recorded cases were imported from outside the country with a small number generated from the local transmission, consistent with the data. Our model predicts that these successful strategies were able to maintain the reproduction number at around 0.21 during the controlled phase, preventing 442,640 cases of COVID-19 and saving more than 1,200 lives in Nepal. However, during the outgrown phase, when the strategies such as border screening and quarantine, lockdown, and detection and isolation, were altered, the reproduction number raised to 1.8, resulting in exponentially growing cases of COVID-19. We further used our model to predict the long-term dynamics of COVID-19 in Nepal and found that without any interventions the current trend may result in about 18.76 million cases (10.70 million detected and 8.06 million undetected) and 89 thousand deaths in Nepal by the end of 2021. Finally, using our predictive model, we evaluated the effects of various control strategies on the long-term outcome of this epidemics and identified ideal strategies to curb the epidemic in Nepal.
当全球大多数国家都在与 COVID-19 大流行作斗争时，其对不同国家的影响程度差异很大。特别是，尼泊尔这个与高度受 COVID-19 影响的国家印度有着开放边境的发展中国家的数据显示，其疫情呈双峰模式，即控制阶段（截至 2020 年 7 月 21 日）和失控阶段（2020 年 7 月 21 日之后）。为了揭示在控制阶段实施的有效策略，我们开发了一个数学模型，该模型能够描述尼泊尔 COVID-19 动态的两个阶段的数据。使用我们的最佳参数估计值及其 95%置信区间，我们发现，在控制阶段，大多数记录的病例都是从国外输入的，只有少数是从当地传播的，与数据一致。我们的模型预测，这些成功的策略在控制阶段使繁殖数保持在 0.21 左右，防止了尼泊尔 442640 例 COVID-19 病例，并拯救了 1200 多人的生命。然而，在失控阶段，当边境筛查和检疫、封锁以及检测和隔离等策略发生变化时，繁殖数上升到 1.8，导致 COVID-19 病例呈指数增长。我们进一步使用我们的模型预测尼泊尔 COVID-19 的长期动态，并发现，如果不进行任何干预，目前的趋势可能会导致尼泊尔在 2021 年底前出现约 1876 万例（检测到 1070 万例，未检测到 806 万例）和 8.9 万例死亡。最后，我们使用预测模型评估了各种控制策略对疫情长期结果的影响，并确定了遏制尼泊尔疫情的理想策略。
