Department of Pharmacology, University of Oxford, Oxford, OX1 3QT, UK.
Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, People's Republic of China.
Sci Rep. 2021 Apr 16;11(1):8412. doi: 10.1038/s41598-021-87882-9.
A reasonable prediction of infectious diseases' transmission process under different disease control strategies is an important reference point for policy makers. Here we established a dynamic transmission model via Python and realized comprehensive regulation of disease control measures. We classified government interventions into three categories and introduced three parameters as descriptions for the key points in disease control, these being intraregional growth rate, interregional communication rate, and detection rate of infectors. Our simulation predicts the infection by COVID-19 in the UK would be out of control in 73 days without any interventions; at the same time, herd immunity acquisition will begin from the epicentre. After we introduced government interventions, a single intervention is effective in disease control but at huge expense, while combined interventions would be more efficient, among which, enhancing detection number is crucial in the control strategy for COVID-19. In addition, we calculated requirements for the most effective vaccination strategy based on infection numbers in a real situation. Our model was programmed with iterative algorithms, and visualized via cellular automata; it can be applied to similar epidemics in other regions if the basic parameters are inputted, and is able to synthetically mimic the effect of multiple factors in infectious disease control.
合理预测不同疾病控制策略下传染病的传播过程，是决策者的重要参考依据。本研究通过 Python 建立了一个动态传播模型，并实现了对疾病控制措施的综合调控。我们将政府干预分为三类，并引入了三个参数来描述疾病控制的关键点，即区域内增长率、区域间传播率和感染者检出率。我们的模拟预测，如果不采取任何干预措施，英国的 COVID-19 感染将在 73 天内失控；同时，群体免疫将从疫情中心开始。引入政府干预后，单一干预措施在疾病控制方面有效，但代价巨大，而联合干预措施则更为有效，其中提高检测数量在 COVID-19 的控制策略中至关重要。此外，我们还根据实际感染人数计算了最有效疫苗接种策略的要求。我们的模型采用迭代算法编程，并通过元胞自动机可视化；如果输入基本参数，它可以应用于其他地区的类似流行病，并能够综合模拟传染病控制中多种因素的影响。
