Treffer: Python 赋能媒介传染病预测预警模型建立及实现.

Objective Based on the Python platform, the entire process of vector-borne infectious disease mathematical models is expanded to better fit models and evaluate intervention effects, to provide new ideas for grassroots prevention and control, and to open up new perspectives. Methods The SmEmIm-SpEpIpApRp model was fitted using the Imfit library, solved with the solve_ivp function, and sensitivity analysis of key model parameters was performed. The Rt calculation was based on the next generation matrix method, and all results were visually displayed with the help of Matplotlib. Results The results showed that R²=0.98 and RMSE-2.07. Rt was 5.607 in the early stage of the epidemic, and Rt<subscript> peak</subscript> was 8.439 on day 41. The period with Rt>1 lasted 81 days, and q had the highest sensitivity (S=35.435). Under a single intervention, when ẞ<subscript>mp</subscript> and B<subscript>pm</subscript><0.01, Rt<1 and the epidemic disappeared. Controlling only y and q would not eliminate the epidemic. Under comprehensive intervention, Scenario 1 could reduce the cumulative cases by 98.64%, and R<subscript>tpeak</subscript>=0.868. Scenario 2 could reduce the cumulative cases by 87.95%, and Rt<subscript>peak</subscript>=1.988. For Scenario 3, Rt<subscript>peak</subscript>=4.78. Although the increase in Rt was smaller and the change rate was low, the longer duration could increase cumulative cases by 161.47%. Scenario 4 could reduce the cumulative cases by 99.38%, and Rt<subscript>peak</subscript>=0.28. Conclusions The high goodness of fit of the model based on the Python platform verifies the necessity of seasonal dynamic modeling, provides an integrated solution for the prevention and control of vector-borne infectious diseases, expands the practical boundaries of theoretical models, and opens a new perspective for precise prevention and control at the grassroots level [ABSTRACT FROM AUTHOR]

基于Python平台,对媒介传染病数学模型全流程进行拓展,以期更好拟合模型并评估干预效果,为基层防 控提供新思路,打开新视角。方法对SmEmIm-SpEpIpApRp 模型采用Imfit库拟合,采用solve_ivp函数求解,对模型关键参数进行敏感性分析,Rt计算依据下一代矩阵法,所有结果借助 Matplotlib 进行可视化展示。结果结果显示R²= 0.98, RMSE-2.07,疫情初期Rt=5.607,第41d达到R<subscript>peak</subscript>=8.439,Rt>1的疫情持续期为81d.q的敏感度最高(S= 35.435),单一干预措施下β<subscript>mp</subscript>β<subscript>pm</subscript><0.01时,Rt<1,疫情消失;仅控制y.g疫情并不会消失;综合干预下场景1可使累积病例减少98.64%,Rt<subscript>peak</subscript>=0.868;场景2可使累积病例下降87.95%,R1=1.988;场景3的R=4.78, Rt<subscript>peak</subscript>上升幅度较小,变化速率低,但持续时间更长,可使累积病例上升161.47%;场景4可使累积病例减少99.38%,Rt<subscript>peak</subscript>=0.28。结论基于 Python 平台的模型高拟合优度验证了季节性动态建模的必要性,为媒介传染病防控提供了 “数据 模型-决策” 的一体化解决方案,拓展了理论模型的实践边界,也为基层精准防控打开了新视角。 关键词:Python;媒介传染病;实时再生数;干预 [ABSTRACT FROM AUTHOR]

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