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Python-enabled development and implementation of a prediction and early warning model for vector-borne infectious diseases
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Guo-qun LI, Xiao-feng DUAN, Hai-qing WANG, Hai-ping CHEN, Wen-qian YANG, Rui LI
Modern Preventive Medicine | 2025, 52(19) : 3490 - 3495
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Modern Preventive Medicine | 2025, 52(19): 3490-3495
Epidemiology and Statistical Methods
Python-enabled development and implementation of a prediction and early warning model for vector-borne infectious diseases
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Guo-qun LI, Xiao-feng DUAN, Hai-qing WANG, Hai-ping CHEN, Wen-qian YANG, Rui LI
Affiliations
  • Yichun University School of Public Health, Yichun, Jiangxi 336000, China
Published: 2025-10-10 doi: 10.20043/j.cnki.MPM.202504517
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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 lmfit 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 Rtpeak 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 βmp and βpm<0.01, Rt<1 and the epidemic disappeared. Controlling only γ and q would not eliminate the epidemic. Under comprehensive intervention, Scenario 1 could reduce the cumulative cases by 98.64%, and Rtpeak=0.868. Scenario 2 could reduce the cumulative cases by 87.95%,and Rtpeak=1.988. For Scenario 3, Rtpeak=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 Rtpeak=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.

Python  /  Vector-borne infectious diseases  /  Real-time reproduction number  /  Intervention
Guo-qun LI, Xiao-feng DUAN, Hai-qing WANG, Hai-ping CHEN, Wen-qian YANG, Rui LI. Python-enabled development and implementation of a prediction and early warning model for vector-borne infectious diseases[J]. Modern Preventive Medicine, 2025 , 52 (19) : 3490 -3495 . DOI: 10.20043/j.cnki.MPM.202504517
Year 2025 volume 52 Issue 19
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doi: 10.20043/j.cnki.MPM.202504517
  • Receive Date:2025-04-27
  • Online Date:2026-03-17
  • Published:2025-10-10
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  • Received:2025-04-27
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    Yichun University School of Public Health, Yichun, Jiangxi 336000, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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