首页|基于ARIMA模型探讨新型冠状病毒感染防控措施对云南省登革热流行的影响

基于ARIMA模型探讨新型冠状病毒感染防控措施对云南省登革热流行的影响

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目的 通过分析云南省登革热的流行特征,探索云南省在新型冠状病毒感染(以下简称新冠)疫情期间采取的防控措施是否对登革热的流行情况产生影响,为制定更具针对性的防控策略提供科学依据。方法 对云南省2013—2023年登革热流行特征进行分析;选取2013—2019年登革热病例数据建立自回归移动平均模型,预测2020—2022年登革热发病数,将预测值和真实值进行比较。结果 2013—2023年云南省共报告登革热病例28131例,其中本地病例24209例,境外输入病例3922例,年均报告发病率为5。35/10万。每年仅有1个发病高峰,主要分布在7—11月。云南省各州市均有登革热病例报告,其中年均发病率最高的4个州市分别为西双版纳州(117。34/10万)、德宏州(56。93/10万)、临沧市(8。44/10万)、普洱市(3。12/10万)。男女比例为1。19∶1,发病年龄主要集中于20~50岁;病例职业主要为农民、商业服务、家务及待业。通过SPSS软件拟合2013—2019年的登革热病例数据建立ARIMA(0,0,1)(1,2,0)12模型,模型拟合效果好。使用模型对2020—2022年云南省各月份登革热发病人数进行预测,只有少部分真实值处于预测值的95%置信区间内。结论 新冠疫情的各项防控措施有效降低了登革热的报告发病数,随着新冠疫情防控政策的持续优化和调整,需对登革热的防控工作提出新的要求,实施更为精准的防控策略。
ARIMA-based modeling to explore the impacts of COVID-19 prevention and control measures on the dengue fever epidemic in Yunnan Province
Objective To analyze the epidemic characteristics of dengue fever in Yunnan Province and explore whether the prevention and control measures taken during the COVID-19 epidemic in Yunnan Province have impacted the epidemic situation of dengue fever,providing a scientific basis for formulating more targeted prevention and control strategies. Methods The epidemiological characteristics of dengue fever in Yunnan Province from 2013 to 2023 were analyzed. The data of dengue fever cases from 2013 to 2019 were selected to establish an Autoregressive Integrated Moving Average (ARIMA) model to predict the number of dengue fever cases from 2020 to 2022. The predicted values were compared with the actual observed values. Results A total of 28131 cases of dengue fever were reported in Yunnan Province from 2013 to 2023,including 24209 local cases and 3922 overseas imported cases,with an average annual reported incidence of 5.35/100000. There was only one peak incidence per year,mainly distributed from July to November. Dengue fever cases were reported in all states and cities in Yunnan Province,with the top four regions having the highest average annual incidence rates being Xishuangbanna (117.34/100000),Dehong (56.93/100000),Lincang (8.44/100000),and Puer (3.12/100000). The male-to-female ratio was 1.08∶1,with the age group predominantly from 20 to 50 years. The main occupations of the cases were farmers,commercial service,housework,and the unemployed. Using SPSS software,an ARIMA (0,0,1)(1,2,0)12 model was fitted to the dengue case data from 2013 to 2019,showing good fitting results. The model was used to predict the number of dengue fever cases in each month in Yunnan Province from 2020 to 2022,with only a few of the actual values falling within the 95% confidence interval of the predicted values. Conclusions The various prevention and control measures effectively reduced the number of reported cases of dengue fever. With the continuous optimization and adjustment of the prevention and control policy of COVID-19,new requirements for dengue fever prevention efforts should be put forward,implementing more precise control strategies.

Dengue feverepidemiology characteristicsautoregressive integrated moving average model

贾豫晨、李宁、郑尔达、邬志薇、王紫鉴、何继波、税铁军

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云南省疾病预防控制中心,云南 昆明 650506

登革热 流行病学特征 自回归移动平均模型

2024

中国热带医学
中华预防医学会,海南疾病预防控制中心

中国热带医学

CSTPCD北大核心
影响因子:0.722
ISSN:1009-9727
年,卷(期):2024.24(10)