Epidemiological characteristics and prediction of epidemic trend of norovirus infections in Yunnan Province

doi:10.20199/j.issn.1672-2302.2025.04.006

Abstract

Abstract:

Objective To analyze the epidemiological characteristics of norovirus infection in Yunnan Province from 2017 to 2023，and assess the application of autoregressive integrated moving average (ARIMA) model and prospective spatialtemporal scan statistics in the incidence trend and early warning of norovirus infection. Methods The case data of norovirus infections in Yunnan Province reported from 2017 to 2024 were collected from the Chinese Disease Prevention and Control Information System, and analyzed regarding the characteristics of temporal, regional, and population distribution using descriptive epidemiology. ARIMA model was established to predict the number of cases, and prospective spatio-temporal scan was used for spatiotemporal aggregation analysis of monthly reported cases. Results Between 2017 and 2024, a total of 15 600 cases of norovirus infections were reported in Yunnan Province, with the highest incidence reported in 2024 (17.473 /100 000). The incidence generally peaked from February to May, during which 53.99% (8 422/15 600) of the cases were reported. The top three prefectures (cities) with higher annual reported incidence were Qujing City (14.167 /100 000), Pu’er City (11.062 /100 000) and Lijiang City (10.706 /100 000). By population distribution, 8 709 cases were males, and 6 891 females. The 0-4 age group had the highest proportion of reported cases, who accounted for 68.93% (10 753/15 600). Children living at home were dominated by occupation distribution, who accounted for 63.23% (9 864/15 600). Ljung Box test of ARIMA (2,0,0)(0,1,0)₁₂ model indicated Q=7.449 (P=0.964), and that the residual sequence was white noise. The model fitting effect appeared sound. The relative error between predicted value and the actual value from January to December 2024 was 14.76%. In the prospective spatiotemporal scanning statistics, 50% of the high-risk population was used as the maximum spatial scanning window, and 14 days were used as the maximum time scanning window for spatiotemporal scanning clustering analysis. The results showed that the spatiotemporal clustering areas in January 2024 were consistent with the actual areas where clustering epidemics occurred, and the warning time was 5 days earlier than the outbreak occurred. Conclusion In recent years, the number of reported cases of norovirus infection in Yunnan Province has risen rapidly, and the areas with reported infections expanded. Children under 5 years old living at home are the main affected population. ARIMA model and prospective spatiotemporal scan statistics can be more accurate for early warning of this infection, which has important application value in practical work.

Key words: Norovirus, Epidemiological characteristics, ARIMA model, Prospective spatiotemporal scan, Prediction

CLC Number:

R512.5

ZHENG Erda, JIA Yuchen, LIU Sixian, SHEN Xiulian, HUANG Tian, HE Jibo, LIU Huihui. Epidemiological characteristics and prediction of epidemic trend of norovirus infections in Yunnan Province[J]. Journal of Tropical Diseases and Parasitology, 2025, 23(4): 222-228.

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References 31

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年份	诺如病毒感染报告病例数（例）	诺如病毒感染报告发病率（/10万）	其他感染性腹泻病报告病例数（例）	其他感染性腹泻病实验室确诊报告病例数（例）	诺如病毒感染病例构成比（%）
2017	1	0.002	28 419	15 441	0.006
2018	16	0.033	37 460	21 683	0.074
2019	72	0.148	50 140	26 654	0.270
2020	272	0.560	37 618	18 242	1.492
2021	288	0.593	48 596	24 230	1.189
2022	385	0.792	35 382	14 276	2.700
2023	6 077	12.508	49 259	27 170	22.367
2024	8 489	17.473	73 573	46 135	18.400

地区	报告病例数（例）									年均报告发病率（/10万）
地区	2017年	2018年	2019年	2020年	2021年	2022年	2023年	2024年	合计	年均报告发病率（/10万）
曲靖市	0	0	0	0	0	2	1 819	1 165	2 986	14.167
普洱市	0	0	0	0	2	5	78	405	490	11.062
丽江市	0	0	0	0	0	0	157	953	1 110	10.706
昆明市	0	2	43	256	230	273	1 835	2 628	5 267	9.557
红河州	0	0	2	0	8	56	959	957	1 982	5.215
德宏州	0	0	0	0	0	7	320	215	542	5.148
临沧市	0	9	24	16	23	20	143	328	563	2.792
楚雄州	0	0	0	0	23	0	257	258	538	2.461
西双版纳州	0	0	0	0	0	0	200	467	667	2.282
玉溪市	0	0	0	0	0	22	28	154	204	2.149
昭通市	0	0	0	0	0	0	96	266	362	1.906
大理州	0	1	2	0	0	0	76	377	456	1.586
文山州	0	0	0	0	0	0	83	222	305	0.620
保山市	1	4	1	0	1	0	19	75	101	0.483
迪庆州	0	0	0	0	0	0	3	11	14	0.440
怒江州	0	0	0	0	1	0	4	8	13	0.304

特征	报告病例数（例）				年均发病率（/10万）
特征	2017—2022年	2023年	2024年	合计	年均发病率（/10万）
性别
男性	572	3 357	4 780	8 709	4.345
女性	462	2 720	3 709	6 891	3.714
年龄（岁）
0~4	788	4 494	5 471	10 753	45.798
5~9	92	482	806	1 380	6.234
10~19	80	441	569	1 090	2.146
20~29	25	196	477	698	1.350
30~39	19	113	357	489	0.724
40~49	13	90	223	326	0.517
50~59	11	128	274	413	0.792
≥60	6	133	312	451	0.835
职业
农民	27	259	568	854
幼托儿童	116	623	776	1 515
学生	146	729	968	1 843
散居儿童	700	4 102	5 062	9 864
其他	45	364	1 115	1 524

ARIMA模型	平稳的R2	正态化BIC	Ljung-Box		MAE	RMSE	MAPE
ARIMA模型	平稳的R2	正态化BIC	Q值	P值	MAE	RMSE	MAPE
(1,0,0)(0,1,0)₁₂	0.725	9.749	22.560	0.164	52.637	116.268	356.303
(0,0,1)(0,1,0)₁₂	0.657	9.969	30.293	0.024	82.020	129.832	758.831
(1,0,1)(0,1,0)₁₂	0.755	9.708	9.698	0.882	54.325	110.581	360.861
(2,0,0)(0,1,0)₁₂	0.766	9.662	7.449	0.964	57.096	108.105	406.568
(2,0,1)(0,1,0)₁₂	0.766	9.734	7.548	0.941	58.059	108.772	423.058
(1,0,1)(1,1,0)₁₂	0.713	9.866	22.809	0.119	52.700	114.195	345.067
(1,0,0)(1,1,1)₁₂	0.713	9.940	22.568	0.094	49.840	120.561	330.183
(2,0,0)(1,1,0)₁₂	0.750	9.800	9.026	0.876	56.331	112.400	408.526
(2,0,0)(1,1,1)₁₂	0.750	9.875	8.971	0.883	56.352	113.252	409.480

月份	预测值（95%CI）（例）	实测值（例）	相对误差（%）
1	333（119~548）	442	-24.66
2	796（477~1 114）	1 196	-33.44
3	1 467（1 104~1 831）	1 634	-10.22
4	1 172（795~1 549）	1 057	10.88
5	1 156（777~1 536）	656	76.22
6	711（332~1 091）	354	100.85
7	635（255~1 015）	326	94.79
8	607（227~987）	440	37.95
9	570（190~951）	483	18.01
10	681（301~1 061）	516	31.98
11	848（468~1 228）	534	58.80
12	766（385~1 146）	851	-9.99
合计	9 742（5 430~14 057）	8 489	14.76