Forecasting and early warning of mosquito density in Nanjing by using the seasonal trend model of moving average

doi:10.3877/cma.j.issn.2095-9133.2021.01.003

Chinese Journal of Hygiene Rescue(Electronic Edition) ›› 2021, Vol. 07 ›› Issue (01): 13-17. doi: 10.3877/cma.j.issn.2095-9133.2021.01.003

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Forecasting and early warning of mosquito density in Nanjing by using the seasonal trend model of moving average

Yanqun Sun¹^,^†(), Hongmin Sun², Shougang Zhang², Tao Ma², Luoju Feng², Chong Wang², Moyuan Lu³, Yan Zhang², Junjun Wang², Qixin Wu², Chengguo Li²

^1. Department Nanjing Municipal Center for Disease Control and Prevention, The Affiliated Nanjing Center for Disease Control and Prevention of Nanjing Medical University, Nanjing 210003, China; State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071
^2. Department Nanjing Municipal Center for Disease Control and Prevention, The Affiliated Nanjing Center for Disease Control and Prevention of Nanjing Medical University, Nanjing 210003, China
^3. Department Nanjing Municipal Center for Disease Control and Prevention, The Affiliated Nanjing Center for Disease Control and Prevention of Nanjing Medical University, Nanjing 210003, China; School of Public Health, Soueast University, Nanjing 210009, China

Received:2020-06-01 Online:2021-02-18 Published:2021-04-25
Contact: Yanqun Sun

Abstract

Abstract:

Objective

To predict the mosquito density in 2019 by using the seasonal trend model of the moving average method based on the mosquito density in Nanjing from 2015 to 2018 to guide mosquito-borne diseases and mosquito control.

Methods

Excel software was used to fit the seasonal trend of the mosquito density in Nanjing during the first half of 2015-2018 on the basis of the regression equation to predict the mosquito density in 2019.

Results

The regression equation after fitting was: y=0.0091x+ 0.5207, and the relative error of the model was -0.04%.

Conclusion

In the 9th and 21st half months of 2019 (that is, from late April to late October), the measured and predicted values are close to each other, and both are lower than the warning value. The model fitting effect is better. This method has guiding significance for the formulation of patriotic health campaign policies and the prevention and control of mosquito-borne diseases.

Key words: Mosquito density, Moving average method, Seasonal trend model, Forecast and warning

Yanqun Sun, Hongmin Sun, Shougang Zhang, Tao Ma, Luoju Feng, Chong Wang, Moyuan Lu, Yan Zhang, Junjun Wang, Qixin Wu, Chengguo Li. Forecasting and early warning of mosquito density in Nanjing by using the seasonal trend model of moving average[J]. Chinese Journal of Hygiene Rescue(Electronic Edition), 2021, 07(01): 13-17.

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Figures/Tables 4

References 15

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半月次	残差				移动平均残差(Et)	移动平均标准差(s)
半月次	2015年	2016年	2017年	2018年	2019年
1	-0.53	-0.75	-1.07	-1.19	-0.95	0.27
2	-0.54	-0.76	-1.08	-1.19	-0.91	0.26
3	-0.55	-0.77	-1.09	-1.20	-0.87	0.25
4	-0.56	-0.78	-1.10	-1.21	-0.87	0.25
5	-0.57	-0.79	-1.080	-1.21	-0.85	0.24
6	-0.55	-0.64	-1.01	-1.13	-0.81	0.24
7	-0.58	-0.77	-1.00	-0.70	-0.67	0.35
8	-0.37	-0.64	-0.89	-0.83	-0.44	0.57
9	-0.28	-0.66	-0.09	0.21	-0.08	0.86
10	0.13	-0.25	0.10	1.33	0.27	1.10
11	0.18	-0.06	1.03	2.53	0.81	1.33
12	0.21	-0.16	1.2	2.34	1.19	1.43
13	2.37	-0.18	4.58	1.24	1.66	1.99
14	4.32	0.58	0.57	1.32	1.72	1.97
15	2.01	0.30	7.76	0.45	1.72	2.04
16	2.03	0.38	2.44	0.02	1.39	1.99
17	0.28	0.29	3.82	-0.09	1.03	1.95
18	0.36	-0.29	1.63	-0.38	0.40	1.18
19	-0.02	-0.14	0.42	-0.71	0.03	1.06
20	-0.42	-0.33	-0.52	-0.70	-0.37	0.63
21	-0.39	-0.55	-0.60	-1.04	-0.65	0.43
22	-0.72	-0.77	-1.09	-1.14	-0.84	0.32
23	-0.73	-0.95	-1.17	-1.39	-0.93	0.28
24	-0.74	-0.96	-1.18	-1.39	-1.00	0.23

半月次	残差				移动平均残差(Et)	移动平均标准差(s)
半月次	2015年	2016年	2017年	2018年	2019年
1	-0.53	-0.75	-1.07	-1.19	-0.95	0.27
2	-0.54	-0.76	-1.08	-1.19	-0.91	0.26
3	-0.55	-0.77	-1.09	-1.20	-0.87	0.25
4	-0.56	-0.78	-1.10	-1.21	-0.87	0.25
5	-0.57	-0.79	-1.080	-1.21	-0.85	0.24
6	-0.55	-0.64	-1.01	-1.13	-0.81	0.24
7	-0.58	-0.77	-1.00	-0.70	-0.67	0.35
8	-0.37	-0.64	-0.89	-0.83	-0.44	0.57
9	-0.28	-0.66	-0.09	0.21	-0.08	0.86
10	0.13	-0.25	0.10	1.33	0.27	1.10
11	0.18	-0.06	1.03	2.53	0.81	1.33
12	0.21	-0.16	1.2	2.34	1.19	1.43
13	2.37	-0.18	4.58	1.24	1.66	1.99
14	4.32	0.58	0.57	1.32	1.72	1.97
15	2.01	0.30	7.76	0.45	1.72	2.04
16	2.03	0.38	2.44	0.02	1.39	1.99
17	0.28	0.29	3.82	-0.09	1.03	1.95
18	0.36	-0.29	1.63	-0.38	0.40	1.18
19	-0.02	-0.14	0.42	-0.71	0.03	1.06
20	-0.42	-0.33	-0.52	-0.70	-0.37	0.63
21	-0.39	-0.55	-0.60	-1.04	-0.65	0.43
22	-0.72	-0.77	-1.09	-1.14	-0.84	0.32
23	-0.73	-0.95	-1.17	-1.39	-0.93	0.28
24	-0.74	-0.96	-1.18	-1.39	-1.00	0.23

半月次	预测值	警戒值	行动值	实测值
1	0.46	0.73	0.89	0.00
2	0.50	0.77	0.92	0.00
3	0.55	0.80	0.95	0.00
4	0.56	0.81	0.96	0.00
5	0.59	0.83	0.97	0.10
6	0.64	0.88	1.02	0.19
7	0.79	1.14	1.34	0.35
8	1.03	1.60	1.94	0.74
9	1.39	2.26	2.78	1.49
10	1.76	2.76	3.36	1.83
11	2.31	3.63	4.43	2.13
12	2.69	4.12	4.98	3.05
13	3.17	5.16	6.35	4.87
14	3.24	5.21	6.39	3.01
15	3.26	5.30	6.53	3.34
16	2.93	4.92	6.11	2.75
17	2.58	4.53	5.70	1.71
18	1.96	3.14	3.85	2.26
19	1.60	2.66	3.30	0.93
20	1.21	1.84	2.21	0.76
21	0.94	1.37	1.63	0.88
22	0.76	1.08	1.27	0.48
23	0.67	0.95	1.12	0.00
24	0.61	0.84	0.97	0.00

半月次	预测值	警戒值	行动值	实测值
1	0.46	0.73	0.89	0.00
2	0.50	0.77	0.92	0.00
3	0.55	0.80	0.95	0.00
4	0.56	0.81	0.96	0.00
5	0.59	0.83	0.97	0.10
6	0.64	0.88	1.02	0.19
7	0.79	1.14	1.34	0.35
8	1.03	1.60	1.94	0.74
9	1.39	2.26	2.78	1.49
10	1.76	2.76	3.36	1.83
11	2.31	3.63	4.43	2.13
12	2.69	4.12	4.98	3.05
13	3.17	5.16	6.35	4.87
14	3.24	5.21	6.39	3.01
15	3.26	5.30	6.53	3.34
16	2.93	4.92	6.11	2.75
17	2.58	4.53	5.70	1.71
18	1.96	3.14	3.85	2.26
19	1.60	2.66	3.30	0.93
20	1.21	1.84	2.21	0.76
21	0.94	1.37	1.63	0.88
22	0.76	1.08	1.27	0.48
23	0.67	0.95	1.12	0.00
24	0.61	0.84	0.97	0.00

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