Spatial-temporal diffusion model of aggregated infectious diseases based on population life characteristics: a case study of COVID-19

Wen Cao; Siqi Zhao; Xiaochong Tong; Haoran Dai; Jiang Sun; Jiaqi Xu; Gongrun Qiu; Jingwen Zhu; Yuzhen Tian; Wen Cao; Siqi Zhao; Xiaochong Tong; Haoran Dai; Jiang Sun; Jiaqi Xu; Gongrun Qiu; Jingwen Zhu; Yuzhen Tian

doi:10.3934/mbe.2023583

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 7: 13086-13112. doi: 10.3934/mbe.2023583

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Spatial-temporal diffusion model of aggregated infectious diseases based on population life characteristics: a case study of COVID-19

1.
School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
2.
School of Geospatial Information, University of Information Engineering, Zhengzhou 450001, China
3.
Northern Information Control Research Institute Group Co. Ltd, Nanjing 211106, China
4.
Beijing QTMap Technology Co. Ltd, Beijing 100192, China
5.
Beijing 61618 troops, Beijing 100088, China

Academic Editor: Xueying Wang

Received: 30 March 2023 Revised: 20 May 2023 Accepted: 24 May 2023 Published: 05 June 2023

Outbreaks of infectious diseases pose significant threats to human life, and countries around the world need to implement more precise prevention and control measures to contain the spread of viruses. In this study, we propose a spatial-temporal diffusion model of infectious diseases under a discrete grid, based on the time series prediction of infectious diseases, to model the diffusion process of viruses in population. This model uses the estimated outbreak origin as the center of transmission, employing a tree-like structure of daily human travel to generalize the process of viral spread within the population. By incorporating diverse data, it simulates the congregation of people, thus quantifying the flow weights between grids for population movement. The model is validated with some Chinese cities with COVID-19 outbreaks, and the results show that the outbreak point estimation method could better estimate the virus transmission center of the epidemic. The estimated location of the outbreak point in Xi'an was only 0.965 km different from the actual one, and the results were more satisfactory. The spatiotemporal diffusion model for infectious diseases simulates daily newly infected areas, which effectively cover the actual patient infection zones on the same day. During the mid-stage of viral transmission, the coverage rate can increase to over 90%, compared to related research, this method has improved simulation accuracy by approximately 18%. This study can provide technical support for epidemic prevention and control, and assist decision-makers in developing more scientific and efficient epidemic prevention and control policies.
- infectious diseases,
- outbreak points,
- temporal evolution,
- diffusion model,
- epidemic prevention
Citation: Wen Cao, Siqi Zhao, Xiaochong Tong, Haoran Dai, Jiang Sun, Jiaqi Xu, Gongrun Qiu, Jingwen Zhu, Yuzhen Tian. Spatial-temporal diffusion model of aggregated infectious diseases based on population life characteristics: a case study of COVID-19[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 13086-13112. doi: 10.3934/mbe.2023583

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Abstract

Outbreaks of infectious diseases pose significant threats to human life, and countries around the world need to implement more precise prevention and control measures to contain the spread of viruses. In this study, we propose a spatial-temporal diffusion model of infectious diseases under a discrete grid, based on the time series prediction of infectious diseases, to model the diffusion process of viruses in population. This model uses the estimated outbreak origin as the center of transmission, employing a tree-like structure of daily human travel to generalize the process of viral spread within the population. By incorporating diverse data, it simulates the congregation of people, thus quantifying the flow weights between grids for population movement. The model is validated with some Chinese cities with COVID-19 outbreaks, and the results show that the outbreak point estimation method could better estimate the virus transmission center of the epidemic. The estimated location of the outbreak point in Xi'an was only 0.965 km different from the actual one, and the results were more satisfactory. The spatiotemporal diffusion model for infectious diseases simulates daily newly infected areas, which effectively cover the actual patient infection zones on the same day. During the mid-stage of viral transmission, the coverage rate can increase to over 90%, compared to related research, this method has improved simulation accuracy by approximately 18%. This study can provide technical support for epidemic prevention and control, and assist decision-makers in developing more scientific and efficient epidemic prevention and control policies.

References

[1]	Y. Sawada, L. Sumulong, Macroeconomic impact of COVID-19 in developing Asia, Social Science Electronic Publishing, 2021. http://dx.doi.org/10.2139/ssrn.3912360
[2]	S. M. Kassa, J. B. H. Njagarah, Y. A. Terefe, Analysis of the mitigation strategies for COVID-19: from mathematical modelling perspective, Chaos, Solitons Fractals, 138 (2020), 109968. https://doi.org/10.1016/j.chaos.2020.109968 doi: 10.1016/j.chaos.2020.109968
[3]	J. Riou, C. L. Althaus, Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020, Eurosurveillance, 25 (2020), 7-11. https://doi.org/10.2807/1560-7917.Es.2020.25.4.2000058
[4]	H. Nishiura, S. M. Jung, N. M. Linton, R. Kinoshita, Y. C. Yang, K. Hayashi, et al., The extent of transmission of novel coronavirus in Wuhan, China, 2020, J. Clin. Med., 9 (2020). https://doi.org/10.3390/jcm9020330
[5]	J. T. Wu, K. Leung, G. M. Leung, Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study, Lancet, 395 (2020), 689-697. https://doi.org/10.1016/S0140-6736(20)30260-9
[6]	Q. Li, X. H. Guan, P. Wu, X. Y. Wang, L. Zhou, Y. Q. Tong, et al., Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia, N. Engl. J. Med., 382 (2020), 1199-1207. https://doi.org/10.1056/NEJMoa2001316 doi: 10.1056/NEJMoa2001316
[7]	H. S. Lee, K. Kim, K. Choi, S. Hong, H. Son, S. Ryu, Incubation period of the coronavirus disease 2019 (COVID-19) in Busan, South Korea, J. Infect. Chemother., 26 (2020), 1011-1013. https://doi.org/10.1016/j.jiac.2020.06.018
[8]	A. Viguerie, G. Lorenzo, F. Auricchio, D. Baroli, T. J. R. Hughes, A. Patton, et al., Simulating the spread of COVID-19 via a spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion, Appl. Math. Lett., 111 (2021). https://doi.org/10.1016/j.aml.2020.106617
[9]	B. F. Maier, D. Brockmann, Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China, Science, 368 (2020), 742-746. https://doi.org/10.1126/science.abb4557 doi: 10.1126/science.abb4557
[10]	N. Crokidakis, COVID-19 spreading in Rio de Janeiro, Brazil: do the policies of social isolation really work? Chaos, Soliton Fractals, 136 (2020). https://doi.org/10.1016/j.chaos.2020.109930
[11]	M. H. D. Ribeiro, R. G. da Silva, V. C. Mariani, L. D. Coelho, Short-term forecasting COVID-19 cumulative confirmed cases: perspectives for Brazil, Chaos, Solitons Fractals, 135 (2020). https://doi.org/10.1016/j.chaos.2020.109853
[12]	A. J. Kucharski, T. W. Russell, C. Diamond, Early dynamics of transmission and control of COVID-19: a mathematical modelling study, Lancet Infect. Dis., 20 (2020), 553-558. https://doi.org/10.1016/S1473-3099(20)30144-4 doi: 10.1016/S1473-3099(20)30144-4
[13]	M. J. Keeling, K. T. D. Eames, Networks and epidemic models, J. R. Soc. Interface, 2 (2005), 295-307. https://doi.org/10.1098/rsif.2005.0051 doi: 10.1098/rsif.2005.0051
[14]	N. A. Christakis, J. H. Fowler, Social network sensors for early detection of contagious outbreaks, PLoS One, 5 (2010). https://doi.org/10.1371/journal.pone.0012948
[15]	H. J. Li, W. Z. Xu, S. P. Song, W. X. Wang, M. Perc, The dynamics of epidemic spreading on signed networks, Chaos, Solitons Fractals, 151 (2021). https://doi.org/10.1016/j.chaos.2021.111294
[16]	Z. S. Wang, C. Y. Xia, Z. Q. Chen, G. R. Chen, Epidemic propagation with positive and negative preventive information in multiplex networks, IEEE Trans. Cybern., 51 (2021), 1454-1462. https://doi.org/10.1109/Tcyb.2019.2960605 doi: 10.1109/Tcyb.2019.2960605
[17]	J. A. Firth, J. Hellewell, P. Klepac, S. Kissler, A. J. Kucharski, L. G. Spurgin, et al., Using a real-world network to model localized COVID-19 control strategies, Nat. Med., 26 (2020), 1616-11622. https://doi.org/10.1038/s41591-020-1036-8 doi: 10.1038/s41591-020-1036-8
[18]	I. N. Lymperopoulos, #stayhome to contain COVID-19: neuro-SIR - neurodynamical epidemic modeling of infection patterns in social networks, Expert Syst. Appl., 165 (2021). https://doi.org/10.1016/j.eswa.2020.113970
[19]	A. Bouchnita, A. Jebrane, A hybrid multi-scale model of COVID-19 transmission dynamics to assess the potential of non-pharmaceutical interventions, Chaos, Solitons Fractals, 138 (2020). https://doi.org/10.1016/j.chaos.2020.109941
[20]	M. Feng, Z. Fang, X. Lu, Z. Xie, S. Xiong, M. Zheng, et al., Traffic analysis zone-based epidemic estimation approach of COVID-19 based on mobile phone data: an example of Wuhan (in Chinese), J. Wuhan Univ., Inf. Sci. Ed., 45 (2020), 651-657. https://doi.org/10.13203/j.whugis20200141 doi: 10.13203/j.whugis20200141
[21]	H. Y. Ren, L. Zhao, A. Zhang, L. Y. Song, Y. L. Liao, W. L. Lu, et al., Early forecasting of the potential risk zones of COVID-19 in China's megacities, Sci. Total Environ., 729 (2020). https://doi.org/10.1016/j.scitotenv.2020.138995
[22]	J. Xia, Y. Zhou, Z. Li, L. Fan, Y. Yue, C. Tao, et al., COVID-19 risk assessment driven by urban spatiotemporal big data: a case study of Guangdong-Hong Kong-Macao Greater Bay Area, Acta Geod. Cartographica Sin., 49 (2020), 671. https://doi.org/10.11947/j.AGCS.2020.20200080 doi: 10.11947/j.AGCS.2020.20200080
[23]	M. M. Sugg, T. J. Spaulding, S. J. Lane, J. D. Runkle, S. R. Harden, A. Hege, et al., Mapping community-level determinants of COVID-19 transmission in nursing homes: a multi-scale approach, Sci. Total Environ., 752 (2021). https://doi.org/10.1016/j.scitotenv.2020.141946
[24]	J. F. Ma, H. H. Zhu, P. Li, C. C. Liu, F. Li, Z. W. Luo, et al., Spatial patterns of the spread of COVID-19 in Singapore and the influencing factors, ISPRS Int. J. Geo-Inf., 11 (2022). https://doi.org/10.3390/ijgi11030152
[25]	Y. Shi, D. Wang, Y. Chen, B. Chen, B. Zhao, M. Deng, An anomaly detection approach from spatio distributions of epidemic based on adjacency constraints in flow space, Acta Geod. Cartographica Sin., 50 (2021), 777-788. https://doi.org/10.11947/j.AGCS.2021.20200350 doi: 10.11947/j.AGCS.2021.20200350
[26]	X. Chen, J. Liu, L. Xu, J. Li, W. Zhang, H. Liu, Construction of the COVID-19 epidemic cases activity knowledge graph: a case study of Zhengzhou City (in Chinese), J. Wuhan Univ., Inf. Sci. Ed., 45 (2020), 816-825. https://doi.org/10.13203/j.whugis20200201 doi: 10.13203/j.whugis20200201
[27]	B. Jiang, X. You, K. Li, X. Zhou, H. Wen, Interactive visual analysis of COVID-19 epidemic situation using geographic knowledge graph (in Chinese), J. Wuhan Univ., Inf. Sci. Ed., 45 (2020), 836-845. https://doi.org/10.13203/j.whugis20200153 doi: 10.13203/j.whugis20200153
[28]	Q. Huang, Q. Liu, C. Song, X. Liu, H. Shu, X. Wang, et al., Urban spatial epidemic simulation model: a case study of the second COVID-19 outbreak in Beijing, China, Trans. GIS, 26 (2022), 297-316. https://doi.org/10.1111/tgis.12850 doi: 10.1111/tgis.12850
[29]	A. J. Tatem, Z. J. Huang, C. Narib, U. Kumar, D. Kandula, D. K. Pindolia, et al., Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning, Malar. J., 13 (2014). https://doi.org/10.1186/1475-2875-13-52
[30]	A. Wesolowski, A. Winter, A. J. Tatem, T. Qureshi, K. Engo-Monsen, C. O. Buckee, et al., Measles outbreak risk in Pakistan: exploring the potential of combining vaccination coverage and incidence data with novel data-streams to strengthen control, Epidemiol. Infect., 146 (2018), 1575-1583. https://doi.org/10.1017/S0950268818001449 doi: 10.1017/S0950268818001449
[31]	J. R. Koo, A. R. Cook, M. Park, Y. X. H. Sun, H. Y. Sun, J. T. Lim, et al., Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study, Lancet Infect. Dis., 20 (2020), 678-688. https://doi.org/10.1016/S1473-3099(20)30162-6 doi: 10.1016/S1473-3099(20)30162-6
[32]	D. Okuonghae, A. Omame, Analysis of a mathematical model for COVID-19 population dynamics in Lagos, Nigeria, Chaos, Solitons Fractals, 139 (2020). https://doi.org/10.1016/j.chaos.2020.110032
[33]	B. Li, Y. Peng, H. He, M. Wang, T. Feng, Built environment and early infection of COVID-19 in urban districts: a case study of Huangzhou, Sustainable Cities Soc., 66 (2021), 102685. https://doi.org/10.1016/j.scs.2020.102685 doi: 10.1016/j.scs.2020.102685
[34]	Q. Guo, Research on the Public Service Facilities' Allocation of Basic Living Unit in Shenzhen, Master's thesis, Harbin Institute of Technology, 2012. Available from: http://kns.cnki.net.zzulib.vpn358.com/KCMS/detail/detail.aspx?dbname = CMFD201401 & filename = 1013038380.nh.
[35]	Z. Cao, J. Wang, Y. Gao, W. Han, X. Feng, G. Zeng, Spatial pattern and heterogeneity risk of severe acute respiratory syndrome epidemic in Guangzhou, Geogr. Res., 27 (2008), 1139-1149. https://doi.org/10.11821/yj2008050017 doi: 10.11821/yj2008050017
[36]	Xinjing News, More than 60 days in Shanghai against the epidemic: how a mega-city fought against Omicron[EB/OL] (in Chinese), 2022. Available from: https://news.sina.com.cn/c/2022-05-01/doc-imcwiwst4954343.shtml.
[37]	Z. Fang, Thinking and challenges of crowd dynamics observation from the perspectives of public health and public security (in Chinese), J. Wuhan Univ., Inf. Sci. Ed., 45 (2020), 1847-1856. https://doi.org/10.13203/j.whugis20200422 doi: 10.13203/j.whugis20200422
[38]	D. Li, Z. Shao, W. Yu, X. Zhu, S. Zhou, Public epidemic prevention and control services based on big data of spatiotemporal location make cities more smart (in Chinese), J. Wuhan Univ., Inf. Sci. Ed., 45 (2020), 475-487. https://doi.org/10.13203/j.whugis20200145 doi: 10.13203/j.whugis20200145
[39]	World Trade Organization, Trade set to plunge as COVID-19 pandemic upends global economy, 2020. Available from: https://www.wto.org/english/news_e/pres20_e/pr855_e.htm.
[40]	B. Pfefferbaum, C. S. North, Mental Health and the COVID-19 Pandemic, N. Engl. J. Med., 383 (2020), 510-512. https://doi.org/10.1056/NEJMp2008017 doi: 10.1056/NEJMp2008017
[41]	J. Y. Qiu, B. Shen, M. Zhao, Z. Wang, B. Xie, Y. F. Xu, A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: implications and policy recommendations, Gen. Psychiatry, 33 (2020). https://doi.org/10.1136/gpsych-2020-100213
[42]	Z. I. Santini, P. E. Jose, E. Y. Cornwell, A. Koyanagi, L. Nielsen, C. Hinrichsen, et al., Social disconnectedness, perceived isolation, and symptoms of depression and anxiety among older Americans (NSHAP): a longitudinal mediation analysis, Lancet Public Health, 5 (2020), E62-E70. https://doi.org/10.1016/S2468-2667(19)30230-0 doi: 10.1016/S2468-2667(19)30230-0
[43]	A. Teslya, T. M. Pham, N. G. Godijk, M. E. Kretzschmar, M. C. J. Bootsma, G. Rozhnova, Impact of self-imposed prevention measures and short-term government-imposed social distancing on mitigating and delaying a COVID-19 epidemic: a modelling study, PLoS Med., 17 (2020). https://doi.org/10.1371/journal.pmed.1003166
[44]	W. Cao, H. Dai, X. Tong, F. Peng, C. Feng, Z. Wu, A model of artificial prevention and control measures for COVID-19 isolation and reception and cure based on discrete grids (in Chinese), J. Wuhan Univ., Inf. Sci. Ed., 46 (2021), 167-176. https://doi.org/10.13203/j.whugis20200343 doi: 10.13203/j.whugis20200343
[45]	D. P. Peng, X. Y. Xing, Y. Wang, K. Y. Zhang, J. Q. Niu, Epidemiological and clinical characteristics of SARS-CoV-2 Omicron variant (in Chinese), Chin. J. Viral Dis., 12 (2022), 385-389. https://doi.org/10.16505/j.2095-0136.2022.0058 doi: 10.16505/j.2095-0136.2022.0058

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