How Effective Is a Traffic Control Policy in Blocking the Spread of COVID-19? A Case Study of Changsha, China
Abstract
:1. Introduction
2. Materials
2.1. Regional Overview and Traffic Control Policies
2.2. Dataset
3. Methods
3.1. Construction of the SEIR Model Considering the Infectivity of the Exposed
- that the susceptible (S) subpopulation is a healthy population that has never been infected with the virus and that is non-immune;
- that the exposed (E) subpopulation is a population that carries the virus and that has an infective capacity during the latent period;
- that all of the infective (I) subpopulation are cured or quarantined immediately after being confirmed and lose infectivity;
- that the recovered (R) subpopulation would not be reinfected after being cured;
- that for the total population size N, mere consideration is given to the effective size of the active population rather than to the births and the deaths within the population and to immigration into and emigration from the population, with all the time;
- that in the early stage of the epidemic, the cumulative confirmed cases are new cases in a single day, whereas the cumulative cured cases are new cured cases in a single day.
3.2. Traffic Control Policy Evaluation Model Based on the SEIR Model Considering the Infectivity of the Exposed
3.3. Parameter Calibration Models
3.3.1. Calibration Model on Initial Infectious Rate and Cure Rate
3.3.2. Calibration Model on Policy Effect Parameter K
3.4. Model Parameter Calibration
4. Results
4.1. Epidemic Development Trend in the Absence of Traffic Control Policies
4.2. Epidemic Development Trend in the Presence of Traffic Control Policies
4.3. Blocking Effects of Different Traffic Control Policies on Epidemic Spread
5. Discussions and Conclusions
5.1. Discussions
5.2. Conclusions
- Based on the classical SIR model, a SEIR model considering the infectivity of the exposed for traffic control policy evaluation has been built. Compared with other traditional SIR models, this model has allowed for the infectivity signature of the exposed and designed the parameter of the infectious rate. This model can evaluate the blocking effects of traffic control policies on the spread of cases in a way.
- According to the calculation results from the model, compared with the natural development state of the epidemic in Changsha, adopting traffic control policies has decreased the peak values of the infective and the exposed by 66.03% and 65.70% and delayed the peak period by 58 days. Among them, the home-quarantine policy is more significantly effective in decreasing the infective and the exposed populations in Changsha, and it can delay the peak period of the epidemic longer, compared to the road traffic control and the public transport suspension policies.
- According to the results in different scenarios, the home-quarantine policy has higher time sensitivity than the road traffic control and the public transport suspension policies: the earlier this traffic control policy is implemented, the more significant its blocking effect on the spread of the epidemic. If simplex traffic control were the only need in an early stage, the home-quarantine policy would be the optimal choice.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- World Health Organization. Timeline: WHO’s COVID-19 Response. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/interactive-timeline#! (accessed on 24 April 2022).
- Duong, B.V.; Larpruenrudee, P.; Fang, T.; Hossain, S.I.; Saha, S.C.; Gu, Y.; Islam, M.S. Is the SARS CoV-2 Omicron Variant Deadlier and More Transmissible Than Delta Variant? Int. J. Environ. Res. Public Health 2022, 19, 4586. [Google Scholar] [CrossRef] [PubMed]
- Magazine, N.; Zhang, T.; Wu, Y.; McGee, M.C.; Veggiani, G.; Huang, W. Mutations and Evolution of the SARS-CoV-2 Spike Protein. Viruses 2022, 14, 640. [Google Scholar] [CrossRef] [PubMed]
- Lindner, C.; Kotta, I.; Marschalko, E.E.; Szabo, K.; Kalcza-Janosi, K.; Retelsdorf, J. Increased Risk Perception, Distress Intolerance and Health Anxiety in Stricter Lockdowns: Self-Control as a Key Protective Factor in Early Response to the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2022, 19, 5098. [Google Scholar] [CrossRef]
- Sheen, J.; Clancy, E.M.; Considine, J.; Dwyer, A.; Tchernegovski, P.; Aridas, A.; Lee, B.E.C.; Reupert, A.; Boyd, L. Did You Bring It Home with You? A Qualitative Investigation of the Impacts of the COVID-19 Pandemic on Victorian Frontline Healthcare Workers and Their Families. Int. J. Environ. Res. Public Health 2022, 19, 4897. [Google Scholar] [CrossRef]
- Khiali, S.; Entezari-Maleki, T. Tocilizumab in COVID-19 management: Addressing time of starting treatment. Eur. J. Hosp. Pharm. 2022. [Google Scholar] [CrossRef] [PubMed]
- Altmann, D.M.; Boyton, R.J. COVID-19 vaccination: The road ahead. Science 2022, 375, 1127–1132. [Google Scholar] [CrossRef] [PubMed]
- World Health Organization. WHO Coronavirus (COVID-19) Dashboard. Available online: https://covid19.who.int/ (accessed on 24 April 2022).
- Goenaga, B.; Matini, N.; Karanam, D.; Underwood, B.S. Disruption and Recovery: Initial Assessment of COVID-19 Traffic Impacts in North Carolina and Virginia. J. Transp. Eng. Part A-Syst. 2021, 147, 1–16. [Google Scholar] [CrossRef]
- Liu, Z.; Stern, R. Quantifying the Traffic Impacts of the COVID-19 Shutdown. J. Transp. Eng. Part A-Syst. 2021, 147. [Google Scholar] [CrossRef]
- Rothan, H.A.; Byrareddy, S.N. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J. Autoimmun. 2020, 109, 102433. [Google Scholar] [CrossRef]
- Maier, B.F.; Brockmann, D. Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China. Science 2020, 368, 742–746. [Google Scholar] [CrossRef] [Green Version]
- Burns, J.; Hoffmann, S.; Kurz, C.; Laxy, M.; Polus, S.; Rehfuess, E. COVID-19 mitigation measures and nitrogen dioxide-A quasi-experimental study of air quality in Munich, Germany. Atmos. Environ. 2021, 246, 118089. [Google Scholar] [CrossRef] [PubMed]
- Poli, P.; Boaga, J.; Molinari, I.; Cascone, V.; Boschi, L. The 2020 coronavirus lockdown and seismic monitoring of anthropic activities in Northern Italy. Sci. Rep. 2020, 10, 9404. [Google Scholar] [CrossRef] [PubMed]
- Yen, M.-Y.; Schwartz, J.; Chen, S.-Y.; King, C.-C.; Yang, G.-Y.; Hsueh, P.-R. Interrupting COVID-19 transmission by implementing enhanced traffic control bundling: Implications for global prevention and control efforts. J. Microbiol. Immunol. Infect. 2020, 53, 377–380. [Google Scholar] [CrossRef]
- Kermack, W.O.; Mckendrick, A.G.A. A Contribution to the Mathematical Theory of Epidemics. Proc. R. Soc. A Math. Phys. Eng. Sci. 1927, 115, 700–721. [Google Scholar]
- Rocchi, E.; Peluso, S.; Sisti, D.; Carletti, M. A Possible Scenario for the Covid-19 Epidemic, Based on the SI(R) Model. SN Compr. Clin. Med. 2020, 2, 501–503. [Google Scholar] [CrossRef] [PubMed]
- Lee, J.; Lee, S.-M.; Jung, E. How Important Is Behavioral Change during the Early Stages of the COVID-19 Pandemic? A Mathematical Modeling Study. Int. J. Environ. Res. Public Health 2021, 18, 9855. [Google Scholar] [CrossRef] [PubMed]
- Leontitsis, A.; Senok, A.; Alsheikh-Ali, A.; Al Nasser, Y.; Loney, T.; Alshamsi, A. SEAHIR: A Specialized Compartmental Model for COVID-19. Int. J. Environ. Res. Public Health 2021, 18, 2667. [Google Scholar] [CrossRef]
- Kang, B.G.; Park, H.-M.; Jang, M.; Seo, K.-M. Hybrid Model-Based Simulation Analysis on the Effects of Social Distancing Policy of the COVID-19 Epidemic. Int. J. Environ. Res. Public Health 2021, 18, 11264. [Google Scholar] [CrossRef] [PubMed]
- Xiang, W.; Chen, L.; Wang, B.; Xue, Q.W.; Hao, W.; Liu, X.M. Policies, population and impacts in metro ridership response to COVID-19 in Changsha. J. Transp. Saf. Security. 2021. [Google Scholar] [CrossRef]
- Yan, C.-X.; Wang, B.; Chen, L.; Xiang, W.; Wang, Y.; Yan, X.-D. Impacts of COVID-19 Traffic Control Policy on Population Flows in Changsha. Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/J. Transp. Syst. Eng. Inf. Technol. 2021, 21, 190–197. [Google Scholar]
- Lin, R.; Lin, S.; Yan, N.; Huang, J. Do prevention and control measures work? Evidence from the outbreak of COVID-19 in China. Cities 2021, 118, 103347. [Google Scholar] [CrossRef] [PubMed]
- Committee, C.C.H.a.W. Prevention and Control of COVID-19-Prevention and Control Efforts. Available online: http://wsjkw.changsha.gov.cn/ztzl_1/fkxxgzbd/fkdt/index_31.html (accessed on 20 April 2022).
- Zhang, J.; Feng, B.; Wu, Y.; Xu, P.; Ke, R.; Dong, N. The effect of human mobility and control measures on traffic safety during COVID-19 pandemic. PLoS ONE 2021, 16, e0243263. [Google Scholar] [CrossRef] [PubMed]
- Chen, M.; Li, M.; Hao, Y.; Liu, Z.; Hu, L.; Wang, L. The introduction of population migration to SEIAR for COVID-19 epidemic modeling with an efficient intervention strategy. Inf. Fusion 2020, 64, 252–258. [Google Scholar] [CrossRef]
- Tang, B.; Wang, X.; Li, Q.; Bragazzi, N.L.; Tang, S.Y.; Xiao, Y.N.; Wu, J.H. Estimation of the Transmission Risk of the 2019-nCoV and Its Implication for Public Health Interventions. J. Clin. Med. 2020, 9, 462. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lauer, S.A.; Grantz, K.H.; Bi, Q.; Jones, F.K.; Zheng, Q.; Meredith, H.R.; Azman, A.S.; Reich, N.G.; Lessler, J. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Ann. Intern. Med. 2020, 172, 577–582. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Linton, N.M.; Kobayashi, T.; Yang, Y.; Hayashi, K.; Akhmetzhanov, A.R.; Jung, S.-m.; Yuan, B.; Kinoshita, R.; Nishiura, H. Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data. J. Clin. Med. 2020, 9, 538. [Google Scholar] [CrossRef] [Green Version]
- Jia, X.-L.; Zhou, W.-X.; Han, X.-J.; Yan, M.-H.; Qin, X.-F. Blocking Effects of Traffic Control Measures on COVID-19 Transmission in City Territories. Zhongguo Gonglu Xuebao/China J. Highw. Transp. 2022, 35, 252–262. [Google Scholar]
- Statistics, C.C.B. Changsha City, the Seventh National Census Bulletin (No. 1). Available online: http://tjj.changsha.gov.cn/tjxx/tjsj/tjgb/202106/t20210603_9988548.html (accessed on 20 April 2022).
- Chinazzi, M.; Davis, J.T.; Ajelli, M.; Gioannini, C.; Litvinova, M.; Merler, S.; Pastore y Piontti, A.; Mu, K.; Rossi, L.; Sun, K.; et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science 2020, 368, 395–400. [Google Scholar] [CrossRef] [Green Version]
- Kraemer, M.U.G.; Yang, C.H.; Gutierrez, B.; Wu, C.H.; Klein, B.; Pigott, D.M.; du Plessis, L.; Faria, N.R.; Li, R.R.; Hanage, W.P.; et al. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 2020, 368, 493–497. [Google Scholar] [CrossRef] [Green Version]
- Bisanzio, D.; Reithinger, R.; Alqunaibet, A.; Almudarra, S.; Alsukait, R.F.; Dong, D.; Zhang, Y.; El-Saharty, S.; Herbst, C.H. Estimating the effect of non-pharmaceutical interventions to mitigate COVID-19 spread in Saudi Arabia. BMC Med. 2022, 20, 51. [Google Scholar] [CrossRef]
- Liu, S.; Yamamoto, T. Role of stay-at-home requests and travel restrictions in preventing the spread of COVID-19 in Japan. Transp. Res. Part A Policy Pract. 2022, 159, 1–16. [Google Scholar] [CrossRef] [PubMed]
- Wang, J.E.; Du, D.; Wei, Y. The development of COVID-19 in China: Spatial diffusion and geographical pattern. Geogr. Res. 2020, 39, 1450–1462. [Google Scholar]
- Sjodin, H.; Wilder-Smith, A.; Osman, S.; Farooq, Z.; Rocklov, J. Only strict quarantine measures can curb the coronavirus disease (COVID-19) outbreak in Italy, 2020. Eurosurveillance 2020, 25, 7–12. [Google Scholar] [CrossRef] [PubMed]
- Lai, S.J.; Ruktanonchai, N.W.; Zhou, L.C.; Prosper, O.; Luo, W.; Floyd, J.R.; Wesolowski, A.; Santillana, M.; Zhang, C.; Du, X.J.; et al. Effect of non-pharmaceutical interventions to contain COVID-19 in China. Nature 2020, 585, 410–413. [Google Scholar] [CrossRef] [PubMed]
Related Work | Modeling Method | Description | This Paper |
---|---|---|---|
[18] | Equation-based model SEIR |
|
|
[19] | Equation-based model SEIR |
| |
[20] | Equation-based model SIR |
| |
[21] | Equation-based model DID (Difference-in-Difference model) |
| |
[22] | Equation-based model DID |
| |
[23] | An equation-based econometric approach to empirically |
|
Order | Date | Traffic Control Policy | Range of Control |
---|---|---|---|
1 | 25 January 2020 | home quarantine | downtown Changsha |
2 | 27 January 2020 | road traffic control | high-risk and medium-risk areas |
3 | 28 January 2020 | public transport suspension | downtown Changsha |
Parameter | Connotation | Parameter | Connotation |
---|---|---|---|
size of subpopulation S at time t | number of the exposed to the susceptible per day | ||
size of subpopulation E at time t | initial infection probability after exposure to the exposed | ||
size of subpopulation I at time t | probability of the exposed subpopulation transmuting into the infective | ||
size of subpopulation R at time t | cure rate | ||
size of the resident population in downtown Changsha | proportion of citywide effective active population |
R2 | adj-R2 | p | Sig. | ||
---|---|---|---|---|---|
5 | 0.049 | 0.907 | 0.899 | 0.808 | 0.000 |
6 | 0.048 | 0.927 | 0.918 | 0.820 | 0.000 |
7 | 0.046 | 0.937 | 0.929 | 0.826 | 0.000 |
8 | 0.045 | 0.945 | 0.937 | 0.831 | 0.000 |
9 | 0.043 | 0.954 | 0.947 | 0.836 | 0.000 |
10 | 0.041 | 0.960 | 0.952 | 0.840 | 0.000 |
11 | 0.040 | 0.968 | 0.961 | 0.844 | 0.000 |
12 | 0.039 | 0.974 | 0.967 | 0.848 | 0.000 |
13 | 0.037 | 0.980 | 0.973 | 0.852 | 0.000 |
14 | 0.036 | 0.985 | 0.975 | 0.855 | 0.000 |
15 | 0.035 | 0.991 | 0.981 | 0.858 | 0.000 |
Parameter Name | Value | Source | Notes |
---|---|---|---|
5~15 individuals | Literature [27] | The number of people with effective exposure varies by date and adjustment of prevention and control policies. | |
0.048~0.5 | Literatures [28,29] | Taken as the reciprocal of the latent period, which lasts 2~21 days | |
2% | Literature [30] | None | |
0.035 | calculated by Formula (14) | None | |
0.001 | calculated by Formula (15) | None | |
−0.597 *** | calculated by Formula (16) | The numerical estimates have significant correlation | |
A | 8,000,000 | Changsha Bureau of Statistics [31] | resident population 10.048 million, including 8 million in urban area, throughout 2021 |
0.011 | calculated by Formula (6) | infectious rate under the home-quarantine policy | |
0.019 | calculated by Formula (6) | infectious rate under the road traffic control | |
0.017 | calculated by Formula (6) | infectious rate under the public transport suspension policy |
Date | I-Actual | I-Estimation | Relative Error | R-Actual | R-Estimation | Relative Error |
---|---|---|---|---|---|---|
25 February 2020 | 242 | 240 | −0.63% | 159 | 135 | −15.29% |
26 February 2020 | 242 | 241 | −0.55% | 164 | 142 | −13.55% |
27 February 2020 | 242 | 241 | −0.48% | 172 | 149 | −13.45% |
28 February 2020 | 242 | 241 | −0.40% | 174 | 156 | −10.36% |
29 February 2020 | 242 | 241 | −0.32% | 178 | 163 | −8.37% |
1 March 2020 | 242 | 241 | −0.24% | 185 | 170 | −7.97% |
2 March 2020 | 242 | 242 | −0.16% | 186 | 177 | −4.62% |
Policy | Scenarios | E-Peak | I-Peak | Peak-Day | Comparison with T = 0 | Comparison with No Policy | ||||
---|---|---|---|---|---|---|---|---|---|---|
Peak Delay | Change of E | Change of I | Peak Delay | Change of E | Change of I | |||||
P1 1 | T = F | 8227 | 5783 | 94 | −16 | −14.04% | −14.20% | −54 | −62.63% | −62.94% |
T = 0 | 9571 | 6740 | 78 | None | None | None | −38 | −56.52% | −56.81% | |
T = −3 | 11,240 | 7929 | 66 | 12 | 17.44% | 17.64% | −26 | −48.94% | −49.19% | |
T = −5 | 11,757 | 8293 | 62 | 16 | 22.84% | 23.04% | −22 | −46.60% | −46.86% | |
T = −7 | 12,296 | 8678 | 58 | 20 | 28.47% | 28.75% | −18 | −44.15% | −44.39% | |
P2 1 | T = F | 13,298 | 9398 | 61 | −3 | −0.14% | −0.11% | −21 | −36.90% | −39.78% |
T = 0 | 13,317 | 9408 | 58 | None | None | None | −18 | 39.51% | −39.72% | |
T = −3 | 15,488 | 10,952 | 51 | 7 | 16.30% | 16.41% | −11 | −29.65% | −29.82% | |
T = −5 | 16,218 | 11,476 | 48 | 10 | 21.78% | 21.98% | −8 | −26.33% | −26.46% | |
T = −7 | 16,873 | 11,946 | 46 | 12 | 26.70% | 26.98% | −6 | −23.36% | −23.45% | |
P3 1 | T = F | 12,090 | 8531 | 67 | −5 | −0.04% | −0.05% | −27 | −45.08% | −45.35% |
T = 0 | 12,095 | 8535 | 62 | None | None | None | −22 | −45.06% | −45.30% | |
T = −3 | 14,096 | 9968 | 53 | 9 | 16.54% | 16.79% | −13 | −35.97% | −36.13% | |
T = −5 | 14,758 | 10,431 | 51 | 11 | 22.02% | 22.21% | −11 | −32.96% | −33.16% | |
T = −7 | 15,385 | 10,878 | 49 | 13 | 27.20% | 27.45% | −9 | −30.12% | −30.30% |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Xiang, W.; Chen, L.; Peng, Q.; Wang, B.; Liu, X. How Effective Is a Traffic Control Policy in Blocking the Spread of COVID-19? A Case Study of Changsha, China. Int. J. Environ. Res. Public Health 2022, 19, 7884. https://doi.org/10.3390/ijerph19137884
Xiang W, Chen L, Peng Q, Wang B, Liu X. How Effective Is a Traffic Control Policy in Blocking the Spread of COVID-19? A Case Study of Changsha, China. International Journal of Environmental Research and Public Health. 2022; 19(13):7884. https://doi.org/10.3390/ijerph19137884
Chicago/Turabian StyleXiang, Wang, Li Chen, Qunjie Peng, Bing Wang, and Xiaobing Liu. 2022. "How Effective Is a Traffic Control Policy in Blocking the Spread of COVID-19? A Case Study of Changsha, China" International Journal of Environmental Research and Public Health 19, no. 13: 7884. https://doi.org/10.3390/ijerph19137884