Research progress on the basic and effective reproductive number in the epidemiology of infectious diseases

ZHANG Xiao-bao; YAN Dan-ying; CHEN Can; JIANG Dai-xi; DING Cheng; LAN Lei; WU Jie; YANG Shi-gui

doi:10.16462/j.cnki.zhjbkz.2021.07.003

Volume 25 Issue 7

Aug. 2021

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2021 > 25(7): 753-757, 790

ZHANG Xiao-bao, YAN Dan-ying, CHEN Can, JIANG Dai-xi, DING Cheng, LAN Lei, WU Jie, YANG Shi-gui. Research progress on the basic and effective reproductive number in the epidemiology of infectious diseases[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(7): 753-757, 790. doi: 10.16462/j.cnki.zhjbkz.2021.07.003

Citation:

ZHANG Xiao-bao, YAN Dan-ying, CHEN Can, JIANG Dai-xi, DING Cheng, LAN Lei, WU Jie, YANG Shi-gui. Research progress on the basic and effective reproductive number in the epidemiology of infectious diseases[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(7): 753-757, 790. doi: 10.16462/j.cnki.zhjbkz.2021.07.003

Citation:

ZHANG Xiao-bao, YAN Dan-ying, CHEN Can, JIANG Dai-xi, DING Cheng, LAN Lei, WU Jie, YANG Shi-gui. Research progress on the basic and effective reproductive number in the epidemiology of infectious diseases[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(7): 753-757, 790. doi: 10.16462/j.cnki.zhjbkz.2021.07.003

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Research progress on the basic and effective reproductive number in the epidemiology of infectious diseases

doi: 10.16462/j.cnki.zhjbkz.2021.07.003

The First Affiliated Hospital, College of Medicine, Zhejiang University, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases of Zhejiang University, National Clinical Research Center for Infectious Diseases of Zhejiang University, Hangzhou 310003, China

Funds:

National Natural Science Foundation of China 81672005

National Natural Science Foundation of China U1611264

National Science and Technology Major Project 2018ZX10715-014-002

Fund of Young and Middle-aged Scientific and technological innovation leaders of the Ministry of Science and Technology 2017RA2120

More Information

Corresponding author: YANG Shi-gui, E-mail: yangshigui@zju.edu.cn
Received Date: 2021-04-08
Rev Recd Date: 2021-06-24

Available Online: 2021-08-13

Publish Date: 2021-07-10

Abstract

Abstract

With the occurrence of an emerging infectious disease, some epidemiological indicators are used to measure the transmission of the disease. Basic reproduction number (R₀) and effective reproduction number (R_e) are two crucial indicators among them. However, the definition, calculation, and interpretation of R₀ and R_e are misunderstood or even misused in many cases. This review introduces the definition, calculation, influence factors, epidemiology significance, notes for application, and the R₀ of some common infectious diseases, aiming to provide scientific guidance for health decision-making departments to prevent and control the epidemic of infectious disease.
- Epidemiology of infectious disease,
- Basic reproduction number,
- Effective reproduction number

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References(33)

References

[1]	Heffernan JM, Smith RJ, Wahl LM. Perspectives on the basic reproductive ratio[J]. J R Soc Interface, 2005, 2(4): 281-293. DOI: 10.1098/rsif.2005.0042.
[2]	Guerra FM, Bolotin S, Lim G, et al. The basic reproduction number (R₀) of measles: a systematic review[J]. Lancet Infect Dis, 2017, 17(12): e420-e428. DOI: 10.1016/s1473-3099(17)30307-9.
[3]	Dietz K. The estimation of the basic reproduction number for infectious diseases[J]. Stat Methods Med Res, 1993, 2(1): 23-41. DOI: 10.1177/096228029300200103.
[4]	Delamater PL, Street E J, Leslie TF, et al. Complexity of the basic reproduction Number (R₀)[J]. Emerg Infect Dis, 2019, 25(1): 1-4. DOI: 10.3201/eid2501.171901.
[5]	周涛. 新型冠状病毒肺炎基本再生数的初步预测[J]. 中国循证医学杂志, 2020, 20(3): 359-364. DOI: 10.7507/1672-2531.202001118. Zhou T. Preliminary prediction of basic reproduction number of COVID-19[J]. Chin J Evid-Based Med, 2020, 20(3): 359-364. DOI: 10.7507/1672-2531.202001118.
[6]	Choi S, Ki M. Estimating the reproductive number and the outbreak size of COVID-19 in Korea[J]. Epidemiol Health, 2020, 42: e2020011. DOI: 10.4178/epih.e2020011.
[7]	Parasite Ecology. Density-dependent vs. Frequency-dependent disease transmission[EB/OL]. (2013-10-17)[2021-03-05]. https://parasiteecology.wordpress.com/2013/10/17/density-dependent-vs-frequency-dependent-disease-transmission/.
[8]	Parasite Ecology. Parasite transmission: density-dependent, frequency-dependent, or neither?[EB/OL]. (2014-02-26)[2021-03-05]. https://parasiteecology.wordpress.com/?s=Parasite+transmission%3A+density-dependent%2C+frequency-dependent%2C+or+neither%3F&submit=Search.
[9]	宋倩倩. 新型冠状病毒肺炎的早期传染病流行病学参数估计研究[J]. 中华流行病学杂志, 2020, 41(4): 461-465. DOI: 10.3760/cma.j.cn112338-20200205-00069. Song QQ. Epidemiological parameter estimation of COVID-19 in early infectious diseases[J]. Chin J Epidemiol, 2020, 41(4): 461-465. DOI: 10.3760/cma.j.cn112338-20200205-00069.
[10]	Barnhart S, Sheppard L, Beaudet N, et al. Tuberculosis in health care settings and the estimated benefits of engineering controls and respiratory protection[J]. J Occup Environ Med, 1997, 39(9): 849-854. DOI: 10.1097/00043764-199709000-00008.
[11]	Zhou ZF, Mei SJ, Chen J, et al. Nonlinear effect of wind velocity on mumps in Shenzhen, China, 2013-2016[J]. Public Health, 2020, 179: 178-185. DOI: 10.1016/j.puhe.2019.10.023.
[12]	Zhang Q, Zhou M, Yang Y, et al. Short-term effects of extreme meteorological factors on childhood hand, foot, and mouth disease reinfection in Hefei, China: a distributed lag non-linear analysis[J]. Sci Total Environ, 2019, 653: 839-848. DOI: 10.1016/j.scitotenv.2018.10.349.
[13]	Lee BY, Bartsch SM, Ferguson MC, et al. The value of decreasing the duration of the infectious period of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection[J]. PLoS Comput Biol, 2021, 17(1): e1008470. DOI: 10.1371/journal.pcbi.1008470.
[14]	Jing QL, Liu MJ, Zhang ZB, et al. Household secondary attack rate of COVID-19 and associated determinants in Guangzhou, China: a retrospective cohort study[J]. Lancet Infect Dis, 2020, 20(10): 1141-1150. DOI: 10.1016/S1473-3099(20)30471-0.
[15]	Edmunds WJ, Kafatos G, Wallinga J, et al. Mixing patterns and the spread of close-contact infectious diseases[J]. Emerg Themes Epidemiol, 2006, 3: 10. DOI: 10.1186/1742-7622-3-10.
[16]	Mossong J, Hens N, Jit M, et al. Social contacts and mixing patterns relevant to the spread of infectious diseases[J]. PLoS Med, 2008, 5(3): e74. DOI: 10.1371/journal.pmed.0050074.
[17]	van den Driessche P, Watmough J. Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission[J]. Math Biosci, 2002, 180: 29-48. DOI: 10.1016/s0025-5564(02)00108-6.
[18]	Xu C, Dong Y, Yu X, et al. Estimation of reproduction numbers of COVID-19 in typical countries and epidemic trends under different prevention and control scenarios[J]. Front Med, 2020, 14(5): 613-622. DOI: 10.1007/s11684-020-0787-4.
[19]	Anderson RM, May RM. Vaccination and herd immunity to infectious diseases[J]. Nature, 1985, 318(6044): 323-329. DOI: 10.1038/318323a0.
[20]	Lipsitch M, Cohen T, Cooper B, et al. Transmission dynamics and control of severe acute respiratory syndrome[J]. Science, 2003, 300(5627): 1966-1970. DOI: 10.1126/science.1086616.
[21]	Riley S, Fraser C, Donnelly CA, et al. Transmission dynamics of the etiological agent of SARS in Hong Kong: impact of public health interventions[J]. Science, 2003, 300(5627): 1961-1966. DOI: 10.1126/science.1086478.
[22]	Breban R, Riou J, Fontanet A. Interhuman transmissibility of Middle East respiratory syndrome coronavirus: estimation of pandemic risk[J]. Lancet, 2013, 382(9893): 694-699. DOI: 10.1016/s0140-6736(13)61492-0.
[23]	Majumder MS, Rivers C, Lofgren E, et al. Estimation of MERS-Coronavirus reproductive number and case fatality rate for the spring 2014 Saudi Arabia outbreak: insights from publicly available data[J]. PLoS Curr, 2014, 6. DOI: 10.1371/ecurrents.outbreaks.98d2f8f3382d84f390736cd5f5fe133c.
[24]	Althaus CL. Estimating the reproduction number of ebola virus (EBOV) during the 2014 outbreak in West Africa[J]. PLoS Curr, 2014, 6. DOI: 10.1371/ecurrents.outbreaks.91afb5e0f279e7f29e7056095255b288.
[25]	Biggerstaff M, Cauchemez S, Reed C, et al. Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature[J]. BMC Infect Dis, 2014, 14: 480. DOI: 10.1186/1471-2334-14-480.
[26]	Tang S, Xiao Y, Yang Y, et al. Community-based measures for mitigating the 2009 H1N1 pandemic in China[J]. PLoS One, 2010, 5(6): e10911. DOI: 10.1371/journal.pone.0010911.
[27]	Ali ST, Kadi AS, Ferguson NM. Transmission dynamics of the 2009 influenza A (H1N1) pandemic in India: the impact of holiday-related school closure[J]. Epidemics, 2013, 5(4): 157-163. DOI: 10.1016/j.epidem.2013.08.001.
[28]	Netto EM, Moreira-Soto A, Pedroso C, et al. High zika virus seroprevalence in Salvador, Northeastern Brazil limits the potential for further outbreaks[J]. mBio, 2017, 8(6)e01390-17. DOI: 10.1128/mBio.01390-17.
[29]	Funk S, Kucharski AJ, Camacho A, et al. Comparative analysis of dengue and zika outbreaks reveals differences by setting and virus[J]. PLoS Negl Trop Dis, 2016, 10(12): e0005173. DOI: 10.1371/journal.pntd.0005173.
[30]	Nishiura H, Kinoshita R, Mizumoto K, et al. Transmission potential of zika virus infection in the South Pacific[J]. Int J Infect Dis, 2016, 45: 95-97. DOI: 10.1016/j.ijid.2016.02.017.
[31]	Zhao S, Lin QY, Ran JJ, et al. Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: a data-driven analysis in the early phase of the outbreak[J]. Int J Infect Dis, 2020, 92: 214-217. DOI: 10.1016/j.ijid.2020.01.050.
[32]	Hong K, Yum SJ, Kim JH, et al. Re-estimation of basic reproduction number of COVID-19 based on the epidemic curve by symptom onset date[J]. Epidemiol Infect, 2021, 149: e53. DOI: 10.1017/s0950268821000431.
[33]	Lai CC, Hsu CY, Jen HH, et al. The Bayesian Susceptible-Exposed-Infected-Recovered model for the outbreak of COVID-19 on the Diamond Princess Cruise Ship[J]. Stoch Environ Res Risk Assess, 2021: 1-15. DOI: 10.1007/s00477-020-01968-w.