Mathematical & Computational Biology

Estimating dates of origin and end of COVID-19 epidemics

10.24072/pcjournal.63 - Peer Community Journal, Volume 1 (2021), article no. e70.

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Estimating the date at which an epidemic started in a country and the date at which it can end depending on interventions intensity are important to guide public health responses. Both are potentially shaped by similar factors including stochasticity (due to small population sizes), superspreading events, and memory effects (the fact that the occurrence of some events, e.g. recovering from an infection, depend on the past, e.g. the number of days since the infection). Focusing on COVID-19 epidemics, we develop and analyse mathematical models to explore how these three factors may affect early and final epidemic dynamics. Regarding the date of origin, we find limited effects on the mean estimates, but strong effects on their variances. Regarding the date of extinction following lockdown onset, mean values decrease with stochasticity or with the presence of superspreading events. These results underline the importance of accounting for heterogeneity in infection history and transmission patterns to accurately capture early and late epidemic dynamics.

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DOI: 10.24072/pcjournal.63
Beneteau, Thomas 1; Elie, Baptiste 1; Sofonea, Mircea T. 1; Alizon, Samuel 1

1 MIVEGEC, Univ Montpellier, CNRS, IRD – Montpellier, France
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Beneteau, Thomas; Elie, Baptiste; Sofonea, Mircea T.; Alizon, Samuel. Estimating dates of origin and end of COVID-19 epidemics. Peer Community Journal, Volume 1 (2021), article  no. e70. doi : 10.24072/pcjournal.63.

Peer reviewed and recommended by PCI : 10.24072/pci.mcb.100004

[1] Althouse, B. M.; Wenger, E. A.; Miller, J. C.; Scarpino, S. V.; Allard, A.; Hébert-Dufresne, L.; Hu, H. Superspreading events in the transmission dynamics of SARS-CoV-2: Opportunities for interventions and control, PLOS Biology, Volume 18 (2020) no. 11 | Article

[2] Anderson RM, ; May RM , Infectious Diseases of Humans. Dynamics and Control, Oxford University Press, Oxford, (1991)

[3] Bedford, T.; Greninger, A. L.; Roychoudhury, P.; Starita, L. M.; Famulare, M.; Huang, M.-L.; Nalla, A.; Pepper, G.; Reinhardt, A.; Xie, H.; Shrestha, L.; Nguyen, T. N.; Adler, A.; Brandstetter, E.; Cho, S.; Giroux, D.; Han, P. D.; Fay, K.; Frazar, C. D.; Ilcisin, M.; Lacombe, K.; Lee, J.; Kiavand, A.; Richardson, M.; Sibley, T. R.; Truong, M.; Wolf, C. R.; Nickerson, D. A.; Rieder, M. J.; Englund, J. A.; Hadfield, J.; Hodcroft, E. B.; Huddleston, J.; Moncla, L. H.; Müller, N. F.; Neher, R. A.; Deng, X.; Gu, W.; Federman, S.; Chiu, C.; Duchin, J. S.; Gautom, R.; Melly, G.; Hiatt, B.; Dykema, P.; Lindquist, S.; Queen, K.; Tao, Y.; Uehara, A.; Tong, S.; MacCannell, D.; Armstrong, G. L.; Baird, G. S.; Chu, H. Y.; Shendure, J.; Jerome, K. R.; Chu, H. Y.; Boeckh, M.; Englund, J. A.; Famulare, M.; Lutz, B. R.; Nickerson, D. A.; Rieder, M. J.; Starita, L. M.; Thompson, M.; Shendure, J.; Bedford, T.; Adler, A.; Brandstetter, E.; Cho, S.; Frazar, C. D.; Giroux, D.; Han, P. D.; Hadfield, J.; Huang, S.; Jackson, M. L.; Kiavand, A.; Kimball, L. E.; Lacombe, K.; Logue, J.; Lyon, V.; Newman, K. L.; Richardson, M.; Sibley, T. R.; Zigman Suchsland, M. L.; Truong, M.; Wolf, C. R. Cryptic transmission of SARS-CoV-2 in Washington state, Science, Volume 370 (2020) no. 6516, pp. 571-575 | Article

[4] Britton, T.; Scalia Tomba, G. Estimation in emerging epidemics: biases and remedies, Journal of The Royal Society Interface, Volume 16 (2019) no. 150 | Article

[5] Danesh, G.; Elie, B.; Michalakis, Y.; Sofonea, M. T.; Bal, A.; Behillil, S.; Destras, G.; Boutolleau, D.; Burrel, S.; Marcelin, A.-G.; Plantier, J.-C.; Thibault, V.; Simon-Loriere, E.; van der Werf, S.; Lina, B.; Josset, L.; Enouf, V.; Alizon, S. Early phylodynamics analysis of the COVID-19 epidemic in France, Peer Community Journal, Volume 1 (2021) | Article

[6] Danesh, G.; Saulnier, E.; Gascuel, O.; Choisy, M.; Alizon, S. Simulating trajectories and phylogenies from population dynamics models with TiPS, Simulating trajectories and phylogenies from population dynam- ics models with TiPS. bioRxiv, (2020) | Article

[7] Djaafara, B. A.; Imai, N.; Hamblion, E.; Impouma, B.; Donnelly, C. A.; Cori, A. A Quantitative Framework for Defining the End of an Infectious Disease Outbreak: Application to Ebola Virus Disease, American Journal of Epidemiology, Volume 190 (2020) no. 4, pp. 642-651 | Article

[8] Eichner, M.; Dietz, K. Eradication of Poliomyelitis: When Can One Be Sure That Polio Virus Transmission Has Been Terminated?, American Journal of Epidemiology, Volume 143 (1996) no. 8, pp. 816-822 | Article

[9] Endo, A.; Abbott, S.; Kucharski, A. J.; Funk, S. Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China, Wellcome Open Research, Volume 5 (2020) | Article

[10] Grant, A. Dynamics of COVID-19 epidemics: SEIR models underestimate peak infection rates and overestimate epidemic duration, Dynamics of COVID-19 epidemics: SEIR models underestimate peak infection rates and overestimate epidemic duration. medRxiv, (2020) | Article

[11] Hartfield, M.; Alizon, S. Introducing the Outbreak Threshold in Epidemiology, PLoS Pathogens, Volume 9 (2013) no. 6 | Article

[12] He, X.; Lau, E. H. Y.; Wu, P.; Deng, X.; Wang, J.; Hao, X.; Lau, Y. C.; Wong, J. Y.; Guan, Y.; Tan, X.; Mo, X.; Chen, Y.; Liao, B.; Chen, W.; Hu, F.; Zhang, Q.; Zhong, M.; Wu, Y.; Zhao, L.; Zhang, F.; Cowling, B. J.; Li, F.; Leung, G. M. Temporal dynamics in viral shedding and transmissibility of COVID-19, Nature Medicine, Volume 26 (2020) no. 5, pp. 672-675 | Article

[13] Hellewell, J.; Abbott, S.; Gimma, A.; Bosse, N. I.; Jarvis, C. I.; Russell, T. W.; Munday, J. D.; Kucharski, A. J.; Edmunds, W. J.; Funk, S.; Eggo, R. M.; Sun, F.; Flasche, S.; Quilty, B. J.; Davies, N.; Liu, Y.; Clifford, S.; Klepac, P.; Jit, M.; Diamond, C.; Gibbs, H.; van Zandvoort, K. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts, The Lancet Global Health, Volume 8 (2020) no. 4 | Article

[14] Li, Q.; Guan, X.; Wu, P.; Wang, X.; Zhou, L.; Tong, Y.; Ren, R.; Leung, K. S.; Lau, E. H.; Wong, J. Y.; Xing, X.; Xiang, N.; Wu, Y.; Li, C.; Chen, Q.; Li, D.; Liu, T.; Zhao, J.; Liu, M.; Tu, W.; Chen, C.; Jin, L.; Yang, R.; Wang, Q.; Zhou, S.; Wang, R.; Liu, H.; Luo, Y.; Liu, Y.; Shao, G.; Li, H.; Tao, Z.; Yang, Y.; Deng, Z.; Liu, B.; Ma, Z.; Zhang, Y.; Shi, G.; Lam, T. T.; Wu, J. T.; Gao, G. F.; Cowling, B. J.; Yang, B.; Leung, G. M.; Feng, Z. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia, New England Journal of Medicine, Volume 382 (2020) no. 13, pp. 1199-1207 | Article

[15] Linton, N.; Kobayashi, T.; Yang, Y.; Hayashi, K.; Akhmetzhanov, A.; 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, Journal of Clinical Medicine, Volume 9 (2020) no. 2 | Article

[16] Liu, Y.; Eggo, R. M.; Kucharski, A. J. Secondary attack rate and superspreading events for SARS-CoV-2, The Lancet, Volume 395 (2020) no. 10227 | Article

[17] Lloyd-Smith, J. O.; Schreiber, S. J.; Kopp, P. E.; Getz, W. M. Superspreading and the effect of individual variation on disease emergence, Nature, Volume 438 (2005) no. 7066, pp. 355-359 | Article

[18] Nishiura, H.; Linton, N. M.; Akhmetzhanov, A. R. Serial interval of novel coronavirus (COVID-19) infections, International Journal of Infectious Diseases, Volume 93 (2020), pp. 284-286 | Article

[19] Nishiura, H.; Miyamatsu, Y.; Mizumoto, K. Objective Determination of End of MERS Outbreak, South Korea, 2015, Emerging Infectious Diseases, Volume 22 (2016) no. 1, pp. 146-148 | Article

[20] du Plessis, L.; McCrone, J. T.; Zarebski, A. E.; Hill, V.; Ruis, C.; Gutierrez, B.; Raghwani, J.; Ashworth, J.; Colquhoun, R.; Connor, T. R.; Faria, N. R.; Jackson, B.; Loman, N. J.; O’Toole, Á.; Nicholls, S. M.; Parag, K. V.; Scher, E.; Vasylyeva, T. I.; Volz, E. M.; Watts, A.; Bogoch, I. I.; Khan, K.; Aanensen, D. M.; Kraemer, M. U. G.; Rambaut, A.; Pybus, O. G. Establishment and lineage dynamics of the SARS-CoV-2 epidemic in the UK, Science, Volume 371 (2021) no. 6530, pp. 708-712 | Article

[21] R Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, (2020)

[22] Ritchie, H.; Mathieu, E.; Rodés-Guirao, L.; Appel, C.; Giattino, C.; Ortiz-Ospina, E.; Hasell, J.; Macdonald, B.; Beltekian, D.; Roser, M. Coronavirus Pandemic (COVID-19). Our World In Data., (2020)

[23] Sofonea, M. T.; Reyné, B.; Elie, B.; Djidjou-Demasse, R.; Selinger, C.; Michalakis, Y.; Alizon, S. Epidemiological monitoring and control perspectives: application of a parsimonious modelling framework to the COVID-19 dynamics in France, Epidemiological monitoring and control perspectives: application of a parsi- monious modelling framework to the COVID-19 dynamics in France. medRxiv, 2020.05.22.20110593., (2020) | Article

[24] Sun, J.; Tang, X.; Bai, R.; Liang, C.; Zeng, L.; Lin, H.; Yuan, R.; Zhou, P.; Huang, X.; Xiong, Q.; Peng, J.; Cui, F.; Ke, B.; Su, J.; Liu, Z.; Lu, J.; Tian, J.; Sun, R.; Ke, C. The kinetics of viral load and antibodies to SARS-CoV-2, Clinical Microbiology and Infection, Volume 26 (2020) no. 12 | Article

[25] Thompson, R. N.; Morgan, O. W.; Jalava, K. Rigorous surveillance is necessary for high confidence in end-of-outbreak declarations for Ebola and other infectious diseases, Philosophical Transactions of the Royal Society B: Biological Sciences, Volume 374 (2019) no. 1776 | Article

[26] Trapman, P.; Ball, F.; Dhersin, J.-S.; Tran, V. C.; Wallinga, J.; Britton, T. Inferring R0 in emerging epidemics—the effect of common population structure is small, Journal of The Royal Society Interface, Volume 13 (2016) no. 121 | Article

[27] Verity, R.; Okell, L. C.; Dorigatti, I.; Winskill, P.; Whittaker, C.; Imai, N.; Cuomo-Dannenburg, G.; Thompson, H.; Walker, P. G. T.; Fu, H.; Dighe, A.; Griffin, J. T.; Baguelin, M.; Bhatia, S.; Boonyasiri, A.; Cori, A.; Cucunubá, Z.; FitzJohn, R.; Gaythorpe, K.; Green, W.; Hamlet, A.; Hinsley, W.; Laydon, D.; Nedjati-Gilani, G.; Riley, S.; van Elsland, S.; Volz, E.; Wang, H.; Wang, Y.; Xi, X.; Donnelly, C. A.; Ghani, A. C.; Ferguson, N. M. Estimates of the severity of coronavirus disease 2019: a model-based analysis, The Lancet Infectious Diseases, Volume 20 (2020) no. 6, pp. 669-677 | Article

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