After the many failures in the control of the COVID-19 pandemic, identifying robust principles of epidemic control will be key in future preparedness. In this work, we propose an optimal control model of an age-of-infection transmission model under a two-phase control regime where social distancing is the only available control tool in the first phase, while the second phase also benefits from the arrival of vaccines. We analyzed the problem by an ad-hoc numerical algorithm under a strong hypothesis implying a high degree of prioritization to the protection of health from the epidemic attack, which we termed the "low attack rate" hypothesis. The outputs of the model were also compared with the data from the Italian COVID-19 experience to provide a crude assessment of the goodness of the enacted interventions prior to the onset of the Omicron variant.
Citation: Alberto d'Onofrio, Mimmo Iannelli, Piero Manfredi, Gabriela Marinoschi. Epidemic control by social distancing and vaccination: Optimal strategies and remarks on the COVID-19 Italian response policy[J]. Mathematical Biosciences and Engineering, 2024, 21(7): 6493-6520. doi: 10.3934/mbe.2024283
After the many failures in the control of the COVID-19 pandemic, identifying robust principles of epidemic control will be key in future preparedness. In this work, we propose an optimal control model of an age-of-infection transmission model under a two-phase control regime where social distancing is the only available control tool in the first phase, while the second phase also benefits from the arrival of vaccines. We analyzed the problem by an ad-hoc numerical algorithm under a strong hypothesis implying a high degree of prioritization to the protection of health from the epidemic attack, which we termed the "low attack rate" hypothesis. The outputs of the model were also compared with the data from the Italian COVID-19 experience to provide a crude assessment of the goodness of the enacted interventions prior to the onset of the Omicron variant.
[1] | J. D. Sachs, S. S. Abdool Karim, L. Aknin, J. Allen, K. Brosbol, F. Colombo, et al., The lancet commission on lessons for the future from the COVID-19 pandemic, The Lancet, 400 (2022), 1224–1280. https://doi.org/10.1016/S0140-6736(22)01585-9 doi: 10.1016/S0140-6736(22)01585-9 |
[2] | N. M. Ferguson, D. Laydon, G. Nedjati-Gilani, N. Imai, K. Ainslie, M. Baguelin, et al., Impact of non-pharmaceutical interventions (npis) to reduce COVID-19 mortality and healthcare demand, imperial college COVID-19 response team, Imperial College COVID-19 Response Team, Report No. 9, 2020" (2020), 20. |
[3] | M. G. Baker, N. Wilson, T. Blakely, Elimination could be the optimal response strategy for COVID-19 and other emerging pandemic diseases, BMJ, 371 (2020). https://doi.org/10.1136/bmj.m4907 doi: 10.1136/bmj.m4907 |
[4] | S. Wu, R. Neill, C. De Foo, A. Qijia Chua, A. Jung, V. Haldane, et al., Aggressive containment, suppression, and mitigation of COVID-19: Lessons learnt from eight countries, BMJ, 375 (2021). https://doi.org/10.1136/bmj-2021-067508 doi: 10.1136/bmj-2021-067508 |
[5] | M. Oliu-Barton, B. S.R. Pradelski, P. Aghion, P. Artus, I. Kickbusch, J. V. Lazarus, et al., Sars-cov-2 elimination, not mitigation, creates best outcomes for health, the economy, and civil liberties, The Lancet, 397 (2021), 2234–2236. https://doi.org/10.1016/S0140-6736(21)00978-8 doi: 10.1016/S0140-6736(21)00978-8 |
[6] | D. H. Morris, F. W. Rossine, J. B. Plotkin, S. A. Levin, Optimal, near-optimal, and robust epidemic control, Commun. Phys., 4 (2021), 1–78. https://doi.org/10.1038/s42005-021-00570-y doi: 10.1038/s42005-021-00570-y |
[7] | F. Alvarez, D. Argente, F. Lippi, A simple planning problem for COVID-19 lock-down, testing, and tracing, Am. Econom. Rev. Insigh., 3 (2021), 367–382. https://doi.org/10.1257/aeri.20200201 doi: 10.1257/aeri.20200201 |
[8] | D. Acemoglu, V. Chernozhukov, I. Werning, M. D. Whinston, Optimal targeted lockdowns in a multigroup sir model, Am. Econom. Rev. Insigh., 3 (2021), 487–502. https://doi.org/10.1257/aeri.20200590 doi: 10.1257/aeri.20200590 |
[9] | W. Choi, E. Shim, Optimal strategies for social distancing and testing to control COVID-19, J. Theor. Biol., 512 (2021). https://doi.org/10.1016/j.jtbi.2020.110568 doi: 10.1016/j.jtbi.2020.110568 |
[10] | D. Aldila, M. Z. Ndii, B. M. Samiadji, Optimal control on COVID-19 eradication program in indonesia under the effect of community awareness, Math. Biosci. Eng., 17 (2020), 6355–6389. https://doi.org/10.3934/mbe.2020335 doi: 10.3934/mbe.2020335 |
[11] | F. Di Lauro, I. Z. Kiss, D. Rus, C. Della Santina, COVID-19 and flattening the curve: A feedback control perspective. IEEE Control Syst. Lett., 5 (2020), 1435–1440. https://doi.org/10.1109/LCSYS.2020.3039322 doi: 10.1109/LCSYS.2020.3039322 |
[12] | T. A. Perkins, G. España, Optimal control of the COVID-19 pandemic with non-pharmaceutical interventions, Bull. Math. Biol., 82 (2020), 118. https://doi.org/10.1007/s11538-020-00795-y doi: 10.1007/s11538-020-00795-y |
[13] | C. Tsay, F. Lejarza, M. A. Stadtherr, M. Baldea, Modeling, state estimation, and optimal control for the us COVID-19 outbreak,. Sci. Rep., 10 (2020), 10711. https://doi.org/10.1038/s41598-020-67459-8 doi: 10.1038/s41598-020-67459-8 |
[14] | J. Köhler, L. Schwenkel, A. Koch, J. Berberich, P. Pauli, F. Allgöwer, Robust and optimal predictive control of the COVID-19 outbreak, Ann. Rev. Control, 51 (2021), 525–539. https://doi.org/10.1016/j.arcontrol.2020.11.002 doi: 10.1016/j.arcontrol.2020.11.002 |
[15] | S. A. Nowak, P. Nascimento de Lima, R. Vardavas, Optimal non-pharmaceutical pandemic response strategies depend critically on time horizons and costs, Sci. Rep., 13 (2023), 2416. https://doi.org/10.1038/s41598-023-28936-y doi: 10.1038/s41598-023-28936-y |
[16] | G. Pisaneschi, M. Tarani, G. Di Donato, A. Landi, M. Laurino, P. Manfredi, Optimal social distancing in epidemic control: Cost prioritization, adherence and insights into preparedness principles, Sci. Rep., 14 (2024), 4365. https://doi.org/10.1038/s41598-024-54955-4 doi: 10.1038/s41598-024-54955-4 |
[17] | A. d'Onofrio, P. Manfredi, M. Iannelli, Dynamics of partially mitigated multi-phasic epidemics at low susceptible depletion: Phases of COVID-19 control in italy as case study, Math. Biosci., 340 (2021), 108671. https://doi.org/10.1016/j.mbs.2021.108671 doi: 10.1016/j.mbs.2021.108671 |
[18] | A. d'Onofrio, M. Iannelli, P. Manfredi, G. Marinoschi, Multiple pandemic waves vs multi-period/multi-phasic epidemics: Global shape of the COVID-19 pandemic, in press (2023). |
[19] | A. d'Onofrio, M. Iannelli, P. Manfredi, G. Marinoschi, Optimal epidemic control by social distancing and vaccination of an infection structured by time since infection: The COVID-19 case study, SIAM J. Appl. Math., (2023). https://doi.org/10.1137/22M1499406 doi: 10.1137/22M1499406 |
[20] | W. O. Kermack, A. G. McKendrick, A contribution to the mathematical theory of epidemics, Proceedings of the royal society of london. Series A, Containing papers of a mathematical and physical character, 115 (1927), 700–721. https://doi.org/10.1098/rspa.1927.0118 doi: 10.1098/rspa.1927.0118 |
[21] | M. S. Eichenbaum, S. Rebelo, M. Trabandt, The macroeconomics of epidemics, Rev. Financial Studies, 34 (2021), 5149–5187. https://doi.org/10.1093/rfs/hhab040 doi: 10.1093/rfs/hhab040 |
[22] | N. E. MacDonald, the SAGE Working Group on Vaccine Hesitancy, Vaccine hesitancy: Definition, scope and determinants, Vaccine, 33 (2015), 4161–4164. https://doi.org/10.1016/j.vaccine.2015.04.036 doi: 10.1016/j.vaccine.2015.04.036 |
[23] | A. S. Fauci, An hiv vaccine is essential for ending the hiv/aids pandemic, JAMA, 318 (2017), 1535–1536. https://doi.org/10.1001/jama.2017.13505 doi: 10.1001/jama.2017.13505 |
[24] | R. T. Rockafellar, Monotone operators and the proximal point algorithm, SIAM J Control Optimiz., 14 (1976), 877–898. https://doi.org/10.1137/0314056 doi: 10.1137/0314056 |
[25] | G. Guzzetta, F. Riccardo, V. Marziano, P. Poletti, F. Trentini, A. Bella, et al., The impact of a nation-wide lockdown on COVID-19 transmissibility in Italy, arXiv preprint (2020). https://doi.org/10.48550/arXiv.2004.12338 doi: 10.48550/arXiv.2004.12338 |
[26] | N. Andrews, E. Tessier, J. Stowe, C. Gower, F. Kirsebom, R. Simmons, et al., Duration of protection against mild and severe disease by COVID-19 vaccines, New England J. Med., 386 (2022), 340–350. https://doi.org/10.1056/NEJMoa2115481 doi: 10.1056/NEJMoa2115481 |
[27] | A. Zardini, M. Galli, M. Tirani, D. Cereda, M. Manica, F. Trentini, et al., A quantitative assessment of epidemiological parameters required to investigate COVID-19 burden, Epidemics, 37 (2021), 100530. https://doi.org/10.1016/j.epidem.2021.100530 doi: 10.1016/j.epidem.2021.100530 |
[28] | E. Foglia, L. Ferrario, F. Schettini, M. B. Pagani, M. Dalla Bona, E. Porazzi, COVID-19 and hospital management costs: The Italian experience, BMC Health Serv. Res., 22 (2022), 1–10. https://doi.org/10.1186/s12913-022-08365-9 doi: 10.1186/s12913-022-08365-9 |
[29] | ISTAT, First results of the national seroprevalence survey on SARS-COV-2, Rome: National Institute of Statistics (2020). https://www.istat.it/it/files//2020/08/ReportPrimiRisultatiIndagineSiero.pdf |
[30] | P. Sah, T. N. Vilches, S. M. Moghadas, A. Pandey, S. Gondi, E. C. Schneider, et al., Return on investment of the COVID-19 vaccination campaign in new york city, JAMA Network Open, 5 (2022), e2243127–e2243127. https://doi.org/10.1001/jamanetworkopen.2022.43127 doi: 10.1001/jamanetworkopen.2022.43127 |
[31] | E. Hansen, T. Day, Optimal control of epidemics with limited resources, J. Math. Biol., 62 (2011), 423–451. https://doi.org/10.1007/s00285-010-0341-0 doi: 10.1007/s00285-010-0341-0 |
[32] | S. Lee, G. Chowell, C. Castillo-Chávez, Optimal control for pandemic influenza: The role of limited antiviral treatment and isolation, J. Theor. Biol., 265 (2010), 136–150. https://doi.org/10.1016/j.jtbi.2010.04.003 doi: 10.1016/j.jtbi.2010.04.003 |
[33] | S. Lee, G. Chowell, Exploring optimal control strategies in seasonally varying flu-like epidemics, J. Theor. Biol., 412 (2017), 36–47. https://doi.org/10.1016/j.jtbi.2016.09.023 doi: 10.1016/j.jtbi.2016.09.023 |
[34] | P. Manfredi, A. d'Onofrio, Modeling the interplay between human behavior and the spread of infectious diseases, Springer Science & Business Media, (2013). |
[35] | Z. Wang, C. T. Bauch, S. Bhattacharyya, A. d'Onofrio, P. Manfredi, M. Perc, et al., Statistical physics of vaccination, Phys. Rep., 664 (2016), 1–113. https://doi.org/10.1016/j.physrep.2016.10.006 doi: 10.1016/j.physrep.2016.10.006 |
[36] | J. Bedson, L. A. Skrip, D. Pedi, S. Abramowitz, S. Carter, M. F. Jalloh, et al., A review and agenda for integrated disease models including social and behavioural factors, Nat. Human Behav., 5 (2021), 834–846. https://doi.org/10.1038/s41562-021-01136-2 doi: 10.1038/s41562-021-01136-2 |
[37] | E. F. Arruda, S. S. Das, C. M. Dias, D. H. Pastore, Modelling and optimal control of multi strain epidemics, with application to COVID-19, PLoS One, 16 (2021), e0257512. https://doi.org/10.1371/journal.pone.0257512 doi: 10.1371/journal.pone.0257512 |