Modeling, Estimation, and Optimal Control of Anti-COVID-19 Multi-dose Vaccine Administration

The recent trends of the COVID-19 research are being devoted to disease transmission modeling in presence of vaccinated individuals, while the emerging needs are being focused on developing effective strategies for the optimal distribution of vaccine between population. <br>In this context, we propose a novel non-linear time-varying model that effectively supports policy-makers in predicting and analyzing the dynamics of COVID-19 when partially and fully immune individuals are included in the population. Specifically, this paper proposes an accurate SIRUCQHE epidemiological model, with eight compartments (namely, Susceptible, Infected, Removed, Unsusceptible, Contagious, Quarantined, Hospitalized, and Extinct). <br>Differently from the related literature, where the common strategies typically rely on the prioritization of the different classes of individuals, we propose a novel Model Predictive Control approach to optimally control the multi-dose vaccine administration in the case the available number of doses is not sufficient to cover the whole population. Focusing on the minimization of the expected number of deaths, the approach discriminates between the number of first and second doses, thus considering also the possibility that some individuals may receive only one injection if the resulting expected fatalities are low. <br><div>To show the effectiveness of the resulting strategies, we first calibrate the model on the Israeli scenario using real data to get reliable predictions on the pandemic dynamics. Lastly, we estimate the impact of the vaccine administration on the virus dynamics and, in particular, based on validated model, we assess the impact of the first dose of the Pfitzer's vaccine confirming the results of clinical tests.</div><div><br></div><div><br></div><div>Extended version of the paper published in <i>Proceedings of the IEEE 16th International Conference on Automation Science and Engineering (CASE) </i><br> </div><div><b>How to cite:</b> Scarabaggio, P., Carli, R., Cavone, G., Epicoco, N., & Dotoli, M. "Modeling, Estimation, and Optimal Control of Anti-COVID-19 Multi-dose Vaccine Administration." In <i>2021 IEEE 17th International Conference on Automation Science and Engineering</i> (CASE) (pp. 990-995). IEEE.<br></div><div><br></div><div>DOI: <u>https://doi.org/10.1109/CASE49439.2021.9551418</u></div><div><br></div><div><br> </div>

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Modeling, Estimation, and Optimal Control of Anti-COVID-19 Multi-dose Vaccine Administration

10.36227/techrxiv.16794724.v1 ◽

2021 ◽

Author(s):

Paolo Scarabaggio ◽

Raffaele Carli ◽

Graziana Cavone ◽

Nicola Epicoco ◽

Mariagrazia Dotoli

Keyword(s):

Optimal Control ◽

Disease Transmission ◽

Linear Time ◽

Real Data ◽

Science And Engineering ◽

Vaccine Administration ◽

Control Approach ◽

International Conference ◽

The Impact ◽

Dose Vaccine

The recent trends of the COVID-19 research are being devoted to disease transmission modeling in presence of vaccinated individuals, while the emerging needs are being focused on developing effective strategies for the optimal distribution of vaccine between population. <br>In this context, we propose a novel non-linear time-varying model that effectively supports policy-makers in predicting and analyzing the dynamics of COVID-19 when partially and fully immune individuals are included in the population. Specifically, this paper proposes an accurate SIRUCQHE epidemiological model, with eight compartments (namely, Susceptible, Infected, Removed, Unsusceptible, Contagious, Quarantined, Hospitalized, and Extinct). <br>Differently from the related literature, where the common strategies typically rely on the prioritization of the different classes of individuals, we propose a novel Model Predictive Control approach to optimally control the multi-dose vaccine administration in the case the available number of doses is not sufficient to cover the whole population. Focusing on the minimization of the expected number of deaths, the approach discriminates between the number of first and second doses, thus considering also the possibility that some individuals may receive only one injection if the resulting expected fatalities are low. <br><div>To show the effectiveness of the resulting strategies, we first calibrate the model on the Israeli scenario using real data to get reliable predictions on the pandemic dynamics. Lastly, we estimate the impact of the vaccine administration on the virus dynamics and, in particular, based on validated model, we assess the impact of the first dose of the Pfitzer's vaccine confirming the results of clinical tests.</div><div><br></div><div><br></div><div>Extended version of the paper published in <i>Proceedings of the IEEE 16th International Conference on Automation Science and Engineering (CASE) </i><br> </div><div><b>How to cite:</b> Scarabaggio, P., Carli, R., Cavone, G., Epicoco, N., & Dotoli, M. "Modeling, Estimation, and Optimal Control of Anti-COVID-19 Multi-dose Vaccine Administration." In <i>2021 IEEE 17th International Conference on Automation Science and Engineering</i> (CASE) (pp. 990-995). IEEE.<br></div><div><br></div><div>DOI: <u>https://doi.org/10.1109/CASE49439.2021.9551418</u></div><div><br></div><div><br> </div>

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Determination of an optimal control strategy for vaccine administration in COVID-19 pandemic treatment

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2020.105664 ◽

2020 ◽

Vol 196 ◽

pp. 105664 ◽

Cited By ~ 3

Author(s):

Gustavo Barbosa Libotte ◽

Fran Sérgio Lobato ◽

Gustavo Mendes Platt ◽

Antônio J. Silva Neto

Keyword(s):

Optimal Control ◽

Control Strategy ◽

Optimal Control Strategy ◽

Vaccine Administration

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Impact of vaccine supplies and delays on optimal control of the COVID-19 pandemic: mapping interventions for the Philippines

Infectious Diseases of Poverty ◽

10.1186/s40249-021-00886-5 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Carlo Delfin S. Estadilla ◽

Joshua Uyheng ◽

Elvira P. de Lara-Tuprio ◽

Timothy Robin Teng ◽

Jay Michael R. Macalalag ◽

...

Keyword(s):

Optimal Control ◽

Optimal Strategy ◽

Minimum Principle ◽

Treatment Strategies ◽

Optimal Strategies ◽

The Philippines ◽

Case Detection ◽

Local Data ◽

Vaccine Administration ◽

Precautionary Measures

Abstract Background Around the world, controlling the COVID-19 pandemic requires national coordination of multiple intervention strategies. As vaccinations are globally introduced into the repertoire of available interventions, it is important to consider how changes in the local supply of vaccines, including delays in administration, may be addressed through existing policy levers. This study aims to identify the optimal level of interventions for COVID-19 from 2021 to 2022 in the Philippines, which as a developing country is particularly vulnerable to shifting assumptions around vaccine availability. Furthermore, we explore optimal strategies in scenarios featuring delays in vaccine administration, expansions of vaccine supply, and limited combinations of interventions. Methods Embedding our work within the local policy landscape, we apply optimal control theory to the compartmental model of COVID-19 used by the Philippine government’s pandemic surveillance platform and introduce four controls: (a) precautionary measures like community quarantines, (b) detection of asymptomatic cases, (c) detection of symptomatic cases, and (d) vaccinations. The model is fitted to local data using an L-BFGS minimization procedure. Optimality conditions are identified using Pontryagin’s minimum principle and numerically solved using the forward–backward sweep method. Results Simulation results indicate that early and effective implementation of both precautionary measures and symptomatic case detection is vital for averting the most infections at an efficient cost, resulting in $$>99\%$$ > 99 % reduction of infections compared to the no-control scenario. Expanding vaccine administration capacity to 440,000 full immunizations daily will reduce the overall cost of optimal strategy by $$25\%$$ 25 % , while allowing for a faster relaxation of more resource-intensive interventions. Furthermore, delays in vaccine administration require compensatory increases in the remaining policy levers to maintain a minimal number of infections. For example, delaying the vaccines by 180 days (6 months) will result in an $$18\%$$ 18 % increase in the cost of the optimal strategy. Conclusion We conclude with practical insights regarding policy priorities particularly attuned to the Philippine context, but also applicable more broadly in similar resource-constrained settings. We emphasize three key takeaways of (a) sustaining efficient case detection, isolation, and treatment strategies; (b) expanding not only vaccine supply but also the capacity to administer them, and; (c) timeliness and consistency in adopting policy measures. Graphic Abstract

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