(502b) Global Supply Chain Optimization for COVID-19 Vaccines Under Covax Facility
AIChE Annual Meeting
2021
2021 Annual Meeting
Sustainable Engineering Forum
Resilient and Sustainable Supply Chains and Product Systems
Tuesday, November 16, 2021 - 8:25am to 8:50am
However, to end the pandemic, we not only need COVID-19 vaccines, but the development of vaccines must be followed by a fair, fast, and efficient global distribution. The current challenge is the timely delivery of the vaccines. The expected volume of the vaccines per month adds up to around 850 tons and is more than the UNICEF suppliers' capacity combined [3]. Two main concerns regarding the global COVID-19 vaccine distribution are 1) Limited supply of resources for the delivery of the vaccines, e.g., cold chain equipment, and 2) Vaccine nationalism, i.e., governments signing agreements with companies to procure surplus vaccines. To overcome these challenges, the COVAX facility was launched by WHO for multiple vaccines to prevent vaccine nationalism. It aims to ensure fair access to COVID-19 vaccines for all countries, irrespective of their financial status. It allows countries to pool resources for effective vaccine development and distribution. Under COVAX, deals for vaccine procurement have already been made. However, vaccine distribution is still under development. It faces various challenges due to (1) the variety of vaccines, (2) person-specific doses, 2) unknown country-specific demands required for herd immunity threshold, especially low-middle income countries, (5) low-temperature storage protocols for most vaccines, and (6) unanticipated disruptions in both production and distribution.
Addressing these challenges requires a multi-prong approach that must go beyond straightforward pharmacological measures. The two primary goals are to (1) estimate the herd immunity threshold and (2) effect an efficient global vaccine distribution. In this work, we explore the time-varying nature of reproduction number during the first wave of the
COVID-19 pandemic using confirmed cases, recovered cases, fatalities, and timings of lockdown phases. We propose a mean-field epidemiological model for COVID-19 that extends the classical SEIR model [4]. We describe NEIASCFR, an 8-compartmental model with variable transmissibility over time, by considering the time-varying nature of parameter values over different lockdown phases. We estimate our model's parameters with public data on confirmed cases, recovered cases, and fatalities for each country. The parameter values are used to determine reproduction number and herd immunity threshold. The model is applied to study the herd immunity threshold of 9 countries (India, Nigeria, Iran, Pakistan, Thailand, Mexico, Democratic Republic of Congo, Egypt, Thailand), constituting 50% of demand under the COVAX facility.
Using this within the framework of COVAX, we develop a global supply chain model to optimize the production and allocation of vaccines to participating countries from various production centers. The supply chain was optimized considering potential disruptions, vaccine approval dynamics, and vaccine candidates' approval with different risk mitigation strategies. Given: 1) Network configuration consisting of production facilities and different regions. 2) Initial vaccine procurement quantities. 3) Capacity of different storage containers. 4) Costs of procurement, storage, transportation, and risk mitigation per unit. 5) Different scenarios, we determine 1) Production quantities of vaccines at different centers and vaccine allocation to each demand region. 2) Extra procurement of vaccines at each center. 3) Cost of procurement and transportation.
References:
1. Worldometer. COVID-19 CORONAVIRUS PANDEMIC. 2021; Available from: https://www.worldometers.info/coronavirus/.
2. Bloomberg. Covid-19 Tracker. 2021; Available from: https://www.bloomberg.com/graphics/covid-vaccine-tracker-global-distribu....
3. UNICEF, UNICEF outlining plans to transport up to 850 tonnes of COVID-19 vaccines per month on behalf of COVAX. 2020.
4. Li, M.Y. and J.S. Muldowney, Global stability for the SEIR model in epidemiology. Mathematical biosciences, 1995. 125(2): p. 155-164.