Table B.1.

A selective summary of most recent literature on pandemic vaccine supply chain.

Authors	Objectives	Methodology	Variables	Priority group
Uscher-Pines et al. (2006)	To review national pandemic influenzas prioritisation plans	Descriptive statistics	Vaccine and antiviral priority groups, group rankings, goals of pharmaceutical interventions, the inclusion of scenarios and population size	Healthcare workers, essential service providers, people at high risk, children, elderly, key decision-makers, influenzas cases, hospitalised cases and unvaccinated
Kee et al. (2007)	To assess the level of influenza vaccine coverage, to understand the driving forces and barriers to vaccination and determine vaccination interventions for the South Korean population	Cross-sectional descriptive statistics	Demographic data, Vaccination rate, Factors associated with vaccination	The priority groups recommended for annual vaccination includes persons aged $\ge$65 years, persons with chronic illness such as chronic cardiopulmonary disease, diabetes, chronic liver disease and malignancy, residents of long-term care facilities, healthcare personnel and pregnant women.
Medlock and Galvani (2009)	To evaluate current vaccine allocation policies and to determine the optimal strategy	Age-structured Simulation model	Number of mortalities, contact rates, the duration of the infectious period, years of life lost, weighing deaths by the expected remaining years of life for different ages, contingent valuation, cost associated with vaccination, cost associated with illness and valued death.	17 age groups (ages 0, 1 to 4; 5 to 9; 10 to 14; $\dots$; 70 to 74; and 75 and older).
Keeling and White (2011)	To targeting vaccination against novel infections: risk, age and spatial structure for pandemic influenza in Great Britain	SIR (susceptible,infectious, recovered) model	Age groups (5–14 years old and then 15–24 years old), regions, risk-groups, time periods to vaccination	Most affected regions of the country where the virus is most prevalent should be given priority
Araz et al. (2012)	To geographic prioritisation of distributing pandemic vaccines (Arizona, USA)	Geospatial and demographically-structured model, mathematical modelling	Age groups, number of people in county, vaccine Efficacy, vaccination rate, transmission probability, Vaccine Supply Data	Areas with high population size being the priority
Lee et al. (2012)	To determine optimal vaccination allocation policies during the H1N1 pandemic in Mexico	Non-Linear Dynamic mathematical model	The age distribution of the population, age specific vaccine efficacy, hospitalisation rates,	6 age groups (1 $=$ 0–5 yr, 2 $=$ 6–12 yr, 3 $=$ 13–19 yr, 4 $=$ 20–39 yr, 5 $=$ 40–59 yr, 6 $\ge$ 60 yr)
Buccieri and Gaetz (2013)	To evaluate ethical pandemic planning policies	Mixed method (Descriptive statistics and interviews)	Gender, demographic factors, fear of infection, lack of concern, access to community-based clinics, access to a regular doctor, promotional campaign.	Homeless individuals in Toronto
Huang et al. (2017)	To explore the optimal allocation of several vaccine types to certain priority groups.	Optimisation model	The five priority groups and regions were taken as input.	Pregnant women, infants (0–3 years old); people between ages of 4–24; and adults at high risk and infant care givers.
Takahashi et al. (2017)	Toe targeting at-risk areas to vaccination against the measles in the African Great Lakes region	Sensitivity analysis, generalised additive models (GAMs)	Vaccinated and unvaccinated population variables, population age,	Some areas had never been vaccinated and were ’hot spots’ in terms of risk of disease and thus given priority for vaccination
Lessler et al. (2018)	To map cholera burden in sub-Saharan Africa and assess how geographical targeting could lead to more efficient interventions and vaccination	Descriptive statistics and Bayesian mapping	Population variables, disease data,	Countries located in central and east Africa (at high risk of disease) should be given priority
McMorrow et al. (2019)	To prioritise between different influenza vaccine risk groups	Descriptive statistics	Socio-economic variables, Rates of influenza, Vaccine efficacy,	Pregnant women, HIV-infected adults aged 15–64 years, Children aged 6–23 months, Adults aged $>$65 years, Healthcare workers, Adults and children with TB and chronic illnesses.
Venkatramanan et al. (2019)	To optimise spatial allocation of seasonal influenza vaccine under temporal constraints, USA	Greedy optimisation algorithm	Population, Airline flows, Commuter flows, Disease dynamics, population mobility	Southern and southeastern states of the United States were identified as priority centres
Acharya and Porwal (2020)	To provide vulnerability index for identification of vulnerable regions in India in terms of COVID-19 epidemic prevalence	Descriptive statistics and mapping	The comprehensive socioeconomic, epidemiological and availability of healthcare variables	Central and eastern regions are a priority
Chen et al. (2020)	To determine the optimal allocation policies for the COVID-19 vaccine.	Age-structured Simulation model	The number of individuals in each of the seven compartments, the population size, the transmission rate, the contact rate, the discount factor of the transmission rate, the average time (from exposed to infectious, from pre-symptomatic infectious to symptomatic infectious, from symptomatic infectious to recovered, from ascertained infectious to isolation, from isolation to recovered), the fraction of ascertainment in each age group, the level of permitted economic activities, the amount of vaccination allocated to each age group.	Seven compartments (susceptible, exposed, Pre-symptomatic infectious, un-ascertained infectious, ascertained infectious, isolated, and removed) and five age group (0–17, 18–44, 45–64, 65–74 and 75+).
Deo et al. (2020)	To examine the challenges of COVID-19 vaccine distribution	Descriptive statistics, Multi-parameter model	Age, co-morbidity, income and profession	60 Plus Years, Moderate or severe comorbidities, Frontline healthcare and other essential workers, Low Income
Gamchi et al. (2020)	To prioritisation of Tehran city districts and individuals for vaccine distribution	Infected–recovered (SIR) model and bi-objective vehicle routing problem (VRP) method	Number of susceptible individuals, Number of infected individuals, Number of recovered individuals, Disease transmission rate, Natural death rate, Fixed cost of immunisation, Total available doses of required vaccine, Vaccination time in regions, Distance between regions, etc.	Areas in which ratio of Pregnant women is high, Areas where ratio of children under 6 months of age is high
Liu and Xian (2020)	To identify and locate potentially vulnerable demographic groups in terms of epidemic prevalence against the transmission of COVID-19 disease	COVID-19 Susceptibility Index	Age, cancer, diabetes, cardiovascular disease, obesity and lung disease	Vulnerable segments of the population are generally situated away from capital cities
Persad et al. (2020)	To prioritise access to COVID-19 vaccines	Descriptive statistics	None	Healthcare workers; other essential workers and people in high-transmission settings; and people with medical vulnerabilities associated with poorer COVID-19 outcomes, such as diabetes, pulmonary disease, cardiac disease, and obesity.