Analytics for Resource Allocation in Pandemic Management

References

• Abdin AF, Fang Y-P, Caunhye A, Alem D, Barros A, Zio E (2023) An optimization model for planning testing and control strategies to limit the spread of a pandemic—The case of COVID-19. Eur. J. Oper. Res. 304(1):308–324.Google Scholar
• Adam DC, Wu P, Wong JY, Lau EHY, Tsang TK, Cauchemez S, Leung GM, Cowling BJ (2020) Clustering and superspreading potential of SARS-CoV-2 infections in Hong Kong. Nature Medicine 26(11):1714–1719.Google Scholar
• Alcock R, Boutilier JJ, Siddiq A (2022) Shield-Net: Matching supply with demand for face shields during the COVID-19 pandemic. INFORMS J. Appl. Anal. 52(6):485–507.Link, Google Scholar
• Aleman DM, Wibisono TG, Schwartz B (2011) A nonhomogeneous agent-based simulation approach to modeling the spread of disease in a pandemic outbreak. Interfaces 41(3):301–315.Link, Google Scholar
• Alizadeh-Meghrazi M, Tosarkani BM, Amin SH, Popovic MR, Ahi P (2022) Design and optimization of a sustainable and resilient mask supply chain during the COVID-19 pandemic: A multi-objective approach. Environ. Development Sustainability 27:22217–22262.Google Scholar
• Axelsen JB, Yaari R, Grenfell BT, Stone L (2014) Multiannual forecasting of seasonal influenza dynamics reveals climatic and evolutionary drivers. Proc. Natl. Acad. Sci. USA 111(26):9538–9542.Google Scholar
• Baloch G, Gzara F, Elhedhli S (2023) Risk-based allocation of COVID-19 personal protective equipment under supply shortages. Eur. J. Oper. Res. 310(3):1085–1100.Google Scholar
• Becker AD, Grantz KH, Hegde ST, Bérubé S, Cummings DAT, Wesolowski A (2021) Development and dissemination of infectious disease dynamic transmission models during the COVID-19 pandemic: What can we learn from other pathogens and how can we move forward? Lancet Digital Health 3(1):e41–e50.Google Scholar
• Bicher M, Rippinger C, Urach C, Brunmeir D, Siebert U, Popper N (2021) Evaluation of contact-tracing policies against the spread of SARS-CoV-2 in Austria: An agent-based simulation. Medical Decision Making 41(8):1017–1032.Google Scholar
• Bicher M, Zuba M, Rainer L, Bachner F, Rippinger C, Ostermann H, Popper N, Thurner S, Klimek P (2022) Supporting COVID-19 policy-making with a predictive epidemiological multi-model warning system. Comm. Medicine 2(1):157.Google Scholar
• Blasioli E, Mansouri B, Tamvada SS, Hassini E (2023) Vaccine allocation and distribution: A review with a focus on quantitative methodologies and application to equity, hesitancy, and COVID-19 pandemic. Oper. Res. Forum 4(2):27.Google Scholar
• Bonabeau E (2002) Agent-based modeling: Methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. USA 99(Suppl_3):7280–7287.Google Scholar
• Borchering RK, Healy JM, Cadwell BL, Johansson MA, Slayton RB, Wallace M, Biggerstaff M (2023) Public health impact of the U.S. Scenario Modeling Hub. Epidemics 44:100705.Google Scholar
• Boyd JR (1996) The essence of winning and losing. Unpublished briefing.Google Scholar
• Bubar KM, Reinholt K, Kissler SM, Lipsitch M, Cobey S, Grad YH, Larremore DB (2021) Model-informed COVID-19 vaccine prioritization strategies by age and serostatus. Science 371(6532):916–921.Google Scholar
• Buckner JH, Chowell G, Springborn MR (2021) Dynamic prioritization of COVID-19 vaccines when social distancing is limited for essential workers. Proc. Natl. Acad. Sci. USA 118(16):e2025786118.Google Scholar
• Calafiore GC, Parino F, Zino L, Rizzo A (2023) Dynamic planning of a two-dose vaccination campaign with uncertain supplies. Eur. J. Oper. Res. 304(3):1269–1278.Google Scholar
• Caulkins JP, Grass D, Feichtinger G, Hartl RF, Kort PM, Kuhn M, Prskawetz A, Sanchez-Romero M, Seidl A, Wrzaczek S (2023) The hammer and the jab: Are COVID-19 lockdowns and vaccinations complements or substitutes? Eur. J. Oper. Res. 311(1):233–250.Google Scholar
• Cheng G, Gao SY, Yuan Y, Zhang C, Zheng Z (2022) On the test accuracy and effective control of the COVID-19 pandemic: A case study in Singapore. INFORMS J. Appl. Anal. 52(6):524–538.Link, Google Scholar
• Choi T-M (2021) Fighting against COVID-19: What operations research can help and the sense-and-respond framework. Ann. Oper. Res. 360:97–113.Google Scholar
• Choudhury NA, Ramkumar M, Schoenherr T, Singh S (2023) The role of operations and supply chain management during epidemics and pandemics: Potential and future research opportunities. Transportation Res. Part E Logist. Transportation Rev. 175:103139.Google Scholar
• Coletti P, Wambua J, Gimma A, Willem L, Vercruysse S, Vanhoutte B, Jarvis CI, et al. (2020) CoMix: Comparing mixing patterns in the Belgian population during and after lockdown. Sci. Rep. 10(1):1–10.Google Scholar
• Coroneo L, Iacone F, Paccagnini A, Santos Monteiro P (2023) Testing the predictive accuracy of COVID-19 forecasts. Internat. J. Forecasting 39(2):606–622.Google Scholar
• Cramer EY, Ray EL, Lopez VK, Bracher J, Brennen A, Castro Rivadeneira AJ, Gerding A, et al. (2022) Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proc. Natl. Acad. Sci. USA 119(15):e2113561119.Google Scholar
• Dattani S (2023) What were the death tolls from pandemics in history? Our World in Data, archived May 21, 2026, https://archive.ourworldindata.org/20260521-064622/historical-pandem ics.html.Google Scholar
• Davidsson P, Persson JA, Holmgren J (2007) On the integration of agent-based and mathematical optimization techniques. Nguyen NT, Grzech A, Howlett RJ, Jain LC, eds. Agent Multi-Agent Systems Tech. Appl. KES-AMSTA 2007 (Springer, Berlin), 1–10.Google Scholar
• Dennis A, Robin C, Carter H (2022) The social media response to twice-weekly mass asymptomatic testing in England. BMC Public Health 22(1):182.Google Scholar
• Dolgui A, Gusikhin O, Ivanov D, Li X, Stecke K (2024) A network-of-networks adaptation for cross-industry manufacturing repurposing. IISE Trans. 56(6):666–682.Google Scholar
• Dönmez Z, Turhan S, Karsu Ö, Kara BY, Karaşan O (2022) Fair allocation of personal protective equipment to health centers during early phases of a pandemic. Comput. Oper. Res. 141:105690.Google Scholar
• Endo A, Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Abbott S, Kucharski AJ, Funk S (2020) Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China. Wellcome Open Res. 5:67.Google Scholar
• Ferguson NM, Laydon D, Nedjati-Gilani G, Imai N, Ainslie K, Baguelin M, Bhatia S, et al. (2020) Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand. Report, Imperial College, London.Google Scholar
• Flahault A, Dias-Ferrao V, Chaberty P, Esteves K, Valleron A-J, Lavanchy D (1998) FluNet as a tool for global monitoring of influenza on the web. JAMA 280(15):1330–1332.Google Scholar
• Funk S, Abbott S, Atkins B, Baguelin M, Baillie J, Birrell P, Blake J, et al. (2020) Short-term forecasts to inform the response to the Covid-19 epidemic in the UK. Preprint, submitted December 4, https://www.medrxiv.org/content/10.1101/2020.11.11.20220962v2.Google Scholar
• Ghasemi P, Ehmke JF, Bicher M (2025) Managing equitable contagious disease testing: A mathematical model for resource optimization. Omega 135:103305.Google Scholar
• Gilani H, Sahebi H (2022) A data-driven robust optimization model by cutting hyperplanes on vaccine access uncertainty in COVID-19 vaccine supply chain. Omega 110:102637. IGoogle Scholar
• Gnanvi JE, Salako KV, Kotanmi GB, Glèlè Kakaï R (2021) On the reliability of predictions on Covid-19 dynamics: A systematic and critical review of modelling techniques. Infectious Disease Model. 6:258–272.Google Scholar
• Gomez LRE, Malysheva N, Pfeil J, Yang Z, Bütow SM, Irrgang C, Körber N, Hattab G, Kühnert D, Ladewig K (2025) Holistic forecasting for future pandemics: A review of pathogens, models, and data. Discover Public Health 22(1):211.Google Scholar
• González-Parra G, Shahriar Mahmud M, Kadelka C (2024) Learning from the COVID-19 pandemic: A systematic review of mathematical vaccine prioritization models. Infectious Disease Model. 9(4):1057–1080.Google Scholar
• Goodarzian F, Navaei A, Ehsani B, Ghasemi P, Muñuzuri J (2023) Designing an integrated responsive-green-cold vaccine supply chain network using Internet-of-Things: Artificial intelligence-based solutions. Ann. Oper. Res. 328(1):531–575.Google Scholar
• Gupta S, Starr MK, Farahani RZ, Asgari N (2021) OM Forum—Pandemics/epidemics: Challenges and opportunities for operations management research. Manufacturing Service Oper. Management 24(1):1–23.Google Scholar
• Guz AN, Rushchitsky JJ (2009) Scopus: A system for the evaluation of scientific journals. Internat. Appl. Mech. 45:351–362.Google Scholar
• Hadfield J, Megill C, Bell SM, Huddleston J, Potter B, Callender C, Sagulenko P, Bedford T, Neher RA (2018) Nextstrain: Real-time tracking of pathogen evolution. Bioinformatics 34(23):4121–4123.Google Scholar
• Hadley L, Rich C, Tasker A, Restif O, Funk S (2025) How does policy modelling work in practice? A global analysis on the use of epidemiological modelling in health crises. PLoS Global Public Health 5(6):1–18.Google Scholar
• Hale T, Angrist N, Goldszmidt R, Kira B, Petherick A, Phillips T, Webster S, et al. (2021) A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nature Human Behav. 5(4):529–538.Google Scholar
• Han S, Cai J, Yang J, Zhang J, Wu Q, Zheng W, Shi H, Ajelli M, Zhou X-H, Yu H (2021) Time-varying optimization of COVID-19 vaccine prioritization in the context of limited vaccination capacity. Nature Comm. 12(1):4673.Google Scholar
• Heins J, Schoenfelder J, Heider S, Heller AR, Brunner JO (2022) A scalable forecasting framework to predict COVID-19 hospital bed occupancy. INFORMS J. Appl. Anal. 52(6):508–523.Link, Google Scholar
• Hosseini-Motlagh S-M, Samani MRG, Farokhnejad P (2021) Designing a testing kit supply network for suspected COVID-19 cases under mixed uncertainty approach. Appl. Soft Comput. 111:107696.Google Scholar
• Hosseini-Motlagh S-M, Samani MRG, Homaei S (2023) Design of control strategies to help prevent the spread of COVID-19 pandemic. Eur. J. Oper. Res. 304(1):219–238.Google Scholar
• Ioannidis JPA, Cripps S, Tanner MA (2022) Forecasting for COVID-19 has failed. Internat. J. Forecasting 38(2):423–438.Google Scholar
• Ivanov D (2020) Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Res. Part E Logist. Transportation Rev. 136:101922.Google Scholar
• Ivanov D (2021) Supply chain viability and the COVID-19 pandemic: A conceptual and formal generalisation of four major adaptation strategies. Internat. J. Production Res. 59(12):3535–3552.Google Scholar
• Ivanov D (2022) Viable supply chain model: Integrating agility, resilience and sustainability perspectives—Lessons from and thinking beyond the COVID-19 pandemic. Ann. Oper. Res. 319(1):1411–1431.Google Scholar
• Jahn B, Sroczynski G, Bicher M, Rippinger C, Mühlberger N, Santamaria J, Urach C, et al. (2021) Targeted COVID-19 vaccination (TAV-COVID) considering limited vaccination capacities—An agent-based modeling evaluation. Vaccines 9(5):434.Google Scholar
• Jentsch PC, Anand M, Bauch CT (2021) Prioritising COVID-19 vaccination in changing social and epidemiological landscapes: A mathematical modelling study. Lancet Infectious Diseases 21(8):1097–1106.Google Scholar
• Kamalov F, Rajab K, Kumar Cherukuri A, Elnagar A, Safaraliev M (2022) Deep learning for Covid-19 forecasting: State-of-the-art review. Neurocomputing 511:142–154.Google Scholar
• Kamran MA, Kia R, Goodarzian F, Ghasemi P (2023) A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms. Socioeconom. Planning Sci. 85:101378.Google Scholar
• Kermack WO, McKendrick AG (1927) Contributions to the mathematical theory of epidemics—I. Proc. Roy. Soc. 115(772):700–721.Google Scholar
• Kermack WO, McKendrick AG (1932) Contributions to the mathematical theory of epidemics—II. The problem of endemicity. Proc. Roy. Soc. 138(834):55–83.Google Scholar
• Kermack WO, McKendrick AG (1933) Contributions to the mathematical theory of epidemics. III.—Further studies of the problem of endemicity. Proc. Roy. Soc. 141(843):94–122.Google Scholar
• Keskin BB, Melouk SH, Meyer IL (2010) A simulation-optimization approach for integrated sourcing and inventory decisions. Comput. Oper. Res. 37(9):1648–1661.Google Scholar
• Kochakkashani F, Kayvanfar V, Haji A (2023) Supply chain planning of vaccine and pharmaceutical clusters under uncertainty: The case of COVID-19. Socioeconom. Planning Sci. 87:101602.Google Scholar
• Kooijmans ECM, Hoogendijk EO, Drapała N, Antonenko O, Burchell GL, Barańska I, Pokladníková J, et al. (2025) Defining and categorizing nonpharmacologic interventions in the older population: A systematic review. J. Amer. Medical Directors Assoc. 26(1):105306.Google Scholar
• Kretzschmar ME, Rozhnova G, Bootsma MCJ, van Boven M, van de Wijgert JH, Bonten MJM (2020) Impact of delays on effectiveness of contact tracing strategies for COVID-19: A modelling study. Lancet Public Health 5(8):e452–e459.Google Scholar
• Kumar P, Singh RK, Shahgholian A (2024) Learnings from COVID-19 for managing humanitarian supply chains: Systematic literature review and future research directions. Ann. Oper. Res. 335(3):899–935.Google Scholar
• Larremore DB, Wilder B, Lester E, Shehata S, Burke JM, Hay JA, Tambe M, Mina MJ, Parker R (2021) Test sensitivity is secondary to frequency and turnaround time for COVID-19 screening. Sci. Adv. 7(1):eabd5393.Google Scholar
• Lee EK, Yuan F, Pietz FH, Benecke BA, Burel G (2015) Vaccine prioritization for effective pandemic response. Interfaces 45(5):425–443.Link, Google Scholar
• Lin K, Musa SN, Yap HJ (2022) Vehicle routing optimization for pandemic containment: A systematic review on applications and solution approaches. Sustainability 14(4):2053.Google Scholar
• Liu K, Liu C, Xiang X, Tian Z (2023) Testing facility location and dynamic capacity planning for pandemics with demand uncertainty. Eur. J. Oper. Res. 304(1):150–168.Google Scholar
• Locke J, Taghavi M, Mansouri B, Saif A (2025) Optimizing vaccine logistics: A taxonomy and narrative review. INFOR Inform. Systems Oper. Res. 63(3):498–539.Google Scholar
• Loo SL, Howerton E, Contamin L, Smith CP, Borchering RK, Mullany LC, Bents S, et al. (2024) The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy. Epidemics 46:100738.Google Scholar
• Lorig F, Johansson E, Davidsson P (2021) Agent-based social simulation of the Covid-19 pandemic: A systematic review. J. Artificial Soc. Soc. Simulation 24(3):5.Google Scholar
• Matrajt L, Eaton J, Leung T, Brown ER (2021) Vaccine optimization for COVID-19: Who to vaccinate first? Sci. Adv. 7(6):eabf1374.Google Scholar
• Miksch F, Jahn B, Espinosa KJ, Chhatwal J, Siebert U, Popper N (2019) Why should we apply ABM for decision analysis for infectious diseases?—An example for dengue interventions. PLoS One 14(8):e0221564.Google Scholar
• Mills A, Ziya S (2025) Test allocation and pool composition in heterogenous populations under strict capacity constraints. Manufacturing Service Oper. Management 27(5):1362–1376.Abstract, Google Scholar
• Mitze T, Kosfeld R, Rode J, Wälde K (2020) Face masks considerably reduce COVID-19 cases in Germany. Proc. Natl. Acad. Sci. USA 117(51):32293–32301.Google Scholar
• Mohammadi M, Dehghan M, Pirayesh A, Dolgui A (2022) Bi‐objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID‐19 pandemic. Omega 113:102725.Google Scholar
• Moran KR, Fairchild G, Generous N, Hickmann K, Osthus D, Priedhorsky R, Hyman J, Del Valle SY (2016) Epidemic forecasting is messier than weather forecasting: The role of human behavior and internet data streams in epidemic forecast. J. Infectious Diseases 214(Suppl. 4):S404–S408.Google Scholar
• Mosallanezhad B, Chouhan VK, Paydar MM, Hajiaghaei-Keshteli M (2021) Disaster relief supply chain design for personal protection equipment during the COVID-19 pandemic. Appl. Soft Comput. 112:107809.Google Scholar
• Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R, Massari M, et al. (2017) POLYMOD social contact data. Zenodo (November 7), https://zenodo.org/records/3746312.Google Scholar
• National Academies of Sciences, Engineering, and Medicine (2017) Using 21st Century Science to Improve Risk-Related Evaluations (National Academies Press, Washington, DC).Google Scholar
• Nutton V (1990) The reception of Fracastoro’s theory of contagion: The seed that fell among thorns? Osiris 6(1):196–234.Google Scholar
• Ojokoh BA, Aribisala B, Sarumi OA, Gabriel AJ, Omisore O, Taiwo AE, Igbe T, et al. (2022) Contact tracing strategies for COVID-19 prevention and containment: A scoping review. Big Data Cognitive Comput. 6(4):111.Google Scholar
• Ozdemir I, Dursunoglu CF, Kara BY, Dora M (2022) Logistics of temporary testing centers for coronavirus disease. Transportation Res. Part C Emerging Tech. 145:103954.Google Scholar
• Pittman KJ, Glover LC, Wang L, Ko DC (2016) The legacy of past pandemics: Common human mutations that protect against infectious disease. PLoS Pathogens 12(7):e1005680.Google Scholar
• Porta M, ed. (2008) A Dictionary of Epidemiology, 5th ed. (Oxford University Press, Oxford, UK).Google Scholar
• Powell WB (2019) Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Powell WB, Ryzhov IO (2012) Optimal Learning (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Queiroz MM, Fosso Wamba S (2024) A structured literature review on the interplay between emerging technologies and COVID-19—Insights and directions to operations fields. Ann. Oper. Res. 335(3):937–963.Google Scholar
• Queiroz MM, Ivanov D, Dolgui A, Wamba SF (2022) Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Ann. Oper. Res. 319(1):1159–1196.Google Scholar
• Rahimi I, Chen F, Gandomi AH (2023) A review on COVID-19 forecasting models. Neural Comput. Appl. 35(33):23671–23681.Google Scholar
• Ramsay MAE (2006) John Snow, MD: Anaesthetist to the Queen of England and pioneer epidemiologist. Proc. (Baylor Univ. Medical Center) 19(1):24–28.Google Scholar
• Roser M (2021) What is the COVID-19 Stringency Index? Our World in Data, archived May 23, 2026, https://archive.ourworldindata.org/20260523-063512/metrics-explained-covid19-stringency-index.html.Google Scholar
• Santini A (2021) Optimising the assignment of swabs and reagent for PCR testing during a viral epidemic. Omega 102:102341.Google Scholar
• Scott N, Saul A, Spelman T, Stoove M, Pedrana A, Saeri A, Grundy E, et al. (2021) The introduction of a mandatory mask policy was associated with significantly reduced COVID-19 cases in a major metropolitan city. PLoS One 16(7):e0253510.Google Scholar
• Shakeel SM, Kumar NS, Madalli PP, Srinivasaiah R, Swamy DR (2021) COVID-19 prediction models: A systematic literature review. Osong Public Health Res. Perspect. 12(4):215–229.Google Scholar
• Sherratt K, Gruson H, Johnson H, Niehus R, Prasse B, Sandmann F, Deuschel J, et al. (2023) Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations. eLife 12:e81916.Google Scholar
• Silva PJS, Sagastizábal C, Nonato LG, Struchiner CJ, Pereira T (2021) Optimized delay of the second COVID-19 vaccine dose reduces ICU admissions. Proc. Natl. Acad. Sci. USA 118(35):e2104640118.Google Scholar
• Sinha P, Kumar S, Chandra C (2023) Strategies for ensuring required service level for COVID-19 herd immunity in Indian vaccine supply chain. Eur. J. Oper. Res. 304(1):339–352.Google Scholar
• Snyder H (2019) Literature review as a research methodology: An overview and guidelines. J. Bus. Res. 104:333–339.Google Scholar
• Tang L, Li Y, Bai D, Liu T, Coelho LC (2022) Bi-objective optimization for a multi-period COVID-19 vaccination planning problem. Omega 110:102617.Google Scholar
• Tavana M, Govindan K, Nasr AK, Heidary MS, Mina H (2021) A mathematical programming approach for equitable COVID-19 vaccine distribution in developing countries. Ann. Oper. Res. 360:391–424.Google Scholar
• Taylor JW, Taylor KS (2023) Combining probabilistic forecasts of COVID-19 mortality in the United States. Eur. J. Oper. Res. 304(1):25–41.Google Scholar
• Thul L, Powell W (2023) Stochastic optimization for vaccine and testing kit allocation for the COVID-19 pandemic. Eur. J. Oper. Res. 304(1):325–338.Google Scholar
• Tippong D, Petrovic S, Akbari V (2022) A review of applications of operational research in healthcare coordination in disaster management. Eur. J. Oper. Res. 301(1):1–17.Google Scholar
• Tirkolaee EB, Goli A, Ghasemi P, Goodarzian F (2022) Designing a sustainable closed-loop supply chain network of face masks during the COVID-19 pandemic: Pareto-based algorithms. J. Cleaner Production 333:130056.Google Scholar
• Tsay C, Lejarza F, Stadtherr MA, Baldea M (2020) Modeling, state estimation, and optimal control for the US COVID-19 outbreak. Sci. Rep. 10(1):10711.Google Scholar
• Vahdani B, Mohammadi M, Thevenin S, Gendreau M, Dolgui A, Meyer P (2023) Fair-split distribution of multi-dose vaccines with prioritized age groups and dynamic demand: The case study of COVID-19. Eur. J. Oper. Res. 310(3):1249–1272.Google Scholar
• Wade L (2020) From Black Death to fatal flu, past pandemics show why people on the margins suffer most. Science (May 14), https://www.sciencemag.org/news/2020/05/black-death-fatal-flu-past-pandemics-show-why-people-margins-suffer-most.Google Scholar
• Wang L, Didelot X, Yang J, Wong G, Shi Y, Liu W, Gao GF, Bi Y (2020) Inference of person-to-person transmission of COVID-19 reveals hidden super-spreading events during the early outbreak phase. Nature Comm. 11(1):5006.Google Scholar
• Wang H, Paulson KR, Pease SA, Watson S, Comfort H, Zheng P, Aravkin AY, et al. (2022) Estimating excess mortality due to the COVID-19 pandemic: A systematic analysis of COVID-19-related mortality, 2020–21. Lancet 399(10334):1513–1536.Google Scholar
• Wolfinger D, Gansterer M, Doerner KF, Popper N (2023) A large neighbourhood search metaheuristic for the contagious disease testing problem. Eur. J. Oper. Res. 304(1):169–182.Google Scholar
• Worby CJ, Chang H-H (2020) Face mask use in the general population and optimal resource allocation during the COVID-19 pandemic. Nature Comm. 11(1):4049.Google Scholar
• World Health Organization (2025) A Decision Framework for Effective, Equitable and Context-Specific Public Health and Social Measures During Public Health Emergencies (World Health Organization, Geneva).Google Scholar
• Yang Y, Bidkhori H, Rajgopal J (2021) Optimizing vaccine distribution networks in low and middle-income countries. Omega 99:102197.Google Scholar
• Yong SEF, Anderson DE, Wei WE, Pang J, Chia WN, Tan CW, Teoh YL, et al. (2020) Connecting clusters of COVID-19: An epidemiological and serological investigation. Lancet Infectious Diseases 20(7):809–815.Google Scholar
• Younis H, Alsharairi M, Younes H, Sundarakani B (2023) The impact of COVID-19 on supply chains: Systematic review and future research directions. Oper. Res. 23(3):48.Google Scholar
• Zhu X, Liu M, Zio E (2026) Optimization strategy for testing resource allocation and ICU bed capacity planning during a pandemic. Expert Systems Appl. 297:129545.Google Scholar