Applications of digital twins in public health: A critical review of current capabilities and a roadmap for translational readiness

Abstract

Aging populations, climate change, and emerging infectious disease threats are increasingly straining traditional public health models. Digital twins (DTs), virtual replicas continuously updated with real-world data, have recently expanded from engineering into healthcare, with growing interest in population-level applications. In practice, DTs can integrate diverse data streams, including epidemiological, environmental, and emerging molecular surveillance data, to support dynamic population-level health modeling. Existing DT pilot projects demonstrate technical feasibility in controlled settings, but most applications in public health remain at an early developmental stage, with limited scale, validation, and real-world implementation. Progress will depend on robust data ecosystems, interoperable platforms, and transparent governance frameworks, while integration with advanced artificial intelligence may further expand future predictive capabilities. We review the potential of DTs in public health, with emphasis on infectious disease surveillance, emergency preparedness, environmental monitoring, chronic disease management, and policy evaluation. In addition, this review proposes a preliminary multi-dimensional framework that evaluates axes such as data integration capacity, predictive validity, operational usability, and governance, thereby providing a basis for assessing the maturity and translational readiness of DT systems in public health. DTs hold potential to support a shift in public health from reactive approaches toward more proactive and predictive practice. However, realizing this vision requires overcoming substantial foundational and translational challenges.

Keywords

Digital twins / Public health / Surveillance / Emergency preparedness / Evidence gaps

References

[1] Schork NJ, Goetz LH. From precision interventions to precision health. Nat Commun. May 30 2025; 16(1): 5024. https://doi.org/10.1038/s41467—025—60395—z

[2] Iranshahi K, Brun J, Arnold T, Sergi T, Müller UC. Digital twins: recent advances and future directions in engineering fields. 2025/06/01/ Intell Syst Appl. 2025; 26: 200516. https://doi.org/10.1016/j.iswa.2025.200516

[3] Ren Y, Pieper AA, Cheng F. Utilization of precision medicine digital twins for drug discovery in Alzheimer's disease. Neurotherapeutics. Apr 2025; 22(3): e00553. https://doi.org/10.1016/j.neurot.2025.e00553

[4] Katsoulakis E, Wang Q, Wu H, et al. Digital twins for health: a scoping review. npj Digit Med. Mar 22 2024; 7(1): 77. https://doi.org/10.1038/s41746—024—01073—0

[5] Sel K, Osman D, Zare F, et al. Building digital twins for cardiovascular health: from principles to clinical impact. J Am Heart Assoc. Oct 2024; 13(19): e031981. https://doi.org/10.1161/JAHA.123.031981

[6] Lal A, Li G, Cubro E, et al. Development and verification of a digital twin patient model to predict specific treatment response during the first 24 h of sepsis. Crit Care Explor. Nov 2020; 2(11): e0249. https://doi.org/10.1097/CCE.0000000000000249

[7] Machado TM, Berssaneti FT. Literature review of digital twin in healthcare. Heliyon. Sep 2023; 9(9): e19390. https://doi.org/10.1016/j.heliyon.2023.e19390

[8] Barricelli BR, Casiraghi E, Fogli D. A survey on digital twin: definitions, characteristics, applications, and design implications. IEEE Access. 2019; 7: 167653-167671. https://doi.org/10.1109/ACCESS.2019.2953499

[9] Ferko E, Bucaioni A, Behnam M. Architecting digital twins. IEEE Access. 2022; 10: 50335-50350. https://doi.org/10.1109/ACCESS.2022.3172964

[10] Yao J—F, Yang Y, Wang X—C, Zhang X—P. Systematic review of digital twin technology and applications. 2023/05/30 Vis Comput Ind Biomed Art. 2023; 6(1): 10. https://doi.org/10.1186/s42492—023—00137—4

[11] Zheng Y, Yang S, Cheng H. An application framework of digital twin and its case study. 2019/03/01 J Ambient Intell Humaniz Comput. 2019; 10(3): 1141-1153. https://doi.org/10.1007/s12652—018—0911—3

[12] Bjornsson B, Borrebaeck C, Elander N, et al. Digital twins to personalize medicine. Genome Med. Dec 31 2019; 12(1): 4. https://doi.org/10.1186/s13073—019—0701—3

[13] Vallee A. Digital twin for healthcare systems. Front Digit Health. 2023; 5: 1253050. https://doi.org/10.3389/fdgth.2023.1253050

[14] Cluitmans MJM, Plank G, Heijman J. Digital twins for cardiac electrophysiology: state of the art and future challenges. (Digitale Zwillinge in der kardialen Elektrophysiologie — aktueller Entwicklungsstand und kommende Herausforderungen). Herzschrittmacher Elektro. Jun 2024; 35(2): 118-123. https://doi.org/10.1007/s00399—024—01014—0

[15] Shen S, Qi W, Liu X, et al. From virtual to reality: innovative practices of digital twins in tumor therapy. J Transl Med. Mar 19 2025; 23(1): 348. https://doi.org/10.1186/s12967—025—06371—z

[16] Mosquera—Lopez C, Jacobs PG. Digital twins and artificial intelligence in metabolic disease research. Trends Endocrinol Metab. Jun 2024; 35(6): 549-557. https://doi.org/10.1016/j.tem.2024.04.019

[17] Laubenbacher R, Mehrad B, Shmulevich I, Trayanova N. Digital twins in medicine. Nat Comput Sci. Mar 2024; 4(3): 184-191. https://doi.org/10.1038/s43588—024—00607—6

[18] Wickramasinghe N, Ulapane N, Sloane EB, Gehlot V. Digital Twins for More Precise and Personalized Treatment. Stud Health Technol Inform. Jan 25 2024; 310: 229-233. https://doi.org/10.3233/SHTI230961

[19] Kühn A, Lehrach H. The “Virtual Patient” system: modeling cancer using deep sequencing technologies for personalized cancer treatment. 2012/03/01 J für Verbrauch und Lebensm. 2012; 7(1): 55-62. https://doi.org/10.1007/s00003—011—0755—7

[20] Kamel Boulos MN, Zhang P. Digital Twins: From Personalised Medicine to Precision Public Health. J Pers Med. Jul 29 2021; 11(8). https://doi.org/10.3390/jpm11080745

[21] De Domenico M, Allegri L, Caldarelli G, et al. Challenges and opportunities for digital twins in precision medicine from a complex systems perspective. NPJ Digit Med. Jan 17 2025; 8(1): 37. https://doi.org/10.1038/s41746—024—01402—3

[22] Attaran S, Attaran M. Advancing healthcare through the integration of digital twins technology: personalized medicine’s next frontier. Future Internet. 2024; 16(12). https://doi.org/10.3390/fi16120477

[23] Vallée A. Digital twins for personalized medicine require epidemiological data and mathematical modeling: viewpoint. J Med Internet Res. Aug 5 2025; 27: e72411. https://doi.org/10.2196/72411

[24] Yu Z, Hong W, Green A. A new era of convergence: molecular and digital medicine in the age of AI. 2026/01/01/ Mol Digit Med. 2026; 1(1): 100003. https://doi.org/10.1016/j.mdmed.2026.100003

[25] Murray CJL, Alamro NMS, Hwang H, Lee U. Digital public health and COVID—19. Lancet Public Health. Sep 2020; 5(9): e469-e470. https://doi.org/10.1016/S2468—2667(20)30187—0

[26] Ghatti S, Yurish L, Shen H, et al. Digital twins in healthcare: a survey of current methods. Arch Clin Biomed Res. 01/01 2023; 07. https://doi.org/10.26502/acbr.50170352

[27] Wang W, Li R, Chen S, et al. Wastewater—Based Surveillance of SARS—CoV—2 and Modeling of COVID—19 Infection Trends. Trop Med Infect Dis. Sep 16 2025; 10(9). https://doi.org/10.3390/tropicalmed10090264

[28] Fonseca ICP, Garcia ISJ, Corominas L, Bosch LM. Applying a Digital Twin and wastewater analysis for robust validation of COVID—19 pandemic forecasts: insights from Catalonia. J Water Health. Mar 2024; 22(3): 584-600. https://doi.org/10.2166/wh.2024.345

[29] Yousif M, Rachida S, Taukobong S, et al. SARS—CoV—2 genomic surveillance in wastewater as a model for monitoring evolution of endemic viruses. Nat Commun. Oct 10 2023; 14(1): 6325. https://doi.org/10.1038/s41467—023—41369—5

[30] Eriksen Graesboll K, Kirkeby RS, Christiansen C. LE. Digital twin simulation modelling shows that mass testing and local lockdowns effectively controlled COVID—19 in Denmark. Commun Med (Lond). Oct 4 2024; 4(1): 192. https://doi.org/10.1038/s43856—024—00621—9

[31] Venkatachalam I, Conceicao EP, Sim JXY, et al. Three—Dimensional Disease Outbreak Surveillance System in a Tertiary Hospital in Singapore: A Proof of Concept. Mayo Clin Proc Digit Health. Jun 2023; 1(2): 172-184. https://doi.org/10.1016/j.mcpdig.2023.04.001

[32] Mason DM, Kapinaj M, Martínez AP, Stella L. Impact of Social Distancing to Mitigate the Spread of COVID—19 in a Virtual Environment. presented at: Proceedings of the 26th ACM Symposium on Virtual Reality Software and Technology . Canada: Virtual Event; 2020. https://doi.org/10.1145/3385956.3422093

[33] Deren L, Wenbo Y, Zhenfeng S. Smart city based on digital twins. 2021/03/29 Comput Urban Sci. 2021; 1(1): 4. https://doi.org/10.1007/s43762—021—00005—y

[34] El Azzaoui A, Kim TW, Loia V, Park JH. Blockchain—Based Secure Digital Twin Framework for Smart Healthy City. Springer Singapore; 2021: 107-113.

[35] Steinhubl SR, Sekaric J, Gendy M, et al. Development of a personalized digital biomarker of vaccine—associated reactogenicity using wearable sensors and digital twin technology. Commun Med (Lond). Apr 13 2025; 5(1): 115. https://doi.org/10.1038/s43856—025—00840—8

[36] Malone B, Simovski B, Moliné C, et al. Artificial intelligence predicts the immunogenic landscape of SARS—CoV—2 leading to universal blueprints for vaccine designs. Sci Rep. Dec 23 2020; 10(1): 22375. https://doi.org/10.1038/s41598—020—78758—5

[37] Mohammadi N, Taylor JE. Thinking fast and slow in disaster decision—making with Smart City Digital Twins. 2021/ 12/01 Nat Comput Sci. 2021; 1(12): 771-773. https://doi.org/10.1038/s43588—021—00174—0

[38] Wolf K, Dawson RJ, Mills JP, Blythe P, Morley J. Towards a digital twin for supporting multi—agency incident management in a smart city. Sci Rep. Sep 28 2022; 12(1): 16221. https://doi.org/10.1038/s41598—022—20178—8

[39] Sun T, Lu J, Li Q, Kou Y. Can digital twin technology revolutionize public health emergency management? Insights from the PPRR framework. Front Public Health. 2025; 13: 1631339. https://doi.org/10.3389/fpubh.2025.1631339

[40] Gowran L.M. EU to develop a digital twin of Earth to better predict climate impact. 2022// 2022.

[41] Voosen P. Europe builds ‘digital twin’ of Earth to hone climate forecasts. 2020/10/02 Science. 2020; 370(6512): 16-17. https://doi.org/10.1126/science.370.6512.16

[42] Seong K, Jiao J, Lewis RH, et al. Towards a digital twin for smart resilient cities: real—time fire and smoke tracking and prediction platform for community awareness (FireCom). Comput Urban Sci. 2025; 5(1): 49. https://doi.org/10.1007/s43762—025—00212—x

[43] Pilati F, Tronconi R, Nollo G, Heragu SS, Zerzer F. Digital Twin of COVID—19 Mass Vaccination Centers. Sustainability. 2021; 13(13). https://doi.org/10.3390/su13137396

[44] Karakra A, Lamine E, Fontanili F, Lamothe J. HospiT’Win: a digital twin framework for patients’ pathways real—time monitoring and hospital organizational resilience capacity enhancement. Proc 9th Int Workshop Innov Simul Healthc (IWISH 2020). 2020.

[45] Re Cecconi F., De Angelis E., Tagliabue L., Ciribini A., Rinaldi S., Bellagente P. Soft Digital Twin for IEQ enabling the COVID risk mitigation in educational spaces. 2021.

[46] Zohdi TI. A digital—twin and machine—learning framework for ventilation system optimization for capturing infectious disease respiratory emissions. Arch Comput Methods Eng. 2021; 28(6): 4317-4329. https://doi.org/10.1007/s11831—021—09609—3

[47] Costa G, Arroyo O, Rueda P, Briones A. A ventilation early warning system (VEWS) for diaphanous workspaces considering COVID—19 and future pandemics scenarios. Heliyon. Mar 2023; 9(3): e14640. https://doi.org/10.1016/j.heliyon.2023.e14640

[48] Pan X, Mavrokapnidis D, Ly HT, Mohammadi N, Taylor JE. Assessing and forecasting collective urban heat exposure with smart city digital twins. Sci Rep. Apr 26 2024; 14(1): 9653. https://doi.org/10.1038/s41598—024—59228—8

[49] Abdel—Mooty MN, Coulibaly P, El—Dakhakhni W. A scalable data driven geospatial framework for climate risk assessment. Sci Rep. Dec 20 2025; 16(1): 2495. https://doi.org/10.1038/s41598—025—32370—7

[50] Cui Y, Li Y, Tang H, et al. A digital—twin platform for cryospheric disaster warning. Natl Sci Rev. Oct 2024; 11(10): nwae300. https://doi.org/10.1093/nsr/nwae300

[51] Borkenhagen A, Babic A. Establishing a Digital Twin Archetype Through Climate Concerns and Quality of Life Data. Stud Health Technol Inform. Jun 26 2025; 328: 550-554. https://doi.org/10.3233/shti250780

[52] Bibri SE, Huang J, Jagatheesaperumal SK, Krogstie J. The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: a comprehensive systematic review. 2024/07/01/ Environ Sci Ecotechnology. 2024; 20: 100433. https://doi.org/10.1016/j.ese.2024.100433

[53] International Telecommunication U. UK city to use digital twin to support pandemic recovery. 2021/05// 2021;

[54] Cavada M, Tight MR, Rogers CDF. 14 — A smart city case study of Singapore—Is Singapore truly smart? In: Anthopoulos L, ed. Smart City Emergence . Elsevier; 2019: 295-314.

[55] Cappon G, Facchinetti A. Digital Twins in Type 1 Diabetes: A Systematic Review. J Diabetes Sci Technol. Jun 17 2024:19322968241262112. https://doi.org/10.1177/19322968241262112

[56] Iqbal JD, Krauthammer M, Biller—Andorno N. The Use and Ethics of Digital Twins in Medicine. J Law Med Ethics. 2022; 50(3): 583-596. https://doi.org/10.1017/jme.2022.97

[57] Du Y, Agrawal V, Hong W. Boopathy GTK. P2Y2 signaling in cancer: mechanisms, biomarkers, and therapeutic horizons. 2026/01/01/ Mol Digit Med. 2026; 1(1): 100001. https://doi.org/10.1016/j.mdmed.2026.100001

[58] Shi Y—B, Liu Z—J, Hu Y, Li S—R, Liu J—Q. Histamine signaling and antihistamines in cancer progression and immunotherapy: current perspectives. 2026/01/01/ Mol Digit Med. 2026; 1(1): 100007. https://doi.org/10.1016/j.mdmed.2026.100007

[59] Yang Y, Yang J, Liang Y, Yang Q. Breast cancer organoids: a precision platform for modeling heterogeneity and advancing personalized therapy. 2026/01/01/ Mol Digit Med. 2026; 1(1): 100004. https://doi.org/10.1016/j.mdmed.2026.100004

[60] Coorey G, Figtree GA, Fletcher DF, et al. The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field. NPJ Digit Med. Aug 26 2022; 5(1): 126. https://doi.org/10.1038/s41746—022—00640—7

[61] Vallee A. Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins. J Med Internet Res. May 13 2024; 26: e50204. https://doi.org/10.2196/50204

[62] Shen MD, Chen SB, Ding XD. The effectiveness of digital twins in promoting precision health across the entire population: a systematic review. NPJ Digit Med. Jun 3 2024; 7(1): 145. https://doi.org/10.1038/s41746—024—01146—0

[63] Zhang B, Li J, Zhou J, et al. A three—dimensional liquid diode for soft, integrated permeable electronics. Nature. Apr 2024; 628(8006): 84-92. https://doi.org/10.1038/s41586—024—07161—1

[64] Guo J, Lv Z. Application of digital twins in multiple fields. 2022/08/01 Multimed Tools Appl. 2022; 81(19): 26941-26967. https://doi.org/10.1007/s11042—022—12536—5

[65] Saddik AE. Digital twins: the convergence of multimedia technologies. IEEE Multimed. 2018; 25(2): 87-92. https://doi.org/10.1109/MMUL.2018.023121167

[66] Wang M, Yang Y, Min J, et al. A wearable electrochemical biosensor for the monitoring of metabolites and nutrients. 2022/ 11/01 Nat Biomed Eng. 2022; 6(11): 1225-1235. https://doi.org/10.1038/s41551—022—00916—z

[67] Sarani Rad F, Hendawi R, Yang X, Li J. Personalized diabetes management with digital twins: a patient—centric knowledge graph approach. J Pers Med. Mar 28 2024; 14(4). https://doi.org/10.3390/jpm14040359

[68] Mulder ST, Omidvari AH, Rueten—Budde AJ, et al. Dynamic digital twin: diagnosis, treatment, prediction, and prevention of disease during the life course. J Med Internet Res. Sep 14 2022; 24(9): e35675. https://doi.org/10.2196/35675

[69] Baranov L, Tikhonova O, Tsarev A, Tverdynin N, Grishakina N. Exp Creat Digit Twins a Result Monit Health Ind Pers. 2022.

[70] Thangaraj PM, Shankar SV, Huang S, et al. A novel digital twin strategy to examine the implications of randomized clinical trials for real—world populations. medRxiv. Sep 6 2024. https://doi.org/10.1101/2024.03.25.24304868

[71] Thangaraj PM, Shankar SV, Oikonomou EK, Khera R. RCT—Twin—GAN generates digital twins of randomized control trials adapted to real—world patients to enhance their inference and application. medRxiv. Dec 15 2023. https://doi.org/10.1101/2023.12.06.23299464

[72] Johnson B, Curtius K. Digital twins are integral to personalizing medicine and improving public health. Nat Rev Gastroenterol Hepatol. Nov 2024; 21(11): 740-741. https://doi.org/10.1038/s41575—024—00992—3

[73] Knudsen AB, Rutter CM, Peterse EFP, et al. Colorectal cancer screening: an updated modeling study for the US preventive services task force. JAMA. May 18 2021; 325(19): 1998-2011. https://doi.org/10.1001/jama.2021.5746

[74] Liew TM, Koay WI. Cost—effective case—finding strategies for cognitive impairment with digital twin simulations. 2026/03/03 Nat Health. 2026. https://doi.org/10.1038/s44360—026—00076—3 2026/03/03.

[75] Qiao J, Zhou Z, Zhang Z, Yang H, Zhang W, Sun J. Integrating multimodal neuroimaging genetics and deep learning to predict Alzheimer's disease: a systematic review. 2026/01/01/ Mol Digit Med. 2026; 1(1): 100006. https://doi.org/10.1016/j.mdmed.2026.100006

[76] Popa EO, van Hilten M, Oosterkamp E, Bogaardt MJ. The use of digital twins in healthcare: socio—ethical benefits and socio—ethical risks. Life Sci Soc Policy. Jul 5 2021; 17(1): 6. https://doi.org/10.1186/s40504—021—00113—x

[77] Botin D, Mihaita A—S, Peimbert—García R, Ramírez Moreno MA, Ramirez—Mendoza R, Lozoya—Santos J. Digital Twin Technology Challenges and Applications: A Comprehensive Review. Remote Sens. 03/09 2022; 14: 1335. https://doi.org/10.3390/rs14061335

[78] Khan A, Milne—Ives M, Meinert E, Iyawa GE, Jones RB, Josephraj AN. A Scoping Review of Digital Twins in the Context of the Covid—19 Pandemic. Biomed Eng Comput Biol. 2022; 13: 11795972221102115. https://doi.org/10.1177/11795972221102115

[79] European C. Destination Earth (DestinE). 2022// 2022;

[80] Centre for Digital Built B. National Digital Twin programme: Building momentum. 2021// 2021;

[81] Li X, He P. Construction and pilot application of digital twin capability model for clinical research under data—intelligence integration. 22 China Digit Med. 2024; 19(7): 7-11. https://doi.org/10.3969/j.issn.1673—7571.2024.07.002

[82] Zhang K, Zhou HY, Baptista—Hon DT, et al. Concepts and applications of digital twins in healthcare and medicine. Patterns (N Y). Aug 9 2024; 5(8): 101028. https://doi.org/10.1016/j.patter.2024.101028

[83] Chen J., Ren Y., Zhou Y., et al. A generative AI framework unifies human multi—omics to model aging, metabolic health, and intervention response. Cell Metabolism. doi: 10.1016/j.cmet.2026.03.014.

[84] Sahal R, Alsamhi SH, Brown KN, O'Shea D, Alouffi B. Blockchain—based digital twins collaboration for smart pandemic alerting: decentralized COVID—19 pandemic alerting use case. Comput Intell Neurosci. 2022; 2022: 7786441. https://doi.org/10.1155/2022/7786441

[85] Vasiliu—Feltes I, Mylrea M, Zhang CY, Wood TC, Thornley B. Impact of blockchain—digital twin technology on precision health, pharmaceutical industry, and life sciences: conference proceedings, Conv2X. Block Healthc Today. 2023; 6: 2023. https://doi.org/10.30953/bhty.v6.281

[86] Coveney P, Highfield R, Stahlberg E, Vazquez M. Digital twins and Big AI: the future of truly individualised healthcare. NPJ Digit Med. Aug 1 2025; 8(1): 494. https://doi.org/10.1038/s41746—025—01874—x

[87] Dai Y, Ge J. Artificial intelligence in medical practice: current status and future perspectives. Cardiology. 2023; 8(1).

[88] Geanta M, Tanwar AS, Lehrach H, Satyamoorthy K, Brand A. Horizon scanning: rise of planetary health genomics and digital twins for pandemic preparedness. OMICS. Feb 2022; 26(2): 93-100. https://doi.org/10.1089/omi.2021.0062

[89] Gkouskou K, Vlastos I, Karkalousos P, Chaniotis D, Sanoudou D, Eliopoulos A. The "Virtual Digital Twins" concept in precision nutrition. Adv Nutr (Bethesda Md). 08/08 2020; 11. https://doi.org/10.1093/advances/nmaa089

[90] Reicher L, Shilo S, Godneva A, et al. Deep phenotyping of health—disease continuum in the Human Phenotype Project. Nat Med. Sep 2025; 31(9): 3191-3203. https://doi.org/10.1038/s41591—025—03790—9

[91] Smith T, Holm RH, Yeager R, et al. Combining community wastewater genomic surveillance with state clinical surveillance: a framework for SARS—CoV—2 public health practice. Food Environ Virol. Dec 2022; 14(4): 410-416. https://doi.org/10.1007/s12560—022—09531—2

[92] Stahlberg EA, Abdel—Rahman M, Aguilar B, et al. Exploring approaches for predictive cancer patient digital twins: opportunities for collaboration and innovation. Front Digit Health. 2022; 4: 1007784. https://doi.org/10.3389/fdgth.2022.1007784