Your privacy, your choice

We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media.

By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection.

See our privacy policy for more information on the use of your personal data.

for further information and to change your choices.
Skip to main content
Download PDF
Download ePub

The role of economic evaluation in modelling public health and social measures for pandemic policy: a systematic review

Cost Effectiveness and Resource Allocation volume 22, Article number: 77 (2024) Cite this article

Abstract

Background

Dynamic transmission models are often used to provide epidemiological guidance for pandemic policy decisions. However, how economic evaluation is typically incorporated into this technique to generate cost-effectiveness estimates of pandemic policy responses has not previously been reviewed.

Methods

We systematically searched the Embase, PubMed and Scopus databases for dynamic epidemiological modelling studies that incorporated economic evaluation of public health and social measures (PHSMs), with no date restrictions, on 7 July 2024.

Results

Of the 2,719 screened studies, 51 met the inclusion criteria. Most studies (n = 42, 82%) modelled SARS-CoV-2. A range of PHSMs were examined, including school closures, testing/screening, social distancing and mask use. Half of the studies utilised an extension of a Susceptible-Exposed-Infectious-Recovered (SEIR) compartmental model. The most common type of economic evaluation was cost-effectiveness analysis (n = 24, 47%), followed by cost-utility analysis (n = 17, 33%) and cost–benefit analysis (n = 17, 33%).

Conclusions

Economic evaluation is infrequently incorporated into dynamic epidemiological modelling studies of PHSMs. The scope of this research should be expanded, given the substantial cost implications of pandemic PHSM policy responses.

Background

Public health and social measures (PHSMs, also referred to as non-pharmaceutical interventions) are implemented during pandemics to suppress or eliminate the transmission of infectious diseases. PHSMs are utilised when vaccines and pharmaceutical treatments are unavailable or insufficient to control the spread of the infectious agent [1]. PHSMs—particularly restrictions on social mobility and lockdowns—can yield significant benefits for population health and health system expenditure; however, they may also result in substantial social costs. Consequently, decision-making regarding the implementation and timing of PHSMs is complex. During the COVID-19 pandemic, the prevailing approach involved using simulation modelling of the health impacts of PHSMs, sometimes compared with parallel estimates of the social and other costs of PHSMs, or brought together in a multicriteria decision making process[2]. Integrated epidemiological and economic modelling that considers health and cost impacts within a single framework has the potential to enhance planning and response strategies for future pandemics [3].

Infectious disease modelling can provide quantitative estimates regarding past or future response scenarios that have not been observed, based on available and projected data [4]. In a public health crisis, such as the recent COVID-19 pandemic, models can enhance our understanding of disease impacts on society, either unmitigated or in the presence of various interventions [5]. Dynamic transmission models allow for the risk of infection to be dependent on the prevalence of infectious individuals in the population, thereby capturing the indirect effects of infectious disease interventions and facilitating understanding of non-linear transmission effects [6,7,8,9]. Common types of dynamic models include compartmental models such as Susceptible-Infectious-Recovered (SIR) models, and agent-based models (ABMs).

Economic evaluation can be integrated into infectious disease modelling, providing an additional metric for comparison between intervention strategies. The perspective used in an economic evaluation can significantly influence the conclusions drawn regarding intervention impacts and policy recommendations and should therefore be selected with consideration of the specific context being modelled [10]. From a health system perspective, determining the most cost-effective intervention can facilitate resource allocation to mitigate morbidity and mortality resulting from a given disease. However, in a public health crisis such as the COVID-19 pandemic, impacts extend beyond the health system, necessitating the consideration of broader social and economic costs. Integrating economic evaluations, preferably extending beyond the health system to include broader societal impacts, into epidemiological models can help assess the proportionality of health responses and guide the selection of appropriate policy options [11].

Previous systematic reviews have summarised economic evaluations of pandemic disease intervention strategies [7, 11,12,13,14,15,16,17,18,19,20]. However, only two of these reviews have specifically examined the use of integrated epidemiological and economic models. These reviews focused on low- and middle-income settings [7], and on pandemic influenza [12]. A recent scoping review provided an evaluation of PHSMs against viral pandemics and had a similar focus but did not require the integration of dynamic transmission modelling. In Ref. [14] Therefore, we conducted a systematic review that builds upon the search strategy of Rasmussen and colleagues (2022) [14], narrowing the inclusion criteria by adding search terms for PHSMs and epidemiological models. The objective of our systematic review was to characterise publications that utilised integrated epidemiological and economic models to evaluate PHSMs against pathogens with pandemic potential.

Methods

This systematic review was conducted in accordance with PRISMA guidelines.[21].

Eligibility criteria and search strategy

We searched for studies that used a dynamic transmission model and incorporated an economic evaluation (reporting both cost and health impacts) of PHSMs (Table 1). Eligible studies modelled pathogens with pandemic potential (specifically Ebolavirus, Zika virus, influenza H1N1, influenza H5N1, MERS, SARS, or SARS-CoV-2 viruses).

Table 1 Inclusion and Exclusion Criteria
Full size table

Literature searches were conducted using Embase, PubMed, and Scopus from inception to the date of search, 7 July 2024. We narrowed the search strategy developed by Rasmussen et al.,[14] including search terms for PHSMs and dynamic transmission models (see Appendix 1 for search strategies).

All recovered citations were imported into Covidence, and duplicates were removed. Two reviewers (SR and SH) independently screened titles and abstracts of all citations for eligibility, followed by the full texts. During full-text review, articles were excluded hierarchically by assessing against exclusion criteria. The articles were excluded based on the first exclusion criteria of Table 1 that the record did not meet. Disagreements were resolved by a third reviewer (JS).

Data analysis

We extracted data using a pre-designed template (Appendix 2) that included fields for: authors, year of publication, study location, study population, intervention administrative level, type of PHSM(s) modelled, type(s) of economic evaluation, optimal decision principle (the principle used by the authors to choose the most cost-effective intervention), dynamic model type and features, virus modelled, and funding source. Data were extracted by SR and reviewed by SH and JS. The data was analysed using frequency tables and narrative summaries.

Results

Our search identified 4,048 citations, which was reduced to 2,719 unique citations after duplicates were removed (Fig. 1). Of these, 51 citations met all eligibility criteria.

Fig. 1
figure 1

PRISMA Flow Diagram of Selected Studies

Full size image

Characteristics of the studies

The characteristics of the 51 eligible studies are summarised in Table 2. The studies were published between 2010 and 2024, with most (n = 43, 84%) published from 2020 onwards. Models were typically parameterised using available country-level demographic, cost, and disease transmission data. All continents were represented among eligible studies, with over one-third of the simulated epidemics occurring in North America (n = 19, 37%). Eligible analyses of MERS, Zika, and Influenza H5N1 were not identified. The majority of eligible studies modelled SARS-CoV-2 (n = 42, 82%), with approximately one-quarter of these (n = 10, 24%) modelling a specific SARS-CoV-2 variant, identified as beta [22], delta [23, 24], and omicron [25,26,27,28,29,30,31].

Table 2 Characteristics of included studies
Full size table

Types of interventions

The PHSMs modelled in the included studies were isolation/quarantine, lockdowns, mask use, school closures, social distancing, and testing/screening policies (Tables 2 and 3). These intervention categories are not mutually exclusive, as many studies modelled packages of interventions (n = 28, 55%). Some studies also modelled PHSMs combined with pharmaceutical interventions [23,24,25,26, 33, 36, 59, 62]. A comparison of results between studies proved difficult due to substantial study heterogeneity, including differing outputs, timeframes, intervention specifications, and study populations.

Table 3 Summary of modelled interventions
Full size table

Testing/screening policies were the most frequently modelled intervention and were exclusively considered in SARS-CoV-2 models. Approximately half of these studies (n = 11, 48%) modelled testing/screening policies within a package of interventions (Table 3). This intervention was predominately modelled at a sub-jurisdictional administrative level in specific local settings (n = 16, 57%), rather than being implemented across an entire administrative region. These local contexts comprised, nursing homes[29, 30, 52], university campuses [24, 51, 52, 56, 66], homeless shelters [42], hospitals [62], schools [23, 31], sporting events [64], and workplaces [28].

School closures were typically modelled independently (n = 5, 45%) without incorporating other PHSMs. Notably, all H1N1 studies (n = 7) modelled school closure policies and were published between 2011 and 2016. Among SARS-CoV-2 studies, school closures were modelled in conjunction with other PHSMs [30, 35, 62]. This intervention was most frequently implemented at a state administrative level.

Isolation/quarantine, lockdowns, mask use, and social distancing measures were predominately modelled within a package of interventions in SARS-CoV-2 models. The only included SARS model, published in 2010, investigated quarantine strategies incorporating contact tracing measures at the national level in Hong Kong [40]. Isolation/quarantine and social distancing measures were exclusively modelled within packages of interventions. Face mask use was typically modelled in specific local settings (n = 6, 45%), such as university campuses [51, 66] and hospitals [62, 70, 71]. Studies that modelled lockdowns were primarily implemented at the national level (n = 8, 62%).

Model designs

The characteristics of the epidemiological models are summarised in Table 2. Most included studies (n = 27, 53%) used an adaptation of the classic Susceptible-Exposed-Infectious-Recovered (SEIR)[22, 24, 26, 32, 39, 40, 42,43,44,45, 49, 50, 52, 54,55,56,57, 61, 62, 65,66,67,68,69,70,71,72]. These extended SEIR models can differentiate between various categories of infectiousness, with most included models (n = 37, 73%) explicitly accounting for asymptomatic infections [22,23,24,25,26,27,28,29,30,31,32, 35, 36, 39, 42, 44,45,46, 48, 49, 51, 54,55,56,57,58,59, 61, 62, 64,65,66,67, 69,70,71,72]. Additionally, most models (n = 40, 78%) incorporated a latent compartment to address the delay between infection and onset of infectiousness [22,23,24,25, 27,28,29,30,31,32,33,34,35,36,37,38,39,40, 42,43,44,45,46, 48, 52, 54,55,56,57, 59, 61,62,63, 65,66,67,68, 70,71,72]. Twelve studies used agent-based models (ABM), which more frequently accounted for vaccine introduction (n = 9, 75%) [23, 25, 27,28,29,30,31, 35, 64] and waning natural immunity from previous infection (n = 5, 42%) [25, 28, 29, 31, 35].

Types of economic evaluation

Various methods for economic evaluation were reported in the eligible studies (Table 2), with some studies reporting multiple approaches. Cost-effectiveness analysis (e.g. cost per infection prevented) was the most frequently utilised, followed by cost-utility analysis (e.g. cost per quality-adjusted life year (QALY) gained), and cost–benefit analysis (e.g. monetised health gains using a net monetary benefit approach). Costing perspectives were also reported, with most studies (n = 30, 59%) considering both a health system and societal perspective. Considering both perspectives allowed the investigators to incorporate costs beyond the health sector. Lastly, when determining the most cost-effective intervention, most studies used an incremental cost-effectiveness ratio (ICER) (n = 27, 53%), followed by net monetary benefit (n = 8, 15%), and cost per case averted (n = 5, 10%).

Discussion

Our review highlights the sparsity of integrated epidemiological and economic models used in the evaluation of PHSMs directed against infectious disease pathogens with pandemic potential. We identified 51 studies with disparate scopes. Most studies were published from 2020 onwards and modelled SARS-CoV-2 infection, indicating growth in the need for or interest in these interdisciplinary models during the pandemic era. However, given the importance of the subject matter and the volume of modelling conducted during the COVID-19 pandemic, relatively few published studies were found that addressed both health and economic impacts within a dynamic epidemiological model.

The COVID-19 pandemic has emphasised that pandemics generate both health and socioeconomic crises, requiring intersectoral collaboration for optimal policy implementation [3, 73]. Integrated epidemiological and economic models can be used as a tool to transparently assist in weighing socioeconomic trade-offs and support evidence-informed policy making [8, 74]. These interdisciplinary models can be used to appraise and compare multiple PHSMs. While aimed at reducing the risk and scale of transmission, the implementation of PHSMs frequently has unintended negative social and economic consequences [3]. Modelling multiple interventions, as done by approximately half of included studies, enables ranking of interventions against one another, or assessment of the effectiveness of the interventions as a ‘package’ against a comparator. In the context of a pandemic, wherein multiple PHSMs are implemented concurrently, this approach is likely to be more realistic [75]. The consideration of multiple interventions is therefore important for decision making in a pandemic, as society seeks to minimise health loss while avoiding unintended negative social and economic consequences.

Regarding health economic methods, most of the identified studies adopted a health system and societal costing perspective. A societal perspective estimates the broader costs to society, such as productivity loss [76]. The utilisation of both perspectives is valuable and complementary, as pandemics have societal and health system economic impacts. Moreover, PHSMs have broader macroeconomic implications than traditional healthcare treatments or pharmaceutical interventions [1].

A common limitation of the included studies was a lack of transparent reporting. Integrated epidemiological and economic models are inherently complex analyses; however, across the studies, the approach to reporting was highly variable. While reporting standards for this type of modelling remain lacking, documentation could have been enhanced by following existing guidelines for economic evaluation and dynamic transmission modelling [76, 77]. Nevertheless, the development of specific guidelines for integrated epidemiological and economic modelling is preferable. Moreover, only a few studies made their model code publicly available. Providing open access to code improves transparency and reproducibility of research, benefiting both scientific progress and clarity of model methodologies, and a rapid response in the event of a newly emerging pandemic.

Our review expands upon existing systematic reviews in the field of integrated epidemiological and economic modelling of pandemic interventions. In contrast to previous reviews, our search strategy imposed no geographical restrictions and encompassed multiple pathogens of pandemic potential while focusing on PHSMs. Additionally, we examined the administrative level of the modelled PHSMs and investigated whether packages of interventions were considered. Limitations of our review include that our search was confined to key databases that we deemed more likely to identify relevant studies. It is possible that other integrated epidemiological and economic models exist that have not been published. Further, non-English language studies were excluded by the search strategy, potentially omitting relevant studies.

The findings of our review can inform future work comparing and evaluating integrated epidemiological and economic model outputs. This may include a critical appraisal of specific PHSMs identified in this wider review, such as school closures or testing and screening policies, to enhance the understanding of their cost-effectiveness. An additional avenue of investigation may be to evaluate the timing of PHSM implementation and at which pandemic stage specific interventions are most cost-effective. Historical comparative SARS-CoV-2 data may be used to support this work. Furthermore, the development of an integrated epidemiological and economic modelling framework could facilitate the establishment of standardised methodologies at an international level to generate more comparable outputs and potentially expedite model construction when required.

Conclusion

This systematic review demonstrated that a limited number of dynamic modelling studies of PHSMs have incorporated economic evaluation, and those identified varied in scope. In light of the COVID-19 pandemic, which necessitated rapid policy responses with substantial cost implications across multiple sectors of society, there is a need to expand the scope of this research going forward.

Data availability statement

All data analysed during this study are included in this published article and its supplementary information files.

Abbreviations

ICER:

Incremental cost-effectiveness ratio

NPI:

Non-pharmaceutical intervention

PHSM:

Public health and social measure

SEIR model:

Susceptible-Exposed-Infectious-Recovered model

References

  1. Kretzschmar ME, Ashby B, Fearon E, Overton CE, Panovska-Griffiths J, Pellis L, et al. Challenges for modelling interventions for future pandemics. Epidemics. 2022;38: 100546.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Raboisson D, Lhermie G. Living with COVID-19: a systemic and multi-criteria approach to enact evidence-based health policy. Front Public Health. 2020;8:294.

    Article  PubMed  PubMed Central  Google Scholar 

  3. World Health Organization, Organisation for Economic Co-operation and Development, The World Bank. Strengthening pandemic preparedness and response through integrated modelling. Geneva; 2024.

  4. Pollett S, Johansson M, Biggerstaff M, Morton LC, Bazaco SL, Brett Major DM, et al. Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action. Epidemics. 2020;33: 100400.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Squazzoni F, Polhill J, Edmonds B, Ahrweiler P, Antosz P, Scholz G, et al. Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action. Journal of Artificial Societies and Social Simulation. 2020;23.

  6. Anonychuk A, Krahn M. Health Economic and Infectious Disease Modelling. Pharmacoeconomics. 2011;29(5):367–9.

    Article  PubMed  Google Scholar 

  7. Drake TL, Devine A, Yeung S, Day NP, White LJ, Lubell Y. Dynamic Transmission Economic Evaluation of Infectious Disease Interventions in Low- and Middle-Income Countries: A Systematic Literature Review. Health Econ. 2016;25 Suppl 1(Suppl Suppl 1):124–39.

  8. Jit M, Brisson M. Modelling the epidemiology of infectious diseases for decision analysis: a primer. Pharmacoeconomics. 2011;29(5):371–86.

    Article  PubMed  Google Scholar 

  9. Trotter CL, Edmunds WJ. Reassessing the cost-effectiveness of meningococcal serogroup C conjugate (MCC) vaccines using a transmission dynamic model. Med Decis Making. 2006;26(1):38–47.

    Article  PubMed  Google Scholar 

  10. Eisman AB, Kilbourne AM, Dopp AR, Saldana L, Eisenberg D. Economic evaluation in implementation science: making the business case for implementation strategies. Psychiatry Res. 2020;283: 112433.

    Article  PubMed  Google Scholar 

  11. Juneau C-E, Pueyo T, Bell M, Gee G, Collazzo P, Potvin L. Lessons from past pandemics: a systematic review of evidence-based, cost-effective interventions to suppress COVID-19. Syst Rev. 2022;11(1):90.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Lugnér AK, Postma MJ. Mitigation of pandemic influenza: review of cost–effectiveness studies. Expert Rev Pharmacoecon Outcomes Res. 2009;9(6):547–58.

    Article  PubMed  Google Scholar 

  13. Vandepitte S, Alleman T, Nopens I, Baetens J, Coenen S, De Smedt D. Cost-effectiveness of COVID-19 policy measures: a systematic review. Value Health. 2021;24(11):1551–69.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Rasmussen MK, Kronborg C, Fasterholdt I, Kidholm K. Economic evaluations of interventions against viral pandemics: a scoping review. Public Health. 2022;208:72–9.

    Article  CAS  PubMed  Google Scholar 

  15. Dawoud DM, Soliman KY. Cost-effectiveness of antiviral treatments for pandemics and outbreaks of respiratory illnesses, including COVID-19: a systematic review of published economic evaluations. Value Health. 2020;23(11):1409–22.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Rezapour A, Souresrafil A, Peighambari MM, Heidarali M, Tashakori-Miyanroudi M. Economic evaluation of programs against COVID-19: a systematic review. Int J Surg. 2021;85:10–8.

    Article  PubMed  Google Scholar 

  17. Pérez Velasco R, Praditsitthikorn N, Wichmann K, Mohara A, Kotirum S, Tantivess S, et al. Systematic review of economic evaluations of preparedness strategies and interventions against influenza pandemics. PLoS ONE. 2012;7(2): e30333.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Podolsky MI, Present I, Neumann PJ, Kim DD. A systematic review of economic evaluations of COVID-19 interventions: considerations of non-health impacts and distributional issues. Value in Health. 2022;25(8):1298–306.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Vardavas C, Nikitara K, Zisis K, Athanasakis K, Phalkey R, Leonardi-Bee J, et al. Cost-effectiveness of emergency preparedness measures in response to infectious respiratory disease outbreaks: a systematic review and econometric analysis. BMJ Open. 2021;11(4): e045113.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Zhou L, Yan W, Li S, Yang H, Zhang X, Lu W, et al. Cost-effectiveness of interventions for the prevention and control of COVID-19: Systematic review of 85 modelling studies. J Glob Health. 2022;12:05022.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372: n71.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Ferguson EA, Brum E, Chowdhury A, Chowdhury S, Kundegorski M, Mahmud AS, et al. Modelling how face masks and symptoms-based quarantine synergistically and cost-effectively reduce SARS-CoV-2 transmission in Bangladesh. Epidemics. 2022;40 (no pagination).

  23. Bilinski A, Ciaranello A, Fitzpatrick MC, Giardina J, Shah M, Salomon JA, et al. Estimated transmission outcomes and costs of SARS-CoV-2 diagnostic testing, screening, and surveillance strategies among a simulated population of primary school students. JAMA Pediatr. 2022;176(7):679–89.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Johnson KE, Pasco R, Woody S, Lachmann M, Johnson-Leon M, Bhavnani D, et al. Optimizing COVID-19 testing strategies on college campuses: evaluation of the health and economic costs. PLoS Comput Biol. 2023;19(12): e1011715.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Szanyi J, Wilson T, Howe S, Zeng J, Andrabi H, Blakely T. An integrated epidemiologic and economic model to assess optimal COVID-19 pandemic policy. medRxiv. 2022;02.

  26. Tan C, Luo X, Zhou Z, Zeng X, Wan X, Yi L, et al. Dynamic zero-COVID strategy in controlling COVID-19 in Shanghai, China: a cost-effectiveness analysis. J Infect Public Health. 2023;16(6):893–900.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Li H, Zhang H. Cost-effectiveness analysis of COVID-19 screening strategy under China’s dynamic zero-case policy. Front Public Health. 2023;11:1099116.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Vilches TN, Rafferty E, Wells CR, Galvani AP, Moghadas SM. Economic evaluation of COVID-19 rapid antigen screening programs in the workplace. BMC Med. 2022;20(1):452.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Dong S, Jutkowitz E, Giardina J, Bilinski A. Screening Strategies to Reduce COVID-19 Mortality in Nursing Homes. JAMA Health Forum. 2024;5(4): e240688.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Bartsch SM, Weatherwax C, Martinez MF, Chin KL, Wasserman MR, Singh RD, et al. Cost-effectiveness of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) testing and isolation strategies in nursing homes. Infect Control Hosp Epidemiol. 2024;45(6):754–61.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Chevalier JM, Han AX, Hansen MA, Klock E, Pandithakoralage H, Ockhuisen T, et al. Impact and cost-effectiveness of SARS-CoV-2 self-testing strategies in schools: a multicountry modelling analysis. BMJ Open. 2024;14(2): e078674.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Kellerborg K, Brouwer W, Van Baal P. Costs and benefits of early response in the Ebola virus disease outbreak in Sierra Leone. Cost Effectiveness and Resource Allocation. 2020;18(1) (no pagination).

  33. Araz OM, Damien P, Paltiel DA, Burke S, van de Geijn B, Galvani A, et al. Simulating school closure policies for cost effective pandemic decision making. BMC Public Health. 2012;12:449.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Brown ST, Tai JH, Bailey RR, Cooley PC, Wheaton WD, Potter MA, et al. Would school closure for the 2009 H1N1 influenza epidemic have been worth the cost?: a computational simulation of Pennsylvania. BMC Public Health. 2011;11:353.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Cao J, Yang F, Geng Z, Shi X, editors. Evaluating the impacts of vaccination, antiviral treatment and school closure on H1N1 influenza epidemic2015: Institute of Electrical and Electronics Engineers Inc.

  36. Halder N, Kelso JK, Milne GJ. Cost-effective strategies for mitigating a future influenza pandemic with H1N1 2009 characteristics. PLoS ONE. 2011;6(7) (no pagination).

  37. Nishiura H, Ejima K, Mizumoto K, Nakaoka S, Inaba H, Imoto S, et al. Cost-effective length and timing of school closure during an influenza pandemic depend on the severity. Theor Biol Med Model. 2014;11:5.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Wong ZSY, Goldsman D, Tsui KL. Economic evaluation of individual school closure strategies: The Hong Kong 2009 H1N1 pandemic. PLoS ONE. 2016;11(1) (no pagination).

  39. Xue Y, Kristiansen IS, de Blasio BF. Dynamic modelling of costs and health consequences of school closure during an influenza pandemic. BMC Public Health. 2012;12:962.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Mubayi A, Zaleta CK, Martcheva M, Castillo-Chávez C. A cost-based comparison of quarantine strategies for new emerging diseases. Math Biosci Eng. 2010;7(3):687–717.

    Article  PubMed  Google Scholar 

  41. Atherly A, Van Den Broek-Altenburg E, Sils B, Ciarametaro M, Dubois B. EE408 the effect of medical innovation on the cost-effectiveness of COVID 19-related policies in the United States using a sir model. Value in Health. 2022;25(7 Supplement):S415.

    Article  PubMed Central  Google Scholar 

  42. Baggett TP, Scott JA, Le MH, Shebl FM, Panella C, Losina E, et al. Clinical Outcomes, Costs, and Cost-effectiveness of Strategies for Adults Experiencing Sheltered Homelessness during the COVID-19 Pandemic. JAMA Network Open. 2020;(no pagination).

  43. Barnett-Howell Z, Watson OJ, Mobarak AM. The benefits and costs of social distancing in high- And low-income countries. Trans R Soc Trop Med Hyg. 2021;115(7):807–19.

    Article  CAS  PubMed  Google Scholar 

  44. Bartsch SM, O’Shea KJ, Chin KL, Strych U, Ferguson MC, Bottazzi ME, et al. Maintaining face mask use before and after achieving different COVID-19 vaccination coverage levels: a modelling study. Lancet Public Health. 2022;7(4):e356–65.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Bertsimas D, Li ML, Soni S. THEMIS: A Framework for Cost-Benefit Analysis of COVID-19 Non-Pharmaceutical Interventions. medRxiv. 2022;10.

  46. Blakely T, Thompson J, Bablani L, Andersen P, Ait Ouakrim D, Carvalho N, et al. Association of simulated COVID-19 policy responses for social restrictions and lockdowns with health-adjusted life-years and costs in Victoria, Australia. JAMA Health Forum. 2021;2(7): e211749.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Cook DC, Fraser RW, McKirdy SJ. A benefit-cost analysis of different response scenarios to COVID-19: A case study. Health Science Reports. 2021;4(2) (no pagination).

  48. Du Z, Pandey A, Bai Y, Fitzpatrick MC, Chinazzi M, Pastore y Piontti A, et al. Comparative cost-effectiveness of SARS-CoV-2 testing strategies in the USA: a modelling study. The Lancet Public Health. 2021;6(3):e184-e91.

  49. Jiang Y, Cai D, Chen D, Jiang S. The cost-effectiveness of conducting three versus two reverse transcription-polymerase chain reaction tests for diagnosing and discharging people with COVID-19: evidence from the epidemic in Wuhan, China. BMJ Glob Health. 2020;5(7).

  50. Kompas T, Grafton RQ, Che TN, Chu L, Camac J. Health and economic costs of early and delayed suppression and the unmitigated spread of COVID-19: The case of Australia. PLoS ONE. 2021;16(6 June) (no pagination).

  51. Losina E, Leifer V, Millham L, Panella C, Hyle EP, Mohareb AM, et al. College campuses and COVID-19 mitigation: Clinical and economic value. Ann Int Med. 2021; 174(4): 472–83.

  52. Love J, Wimmer MT, Toth DJA, Chandran A, Makhija D, Cooper CK, et al. Comparison of antigen- And RT-PCR-based testing strategies for detection of SARS-CoV-2 in two high-exposure settings. PLoS ONE. 2021;16(9 September) (no pagination).

  53. Miles DK, Heald AH, Stedman M. How fast should social restrictions be eased in England as COVID-19 vaccinations are rolled out? International Journal of Clinical Practice. 2021;75(7) (no pagination).

  54. Neilan AM, Losina E, Bangs AC, Flanagan C, Panella C, Eskibozkurt GE, et al. Clinical Impact, Costs, and Cost-effectiveness of Expanded Severe Acute Respiratory Syndrome Coronavirus 2 Testing in Massachusetts. Clinical Infectious Diseases. 2021;73(9):E2908-E17.

  55. Paltiel AD, Zheng A, Sax PE. Clinical and economic effects of widespread rapid testing to decrease SARS-CoV-2 transmission. Ann Intern Med. 2021;174(6):803–10.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Paltiel AD, Zheng A, Walensky RP. Assessment of SARS-CoV-2 screening strategies to permit the safe reopening of college campuses in the United States. JAMA Netw Open. 2020. https://doi.org/10.1001/jamanetworkopen.2020.16818.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Reddy KP, Shebl FM, Foote JHA, Harling G, Scott JA, Panella C, et al. Cost-effectiveness of public health strategies for COVID-19 epidemic control in South Africa: a microsimulation modelling study. The Lancet Global Health. 2021;9(2):e120-e9

  58. Robles-Zurita J. Reducing the basic reproduction number of COVID-19: a model simulation focused on QALYs, hospitalisation, productivity costs and optimal (soft) lockdown. Eur J Health Econ. 2022. https://doi.org/10.1007/s10198-022-01500-7.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Sandmann FG, Davies NG, Vassall A, Edmunds WJ, Jit M, Sun FY, et al. The potential health and economic value of SARS-CoV-2 vaccination alongside physical distancing in the UK: a transmission model-based future scenario analysis and economic evaluation. Lancet Infect Dis. 2021;21(7):962–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Shimul SN, Alradie-Mohamed A, Kabir R, Al-Mohaimeed A, Mahmud I. Effect of easing lockdown and restriction measures on COVID-19 epidemic projection: a case study of Saudi Arabia. PLoS ONE. 2021;16:e0256958.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Shlomai A, Leshno A, Sklan EH, Leshno M. Modeling social distancing strategies to prevent SARS-CoV-2 spread in Israel: a cost-effectiveness analysis. Value in Health. 2021;24(5):607–14.

    Article  PubMed  Google Scholar 

  62. Smith DRM, Duval A, Zahar JR, Hendrickx N, Jean K, Jijón S, et al. Rapid antigen testing as a reactive response to surges in nosocomial SARS-CoV-2 outbreak risk. Nat Commun. 2022. https://doi.org/10.1038/s41467-021-27845-w.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Wang Q, Shi N, Huang J, Yang L, Cui T, Ai J, et al. Cost-effectiveness of public health measures to control COVID-19 in China: a microsimulation modeling study. Front Public Health. 2021;9: 726690.

    Article  PubMed  Google Scholar 

  64. Wang X, Cai Y, Zhang B, Zhang X, Wang L, Yan X, et al. Cost-effectiveness analysis on COVID-19 surveillance strategy of large-scale sports competition. Infect Dis Pov. 2022. https://doi.org/10.1186/s40249-022-00955-3.

    Article  Google Scholar 

  65. Yu J, Huang Y, Shen ZJ. Optimizing and evaluating PCR-based pooled screening during COVID-19 pandemics. Sci Rep. 2021;11(1):21460.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Zafari Z, Goldman L, Kovrizhkin K, Muennig PA. The cost-effectiveness of common strategies for the prevention of transmission of SARSCoV- 2 in universities. PLoS ONE. 2021;16:e0257806.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Zhang AZ, Enns EA. Optimal timing and effectiveness of COVID-19 outbreak responses in China: a modelling study. BMC Public Health. 2022;22(1):679.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Zhao J, Jin H, Li X, Jia J, Zhang C, Zhao H, et al. Disease burden attributable to the first wave of COVID-19 in China and the effect of timing on the cost-effectiveness of movement restriction policies. Value in Health. 2021;24(5):615–24.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Boloori A, Saghafian S. Health and economic impacts of lockdown policies in the early stage of COVID-19 in the United States. Serv Sci. 2023;15(3):188–211.

    Article  Google Scholar 

  70. Ozcelik E, Lerouge A, Cecchini M, Cassini A, Allegranzi B. Estimating the return on investment of selected infection prevention and control interventions in healthcare settings for preparing against novel respiratory viruses: modelling the experience from SARS-CoV-2 among health workers. EClinicalMedicine. 2024;68: 102388.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Sharma M, Sra H, Painter C, Pan-Ngum W, Luangasanatip N, Chauhan A, et al. Cost-effectiveness analysis of surgical masks, N95 masks compared to wearing no mask for the prevention of COVID-19 among health care workers: evidence from the public health care setting in India. PLoS ONE. 2024;19(5): e0299309.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Wang X, Pei S, Wang L, La B, Zhao M, Zhang X, et al. Investigation on the possibility of dynamic COVID-Zero strategy in China: a population-based transmission model analysis and economic evaluation. BMJ Open. 2023;13(8): e067294.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Bonnet G, Pearson CAB, Torres-Rueda S, Ruiz F, Lines J, Jit M, et al. A scoping review and taxonomy of epidemiological-macroeconomic models of COVID-19. Value Health. 2024;27(1):104–16.

    Article  PubMed  Google Scholar 

  74. Berger L, Berger N, Bosetti V, Gilboa I, Hansen LP, Jarvis C, et al. Rational policymaking during a pandemic. Proc Natl Acad Sci. 2021;118(4): e2012704118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. World Health Organization. Non-pharmaceutical public health measures for mitigating the risk and impact of epidemic and pandemic influenza 2019.

  76. Husereau D, Drummond M, Augustovski F, de Bekker-Grob E, Briggs AH, Carswell C, et al. Consolidated health economic evaluation reporting standards (CHEERS) 2022 explanation and elaboration: a report of the ISPOR CHEERS II good practices task force. Value in Health. 2022;25(1):10–31.

    Article  PubMed  Google Scholar 

  77. Pitman R, Fisman D, Zaric GS, Postma M, Kretzschmar M, Edmunds J, et al. Dynamic transmission modeling: a report of the ISPOR-SMDM modeling good research practices task force working group-5. Med Decis Making. 2012;32(5):712–21.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This research was supported by an Australian Government Research Training Program (RTP) Scholarship.

Author information

Authors and Affiliations

  1. Population Interventions Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia

    Shania Rossiter, Samantha Howe, Joshua Szanyi, Tim Wilson & Tony Blakely

  2. Epidemiological Modelling Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia

    James M. Trauer

Authors
  1. Shania Rossiter
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Samantha Howe
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Joshua Szanyi
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. James M. Trauer
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Tim Wilson
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Tony Blakely
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

SR, JT, TW and TB contributed to conceptualisation, methodology, and writing – original draft, review and editing. SR, SH and JS contributed to screening and data extraction. SH and JS also contributed to writing – review and editing. JT, TW and TB were responsible for supervision. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Shania Rossiter.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Appendices

Appendix 1

Search strategies

Embase search details

Searched on 07/07/2024—2064 results.

  1. 1

    Ebola hemorrhagic fever/

  2. 2

    Zika fever/

  3. 3

    “influenza a virus (h5n1)”/

  4. 4

    Middle East respiratory syndrome/

  5. 5

    severe acute respiratory syndrome/

  6. 6

    “influenza a virus (h1n1)”/

  7. 7

    (ebola or zika or H5N1 or mers or sars or H1N1).ti,ab.

  8. 8

    (((pneumonia or covid* or coronavirus* or corona virus* or ncov* or 2019-ncov or sars*) adj5 Wuhan) or (SARS coronavirus* and Wuhan)).mp.

  9. 9

    (2019-ncov or ncov19 or ncov-19 or 2019-novel CoV or sars-cov2 or sars-cov-2 or sarscov2 or sarscov-2 or Sars-coronavirus2 or Sars-coronavirus-2 or coronavirus-19 or covid19 or covid-19 or covid 2019 or "novel coronavirus" or "new coronavirus" or "nouveau coronavirus" or CoV or nCoV or covid or covid19 or covid-19 or "coronavirus 2" or ((novel or new or nouveau) adj1 (CoV or nCoV or covid or coronavirus* or "corona virus" or Pandemi*2)) or ((covid or covid19 or covid-19) and pandemic*2)).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word, candidate term word]

  10. 10

    (COVID-19 or "severe acute respiratory syndrome 2" or 2019 pandemic or 2020 pandemic).mp. or SARS-like coronavirus*.ti,ab,ct.

  11. 11

    or/1–10.

  12. 12

    exp economic evaluation/

  13. 13

    economic evaluation.ti,ab.

  14. 14

    (cost* adj2 (effective* or utilit* or benefit* or minimi* or illness*)).ti,ab.

  15. 15

    or/12–14.

  16. 16

    (Simulation or model*).mp.

  17. 17

    susceptible exposed infectious recovered model/ or model/ or mathematical model/ or susceptible infected recovered model/ or population model/ or theoretical model/or statistical model/ or stochastic model/ or epidemiological model/ or susceptible infected susceptible model/

  18. 18

    or/17–18.

  19. 19

    11 and 15 and 18.

  20. 20

    limit 16 to english language.

Pubmed search details

Searched on 07/07/2024 – 473 results

(((((((("SARS Virus"[Mesh]) AND "Middle East Respiratory Syndrome Coronavirus"[Mesh]) AND "Ebolavirus"[Mesh]) AND "Zika Virus"[Mesh]) OR "Influenza A Virus, H1N1 Subtype"[Mesh]) OR "Influenza A Virus, H5N1 Subtype"[Mesh]) OR (SARS[Title/Abstract] OR MERS[Title/Abstract] OR Ebola[Title/Abstract] OR Zika[Title/Abstract] OR H1N1[Title/Abstract] OR H5N1[Title/Abstract])).

OR


(((“coronavirus”[MeSH Terms] OR “severe acute respiratory syndrome coronavirus 2”[Supplementary Concept] OR “coronavirus infections”[MeSH Terms] OR (“coronavirus*”[Title/Abstract] OR “coronovirus*”[Title/Abstract] OR “coronavirinae*”[Title/Abstract] OR “wuhan*”[Title/Abstract] OR “hubei*”[Title/Abstract] OR “huaian”[Title/Abstract] OR “2019 ncov”[Title/Abstract] OR “2019nCoV”[Title/Abstract] OR “nCoV2019”[Title/Abstract] OR “nCoV 2019”[Title/Abstract] OR “covid 19”[Title/Abstract] OR “COVID19”[Title/Abstract] OR “covid 19”[Title/Abstract] OR “HCoV 19”[Title/Abstract] OR “HCoV19”[Title/Abstract] OR “CoV”[Title/Abstract] OR “2019 novel*”[Title/Abstract] OR “ncov”[Title/Abstract] OR “n cov”[Title/Abstract] OR “SARS CoV 2”[Title/Abstract] OR “SARSCoV 2”[Title/Abstract] OR “SARSCoV2”[Title/Abstract] OR “SARS CoV2”[Title/Abstract] OR “SARSCov19”[Title/Abstract] OR “SARS Cov19”[Title/Abstract] OR “SARS Cov 19”[Title/Abstract] OR “novo”[Title/Abstract] OR “ncorona*”[Title/Abstract] OR “severe acute respiratory syndrome coronavirus 2”[Title/Abstract].

OR

“SARS2”[Title/Abstract] OR “2019 ncov”[Title/Abstract] OR “severe acute respiratory syndrome”[Title/Abstract] OR "SARS"[Title/Abstract])))))).

AND

(((“Cost–Benefit Analysis”[MeSH Terms] OR “economic evaluation”[Title/Abstract] OR “cost effectiv*”[Title/Abstract] OR “cost utility*”[Title/Abstract] OR “cost benefit*”[Title/Abstract] OR “cost minimi*”[Title/Abstract] OR “cost illness*”[Title/Abstract]))).

AND

(((“simulation*”[Title/Abstract] OR “model*”[MeSH Terms] OR “susceptible exposed infectious recovered model*”[Title/Abstract] OR “mathematical model*”[Title/Abstract] OR “susceptible infected recovered model*”[Title/Abstract] OR “population model*”[Title/Abstract] OR “theoretical model*”[Title/Abstract] OR “statistical model*”[Title/Abstract] OR “stochastic model*”[Title/Abstract] OR “epidemiological model*” [Title/Abstract] OR “susceptible infected susceptible model*”[Title/Abstract]))).

Filters: english

Scopus search details

Adjusted search on 07/07/2024—1511 results

(TITLE-ABS-KEY (coronavirus OR "Middle East respiratory syndrome" OR "Severe acute respiratory syndrome" OR "Porcine epidemic diarrhea virus" OR "Feline infectious peritonitis virus" OR "Murine hepatitis virus" OR "Avian infectious bronchitis virus") OR TITLE-ABS-KEY (ebola OR zika OR h1n1 OR h5n1)).

AND

(TITLE-ABS-KEY (cost* PRE/2 effectiv*) OR TITLE-ABS-KEY (cost* PRE/2 utilit*) OR TITLE-ABS-KEY (cost* PRE/2 benefit*) OR TITLE-ABS-KEY (cost* PRE/2 illness*) OR TITLE-ABS-KEY (cost* PRE/2 analys*) OR TITLE-ABS-KEY (economic* PRE/2 evaluation*)).

AND

(TITLE-ABS-KEY (simulation OR model) OR TITLE-ABS-KEY (“Susceptible exposed infectious recovered model” OR “Susceptible infected recovered model” OR “Susceptible infected susceptible model”) OR TITLE-ABS-KEY (math* PRE/2 model*) OR TITLE-ABS-KEY (populat* PRE/2 model*) OR TITLE-ABS-KEY (theor* PRE/2 model*) OR TITLE-ABS-KEY (stat* PRE/2 model*) OR TITLE-ABS-KEY (stochastic* PRE/2 model*) OR TITLE-ABS-KEY (epidemiolog* PRE/2 model*)).

AND

(LIMIT-TO(LANGUAGE, "English")).

Appendix 2

Data extraction tool

The data extraction tool was used to assist in extracting relevant data from eligible studies. All reviewers used this tool.

  1. 1.

    Identifier and article information

    1. 1.1

      Initials of person extracting the data.

    2. 1.2

      Initials of second reviewer.

    3. 1.3

      Date of data extraction.

    4. 1.4

      Article identifier.

    5. 1.5

      Article title.

    6. 1.6

      First author’s last name.

    7. 1.7

      Year of publication.

  1. 2.

    Study design information

    1. 2.1

      Aim of the study.

    2. 2.2

      Study location.

    3. 2.3

      Study population.

    4. 2.4

      Administrative level of intervention.

    5. 2.5

      Intervention/s of interest.

    6. 2.6

      Were packages of interventions used?

    7. 2.7

      Comparator for intervention.

  1. 3.

    Economic evaluation

  2. 4.

    How was the optimal intervention chosen?

  3. 5.

    Was cost-effectiveness analysis used?

  4. 6.

    Was cost-utility analysis used?

  5. 7.

    Was cost–benefit analysis used?

  6. 8.

    Was cost-minimisation analysis used?

  7. 9.

    Was another type of economic evaluation used?

  8. 10.

    Time horizon.

  9. 11.

    Was discounting used?

  10. 12.

    Which perspective/s have been used when measuring costs?

  11. 13.

    Was a sensitivity analysis used?

  1. 4.

    Dynamic transmission model

  2. 5.

    Model type.

  3. 6.

    Infection.

  4. 7.

    Does the model have a latent compartment?

  5. 8.

    Does the model have an asymptomatic compartment?

  6. 9.

    Does the model allow for reinfections?

  7. 10.

    Does the model account for changes in the virus?

  8. 11.

    For SARS-CoV-2, is the variant being modelled specified?

  9. 12.

    Does the model account for vaccinated population/introduction of vaccines?

  10. 13.

    Does the model account for vaccine waning?

  11. 14.

    Does the model account for natural immunity?

  1. 5.

    Conclusions

    1. 5.1

      Key conclusions of study

  1. 6.

    Other

    1. 6.1

      Funding source.

    2. 6.2

      Is the model code provided?

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rossiter, S., Howe, S., Szanyi, J. et al. The role of economic evaluation in modelling public health and social measures for pandemic policy: a systematic review. Cost Eff Resour Alloc 22, 77 (2024). https://doi.org/10.1186/s12962-024-00585-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12962-024-00585-6

Keywords

Cost Effectiveness and Resource Allocation

ISSN: 1478-7547

Contact us