Obtaining market approval and reimbursement are necessary but not sufficient conditions for the implementation of new vaccines in high-income countries to maximize their long-term preventative value. Comprehensive pre-launch and launch-phase economic evaluations of the disease and the vaccine are necessary to support long-term public health improvement by the vaccination program. This review highlights the construction of these evaluations conceived as a plan, methods, and a tool. They can be generated by different stakeholders (e.g., payers, producers, target groups) interested in the value success of vaccination. A Vaccine Launching Value Project (VLVP) has been developed based on the experience gained from helping to launch 10 new vaccines worldwide over 15 years (2005-2020). It comprises information on the following: (1) identification of new vaccines that should require a VLVP approach; (2) country-specific characteristics of healthcare; (3) methods to assess economic values for specific stakeholders; (4) identification of the money flow in managing the disease and infection spread; and (5) optimal implementation strategies at the initiation of new vaccination programs. The benefits of applying the VLVP are illustrated using rotavirus vaccination as an example. The VLVP program starts with the development of a Broad Country Linked Inventory (Brocoli) Plan that interconnects eight baskets of information specifying a framework of activities. This is followed by the Cauliflower and Artichoke Methods to assess the vaccine value for additional key stakeholders (e.g., employers, hospital managers, working mothers, the Ministry of Finance) and the money flow amongst the payers (who pays what to whom, when, for what, and how). The evaluation process finishes with the Total Management Tool (Tomato) to identify the optimal implementation conditions at the start of a new vaccination program necessary to obtain the best long-term value for the stakeholders selected. The critical interconnections between these information blocks are discussed. This improves the positioning of a new vaccine by articulating its total economic value within a societal and public health environment over time, outside the conventional Health Technology Assessment box. The Tomato Tool emerges as the most pivotal component of the VLVP. It provides the best assurance of long-term economic value with strong sustainability support.

Vaccination has resulted in substantial public health benefits for human populations worldwide since it was first introduced more than a century ago. This article presents an overview of the history of vaccine development, its implementation, and price setting, the latter mainly from a developed world perspective. It considers potential issues and challenges. Over time, vaccine development and production has evolved to a market-driven approach, conducted largely by private commercial entities. The complex processes of identifying potential vaccine targets and developing and producing vaccines at scale have now become more efficient. However, vaccine pricing is an emerging concern. The elements that maximize the overall health benefit of vaccination include high volume, high coverage, and rapid initial implementation to achieve the high coverage with the vaccine as quickly as possible. It therefore requires substantial initial investment. Consequently, the price set for the vaccine should be reasonable to avoid limiting the coverage given the available budget. Suboptimal coverage leads to suboptimal benefit if herd protection is not fully achieved. This may disappoint health authorities and may result in program discontinuation. Conventional cost-effectiveness analysis is therefore not ideally suited to vaccine price setting, as it is based on the concept of ‘more for more’, i.e., higher health gain achieved at a higher reimbursement cost that does not account for limited budgets. Constrained optimization (CO) combines value assessment with constrained budget allocation into one analysis method and may therefore be the better option for vaccine pricing.

New vaccination programs measure economic success through cost-effectiveness analysis (CEA) based on an outcome evaluated over a certain time frame. The reimbursement price of the newly approved vaccine is then often reliant on a simulated ideal effect projection because of limited long-term data availability. This optimal cost-effectiveness result is later rarely adjusted to the observed effect measurements, barring instances of market competition-induced price erosion through the tender process. However, comprehensive and systematic monitoring of the vaccine effect (VE) for the evaluation of the real long-term economic success of vaccination is critical. It informs expectations about vaccine performance with success timelines for the investment. Here, an example is provided by a 15-year assessment of the rotavirus vaccination program in Belgium (RotaBIS study spanning 2005 to 2019 across 11 hospitals). The vaccination program started in late 2006 and yielded sub-optimal outcomes. Long-term VE surveillance data provided insights into the infection dynamics, disease progression, and vaccine performance. The presented analysis introduces novel conceptual frameworks and methodologies about the long-term economic success of vaccination programs. The CEA evaluates the initial target vaccination population, considering vaccine effectiveness compared with a historical unvaccinated group. Cost-impact analysis (CIA) covers a longer period and considers the whole vaccinated and unvaccinated population in which the vaccine has direct and indirect effects. The economic success index ratio of CIA over CEA outcomes evaluates long-term vaccination performance. Good performance is close to the optimal result, with an index value ≤1, combined with a low CEA. This measurement is a valuable aid for new vaccine introductions. It supports the establishment of robust monitoring protocols over time.

Healthcare is a huge business sector in many countries, focusing on the social function of delivering quality health when people develop illness. The system is essentially financed by public funds based on the solidarity principle. With a large financial outlay, the sector must use economic evaluation methods to achieve better efficiency. The objective of our study was to evaluate and to understand how health economics is used today, taking Belgium as an example of a high-income country. The evaluation started with a historical view of healthcare development and ended with potential projections for its future. A literature review focused on country-specific evaluation reports to identify the health economic methods used, with a search for potential gaps. The first results indicated that Belgium in 2021 devoted 11% of its GDP, 17% of its total tax revenue, and 30% of the national Social Security Fund to health-related activities, totalizing EUR 55.5 billion spending. The main health economic method used was a cost-effectiveness analysis linked to budget impact, assigning reimbursable monetary values to new products becoming available. However, these evaluation methods only impacted at most 20% of the money circulating in healthcare. The remaining 80% was subject to financial regulations (70%) and budgeting (10%), which could use many other techniques of an economic analysis. The evaluation indicated two potentially important changes in health economic use in Belgium. One was an increased focus on budgeting with plans, time frames, and quantified treatment objectives on specific disease problems. Economic models with simulations are very supportive in those settings. The other was the application of constrained optimization methods, which may become the new standard of practice when switching from fee-for-service to pay-per-performance as promoted by value-based healthcare and value-based health management. This economic refocusing to a more constrained approach may help to keep the healthcare system sustainable and affordable in the face of the many future challenges including ageing, climate change, migration, pandemics, logistical limitations, and financial instability.

Background: Arithmetic average values about disease burden across aging adults are often used in the absence of having reliable access to real life data. Those values however assume group homogeneity in characteristics such as age, sex, disease incidence rates, or costs. The question arises about how much the overall outcome results, like total disease management costs, obtained under those homogeneity assumptions may deviate from real-world population data that may manifest non-homogeneous distributions. Without being able to have easily access to those real-world data and for getting a good approximation of the deviations in outcome results, a calculation method is proposed that should also indicate which factor may have a dominant influence on the cost difference between homogeneous and non-homogeneous results. The method should help focus the research for obtaining more accurate information from real-world data in subsequent steps that better estimate control gain of infectious diseases through new interventions. Methods: The method explores, as the outcome measure to assess, the relative deviation in overall infection management costs measured with homogeneity versus non-homogeneity design in the datasets of aging adults. Population modelling is used with an Extended Sensitivity Analysis Plan (ESAP) that simulates non-homogeneous, but realistic, approximates of age-specific distributional spread in demography, infectious disease, and its severity in people aged > 65 years old over a 1-year period in univariant and multivariant assessments. Disease management costs are adjusted for 3 infection severity levels with increased differences between them using multiplication factors up to 20 times the initial unit cost. Results: The assumed full homogenous dataset systematically overestimates up to 10% the overall disease management cost in aging adults when compared with a group simulated with non-homogeneous, but realistic distributions for age, infection, severity, and cost, mainly due to the difference in the demographic age composition. However, overall costs of a proposed homogeneous condition tend to underestimate the spending of non-homogeneous conditions when the reference case has a partially homogeneous setup instead of a full condition or when the demographic age-change in the non-homogeneous condition evolves towards age-demographic homogeneity (same number of people at each age with increasing age), a likely evolution in the coming 15 to 30 years. Conclusion: Assessing the current cost burden of infectious diseases in aging adults must consider exact age-composition of the demography, the type of infection spread with severity levels in function of age and their cost differences between severity levels to avoid unrealistic cost estimates when assuming unreal homogeneous group conditions that could currently overestimate the real costs.

Rotavirus vaccination was introduced in high-income countries starting in 2006, with no recommendation for optimal implementation. Economic evaluations were presented before launch projecting potential impacts. Few economic reassessments have been reported following reimbursement. This study compares the short- to long-term economic value of rotavirus vaccination between pre-launch predictions and real-world evidence collected over 15 years, proposing recommendations for optimal vaccine launch. A cost-impact analysis compared rotavirus hospitalisation data after the introduction of vaccination between pre-launch modelled projections and observed data collected in the RotaBIS study in Belgium. A best model fit of the observed data was used to simulate launch scenarios to identify the optimal strategy. Data from other countries in Europe were used to confirm the potential optimal launch assessment. The Belgian analysis in the short term (first 8 years) indicated a more favourable impact for the observed data than predicted pre-launch model results. The long-term assessment (15 years) showed bigger economic disparities in favour of the model-predicted scenario. A simulated optimal vaccine launch, initiating the vaccination at least 6 months prior the next seasonal disease peak with an immediate very high vaccine coverage, indicated important additional potential gains, which would make vaccination very cost impactful. Finland and the UK are on such a route leading to long-term vaccination success, whereas Spain and Belgium have difficulties in achieving optimum vaccine benefits. An optimal launch of rotavirus vaccination may generate substantial economic gains over time. For high-income countries that are considering implementing rotavirus vaccination, achieving an optimal launch is a critical factor for long-term economic success.

Abstract Background arithmetic average values about disease burden across ageing adults are often used, which assumes homogeneity in group characteristics such as age, sex, disease frequency (incidence rates), and cost distributions. The question arises about how much outcome results such as overall cost obtained under this homogeneity assumption deviate from real-world population data that may manifest non-homogeneous distributions. Methods the method explores the amount of deviation measured between homogeneity versus non-homogeneity for overall infection costs in ageing adults as the outcome measure to assess. Population modelling is used with an extended sensitivity analysis plan (ESAP) that simulates non-homogeneous, age-specific distributional spread for demography, infectious disease, and its severity in people aged > 65 years old over a 1-year period in univariant and multivariant assessments. Costs are adjusted for 3 severity levels with increased difference between them using multiplication factors. Results the assumed full homogenous dataset systematically overestimates up to 10% the overall cost in ageing adults when compared with a group simulated with non-homogeneous distributions for age, infection, severity, and cost, mainly due to the demographic age-composition. Overall cost of a proposed homogeneous condition tends to underestimate the spending of non-homogeneous conditions when the reference case has a partial homogeneous set-up or when the demographic change in the non-homogeneous condition evolves towards age-demographic homogeneity (same number of people with increasing age), a likely evolution in the coming 10 to 20 years. Conclusion assessing the current cost burden of infectious diseases in ageing adults must consider exact age-composition of demography, infection spread with severity levels and their cost differences to avoid unrealistic cost estimates when assuming homogeneous group conditions.

At least since the Age of Enlightenment, good health has been a tenet for society. Healthy societies could learn better, work harder, improve their wealth, and live longer. Today societies focus on life expectancy, as we value long and healthy lives. As illustrated by the provision of COVID-19 vaccines first for the elderly, societies value life-saving actions. Paradoxically, health economic assessments conventionally devalue long-lasting health through the practice of discounting health benefits along with costs. However, health, with its intrinsic and instrumental characteristics, is not synonymous with money cash, a tradeable asset that devalues with time. If improving healthy life expectancy is a societal ambition, it seems counter-intuitive to value future health less as a result of an artificial mathematical construct when evaluating economically new medical interventions. In this paper, we investigate the application of discounting health in healthcare and consider paradoxical findings, especially in relation to disease prevention with vaccination. We argue that there is no economically sustainable argument to discount health gains, except for the benefit of the payer with a goal of spending less on life-saving products. If that is the objective for discounting health, there are other means to achieve the same goal in a more transparent and simpler way. From the long-term perspective of healthcare development, not discounting health gains would encourage research that values long-term effects. This in turn has the potential to benefit the investor, the payer, and the patient/consumer, improving the situation from multiple perspectives.

Presently, there are at least five important vaccine producers that have already launched or intend to launch a new vaccine designed to prevent infections caused by the Respiratory Syncytial Virus (RSV), which is highly prevalent in the youngest as well as the oldest age groups [...]

Healthcare decision-makers face difficult decisions regarding COVID-19 booster selection given limited budgets and the need to maximize healthcare gain. A constrained optimization (CO) model was developed to identify booster allocation strategies that minimize bed-days by varying the proportion of the eligible population receiving different boosters, stratified by age, and given limited healthcare expenditure. Three booster options were included: B1, costing US $1 per dose, B2, costing US $2, and no booster (NB), costing US $0. B1 and B2 were assumed to be 55%/75% effective against mild/moderate COVID-19, respectively, and 90% effective against severe/critical COVID-19. Healthcare expenditure was limited to US$2.10 per person; the minimum expected expense using B1, B2, or NB for all. Brazil was the base-case country. The model demonstrated that B1 for those aged <70 years and B2 for those ≥70 years were optimal for minimizing bed-days. Compared with NB, bed-days were reduced by 75%, hospital admissions by 68%, and intensive care unit admissions by 90%. Total costs were reduced by 60% with medical resource use reduced by 81%. This illustrates that the CO model can be used by healthcare decision-makers to implement vaccine booster allocation strategies that provide the best healthcare outcomes in a broad range of contexts.

Observational data over 15 years of rotavirus vaccine introduction in Belgium have indicated that rotavirus hospitalisations in children aged <5 years plateaued at a higher level than expected, and was followed by biennial disease peaks. The research objective was to identify factors influencing these real-world vaccine impact data. We constructed mathematical models simulating rotavirus-related hospitalisations by age group and year for those children. Two periods were defined using different model constructs. First, the vaccine uptake period encompassed the years required to cover the whole at-risk population. Second, the post-uptake period covered the years in which a new infection/disease equilibrium was reached. The models were fitted to the observational data using optimisation programmes with regression and differential equations. Modifying parameter values identified factors affecting the pattern of hospitalisations. Results indicated that starting vaccination well before the peak disease season in the first year and rapidly achieving high coverage was critical in maximising early herd effect and minimising secondary sources of infection. This, in turn, would maximise the reduction in hospitalisations and minimise the size and frequency of subsequent disease peaks. The analysis and results identified key elements to consider for countries initiating an optimal rotavirus vaccine launch programme.

Background:Infectious disease in aging adults (≥61 years) often occurs in combination with other health conditions leading to long hospital stays. Detailed studies on infection in aging adults investigating this problem are sparse.Aim:To quantify the effect of primary and secondary diagnosed infections on hospitalization bed-days among aging adult patients.Design:Retrospective patient-file study.Setting:Ziekenhuis Netwerk Antwerpen (ZNA) Hospital, a 1,858-bed general hospital in Belgium, with 364 beds allocated to geriatric patients.Data source:Database of hospitalized adult patients aged ≥61 years.Methods:All adult patients aged ≥61 years hospitalized on two wards, Geriatrics and Pulmonology, from 2010 to 2014 were included. Primary diagnosed infections were defined as infections known at entry to be treated first. Secondary diagnosed infections included infections known at entry but treated in parallel to primary non-infectious causes of entry, infections unknown at entry, and hospital-acquired (nosocomial) infections. Data were analyzed by patient age, gender, year, ward type, bed-days of hospitalization, infection rates, and seasonality.Results:There were 3,306 primary diagnosed infections (18%) and 14,758 secondary infections (82%) identified in the two wards combined (54.7% of all hospital stays at those 2 wards). Secondary diagnosed infections accounted for a significantly higher proportion of hospitalizations in both wards (+40% for Geriatric ward; +20% for Pulmonology ward; p < 0.001) and were associated with a significantly longer average hospital stay (+4 days for Geriatric ward; +5 days for Pulmonology ward; p < 0.001). Nosocomial infections (12% for Geriatric ward; 7% for Pulmonology ward) were associated with particularly high bed-days of hospitalization, at approximately +15 days and +12 days on Geriatric and Pulmonology wards, respectively. Both wards showed marked seasonality for respiratory infections with winter peaks.Conclusion:Real-world data showed that secondary diagnosed infections in aging adults imposed a high burden on hospital care along with longer hospital stays. This hampered bed availability during peak seasons.

Cohort models are often used to assess disease progression over different health states, for measuring total burden over time. That logic becomes complicated when the population composition is highly dynamic in function of age and sex, with changes in health condition, in decreases of immune responses, being relocated over different healthcare settings with different infection transmission risks. Our objective is to identify an alternative to cohort modelling, which also estimates adequately the infection burden, using a transparent evaluation method, that supports decision makers in their choices of applying vaccination of older adults. We compare the disease burden assessment using 2 different approaches. One is the cohort modelling following people until everyone is dead. The other is the cross-sectional 1-year population approach having the same infectious disease problem. The cohort modelling simulates progression to different infectious disease levels moving people to different settings (home, home care, nursing home, hospital), introducing many assumptions on the probabilities of event occurrence because of missing data. The cross-section evaluation makes the inventory of all infectious events happening at the different settings. It presents a snapshot of the problem based on real observations without patient flows. Although the 2 approaches can be forced to reach a same accumulated burden result of health events, the cross-sectional approach needs less assumptions, is easier to verify and validate, and facilitates the impact measurement of preventative interventions. The cohort modelling gets complicated when more health states are to be considered. It then becomes driven by assumptions, rather than having real-world data presented. When exposed to various dynamics in the population, like age, sex, health condition, place of living, infection’s types, severity, and mortality, having straightforward inventory figures over a fixed time period are more insightful for understanding the problem, rather than using a cohort modelling approach.

Discounting health is routinely applied in health economic evaluations causing debates about its appropriate level and whether it should be equivalent or lower than discounting cost. Different fundamental arguments were put forward to keep the discount rate for health the same as for cost. Differential discounting may cause inconsistencies in the interpretation of the results or may cause paradoxes in initiating prevention. The question we have however is different, is health gain a suitable measure to be discounted like money is?

COVID-19 vaccine boosters are available in many countries. Public health policymakers face difficult choices over which booster brand to recommend, given limited budgets and the need to maximize health gains. Here, we provide a conceptual model to identify the best booster strategies for age-identified subpopulations under different conditions. A constrained optimization model with an objective function to minimize bed-days was developed that varied population proportion receiving different booster options by age, to identify the best booster strategy that minimized bed-days with a constraint of maximum healthcare expenditure of US$2.10/person. It included a 3-month decision-tree model to calculate bed-days, with the following health states: healthy/asymptomatic; mild (not hospitalized); moderate (general ward); severe (intensive care unit [ICU], no mechanical ventilation); critical (requiring mechanical ventilation); and death. Medical resource utilization (MRU) costs and hospital bed-days were calculated for each health state. The base country was Brazil. Three booster options, B1 (US$1), B2 (US$2), and no-booster (NB, US$0) were considered. Based on real-world effectiveness estimates, B1 and B2 were assumed to be 55% and 75% effective against mild/moderate COVID-19, respectively. Both reduced severe/critical COVID-19 by 90%. The target population was adults eligible for boosters, stratified by age. The best booster strategy identified recommended 100% coverage of those eligible, with B1 for population <70 years and B2 for population ≥70 years. Compared with NB, bed-days were reduced by 75%, hospitalizations by 68%, and ICU admissions by 90% leading to a 60% reduction in total costs (81% reduction in MRU costs). Within individual age-groups, costs were reduced by 57%-66% based on the age-specific disease risk. A constrained optimization model identifies the best age-specific booster allocation strategy to minimize hospital bed-days across different age groups without exceeding a predefined budget. Decision-makers could use this method to achieve the best possible health outcomes when healthcare resources are limited.

Background: Observational data on the reduction in hospitalisations after rotavirus vaccine introduction in Belgium suggest that vaccine impact plateaued at an unexpectedly high residual hospitalisation rate. The objective of this analysis was to identify factors that influence real-world vaccine impact. Methods: Data were collected on hospitalisations in children aged < 5 years with rotavirus disease from 11 hospitals since 2005 (the RotaBIS study). The universal rotavirus vaccination campaign started late in 2006. A mathematical model simulated rotavirus hospitalisations in different age groups using vaccine efficacy and herd effect, influenced by vaccine coverage, vaccine waning, and secondary infection sources. The model used optimisation analysis to fit the simulated curve to the observed data, applying Solver add-in software. It also simulated an 'ideal' vaccine introduction maximising hospitalisation reduction (maximum coverage, maximum herd effect, no waning), and compared this with the best-fit simulated curve. Modifying model input values identified factors with the largest impact on hospitalisations. Results: Compared with the 'ideal' simulation, observed data showed a slower decline in hospitalisations and levelled off after three years at a higher residual hospitalisation rate. The slower initial decline was explained by the herd effect in unvaccinated children. The higher residual hospitalisation rate was explained by starting the vaccine programme in November, near the rotavirus seasonal peak. This resulted in low accumulated vaccine coverage during the first rotavirus disease peak season, with the consequential appearance of secondary infection sources. This in turn reduced the herd effect, resulting in a diminished net impact. Conclusions: Our results indicate that countries wishing to maximise the impact of rotavirus vaccination should start vaccinating well ahead of the rotavirus seasonal disease peak. This maximises herd effect during the first year leading to rapid and high reduction in hospitalisations. Secondary infection sources explain the observed data in Belgium better than vaccine waning. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

ABSTRACT Vaccine impact models against rotavirus disease (RD) and pneumococcal disease (PD) in low- and middle-income countries assume vaccine coverage based on other vaccines. We propose to assess the impact on severe disease cases and deaths avoided based on vaccine doses delivered by one manufacturer to Gavi-supported countries. From the number of human rotavirus vaccine (HRV) and pneumococcal polysaccharide protein D-conjugate vaccine (PHiD-CV) doses delivered, we estimated the averted burden of disease 1) in a specific year and 2) for all children vaccinated during the study period followed-up until 5 years (y) of age. Uncertainty of the estimated impact was assessed in a probabilistic sensitivity analysis using Monte-Carlo simulations to provide 95% confidence intervals. From 2009 to 2019, approximately 143 million children received HRV in 57 Gavi-supported countries, avoiding an estimated 18.7 million severe RD cases and 153,000, deaths. From 2011 to 2019, approximately 146 million children received PHiD-CV in 36 countries, avoiding an estimated 5.0 million severe PD cases and 587,000 deaths. The number of severe cases and deaths averted for all children vaccinated during the study period until 5 years of age were about 23.2 million and 190,000, respectively, for HRV, and 6.6 million and 749,000, respectively, for PHiD-CV. Models based on doses delivered help to assess the impact of vaccination, plan vaccination programs and understand public health benefits. In 2019, HRV and PHiD-CV doses delivered over a 5-y period may have, on average, averted nine severe disease cases every minute and one child death every 4 min. Plain Language Summary What is the context? The WHO added the pneumococcal conjugate vaccine and the rotavirus vaccine in the recommended vaccination schedule of all countries in 2007 and 2009, respectively. Previous studies estimated the public health benefit of these vaccines by approximating the number of children who received them. What is new? We used an alternative approach to estimate the benefit based on actual number of doses of the vaccines, human rotavirus vaccine (HRV; Rotarix) and pneumococcal polysaccharide protein D-conjugate vaccine (PHiD-CV; Synflorix) delivered to each country considered. The study analyzed data from children under 5 years of age in 60 Gavi-supported countries by identifying the number of vaccine doses delivered, estimating the number of children fully covered, applying the country-specific disease epidemiology, estimating the number of severe disease cases and deaths avoided. From 2009 to 2019, approximately 143 million children were vaccinated with HRV avoiding an estimated 18.7 million severe rotavirus disease cases and 153,000 deaths. From 2011 to 2019, about 146 million children were vaccinated with pneumococcal vaccine avoiding an estimated 5.0 million severe pneumococcal disease cases and 587,000 deaths. What is the impact? The benefit of HRV and PHiD-CV in Gavi-supported countries is often estimated based on assumptions of vaccine coverage rates. A modeling approach based on doses delivered by the vaccine manufacturer can provide an additional view on the potential vaccine benefits and improve planning, contribution, and sustainability of the immunization programs at a country level. In 2019, HRV and PHiD-CV together averted nine cases of severe disease each minute and one child death every 4 minutes. Graphical Abstract

To collect and review the current state of infectious disease modelling in the elderly in six pre-selected diseases as part of VITAL H2020 project. MEDLINE (PubMed) database was searched for all published models, between 1/1/2000 and 25/6/2020, that have investigated infectious diseases in ageing adults. SLR focusses on the following infectious diseases identified as diseases with potentially high burden in the elderly: extra-intestinal pathogenic Escherichia coli, Respiratory Syncytial virus (RSV), influenza, pneumococcal disease, Staphylococcus aureus, and norovirus. We identified 88 eligible articles. Influenza was subject matter in 44, pneumococcal diseases in 36, while pneumococcal diseases and influenza were modelled together in 5 publications. Only 2 models were identified for norovirus, 1 for E. Coli and none for Staphylococcus aureus and RSV. Models of influenza are mostly built as cost-utility studies, with costs, QALYs and ICER being their main outcomes (32/44), more frequently static and of a decision tree structure (14/44 vs 7/44 for Markov model) with time horizon of one influenza season (13/24). They were mostly focused on analyzing the impact of vaccination strategies (36/44), considerably less assessing antiviral drugs (5/44). Elderly population was defined by the age limit of 65+(33/40). As for the pneumococcal models, effectiveness was expressed in QALYs (21/36), or life years gained (9/36). Models remain mostly static (31/36) with Markov model structure dominating (24/36). All selected pneumococcal models were focused on vaccines as interventions, conducted under a lifetime horizon (11/21) and elderly population defined by the same age limit of 65+(18/36). The two models concerning norovirus where of deterministic compartmental structure without estimates for costs or effectiveness. Majority of identified models concerned influenza and pneumococcal disease. Presented SLR collected information on several relevant categories per each of publication and can quantitatively explain predominant approaches in modelling selected diseases for the elderly.