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2 Minute Medicine Rewind December 29th, 2025 – 2 Minute Medicine
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Osborne Clarke advises ALER Milano in the €200 million+ mixed concession for energy redevelopment and EPC services for a substantial public housing portfolio
Osborne Clarke assisted ALER Milano (Azienda Lombarda di Edilizia Residenziale di Milano) in all phases of the project financing for the award of the energy performance contract (EPC) and for the energy redevelopment of a total of 152 properties intended for public housing, eligible for both the incentives recognised by the GSE and provided for by the so-called Conto Termico 3.0, and the NRRP measure ‘M7 Investment 17 Repower – Regulation (EU) 2023/435 of 27 February 2023’.
In particular, the Firm provided assistance throughout all stages of the initiative: initially in preparing a public notice for the receipt of project financing proposals, then in evaluating a total of 13 proposals received, as well as in the subsequent definition of mixed EPC and PPP contract schemes, and in the legal structuring of the subsequent tender procedures.
Osborne Clarke also advised in relation to the proceedings before the Milan Regional Administrative Court brought by an operator whose proposal was not considered worthy of consideration.
As part of the transaction, worth over €200 million, Osborne Clarke worked with its public law department, coordinated throughout by partner Giorgio Lezzi, assisted, with regard to the preparation of the public notice and assistance for the purposes of preliminary assessment, by senior associate Angelo Maria Quintieri, in relation to the assessment of the public utility of the project proposals and assistance in the preparation of the tender documents, by associate Federico Milani and trainee Francesco Antonio Maria Notti, and in the judicial phase before the Regional Administrative Court by senior associate Sabrina Maiello.
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The global macroeconomic burden of diabetes mellitus
This study complies with all relevant ethical regulations. The analyses were conducted using aggregated, publicly available data from international repositories and previously published sources. No individual-level human or animal data were collected, and therefore, ethical approval from an institutional review board or ethics committee was not required.
Model description
We estimated the macroeconomic burden of diabetes mellitus for 204 countries. The definition of diabetes mellitus followed the GBD study’s diabetes mellitus category39. Of the 204 studied countries, data from 144 were completed for our projections. We directly calculated the macroeconomic burden of diabetes mellitus for these 144 countries using the health macroeconomic model described in detail in the previous studies15,16,17,18,19,20. In this model, diabetes mellitus affects the economy through three main pathways. First, it reduces effective labor supply through mortality and morbidity. Diabetes mellitus deaths shrink the population, including working-age individuals, while diabetes mellitus morbidity reduces productivity and increases absenteeism. We adjust labor loss using age-specific and sex-specific labor force participation rates, reducing the potential for overestimation. Second, diabetes-related treatment costs reduce aggregate savings and investment by reallocating resources from capital accumulation to healthcare consumption. While reductions in such costs may boost investment, some resources may be redirected to other diseases, slightly overstating the net economic gains. Third, we estimate only the excess informal caregiving time caused by diabetes mellitus, excluding care related to coexisting conditions. This avoids overstating the informal care burden.
We estimated the additional cost associated with the rise in diabetes mellitus cases and increased mortality among patients with diabetes mellitus attributable to COVID-19. The number of COVID-19 cases was based on daily counts of individuals infected with COVID-19, as estimated by the Institute for Health Metrics and Evaluation40. We analyzed the long-term (2020–2050) impact of infections during the first 3 years of the pandemic—1 January 2020 to 1 September 2022—according to updated COVID-19 infection projections from the Institute for Health Metrics and Evaluation. To do so, we first derived the number of additional cases of diabetes based on the increased risk of incident diabetes in COVID-19 patients; a cohort study of 181,280 participants between 1 March 2020 and 30 September 2021 found an HR of 1.40 (95% CI = 1.36–1.44) for incident diabetes in people who survived the first 30 days of severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection relative to those who had not contracted SARS-CoV-2 (ref. 8). Then, we calculated the increased mortality rate among diabetic patients due to the increased risk of death from COVID-19 infection; a cohort study of 6,014 inpatients with diabetes—either COVID-19 positive (n = 698) or negative (n = 5,316)—revealed that diabetic patients hospitalized with COVID-19 were 3.6 times more likely to die than those not infected7. Finally, we estimated the macroeconomic cost associated with the increased mortality and morbidity of diabetes due to COVID-19. The projected long-term burden (2020–2050) reflects the elevated diabetes risk among individuals with prior COVID-19 infection from 2020 to 2022, who had a 40% higher incidence (HR = 1.40, 95% CI = 1.36–1.44) compared to controls.
Providing informal or unpaid care—which constitutes a substantial proportion of diabetes mellitus care—reduces the formal labor hours of caregivers. We considered the labor impact of informal care related to diabetes mellitus by subtracting the following estimate of effective labor from the labor supply for each diabetes mellitus patient. Specifically, we assumed informal care time as 4.0 h for each diabetes patient for each week, based on the estimation provided in ref. 29, and assumed that full-time employees work an average of 35.9 h per week, as reported by the International Labour Organization41. Consequently, for each patient with diabetes mellitus, the labor supply is reduced by 0.11 (4.0 divided by 35.9) units of labor due to informal caregiving. We also considered the detailed age distribution of informal caregivers to estimate the impact of informal labor loss on the macroeconomic burden. For sensitivity analyses, we revised our estimates of weekly informal caregiving hours. We set the lower bound at 0.285 h per week, calculated by multiplying the lowest reported disability prevalence among diabetic adults (15%42) by the conservative weekly caregiving time (1.9 h per week29) for individuals with mild diabetes. The upper bound remained at 8.3 h per week29, reflecting the higher caregiving needs observed among older populations with more severe diabetes. Formal caregiving is not considered an economic loss, as it involves paid labor and generates economic value. It is treated as part of the overall economy in our accounting framework.
To quantify the macroeconomic burden of diabetes mellitus, we compared aggregate output (using GDP) across three scenarios over the period 2020–2050: (1) the status quo scenario, in which no interventions are implemented that could reduce the mortality, morbidity, or prevalence of diabetes mellitus relative to current and projected rates; (2) a counterfactual scenario, in which we assumed the complete elimination of diabetes mellitus at zero cost; and (3) a COVID-19 scenario, in which we estimated the increased mortality and morbidity of diabetes mellitus due to COVID-19 between 1 January 2020 and 1 September 2022. The macroeconomic burden of diabetes mellitus was calculated as the cumulative difference in projected GDP between scenarios (1) and (2), which served as the baseline. Furthermore, because COVID-19 increases the incidence of, and mortality from, diabetes mellitus, we calculated the additional macroeconomic burden attributable to COVID-19 as the cumulative difference due to the increased diabetes mellitus cases between scenarios (2) and (3) during this period. We describe our counterfactual assumptions in detail below.
In the counterfactual scenario, we assume the complete elimination of diabetes mellitus starting in 2020, consistent with the comparative risk assessment framework adopted by the GBD study. In this scenario, all diabetes-related mortality and morbidity are fully averted, while risks from other causes remain unchanged. This approach facilitates consistent cause-specific attribution of economic burden but may overestimate benefits, especially among older adults with substantial competing mortality risks. In translating this health shock into economic outcomes, our health macroeconomic model assumes that eliminating diabetes would increase the effective labor supply by reducing disease-related absenteeism, presenteeism and premature mortality. It would also reduce healthcare expenditures for diabetes treatment, thereby boosting aggregate savings and physical capital accumulation through increased investment. These health-induced changes then generate downstream effects on GDP growth over time. We do not model general equilibrium feedbacks such as changes in wages, labor force participation preferences or government budget reallocation across sectors. Instead, we apply a partial equilibrium framework with fixed labor participation rates and savings behaviors, where changes stem only from shifts in the disease burden. As such, we provide a structured yet conservative estimate of the macroeconomic burden of diabetes mellitus. These estimates are based on a simulation model and should not be interpreted as precise causal effects; rather, they are indicative projections based on clearly defined and transparent assumptions.
Data
We considered data for 204 countries and a set of World Bank regions. GDP projections for the status quo scenario, saving rates and health expenditures were taken from the World Bank’s database43,44,45. The mortality and morbidity data (years of life lost due to premature mortality and years lost due to disability) were obtained from the recently updated GBD 2021 (refs. 39,46). We relied on the International Labour Organization for age–sex-specific labor force projections47 and the Barro–Lee education database for age–sex-specific data on average years of schooling48. We obtained the age–sex-specific population from the Department of Economic and Social Affairs population dynamics database49. Using these data sources, we calculated human capital according to the Mincer equation50 and inferred the experience-related human capital component by relying on the corresponding estimates discussed in ref. 51. The physical capital data were taken from the Penn World Table projections52, and we followed standard economic estimates for the value of the output elasticity of physical capital (that is, the percentage change in output for a 1% change in the physical capital stock)53.
We used data to calculate treatment costs (ref. 38); these data include both inpatient and outpatient medical costs of diabetes mellitus. Supplementary Table 1 shows country-specific treatment data and Supplementary Table 2 shows other parameter values and data sources used in the macroeconomic model. To make country estimates comparable, all costs were converted to 2017 international dollars (2017 INT$). For 60 countries, some data—mostly on education, physical capital and the saving rate—were incomplete (see Supplementary Table 3 for details); reliable data on GDP and the prevalence rate of diabetes mellitus were available for these countries. Similar to the previous research18, we used a linear projection to approximate the economic burden of diabetes mellitus for these countries, which is shown in detail in Supplementary Table 4.
Modeling details
The goal was to calculate the economic effect of diabetes mellitus due to healthcare expenses and productivity losses from death, morbidity and informal care. For each country, we performed the following analysis:
In step 1, we identified the disease burden of diabetes mellitus (in terms of mortality, morbidity and treatment costs).
In step 2, we constructed economic projections for the following two scenarios: a status quo scenario, in which GDP is projected to grow based on current estimates and projections of disease prevalence, and a counterfactual scenario, in which diabetes mellitus prevalence is eliminated from the beginning of the time frame. The economic projections use a macroeconomic production function and can be further decomposed into the following two parts:
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1.
Projections of effective labor supply; and
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2.
Projections of physical capital accumulation.
In step 3, we calculated the economic loss as the cumulative difference in projected annual GDP between these two scenarios for various discount rates.
In the counterfactual scenario where diabetes mellitus is eliminated, we assume that diabetes-related morbidity and mortality are fully averted, while the risks of morbidity and mortality from other causes remain unchanged. This assumption follows the GBD comparative risk assessment framework, allowing for consistent estimation across causes. However, it may overestimate the benefits of eliminating diabetes, particularly in older populations, due to unmodeled competing risks. This detailed model description follows our previous contributions, in which we applied the framework to estimate the economic burden of noncommunicable diseases in China, Japan and South Korea15, as well as in the United States and European countries17,54, and the economic burden of noncommunicable diseases and other risk factors18.
Production function
Consider an economy in which time (t=mathrm{1,2},ldots ,infty) evolves discretely. Building upon the details in ref. 55, we considered the following production function for this economy:
$${Y}_{t}={A}_{t}{K}_{t}^{alpha }{H}_{t}^{1-alpha },$$
where ({Y}_{t}) is aggregate output; ({A}_{t}) is the technological level at time (t), which we assumed evolves exogenously; ({K}_{t}) is the physical capital stock (that is, machines, factory buildings, and so on); and ({H}_{t}) represents aggregate human capital. The parameter (alpha) is the elasticity of final output with respect to physical capital. The aggregate production function recognizes that output is not only produced with physical capital and ‘raw labor’ as in the framework discussed in ref. 56, on which the original EPIC model is based57, but with ‘effective labor’, of which health is a crucial determinant.
Physical capital evolves according to
$${K}_{t+1}=left(1-delta right){K}_{t}+{Y}_{t}-{C}_{t}-{rm{TC}}_{t}=left(1-delta right){K}_{t}+{s}_{t}{Y}_{t},$$
where (delta) refers to the depreciation rate, ({s}_{t}) refers to the saving rate, ({rm{TC}}_{t}) refers to the costs of the ongoing treatment of diabetes mellitus and ({C}_{t}) refers to the amount of consumption. From the above Equation, it follows that the saving rate is defined as
$${s}_{t}=1-frac{{C}_{t}+{rm{TC}}_{t}}{{Y}_{t}}.$$
Of note, aggregate output ({Y}_{t}) is used for the following three purposes: (1) to pay treatment costs ({rm{TC}}_{t}) (hospitalization, medication, and so on), (2) to consume the amount ({C}_{t}) and (3) to save.
Individuals of age group (a) are endowed with ({h}_{t}^{(a)}) units of human capital and supply ({{mathcal{l}}}_{t}^{(a)}) units of labor from the age of 15 up to their retirement at age (R), that is, for (ain [15,R]). Children younger than 15 years of age and retirees older than (R) do not work. R varies by country and could correspond to a high age (for example, some people aged above 80 years could also be working). In the theoretical derivations, R indicates the upper bound of the summation. In our simulations, we used labor projections data from the International Labour Organization, and positive values for the labor force exist for cohorts above the age of 65 years. Aggregate human capital in the production function (1) is then defined as the sum over the age-specific effective labor supply of each age group:
$${H}_{t}=mathop{sum }limits_{a=15}^{R}{h}_{t}^{left(aright)}{{mathcal{l}}}_{t}^{left(aright)}{n}_{t}^{left(aright)},$$
where ({n}_{t}^{a}) denotes the number of individuals in age group (a). Of note, aggregate human capital increases with the number of working-age individuals who live in the economy (that is, with a higher ({n}_{t}={sum }_{a=15}^{R}{n}_{t}^{(a)})), with individual human capital endowment (that is, with a higher ({h}_{t}^{(a)}) for at least one (a)), and with labor supply (that is, with a higher ({{mathcal{l}}}_{t}^{(a)}) for at least one (a)).
We followed ref. 50 and constructed the average human capital of the cohort aged (a) according to an exponential function of education and work experience:
$${h}_{t}^{left(aright)}=exp left[{eta }_{1}left(y{s}_{t}^{left(aright)}right)+{eta }_{2}left(a-y{s}_{t}^{left(aright)}-5right)+{eta }_{3}{left(a-y{s}_{t}^{left(aright)}-5right)}^{2}right],$$
where ({eta }_{1}) is the semi-elasticity of human capital with respect to average years of education as given by (y{s}_{t}^{left(aright)}), and ({eta }_{2}) and ({eta }_{3}) are the semi-elasticities of human capital with respect to the experience of the workforce (left(a-y{s}_{t}^{left(aright)}-5right)) and the experience of the workforce squared ({left(a-y{s}_{t}^{left(aright)}-5right)}^{2}), respectively. Here we assumed a school entry age of 5 years throughout.
Impact of diabetes mellitus on labor supply
Following refs. 15,17,18, the evolution of labor supply in the status quo scenario is given by
$${L}_{t}^{left(aright)}={{mathcal{l}}}_{t}^{left(aright)}{n}_{t}^{left(aright)},mathrm{with},{n}_{t}^{left(aright)}=left[1-{sigma }_{t-1}^{left(a-1right)}right]{n}_{t-1}^{left(a-1right)},$$
where ({sigma }_{t}^{left(aright)}) is the overall mortality rate of age group (a) in time (t). Mortality and morbidity reduce effective labor supply.
Let ({sigma }_{r,t}^{left(aright)}) denote the mortality rate of people in age group (a) due to diabetes mellitus, and let ({sigma }_{-r,t}^{left(aright)}) be the overall mortality rate due to the causes other than diabetes mellitus. Then we have
$$left(1-{sigma }_{t}^{left(aright)}right)=(1-{sigma }_{r,t}^{left(aright)})(1-{sigma }_{-r,t}^{left(aright)}).$$
Mortality from diabetes mellitus reduces the labor supply by reducing the population ({n}_{t}^{left(aright)}) (through ({sigma }_{r,t}^{left(aright)})). In the counterfactual case, in which diabetes mellitus is eliminated from time (t=0) onward, the evolution of labor supply is defined similarly to the evolution of labor supply equation, but with a different overall mortality rate (({sigma }_{-r,t}^{left(aright)}) instead of ({sigma }_{t}^{left(aright)})). For simplicity, we assumed that the number of births is the same in both cases at each point in time (t).
In the counterfactual scenario, the size of the cohort aged (a) at time (t({bar{n}}_{t}^{(a)})) evolves according to
$${bar{n}}_{t}^{(a)}=left[1-{sigma }_{-r,t-1}^{left(a-1right)}right]{bar{n}}_{t-1}^{(a-1)},,{bar{n}}_{0}^{(a)}={n}_{0}^{left(aright)},,{bar{n}}_{t}^{(0)}={n}_{t}^{left(0right)},$$
Following ref. 15, the loss of labor due to mortality accumulates over the years according to
$${bar{n}}_{t}^{(a)}={n}_{t}^{left(aright)}/mathop{prod }limits_{tau =0}^{min left{t,aright}-1}left[1-{sigma }_{r,t-1-tau }^{left(a-1-tau right)}right].$$
The morbidity effect is captured by a reduction in the labor participation rate ({{mathcal{l}}}_{t}^{left(aright)}) because people with an illness typically reduce their labor supply, either by reducing their working hours or by leaving the workforce. Following ref. 15, the labor participation rate in the counterfactual scenario ({bar{{mathcal{l}}}}_{t}^{(a)}) can be calculated as
$${bar{{mathcal{l}}}}_{t}^{(a)}approx {{mathcal{l}}}_{t}^{left(aright)}/mathop{prod }limits_{tau =0}^{min left{t,aright}-1}left[1-{p}^{tau }{sigma }_{r,t-1-tau }^{left(a-1-tau right)}{xi }^{,left(a-1-tau right)}right],$$
where ({xi }^{,left(aright)}) measures the size of the morbidity effect relative to the relevant mortality rate, and where ({p}^{tau }) is the probability that a patient died from diabetes mellitus before time (t).
Because the impact of morbidity is hard to estimate directly, we first defined
$${xi }^{,left(aright)}=frac{mathrm{loss},mathrm{of},mathrm{labor},mathrm{due},mathrm{to},mathrm{morbidity},mathrm{in},mathrm{age},mathrm{group},a}{mathrm{loss},mathrm{of},mathrm{labor},mathrm{due},mathrm{to},mathrm{mortality},mathrm{in},mathrm{age},mathrm{group},a}.$$
Next, we assumed that the following holds in any given year for each age group (a):
$${xi }^{,left(aright)}=frac{{mathrm{YLD}}^{left(aright)}}{{mathrm{YLL}}^{left(aright)}},$$
where ({mathrm{YLD}}^{left(aright)}) represents the years lived with diabetes mellitus and ({mathrm{YLL}}^{left(aright)}) represents the years of life lost due to diabetes mellitus. Of note, ({xi }^{,left(aright)}) can be calculated from the corresponding DALY data reported by the GBD study58.
In sum, as a result of the elimination of diabetes mellitus, the ‘counterfactual scenario’ is associated with an increase in labor supply as compared with the status quo scenario. We approximated the change in labor supply (at time (t) for age group (a)) by
$$Delta {L}_{t}^{left(aright)}approx {{mathcal{l}}}_{t}^{left(aright)}{n}_{t}^{left(aright)}mathop{sum }limits_{tau =0}^{min left{t,aright}-1}{sigma }_{r,t-1-tau }^{left(a-1-tau right)}left[1+{p}^{tau }{xi }^{,left(a-1-tau right)}right].$$
For the more general case of a partial reduction in the prevalence of diabetes mellitus by a factor (rho), we obtained the loss of labor for age group (a) at time (t) as
$$Delta {L}_{t}^{left(aright)}left(rho right)approx {{mathcal{l}}}_{t}^{left(aright)}{n}_{t}^{left(aright)}mathop{sum }limits_{tau =0}^{min left{t,aright}-1}{rho sigma }_{r,t-1-tau }^{left(a-1-tau right)}left[1+{p}^{tau }{xi }^{,left(a-1-tau right)}right].$$
The details in ref. 15 showed the mathematical proof.
For the modeling of informal care labor, we simply subtract the labor loss associated with informal care (defined as a fraction of diabetes mellitus prevalence) from the effective labor supply.
Impact of diabetes mellitus on physical capital accumulation
Diabetes mellitus also impedes the accumulation of physical capital because savings finance part of the treatment costs. Following refs. 15,17, physical capital accumulation in the counterfactual scenario can be written as
$${bar{K}}_{t+1}={bar{s}}_{t}{bar{Y}}_{t}+left(1-delta right){bar{K}}_{t},$$
$${bar{s}}_{t}{bar{Y}}_{t}={bar{I}}_{t}={bar{Y}}_{t}-{bar{C}}_{t}={{rm{s}}}_{t}{bar{Y}}_{t}+chi{rm{TC}}_{t},$$
where an overbar indicates the counterfactual scenario and where (chi) is the fraction of the treatment cost that is diverted to savings. The counterfactual saving rate is thus defined by
$${bar{s}}_{t}=frac{{s}_{t}{bar{Y}}_{t}+chi{rm{TC}}_{t}}{{bar{Y}}_{t}}.$$
For more details, see refs. 15,17.
Because diabetes mellitus is assumed to be eliminated in the counterfactual scenario, the resources that were devoted to its treatment can now be used for savings or consumption. Of note, this creates an income effect that, in reality, could affect the division of households’ income between savings and consumption. For tractability, we assumed that aggregate investment consists of two parts in the counterfactual scenario, which are as follows: a fixed share ({{rm{s}}}_{t}) of total output and an additional part from ({rm{TC}}_{t}) that would otherwise have been used to pay to treat diabetes mellitus:
$${bar{I}}_{t}={{rm{s}}}_{t}{bar{Y}}_{t}+chi{rm{TC}}_{t},$$
Similarly, for the case of a partial reduction in diabetes mellitus prevalence by (rho), we have
$${bar{I}}_{t}={{rm{s}}}_{t}{bar{Y}}_{t}+rho chi{rm{TC}}_{t}.$$
The intuition is that if diabetes mellitus were partially eliminated, the treatment cost that is diverted to savings should be added back proportionally.
Sensitivity analyses
We conducted several sensitivity analyses. First, we varied the mortality and morbidity rates. The baseline estimates were calculated with the mean mortality and morbidity data from GBD. In the sensitivity analyses, best-case and worst-case estimates were calculated based on the lower and upper bounds of GBD mortality and morbidity data. Table 1 presents the results of this sensitivity analysis in parentheses next to the baseline estimates. Second, we varied the discount rate. In the main analysis, we generated our estimates using a discount rate of 2%. We present estimates for each country by World Bank region and World Bank income group using discount rates of 0% in Supplementary Table 6 and 3% in Supplementary Table 7. Finally, we conducted sensitivity analyses by varying the weekly informal care hours from 0.285 to 8.3, with 4.0 as the median value (Supplementary Table 8).
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
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New nonstop flights from Austin coming in 2026
If you’re flying out of Austin in 2026, the list of places you’ll be able to reach nonstop is changing, and in many cases, the destinations are tilting in a more vacation-friendly direction.
New flights will make it easier to head straight to ski towns, beach cities and Midwest hubs, even as a handful of familiar nonstop options quietly fade away.
Add it all up, and the number of seats for sale is set to rise nearly 9% in the first three months of 2026 compared to the same period a year earlier, according to airport data.
Much of that change is being driven by Southwest Airlines, the dominant carrier at Austin-Bergstrom International Airport, which is preparing to offer unprecedented numbers of fights this summer.
Southwest announced this month that it will be launching a crew base in March at ABIA, allowing the Dallas-based carrier’s local employees to start and end their days in Austin. The city of Austin and the state of Texas are paying Southwest millions of dollars to go on a hiring spree, with some 2,000 new jobs expected.
“The benefits go to the traveling public,” airport CEO Ghizlane Badawi promised. She said the crew base will mean more nonstop Southwest flights and greater reliability, especially when it gets busy or if there’s bad weather.
Airport expansion throttles up
City of Austin
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Aviation Department
An expansion on the west end of the Barbara Jordan Terminal is set to open in the spring. A children’s play area is depicted on the left with green walls. The large staircase leads up to the mezzanine area with the meditation/quiet space and public outdoor balcony overlooking downtown Austin. As a years-long project to increase ABIA’s capacity kicks into high gear, travelers can expect more construction at the airport. But they will also start to benefit as some of the big customer-facing projects come online in the first half of 2026.
Checkpoint 3, which has been under renovation for almost two years, is scheduled to reopen in early 2026 with more TSA screening lanes and and ticket counters.
An 80,000 square foot expansion at the west end of the Barbara Jordan Terminal is set to open in the spring with three additional gates and new amenities like a children’s play area and a place where pets can pee.
Those projects will create space the airport needs to close and demolish the South Terminal, where discount airlines Allegiant and Frontier operate.
After an intense legal fight, the city paid $88 million to evict the South Terminal’s private operator so the building could be razed to create space for new taxiways and a concourse with at least 20 gates, planned to open in the early 2030s.
Allegiant said it will move to the Barbara Jordan Terminal in February. Frontier did not respond to a request for comment, but an airport spokesperson confirmed the ultra-low-cost airline will move to the Barbara Jordan Terminal as well.
Behind the scenes, a $241 million upgrade of ABIA’s outbound baggage handling system was activated this month, ahead of schedule. The new system can process more than 4,000 bags per hour, more than double the previous volume. The city’s aviation department said this will decrease the potential for flight delays.
Air traffic controller shortage persists
Michael Minasi
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KUT News
Air traffic controllers in Austin worked unpaid through the federal government shutdown in October, which was also the busiest month in ABIA history. Travelers at ABIA will still have to contend with a dire shortage of air traffic controllers in 2026. Even though the Federal Aviation Administration has sent more trainees to the Austin tower, the number of controllers remains about half the level recommended by staffing targets set jointly by the FAA and the union.
That has been more evident lately with an increase in ground delays, when flights to Austin that haven’t taken off yet are forced to wait at their home airports so overworked controllers at ABIA don’t get swamped.
With significant growth in flights planned in 2026 and no immediate end to the controller shortage in sight, those unexpected delays could continue to be an issue in the New Year.
So here’s a look at some of the nonstop flights coming, changing and going away, and what it means for Austin travelers planning trips in 2026.
Southwest Airlines
Gabriel C. Pérez
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KUT News
Southwest Airlines is scheduled to have up to 132 daily departures from Austin this summer, the most it’s ever had at ABIA. Southwest is on track to offer a record-breaking volume of nonstop flights from Austin. Maximum daily departures will rise from 109 in January to an unprecedented 132 between June and August, airport data shows. ABIA’s busiest carrier is starting service to these destinations:
- Hayden/Steamboat Springs, Colorado: Saturday-only seasonal service from March 7 to April 6
- Fort Myers, Florida: Saturday-only seasonal service from March 7 to April 6
- Palm Springs, California: Saturday and Sunday nonstop service during from March 7 to April 6. Then Saturday-only service from April 7 to June 3. This is will be the first year Southwest has flown nonstop from ABIA to Palm Springs International Airport.
- Pensacola, Florida: daily service except Tuesdays and Wednesdays from March 7 to April 6
- Cincinnati: New daily service from June 4 to Aug. 3.
- Seattle: New seasonal service returns June 4 to Aug. 3. Southwest hasn’t flown Austin to Seattle nonstop since 2018.
- Indianapolis: Frequency increasing over the summer to three flights daily.
- San Francisco: Frequency increasing over the summer to two flights a day Monday through Friday
Delta
Michael Minasi
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KUT News
Delta’s expansion into Austin continues in 2026, making it the second biggest carrier at ABIA behind Southwest. The second biggest carrier at ABIA began new nonstop service in November to Denver and Miami. Delta started flying to Cancún, Mexico on Dec. 20, and the airline plans to add more nonstop flights in 2026.
- Columbus, Ohio: starting June 7
- Kansas City, Missouri: starting June 7
- Bozeman, Montana: Saturday-only service from June 13 to Sept. 5
- Kalispell, Montana: Saturday-only service from June 13 to Sept. 5
- Destin–Fort Walton Beach Airport, Florida: Saturday-only service from June 13 to Sept. 5
- San José del Cabo, Mexico: This nonstop service was supposed to end Jan. 5, but will continue through April 12
- Palm Springs, California: Saturday-only nonstop service started in November and will be increased to daily service on Dec. 20 through March 7. The flight will return to Saturday-only through April 25.
- Northwest Florida Beaches International Airport: Delta is decreasing daily nonstop service to this airport outside Panama City, Florida, to Saturday-only from Jan. 5 to March 8.
- Louisville: Service for the Kentucky Derby with one roundtrip flight on April 30 and another on May 3.
Frontier and Allegiant
Gabriel C. Pérez
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KUT News
Allegiant and Frontier will relocate from the South Terminal to the Barbara Jordan Terminal starting in February. The two carriers that now operate out of the low-cost South Terminal will have to start paying higher fees to the airport when they shift to the Barbara Jordan Terminal in early 2026. Ultimately, those costs get passed on to passengers.
Both airlines are planning to reduce their schedules in early 2026.
Frontier is flying nonstop to these 11 cities through Jan. 6:
- Atlanta
- Cincinnati
- Cleveland
- Chicago O’Hare
- Denver
- Las Vegas
- Miami
- Orlando, Florida
- Philadelphia
- Phoenix
- San Diego
From January through March 5, Frontier will suspend service to Cleveland, Cincinnati and Philadelphia.
Allegiant will fly to seven cities through Feb. 10:
- Asheville, North Carolina
- Cincinnati
- Des Moines, Iowa
- Grand Rapids, Michigan
- Provo, Utah
- Sarasota, Florida
- Knoxville, Tennessee
From Feb. 11 to May 13, Allegiant’s offerings will shrink to four destinations:
- Cincinnati
- Des Moines, Iowa
- Grand Rapids, Michigan
- Provo, Utah
American Airlines
In addition to its regular service, the third-largest airlines at ABIA is flying nonstop to Augusta Regional Airport on April 6, 11 and 14 for the Masters Tournament.
American is also offering a special event nonstop flight to Louisville for the Kentucky Derby with an outbound flight on April 30 and a return on May 3 aboard a 128-seat Airbus A319.
Lufthansa
Starting this month, Austin is one of the few U.S. cities where Lufthansa is offering its new Allegris service. For those who can afford it, the premium offering on select flights allows travelers to choose one of five high-end seat configurations.
Lufthansa’s First Class Allegris flights offer suites with beds “as private and individual as a hotel room – only at an altitude of eleven kilometers,” the company said. But Austin’s jetsetters will have to make due with the carrier’s lesser Business Class Allegris seating on their 10-hour flight to Frankfurt, which cost well over $10,000 each way, according to a search of available tickets. Lufthansa will fly to Frankfurt on a Tuesday, Thursday, Sunday schedule for the winter season on a Boeing 787-9 Dreamliner. Starting March 29 and lasting through the summer, the nonstop service to Frankfurt is scheduled to run five times a week on the older and less snazzy Airbus A340-300.
Alaska Airlines
The number five airline at ABIA is stopping twice daily service to San Francisco. The last flight is on Jan. 6.
Alaska will increase nonstop service to San Diego from three to four times per day on Jan. 7.
Viva (formerly Viva Aerobus)
Viva had been planning to fly nonstop from Austin to Mexico City’s newest airport — Felipe Ángeles — starting in November. The low-cost airline had even started selling tickets.
But a U.S. Department of Transportation order in October canceled that route and 12 others between the U.S. and Ángeles Airport. Transportation Secretary Sean Duffy said it was retaliation for the Mexican government blocking expansion of U.S. flights into Mexico.
Rep. Monica De La Cruz, a Republican from South Texas, said the move is punishing American businesses. She has been urging a resolution to the dispute.
But it’s still unclear when Viva Aerobus flights from Austin to Felipe Ángeles International Airport might be allowed to take off.
Other airlines
Lorianne Willett
/
KUT News
Airlines will be making many more smaller tweaks to service throughout 2026. British Airways is currently flying once daily to London Heathrow on an Airbus A350-1000 with 351 seats. On March 29, that will double to twice daily service.
Copa Airlines added a fifth weekly flight to Panama City, Panama, in December and January. The fifth weekly flight will resume again from April to August.
JetBlue began flying twice daily to Fort Lauderdale, Florida, on Nov. 20 aboard a 162-seat Airbus A320. That service will be reduced to once daily from Jan. 7 to Feb. 11.
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