PERKEMBANGAN INDUSTRI PENERBANGAN DAN PERTUMBUHAN EKONOMI DI INDONESIA

Persaingan bisnis layanan jasa transportasi udara pada saat ini mengalami perkembangan yang sangat pesat. Penelitian ini melihat pengaruh pertumbuhan ekonomi, kepadatan penduduk, dan jumlah transportasi darat provinsi di Indonesia terhadap jumlah penumpang dan jumlah cargo. Studi ini menggunakan data statistik transportasi udara yang dirilis Badan Pusat Statistik (BPS). Metode Regresi yang digunakan menggunakan Fixed Effect. Hasil penelitian ini menunjukkan bahwa Produk Domestik Bruto (PDB) per kapita dan kebijakan pemerintah mengenai tarif batas atas maupun bawah berhubungan positif dan signifikan terhadap jumlah penumpang. Selanjutnya variabel transportasi darat yang diwakili truk memiliki hubungan yang positif dan signifikan terhadap cargo. Studi ini juga menemukan kebijakan penerapan batas tarif dapat menganggu keseimbangan pasar. Ketika penerapan tarif batas atas dibawah harga keseimbangan dapat menimbulkan excess demand.

Excess demand prediction for bike sharing systems

One of the most crucial elements for the long-term success of shared transportation systems (bikes, cars etc.) is their ubiquitous availability. To achieve this, and avoid having stations with no available vehicle, service operators rely on rebalancing. While different operators have different approaches to this functionality, overall it requires a demand-supply analysis of the various stations. While trip data can be used for this task, the existing methods in the literature only capture the observed demand and supply rates. However, the excess demand rates (e.g., how many customers attempted to rent a bike from an empty station) are not recorded in these data, but they are important for the in-depth understanding of the systems’ demand patterns that ultimately can inform operations like rebalancing. In this work we propose a method to estimate the excess demand and supply rates from trip and station availability data. Key to our approach is identifying what we term as excess demand pulse (EDP) in availability data as a signal for the existence of excess demand. We then proceed to build a Skellam regression model that is able to predict the difference between the total demand and supply at a given station during a specific time period. Our experiments with real data further validate the accuracy of our proposed method.

Existence and well-posedness for excess demand equilibrium problems

<p style='text-indent:20px;'>In this paper, we study excess demand equilibrium problems in Euclidean spaces. Applying the Glicksberg's fixed point theorem, sufficient conditions for the existence of solutions for the reference problems are established. We introduce a concept of well-posedness, say Levitin–Polyak well-posedness in the sense of Painlevé–Kuratowski, and investigate sufficient conditions for such kind of well-posedness.</p>

Immediate and Long-Term Implications of the COVID-19 Pandemic for People With Disabilities

Some people with disabilities may have greater risk of contracting COVID-19 or experiencing worse outcomes if infected. Although COVID-19 is a genuine threat for people with disabilities, they also fear decisions that might limit lifesaving treatment should they contract the virus. During a pandemic, health systems must manage excess demand for treatment, and governments must enact heavy restrictions on their citizens to prevent transmission. Both actions can have a negative impact on people with disabilities. Ironically, the sociotechnical advances prompted by this pandemic could also revolutionize quality of life and participation for people with disabilities. Preparation for future disasters requires careful consideration.

A Hybrid Parameter Estimation for Multi-asset Modeling and Dynamic Allocation Based on Financial Market Microstructure Model

In the previous works, a discrete-time microstructure (DTMS) model for financial market was constructed by using identification technology and was successfully applied to dynamic asset allocation based on the identified excess demand. However, the initial value setting of the parameters has a great influence on the estimated results of the DTMS model, which may make the estimated model to describe the dynamic characteristics of the financial time series poor and also affect the investment results indirectly. To overcome the weakness, this paper proposes a global optimization method which combines particle swarm optimization (PSO) and genetic algorithm (GA) to estimate the initial parameters. In the paper, the multi-asset DTMS model is established, and a multi-asset dynamic allocation strategy based on excess demand obtained from the DTMS model is also designed. Furthermore, the paper also discusses the impact of mutual correlation of assets on portfolio. Case studies show that, when a portfolio is composed of several stocks which are weak correlation, its total return of the portfolio is more than the sum of two-asset allocation for each stock; while the correlation between stocks is high, the obtained total return is not better than those of two-asset allocation.

Forecasting the impact of the first wave of the COVID-19 pandemic on hospital demand and deaths for the USA and European Economic Area countries

SummaryBackgroundHospitals need to plan for the surge in demand in each state or region in the United States and the European Economic Area (EEA) due to the COVID-19 pandemic. Planners need forecasts of the most likely trajectory in the coming weeks and will want to plan for the higher values in the range of those forecasts. To date, forecasts of what is most likely to occur in the weeks ahead are not available for states in the USA or for all countries in the EEA.MethodsThis study used data on confirmed COVID-19 deaths by day from local and national government websites and WHO. Data on hospital capacity and utilisation and observed COVID-19 utilisation data from select locations were obtained from publicly available sources and direct contributions of data from select local governments. We develop a mixed effects non-linear regression framework to estimate the trajectory of the cumulative and daily death rate as a function of the implementation of social distancing measures, supported by additional evidence from mobile phone data. An extended mixture model was used in data rich settings to capture asymmetric daily death patterns. Health service needs were forecast using a micro-simulation model that estimates hospital admissions, ICU admissions, length of stay, and ventilator need using available data on clinical practices in COVID-19 patients. We assume that those jurisdictions that have not implemented school closures, non-essential business closures, and stay at home orders will do so within twenty-one days.FindingsCompared to licensed capacity and average annual occupancy rates, excess demand in the USA from COVID-19 at the estimated peak of the epidemic (the end of the second week of April) is predicted to be 9,079 (95% UI 253–61,937) total beds and 9,356 (3,526–29,714) ICU beds. At the peak of the epidemic, ventilator use is predicted to be 16,545 (8,083–41,991). The corresponding numbers for EEA countries are 120,080 (119,183–121,107), 32,291 (32,157– 32,425) and 28,973 (28,868–29,085) at a peak of April 6. The date of peak daily deaths varies from March 30 through May 12 by state in the USA and March 27 through May 4 by country in the EEA. We estimate that through the end of July, there will be 60,308 (34,063–140,381) deaths from COVID-19 in the USA and 143,088 (101,131–253,163) deaths in the EEA. Deaths from COVID-19 are estimated to drop below 0.3 per million between May 4 and June 29 by state in the USA and between May 4 and July 13 by country in the EEA. Timing of the peak need for hospital resource requirements varies considerably across states in the USA and across regions of Europe.InterpretationIn addition to a large number of deaths from COVID-19, the epidemic will place a load on health system resources well beyond the current capacity of hospitals in the USA and EEA to manage, especially for ICU care and ventilator use. These estimates can help inform the development and implementation of strategies to mitigate this gap, including reducing non-COVID-19 demand for services and temporarily increasing system capacity. The estimated excess demand on hospital systems is predicated on the enactment of social distancing measures within three weeks in all locations that have not done so already and maintenance of these measures throughout the epidemic, emphasising the importance of implementing, enforcing, and maintaining these measures to mitigate hospital system overload and prevent deaths.FundingBill & Melinda Gates Foundation and the state of Washington

Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months

ImportanceThis study presents the first set of estimates of predicted health service utilization and deaths due to COVID-19 by day for the next 4 months for each state in the US.ObjectiveTo determine the extent and timing of deaths and excess demand for hospital services due to COVID-19 in the US.Design, Setting, and ParticipantsThis study used data on confirmed COVID-19 deaths by day from WHO websites and local and national governments; data on hospital capacity and utilization for US states; and observed COVID-19 utilization data from select locations to develop a statistical model forecasting deaths and hospital utilization against capacity by state for the US over the next 4 months.Exposure(s)COVID-19.Main outcome(s) and measure(s)Deaths, bed and ICU occupancy, and ventilator use.ResultsCompared to licensed capacity and average annual occupancy rates, excess demand from COVID-19 at the peak of the pandemic in the second week of April is predicted to be 64,175 (95% UI 7,977 to 251,059) total beds and 17,380 (95% UI 2,432 to 57,955) ICU beds. At the peak of the pandemic, ventilator use is predicted to be 19,481 (95% UI 9,767 to 39,674). The date of peak excess demand by state varies from the second week of April through May. We estimate that there will be a total of 81,114 (95% UI 38,242 to 162,106) deaths from COVID-19 over the next 4 months in the US. Deaths from COVID-19 are estimated to drop below 10 deaths per day between May 31 and June 6.Conclusions and RelevanceIn addition to a large number of deaths from COVID-19, the epidemic in the US will place a load well beyond the current capacity of hospitals to manage, especially for ICU care. These estimates can help inform the development and implementation of strategies to mitigate this gap, including reducing non-COVID-19 demand for services and temporarily increasing system capacity. These are urgently needed given that peak volumes are estimated to be only three weeks away. The estimated excess demand on hospital systems is predicated on the enactment of social distancing measures in all states that have not done so already within the next week and maintenance of these measures throughout the epidemic, emphasizing the importance of implementing, enforcing, and maintaining these measures to mitigate hospital system overload and prevent deaths.Data availability statementA full list of data citations are available by contacting the corresponding author.Funding StatementBill & Melinda Gates Foundation and the State of WashingtonKey PointsQuestionAssuming social distancing measures are maintained, what are the forecasted gaps in available health service resources and number of deaths from the COVID-19 pandemic for each state in the United States?FindingsUsing a statistical model, we predict excess demand will be 64,175 (95% UI 7,977 to 251,059) total beds and 17,380 (95% UI 2,432 to 57,955) ICU beds at the peak of COVID-19. Peak ventilator use is predicted to be 19,481 (95% UI 9,767 to 39,674) ventilators. Peak demand will be in the second week of April. We estimate 81,114 (95% UI 38,242 to 162,106) deaths in the United States from COVID-19 over the next 4 months.MeaningEven with social distancing measures enacted and sustained, the peak demand for hospital services due to the COVID-19 pandemic is likely going to exceed capacity substantially. Alongside the implementation and enforcement of social distancing measures, there is an urgent need to develop and implement plans to reduce non-COVID-19 demand for and temporarily increase capacity of health facilities.