Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userVaccination strategies and transmission of COVID-19: evidence across leading countries
182
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Vaccination has been perceived as a key to reaching herd immunity in the current COVID-19 pandemic. This paper examines effectiveness of different vaccination strategies. We investigate the effects of two key elements in mass vaccination, which are allocations and timing of first and second doses and types of vaccines, on the spread of COVID-19. Amid limited supply of approved vaccines and constrained medical resources, the choice of a vaccination strategy is fundamentally an economic problem. We employ standard time-series and panel data models commonly used in economic research with real world data to estimate the effects of progress in vaccination and types of vaccines on health outcomes. Potential confounders such as government responses and peoples behavioral changes are also taken into account. Our findings suggest that the share of people vaccinated with at least one dose is significantly negatively associated with new infections and deaths. Conditioning on first dose progress, full vaccination offers no further reductions in new cases and deaths. For vaccines from China, however, we find weaker effects of vaccination progress on health outcomes. Our results support the extending interval between first and second dose policy adopted by Canada and the UK among others for mRNA-based vaccines. As vaccination progressed, peoples mobility increased and it offset the direct effects of vaccination. Therefore, public health measures are still important to contain the transmission by refraining people from being more mobile after vaccinated.
rate research
Read More
We evaluate the efficiency of various heuristic strategies for allocating vaccines against COVID-19 and compare them to strategies found using optimal control theory. Our approach is based on a mathematical model which tracks the spread of disease among different age groups and across different geographical regions, and we introduce a method to combine age-specific contact data to geographical movement data. As a case study, we model the epidemic in the population of mainland Finland utilizing mobility data from a major telecom operator. Our approach allows to determine which geographical regions and age groups should be targeted first in order to minimize the number of deaths. In the scenarios that we test, we find that distributing vaccines demographically and in an age-descending order is not optimal for minimizing deaths and the burden of disease. Instead, more lives could potentially be saved by using strategies which emphasize high-incidence regions and distribute vaccines in parallel to multiple age groups. The level of emphasis that high-incidence regions should be given depends on the overall transmission rate in the population. This observation highlights the importance of updating the vaccination strategy when the effective reproduction number changes due to the general contact patterns changing and new virus variants entering.
During the global spread of COVID-19, Japan has been among the top countries to maintain a relatively low number of infections, despite implementing limited institutional interventions. Using a Tokyo Metropolitan dataset, this study investigated how these limited intervention policies have affected public health and economic conditions in the COVID-19 context. A causal loop analysis suggested that there were risks to prematurely terminating such interventions. On the basis of this result and subsequent quantitative modelling, we found that the short-term effectiveness of a short-term pre-emptive stay-at-home request caused a resurgence in the number of positive cases, whereas an additional request provided a limited negative add-on effect for economic measures (e.g. the number of electronic word-of-mouth (eWOM) communications and restaurant visits). These findings suggest the superiority of a mild and continuous intervention as a long-term countermeasure under epidemic pressures when compared to strong intermittent interventions.
Because of the ongoing Covid-19 crisis, supply chain management performance seems to be struggling. The purpose of this paper is to examine a variety of critical factors related to the application of contingency theory to determine its feasibility in preventing future supply chain bottlenecks. The study reviewed current online news reports, previous research on contingency theory, as well as strategic and structural contingency theories. This paper also systematically reviewed several global supply chain management and strategic decision-making studies in an effort to promote a new strategy. The findings indicated that the need for mass production of products within the United States, as well as within trading partners, is necessary to prevent additional Covid-19 related supply chain gaps. The paper noted that in many instances, the United States has become dependent on foreign products, where the prevention of future supply chain gaps requires the United States restore its manufacturing prowess.
The Asian-pacific region is the major international tourism demand market in the world, and its tourism demand is deeply affected by various factors. Previous studies have shown that different market factors influence the tourism market demand at different timescales. Accordingly, the decomposition ensemble learning approach is proposed to analyze the impact of different market factors on market demand, and the potential advantages of the proposed method on forecasting tourism demand in the Asia-pacific region are further explored. This study carefully explores the multi-scale relationship between tourist destinations and the major source countries, by decomposing the corresponding monthly tourist arrivals with noise-assisted multivariate empirical mode decomposition. With the China and Malaysia as case studies, their respective empirical results show that decomposition ensemble approach significantly better than the benchmarks which include statistical model, machine learning and deep learning model, in terms of the level forecasting accuracy and directional forecasting accuracy.
We provide quantitative predictions of first order supply and demand shocks for the U.S. economy associated with the COVID-19 pandemic at the level of individual occupations and industries. To analyze the supply shock, we classify industries as essential or non-essential and construct a Remote Labor Index, which measures the ability of different occupations to work from home. Demand shocks are based on a study of the likely effect of a severe influenza epidemic developed by the US Congressional Budget Office. Compared to the pre-COVID period, these shocks would threaten around 22% of the US economys GDP, jeopardise 24% of jobs and reduce total wage income by 17%. At the industry level, sectors such as transport are likely to have output constrained by demand shocks, while sectors relating to manufacturing, mining and services are more likely to be constrained by supply shocks. Entertainment, restaurants and tourism face large supply and demand shocks. At the occupation level, we show that high-wage occupations are relatively immune from adverse supply and demand-side shocks, while low-wage occupations are much more vulnerable. We should emphasize that our results are only first-order shocks -- we expect them to be substantially amplified by feedback effects in the production network.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
