Analysis Of Shortfalls Research Articles

Progress Toward Equitable Mpox Vaccination Coverage: A Shortfall Analysis — United States, May 2022–April 2023

More than 30,000 monkeypox (mpox) cases were reported in the United States during the 2022 multinational outbreak; cases disproportionately affected gay, bisexual, and other men who have sex with men (MSM). Substantial racial and ethnic disparities in incidence were also reported (1). The national mpox vaccination strategy* emphasizes that efforts to administer the JYNNEOS mpox vaccine should be focused among the populations at elevated risk for exposure to mpox (2). During May 2022-April 2023, a total of 748,329 first JYNNEOS vaccine doses (of the two recommended) were administered in the United States.† During the initial months of the outbreak, lower vaccination coverage rates among racial and ethnic minority groups were reported (1,3); however, after implementation of initiatives developed to expand access to mpox vaccination,§ coverage among racial and ethnic minority groups increased (1,4). A shortfall analysis was conducted to examine whether the increase in mpox vaccination coverage was equitable across all racial and ethnic groups (5). Shortfall was defined as the percentage of the vaccine-eligible population that did not receive the vaccine (i.e., 100% minus the percentage of the eligible population that did receive a first dose). Monthly mpox vaccination shortfalls were calculated and were stratified by race and ethnicity; monthly percent reductions in shortfall were also calculated compared with the preceding month's shortfall (6). The mpox vaccination shortfall decreased among all racial and ethnic groups during May 2022-April 2023; however, based on analysis of vaccine administration data with race and ethnicity reported, 66.0% of vaccine-eligible persons remained unvaccinated at the end of this period. The shortfall was largest among non-Hispanic Black or African American (Black) (77.9%) and non-Hispanic American Indian or Alaska Native (AI/AN) (74.5%) persons, followed by non-Hispanic White (White) (66.6%) and Hispanic or Latino (Hispanic) (63.0%) persons, and was lowest among non-Hispanic Asian (Asian) (38.5%) and non-Hispanic Native Hawaiian and other Pacific Islander (NH/OPI) (43.7%) persons. The largest percentage decreases in the shortfall were achieved during August (17.7%) and September (8.5%). However, during these months, smaller percentage decreases were achieved among Black persons (12.2% and 4.9%, respectively), highlighting the need for a focus on equity for the entirety of a public health response. Achieving equitable progress in JYNNEOS vaccination coverage will require substantial decreases in shortfalls among Black and AI/AN persons.

Some New Alternative Formulations of Adaptive Kalman Filter for Market Risk Beta Estimation

Kalman filter (KF) provides optimal beta estimate with linear models where the noise covariances are known a priori. Noise covariance adaptation-based adaptive KFs (AKFs) have also been used to get these beta estimates. These AKFs suffer from one typical problem, namely inadequate noise filtering. This paper explores some new formulation of such AKFs to solve this problem in addition to applying other related existing formulations. The proposed methods have been analysed through simulation study along with empirical performance verifications through VaR backtesting, expected shortfall analysis, and in-sample performance analysis. Results show that two new and one existing AKFs are successful to provide smooth beta estimates.

Performance evaluation of modified adaptive Kalman filters, least means square and recursive least square methods for market risk beta and VaR estimation

Adaptive Kalman Filters (AKFs) are well known for their navigational applications. This work bridges the gap in the evolution of AKFs to handle parameter inconsistency problems with adaptive noise covariances. The focus is to apply proposed techniques for beta and VaR estimation of assets. The empirical performance of the proposed filters are compared with the standard least square family and KF with respect to VaR backtesting, expected shortfall analysis and in-sample forecasting performance analysis using Indian market data. Results show that the Modified AKFs are performing at par with the bench mark even with these adaptive noise covariance assumptions.

A simple model of potato growth and yield: Part II. Validation and external sensitivity

A model has been developed which calculates the dates of 50% emergence and of canopy closure in the potato crop and which estimates total dry matter production and yield. The output from the model has been tested against three sets of independent data, one to test the estimation of dates of emergence and canopy closure, a second to examine the sequence of dry matter accumulation and the third to test estimates of total yield. This validation exercise has shown the model to be successful in its aims of providing an estimate of potential yield of a potato crop using only simple inputs and of describing the development of the crop towards that yield. The limitations of the model have been identified and possible developments have been discussed. The sensitivity of the potential yield to changes in times of planting and harvest and to a range of real combinations of weather has been examined to show the possible consequences of altered farm practice and the year-to-year fluctuations in temperature and radiation receipt. The use of the model in the analysis of shortfalls in farm yields has been discussed.