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Managing Volunteer Engagement Non-profit organizations that provide food, shelter, and other services to people in need, rely on volunteers to deliver their services. Unlike paid labor, non-profit organizations have less control over unpaid volunteers' schedules, efforts, and reliability. However, these organizations can invest in volunteer engagement activities to strive for a steady and adequate supply of volunteer labor. The paper, "A Dynamic Model for Managing Volunteer Engagement", models the volunteer management process. The paper derives a dynamic control policy that judiciously uses the non-profit's resources for volunteer engagement activities depending on explicit congestion thresholds, thereby lowering the overall cost of the non-profit operation. Nonprofit organizations that provide food, shelter, and other services to people in need, rely on volunteers to deliver their services. Unlike paid labor, nonprofit organizations have less control over unpaid volunteers' schedules, efforts, and reliability. However, these organizations can invest in volunteer engagement activities to ensure a steady and adequate supply of volunteer labor. We study a key operational question of how a nonprofit organization can manage its volunteer workforce capacity to ensure consistent provision of services. In particular, we formulate a multiclass queueing network model to characterize the optimal engagement activities for the nonprofit organization to minimize the costs of enhancing volunteer engagement, while maximizing productive work done by volunteers. Because this problem appears intractable, we formulate an approximating Brownian control problem in the heavy traffic limit and study the dynamic control of that system. Our solution is a nested threshold policy with explicit congestion thresholds that indicate when the nonprofit should optimally pursue various types of volunteer engagement activities. A numerical example calibrated using data from a large food bank shows that our dynamic policy for deploying engagement activities can significantly reduce the food bank's total annual cost of its volunteer operations while still maintaining almost the same level of social impact. This improvement in performance does not require any additional resources—it only requires that the food bank strategically deploy its engagement activities based on the number of volunteers signed up to work volunteer shifts. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2021.0419. [ABSTRACT FROM AUTHOR]

Latent Agents in Networks: Estimation and Targeting In "Latent Agents in Networks: Estimation and Targeting," Baris Ata, Alexandre Belloni, and Ozan Candogan address the problem of estimating network effects in a setting in which data only on a subset of agents is available. In this setting, the observable agents influence each other's outcomes both directly and indirectly through their influence on the latent agents. Even in sparse networks, the combination of direct and indirect network effects yields a nonsparse influence structure that makes estimation challenging. The authors overcome this challenge and provide an estimation algorithm that performs well in high-dimensional settings. They also establish convergence rates for their proposed estimator and show that their performance guarantees are valid for a large class of networks. Finally, the authors demonstrate the application of their algorithm to a targeted advertising problem, in which it can be used to obtain asymptotically optimal advertising decisions despite the presence of latent agents. We consider a platform that serves (observable) agents, who belong to a larger network that also includes additional agents who are not served by the platform. We refer to the latter group of agents as latent agents. Associated with each agent are the agent's covariate and outcome. The platform has access to past covariates and outcomes of the observable agents, but no data for the latent agents is available to the platform. Crucially, the agents influence each other's outcome through a certain influence structure. In particular, observable agents influence each other both directly and indirectly through the influence they exert on the latent agents. The platform doesn't know the inference structure of either the observable or the latent parts of the network. We investigate how the platform can estimate the dependence of the observable agents' outcomes on their covariates, taking the presence of the latent agents into account. First, we show that a certain matrix succinctly captures the relationship between the outcomes and the covariates. We provide an algorithm that estimates this matrix using historical data of covariates and outcomes for the observable agents under a suitable approximate sparsity condition. We also establish convergence rates for the proposed estimator despite the high dimensionality that allows more agents than observations. Second, we show that the approximate sparsity condition holds under the standard conditions used in the literature. Hence, our results apply to a large class of networks. Finally, we illustrate the applications to a targeted advertising problem. We show that, by using the available historical data with our estimator, it is possible to obtain asymptotically optimal advertising decisions despite the presence of latent agents. Funding: O. Candogan acknowledges NSF award 2216912 for "Institute for Data, Econometrics, Algorithms and Learning". Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2485. [ABSTRACT FROM AUTHOR]

Background: In 2020, SARS-CoV-2 and the COVID-19 pandemic had a huge impact on the access to and provision of ART treatments. Gradually, knowledge of the virus and its transmission has become available, allowing ART activities to resume. Still, questions on the impact of the virus on human gametes and fertility remain.Objective and Rationale: This article summarizes published data, aiming to clarify the impact of SARS-CoV-2 and the COVID-19 disease on human fertility and assisted reproduction, as well as the impact of vaccination, and from this, provide answers to questions that are relevant for people contemplating pregnancy and for health care professionals.Search Methods: PUBMED/MEDLINE and the WHO COVID-19 database were searched from inception to 5 October 2022 with search terms focusing on 'SARS-CoV-2' and gametes, embryos, reproductive function, fertility and ART. Non-English studies and papers published prior to 2020 were excluded, as well as reviews and non-peer reviewed publications. Full papers were assessed for relevance and quality, where feasible.Outcomes: From the 148 papers included, the following observations were made. The SARS-CoV-2-binding proteins, angiotensin-converting enzyme 2 (ACE2) and type II transmembrane serine protease (TMPRSS2), are expressed in the testis, but co-expression remains to be proven. There is some evidence of SARS-CoV-2 RNA in the ejaculate of COVID-19 patients with severe disease, but not in those with mild/moderate disease. SARS-CoV-2 infection can impair spermatogenesis, but this seems to resolve after one spermatogenic cycle. Testosterone levels seem to be lower during and after COVID-19, but long-term data are lacking; disease severity may be associated with testosterone levels. COVID-19 cannot be considered a sexually transmitted disease. There is no co-expression of ACE2 and TMPRSS2 in the myometrium, uterus, ovaries or fallopian tubes. Oocytes seem to have the receptors and protease machinery to be susceptible to SARS-CoV-2 infection; however, viral RNA in oocytes has not been detected so far. Women contemplating pregnancy following COVID-19 may benefit from screening for thyroid dysfunction. There is a possible (transient) impact of COVID-19 on menstrual patterns. Embryos, and particularly late blastocysts, seem to have the machinery to be susceptible to SARS-CoV-2 infection. Most studies have not reported a significant impact of COVID-19 on ovarian reserve, ovarian function or follicular fluid parameters. Previous asymptomatic or mild SARS-CoV-2 infection in females does not seem to negatively affect laboratory and clinical outcomes of ART. There are no data on the minimum required interval, if any, between COVID-19 recovery and ART. There is no evidence of a negative effect of SARS-CoV-2 vaccination on semen parameters or spermatogenesis, ovarian function, ovarian reserve or folliculogenesis. A transient effect on the menstrual cycle has been documented. Despite concerns, cross reactivity between anti-SARS-CoV-2 spike protein antibodies and Syncytin-1, an essential protein in human implantation, is absent. There is no influence of mRNA SARS-CoV-2 vaccine on patients' performance during their immediate subsequent ART cycle. Pregnancy rates post-vaccination are similar to those in unvaccinated patients.Wider Implications: This review highlights existing knowledge on the impact of SARS-CoV-2 infection or COVID-19 on fertility and assisted reproduction, but also identifies gaps and offers suggestions for future research. The knowledge presented should help to provide evidence-based advice for practitioners and couples contemplating pregnancy alike, facilitating informed decision-making in an environment of significant emotional turmoil. [ABSTRACT FROM AUTHOR]

OBJECTIVE: It is thought that there is not enough data about the frequency of acceptance of oral glucose tolerance test (OGTT) in Turkish pregnant women. The aim of this study was to investigate the frequency of acceptance of OGTT among participants in our single tertiary center. METHODS: The data of non-diabetic 344 pregnant women seen at the Obstetrics Clinic of our hospital between September 2016 and September 2017 were obtained from the hospital records. Women who did not have regular follow-up during pregnancy were excluded. One of the two or one-step approaches was used in the diagnosis of gestational diabetes mellitus (GDM) depending on the choice of the physician following the patient. RESULTS: There were 223 subjects eligible for the study. One hundred seventy-seven pregnant women (79.4%) accepted to do OGTT. We determined that 46 women (20.6%) did not complete at least one OGTT, of whom 74% (n=34) never completed the recommended screening test in this cohort. The overall frequency of GDM was approximately 15.2% (n=34). OGTT acceptability was higher among pregnant women with university graduates (p=0.02). Adverse pregnancy outcomes were similar between the accepted and rejected groups. Among the reasons for OGTT rejection, the media had a significant influence (n=35). CONCLUSION: Our results show that a significant percentage of patients refused to do OGTT. Therefore the actual frequency of pregnant women with GDM could not be determined. One way to increase compliance may be recommending only the one-step test for pregnant women in countries with a high rejection rate of OGTT. [ABSTRACT FROM AUTHOR]