The role of inference in AI: Start S.M.A.L.L. with mindful modeling

This chapter explores trustworthiness in AI and penetrates the black-box opacity through explainable, fair, and ethical AI solutions. AI remains a spirited topic within academic, government, and industrial literature. Much has occurred since the last AI winter in the early 1990's;yet, numerous sources indicate the initial successes solving problems like computer vision, speech recognition, and natural sciences may wane — plunging AI into another winter. Many factors contributed to advances in AI: more data science courses in universities producing data-science capable graduates, high venture capital funding levels encouraging startups, and a decade of broadening awareness among corporate executives about AI promises, real or perceived. Nonetheless, could sources like Gartner be right? Are we approaching another AI winter? As the world learned during the COVID-19 pandemic, when we find ourselves in a crisis, focusing on the fundamentals can have a powerful effect to easing the troubles. As AI makes history, it relies on progress from other domains such as data availability, computing power, and algorithmic advances. Balance among elements maintains a healthy system. AI is no different. Too much or too little of any elemental capability can slow down overall progress. This chapter integrates fundamental ideas from psychology (heuristics and bias), mindfulness in modeling (conceptual models in group settings), and inference (both classical and contemporary). Practitioners may find the techniques proposed in this chapter useful next steps in AI evolution aimed at understanding human behavior. The techniques we discuss can protect against negative impacts resulting from a future AI winter through proper preparation: appreciating the fundamentals, understanding AI assumptions and limitations, and approaching AI assurance in a mindful manner as it evolves. This chapter will address the fundamentals in a unifying example focused on healthcare, with opportunities for trustworthy AI that is impartial, fair, and unbiased. © 2023 Elsevier Inc. All rights reserved.

A Tutorial on Modern Bayesian Methods in Clinical Trials.

Clinical trials continue to be the gold standard for evaluating new medical technologies. New advancements in modern computation power have led to increasing interest in Bayesian methods. Despite the multiple benefits of Bayesian approaches, application to clinical trials has been limited. Based on insights from the survey of clinical researchers in drug development conducted by the Drug Information Association Bayesian Scientific Working Group (DIA BSWG), insufficient knowledge of Bayesian approaches was ranked as the most important perceived barrier to implementing Bayesian methods. Results of the same survey indicate that clinical researchers may find the interpretation of results from a Bayesian analysis to be more useful than conventional interpretations. In this article, we illustrate key concepts tied to Bayesian methods, starting with familiar concepts widely used in clinical practice before advancing in complexity, and use practical illustrations from clinical development.

Quantifying the Trendiness of Trends.

News media often report that the trend of some public health outcome has changed. These statements are frequently based on longitudinal data, and the change in trend is typically found to have occurred at the most recent data collection time point-if no change had occurred the story is less likely to be reported. Such claims may potentially influence public health decisions on a national level. We propose two measures for quantifying the trendiness of trends. Assuming that reality evolves in continuous time, we define what constitutes a trend and a change in trend, and introduce a probabilistic Trend Direction Index. This index has the interpretation of the probability that a latent characteristic has changed monotonicity at any given time conditional on observed data. We also define an index of Expected Trend Instability quantifying the expected number of changes in trend on an interval. Using a latent Gaussian process model, we show how the Trend Direction Index and the Expected Trend Instability can be estimated in a Bayesian framework, and use the methods to analyse the proportion of smokers in Denmark during the last 20 years and the development of new COVID-19 cases in Italy from 24 February onwards.

Declining trends in vaccine confidence across sub-Saharan Africa: A large-scale cross-sectional modeling study.

Current WHO/UNICEF estimates of routine childhood immunization coverage reveal the largest sustained decline in uptake in three decades with pronounced setbacks across Africa. Although the COVID-19 pandemic has induced significant supply and delivery disruptions, the impact of the pandemic on vaccine confidence is less understood. We here examine trends in vaccine confidence across eight sub-Saharan countries between 2020 and 2022 via a total of 17,187 individual interviews, conducted via a multi-stage probability sampling approach and cross-sectional design and evaluated using Bayesian methods. Multilevel regression combined with poststratification weighting using local demographic information yields national and sub-national estimates of vaccine confidence in 2020 and 2022 as well as its socio-demographic associations. We identify declines in perceptions toward the importance of vaccines for children across all eight countries, with mixed trends in perceptions toward vaccine safety and effectiveness. We find that COVID-19 vaccines are perceived to be less important and safe in 2022 than in 2020 in six of the eight countries, with the only increases in COVID-19 vaccine confidence detected in Ivory Coast. There are substantial declines in vaccine confidence in the Democratic Republic of Congo and South Africa, notably in Eastern Cape, KwaZulu-Natal, Limpopo, and Northern Cape (South Africa) and Bandundu, Maniema, Kasaï-Oriental, Kongo-Central, and Sud-Kivu (DRC). While over 60-year-olds in 2022 have higher vaccine confidence in vaccines generally than younger age groups, we do not detect other individual-level socio-demographic associations with vaccine confidence at the sample sizes studied, including sex, age, education, employment status, and religious affiliation. Understanding the role of the COVID-19 pandemic and associated policies on wider vaccine confidence can inform post-COVID vaccination strategies and help rebuild immunization system resilience.

4 - Bayesian Statistical Methods in Stem Cell Transplantation and Cellular Therapy

A brief overview of Bayesian statistics is given, followed by illustrative applications of Bayesian methods in allogeneic hematopoietic cell transplantation (allo-HCT) and cellular therapy. Three clinical trial designs are described, including a study to evaluate safety and efficacy of cytotoxic T-lymphocytes for posttransplant viral infections, a trial to optimize chimeric antigen receptor T-cell dose for hematologic malignancies based on efficacy-toxicity trade-offs, and a randomized study of cord blood derived regulatory T-cells for COVID-19 induced acute respiratory distress syndrome. Three data analyses are described, including a sensitivity analysis of preparative regimen effects for allo-HCT that are confounded with institutional effects, regression-based estimation of the effects on survival of antigens in convalescent plasma therapy for COVID-19, and precision pharmacokinetic-guided dosing of intravenous busulfan in allo-HCT.

Bayesian nonlinear expectation for time series modelling and its application to Bitcoin.

This paper proposes a two-stage approach to parametric nonlinear time series modelling in discrete time with the objective of incorporating uncertainty or misspecification in the conditional mean and volatility. At the first stage, a reference or approximating time series model is specified and estimated. At the second stage, Bayesian nonlinear expectations are introduced to incorporate model uncertainty or misspecification in prediction via specifying a family of alternative models. The Bayesian nonlinear expectations for prediction are constructed from closed-form Bayesian credible intervals evaluated using conjugate priors and residuals of the estimated approximating model. Using real Bitcoin data including some periods of Covid 19, applications of the proposed method to forecasting and risk evaluation of Bitcoin are discussed via three major parametric nonlinear time series models, namely the self-exciting threshold autoregressive model, the generalized autoregressive conditional heteroscedasticity model and the stochastic volatility model. Supplementary Information: The online version contains supplementary material available at 10.1007/s00181-022-02255-z.

A comparative research on teachers' knowledge in five Asia-Pacific countries in the COVID-19 pandemic: The case of tourism and hospitality education

The COVID-19 outbreak at the beginning of 2020 has drastically impacted almost every aspect of our daily life. Empirical evidence is lacking on which sector of knowledge in technology-enhanced teaching needs to be developed further for tourism and hospitality programs conducted online. The present study investigated teachers' technology, learners, pedagogy, academic discipline content knowledge, and context knowledge (TLPACK) in tourism and hospitality online education settings using comparative research methods. A total of 173 participants from five countries (Indonesia, Philippines, Taiwan, Thailand, and Vietnam) were surveyed online. The results revealed that, despite the fact that they were from different countries, all teachers reached a consensus that their knowledge about learners was the lowest during the online teaching period of the pandemic;meanwhile, they all ranked academic knowledge as the highest among these five variables except Vietnamese teachers who considered their knowledge on pedagogy to be the highest. Additionally, their TLPACK revealed significant differences in various countries and differences in academic discipline content knowledge are caused by the interaction of nationality and gender. This study overcomes a major limitation of previous studies on how the pandemic has affected educational praxis as the focus of previous research has been on the situation in a single country. Therefore, the present study's findings can serve as a reference for practitioners of tourism and hospitality online education in Asia-Pacific region when facing unprecedented and urgent changes of educational practices during and post the COVID-19 pandemic.

Social Support and Resilience as Predictors of Prosocial Behaviors before and during COVID-19.

The objective of this research was to analyze the relationship between social support and resilience with prosocial behavior before and during the confinement caused by COVID-19. Materials and Methods: The participants were divided into a confined group (228 women and 84 men) and an unconfined group (153 women and 105 men), all of whom were university students. Instruments were applied to measure the variables proposed. Results: Social support predicted 24.4% of the variance in prosocial behavior among women and 12% among men in the confined group; no evidence of this relationship was found in the unconfined groups. Resilience predicted 7% of the variance in prosocial behavior among confined women, 8.4% among confined men, 8.8% among unconfined women, and 5.1% in unconfined men. Discussion and Conclusion: The results show the importance of social support and resilience in prosocial behaviors, which are key elements for the proper functioning of society, especially in the face of a crisis such as COVID-19.

THE ECONOMIC IMPACT OF FALSE POSITIVITY OF COVID-19 PCR TESTING IN THE CZECH REPUBLIC

The COVID-19 pandemic has affected the entire world, causing significant losses to the world's population's health, lives, and economic levels. The process of testing using RT-PCR tests also had other serious economic impacts. The testing process also sometimes results in erroneous results. One of them is false positivity. This article uses the Bayesian approach which estimates the economic impacts of false-positive results. The Bayesian approach takes into account a prior probability distribution depending on the prevalence of the disease in the population. False-positive results can be minimized by retesting positive persons who have no clinical symptoms of COVID-19. The costs of retesting these people are significantly lower than those associated with isolating them and quarantining their contacts.

Bayesian Inference in Census-House Dataset

As one of the most popular probabilistic programming tools, PyMC3 can solve inference problems in many scientific fields. In this paper, we used PyMC3 to build a Bayesian model for the census-house dataset to predict the correspondence between the U.S. population and house prices, and evaluated it using the dataset to determine the validity and accuracy of the established model. Through the evaluation of this dataset, the Bayesian model established in this paper can predict the theoretical data of house prices with high accuracy in the absence of COVID-19, which has implications for the study of the current property prices that have increased significantly because of COVID-19 and the due prices of similar large assets, researchers can predict the house prices in the absence of COVID-19, and then based on the current house prices calculate the difference and thus study the impact of COVID-19 in terms of house prices as well as the impact of similar asset prices. © 2021 IEEE.

Infectious disease phylodynamics with occurrence data

1. Phylodynamic models use pathogen genome sequence data to infer epidemiological dynamics. With the increasing genomic surveillance of pathogens, especially during the SARS-CoV-2 pandemic, new practical questions about their use are emerging. 2. One such question focuses on the inclusion of un-sequenced case occurrence data alongside sequenced data to improve phylodynamic analyses. This approach can be particularly valuable if sequencing efforts vary over time. 3. Using simulations, we demonstrate that birth-death phylodynamic models can employ occurrence data to eliminate bias in estimates of the basic reproductive number due to misspecification of the sampling process. In contrast, the coalescent exponential model is robust to such sampling biases, but in the absence of a sampling model it cannot exploit occurrence data. Subsequent analysis of the SARS-CoV-2 epidemic in the northwest USA supports these results. 4. We conclude that occurrence data are a valuable source of information in combination with birth-death models. These data should be used to bolster phylodynamic analyses of infectious diseases and other rapidly spreading species in the future.

Performance of Three Tests for SARS-CoV-2 on a University Campus Estimated Jointly with Bayesian Latent Class Modeling.

Accurate tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been critical in efforts to control its spread. The accuracy of tests for SARS-CoV-2 has been assessed numerous times, usually in reference to a gold standard diagnosis. One major disadvantage of that approach is the possibility of error due to inaccuracy of the gold standard, which is especially problematic for evaluating testing in a real-world surveillance context. We used an alternative approach known as Bayesian latent class modeling (BLCM), which circumvents the need to designate a gold standard by simultaneously estimating the accuracy of multiple tests. We applied this technique to a collection of 1,716 tests of three types applied to 853 individuals on a university campus during a 1-week period in October 2020. We found that reverse transcriptase PCR (RT-PCR) testing of saliva samples performed at a campus facility had higher sensitivity (median, 92.3%; 95% credible interval [CrI], 73.2 to 99.6%) than RT-PCR testing of nasal samples performed at a commercial facility (median, 85.9%; 95% CrI, 54.7 to 99.4%). The reverse was true for specificity, although the specificity of saliva testing was still very high (median, 99.3%; 95% CrI, 98.3 to 99.9%). An antigen test was less sensitive and specific than both of the RT-PCR tests, although the sample sizes with this test were small and the statistical uncertainty was high. These results suggest that RT-PCR testing of saliva samples at a campus facility can be an effective basis for surveillance screening to prevent SARS-CoV-2 transmission in a university setting. IMPORTANCE Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been vitally important during the COVID-19 pandemic. There are a variety of methods for testing for this virus, and it is important to understand their accuracy in choosing which one might be best suited for a given application. To estimate the accuracy of three different testing methods, we used a data set collected at a university that involved testing the same samples with multiple tests. Unlike most other estimates of test accuracy, we did not assume that one test was perfect but instead allowed for some degree of inaccuracy in all testing methods. We found that molecular tests performed on saliva samples at a university facility were similarly accurate as molecular tests performed on nasal samples at a commercial facility. An antigen test appeared somewhat less accurate than the molecular tests, but there was high uncertainty about that.

Clinical Effectiveness of REGN-COV2 in Patients with COVID-19 in Japan: A Retrospective Cohort Study with a Bayesian Inference.

BACKGROUND: Neutralizing antibody cocktail therapy, REGN-COV2, is promising in preventing a severe form of coronavirus disease 2019 (COVID-19), but its effectiveness in Japan has not been fully investigated. MATERIALS AND METHODS: To evaluate the effectiveness of REGN-COV2, clinical data of 20 patients with COVID-19 who received REGN-COV2 was compared with the control by matching age and sex. The primary outcome was the time from the onset to defervescence, the duration of hospitalization, and oxygen requirement. A sensitivity analysis using Bayesian analysis was also conducted. RESULTS: The time to defervescence was significantly shorter in the treatment group (5.25 vs. 7.95 days, P = 0.02), and so was the duration of hospitalization (7.115 vs. 11.45, P = 0.0009). However, the oxygen therapy requirement did not differ between the two groups (15% vs. 35%, P = 0.27). For Bayesian analysis, the median posterior probability of the time to defervescence since the symptom onset on the REGN-COV2 group was 5.28 days [95% credible interval (CrI): 4.28 - 6.31 days], compared with the control of 7.99 days (95% CrI: 6.81 - 9.24 days). The posterior probability of the duration of the hospitalization on the REGN-COV2 group was 7.17 days (95% CrI: 5.99 - 8.24 days), compared with the control of 11.54 days (95% CrI: 10.28 - 13.14 days). The posterior probability of the oxygen requirement on the REGN-COV2 group was 18% (95% CrI: 3 - 33%), compared with the control of 36% (95% CrI: 16 - 54%). CONCLUSION: REGN-COV2 may be effective in early defervescence and shorter hospitalization. Its effectiveness for preventing a severe form of infection needs to be evaluated by further studies.

Estimating viral prevalence with data fusion for adaptive two-phase pooled sampling.

The COVID-19 pandemic has highlighted the importance of efficient sampling strategies and statistical methods for monitoring infection prevalence, both in humans and in reservoir hosts. Pooled testing can be an efficient tool for learning pathogen prevalence in a population. Typically, pooled testing requires a second-phase retesting procedure to identify infected individuals, but when the goal is solely to learn prevalence in a population, such as a reservoir host, there are more efficient methods for allocating the second-phase samples.To estimate pathogen prevalence in a population, this manuscript presents an approach for data fusion with two-phased testing of pooled samples that allows more efficient estimation of prevalence with less samples than traditional methods. The first phase uses pooled samples to estimate the population prevalence and inform efficient strategies for the second phase. To combine information from both phases, we introduce a Bayesian data fusion procedure that combines pooled samples with individual samples for joint inferences about the population prevalence.Data fusion procedures result in more efficient estimation of prevalence than traditional procedures that only use individual samples or a single phase of pooled sampling.The manuscript presents guidance on implementing the first-phase and second-phase sampling plans using data fusion. Such methods can be used to assess the risk of pathogen spillover from reservoir hosts to humans, or to track pathogens such as SARS-CoV-2 in populations.

A Statistical Model to Assess Risk for Supporting COVID-19 Quarantine Decisions.

In Germany, local health departments are responsible for surveillance of the current pandemic situation. One of their major tasks is to monitor infected persons. For instance, the direct contacts of infectious persons at group meetings have to be traced and potentially quarantined. Such quarantine requirements may be revoked, when all contact persons obtain a negative polymerase chain reaction (PCR) test result. However, contact tracing and testing is time-consuming, costly and not always feasible. In this work, we present a statistical model for the probability that no transmission of COVID-19 occurred given an arbitrary number of negative test results among contact persons. Hereby, the time-dependent sensitivity and specificity of the PCR test are taken into account. We employ a parametric Bayesian model which combines an adaptable Beta-Binomial prior and two likelihood components in a novel fashion. This is illustrated for group events in German school classes. The first evaluation on a real-world dataset showed that our approach can support important quarantine decisions with the goal to achieve a better balance between necessary containment of the pandemic and preservation of social and economic life. Future work will focus on further refinement and evaluation of quarantine decisions based on our statistical model.

Alternative Strategies for the Estimation of a Disease's Basic Reproduction Number: A Model-Agnostic Study.

This work presents a model-agnostic evaluation of four different models that estimate a disease's basic reproduction number. The evaluation presented is twofold: first, the theory behind each of the models is reviewed and compared; then, each model is tested with eight impartial simulations. All scenarios were constructed in an experimental framework that allows each model to fulfill its assumptions and hence, obtain unbiased results for each case. Among these models is the one proposed by Thompson et al. (Epidemics 29:100356, 2019), i.e., a Bayesian estimation method well established in epidemiological practice. The other three models include a novel state-space method and two simulation-based approaches based on a Poisson infection process. The advantages and flaws of each model are discussed from both theoretical and practical standpoints. Finally, we present the evolution of Covid-19 outbreak in Colombia as a case study for computing the basic reproduction number with each one of the reviewed methods.

Effect estimates of COVID-19 non-pharmaceutical interventions are non-robust and highly model-dependent.

OBJECTIVE: To compare the inference regarding the effectiveness of the various non-pharmaceutical interventions (NPIs) for COVID-19 obtained from different SIR models. STUDY DESIGN AND SETTING: We explored two models developed by Imperial College that considered only NPIs without accounting for mobility (model 1) or only mobility (model 2), and a model accounting for the combination of mobility and NPIs (model 3). Imperial College applied models 1 and 2 to 11 European countries and to the USA, respectively. We applied these models to 14 European countries (original 11 plus another 3), over two different time horizons. RESULTS: While model 1 found that lockdown was the most effective measure in the original 11 countries, model 2 showed that lockdown had little or no benefit as it was typically introduced at a point when the time-varying reproduction number was already very low. Model 3 found that the simple banning of public events was beneficial, while lockdown had no consistent impact. Based on Bayesian metrics, model 2 was better supported by the data than either model 1 or model 3 for both time horizons. CONCLUSION: Inferences on effects of NPIs are non-robust and highly sensitive to model specification. In the SIR modeling framework, the impacts of lockdown are uncertain and highly model-dependent.

Daily Forecasting of Regional Epidemics of Coronavirus Disease with Bayesian Uncertainty Quantification, United States.

To increase situational awareness and support evidence-based policymaking, we formulated a mathematical model for coronavirus disease transmission within a regional population. This compartmental model accounts for quarantine, self-isolation, social distancing, a nonexponentially distributed incubation period, asymptomatic persons, and mild and severe forms of symptomatic disease. We used Bayesian inference to calibrate region-specific models for consistency with daily reports of confirmed cases in the 15 most populous metropolitan statistical areas in the United States. We also quantified uncertainty in parameter estimates and forecasts. This online learning approach enables early identification of new trends despite considerable variability in case reporting.

Racial, Ethnic, and Geographic Disparities in Novel Coronavirus (Severe Acute Respiratory Syndrome Coronavirus 2) Test Positivity in North Carolina.

BACKGROUND: Emerging evidence suggests that black and Hispanic communities in the United States are disproportionately affected by coronavirus disease 2019 (COVID-19). A complex interplay of socioeconomic and healthcare disparities likely contribute to disproportionate COVID-19 risk. METHODS: We conducted a geospatial analysis to determine whether individual- and neighborhood-level attributes predict local odds of testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We analyzed 29 138 SARS-CoV-2 tests within the 6-county catchment area for Duke University Health System from March to June 2020. We used generalized additive models to analyze the spatial distribution of SARS-CoV-2 positivity. Adjusted models included individual-level age, gender, and race, as well as neighborhood-level Area Deprivation Index, population density, demographic composition, and household size. RESULTS: Our dataset included 27 099 negative and 2039 positive unique SARS-CoV-2 tests. The odds of a positive SARS-CoV-2 test were higher for males (odds ratio [OR], 1.43; 95% credible interval [CI], 1.30-1.58), blacks (OR, 1.47; 95% CI, 1.27-1.70), and Hispanics (OR, 4.25; 955 CI, 3.55-5.12). Among neighborhood-level predictors, percentage of black population (OR, 1.14; 95% CI, 1.05-1.25), and percentage Hispanic population (OR, 1.23; 95% CI, 1.07-1.41) also influenced the odds of a positive SARS-CoV-2 test. Population density, average household size, and Area Deprivation Index were not associated with SARS-CoV-2 test results after adjusting for race. CONCLUSIONS: The odds of testing positive for SARS-CoV-2 were higher for both black and Hispanic individuals, as well as within neighborhoods with a higher proportion of black or Hispanic residents-confirming that black and Hispanic communities are disproportionately affected by SARS-CoV-2.