estimateR: an R package to estimate and monitor the effective reproductive number.

BACKGROUND: Accurate estimation of the effective reproductive number ([Formula: see text]) of epidemic outbreaks is of central relevance to public health policy and decision making. We present estimateR, an R package for the estimation of the reproductive number through time from delayed observations of infection events. Such delayed observations include confirmed cases, hospitalizations or deaths. The package implements the methodology of Huisman et al. but modularizes the [Formula: see text] estimation procedure to allow easy implementation of new alternatives to the currently available methods. Users can tailor their analyses according to their particular use case by choosing among implemented options. RESULTS: The estimateR R package allows users to estimate the effective reproductive number of an epidemic outbreak based on observed cases, hospitalization, death or any other type of event documenting past infections, in a fast and timely fashion. We validated the implementation with a simulation study: estimateR yielded estimates comparable to alternative publicly available methods while being around two orders of magnitude faster. We then applied estimateR to empirical case-confirmation incidence data for COVID-19 in nine countries and for dengue fever in Brazil; in parallel, estimateR is already being applied (i) to SARS-CoV-2 measurements in wastewater data and (ii) to study influenza transmission based on wastewater and clinical data in other studies. In summary, this R package provides a fast and flexible implementation to estimate the effective reproductive number for various diseases and datasets. CONCLUSIONS: The estimateR R package is a modular and extendable tool designed for outbreak surveillance and retrospective outbreak investigation. It extends the method developed for COVID-19 by Huisman et al. and makes it available for a variety of pathogens, outbreak scenarios, and observation types. Estimates obtained with estimateR can be interpreted directly or used to inform more complex epidemic models (e.g. for forecasting) on the value of [Formula: see text].

From hype to reality: data science enabling personalized medicine.

BACKGROUND: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. CONCLUSIONS: There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.

Causal stability ranking.

Genotypic causes of a phenotypic trait are typically determined via randomized controlled intervention experiments. Such experiments are often prohibitive with respect to durations and costs, and informative prioritization of experiments is desirable. We therefore consider predicting stable rankings of genes (covariates), according to their total causal effects on a phenotype (response), from observational data. Since causal effects are generally non-identifiable from observational data only, we use a method that can infer lower bounds for the total causal effect under some assumptions. We validated our method, which we call Causal Stability Ranking (CStaR), in two situations. First, we performed knock-out experiments with Arabidopsis thaliana according to a predicted ranking based on observational gene expression data, using flowering time as phenotype of interest. Besides several known regulators of flowering time, we found almost half of the tested top ranking mutants to have a significantly changed flowering time. Second, we compared CStaR to established regression-based methods on a gene expression dataset of Saccharomyces cerevisiae. We found that CStaR outperforms these established methods. Our method allows for efficient design and prioritization of future intervention experiments, and due to its generality it can be used for a broad spectrum of applications.

Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2.

The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of Re, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated Re. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent Re estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of Re estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.

Over the past two and a half years, countries around the globe have struggled to control the transmission of the SARS-CoV-2 virus within their borders. To manage the situation, it is important to have an accurate picture of how fast the virus is spreading. This can be achieved by calculating the effective reproductive number (Re), which describes how many people, on average, someone with COVID-19 is likely to infect. If the Re is greater than one, the virus is infecting increasingly more people, but if it is smaller than one, the number of cases is declining. Scientists use various strategies to estimate the Re, which each have their own strengths and weaknesses. One of the main difficulties is that infections are typically recorded only when people test positive for COVID-19, are hospitalized with the virus, or die. This means that the data provides a delayed representation of when infections are happening. Furthermore, changes in these records occur later than measures that change the infection dynamics. As a result, researchers need to take these delays into account when estimating Re. Here, Huisman, Scire et al. have developed a new method for estimating the Re based on available data records, statistically taking into account the above-mentioned delays. An online dashboard with daily updates was then created so that policy makers and the population could monitor the values over time. For over two years, Huisman, Scire et al. have been applying their tool and dashboard to COVID-19 data from 170 countries. They found that public health interventions, such as mask requirements and lockdowns, did help reduce the Re in Europe. But the effects were not uniform across the globe, likely because of variations in how restrictions were implemented and followed during the pandemic. In early 2020, the Re only dropped below one after countries put lockdowns or other severe measures in place. The Re values added to the dashboard over the last two years have been used pro-actively to inform public health policies in Switzerland and to monitor the spread of SARS-CoV-2 in South Africa. The team has also recently released programming software based on this method that can be used to track future disease outbreaks, and extended the method to estimate the Re using SARS-CoV-2 levels in wastewater.

The importance of timely contact tracing - A simulation study.

BACKGROUND: While the role of contact tracing in the containment of the COVID-19 epidemic remains important until vaccines are widely available, literature on objectively measurable indicators for the effectiveness of contact tracing is scarce. We suggest the diagnostic serial interval, the time between the diagnosis of the infector and infectee, as a new indicator for the effectiveness of contact tracing. METHODS: Using an agent-based simulation model, we demonstrate how the diagnostic serial interval correlates with the course of the epidemic. We consider four scenarios of how diagnosis and subsequent isolation are triggered: 1. never, 2. by symptoms, 3. by symptoms and loose contact tracing, 4. by symptoms and tight contact tracing. We further refine scenarios 3 and 4 with different lengths of target diagnostic serial intervals. RESULTS: Scenarios 1 and 2 did not yield a notable difference. In scenarios 3 and 4, however, contact tracing led to a decrease of the height of the epidemic as well as the cumulative proportion of infected agents. Generally, the shorter the diagnostic serial interval was, the smaller the peak of the epidemic became, and the more proportion of the population remained susceptible at the end of the epidemic. CONCLUSION: A short target diagnosis interval is critical for contact tracing to be effective in the epidemic control. The diagnosis interval can be used to assess and guide the contact tracing strategy.

Comprehensive Statistical Exploration of Prognostic (Bio-)Markers for Responses to Immune Checkpoint Inhibitor in Patients with Non-Small Cell Lung Cancer.

Metastatic non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs) may suffer from heavy side effects and not all patients benefit from the treatment. We conducted a comprehensive statistical analysis to identify promising (bio-)markers for treatment response. We analyzed retrospective data from NSCLC patients treated with ICIs in first- or further-line therapy settings at the University Hospital Zurich. We investigated 16 possible prognostic markers with respect to overall survival, tumor size reduction, and the development of an immune-related adverse event (irAE) and assessed the robustness of our results. For the further-line patient group, the most significant result was that increased basophil counts were associated with increased odds of tumor size reduction within three months and with the development of an irAE. For the first-line patient group, the most significant results were that increased lymphocyte counts, the histology of adenocarcinoma, and the intake of non-steroidal anti-rheumatic drugs (NSAR) were associated with decreased hazards of dying. Our study yielded new hypotheses for predictive (bio-)markers for response to ICIs in NSCLC patients. The possibly beneficial role of high basophil counts is a particularly interesting finding. Our results should be tested on independent data in a prospective fashion.

Understanding human functioning using graphical models.

BACKGROUND: Functioning and disability are universal human experiences. However, our current understanding of functioning from a comprehensive perspective is limited. The development of the International Classification of Functioning, Disability and Health (ICF) on the one hand and recent developments in graphical modeling on the other hand might be combined and open the door to a more comprehensive understanding of human functioning. The objective of our paper therefore is to explore how graphical models can be used in the study of ICF data for a range of applications. METHODS: We show the applicability of graphical models on ICF data for different tasks: Visualization of the dependence structure of the data set, dimension reduction and comparison of subpopulations. Moreover, we further developed and applied recent findings in causal inference using graphical models to estimate bounds on intervention effects in an observational study with many variables and without knowing the underlying causal structure. RESULTS: In each field, graphical models could be applied giving results of high face-validity. In particular, graphical models could be used for visualization of functioning in patients with spinal cord injury. The resulting graph consisted of several connected components which can be used for dimension reduction. Moreover, we found that the differences in the dependence structures between subpopulations were relevant and could be systematically analyzed using graphical models. Finally, when estimating bounds on causal effects of ICF categories on general health perceptions among patients with chronic health conditions, we found that the five ICF categories that showed the strongest effect were plausible. CONCLUSIONS: Graphical Models are a flexible tool and lend themselves for a wide range of applications. In particular, studies involving ICF data seem to be suited for analysis using graphical models.

Diagnostic serial interval as a novel indicator for contact tracing effectiveness exemplified with the SARS-CoV-2/COVID-19 outbreak in South Korea.

BACKGROUND: The clinical onset serial interval is often used as a proxy for the transmission interval of an infectious disease. For SARS-CoV-2/COVID-19, data on clinical onset serial intervals is limited, since symptom onset dates are not routinely recorded and do not exist in asymptomatic carriers. METHODS: We define the diagnostic serial interval as the time between the diagnosis dates of the infector and infectee. Based on the DS4C project data on SARS-CoV-2/COVID-19 in South Korea, we estimate the means of the diagnostic serial interval, the clinical onset serial interval, and the difference between the two. We use the balanced cluster bootstrap method to construct 95% bootstrap confidence intervals. RESULTS: The mean of the diagnostic serial interval was estimated to be 3.63 days (95% CI: 3.24, 4.01). The diagnostic serial interval was significantly shorter than the clinical onset serial interval (estimated mean difference -1.12 days, 95% CI: -1.98, -0.26). CONCLUSIONS: The relatively short diagnostic serial intervals of SARS-CoV-2/COVID-19 in South Korea are likely due to the country's extensive efforts towards contact tracing. We propose the mean diagnostic serial interval as a new indicator for the effectiveness of a country's contact tracing as part of the epidemic surveillance.

Loss to follow-up correction increased mortality estimates in HIV-positive people on antiretroviral therapy in Mozambique.

OBJECTIVES: People living with human immunodeficiency virus (HIV) on antiretroviral therapy (ART) may be lost to follow-up (LTFU), which hampers the assessment of outcomes. We estimated mortality for patients starting ART in a rural region in sub-Saharan Africa and examined risk factors for death, correcting for LTFU. STUDY DESIGN AND SETTING: We analyzed data from Ancuabe, Mozambique, where patients LTFU are traced by phone and home visits. We used cumulative incidence functions to estimate mortality and LTFU. To correct for LTFU, we revised outcomes based on tracing data using different inverse probability weights (maximum likelihood, Ridge regression, or Bayesian model averaging). We fitted competing risk models to identify risk factors for death and LTFU. RESULTS: The analyses included 4,492 patients; during 8,152 person-years of follow-up, 486 patients died, 2,375 were LTFU, 752 were traced, and 603 were found. At 4 years after starting ART, observed mortality was 11.9% (95% confidence interval [CI]: 10.9-13.0), but 23.5% (95% CI: 19.8-28.0), 21.6% (95% CI: 18.7-25.0), and 23.3% (95% CI: 19.7-27.6) after correction with maximum likelihood, Ridge regression, and Bayesian model averaging weights, respectively. The risk factors for death included male sex, lower CD4 cell counts, and more advanced clinical stage. CONCLUSION: In ART programs with substantial LTFU, mortality estimates need to take LTFU into account.

Socio-behavioural characteristics and HIV: findings from a graphical modelling analysis of 29 sub-Saharan African countries.

INTRODUCTION: Socio-behavioural factors may contribute to the wide variance in HIV prevalence between and within sub-Saharan African (SSA) countries. We studied the associations between socio-behavioural variables potentially related to the risk of acquiring HIV. METHODS: We used Bayesian network models to study associations between socio-behavioural variables that may be related to HIV. A Bayesian network consists of nodes representing variables, and edges representing the conditional dependencies between variables. We analysed data from Demographic and Health Surveys conducted in 29 SSA countries between 2010 and 2016. We predefined and dichotomized 12 variables, including factors related to age, literacy, HIV knowledge, HIV testing, domestic violence, sexual activity and women's empowerment. We analysed data on men and women for each country separately and then summarized the results across the countries. We conducted a second analysis including also the individual HIV status in a subset of 23 countries where this information was available. We presented summary graphs showing associations that were present in at least six countries (five in the analysis with HIV status). RESULTS: We analysed data from 190,273 men (range across countries 2295 to 17,359) and 420,198 women (6621 to 38,948). The two variables with the highest total number of edges in the summary graphs were literacy and rural/urban location. Literacy was negatively associated with false beliefs about AIDS and, for women, early sexual initiation, in most countries. Literacy was also positively associated with ever being tested for HIV and the belief that women have the right to ask their husband to use condoms if he has a sexually transmitted infection. Rural location was positively associated with false beliefs about HIV and the belief that beating one's wife is justified, and negatively associated with having been tested for HIV. In the analysis including HIV status, being HIV positive was associated with female-headed household, older age and rural location among women, and with no variables among men. CONCLUSIONS: Literacy and urbanity were strongly associated with several factors that are important for HIV acquisition. Since literacy is one of the few variables that can be improved by interventions, this makes it a promising intervention target.

CURRENT STATUS DATA WITH COMPETING RISKS: LIMITING DISTRIBUTION OF THE MLE.

We study nonparametric estimation for current status data with competing risks. Our main interest is in the nonparametric maximum likelihood estimator (MLE), and for comparison we also consider a simpler 'naive estimator'. Groeneboom, Maathuis and Wellner [8] proved that both types of estimators converge globally and locally at rate n(1/3). We use these results to derive the local limiting distributions of the estimators. The limiting distribution of the naive estimator is given by the slopes of the convex minorants of correlated Brownian motion processes with parabolic drifts. The limiting distribution of the MLE involves a new self-induced limiting process. Finally, we present a simulation study showing that the MLE is superior to the naive estimator in terms of mean squared error, both for small sample sizes and asymptotically.

Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example.

Most psychiatric disorders are associated with subtle alterations in brain function and are subject to large interindividual differences. Typically, the diagnosis of these disorders requires time-consuming behavioral assessments administered by a multidisciplinary team with extensive experience. While the application of Machine Learning classification methods (ML classifiers) to neuroimaging data has the potential to speed and simplify diagnosis of psychiatric disorders, the methods, assumptions, and analytical steps are currently opaque and not accessible to researchers and clinicians outside the field. In this paper, we describe potential classification pipelines for autism spectrum disorder, as an example of a psychiatric disorder. The analyses are based on resting-state fMRI data derived from a multisite data repository (ABIDE). We compare several popular ML classifiers such as support vector machines, neural networks, and regression approaches, among others. In a tutorial style, written to be equally accessible for researchers and clinicians, we explain the rationale of each classification approach, clarify the underlying assumptions, and discuss possible pitfalls and challenges. We also provide the data as well as the MATLAB code we used to achieve our results. We show that out-of-the-box ML classifiers can yield classification accuracies of about 60-70%. Finally, we discuss how classification accuracy can be further improved, and we mention methodological developments that are needed to pave the way for the use of ML classifiers in clinical practice.

The causal effect of switching to second-line ART in programmes without access to routine viral load monitoring.

OBJECTIVES: We examined the effect of switching to second-line antiretroviral therapy (ART) on mortality in patients who experienced immunological failure in ART programmes without access to routine viral load monitoring in sub-Saharan Africa. DESIGN AND SETTING: Collaborative analysis of two ART programmes in Lusaka, Zambia and Lilongwe, Malawi. METHODS: We included all adult patients experiencing immunological failure based on WHO criteria. We used Cox proportional hazards models weighted by the inverse probability of switching to compare mortality between patients who switched and patients who did not; and between patients who switched immediately and patients who switched later. Results are expressed as hazard ratios with 95% credible intervals (95% CI). RESULTS: Among 2411 patients with immunological failure 324 patients (13.4%) switched to second-line ART during 3932 person-years of follow-up. The median CD4 cell count at start of ART and failure was lower in patients who switched compared to patients who did not: 80 versus 155 cells/µl (P < 0.001) and 77 versus 146 cells/µl (P < 0.001), respectively. Adjusting for baseline and time-dependent confounders, mortality was lower among patients who switched compared to patients remaining on failing first-line ART: hazard ratio 0.25 (95% CI 0.09-0.72). Mortality was also lower among patients who remained on failing first-line ART for shorter periods: hazard ratio 0.70 (95% CI 0.44-1.09) per 6 months shorter exposure. CONCLUSION: In ART programmes switching patients to second-line regimens based on WHO immunological failure criteria appears to reduce mortality, with the greatest benefit in patients switching immediately after immunological failure is diagnosed.

Inconsistency of the MLE for the Joint Distribution of Interval-Censored Survival Times and Continuous Marks.

This paper considers the non-parametric maximum likelihood estimator (MLE) for the joint distribution function of an interval-censored survival time and a continuous mark variable. We provide a new explicit formula for the MLE in this problem. We use this formula and the mark-specific cumulative hazard function of Huang & Louis (1998) to obtain the almost sure limit of the MLE. This result leads to necessary and sufficient conditions for consistency of the MLE, which imply that the MLE is inconsistent in general. We show that the inconsistency can be repaired by discretizing the marks. Our theoretical results are supported by simulations.

Nonparametric estimation of the joint distribution of a survival time subject to interval censoring and a continuous mark variable.

This article considers three nonparametric estimators of the joint distribution function for a survival time and a continuous mark variable when the survival time is interval censored and the mark variable may be missing for interval-censored observations. Finite and large sample properties are described for the nonparametric maximum likelihood estimator (NPMLE) as well as estimators based on midpoint imputation (MIDMLE) and coarsening the mark variable (CMLE). The estimators are compared using data from a simulation study and a recent phase III HIV vaccine efficacy trial where the survival time is the time from enrollment to infection and the mark variable is the genetic distance from the infecting HIV sequence to the HIV sequence in the vaccine. Theoretical and empirical evidence are presented indicating the NPMLE and MIDMLE are inconsistent. Conversely, the CMLE is shown to be consistent in general and thus is preferred.