Recent years have witnessed an explosion of extensive geolocated datasets related to human movement, enabling scientists to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. The study of human mobility is especially important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling. In this survey, we review the approaches developed to reproduce various mobility patterns, with the main focus on recent developments. This review can be used both as an introduction to the fundamental modeling principles of human mobility, and as a collection of technical methods applicable to specific mobility-related problems. The review organizes the subject by differentiating between individual and population mobility and also between shortrange and long-range mobility. Throughout the text the description of the theory is intertwined with real-world applications.

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Background: Herpes simplex virus type 2 (HSV-2) infection is a prevalent, sexually transmitted infection with a sizable disease burden that is highest in sub-Saharan Africa. This study aimed to characterize HSV-2 epidemiology in this region. Methods: Cochrane and PRISMA guidelines were followed to systematically review, synthesize, and report HSV-2 related findings up to August 23, 2020. Meta-analyses and meta-regressions were conducted. Findings: From 218 relevant publications, 451 overall outcome measures and 869 stratified measures were extracted. Pooled incidence rates ranged between 2.4À19.4 per 100 person-years across populations. Pooled seroprevalence was lowest at 37.3% (95% confidence interval (CI): 34.9À39.7%) in general populations and high in female sex workers and HIV-positive individuals at 62.5% (95% CI: 54.8À70.0%) and 71.3% (95% CI: 66.5À75.9%), respectively. In general populations, pooled seroprevalence increased steadily with age. Compared to women, men had a lower seroprevalence with an adjusted risk ratio (ARR) of 0.61 (95% CI: 0.56À0.67). Seroprevalence has decreased in recent decades with an ARR of 0.98 (95% CI: 0.97À0.99) per year. Seroprevalence was highest in Eastern and Southern Africa. Pooled HSV-2 proportion in genital ulcer disease was 50.7% (95% CI: 44.7À56.8%) and in genital herpes it was 97.3% (95% CI: 84.4À100%). Interpretation: Seroprevalence is declining by 2% per year, but a third of the population is infected. Age and geography play profound roles in HSV-2 epidemiology. Temporal declines and geographic distribution of HSV-2 seroprevalence mirror that of HIV prevalence, suggesting sexual risk behavior has been declining for three decades. HSV-2 is the etiological cause of half of genital ulcer disease and nearly all genital herpes cases with limited role for HSV-1.

IntroductionHerpes simplex virus (HSV) infection can cause painful, recurrent genital ulcer disease (GUD), which can have a substantial impact on sexual and reproductive health. HSV-related GUD is most often due to HSV type 2 (HSV-2), but may also be due to genital HSV type 1 (HSV-1), which has less frequent recurrent episodes than HSV-2. The global burden of GUD has never been quantified. Here we present the first global and regional estimates of GUD due to HSV-1 and HSV-2 among women and men aged 15–49 years old.MethodsWe developed a natural history model reflecting the clinical course of GUD following HSV-2 and genital HSV-1 infection, informed by a literature search for data on model parameters. We considered both diagnosed and undiagnosed symptomatic infection. This model was then applied to existing infection estimates and population sizes for 2016. A sensitivity analysis was carried out varying the assumptions made.ResultsWe estimated that 187 million people aged 15–49 years had at least one episode of HSV-related GUD globally in 2016: 5.0% of the world’s population. Of these, 178 million (95% of those with HSV-related GUD) had HSV-2 compared with 9 million (5%) with HSV-1. GUD burden was highest in Africa, and approximately double in women compared with men. Altogether there were an estimated 8 billion person-days spent with HSV-related GUD globally in 2016, with 99% of days due to HSV-2. Taking into account parameter uncertainty, the percentage with at least one episode of HSV-related GUD ranged from 3.2% to 7.9% (120–296 million). However, the estimates were sensitive to the model assumptions.ConclusionOur study represents a first attempt to quantify the global burden of HSV-related GUD, which is large. New interventions such as HSV vaccines, antivirals or microbicides have the potential to improve the quality of life of millions of people worldwide.

IMPORTANCE Machine learning algorithms could be used as the basis for clinical decision-making aids to enhance clinical practice. OBJECTIVE To assess the ability of machine learning algorithms to predict dementia incidence within 2 years compared with existing models and determine the optimal analytic approach and number of variables required. DESIGN, SETTING, AND PARTICIPANTSThis prognostic study used data from a prospective cohort of 15 307 participants without dementia at baseline to perform a secondary analysis of factors that could be used to predict dementia incidence. Participants attended National Alzheimer Coordinating Center memory clinics across the United States between 2005 and 2015. Analyses were conducted from March to May 2021. EXPOSURES 258 variables spanning domains of dementia-related clinical measures and risk factors. MAIN OUTCOMES AND MEASURES The main outcome was incident all-cause dementia diagnosed within 2 years of baseline assessment. RESULTS In a sample of 15 307 participants (mean [SD] age, 72.3 [9.8] years; 9129 [60%] women and 6178 [40%] men) without dementia at baseline, 1568 (10%) received a diagnosis of dementia within 2 years of their initial assessment. Compared with 2 existing models for dementia risk prediction (ie, Cardiovascular Risk Factors, Aging, and Incidence of Dementia Risk Score, and theBrief Dementia Screening Indicator), machine learning algorithms were superior in predicting incident all-cause dementia within 2 years. The gradient-boosted trees algorithm had a mean (SD) overall accuracy of 92% (1%), sensitivity of 0.45 (0.05), specificity of 0.97 (0.01), and area under the curve of 0.92 (0.01) using all 258 variables. Analysis of variable importance showed that only 6 variables were required for machine learning algorithms to achieve an accuracy of 91% and area under the curve of at least 0.89. Machine learning algorithms also identified up to 84% of participants who received an initial dementia diagnosis that was subsequently reversed to mild cognitive impairment or cognitively unimpaired, suggesting possible misdiagnosis. CONCLUSIONS AND RELEVANCEThese findings suggest that machine learning algorithms could accurately predict incident dementia within 2 years in patients receiving care at memory clinics using only 6 variables. These findings could be used to inform the development and validation of decision-making aids in memory clinics.

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BackgroundHerpes simplex virus type 2 (HSV-2) infection is a prevalent sexually transmitted infection with a sizable disease burden that is highest in sub-Saharan Africa. This study aimed to characterize HSV-2 epidemiology in this region.MethodsCochrane and PRISMA guidelines were followed to systematically review, synthesize, and report HSV-2 related findings. Meta-analyses and meta-regressions were conducted.FindingsFrom 218 relevant publications, 451 overall outcome measures and 869 stratified measures were extracted. Pooled incidence rates ranged between 2.4-19.4 per 100 person-years across populations. Pooled seroprevalence was lowest at 37.3% (95% confidence interval (CI): 34.9-39.7%) in general populations and high in female sex workers and HIV positive individuals at 62.5% (95% CI: 54.8-70.0%) and 71.3% (95% CI: 66.5-75.9%), respectively. In general populations, pooled seroprevalence increased steadily with age. Compared to women, men had a lower seroprevalence with an adjusted risk ratio (ARR) of 0.61 (95% CI: 0.56-0.67).Seroprevalence decreased in recent decades with an ARR of 0.98 (95% CI: 0.97-0.99) per year. Seroprevalence was highest in Eastern and Southern Africa. Pooled HSV-2 proportion in genital ulcer disease was 50.7% (95% CI: 44.7-56.8%) and in genital herpes it was 97.3% (95% CI: 84.4-100%).InterpretationSeroprevalence is declining by 2% per year, but a third of the population is infected. Age and geography play profound roles in HSV-2 epidemiology. Temporal declines and geographic distribution of HSV-2 seroprevalence mirror that of HIV prevalence, suggesting sexual risk behavior has been declining for three decades. HSV-2 is the etiological cause of half of GUD and nearly all genital herpes cases.FundingThis work was supported by pilot funding from the Biomedical Research Program at Weill Cornell Medicine in Qatar and by the Qatar National Research Fund [NPRP 9-040-3-008].

Background: Expert opinion is that about 20% of emergency stroke patients should receive thrombolysis. Currently, 11% to 12% of patients in England and Wales receive thrombolysis, ranging from 2% to 24% between hospitals. The aim of this study was to assess how much variation is due to differences in local patient populations, and how much is due to differences in clinical decision-making and stroke pathway performance, while estimating a realistic target thrombolysis use. Methods: Anonymised data for 246 676 emergency stroke admissions to 132 acute hospitals in England and Wales between 2016 and 2018 was obtained from the Sentinel Stroke National Audit Programme data. We used machine learning to learn decisions on who to give thrombolysis to at each hospital. We used clinical pathway simulation to model effects of changing pathway performance. Qualitative research was used to assess clinician attitudes to these methods. Three changes were modeled: (1) arrival-to-treatment in 30 minutes, (2) proportion of patients with determined stroke onset times set to at least the national upper quartile, (3) thrombolysis decisions made based on majority vote of a benchmark set of hospitals. Results: Of the modeled changes, any single change was predicted to increase national thrombolysis use from 11.6% to between 12.3% to 14.5% (clinical decision-making having the most effect). Combined, these changes would be expected to increase thrombolysis to 18.3%, but there would still be significant variation between hospitals depending on local patient population. Clinicians engaged well with the modeling, but those from hospitals with lower thrombolysis use were most cautious about the methods. Conclusions: Machine learning and clinical pathway simulation may be applied at scale to national stroke audit data, allowing extended use and analysis of audit data. Stroke thrombolysis rates of at least 18% look achievable in England and Wales, but each hospital should have its own target.