A novel infectious respiratory disease was recognized in Wuhan (Hubei Province, China) in December 2019. In February 2020, the disease was named “coronavirus disease 2019” (COVID-19). COVID-19 became a pandemic in March 2020, and, since then, different countries have implemented a broad spectrum of policies. Thailand is considered to be among the top countries in handling its first wave of the outbreak—12 January to 31 July 2020. Here, we illustrate how Thailand tackled the COVID-19 outbreak, particularly the effects of public health interventions on the epidemiological spread. This study shows how the available data from the outbreak can be analyzed and visualized to quantify the severity of the outbreak, the effectiveness of the interventions, and the level of risk of allowed activities during an easing of a “lockdown.” This study shows how a well-organized governmental apparatus can overcome the havoc caused by a pandemic.

The focus of this article is on failure history of a repairable system for which the relevant data comprise successive event times for a recurrent phenomenon along with an event-count indicator. We undertake an investigation for analyzing failures from repairable systems that are subject to multiple failure modes. Failure data representing a cluster of recurrent events from a single system are studied under the parametric framework of a power-law process, a model that has found considerable attention in industrial applications. Some interesting and nonstandard asymptotic results ensue in this context that are discussed in detail. Extensive simulation has been carried out that supplements the theoretical findings. An extension to the case where the specific cause of failure may be missing is investigated in detail. The methodology has been implemented on recurrent failure data obtained from a warranty claim database for a fleet of automobiles. Supplementary material for this article is available online.

A novel infectious respiratory disease was recognized in Wuhan (Hubei Province, China) in December 2019. In February 2020, the disease was named "coronavirus disease 2019" (COVID-19). COVID-19 became a pandemic in March 2020, and, since then, different countries have implemented a broad spectrum of policies. Thailand is considered to be among the top countries in handling its first wave of the outbreak -- 12 January to 31 July 2020. Here, we illustrate how Thailand tackled the COVID-19 outbreak, particularly the effects of public health interventions on the epidemiological spread. This study shows how the available data from the outbreak can be analyzed and visualized to quantify the severity of the outbreak, the effectiveness of the interventions, and the level of risk of allowed activities during an easing of a "lockdown." This study shows how a well-organized governmental apparatus can overcome the havoc caused by a pandemic.

Probability plots are popular graphical tools used by reliability engineers and other practitioners for assessing parametric distributional assumptions. They are particularly well suited for location-scale families or those that can be transformed to such families. When the plot indicates a reasonable conformity to the assumed family, it is common to estimate the underlying location and scale parameters by fitting a line through the plot. This quick-and-easy method is especially useful with censored data. Indeed, the current version of a popular statistical software package uses this as the default estimation method. In this article we investigate the properties of graphical estimators with multiply right-censored data and compare their performance with that of maximum likelihood estimators. Large-sample results on consistency, asymptotic normality, and asymptotic variance expressions are obtained. Small-sample properties are studied through simulation for selected distributions and censoring patterns. The results presented in this article extend the work of Nair (1984) to right-censored data.

This study aimed to investigate the effect of the lack of keratinized mucosa on the risk of peri-implantitis, while also accounting for possible confounding factors. A literature search was conducted in PubMed and Scopus, including human studies that assessed the presence and width of keratinized mucosa in relation to the occurrence of peri-implantitis. Twenty-two articles were included, and 16 cross-sectional studies we meta-analyzed. The prevalence of peri-implantitis was 6.68–62.3% on patient-level and 4.5–58.1% on implant-level. The overall analysis indicated that the lack of keratinized mucosa was associated with a higher prevalence of peri-implantitis (OR = 2.78, 95% CI 2.07–3.74, p < 0.00001). Similar results were shown when subgroup analyses were performed, including studies with a similar case definition of peri-implantitis (Marginal Bone Loss, MBL ≥ 2 mm) (OR = 1.96, 95% CI 1.41–2.73, p < 0.0001), fixed prostheses only (OR = 2.82, 95% CI 1.85–4.28, p < 0.00001), patients under regular implant maintenance (OR = 2.08, 95% CI 1.41–3.08, p = 0.0002), and studies adjusting for other variables (OR = 3.68, 95% CI 2.32–5.82, p = 0.007). Thus, the lack of keratinized mucosa is a risk factor that increases the prevalence of peri-implantitis and should be accounted for when placing dental implants.