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Context. This is the second paper of a series aiming to determine the birth rates of supernovae (SNe) in the local Universe. Aims. We aimed to estimate the SN rates in the local Universe and fit the delay-time distribution of type Ia SNe (SNe Ia) to put constraints on their progenitor scenarios. Methods. We performed a Monte Carlo simulation to estimate volumetric rates using the nearby SN sample introduced in Paper I. The rate evolution of core-collapse (CC) SNe closely follows the evolution of the cosmic star formation history, while the rate evolution of SNe Ia involves the convolution of the cosmic star formation history and a two-component delay-time distribution including a power law and a Gaussian component. Results. The volumetric rates of type Ia, Ibc, and II SNe are derived as 0.325 ± 0.040_−0.010^+0.016, 0.160 ± 0.028_−0.014^+0.044, and 0.528 ± 0.051_−0.013^+0.162(in units of 10⁻⁴yr⁻¹Mpc⁻³h₇₀³), respectively. The rate of CCSNe (0.688 ± 0.078_−0.027^+0.0206) is consistent with previous estimates, which trace the star formation history. Conversely, the newly derived local SN Ia rate is larger than existing results given at redshifts 0.01 < z < 0.1, favoring an increased rate from the Universe at z ∼ 0.1 to the local Universe at z < 0.01. A two-component model effectively reproduces the rate variation, with the power law component accounting for the rate evolution at larger redshifts and the Gaussian component with a delay time of 12.63 ± 0.38 Gyr accounting for the local rate evolution. This delayed component, with its exceptionally long delay time, suggests that the progenitors of these SNe Ia were formed around 1 Gyr after the birth of the Universe, which could only be explained by a double-degenerate progenitor scenario. Comparison with the Palomar Transient Factory (PTF) sample of SNe Ia at z = 0.073 and the morphology of their host galaxies, reveals that the increased SN Ia rate at z < 0.01 is primarily due to the SNe Ia of massive E and S0 galaxies with old stellar populations. Based on the above results, we estimate the Galactic SN rate as 3.08 ± 1.29 per century. 

Context. This is the first paper in a series aiming to determine the fractions and birth rates of various types of supernovae (SNe) in the local Universe. Aims. In this paper, we aim to construct a complete sample of SNe in the nearby Universe and provide more precise measurements of subtype fractions. Methods. We carefully selected our SN sample at a distance of less than 40 Mpc mainly from wide-field surveys conducted over the years from 2016 to 2023. Results. The sample contains a total of 211 SNe, including 109 SNe II, 69 SNe Ia, and 33 SNe Ibc. With the aid of sufficient spectra, we obtained relatively accurate subtype classifications for all SNe in this sample. After corrections for the Malmquist bias, this volumelimited sample yielded fractions of SNe Ia, SNe Ibc, and SNe II of 30.4_−11.5^+3.7%, 16.3_−7.4^+3.7%, and 53.3_−18.7^+9.5%, respectively. In the SN Ia sample, the fraction of the 91T-like subtype becomes relatively low (~5.4%), while that of the 02cx-like subtype shows a moderate increase (~6.8%). In the SN Ibc sample, we find significant fractions of broadlined SNe Ic (~18.0%) and SNe Ibn (~8.8%). The fraction of the 87A-like subtype was determined to be ~2.3%, indicating rare explosions from blue supergiant stars. We find that SNe Ia show a double peak number distribution in S0- and Sc-type host galaxies, which may serve as straightforward evidence for the presence of “prompt” and “delayed” progenitor components that give rise to SN Ia explosions. Several subtypes of SNe such as 02cx-like SNe Ia, broadlined SNe Ic, and SNe IIn (and perhaps SNe Ibn) are found to occur preferentially in less massive spiral galaxies (i.e., with stellar mass <0.5×10¹⁰M_ʘ), thus favoring their associations with young stellar progenitors. Moreover, the 02cx-like subtype shows a trend of exploding in the outer skirt of their hosts, which is suggestive of metal-poor progenitors. 

The coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective. We developed a machine learning (XGBoost) model to predict during the incubation period a SARS-CoV-2 infection that is subsequently diagnosed after 3 or more days. We used data from multiple sources, including demographic, clinical, treatment, laboratory, and vaccination information from a national network of hemodialysis clinics, socioeconomic information from the Census Bureau, and county-level COVID-19 infection and mortality information from state and local health agencies. We created prediction models and evaluated their performances on a rolling basis to investigate the evolution of prediction power and risk factors. From April 2020 to August 2020, our machine learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.75, an improvement of over 0.07 from a previously developed machine learning model published by Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUROC of 0.6 in December 2021 and January 2022. Over the whole study period, that is, from April 2020 to February 2022, fixing the false-positive rate at 20%, our model was able to detect 40% of the positive patients. We found that features derived from local infection information reported by the Centers for Disease Control and Prevention (CDC) were the most important predictors, and vaccination status was a useful predictor as well. Whether or not a patient lives in a nursing home was an effective predictor before vaccination, but became less predictive after vaccination. As found in our study, the dynamics of the prediction model are frequently changing as the pandemic evolves. County-level infection information and vaccination information are crucial for the success of early COVID-19 prediction models. Our results show that the proposed model can effectively identify SARS-CoV-2 infections during the incubation period. Prospective studies are warranted to explore the application of such prediction models in daily clinical practice.