The ongoing COVID-19 epidemic continues to spread within and outside of China, despite several social distancing measures implemented by the Chinese government. Limited epidemiological data are available, and recent changes in case definition and reporting further complicate our understanding of the impact of the epidemic, particularly in the epidemic’s epicenter. Here we use previously validated phenomenological models to generate short-term forecasts of cumulative reported cases in Guangdong and Zhejiang, China. Using daily reported cumulative case data up until 13 February 2020 from the National Health Commission of China, we report 5- and 10-day ahead forecasts of cumulative case reports. Specifically, we generate forecasts using a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model, which have each been previously used to forecast outbreaks due to different infectious diseases. Forecasts from each of the models suggest the outbreaks may be nearing extinction in both Guangdong and Zhejiang; however, the sub-epidemic model predictions also include the potential for further sustained transmission, particularly in Zhejiang. Our 10-day forecasts across the three models predict an additional 65–81 cases (upper bounds: 169–507) in Guangdong and an additional 44–354 (upper bounds: 141–875) cases in Zhejiang by February 23, 2020. In the best-case scenario, current data suggest that transmission in both provinces is slowing down.

Since the detection of the first case of COVID-19 in Chile on March 3^rd, 2020, a total of 513,188 cases, including ~14,302 deaths have been reported in Chile as of November 2^nd, 2020. Here, we estimate the reproduction number throughout the epidemic in Chile and study the effectiveness of control interventions especially the effectiveness of lockdowns by conducting short-term forecasts based on the early transmission dynamics of COVID-19. Chile’s incidence curve displays early sub-exponential growth dynamics with the deceleration of growth parameter,p, estimated at 0.8 (95% CI: 0.7, 0.8) and the reproduction number,R, estimated at 1.8 (95% CI: 1.6, 1.9). Our findings indicate that the control measures at the start of the epidemic significantly slowed down the spread of the virus. However, the relaxation of restrictions and spread of the virus in low-income neighborhoods in May led to a new surge of infections, followed by the reimposition of lockdowns in Greater Santiago and other municipalities. These measures have decelerated the virus spread withRestimated at ~0.96 (95% CI: 0.95, 0.98) as of November 2^nd, 2020. The early sub-exponential growth trend (p~0.8) of the COVID-19 epidemic transformed into a linear growth trend (p~0.5) as of July 7^th, 2020, after the reimposition of lockdowns. While the broad scale social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing and active case detection and isolation efforts to maintain the epidemic under control.

We consider functional linear regression models with a functional response and multiple functional predictors, with the goal of finding the best finite-dimensional approximation to the signal part of the response function. Defining the integrated squared correlation coefficient between a random variable and a random function, we propose to solve a penalized generalized functional eigenvalue problem, whose solutions satisfy that projections on the original predictors generate new scalar uncorrelated variables and these variables have the largest integrated squared correlation coefficient with the signal function. With these new variables, we transform the original function-on-function regression model to a function-on-scalar regression model whose predictors are uncorrelated, and estimate the model by penalized least-square method. This method is also extended to models with both multiple functional and scalar predictors. We provide the asymptotic consistency and the corresponding convergence rates for our estimates. Simulation studies in various settings and for both one and multiple functional predictors demonstrate that our approach has good predictive performance and is very computational efficient. Supplementary materials for this article are available online.

This study examined how socio-demographic, climate and population health characteristics shaped the geospatial variability in excess mortality patterns during the COVID-19 pandemic in Mexico. We used Serfling regression models to estimate all-cause excess mortality rates for all 32 Mexican states. The association between socio-demographic, climate, health indicators and excess mortality rates were determined using multiple linear regression analyses. Functional data analysis characterized clusters of states with distinct excess mortality growth rate curves. The overall all-cause excess deaths rate during the COVID-19 pandemic in Mexico until April 10, 2021 was estimated at 39.66 per 10 000 population. The lowest excess death rates were observed in southeastern states including Chiapas (12.72) and Oaxaca (13.42), whereas Mexico City had the highest rate (106.17), followed by Tlaxcala (51.99). We found a positive association of excess mortality rates with aging index, marginalization index, and average household size (P < 0.001) in the final adjusted model (Model R²=77%). We identified four distinct clusters with qualitatively similar excess mortality curves. Central states exhibited the highest excess mortality rates, whereas the distribution of aging index, marginalization index, and average household size explained the variability in excess mortality rates across Mexico.

Previous studies have found a high prevalence of vitamin D deficiency in children, yet few validated dietary vitamin D assessment tools are available for use in children. Our objective was to determine whether a short food frequency questionnaire (SFFQ) can effectively assess vitamin D intake in children. Vitamin D intake ascertained by a SFFQ was compared with assessments by a previously validated long food frequency questionnaire (LFFQ) in a population of 296 healthy 6- to 14-y-old children (54% male, 60% African American) from Pittsburgh, PA. The questionnaires were completed at two points 6 mo apart. Median reported daily vitamin D intake from the SFFQ (baseline: 380 IU, follow-up: 363 IU) was higher than the LFFQ (255 IU and 254 IU, respectively). Reported median dairy intake, including milk, cheese, and yogurt, was 3.7 cups/day, which meets the USDA recommendation for children. Vitamin D intake reported by the 2 questionnaires was modestly correlated at baseline and follow-up (r = 0.35 and r = 0.37,...