Growth Rate and Acceleration Analysis of the COVID-19 Pandemic Reveals the Effect of Public Health Measures in Real Time.

Background: Ending the COVID-19 pandemic is arguably one of the most prominent challenges in recent human history. Following closely the growth dynamics of the disease is one of the pillars toward achieving that goal. Objective: We aimed at developing a simple framework to facilitate the analysis of the growth rate (cases/day) and growth acceleration (cases/day2) of COVID-19 cases in real-time. Methods: The framework was built using the Moving Regression (MR) technique and a Hidden Markov Model (HMM). The dynamics of the pandemic was initially modeled via combinations of four different growth stages: lagging (beginning of the outbreak), exponential (rapid growth), deceleration (growth decay), and stationary (near zero growth). A fifth growth behavior, namely linear growth (constant growth above zero), was further introduced to add more flexibility to the framework. An R Shiny application was developed, which can be accessed at https://theguarani.com.br/ or downloaded from https://github.com/adamtaiti/SARS-CoV-2. The framework was applied to data from the European Center for Disease Prevention and Control (ECDC), which comprised 3,722,128 cases reported worldwide as of May 8th 2020. Results: We found that the impact of public health measures on the prevalence of COVID-19 could be perceived in seemingly real-time by monitoring growth acceleration curves. Restriction to human mobility produced detectable decline in growth acceleration within 1 week, deceleration within ~2 weeks and near-stationary growth within ~6 weeks. Countries exhibiting different permutations of the five growth stages indicated that the evolution of COVID-19 prevalence is more complex and dynamic than previously appreciated. Conclusions: These results corroborate that mass social isolation is a highly effective measure against the dissemination of SARS-CoV-2, as previously suggested. Apart from the analysis of prevalence partitioned by country, the proposed framework is easily applicable to city, state, region and arbitrary territory data, serving as an asset to monitor the local behavior of COVID-19 cases.

Home and Family Environment Related to Development of Obesity: A 21-Year Longitudinal Study.

BACKGROUND: Early-life conditions are important for the development of obesity. We hypothesized that home and family characteristics reflective of less supportive environments during childhood will be associated with higher adult BMI and faster BMI growth between ages 5 and 21 years. We also examined the timing and acceleration of BMI increase by adult weight status (normal weight, overweight, obese, and extremely obese) to discern how BMI increase differs across group and across time. METHODS: BMI was assessed in 1000 Chilean youth (52% female) at ages 5, 10, 15, and 21 years. Latent growth curve analysis modeled BMI trajectories from 5 to 21 years. Observer and maternal ratings assessed children's home and family environments and parenting at 1 and 10 years. RESULTS: The four weight groups differed in acceleration of BMI increase starting at age 5, with bigger children getting bigger faster. Higher 21-year BMI related to family stress, father absence, maternal depression, frequent child confinement (in playpen), an unclean home environment at 1 year, and low provision for active stimulation and few stimulating experiences at 10 years. Accelerated BMI increase related to lower learning stimulation in the home at 1 year and less parental warmth and acceptance at child age 10. CONCLUSIONS: Home and family characteristics that reflect an absence of support for children's development were associated with overweight/obesity in young adulthood and accelerated BMI growth. Findings identify several home and family characteristics that can serve as preventive or intervention targets.