Study of the trend pattern of COVID-19 using spline-based time series model: a Bayesian paradigm.

A vast majority of the countries are under economic and health crises due to the current epidemic of coronavirus disease 2019 (COVID-19). The present study analyzes the COVID-19 using time series, an essential gizmo for knowing the enlargement of infection and its changing behavior, especially the trending model. We consider an autoregressive model with a non-linear time trend component that approximately converts into the linear trend using the spline function. The spline function splits the series of COVID-19 into different piecewise segments between respective knots in the form of various growth stages and fits the linear time trend. First, we obtain the number of knots with their locations in the COVID-19 series to identify the transmission stages of COVID-19 infection. Then, the estimation of the model parameters is obtained under the Bayesian setup for the best-fitted model. The results advocate that the proposed model appropriately determines the location of knots based on different transmission stages and know the current transmission situation of the COVID-19 pandemic in a country.

A pairwise likelihood-based approach for changepoint detection in multivariate time series models.

This paper develops a composite likelihood-based approach for multiple changepoint estimation in multivariate time series. We derive a criterion based on pairwise likelihood and minimum description length for estimating the number and locations of changepoints and for performing model selection in each segment. The number and locations of the changepoints can be consistently estimated under mild conditions and the computation can be conducted efficiently with a pruned dynamic programming algorithm. Simulation studies and real data examples demonstrate the statistical and computational efficiency of the proposed method.

Concurrent epidemics of influenza A/H3N2 and A/H1N1pdm in Southern China: A serial cross-sectional study.

OBJECTIVES: This study aimed to elucidate the antibody response pattern of multiple influenza subtypes through a 4-year serological study of a general population in Shenzhen, Southern China. METHODS: A serial cross-sectional serological survey was conducted at eight time points between 2009 and 2012. A total number of 5876 subjects were recruited from all age groups. The influenza subtypes tested were A/H1N1, A/H3N2, B/Yamagata, B/Victoria, and A/H1N1pdm. Genetic sequencing and phylogenetic analysis were performed on 127 H3 genes and 28 H1pdm genes. RESULTS: We found concurrent epidemics of A/H3N2 and A/H1N1pdm with simultaneous peak times at March 2011. A/H3N2 was the dominant subtype. Ten residue substitutions (S61N, T64I, K78E, K160N, N161S, A214S, T228A, A229V, V239I, N294K, and N328S) were found in the H3 gene that might underlie its epidemic. The elderly group showed an antibody response cycle that was weaker in magnitude and slower in peak time than in younger groups. CONCLUSIONS: The study provides a broad transmission picture and epidemiological characteristics of the major flu subtypes. The findings suggest that it may be necessary to include the A/H1N1pdm strain to the current trivalent or quadrivalent vaccine design. The delayed antibody response cycle in the elderly group indicates the need for better protection of elderly people that might be achieved by an earlier vaccination at a higher dose.

Inequality in obesigenic environments: fast food density in New York City.

The high prevalence of obesity in African American populations may be due to the food environment in residential communities, and the density of fast food restaurants is an important aspect of the restaurant landscape in US cities. This study investigated racial and socioeconomic correlates of fast food density in New York City. We found that predominantly Black areas had higher densities of fast food than predominantly White areas; high-income Black areas had similar exposure as low-income Black areas; and national chains were most dense in commercial areas. The results highlight the importance of policy level interventions to address disparities in food environments as a key goal in obesity prevention efforts.