Vector Autoregressive Modeling of COVID-19 Incidence Rate in Nigeria

With the outbreak of COVID-19, a lot of studies have been carried out in various science disciplines to either reduce the spread or control the increasing trend of the disease. Modeling the outbreak of a pandemic is pertinent for inference making and implementation of policies. In this study, we adopted the Vector autoregressive model which takes into account the dependence that exists between both multivariate variables in modeling and forecasting the number of confirmed COVID-19 cases and deaths in Nigeria. A co-integration test was carried out prior to the application of the Vector Autoregressive model. An autocorrelation test and a test for heteroscedasticity were further carried out where it was observed that there exists no autocorrelation at lag 3 and 4 and there exists no heteroscedasticity respectively. It was observed from the study that there is a growing trend in the number of COVID-19 cases and deaths. A Vector Autoregressive model of lag 4 was adopted to make a forecast of the number of cases and death. The forecast also reveals a rising trend in the number of infections and deaths. The government therefore needs to put further measures in place to curtail the spread of the virus and aim towards flattening the curve.

Using vector-autoregressive modeling to reveal bidirectional relationships in sex-related interactions in mother infant dyads

Vector autoregression (VAR) modeling allows probing bidirectional relationships in gender development and may support hypothesis testing following multi-modal data collection. We show VAR in three lights: supporting a hypothesis, rejecting a hypothesis, and opening up new questions. To illustrate these capacities of VAR, we reanalyzed longitudinal data that recorded dyadic mother-infant interactions for 15 boys and 15 girls aged 3 to 11 months of age. We examined monthly counts of 15 infant behaviors and 13 maternal behaviors (Seifert et al., 1994). VAR models demonstrated that infant crawling predicted a subsequently close feedback loop from mothers of boys but a subsequently open ended, branched response from mothers of girls. A different finding showed that boys’ standing independently later predicted significant later increases of four maternal behaviors: rocking/jiggling, lifting, affectionate touching, and stimulation of infant gross-motor activity. In contrast, crawling by girls led mothers to later decrease the same maternal behaviors. Thus, VAR might allow us to identify how mothers respond differently during daily interactions depending on infant sex. The present work intends mainly to showcase the VAR method in the specific context of the empirical study of gender development.

Vector Autoregressive Modeling on Cases of Malaria Based on the Tribal in Tanah Bumbu District

The number of malaria in this area always has the tendency of the most compared to the city/district in South Kalimantan Province. Behavior is internalisation factor from the level of knowledge, attitudes and actions of a person who influenced by customs, customs and belief in certain things that has been handed down by his ancestors. The behavior of a community group can be different from the other groups so that they formed a group behavior or can be said tribal behavior.The purpose of this research predicts that the number of the prevalence of malaria in Tanah Bumbu tribal based with Y_1t : the tribe of Banjar, Y_2t : Javanese, Y_3t : the tribe of Bugis, and Y_4t : other tribes using vector autoregressive (VAR) model. The results of the study showed with Granger Causality approach there is a relationship between the amount of the prevalence of Malaria Javanese with other tribes, Bugis tribe with other tribes. The relationship is strengthened in the VAR model, which is the number of the prevalence of Malaria Javanese influenced by the number of the prevalence of Malaria Javanese at period t-1, and the number of the prevalence of Malaria tribe Bugis at period t-1. While the number of the prevalence of Malaria tribe Bugis influenced by the number of the prevalence of Malaria Other tribes in the period t-2.