ABSTRACT
Aim: Coronavirus is an airborne and infectious disease and it is crucial to check the impact of climatic risk factors on the transmission of COVID-19. The main objective of this study is to determine the effect of climate risk factors using Bayesian regression analysis. Methods: Coronavirus disease 2019, due to the effect of the SARS-CoV-2 virus, has become a serious global public health issue. This disease was identified in Bangladesh on March 8, 2020, though it was initially identified in Wuhan, China. This disease is rapidly transmitted in Bangladesh due to the high population density and complex health policy setting. To meet our goal, The MCMC with Gibbs sampling is used to draw Bayesian inference, which is implemented in WinBUGS software. Results: The study revealed that high temperatures reduce confirmed cases and deaths from COVID-19, but low temperatures increase confirmed cases and deaths. High temperatures have decreased the proliferation of COVID-19, reducing the virus's survival and transmission. Conclusions: Considering only the existing scientific evidence, warm and wet climates seem to reduce the spread of COVID-19. However, more climate variables could account for explaining most of the variability in infectious disease transmission.
ABSTRACT
Since the primary mode of respiratory virus transmission is person-to-person interaction, we are required to reconsider physical interaction patterns to mitigate the number of people infected with COVID-19. While research has shown that non-pharmaceutical interventions (NPI) had an evident impact on national mobility patterns, we investigate the relative regional mobility behaviour to assess the effect of human movement on the spread of COVID-19. In particular, we explore the impact of human mobility and social connectivity derived from Facebook activities on the weekly rate of new infections in Germany between 3 March and 22 June 2020. Our results confirm that reduced social activity lowers the infection rate, accounting for regional and temporal patterns. The extent of social distancing, quantified by the percentage of people staying put within a federal administrative district, has an overall negative effect on the incidence of infections. Additionally, our results show spatial infection patterns based on geographical as well as social distances.
ABSTRACT
Statistical modelling is pivotal in assessing intensity of a stochastic processes. Novel Corona virus disease demanded proactive measures to understand the severity of disease spread and to plan its control accordingly. We propose estimation of reproduction number as a crucial factor to monitor the random dynamics of Covid-19 in India. In the present paper, semi-parametric regression based on penalized splines embedded under Bayesian formulation is utilised to estimate reproduction number while incorporating effects of underreporting and delay in reporting for the actual number of daily occurrences. Monte Carlo Markov Chain approximations are utilised to perform simulation study and thereby to assess the impact of the reporting probability and misspecification of delay pattern on potential for further substance of the pandemic. For a cycle of reporting on weekly basis, the proposed penalized spline Bayesian framework fits closest to the empirical data drawn for a two-day delay in reporting with approximately half of the actual cases being reported. The present paper is a contribution towards estimation of the true daily reproduction number of Covid-19 incidences in its next generation cycle.
ABSTRACT
Infectious diseases such as severe acute respiratory syndrome (SARS) and influenza are influenced by weather conditions. Climate variables, for example, temperature and humidity, are two important factors in the severity of COVID-19's impact on the human respiratory system. This study aims to examine the effects of these climate variables on COVID-19 mortality. The data are collected from March 08, 2020, to April 30, 2022. The parametric regression under GAM and semiparametric regression under GAMLSS frameworks are used to analyze the daily number of death due to COVID-19. Our findings revealed that temperature and relative humidity are commencing to daily deaths due to COVID-19. A positive association with COVID-19 daily death counts was observed for temperature range and a positive association for humidity. In addition, one-unit increase in daily temperature range was only associated with a 1.08% (95% CI: 1.06%, 1.10%), and humidity range was only associated with a 1.03% (95% CI: 1.02%, 1.03%) decrease in COVID-19 deaths. A flexible regression model within the framework of Generalized Additive Models for Location Scale and Shape is used to analyze the data by adjusting the time effect. We used two adaptable predictor models, such as (i) the Fractional polynomial model and (ii) the B-spline smoothing model, to estimate the systematic component of the GAMLSS model. According to both models, high humidity and temperature significantly (and drastically) lessened the severity of COVID-19 death. The findings on the epidemiological trends of the COVID-19 pandemic and weather changes may interest policymakers and health officials.
ABSTRACT
Climate change has several negative effects on health, including cardiovascular disease. Many studies have considered the effect of temperature on cardiovascular disease and found that there is an association between extreme levels of temperature, cold and hot, and cardiovascular disease. However, the number of articles that have studied the change point or the threshold in temperature is very limited. To the best of our knowledge, there have been no studies focusing on detecting and testing the significance of the change point in the temperature–cardiovascular relationship. Identifying the change point in cities may help to design better adaptive strategies in view of predicted weather changes in the future. Knowing the change points of temperature may prevent further mortality associated with the weather changes. Therefore, in this paper, we propose a unified approach that simultaneously estimates the semiparametric relationship and detects the significant point. A semiparametric generalized change point single index model is introduced as our unified approach by adjusting for several weather variables. A permutation-based testing procedure to detect the change point is introduced as well. A simulation study is conducted to evaluate the proposed algorithm. The advantage of our proposed approach is demonstrated using the cardiovascular mortality data of the city of Chicago, USA.
ABSTRACT
Mean residual life (MRL) function defines the remaining life expectancy of a subject who has survived to a time point and is an important alternative to the hazard function for characterizing the distribution of a time-to-event variable. Existing MRL models primarily focus on studying the association between risk factors and disease risks using linear model specifications in multiplicative or additive scale. When risk factors have complex correlation structures, nonlinear effects, or interactions, the prefixed linearity assumption may be insufficient to capture the relationship. Single-index modeling framework offers flexibility in reducing dimensionality and modeling nonlinear effects. In this article, we propose a class of partially linear single-index generalized MRL models, the regression component of which consists of both a semiparametric single-index part and a linear regression part. Regression spline technique is employed to approximate the nonparametric single-index function, and parameters are estimated using an iterative algorithm. Double-robust estimators are also proposed to protect against the misspecification of censoring distribution or MRL models. A further contribution of this article is a nonparametric test proposed to formally evaluate the linearity of the single-index function. Asymptotic properties of the estimators are established, and the finite-sample performance is evaluated through extensive numerical simulations. The proposed models and inference approaches are demonstrated by a New York University Langone Health (NYULH) COVID-19 dataset.