The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004–2018

Evidence on the long-term influence of climatic variables on pertussis is limited. This study aims to explore the long-term quantitative relationship between weather variability and pertussis. Data on the monthly number of pertussis cases and weather parameters in Chongqing in the period of 2004–2018 were collected. Then, we used a negative binomial multivariable regression model and cointegration testing to examine the association of variations in monthly meteorological parameters and pertussis. Descriptive statistics exhibited that the pertussis incidence rose from 0.251 per 100,000 people in 2004 to 3.661 per 100,000 persons in 2018, and pertussis was a seasonal illness, peaked in spring and summer. The results from the regression model that allowed for the long-term trends, seasonality, autoregression, and delayed effects after correcting for overdispersion showed that a 1 hPa increment in the delayed one-month air pressure contributed to a 3.559% (95% CI 0.746–6.293%) reduction in the monthly number of pertussis cases; a 10 mm increment in the monthly aggregate precipitation, a 1 °C increment in the monthly average temperature, and a 1 m/s increment in the monthly average wind velocity resulted in 3.641% (95% CI 0.960–6.330%), 19.496% (95% CI 2.368–39.490%), and 3.812 (95% CI 1.243–11.690)-fold increases in the monthly number of pertussis cases, respectively. The roles of the mentioned weather parameters in the transmission of pertussis were also evidenced by a sensitivity analysis. The cointegration testing suggested a significant value among variables. Climatic factors, particularly monthly temperature, precipitation, air pressure, and wind velocity, play a role in the transmission of pertussis. This finding will be of great help in understanding the epidemic trends of pertussis in the future, and weather variability should be taken into account in the prevention and control of pertussis.

The ARDL Method in the Energy-Growth Nexus Field; Best Implementation Strategies

A vast number of the energy-growth nexus researchers, as well as other “X-variable-growth nexus” studies, such as for example the tourism-growth nexus, the environment-growth nexus or the food-growth nexus have used the autoregressive distributed lag model (ARDL) bounds test approach for cointegration testing. Their research papers rarely include all the ARDL procedure steps in a detailed way and thus they leave other researchers confused with the series of steps that must be followed and the best implementation paradigms so that they not allow any obscure aspects. This paper is a comprehensive review that suggests the steps that need to be taken before the ARDL procedure takes place as well as the steps that should be taken afterward with respect to causality investigation and robust analysis.

Cointegration Testing and Dynamic Simulations of Autoregressive Distributed Lag Models

In this article, we introduce dynamac, a suite of commands designed to assist users in modeling and visualizing the effects of autoregressive distributed lag models and in testing for cointegration. We discuss the bounds cointegration test proposed by Pesaran, Shin, and Smith (2001, Journal of Applied Econometrics 16: 289–326), which we have adapted into a command. Because the resulting models can be dynamically complex, we follow the advice of Philips (2018, American Journal of Political Science 62: 230–244) by introducing a flexible command designed to dynamically simulate and plot a variety of types of autoregressive distributed lag models, including error-correction models.