Role of meteorological factors on SARS-CoV-2 infection incidence in Italy and Spain before the vaccination campaign. A multi-city time series study

Association between meteorological factors and COVID-19 transmission in low- and middle-income countries: A time-stratified case-crossover study

“…However, epidemiological evidence regarding the effect of humidity is still in debate. A city-level study in Italy and Span revealed a reversed U-shape association of COVID-19 transmission with RH and absolute humidity ( Donzelli et al, 2022 ). Another study enrolling 50 cities indicated a positive association for RH but a negative association for specific humidity that is the mass of water vapor per unit mass of moist air ( Sajadi et al, 2020 ).…”

Insight into vaccination and meteorological factors on daily COVID-19 cases and mortality in Bangladesh

“…Datasets for human serum antibody levels and SARS-CoV-2 vaccination are still lacking. Therefore, additional research could be done to explore the factors affecting vaccination ( Donzelli et al, 2022 ).…”

Association of Ambient Temperature and Absolute Humidity with the Effective Reproduction Number of COVID-19 in Japan

“…Changes in the national-level government public health and social measures were controlled by a linear term that incorporated the GSI from the OxCGRT. Furthermore, varying baseline risk on top of shared long-term seasonal variation and cycle as well as short-term trends were modeled by incorporating natural cubic splines of time (6 df per year for the main analysis), week of year (category), day of week (category), and public holiday (category) fixed effects variables as possible confounders [16,32,39,57]. The autocorrelation term of residuals in the case of infectious disease is pathogen-specific and needs to be accounted for; therefore, autoregressive terms at the logarithm of the order of one to five were incorporated into the statistical models [36].…”

“…The variability in the results of previous studies may be partly explained by differences in the sample size, observation period, breadth of the spatiotemporal scale of analysis, the application of different statistical modeling techniques with varying degrees of sophistication, and the degree of consideration of potential confounding factors [16,32]. In particularl, previous systematic reviews have indicated that these modeling studies have significant methodological limitations that may introduce bias and limit causal inferences [33,34].…”

“…In particular, the time-varying distributed lag nonlinear models (DLNM), which were developed in the last decade, have been relatively underused despite being well suited for time-series statistical modeling of infectious disease dynamics [37,38]. Moreover, despite the advantages of the method, DLNM has not been overly used in research, and only some disease modeling studies have utilized it [16,32,[39][40][41].…”