MOOD publications

@article{nokey_41,

title = {A population-based cohort approach  to assess excess mortality due  to the spread of COVID-19 in Italy,  January-May 2020},

author = {Maria Dorrucci and Giada Minelli and Stefano Boros and Valerio Manno and Sabrina Prati and 

Marco Battaglini and Gianni Corsetti and Xanthi Andrianou and Flavia Riccardo and 

Massimo Fabiani and Maria Fenicia Vescio and Matteo Spuri and Alberto Mateo-Urdiales and 

Martina Del Manso and Patrizio Pezzotti and Antonino Bella and the Italian Integrated 

Surveillance COVID-19 Group},

url = {https://doi.org/10.4415/ann_22_01_04},

doi = {10.4415/ANN_22_01_04},

volume = {58},

number = {1},

pages = {25-33},

abstract = {Aims. To assess the impact of the COVID-19 pandemic on all-cause mortality in Italy 

during the first wave of the epidemic, taking into consideration the geographical heterogeneity of the spread of COVID-19.

Methods. This study is a retrospective, population-based cohort study using national statistics throughout Italy. Survival analysis was applied to data aggregated by day of death, 

age groups, sex, and Italian administrative units (107 provinces). We applied Cox models 

to estimate the relative hazards (RH) of excess mortality, comparing all-cause deaths in 

2020 with the expected deaths from all causes in the same time period. The RH of excess 

deaths was estimated in areas with a high, moderate, and low spread of COVID-19. We 

reported the estimate also restricting the analysis to the period of March-April 2020 (first 

peak of the epidemic).

Results. The study population consisted of 57,204,501 individuals living in Italy as of 

January 1, 2020. The number of excess deaths was 36,445, which accounts for 13.4% 

of excess mortalities from all causes during January-May 2020 (i.e., RH = 1.134; 95% 

confidence interval (CI): 1.129-1.140). In the macro-area with a relatively higher spread 

of COVID-19 (i.e., incidence rate, IR): 450-1,610 cases per 100,000 residents), the RH 

of excess deaths was 1.375 (95% CI: 1.364-1.386). In the area with a relatively moderate 

spread of COVID-19 (i.e., IR: 150-449 cases) it was 1.049 (95% CI: 1.038-1.060). In 

the area with a relatively lower spread of COVID-19 (i.e., IR: 30-149 cases), it was 0.967 

(95% CI: 0.959-0.976). Between March and April (peak months of the first wave of the 

epidemic in Italy), we estimated an excess mortality from all causes of 43.5%. The RH of 

all-cause mortality for increments of 500 cases per 100,000 residents was 1.352 (95% CI: 

1.346-1.359), corresponding to an increase of about 35%.

Conclusions. Our analysis, making use of a population-based cohort model, estimated 

all-cause excess mortality in Italy taking account of both time period and of COVID-19 

geographical spread. The study highlights the importance of a temporal/geographic 

framework in analyzing the risk of COVID-19-epidemy related mortality.},

keywords = {Covid-19 (Coronavirus)},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Early Genomic Surveillance and Phylogeographic Analysis of Getah Virus, a Reemerging Arbovirus, in Livestock in China},

author = {Jin Zhao and Simon Dellicour and Ziqing Yan and Michael Veit and Mandev S. Gill and Wan-Ting He and Xiaofeng Zhai and Xiang Ji and Marc A. Suchard and Philippe Lemey and Shuo Su},

url = {https://journals.asm.org/doi/10.1128/jvi.01091-22},

doi = {10.1128/jvi.01091-22},

journal = {Journal of Virology},

abstract = {Getah virus (GETV) mainly causes disease in livestock and may pose an epidemic risk due to its expanding host range and the potential of long-distance dispersal through animal trade. Here, we used metagenomic next-generation sequencing (mNGS) to identify GETV as the pathogen responsible for reemerging swine disease in China and subsequently estimated key epidemiological parameters using phylodynamic and spatially-explicit phylogeographic approaches. The GETV isolates were able to replicate in a variety of cell lines, including human cells, and showed high pathogenicity in a mouse model, suggesting the potential for more mammal hosts. We obtained 16 complete genomes and 79 E2 gene sequences from viral strains collected in China from 2016 to 2021 through large-scale surveillance among livestock, pets, and mosquitoes. Our phylogenetic analysis revealed that three major GETV lineages are responsible for the current epidemic in livestock in China. We identified three potential positively selected sites and mutations of interest in E2, which may impact the transmissibility and pathogenicity of the virus. Phylodynamic inference of the GETV demographic dynamics identified an association between livestock meat consumption and the evolution of viral genetic diversity. Finally, phylogeographic reconstruction of GETV dispersal indicated that the sampled lineages have preferentially circulated within areas associated with relatively higher mean annual temperature and pig population density. Our results highlight the importance of continuous surveillance of GETV among livestock in southern Chinese regions associated with relatively high temperatures.},

keywords = {OpenDataSet},

pubstate = {published},

tppubtype = {article}

}