Molecular characterization and phylogenetic analysis of porcine epidemic diarrhea virus strains circulating in China from 2020 to 2021.

BACKGROUND: Porcine epidemic diarrhea virus (PEDV), an enteric coronavirus, has become the major causative agent of acute gastroenteritis in piglets since 2010 in China. RESULTS: In the current study, 91 complete spike (S) gene sequences were obtained from PEDV positive samples collected from 17 provinces in China from March 2020 to March 2021. A phylogenetic analysis showed that 92.3% (84 out of 91) of the identified strains belonged to GII subtype, while 7.7% (7 out of 91) were categorized as S-INDEL like strains and grouped within GI-c clade. Based on a recombination analysis, six of S-INDEL like strains were recombinant strains originated from S-INDEL strain FR/001/2014 and virulent strain AJ1102. In addition, PEDV variant strains (CH/GDMM/202012, CH/GXDX/202010 et al) carrying novel insertions (360QGRKS364 and 1278VDVF1281) in the S protein were observed. Furthermore, the deduced amino acid sequences for the S protein showed that multiple amino acid substitutions in the antigenic epitopes in comparison with the vaccine strains. CONCLUSIONS: In conclusion, these data provide novel molecular evidence on the epidemiology and molecular diversity of PEDV in 2020-2021. This information may help design a strategy for controlling and preventing the prevalence of PEDV variant strains in China.

Modelling the epidemic dynamics of COVID-19 with consideration of human mobility.

So far COVID-19 has resulted in mass deaths and huge economic losses across the world. Various measures such as quarantine and social distancing have been taken to prevent the spread of this disease. These prevention measures have changed the transmission dynamics of COVID-19 and introduced new challenges for epidemic modelling and prediction. In this paper, we study a novel disease spreading model with two important aspects. First, the proposed model takes the quarantine effect of confirmed cases on transmission dynamics into account, which can better resemble the real-world scenario. Second, our model incorporates two types of human mobility, where the intra-region human mobility is related to the internal transmission speed of the disease in the focal area and the inter-region human mobility reflects the scale of external infectious sources to a focal area. With the proposed model, we use the human mobility data from 24 cities in China and 8 states in the USA to analyse the disease spreading patterns. The results show that our model could well fit/predict the reported cases in both countries. The predictions and findings shed light on how to effectively control COVID-19 by managing human mobility behaviours.