Effect of flight connectivity on the introduction and evolution of the COVID-19 outbreak in Canadian provinces and territories.

BACKGROUND: The COVID-19 pandemic has challenged health services and governments in Canada and around the world. Our research aims to evaluate the effect of domestic and international air travel patterns on the COVID-19 pandemic in Canadian provinces and territories. METHODS: Air travel data were obtained through licensed access to the 'BlueDot Intelligence Platform', BlueDot Inc. Daily provincial and territorial COVID-19 cases for Canada and global figures, including mortality, cases recovered and population data were downloaded from public datasets. The effects of domestic and international air travel and passenger volume on the number of local and non-local infected people in each Canadian province and territory were evaluated with a semi-Markov model. Provinces and territories are grouped into large (>100 000 confirmed COVID-19 cases and >1 000 000 inhabitants) and small jurisdictions (≤100 000 confirmed COVID-19 cases and ≤1 000 000 inhabitants). RESULTS: Our results show a clear decline in passenger volumes from March 2020 due to public health policies, interventions and other measures taken to limit or control the spread of COVID-19. As the measures were eased, some provinces and territories saw small increases in passenger volumes, although travel remained below pre-pandemic levels. During the early phase of disease introduction, the burden of illness is determined by the connectivity of jurisdictions. In provinces with a larger population and greater connectivity, the burden of illness is driven by case importation, although local transmission rapidly replaces imported cases as the most important driver of increasing new infections. In smaller jurisdictions, a steep increase in cases is seen after importation, leading to outbreaks within the community. CONCLUSIONS: Historical travel volumes, combined with data on an emerging infection, are useful to understand the behaviour of an infectious agent in regions of Canada with different connectivity and population size. Historical travel information is important for public health planning and pandemic resource allocation.

Towards a phenomenological based model for predicting the hardness of a processed meat product.

This study aims to build a model for predicting the hardness of meat products by considering their protein fractions and protein structural changes during production. Protein solubility is considered an indicator of protein structural changes. The obtained model results show that structural changes of sarcoplasmic and myofibrillar proteins occur during production. The gelling capacity is formed by the effect of the three protein fractions, namely myofibrillar, sarcoplasmic and stromal. The obtained model allows the prediction of the hardness of meat products based on their protein fraction contents with error below 5%, thus reaching a significant adjustment between real process data and the simulated model.