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The 1918 influenza pandemic was characterized by multiple epidemic waves. We investigated into reactive social distancing, a form of behavioral responses, and its effect on the multiple influenza waves in the United Kingdom. Two forms of reactive social distancing have been used in previous studies: Power function, which is a function of the proportion of recent influenza mortality in a population, and Hill function, which is a function of the actual number of recent influenza mortality. Using a simple epidemic model with a Power function and one common set of parameters, we provided a good model fit for the observed multiple epidemic waves in London boroughs, Birmingham and Liverpool. Our approach is different from previous studies where separate models are fitted to each city. We then applied these model parameters obtained from fitting three cities to all 334 administrative units in England and Wales and including the population sizes of individual administrative units. We computed the Pearsons correlation between the observed and simulated data for each administrative unit. We achieved a median correlation of 0.636, indicating our model predictions perform reasonably well. Our modelling approach which requires reduced number of parameters resulted in computational efficiency gain without over-fitting the model. Our works have both scientific and public health significance.
The novel coronavirus disease (COVID-19) is a highly contagious respiratory disease that was first detected in Wuhan, China in December 2019, and has since spread around the globe, claiming more than 69,000 lives by the time this protocol is written.
We show that the COVID-19 pandemic under social distancing exhibits universal dynamics. The cumulative numbers of both infections and deaths quickly cross over from exponential growth at early times to a longer period of power law growth, before even
The outbreak of the novel coronavirus, COVID-19, has been declared a pandemic by the WHO. The structures of social contact critically determine the spread of the infection and, in the absence of vaccines, the control of these structures through large
Social distancing remains an important strategy to combat the COVID-19 pandemic in the United States. However, the impacts of specific state-level policies on mobility and subsequent COVID-19 case trajectories have not been completely quantified. Usi
We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several of these (epidemic) models to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases tha