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10.1098/rsif.2020.0518

http://scihub22266oqcxt.onion/10.1098/rsif.2020.0518
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suck abstract from ncbi


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pmid32993434      J+R+Soc+Interface 2020 ; 17 (170): 20200518
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  • Patterns of the COVID-19 pandemic spread around the world: exponential versus power laws #MMPMID32993434
  • Komarova NL; Schang LM; Wodarz D
  • J R Soc Interface 2020[Sep]; 17 (170): 20200518 PMID32993434show ga
  • We have analysed the COVID-19 epidemic data of more than 174 countries (excluding China) in the period between 22 January and 28 March 2020. We found that some countries (such as the USA, the UK and Canada) follow an exponential epidemic growth, while others (like Italy and several other European countries) show a power law like growth. Regardless of the best fitting law, many countries can be shown to follow a common trajectory that is similar to Italy (the epicentre at the time of analysis), but with varying degrees of delay. We found that countries with 'younger' epidemics, i.e. countries where the epidemic started more recently, tend to exhibit more exponential like behaviour, while countries that were closer behind Italy tend to follow a power law growth. We hypothesize that there is a universal growth pattern of this infection that starts off as exponential and subsequently becomes more power law like. Although it cannot be excluded that this growth pattern is a consequence of social distancing measures, an alternative explanation is that it is an intrinsic epidemic growth law, dictated by a spatially distributed community structure, where the growth in individual highly mixed communities is exponential but the longer term, local geographical spread (in the absence of global mixing) results in a power law. This is supported by computer simulations of a metapopulation model that gives rise to predictions about the growth dynamics that are consistent with correlations found in the epidemiological data. Therefore, seeing a deviation from straight exponential growth may be a natural progression of the epidemic in each country. On the practical side, this indicates that (i) even in the absence of strict social distancing interventions, exponential growth is not an accurate predictor of longer term infection spread, and (ii) a deviation from exponential spread and a reduction of estimated doubling times do not necessarily indicate successful interventions, which are instead indicated by a transition to a reduced power or by a deviation from power law behaviour.
  • |*Computer Simulation[MESH]
  • |*Public Health Informatics[MESH]
  • |Betacoronavirus[MESH]
  • |COVID-19[MESH]
  • |Communicable Disease Control[MESH]
  • |Coronavirus Infections/*epidemiology[MESH]
  • |Data Collection[MESH]
  • |Geography[MESH]
  • |Global Health[MESH]
  • |Humans[MESH]
  • |Kinetics[MESH]
  • |Pandemics[MESH]
  • |Pneumonia, Viral/*epidemiology[MESH]


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