A simple method to describe the COVID-19 trajectory and dynamics in any country based on Johnson cumulative density function fitting
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Abstract
A simple method is utilised to study and compare COVID-19 infection dynamics between countries based on curve fitting to publicly shared data of confirmed COVID-19 infections. The method was tested using data from 80 countries from 6 continents. We found that Johnson cumulative density functions (CDFs) were extremely well fitted to the data (R 2 > 0.99) and that Johnson CDFs were much better fitted to the tails of the data than either the commonly used normal or lognormal CDFs. Fitted Johnson CDFs can be used to obtain basic parameters of the infection wave, such as the percentage of the population infected during an infection wave, the days of the start, peak and end of the infection wave, and the duration of the wave’s increase and decrease. These parameters can be easily interpreted biologically and used both for describing infection wave dynamics and in further statistical analysis. The usefulness of the parameters obtained was analysed with respect to the relation between the gross domestic product (GDP) per capita, the population density, the percentage of the population infected during an infection wave, the starting day and the duration of the infection wave in the 80 countries. We found that all the above parameters were significantly associated with GDP per capita, but only the percentage of the population infected was significantly associated with population density. If used with caution, this method has a limited ability to predict the future trajectory and parameters of an ongoing infection wave.
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SciScore for 10.1101/2020.12.05.20244178: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources The method was tested on 80 countries from 6 regions: 1) Africa (Democratic Republic of Congo, Egypt, Ethiopia, Kenya, Morocco, Nigeria, Somalia, South Africa, South Sudan, Sudan and Zimbabwe); 2) Asia (Afghanistan, Bangladesh, Cambodia, China, India, Indonesia, Iran, Iraq, Israel, Japan, Lebanon, Myanmar, Pakistan, Philippines, Saudi Arabia, Singapore, South Korea, Sri Lanka, Syria, Taiwan, Thailand, Turkey, Vietnam); 3) Europe (Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czechia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Netherlands, North … SciScore for 10.1101/2020.12.05.20244178: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources The method was tested on 80 countries from 6 regions: 1) Africa (Democratic Republic of Congo, Egypt, Ethiopia, Kenya, Morocco, Nigeria, Somalia, South Africa, South Sudan, Sudan and Zimbabwe); 2) Asia (Afghanistan, Bangladesh, Cambodia, China, India, Indonesia, Iran, Iraq, Israel, Japan, Lebanon, Myanmar, Pakistan, Philippines, Saudi Arabia, Singapore, South Korea, Sri Lanka, Syria, Taiwan, Thailand, Turkey, Vietnam); 3) Europe (Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czechia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Russia, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, United Kingdom); 4) North America (Canada, Jamaica, Mexico, United States of America); 5) Oceania (Australia, Fiji, New Zealand, Papua New Guinea); and 6) South America (Argentina, Bolivia, Brazil, Chile, Colombia, Paraguay, Peru, Uruguay, Venezuela). Fijisuggested: (Fiji, RRID:SCR_002285)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:One must bear in mind, however, the method’s limitations (see above), as well as those resulting from the data collection and reporting, which are discussed later in this section. The results obtained from this application of parameters describing COVID-19 dynamics have shown that the higher the GDP per capita, the higher the percentage of the population infected. This is quite an unexpected result, but consistent with a very recent report by Liu et al. (2020), who found a positive correlation between the human development index (HDI) and the risk of infection and death from COVID-19 in Italy. Other results have shown that, excluding countries where the infection wave peaked very early and its duration was short, the higher the GDP per capita, the earlier the peak and the shorter the first epidemic wave. This result, in turn, is similar to that reported in another very recent paper, in which the date of the first COVID-19 cases was shown to co-vary positively with GDP across countries, most probably because of their closer involvement in global tourism and transport (Jankowiak et al., 2020). Another example showed that the higher the population density, the lower the percentage of a population infected during the first wave of infections. This, too, seems unexpected; but this negative dependence results from the fact that infections are presented as a percentage, which does not scale proportionally with population density. A further possible explanation is that in countries w...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
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- No protocol registration statement was detected.
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