Comparative Analysis of Early Dynamic Trends in Novel Coronavirus Outbreak: A Modeling Framework
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Abstract
Background
The 2019 coronavirus disease (COVID-19) represents a significant public health threat globally. Here we describe efforts to compare epidemic growth, size and peaking time for countries in Asia, Europe, North America, South America and Australia in the early epidemic phase.
Methods
Using the time series of cases reported from January 20, 2020 to February 13, 2020 and transportation data from December 1, 2019 to January 23, 2020 we have built a novel time-varying growth model to predict the epidemic trend in China. We extended our method, using cases reported from January 26, 2020 - or the date of the earliest case reported, to April 9, 2020 to predict future epidemic trend and size in 41 countries. We estimated the impact of control measures on the epidemic trend.
Results
Our time-varying growth model yielded high concordance in the predicted epidemic size and trend with the observed figures in C hina. Among the other 41 countries, the peak time has been observed in 28 countries before or around April 9, 2020; the peak date and epidemic size were highly consistent with our estimates. We predicted the remaining countries would peak in April or May 2020, except India in July and Pakistan in August. The epidemic trajectory would reach the plateau in May or June for the majority of countries in the current wave. Countries that could emerge to be new epidemic centers are India, Pakistan, Brazil, Mexico, and Russia with a prediction of 10 5 cases for these countries. The effective reproduction number R t displayed a downward trend with time across countries, revealing the impact of the intervention remeasures i.e. social distancing. R t remained the highest in the UK (median 2.62) and the US (median 2.19) in the fourth week after the epidemic onset.
Conclusions
New epidemic centers are expected to continue to emerge across the whole world. Greater challenges such as those in the healthcare system would be faced by developing countries in hotspots. A domestic approach to curb the pandemic must align with joint international efforts to effectively control the spread of COVID-19. Our model promotes a reliable transmissibility characterization and epidemic forecasting using the incidence of cases in the early epidemic phase.
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SciScore for 10.1101/2020.02.21.20026468: (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
No key resources detected.
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:Our study has several limitations. Firstly, the projection of the temporal trend of an outbreak using the early-stage dataset could be dramatically influenced by the changes in the intervention strategies later on. For example, using data up to March 15, our previous analysis overestimated the epidemic size in U.S as the social distance …
SciScore for 10.1101/2020.02.21.20026468: (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
No key resources detected.
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:Our study has several limitations. Firstly, the projection of the temporal trend of an outbreak using the early-stage dataset could be dramatically influenced by the changes in the intervention strategies later on. For example, using data up to March 15, our previous analysis overestimated the epidemic size in U.S as the social distance measures (i.e. quarantine) were implemented starting from March 21. The performance of models used in this work will be continuously improved with data coming in from an ongoing outbreak, thus real-time estimates of key epidemiological parameters can be available before the epidemic fully ends. Secondly, the number of infections estimated might not be comparable across the countries; for example, the number of infections in Germany is not comparable to the number of infections in Italy or China, as the latter did not perform large-scale population-based testing and thus the cases could be more severe. Lastly, the analyses are highly reliant on the reporting criteria and quality of the data. The under-reporting of infection is likely a common scenario in the majority of countries. For example, a recent study showed that the reported number of confirmed positive cases was 50-85-fold lower than the actual number of infections in 3330 people in Santa Clara County, US47. A more realistic and comprehensive analysis could be performed that includes accurate epidemic data and information. In current imperfect situation, our model could still be used f...
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.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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- No protocol registration statement was detected.
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