COVID-19 Asymptomatic Infection Estimation
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Abstract
Background
Mounting evidence suggests that there is an undetected pool of COVID-19 asymptomatic but infectious cases. Estimating the number of asymptomatic infections has been crucial to understand the virus and contain its spread, which is, however, hard to be accurately counted.
Methods
We propose an approach of machine learning based fine-grained simulator (ML-Sim), which integrates multiple practical factors including disease progress in the incubation period, cross-region population movement, undetected asymptomatic patients, and prevention and containment strength. The interactions among these factors are modeled by virtual transmission dynamics with several undetermined parameters, which are determined from epidemic data by machine learning techniques. When MLSim learns to match the real data closely, it also models the number of asymptomatic patients. MLSim is learned from the open Chinese global epidemic data.
Findings
MLSim showed better forecast accuracy than the SEIR and LSTM-based prediction models. The MLSim learned from the data of China’s mainland reveals that there could have been 150,408 (142,178-157,417) asymptomatic and had self-healed patients, which is 65% (64% – 65%) of the inferred total infections including undetected ones. The numbers of asymptomatic but infectious patients on April 15, 2020, were inferred as, Italy: 41,387 (29,037 – 57,151), Germany: 21,118 (11,484 – 41,646), USA: 354,657 (277,641 – 495,128), France: 40,379 (10,807 – 186,878), and UK: 144,424 (127,215 – 171,930). To control the virus transmission, the containment measures taken by the government were crucial. The learned MLSim also reveals that if the date of containment measures in China’s mainland was postponed for 1, 3, 5, and 7 days later than Jan. 23, there would be 109,039 (129%), 183,930 (218%), 313,342 (371%), 537,555 (637%) confirmed cases on June 12.
Conclusions
Machine learning based fine-grained simulators can better model the complex real-world disease transmission process, and thus can help decision-making of balanced containment measures. The simulator also revealed the potential great number of undetected asymptomatic infections, which poses a great risk to the virus containment.
Funding
National Natural Science Foundation of China.
Article activity feed
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SciScore for 10.1101/2020.04.19.20068072: (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: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:An appropriate limitation on the search space of parameters can benefit the model with sufficient representation ability and fewer unreasonable solutions. Appendix D shows the settings of the search space of parameters in all experiments. In general, the multi-solution problem exists but can be eliminated by introducing more domain knowledge, i.e. more strict assumptions. There are other potentially important factors we did not consider in our …
SciScore for 10.1101/2020.04.19.20068072: (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: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:An appropriate limitation on the search space of parameters can benefit the model with sufficient representation ability and fewer unreasonable solutions. Appendix D shows the settings of the search space of parameters in all experiments. In general, the multi-solution problem exists but can be eliminated by introducing more domain knowledge, i.e. more strict assumptions. There are other potentially important factors we did not consider in our simulator, mainly due to the lack of data. The factors can include the capacity of daily tests of infections, which upper bounds the daily maximum confirmation number, and the ICU capacity, which directly affects the death rate.
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.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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