Forecasting the Worldwide Spread of COVID-19 based on Logistic Model and SEIR Model

  1. Xiang Zhou
  2. Xudong Ma
  3. Na Hong
  4. Longxiang Su
  5. Yingying Ma
  6. Jie He
  7. Huizhen Jiang
  8. Chun Liu
  9. Guangliang Shan
  10. Weiguo Zhu
  11. Shuyang Zhang
  12. Yun Long

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Abstract

Background

With the outbreak of coronavirus disease 2019 (COVID-19), a sudden case increase in late February 2020 led to deep concern globally. Italy, South Korea, Iran, France, Germany, Spain, the US and Japan are probably the countries with the most severe outbreaks. Collecting epidemiological data and predicting epidemic trends are important for the development and measurement of public intervention strategies. Epidemic prediction results yielded by different mathematical models are inconsistent; therefore, we sought to compare different models and their prediction results to generate objective conclusions.

Methods

We used the number of cases reported from January 23 to March 20, 2020, to estimate the possible spread size and peak time of COVID-19, especially in 8 high-risk countries. The logistic growth model, basic SEIR model and adjusted SEIR model were adopted for prediction. Given that different model inputs may infer different model outputs, we implemented three model predictions with three scenarios of epidemic development.

Results

When comparing all 8 countries’ short-term prediction results and peak predictions, the differences among the models were relatively large. The logistic growth model estimated a smaller epidemic size than the basic SERI model did; however, once we added parameters that considered the effects of public health interventions and control measures, the adjusted SERI model results demonstrated a considerably rapid deceleration of epidemic development. Our results demonstrated that contact rate, quarantine scale, and the initial quarantine time and length are important factors in controlling epidemic size and length.

Conclusions

We demonstrated a comparative assessment of the predictions of the COVID-19 outbreak in eight high-risk countries using multiple methods. By forecasting epidemic size and peak time as well as simulating the effects of public health interventions, the intent of this paper is to help clarify the transmission dynamics of COVID-19 and recommend operation suggestions to slow down the epidemic. It is suggested that the quick detection of cases, sufficient implementation of quarantine and public self-protection behaviors are critical to slow down the epidemic.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.26.20044289: (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:
    The R0 is expected to decrease substantially compared to values at the early stage after governments implement control measures; however, regardless of the kind of policy each country executes, each policy has its limitations in defending against COVID-19, and sustained transmission chains will occur until there is a vaccine or the until the virus disappears due to seasonal or population immunity25. Therefore, detecting all transmission events is the most critical issue of COVID-19 control in the current stage because any undetected case in a local area could begin a new epidemic chain of transmission. In addition, the public should take adequate protective measures against the transmission of COVID-19. From the view of mathematical models, the SEIR model is designed for infectious disease estimation; however, the logistic growth model is designed to fit the development of the curves. The logistic model may fit the existing data better when compared with the SEIR model, since it is trained from the existing data, but it cannot be accurately judged and incorporates infectious characteristics. Therefore, we believe that the logistic model is better for near-term predictions. On the other hand, the SEIR model introduces more variables and factors by considering the interaction and association among multiple groups of people, and it is more reasonable than the logistic model as it follows the rules of infectious disease development, but the prediction results vary greatly with re...

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    • 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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  2. Version published to 10.1101/2020.03.26.20044289v2 on medRxiv
  3. Version published to 10.1101/2020.03.26.20044289v1 on medRxiv