Surveillance by age-class and prefecture for emerging infectious febrile diseases with respiratory symptoms, including COVID-19

  1. Tomoaki Ueno
  2. Junko Kurita
  3. Tamie Sugawara
  4. Yoshiyuki Sugishita
  5. Yasushi Ohkusa
  6. Hirokazu Kawanohara
  7. Miwako Kamei

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Abstract

Object

The COVID-19 outbreak emerged in late 2019 in China, expanding rapidly thereafter. Even in Japan, epidemiological linkage of transmission was probably lost already by February 18, 2020. From that time, it has been necessary to detect clusters using syndromic surveillance.

Method

We identified common symptoms of COVID-19 as fever and respiratory symptoms. Therefore, we constructed a model to predict the number of patients with antipyretic analgesics (AP) and multi-ingredient cold medications (MIC) controlling well-known pediatric infectious diseases including influenza or RS virus infection. To do so, we used the National Official Sentinel Surveillance for Infectious Diseases (NOSSID), even though NOSSID data are weekly data with 10 day delays, on average. The probability of a cluster with unknown febrile disease with respiratory symptoms is a product of the probabilities of aberrations in AP and MIC, which is defined as one minus the probability of the number of patients prescribed a certain type of drug in PS compared to the number predicted using a model. This analysis was conducted prospectively in 2020 using data from October 1, 2010 through 2019 by prefecture and by age-class.

Results

The probability of unknown febrile disease with respiratory symptom cluster was estimated as less than 60% in 2020.

Discussion

The most severe limitation of the present study is that the proposed model cannot be validated. A large outbreak of an unknown febrile disease with respiratory symptoms must be experienced, at which time, practitioners will have to “wing it”. We expect that no actual cluster of unknown febrile disease with respiratory symptoms will occur, but if it should occur, we hope to detect it.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.11.20061697: (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 most severe limitation of the present study is that the validity of the proposed model has not been verified. Verifying it requires that we experience a large outbreak of COVID-19. With such a system, practitioners would have to wing it during its first trial. Hopefully we expect to be able to detect any cluster of COVID-19 correctly if such a cluster were to exist. The prior day’s data of detected clusters have been published on the internet (http://prescription.orca.med.or.jp/syndromic/kanjyasuikei/index.php) since the beginning of March. To date, fortunately, we have no detection from this survey method. The present study supports the author’s opinions, which are unrelated to any stance or policy of professionally affiliated bodies.

    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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  2. Version published to 10.1101/2020.04.11.20061697v1 on medRxiv