An assessment of self-reported COVID-19 related symptoms of 227,898 users of a social networking service in Japan: Has the regional risk changed after the declaration of the state of emergency?

Abstract

Background: In the absence of widespread testing, symptomatic monitoring efforts may allow for understanding the epidemiological situation of the spread of coronavirus disease 2019 (COVID-19) in Japan. We obtained data from a social networking service (SNS) messaging application that monitors self-reported COVID-19 related symptoms in real time in Fukuoka Prefecture, Japan. We aimed at not only understanding the epidemiological situation of COVID-19 in the prefecture, but also highlighting the usefulness of symptomatic monitoring approaches that rely on self-reporting using SNS during a pandemic, and informing the assessment of Japan's emergency declaration over COVID-19.

Methods: We analysed symptoms data (fever over 37.5° and a strong feeling of weariness or shortness of breath), reported voluntarily via SNS chatbot by 227,898 residents of Fukuoka Prefecture during March 27 to May 3, 2020, including April 7, when a state of emergency was declared. We estimated the spatial correlation coefficient between the number of the self-reported cases of COVID-19 related symptoms and the number of PCR confirmed COVID-19 cases in the period (obtained from the prefecture website); and estimated the empirical Bayes age- and sex-standardised incidence ratio (EBSIR) of the symptoms in the period, compared before and after the declaration. The number of symptom cases was weighted by age and sex to reflect the regional population distribution according to the 2015 national census.

Findings: Of the participants, 3.47% reported symptoms. There was a strong spatial correlation of 0.847 (p < 0.001) at municipality level between the weighted number of self-reported symptoms and the number of COVID-19 cases for both symptoms. The EBSIR at post-code level was not likely to change remarkably before and after the declaration of the emergency, but the gap in EBSIR between high-risk and low-risk areas appeared to have increased after the declaration.

Interpretation: While caution is necessary as the data was limited to SNS users, the self-reported COVID-19 related symptoms considered in the study had high epidemiological evaluation ability. In addition, though based on visual assessment, after the declaration of the emergency, regional containment of the infection risk might have strengthened to some extent. SNS, which can provide a high level of real-time, voluntary symptom data collection, can be used to assess the epidemiology of a pandemic, as well as to assist in policy assessments such as emergency declarations.

Funding: The present work was supported in part by a grant from the Ministry of Health, Labour and Welfare of Japan (H29-Gantaisaku-ippan-009).

Keywords: COVID-19; Japan; Social networking service; State of emergency declaration.

Fig. 1

Daily trend in prevalence of conditions A–D in Fukuoka Prefecture among study participants from March 1 to April 30, 2020.  Red, green, orange, and purple lines indicate the prevalence of participants with condition A–D, respectively. Gray bars indicate the number of confirmed PCR cases in Fukuoka . (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Maps plotting weighted number of cases with conditions A–D at post-code level in Fukuoka Prefecture during March 27 to May 3, 2020. The black line represents a railroad track. The gray areas represent that there were no participants. The number of participants with conditions was weighted by age and sex to reflect the regional population distribution according to the 2015 national census . (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Maps plotting the empirical Bayesian estimates of age- and sex-standardised incidence ratio of conditions A–D at post-code level in Fukuoka Prefecture during March 27 to May 3, 2020. The black line represents a railroad track. The spatial neighbourhood and its associated local adjacency matrix were defined based on the k-nearest neighbourhood method with k = 60. The gray areas represent that there were no participants or an estimation was not possible. The number of participants with conditions was weighted by age and sex to reflect the regional population distribution according to the 2015 national census .

Fig. 4

Maps plotting the empirical Bayesian estimates of age- and sex-standardised incidence ratio of conditions A–D at post-code level in Fukuoka Prefecture before (A) and after (B) the declaration of state of emergency. The black line represents a railroad track. The spatial neighbourhood and its associated local adjacency matrix were defined based on the k-nearest neighbourhood method with k = 100. The gray areas represent that there were no participants or an estimation was not possible. The number of participants with conditions was weighted by age and sex to reflect the regional population distribution according to the 2015 national census . Before: during March 27 to April 6, 2020; and After: during April 7 to May 3, 2020. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)