https://ojs.wpro.who.int/ 1WPSAR Vol 13, No 1, 2022 | doi: 10.5365/wpsar.2022.13.1.889 Surveillance System Implementation/Evaluation T he establishment of reliable and real-time epidemiological data on emerging and re-emerging infectious diseases is crucial for understanding transmission patterns and assessing the impact of public health intervention to mitigate outbreaks.1 However, during public health emergencies, routine surveillance channels can be overwhelmed; thus, real- time assessment may be hampered by insufficient information.2 In 1981, the Government of Japan established a laboratory-based surveillance system for infectious dis- eases. In 1999, this was expanded to include a system for patient reporting, and the Act on the Prevention of Infectious Diseases and Medical Care for Patients with Infectious Diseases (hereafter, the Infectious Diseases Control Law) was enacted.3 Collected data were inte- grated into this national surveillance system: the National Epidemiological Surveillance of Infectious Diseases (NESID) programme.3 To respond to emerging or re- emerging diseases not defined in the surveillance system, surveillance of “undiagnosed serious infectious illness” is included in NESID to promote early detection of patho- gens during public health emergencies.4 This surveillance system was used to detect and monitor coronavirus disease 2019 (COVID-19) cases5 even before COVID-19 was labelled as a “designated infectious disease” in Japan by cabinet order on 28 January 2020.6 The designation mandated that health-care providers report all confirmed cases to public health centres in their local jurisdictions a Data Management Team, The National COVID-19 Cluster Taskforce, The Ministry of Health, Labour and Welfare, Tokyo, Japan. b Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan. c National Institute of Public Health, Saitama, Japan. d Jumonji University, Saitama, Japan. e Department of Virology, Tohoku University Graduate School of Medicine, Sendai, Japan. f Department of Healthcare Epidemiology, Kyoto University, Kyoto, Japan. g Center for Surveillance, Immunization and Epidemiologic Research, National Institute of Infectious Diseases, Tokyo, Japan. h Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, Japan. i Hakubi Center for Advanced Research, Kyoto University, Kyoto, Japan. * These authors contributed equally. Published: 31 March 2022 doi: 10.5365/wpsar.2022.13.1.889 In response to the outbreak of coronavirus disease 2019 (COVID-19) in Japan, a national COVID-19 cluster taskforce (comprising governmental and nongovernmental experts) was established to support the country’s Ministry of Health, Labour and Welfare in conducting daily risk assessment. The assessment was carried out using established infectious disease surveillance systems; however, in the initial stages of the pandemic these were not sufficient for real-time risk assessment owing to limited accessibility, delay in data entry and inadequate case information. Also, local governments were publishing anonymized data on confirmed COVID-19 cases on their official websites as daily press releases. We developed a unique database for nationwide real-time risk assessment that included these case lists from local government websites and integrated all case data into a standardized format. The database was updated daily and checked systematically to ensure comprehensiveness and quality. Between 15 January 2020 and 15 June 2021, 776 459 cases were logged in the database, allowing for analysis of real-time risk from the pandemic. This semi-automated database was used in daily risk assessments, and to evaluate and update control measures to prevent community transmission of COVID-19 in Japan. The data were reported almost every week to the Japanese Government Advisory Panel on COVID-19 for public health responses. Integration of publicly available case-based data for real-time coronavirus disease 2019 risk assessment, Japan Kota Ninomiya,a,b,c* Mariko Kanamori,a* Naomi Ikeda,a,d* Kazuaki Jindai,e,f* Yura K Ko,g Kanako Otani,g Yuki Furuse,h,i Hiroki Akaba,e Reiko Miyahara,g Mayuko Saito,e Motoi Suzukig and Hitoshi Oshitanie Correspondence to Hitoshi Oshitani (email: oshitanih@med.tohoku.ac.jp) WPSAR Vol 13, No 1, 2022 | doi: 10.5365/wpsar.2022.13.1.889 https://ojs.wpro.who.int/2 Ninomiya et alPublicly available patient data for real-time coronavirus disease 2019 tion for more accurate situation analysis and rapid risk assessment to bolster public health decision-making. In this report, we describe the development of such a database that supported real-time risk assessment and its impact on policies for managing the COVID-19 pandemic. DATABASE DEVELOPMENT The new database used publicly available data from daily press releases published on local government websites.9 Each local government releases anonymized individual case data and aggregated daily case numbers on their website. Although the Cabinet Secretariat (htt- ps://cio.go.jp/policy-opendata) recommended that local governments share data with the public in a universal format (e.g. a file of comma-separated values) in line with the five-star open data model,10 the press release format was not standardized or consistent across local governments, especially during the early phase of the pandemic. Some press releases were published as PDF files or embedded directly in the HTML, whereas others described cases in text not in tables. Therefore, the case information needed to be converted and integrated into a standardized data format within a single database. Initially, we extracted the case information manu- ally from each local government website with support from volunteers; however, this was labour-intensive and required significant resources. Subsequently, we developed a programme written in Python programming language11 to automate extraction of information directly from the websites and its conversion to tabular form. The database contained the following variables for each case: official reporting date, reporting prefecture, prefectural case identification number, reporting mu- nicipality, municipal case identification number, age, sex, occupation, residence (limited to prefecture and municipality), onset date, confirmation date, presence of symptoms at the time of confirmation, history of overseas travel, history of domestic travel and epide- miological link with other confirmed cases. DATABASE MANAGEMENT Initially, quality assurance of the data was performed manually, with an algorithm developed in Python to detect possible data errors, including abnormal values, under the Infectious Diseases Control Law.3 Thereafter, the collected data were confirmed by local governments and ultimately reported to the Ministry of Health, Labour and Welfare (MHLW). The government also established the national COVID-19 cluster taskforce (comprising governmental and nongovernmental experts) on 25 February 2020, to support the MHLW’s efforts.7 The taskforce members analysed current case and cluster data, conducted daily risk assessments and provided technical advice for pub- lic health decision-making. The surveillance data from NESID were initially used for this analysis; however, the data were not available to nongovernmental experts owing to privacy issues. Also, the surveillance data were inadequate for timely analyses of community transmis- sion of this emerging infection because they were not designed to report cases on a day-to-day basis. The standard surveillance process involves health-care facilities that diagnose cases sending case information to the public health centre in their jurisdiction via fax for registration; at the health centre, the data are manu- ally entered into the system for further verification and reporting.3 As the COVID-19 case numbers grew, the reporting process became overwhelmed, leading to sig- nificant delays in reporting to public health authorities. Also, the standard reporting form did not include vari- ables essential for risk analysis of COVID-19 transmis- sion, such as history of contact with confirmed cases, being in a possible spreading event or travel history from an epidemic area or country. To alleviate these issues, the MHLW asked local governments to send daily case information in a spreadsheet directly to the MHLW via email.8 This updated surveillance process also had report- ing delays and discrepancies between the number of cases reported by local governments and those reported by the MHLW as the pandemic unfolded throughout Japan. The reporting delay by local governments to the MHLW could exceed 1 week in some instances. Data entry errors such as spelling and typographical errors were also recognized. These shortcomings made it difficult for the national COVID-19 cluster taskforce to analyse the epidemiological situation and conduct real- time risk assessment to inform decision-making. Therefore, another system of data collection was urgently needed to collect and integrate case informa- WPSAR Vol 13, No 1, 2022 | doi: 10.5365/wpsar.2022.13.1.889https://ojs.wpro.who.int/ 3 Publicly available patient data for real-time coronavirus disease 2019Ninomiya et al Fig. 1. Daily operating procedures of the COVID-19 database, Japan most cases were younger and either pre-symptomatic or asymptomatic.14 Epidemiological analysis and real-time risk as- sessment results based on this database have been presented to the Japanese Government Advisory Panel on COVID-19 to evaluate the effectiveness of public health measures against COVID-19. The government’s COVID-19 dashboard (https://covid19.mhlw.go.jp), which uses data not accessible to the public, was also refined to be used for the visualization of epidemiological data in parallel with our database. Because our database and the government’s database were not cross-checked or merged, the Advisory Panel analysed data summaries from both databases to identify discrepancies between them and improve their quality. Furthermore, comparing analyses from both datasets provided a multifaceted perspective to help experts conduct risk assessments. DISCUSSION The development of a reliable database during a public health emergency is challenging. High numbers of cases can overwhelm pre-existing surveillance systems, necessitating an alternative system. Basic demographic information derived from a reliable database is required for real-time risk assessment and policy-making. We successfully developed an integrated database platform missing data, inconsistencies in Japanese characters (Kanji) and categorical variables (e.g. occupation and place of residence). These errors were corrected daily. We developed a standard operating procedure for up- dating case information without errors (Fig. 1). The database automatically collected press releases from websites in the morning, then a manual verification process was used to ensure the data entry was cor- rect. Next, we semi-automatically modified abnormal values before calculating epidemiological parameters, which were then used by the cluster taskforce for their risk assessment. Close collaboration with data users, such as governmental officers and experts, allowed their feedback to improve database development and maintenance. RESULTS The database included 776 459 cases from 15 Janu- ary 2020 to 15 June 2021 (Table 1), from which daily epidemic curves and geographical maps were created.12 Epidemiological information on the first few hundred cases was derived from the database.13 Further epidemiological parameters (e.g. effective reproduction number, proportion of unlinked cases, age and sex dis- tribution, symptom onset and confirmation delay) were derived from the database for real-time risk assessment. An analysis of clusters in the early phase showed that Local governments report case information as of the previous day Local governments report case information as of the current day Automatically collect press releases and standardize case information Manually correct mistakes that require human judgement 0:00 6:00 12:00 18:00 24:00 Time Semi-automatically modify abnormal values for quality assurance Calculate epidemiological parameters Make risk assessment WPSAR Vol 13, No 1, 2022 | doi: 10.5365/wpsar.2022.13.1.889 https://ojs.wpro.who.int/4 Ninomiya et alPublicly available patient data for real-time coronavirus disease 2019 Table 1. List of variables included in the COVID-19 database from 15 January 2020 through 15 June 2021 (N = 776 459), Japan Balancing privacy protection with the need for granular personal data for public health analyses dur- ing health emergencies has been controversial. Using press release data that do not include identifying in- formation (as per local government policy) allowed for the sharing of outputs with nongovernmental officers with minimal risk to private information. The database provided consistent reporting of cases nationally, which in turn allowed for information to be shared among local governments, particularly where cases had travelled to multiple prefectures. that collected press release information from local gov- ernment websites. Similar processes have been used in other Asian countries and regions, such as Taiwan (China), Hong Kong Special Administrative Region (China) and Singapore, where data from government- issued press releases on new COVID-19 cases have been used for studies to investigate transmission pat- terns or to evaluate interventions.15,16 Our database was used for conducting research activities, and also served as a real-time monitoring tool to support public health decision-making in the outbreak setting. Variables Description Coverage, n (%) Reporting prefecture Name of the prefecture where the case was reported 776 459 (100) Case identification number by prefecture Serial number of the case in the reporting prefecture 720 726 (92.8) Reporting municipality Name of the municipality where the case was reported 275 149 (35.4) Case ID by municipality Serial number of the case in the reporting municipality 263 564 (33.9) Age Age stratum by decade 668 250 (86.1) Sex Male or female 662 444 (85.3) Occupation Case’s occupation type: health-care professional, public servant, office worker, corporate executive, educational professional, self-employment, unemployment, other 386 874 (49.8) Residential prefecture Name of the prefecture where the case resides 692 084 (89.1) Residential municipality Name of the municipality where the case resides 506 075 (65.2) Onset date Date of illness onset 402 855 (51.9)a Date of confirmation Date that SARS-CoV-2 infection was confirmed 540 027 (69.5) Date of official announcement Date the case was announced by the prefecture or municipality 776 024 (99.9) Presence of symptoms at the time of confirmation Whether the case had any symptoms when SARS-CoV-2 infection was confirmed 416 837 (53.7) History of overseas travel Whether the case had a history of overseas travel before the infection was confirmed 474 (0.1) History of domestic travel Whether the case had a history of domestic travel before the infection was confirmed 13 558 (1.7) Epidemiological link Whether the case had a history of close contact with other positive cases or a history of staying in a clustered location known to be associated with more than one infected case before the infection was confirmed 326 693 (42.1) Re-positive If an infected person tested positive again after testing negative 353 (0.05) a These calculations included asymptomatic cases. If asymptomatic cases were excluded (N = 710 553), the coverage of onset date would be 56.3% (n = 400 350). WPSAR Vol 13, No 1, 2022 | doi: 10.5365/wpsar.2022.13.1.889https://ojs.wpro.who.int/ 5 Publicly available patient data for real-time coronavirus disease 2019Ninomiya et al Ethics approval This study was exempt from institutional review board approval because it involved secondary analysis of pub- licly available, de-identified data. Funding This work was funded by research grants from the Ministry of Health, Labour and Welfare, Japan (grant no. JMPH20HA2007) and the Japan Agency for Medical Research Development (grant no. JP19fk0108104). References 1. Morgan OW, Aguilera X, Ammon A, Amuasi J, Fall IS, Frieden T, et al. Disease surveillance for the COVID-19 era: time for bold changes. Lancet. 2021;397(10292):2317–9. doi:10.1016/ S0140-6736(21)01096-5 pmid:34000258 2. White LF, Pagano M. Reporting errors in infectious disease outbreaks, with an application to pandemic influenza A/H1N1. Epidemiol Perspect Innov. 2010;7:12. doi:10.1186/1742-5573- 7-12 pmid:21159178 3. Infectious disease surveillance system in Japan. Tokyo: Center for Surveillance, Immunization and Epidemiologic Research, National Institute of Infectious Diseases; 2018. Available from: https://www.niid.go.jp/niid/images/epi/nesid/nesid_en.pdf, ac- cessed 18 November 2021. 4. 10th taskforce meeting for scientific innovation for Tokyo 2020 Olympic and Paralympic Games [in Japanese]. Tokyo: Cabinet Office, Government of Japan; 2019. Available from: https:// www8.cao.go.jp/cstp/tyousakai/olyparasuishin/10kai/10kai. html, accessed 18 November 2021. 5. National Institute of Infectious Diseases. Demographics of the first 12 cases of COVID-19 reported in Japan [in Japanese]. In- fectious Agents Surveillance Report. 2020;41:48–9. Available from: https://www.niid.go.jp/niid/ja/2019-ncov/2488-idsc/iasr- news/9387-481p01.html, accessed 18 November 2021. 6. Japan: Cabinet issues orders relating to infectious disease control act and quarantine act. Washington, DC: Library of Congress; 2020. Available from: https://www.loc.gov/item/global-legal- monitor/2020-03-11/japan-cabinet-issues-orders-relating-to- infectious-disease-control-act-and-quarantine-act/, accessed 29 March 2022. 7. Oshitani H. Expert Members of the National COVID-19 Cluster Taskforce at the Ministry of Health, Labour and Welfare, Japan. Cluster-based approach to coronavirus disease 2019 (COVID-19) response in Japan, from February to April 2020. Jpn J Infect Dis. 2020;73(6):491–3. doi:10.7883/yoken.JJID.2020.363 pmid:32611985 8. Notification by the Ministry of Health, Labour and Welfare. Re- quest of number of cases who visit COVID-19 consultation centers [in Japanese]. Okayama Prefecture: Ministry of Health, Labour and Welfare; 2020 Feb 1. Available from: https://www.pref. okayama.jp/uploaded/attachment/267818.pdf, accessed 18 No- vember 2021. One of the limitations of this database is that the reporting format for some variables differed across local governments and phases. Also, some variables were partially or entirely missing from the data recorded by some local governments,9 and local reporting forms were sometimes reformatted without notification, requir- ing updating of the database processes; hence, it was difficult to share consistent coding scripts for collecting information. Furthermore, the quality of the data from each local government varied depending on workload, especially for data on epidemiological links from active surveillance at public health centres. Detailed clinical data and disease outcomes were not publicly available. Despite the limitations, this semi-automated data- base based on publicly available data was useful for monitoring pandemic trends, conducting real-time risk assessment and assessing the impacts of public health policies. No additional cost for computing resources or software was required apart from a few personal computers to manage our database; also, abstracting sufficient data from open-source data was techni- cally straightforward. Therefore, our method could be adapted for use in resource-constrained settings and could serve as a meaningful model for other countries to create similar databases during public health emer- gencies. Based on this experience, we recommend that countries develop legislation and establish systems that can extract and store anonymized case information from publicly provided information to supplement routine surveillance systems. Acknowledgements The authors thank the local governments, public health centres, public health institutes and the National Institute of Infectious Diseases, Japan, for their work on surveil- lance, laboratory testing, epidemiological investigations and data collection. We also thank all members of the National COVID-19 Cluster Taskforce and volunteers in its Data Management Team. Conflicts of interest Mariko Kanamori was a Research Fellow of the Japan Society for the Promotion of Science. The other authors declare no conflicts of interest. WPSAR Vol 13, No 1, 2022 | doi: 10.5365/wpsar.2022.13.1.889 https://ojs.wpro.who.int/6 Ninomiya et alPublicly available patient data for real-time coronavirus disease 2019 14. Furuse Y, Sando E, Tsuchiya N, Miyahara R, Yasuda I, Ko YK, et al. Clusters of coronavirus disease in communities, Japan, January–April 2020. Emerg Infect Dis. 2020;26(9):2176–9. doi:10.3201/eid2609.202272 pmid:32521222 15. Summers J, Cheng HY, Lin HH, Barnard LT, Kvalsvig A, Wilson N, et al. Potential lessons from the Taiwan and New Zealand health responses to the COVID-19 pandemic. Lancet Reg Health West Pac. 2020;4:100044. doi:10.1016/j.lan- wpc.2020.100044 pmid:34013216 16. Adam DC, Wu P, Wong JY, Lau EHY, Tsang TK, Cauchemez S, et al. Clustering and superspreading potential of SARS-CoV-2 infections in Hong Kong. Nat Med. 2020;26(11):1714–9. doi:10.1038/s41591-020-1092-0 pmid:32943787 9. Notification by the Ministry of Health, Labour and Welfare (Japan). Disclosure of information for COVID-19 outbreak [in Japanese]. Tokyo: Ministry of Health, Labour and Welfare; 2020. Available from: https://www.mhlw.go.jp/content/000652973.pdf, ac- cessed 11 July 2021. 10. Berners-Lee T. 5-Star Open Data [Internet]. Available from: http://5stardata.info/, accessed 18 November 2021. 11. Van Rossum G, Drake FL. Python 3 Reference Manual. Scotts Valley, CA: CreateSpace; 2009. 12. Japan data. Response COVID-19; 2020. Available from: https://www. responsecovid19.org/japan/data/, accessed 11 July 2021. 13. Furuse Y, Ko YK, Saito M, Shobugawa Y, Jindai K, Saito T, et al. Epidemiology of COVID-19 outbreak in Japan, from January– March 2020. Jpn J Infect Dis. 2020;73(5):391–3. doi:10.7883/ yoken.JJID.2020.271 pmid:32350228