key: cord-288264-xs08g2cy authors: Ulahannan, Jijo Pulickiyil; Narayanan, Nikhil; Thalhath, Nishad; Prabhakaran, Prem; Chaliyeduth, Sreekanth; Suresh, Sooraj P; Mohammed, Musfir; Rajeevan, E; Joseph, Sindhu; Balakrishnan, Akhil; Uthaman, Jeevan; Karingamadathil, Manoj; Thomas, Sunil Thonikkuzhiyil; Sureshkumar, Unnikrishnan; Balan, Shabeesh; Vellichirammal, Neetha Nanoth title: A citizen science initiative for open data and visualization of COVID-19 outbreak in Kerala, India date: 2020-08-06 journal: J Am Med Inform Assoc DOI: 10.1093/jamia/ocaa203 sha: doc_id: 288264 cord_uid: xs08g2cy OBJECTIVE: India reported its first COVID-19 case in the state of Kerala and an outbreak initiated subsequently. The Department of Health Services, Government of Kerala, initially released daily updates through daily textual bulletins for public awareness to control the spread of the disease. However, this unstructured data limits upstream applications, such as visualization, and analysis, thus demanding refinement to generate open and reusable datasets. MATERIALS AND METHODS: Through a citizen science initiative, we leveraged publicly available and crowd-verified data on COVID-19 outbreak in Kerala from the government bulletins and media outlets to generate reusable datasets. This was further visualized as a dashboard through a frontend web application and a JSON repository, which serves as an API for the frontend. RESULTS: From the sourced data, we provided real-time analysis, and daily updates of COVID-19 cases in Kerala, through a user-friendly bilingual dashboard (https://covid19kerala.info/) for non-specialists. To ensure longevity and reusability, the dataset was deposited in an open-access public repository for future analysis. Finally, we provide outbreak trends and demographic characteristics of the individuals affected with COVID-19 in Kerala during the first 138 days of the outbreak. DISCUSSION: We anticipate that our dataset can form the basis for future studies, supplemented with clinical and epidemiological data from the individuals affected with COVID-19 in Kerala. CONCLUSION: We reported a citizen science initiative on the COVID-19 outbreak in Kerala to collect and deposit data in a structured format, which was utilized for visualizing the outbreak trend and describing demographic characteristics of affected individuals. In December 2019, an outbreak of cases presenting with pneumonia of unknown etiology was reported in Wuhan, China. The outbreak, caused by a novel severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2), later evolved as a pandemic (coronavirus disease 2019; , claiming thousands of lives globally. [1] [2] [3] [4] Initial studies revealed the clinical and prognostic features of COVID-19 along with its transmission dynamics and stressed the need for implementing public health measures for containment of infection and transmission among the population at high-risk. [2 5-9] In response to this, several countries have implemented measures including travel restrictions and physical distancing by community-wide quarantine. [2 6 10] These extensive measures were imposed, taking into consideration the lack of adequate testing kits for detection, a vaccine, or proven antivirals for preventing or treating this disease along with reports of considerable strain on the health system leading to unprecedented loss of human life. India-the second most populated country in the world-reported its first case in the state of Kerala on January 30, 2020, among individuals with travel history from Wuhan, the epicenter of the COVID-19 outbreak. [11] With the subsequent reports of an outbreak in the Middle East and Europe, Kerala has been on high-alert for a potential outbreak, as an estimated 10% of the population work abroad and being an international tourist destination. [12 13 ] The state has a high population density, with a large proportion of the population falling in the adult and older age group. [14] This population also shows a high incidence of COVID-19-associated comorbidities such as hypertension, diabetes, and cardiovascular disease. [9 15-17] As evidenced by reports of other countries, these factors pose a significant threat for an outbreak and would exert a tremendous burden on the public healthcare system. [18] [19] [20] Severe public health measures were implemented in the state of Kerala and across India to prevent an outbreak. International flights were banned by March 22, 2020 , and a nation-wide lockdown was initiated on March 25, 2020. [21] However, before these measures were implemented, several cases (including travelers from Europe and the Middle East), along with a few reports of secondary transmission, were reported in Kerala. Since the first case was reported, the Department of Health Services (DHS), Government of Kerala, initiated diagnostic testing, isolation, contact tracing, and social distancing through quarantine, and the details of cases were released for the public through daily textual bulletins. For pandemics such as COVID-19, public awareness via dissemination of reliable information in real-time plays a significant role in controlling the spread of the disease. Besides, real-time monitoring for identifying the magnitude of spread helps in hotspot identification, potential intervention measures, resource allocation, and crisis management. [22] The lack of such a real-time data visualization dashboard for the public with granular information specific to Kerala in the local language (Malayalam), during the initial days of the outbreak, was the motivation for this work. To achieve this, the collection of relevant information on infection and refining the dataset in a structured manner for upstream purposes such as visualization and/or epidemiological analysis is essential. Open/crowd-sourced data has immense potential during the early stage of an outbreak, considering the limitation of obtaining detailed clinical and epidemiological data in real-time during an outbreak. [23] [24] [25] Furthermore, the structured datasets, when deposited in open repositories and archived, can ensure longevity for future analytical efforts and policymaking. Here, we report a citizen science initiative to leverage publicly available data on COVID-19 cases in Kerala from the daily bulletins released by the DHS, Government of Kerala, and various news outlets. The multi-sourced data was refined to make a structured live dataset to provide real-time analysis and daily updates of COVID-19 cases in Kerala through a bilingual (English and Malayalam) user-friendly dashboard (https://covid19kerala.info/). We aimed to disseminate the data of the outbreak trend, hotspots maps, and daily statistics in a comprehensible manner for non-specialists with bilingual (Malayalam and English) interpretation. Next, we aimed for the longevity and reusability of the datasets by depositing it in a public repository, aligning with open data principles for future analytical efforts. [26] Finally, to show the scope of the sourced data, we provide a snapshot of outbreak trends and demographic characteristics of the individuals affected with COVID-19 in Kerala during the first 138 days of the outbreak. The CODD-K constituting, members from different domains, who shared the interest for sourcing data, building the dataset, visualizing, distributing, and interpreting the data on infection outbreak volunteered this effort (https://team.covid19kerala.info/). This initiative was in agreement with definitions proposed by different citizen-science initiatives. [27 28 ] The CODD-K invited participation in this initiative from the public through social media. The domain experts in the collective defined the data of interest to be collected, established the informatics workflow, and the web application for data visualization. The volunteers contributed by sourcing data from various media outlets for enriching the data. Social media channels (Telegram channels and WhatsApp groups) were used for data collection, which was verified independently and curated by data validation team members. The collective defined the data of interest as minimal structured metadata of the COVID-19 infections in Kerala, covering the possible facets of its spatial and temporal nature, excluding the clinical records ( Figure 1 ). The resulting datasets should maintain homogeneity and consistency, assuring the privacy and anonymity of the individuals. The notion of this data definition is to make the resulting datasets reusable and interoperable with similar or related datasets. A set of controlled vocabularies were formed as a core of this knowledge organization system to reduce anomalies, prevent typographical errors, and duplicate entries. Together with the controlled vocabularies, identifiers of individual entries in each dataset make the datasets interlinked. An essential set of authority control is used in populating spatial data to make it accurate in the naming and hierarchy. A substantial set of secondary datasets were also produced and maintained along with the primary datasets, including derived and combined information from the primary datasets and external resources. We primarily sourced publicly available de-identified data, released daily as textual bulletins (from January 31, 2020) by the DHS, Government of Kerala, India (https://dhs.kerala.gov.in), of the individuals diagnostically confirmed positive for SARS-CoV-2 by reverse transcription-polymerase chain reaction (RT-PCR) at the government-approved test centers. We also collected and curated reports from print and visual media for supplementing the data. The quality of the data in terms of veracity and selection bias has been ensured as described (Supplementary Methods). Utmost care was taken to remove any identifiable information to ensure the privacy of the subjects. Entries were verified independently by CODD-K data validation team members and rectified for inconsistencies ( Figure 2 ). Since the data collected were publicly available, no individual consent and ethical approval were required for the study. To demonstrate the utility of the collected dataset, we provided the status of the first 138 days (between January 30, 2020, and June 15, 2020) of the COVID-19 outbreak in Kerala, and also described demographic characteristics of the individuals affected with COVID-19. We ensured that the sourced dataset complied with the Open Definition 2.1 laid down by Open Knowledge Foundation. [26] https://mc.manuscriptcentral.com/jamia We further visualized the data through a user-friendly bilingual progressive web application (PWA) designed to be both device and browser agnostic. For the convenience of the public, the dashboard mainly highlighted the number of individuals who are hospitalized, tested, confirmed, currently active, deceased, recovered, and people under observation (State-wise and District Data), updated daily. We also visualized maps for hotspots, and active patients, along with outbreak spread trend (new, active, and recovered cases), new cases by day, diagnostic testing trend, patients-age breakup, confirmed case trajectories at the district administration level (Figure 4A Since the SARS-CoV-2 infection outbreak occurs in clusters, early identification and isolation of these clusters are essential to contain the outbreak. Accurate tracking of the new cases and real-time surveillance is essential for the effective mitigation of COVID-19. However, the daily public bulletins by DHS did not have any unique identification code for the COVID-19 infected individuals and also for secondary contacts who have contracted the infection through contact transmission. This limited us from tracking the transmission dynamics. As an alternative, we resorted to https://mc.manuscriptcentral.com/jamia mapping hotspots for infection as a proxy measure to indicate possible outbreak areas. Initially, red, orange, and green zones based on the number of cases were designated to each district by the Government of India. Later, the Government of Kerala started releasing COVID-19 hotspot regions at the level of LSG administration areas. We manually curated the hotspot information from the DHS bulletins, and the dataset was published as a static JSON file in the GeoJSON format, which improves the browser caching and drops the requirement of server-sided API services. The hotspot locations were highlighted as red dots with descriptions, and when zoomed, the LSG administration area will be displayed on the map. In order to improve the visual clarity of hotspots with varying sizes of the LSGs and different zoom levels in browsers, an identifiable spot is placed on the visual center of the LSG area polygon. This inner center of the polygon was calculated with an iterative grid algorithm. To the best of our knowledge, this feature is unique to our dashboard. We also provided a toggle bar to visualize district boundaries and areas declared as hotspots at LSG resolution ( Figure 4C ). Owing to the lack of data, additional information such as the number of active cases in these hotspots could not be plotted. In this report, we describe a citizen science initiative that leveraged publicly available [ 35 36 ] Thus, our model also sets an example for efficient data management in such citizen-science initiatives. While the real-time information serves the public for assessing potential risk India. [23] [24] [25] However, our approach differed from those, as we sourced unstructured data released by the government, supplemented with the information from media outlets. This strategy not only ensures authenticity but also enriches the data available in the public domain into a structured dataset, though it depends on the data release policies adopted by the different state governments. Kerala is one of the many states in India with a transparent data release policy, which ensured the authenticity of data collected through our initiative. Furthermore, the granularity of the data at the LSG levels, which are manually verified (as released in local language) gives an added advantage, in terms of data depth, over other Pan-Indian dashboards that mainly rely on APIs to fetch cumulative data. Although this approach seems to be efficient, an unexpected surge in cases can jeopardize the data collection, thus limiting the feasibility. During such a scenario, a trade-off between depth and breadth of data collected has to be decided. Moreover, this approach also has inherent limitations, including issues with the veracity of data, owing to the anonymity, and depth of the data released, including clinical symptoms. Since each infected case identified in Kerala was not provided with a unique ID, it was impossible to track these cases for the assessment of vital epidemiological parameters like the reproduction number (R 0 ). Based on our experience of collating and analyzing COVID-19 data from the public domain in Kerala, we propose to frame specific guidelines for the public data release for COVID-19 or other epidemics. We recommend the release of official COVID-19 data in a consistent, structured and machine-readable format, in addition to the bulletins, which could be provided with a permanent URL and also archived in a public repository for future retrospective analyses. We also suggest releasing the assigned unique ID for the individuals affected with COVID-19, to avoid inconsistencies in reporting and to enable tracking the secondary transmission. Furthermore, providing COVID-19 associated symptomatic information, without compromising the privacy of the infected individuals will also aid in the basic understanding of the disease through analytical approaches. Our dataset, compiled between January 30, 2020, to June 15, 2020, indicates that the infections reported in Kerala were mainly among working-age men, with a travel history of places with COVID-19 outbreak. Active tracking and isolation of cases with travel history lead to better management of outbreak. Since the majority of cases reported in Kerala were within the age group of 20-40 years, and the patients being in constant inpatient care possibly contributed to a better outcome and lesser mortality rate, respectively. Kerala implemented vigorous COVID-19 testing, and even though the test rate was relatively low (4,359 tests per million of the population), the average number of positives detected for 1,000 tests (individuals) was lesser when compared to other states in India. Data from Kerala also provides insights about the mean duration of illness and the effect of increasing age on this parameter. Collectively, we report a citizen science initiative on the COVID-19 outbreak in Kerala to collect data in a structured format, utilized for visualizing the outbreak trend, and describing demographic characteristics of affected individuals. While the core aim of this initiative is to document COVID-19 related information for the public, researchers, and policymakers, the implemented data visualization tool also alleviates the citizen's anxiety around the pandemic in Kerala. 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The Lancet Digital Health Open access epidemiological data from the COVID-19 outbreak Open Knowledge Foundation. Open Definition 2.1. Secondary Open Definition 2 Ten principles of citizen science Opinion: Toward an international definition of citizen science The geojson format Secondary 2020 Frictionless Data Specs. Secondary Frictionless Data Specs May 2 info-Data: A collective open dataset of COVID-19 outbreak in the south Indian state of Kerala Linked Data Glossary Genomic Epidemiology of SARS-CoV-2 in Guangdong Province Crowdsourcing biomedical research: leveraging communities as innovation engines Role of technology in responding to disasters: insights from the great deluge in Kerala Architectural Considerations for Building a Robust Crowdsourced Disaster Relief Application. 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS); 2020 Space-based monitoring of severe flooding of a southern state in India during south-west monsoon season We acknowledge Shane Reustle for his help and support for forking the Japan COVID- 19 Coronavirus Tracker repository and implementation of the dashboard. We thank Jiahui Zhou for the original concept and design of the tracker. We also thank Sajjad Anwar for generously providing the administrative boundary shapefiles and geoJSONS for Kerala. Maps were generously provided by the Mapbox community team. We also thank the volunteers who have contributed to the sourcing of data from various media outlets for enriching the data. The authors declare no competing interests This study was not funded by any agencies and was purely a voluntary effort during the community-wide quarantine period by a team of technologists, academicians, students, and the general public advocating open data and citizen science.