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.

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.

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The COVID-19 infection rapidly spread globally, mostly affecting the extremely vulnerable category of the elderly with comorbidities. There are inconsistencies in the findings on the type of comorbidity of the elderly and its association with fatalities. In this context, this research investigated the impact of comorbidity in the fatality of elderly COVID-19 patients in Kerala based on their healthcare status, functionality, and morbidity profiles. A concurrent mixed-method approach was adopted for the study to achieve the objectives, where the quantitative and qualitative data had been collected in the COVID-19 situation, from June to November 2020. This study’s findings have been further triangulated with the COVID-19 elderly fatality data, which is available from the crowdsourced dashboard of the research team and two other volunteering dashboards. This paper establishes that comorbidities can predict potential fatality among elderly COVID-19 patients. While facing an epidemic like the present zoonotic disease, better knowledge of these high-risk factors will help clinicians to pinpoint the situation and implement therapeutic and preventive methodologies and interventions. The comorbidity level of the elderly in Kerala matches with the profile of COVID-19 death cases where heart disease, diabetes, cancer, and hypertension are the significant predictors of COVID-19 elderly fatality in Kerala.

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