Purpose This study aims to use financial stability and health facilities of countries, to cluster them for making a more consensus environment for manifesting the status of Covid-19 in a justifiable manner. The scarcity of the categorisation of the countries of the world in a common platform, and the requirement of manifesting the pandemic status such as Covid-19 in a justifiable manner create the demanding requirement. This study mainly focusses on assisting to generate a liable manifesto to criticise the span of viral infection of the severe acute respiratory syndrome coronavirus-2 over the globe. Design/methodology/approach Data for this study has been gathered from official websites of the World Bank, and the world in data. The Louvain clustering method has been used to cluster the countries based on their financial strength and health facilities. The resulted clusters are visualised using Silhouette plots. The anomalies of the clusters had been used to quantify the pandemic situation. The status of Covid-19 has been manifested with the time series analysis through python programming. Findings The countries of the world have been clustered into seven, where developed countries divided into three clusters and the countries with transition economies and developing clustered together into four clusters. The time series analysis of recognised anomalies of the clusters assist to monitor the government responses and analyse the efficiency of used safety measures against the pandemic. Originality/value This study’s resulted clusters are highly valuable as a division of countries of the whole world for evaluating the health systems and for the regional levels. Further, the results of time series analysis are beneficial in monitoring the government responses and analysing the efficiency of used safety measures against the pandemic.

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Exploring the sociodemographic factors of a cohort is a vital phase in revealing significant aspects of the societal health status. The health care sector utilises the results of exploratory analysis of the sociodemographic nature to fulfil various purposes such as constructing health care policies, allocating adequate resources, imposing necessary medications and many more. A large and growing body of evidence shows that understanding the pervasiveness of sociodemographic factors: age, ethnicity, gender, reveal crucial information. Therefore, this study aims to disclose the knowledge through analysing the sociodemographic details of a New Zealand diabetes cohort. Diabetes mellitus is a chronic fatal disease that occurs due to the inability to control proper blood sugar levels, which causes multitudinous acute and chronic complications. Diabetes became a high prevalence disease in the region of Waikato. Analysing the cohort of diabetes patients associated with complications of diabetes illustrate the prevalence of complications of diabetes among the patients. The dataset of the study has been collected from the Waikato district health board. This study intends to report the initial scanning of the dataset profile with visualising the resulting patterns of sociodemographic details from the samples and their association with complications of diabetes. The Sankey diagrams use to visualise the results of exploratory data analysis. The resulted graphs of the data screening descriptively illustrate the characteristics of the cohort associated with demographic factors. Maori population shows higher percentage (0.68) of diabetes patients than the other ethnicities, while having narrower age expansion (13-95) with early onset age, compared to others (20-103). Males (0.61%) are more vulnerable to diabetes than females (0.55%). Additionally, hypertension and cardiovascular diseases are common among the diabetes patients' in the Waikato region. Maori male population is highly vulnerable to diabetes. This study will be beneficial in constructing and analysing the demographical categories of the cohort to comparatively study the pervasiveness of the diseases among resulting classes.