Clustering of countries for COVID-19 cases based on disease prevalence, health systems and environmental indicators

Monkeypox outbreaks in the context of the COVID-19 pandemic: Network and clustering analyses of global risks and modified SEIR prediction of epidemic trends

Background

Ninety-eight percent of documented cases of the zoonotic disease human monkeypox (MPX) were reported after 2001, with especially dramatic global spread in 2022. This longitudinal study aimed to assess spatiotemporal risk factors of MPX infection and predict global epidemiological trends.

Method

Twenty-one potential risk factors were evaluated by correlation-based network analysis and multivariate regression. Country-level risk was assessed using a modified Susceptible-Exposed-Infectious-Removed (SEIR) model and a risk-factor-driven k-means clustering analysis.

Results

Between historical cases and the 2022 outbreak, MPX infection risk factors changed from relatively simple [human immunodeficiency virus (HIV) infection and population density] to multiple [human mobility, population of men who have sex with men, coronavirus disease 2019 (COVID-19) infection, and socioeconomic factors], with human mobility in the context of COVID-19 being especially key. The 141 included countries classified into three risk clusters: 24 high-risk countries mainly in West Europe and Northern America, 70 medium-risk countries mainly in Latin America and Asia, and 47 low-risk countries mainly in Africa and South Asia. The modified SEIR model predicted declining transmission rates, with basic reproduction numbers ranging 1.61-7.84 in the early stage and 0.70-4.13 in the current stage. The estimated cumulative cases in Northern and Latin America may overtake the number in Europe in autumn 2022.

Conclusions

In the current outbreak, risk factors for MPX infection have changed and expanded. Forecasts of epidemiological trends from our modified SEIR models suggest that Northern America and Latin America are at greater risk of MPX infection in the future.

Understanding the COVID-19 pandemic prevalence in Africa through optimal feature selection and clustering: evidence from a statistical perspective

The COVID-19 pandemic, which outbroke in Wuhan (China) in December 2019, severely hit almost all sectors of activity in the world as a consequence of the restrictive measures imposed. Two years later, Africa still emerges as the least affected continent by the pandemic. This study analyzed COVID-19 prevalence across African countries through country-level variables prior to clustering. Using Spearman-rank correlation, multicollinearity analysis and univariate filtering, 9 country-level variables were identified from an initial set of 34 variables. These variables relate to socioeconomic status, population structure, healthcare system and environment and the climatic setting. A clustering of the 54 African countries is further carried out through the use of agglomerative hierarchical clustering (AHC) method, which generated 3 distinctive clusters. Cluster 1 (11 countries) is the most affected by COVID-19 (median of 63,508.6 confirmed cases and 946.5 deaths per million) and is composed of countries with the highest socioeconomic status. Cluster 2 (27 countries) is the least affected (median of 4473.7 confirmed cases and 81.2 deaths per million), and mainly features countries with the least socioeconomic features and international exposure. Cluster 3 (16 countries) is intermediate in terms of COVID-19 prevalence (median of 2569.3 confirmed cases and 35.7 deaths per million) and features countries the least urbanized and geographically close to the equator, with intermediate international exposure and socioeconomic features. These findings shed light on the main features of COVID-19 prevalence in Africa and might help refine effectively coping management strategies of the ongoing pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-022-02646-3.

Application of Data Science for Cluster Analysis of COVID-19 Mortality According to Sociodemographic Factors at Municipal Level in Mexico

Mexico is among the five countries with the largest number of reported deaths from COVID-19 disease, and the mortality rates associated to infections are heterogeneous in the country due to structural factors concerning population. This study aims at the analysis of clusters related to mortality rate from COVID-19 at the municipal level in Mexico from the perspective of Data Science. In this sense, a new application is presented that uses a machine learning hybrid algorithm for generating clusters of municipalities with similar values of sociodemographic indicators and mortality rates. To provide a systematic framework, we applied an extension of the International Business Machines Corporation (IBM) methodology called Batch Foundation Methodology for Data Science (FMDS). For the study, 1,086,743 death certificates corresponding to the year 2020 were used, among other official data. As a result of the analysis, two key indicators related to mortality from COVID-19 at the municipal level were identified: one is population density and the other is percentage of population in poverty. Based on these indicators, 16 municipality clusters were determined. Among the main results of this research, it was found that clusters with high values of mortality rate had high values of population density and low poverty levels. In contrast, clusters with low density values and high poverty levels had low mortality rates. Finally, we think that the patterns found, expressed as municipality clusters with similar characteristics, can be useful for decision making by health authorities regarding disease prevention and control for reinforcing public health measures and optimizing resource distribution for reducing hospitalizations and mortality.

Combining rank-size and k-means for clustering countries over the COVID-19 new deaths per million

This paper deals with the cluster analysis of selected countries based on COVID-19 new deaths per million data. We implement a statistical procedure that combines a rank-size exploration and a k-means approach for clustering. Specifically, we first carry out a best-fit exercise on a suitable polynomial rank-size law at an individual country level; then, we cluster the considered countries by adopting a k-means clustering procedure based on the calibrated best-fit parameters. The investigated countries are selected considering those with a high value for the Healthcare Access and Quality Index to make a consistent analysis and reduce biases from the data collection phase. Interesting results emerge from the meaningful interpretation of the parameters of the best-fit curves; in particular, we show some relevant properties of the considered countries when dealing with the days with the highest number of new daily deaths per million and waves. Moreover, the exploration of the obtained clusters allows explaining some common countries' features.

Challenges and Drawbacks of the EU Medical System Generated by the COVID-19 Pandemic in the Field of Health Systems' Digitalization

The COVID-19 pandemic and the digitalization of medical services present significant challenges for the medical sector of the European Union, with profound implications for health systems and the provision of high-performance public health services. The sustainability and resilience of health systems are based on the introduction of information and communication technology in health processes and services, eliminating the vulnerability that can have significant consequences for health, social cohesion, and economic progress. This research aims to assess the impact of digitalization on several dimensions of health, introducing specific implications of the COVID-19 pandemic. The research methodology consists of three procedures: cluster analysis performed through vector quantization, agglomerative clustering, and an analytical approach consisting of data mapping. The main results highlight the importance of effective national responses and provide recommendations, various priorities, and objectives to strengthen health systems at the European level. Finally, the results reveal the need to reduce the gaps between the EU member states and a new approach to policy, governance, investment, health spending, and the performing provision of digital services.

Clustering of countries according to the COVID-19 incidence and mortality rates

Background

Two years after the beginning of the COVID-19 pandemic on December 29, 2021, there have been 281,808,270 confirmed cases of COVID-19, including 5,411,759 deaths. This information belongs to almost 216 Countries, areas, or territories facing COVID-19. The disease trend was not homogeneous across these locations, and studying this variation is a crucial source of information for policymakers and researchers. Therefore, we address different patterns in mortality and incidence of COVID-19 across countries using a clustering approach.

Methods

The daily records of new cases and deaths of 216 countries were available on the WHO online COVID-19 dashboard. We used a three-step approach for identifying longitudinal patterns of change in quantitative COVID-19 incidence and mortality rates. At the first, we calculated 27 summary measurements for each trajectory. Then we used factor analysis as a dimension reduction method to capture the correlation between measurements. Finally, we applied a K-means algorithm on the factor scores and clustered the trajectories.

Results

We determined three different patterns for the trajectories of COVID-19 incidence and the three different ones for mortality rates. According to incidence rates, among 206 countries the 133 (64.56) countries belong to the second cluster, and 15 (7.28%) and 58 (28.16%) belong to the first and 3rd clusters, respectively. All clusters seem to show an increased rate in the study period, but there are several different patterns. The first one exhibited a mild increasing trend; however, the 3rd and the second clusters followed the severe and moderate increasing trend. According to mortality clusters, the frequency of sets is 37 (18.22%) for the first cluster with moderate increases, 157 (77.34%) for the second one with a mild rise, and 9 (4.34%) for the 3rd one with severe increase.

Conclusions

We determined that besides all variations within the countries, the pattern of a contagious disease follows three different trajectories. This variation looks to be a function of the government’s health policies more than geographical distribution. Comparing this trajectory to others declares that death is highly related to the nature of epidemy.