scholarly journals 1492Ordering the chaos: The global clustering of COVID-19 incidence and mortality

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Vivek Jason Jayaraj ◽  
Sanjay Rampal ◽  
Chiu-Wan Ng ◽  
Diane Woei Quan Chong
Keyword(s):  
South America ◽  
Clustering Algorithm ◽  
Mortality Rates ◽  
Data Repositories ◽  
Partitional Clustering ◽  
Open Source Data ◽  
Incidence And Mortality ◽  
Source Data ◽  
Clustering Approach ◽  
Country Specific

Abstract Background The propagation of COVID-19 has been dynamic across countries and time. We utilised a temporal clustering approach in exploring trends of incidence and mortality across 202 countries. Methods COVID-19 case and death data between 1 January 2020 and 30 April 2021 were extracted from open-source data repositories. A partitional clustering algorithm, using Euclidean distances and partition around medoids, was utilised in exploring 14-day incidence and mortality rates across 202 countries. Inter-cluster comparisons were carried out using the 14-day incidence and mortality rates across clusters. Results Country-specific trends of incidence and mortality across the study period were agglomerated into one of six clusters. The overall trend of incidence and mortality during this period peaked between November 2020 and January 2021. However, four of the six clusters have an upward trajectory. Countries in cluster four, mostly situated in Europe, reported the highest overall incidence of 192 cases per 100,000 population (95% CI: 166, 220). Countries in cluster three, a mix of countries from South America, Eastern Europe, and Africa, were observed to have the highest overall mortality rate of 32 deaths per 1,000,000 population (95% CI: 23, 45). Conclusions The high global burden of disease and inequity in vaccine access highlights the need for a consolidated global effort in mitigating the pandemic. Key messages Increasing trajectories of incidence and mortality in Asia, South America, and Africa suggest that the worst of the pandemic may be ahead of us.

scholarly journals Untangling factors associated with country-specific COVID-19 incidence, mortality and case fatality rates during the first quarter of 2020

2020 ◽  
Author(s):  
Lev Shagam
Keyword(s):  
Case Fatality Rate ◽  
Case Fatality ◽  
Mortality Rates ◽  
Press Freedom ◽  
Gdp Per Capita ◽  
Model Case ◽  
Factors Associated ◽  
Incidence And Mortality ◽  
Per Capita ◽  
Country Specific

AbstractAt early stages of the COVID-19 pandemic which we are experiencing, the publicly reported incidence, mortality and case fatality rates (CFR) vary significantly between countries. Here we aim to untangle factors that are associated with the differences during the first quarter of the year 2020. Number of performed COVID-19 tests has a strong correlation with country-specific incidence (p < 2 × 10−16) and mortality rate (p = 5.1 × 10−8). Using multivariate linear regression we show that incidence and mortality rates correlate significantly with GDP per capita (p = 2.6 × 10−15 and 7.0 × 10−4, respectively), country-specific duration of the outbreak (2.6 × 10−4 and 0.0019), fraction of citizens over 65 years old (p = 0.0049 and 3.8 × 10−4) and level of press freedom (p = 0.021 and 0.019) which cumulatively explain 80% of variability of incidence and more than 60% of variability of mortality of the disease during the period analyzed. Country hemisphere demonstrated significant correlation only with mortality (p = 0.17 and 0.036) whereas population density (p = 0.94 and p = 0.75) and latitude (p = 0.61 and 0.059) did not reach significance in our model. Case fatality rate is shown to rise as the outbreak progresses (p=0.028). We rank countries by COVID-19 mortality corrected for incidence and the factors that were shown to affect it, and by CFR corrected for outbreak duration, yielding very similar results. Among the countries where the outbreak started after the 15th of February and with at least 1000 registered patients during the period analyzed, the lowest corrected CFR are seen in Israel, South Africa and Chile. The ranking results should be considered with caution as they do not consider all confounding factors or data reporting biases.

scholarly journals High Proportion of Potential Candidates for Immunotherapy in a Chilean Cohort of Gastric Cancer Patients: Results of the FORCE1 Study

Cancers ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1275 ◽  
Author(s):  
Miguel Cordova-Delgado ◽  
Mauricio P. Pinto ◽  
Ignacio N. Retamal ◽  
Matías Muñoz-Medel ◽  
María Loreto Bravo ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
South America ◽  
Clinical Data ◽  
Inverse Correlation ◽  
Mortality Rates ◽  
Molecular Profile ◽  
Barr Virus ◽  
Geographical Variations ◽  
Mutational Status ◽  
Incidence And Mortality

Gastric cancer (GC) is a heterogeneous disease. This heterogeneity applies not only to morphological and phenotypic features but also to geographical variations in incidence and mortality rates. As Chile has one of the highest mortality rates within South America, we sought to define a molecular profile of Chilean GCs (ClinicalTrials.gov identifier: NCT03158571/(FORCE1)). Solid tumor samples and clinical data were obtained from 224 patients, with subsets analyzed by tissue microarray (TMA; n = 90) and next generation sequencing (NGS; n = 101). Most demographic and clinical data were in line with previous reports. TMA data indicated that 60% of patients displayed potentially actionable alterations. Furthermore, 20.5% were categorized as having a high tumor mutational burden, and 13% possessed micro-satellite instability (MSI). Results also confirmed previous studies reporting high Epstein-Barr virus (EBV) positivity (13%) in Chilean-derived GC samples suggesting a high proportion of patients could benefit from immunotherapy. As expected, TP53 and PIK3CA were the most frequently altered genes. However, NGS demonstrated the presence of TP53, NRAS, and BRAF variants previously unreported in current GC databases. Finally, using the Kendall method, we report a significant correlation between EBV+ status and programmed death ligand-1 (PDL1)+ and an inverse correlation between p53 mutational status and MSI. Our results suggest that in this Chilean cohort, a high proportion of patients are potential candidates for immunotherapy treatment. To the best of our knowledge, this study is the first in South America to assess the prevalence of actionable targets and to examine a molecular profile of GC patients.

scholarly journals Determinants of the incidence and mortality rates of COVID-19 during the first six months of the pandemic; a cross-country study

2021 ◽  
Author(s):  
Noha Asem ◽  
Ahmed Ramadan ◽  
Mohamed Hassany ◽  
Ramy Mohamed Ghazy ◽  
Mohamed Abdallah ◽  
...  
Keyword(s):  
Mortality Rates ◽  
Healthcare Systems ◽  
Multivariate Techniques ◽  
Average Temperature ◽  
Incidence And Mortality ◽  
Contact Frequency ◽  
Country Study ◽  
Proxy Measure ◽  
Cross Country ◽  
Country Specific

AbstractCOVID-19 pandemic raises an extraordinary challenge to the healthcare systems globally. The governments are taking key measures to constrain the corresponding health, social, and economic impacts, however, these measures vary depending on the nature of the crisis and country-specific circumstances.ObjectivesConsidering different incidence and mortality rates across different countries, we aimed at explaining variance of these variables by performing accurate and precise multivariate analysis with aid of suitable predictors, accordingly, the model would proactively guide the governmental responses to the crisis.MethodsUsing linear and exponential time series analysis, this research aimed at studying the incidence and mortality rates of COVID-19 in 18 countries during the first six months of the pandemic, and further utilize multivariate techniques to explain the variance in monthly exponential growth rates of cases and deaths with aid of a set of different predictors: the recorded Google mobility trends towards six categories of places, daily average temperature, daily humidity, and key socioeconomic attributes of each country.ResultsThe analysis showed that changes in mobility trends were the most significant predictors of the incidence and mortality rates, temperature and humidity were also significant but to a much lesser extent, on the other hand, the socioeconomic attributes did not contribute significantly to explaining different incidence and mortality rates across countries.ConclusionChanges in mobility trends across countries dramatically affected the incidence and mortality rates across different countries, thus, it might be used as a proxy measure of contact frequency.

scholarly journals Adjustable Algorithmic Tool for Assessing the Effectiveness of Maternal Respiratory Syncytial Virus (RSV) Vaccination on Infant Mortality in Developing Countries

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Rachel Cevigney ◽  
Christopher Leary ◽  
Bernard Gonik
Keyword(s):  
Developing Countries ◽  
Infant Mortality ◽  
Case Fatality ◽  
The United States ◽  
Mortality Rates ◽  
Incidence Rates ◽  
Acute Lower Respiratory Infection ◽  
Maternal Child Health ◽  
Incidence And Mortality ◽  
Country Specific

Acute lower respiratory infection (ALRI) due to RSV is a common cause of global infant mortality, with most cases occurring in developing countries. Using data aggregated from priority countries as designated by the United States Agency for International Development’s (USAID) Maternal Child Health and Nutrition (MCHN) program, we created an adjustable algorithmic tool for visualizing the effectiveness of candidate maternal RSV vaccination on infant mortality. Country-specific estimates for disease burden and case fatality rates were computed based on established data. Country-specific RSV-ALRI incidence rates for infants 0-5 months were scaled based on the reported incidence rates for children 0-59 months. Using in-hospital mortality rates and predetermined “inflation factor,” we estimated the mortality of infants aged 0-5 months. Given implementation of a candidate maternal vaccination program, estimated reduction in infant RSV-ALRI incidence and mortality rates were calculated. User input is used to determine the coverage of the program and the efficacy of the vaccine. Using the generated algorithm, the overall reduction in infant mortality varied considerably depending on vaccine efficacy and distribution. Given a potential efficacy of 70% and a maternal distribution rate of 50% in every USAID MCHN priority country, annual RSV-ALRI-related infant mortality is estimated to be reduced by 14,862 cases. The absolute country-specific reduction is dependent on the number of live births; countries with the highest birth rates had the greatest impact on annual mortality reduction. The adjustable algorithm provides a standardized analytical tool in the evaluation of candidate maternal RSV vaccines. Ultimately, it can be used to guide public health initiatives, research funding, and policy implementation concerning the effectiveness of potential maternal RSV vaccination on reducing infant mortality.

scholarly journals Long-Term Exposure to Fine and Coarse Particulate Matter and COVID-19 Incidence and Mortality Rate in Chile during 2020

2021 ◽  
Vol 18 (14) ◽  
pp. 7409
Author(s):  
Macarena Valdés Salgado ◽  
Pamela Smith ◽  
Mariel Opazo ◽  
Nicolás Huneeus
Keyword(s):  
Mortality Rate ◽  
Incidence Rate ◽  
Air Pollutants ◽  
Negative Binomial ◽  
Transport Model ◽  
Surface Concentration ◽  
Mortality Rates ◽  
Incidence And Mortality ◽  
Pm2.5 And Pm10

Background: Several countries have documented the relationship between long-term exposure to air pollutants and epidemiological indicators of the COVID-19 pandemic, such as incidence and mortality. This study aims to explore the association between air pollutants, such as PM2.5 and PM10, and the incidence and mortality rates of COVID-19 during 2020. Methods: The incidence and mortality rates were estimated using the COVID-19 cases and deaths from the Chilean Ministry of Science, and the population size was obtained from the Chilean Institute of Statistics. A chemistry transport model was used to estimate the annual mean surface concentration of PM2.5 and PM10 in a period before the current pandemic. Negative binomial regressions were used to associate the epidemiological information with pollutant concentrations while considering demographic and social confounders. Results: For each microgram per cubic meter, the incidence rate increased by 1.3% regarding PM2.5 and 0.9% regarding PM10. There was no statistically significant relationship between the COVID-19 mortality rate and PM2.5 or PM10. Conclusions: The adjusted regression models showed that the COVID-19 incidence rate was significantly associated with chronic exposure to PM2.5 and PM10, even after adjusting for other variables.

scholarly journals (late submission poster) Incidence and mortality rates of cholangiocarcinoma in England

2019 ◽  
Vol 30 ◽  
pp. iv155
Author(s):  
Tracey Genus ◽  
Daniela Tataru ◽  
Helen Morement ◽  
Mireille Toledano ◽  
Shahid Khan
Keyword(s):  
Mortality Rates ◽  
Incidence And Mortality

A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation

2017 ◽  
Vol 32 (04) ◽  
pp. 1850012 ◽  
Author(s):  
R. R. Gharieb ◽  
G. Gendy ◽  
H. Selim
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Local Data ◽  
Cluster Membership ◽  
Kl Divergence ◽  
Clustering Approach ◽  
Center Distance

In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.

Sign in / Sign up

Export Citation Format

Share Document