Variations of COVID-19 mortality are affected by economic disparities across countries.

BACKGROUND: When the COVID-19 case number reaches a maximum in a country, its capacity and management of health system face greatest challenge. METHODS: We performed a cross-sectional study on data of turning points for cases and deaths for the first three waves of COVID-19 in countries with more than 5000 cumulative cases, as reported by Worldometers and WHO Coronavirus (COVID-19) Dashboard. We compared the case fatality rates (CFRs) and time lags (in unit of day) between the turning points of cases and deaths among countries in different development stages and potential influence factors. As of May 10, 2021, 106 out of 222 countries or regions (56%) reported more than 5000 cases. Approximately half of them have experienced all the three waves of COVID-19 disease. The average mortality rate at the disease turning point was 0.038 for the first wave, 0.020 for the second wave, and 0.023 for wave 3. In high-income countries, the mortality rates during the first wave are higher than that of the other income levels. However, the mortality rates during the second and third waves of COVID-19 were much lower than those of the first wave, with a significant reduction from 5.7% to 1.7% approximately 70%. At the same time, high-income countries exhibited a 2-fold increase in time lags during the second and the third waves compared to the first wave, suggesting that the periods between the cases and deaths turning point extended. High rates in the first wave in developed countries are associated to multiple factors including transportation, population density, and aging populations. In upper middle- and lower middle-income countries, the decreasing of mortality rates in the second and third waves were subtle or even reversed, with increased mortality during the following waves. In the upper and lower middle-income countries, the time lags were about 50% of the durations observed from high-income countries. INTERPRETATION: Economy and medical resources affect the efficiency of COVID-19 mitigation and the clinical outcomes of the patients. The situation is likely to become even worse in the light of these countries' limited ability to combat COVID-19 and prevent severe outcomes or deaths as the new variant transmission becomes dominant.

Graphical Trajectory Comparison to Identify Errors in Data of COVID-19: A Cross-Country Analysis.

Data from the early stage of a novel infectious disease outbreak provide vital information in risk assessment, prediction, and precise disease management. Since the first reported case of COVID-19, the pattern of the novel coronavirus transmission in Wuhan has become the interest of researchers in epidemiology and public health. To thoroughly map the mechanism of viral spreading, we used the patterns of data at the early onset of COVID-19 from seven countries to estimate the time lag between peak days of cases and deaths. This study compared these data with those of Wuhan and estimated the natural history of disease across the infected population and the time lag. The findings suggest that comparative analyses of data from different regions and countries reveal the differences between peaks of cases and deaths caused by COVID-19 and the incomplete and underestimated cases in Wuhan. Different countries may show different patterns of cases peak days, deaths peak days, and peak periods. Error in the early COVID-19 statistics in Brazil was identified. This study provides sound evidence for policymakers to understand the local circumstances in diagnosing the health of a population and propose precise and timely public health interventions to control and prevent infectious diseases.

Democracy and case fatality rate of COVID-19 at early stage of pandemic: a multicountry study.

Many studies have evaluated factors that influence the course of the COVID-19 pandemic in different countries. This multicountry study assessed the influence of democracy and other factors on the case fatality rate of COVID-19 during the early stage of the pandemic. We accessed the World Health Organization, World Bank, and the Democracy Index 2019 databases for data from the 148 countries. Multiple analyses were conducted to examine the association between the Democracy Index and case fatality rate of COVID-19. Within 148 countries, the percentage of the population aged 65 years and above (p = 0.0193), and health expenditure as a percentage of GDP (p = 0.0237) were positively associated with countries' case fatality rates. By contrast, hospital beds per capita helped to reduce the case fatality rates. In particular, the Democracy Index was positively associated with case fatality rates in a subgroup of 47 high-income countries. This study suggests that enhancing the health system with increased hospital beds and healthcare workforce per capita should reduce case fatality rate. The findings suggest that a higher Democracy Index is associated with more deaths from COVID-19 at the early stage of the pandemic, possibly due to the decreased ability of the government.

The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19.

The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were chosen as representatives from the perspective of the coronavirus outbreak in China. Data were copied and pasted onto Excel spreadsheets to perform data analysis. A new methodology, Patient Information Based Algorithm (PIBA) [1], has been adapted to process the data and used to estimate the death rate of COVID-19 in real-time. Assumption is that the number of days from inpatients to death fall into a pattern of normal distribution and the scores in normal distribution can be obtained by observing 33 death cases and analysing the data [2]. We selected 5 scores in normal distribution of these durations as lagging days, which will be used in the following estimation of death rate. We calculated each death rate on accumulative confirmed cases with each lagging day from the current data and then weighted every death rate with its corresponding possibility to obtain the total death rate on each day. While the trendline of these death rate curves meet the curve of current ratio between accumulative death cases and confirmed cases at some points in the near future, we considered that these intersections are within the range of real death rates. Six tables were presented to illustrate the PIBA method using data from China and South Korea. One figure on estimated rate of infection and patients in serious condition and retrospective estimation of initially occurring time of CORID-19 based on PIBA.

Toward a Country-Based Prediction Model of COVID-19 Infections and Deaths Between Disease Apex and End: Evidence From Countries With Contained Numbers of COVID-19.

The complexity of COVID-19 and variations in control measures and containment efforts in different countries have caused difficulties in the prediction and modeling of the COVID-19 pandemic. We attempted to predict the scale of the latter half of the pandemic based on real data using the ratio between the early and latter halves from countries where the pandemic is largely over. We collected daily pandemic data from China, South Korea, and Switzerland and subtracted the ratio of pandemic days before and after the disease apex day of COVID-19. We obtained the ratio of pandemic data and created multiple regression models for the relationship between before and after the apex day. We then tested our models using data from the first wave of the disease from 14 countries in Europe and the US. We then tested the models using data from these countries from the entire pandemic up to March 30, 2021. Results indicate that the actual number of cases from these countries during the first wave mostly fall in the predicted ranges of liniar regression, excepting Spain and Russia. Similarly, the actual deaths in these countries mostly fall into the range of predicted data. Using the accumulated data up to the day of apex and total accumulated data up to March 30, 2021, the data of case numbers in these countries are falling into the range of predicted data, except for data from Brazil. The actual number of deaths in all the countries are at or below the predicted data. In conclusion, a linear regression model built with real data from countries or regions from early pandemics can predict pandemic scales of the countries where the pandemics occur late. Such a prediction with a high degree of accuracy provides valuable information for governments and the public.

The threat of the COVID-19 pandemic on reversing global life-saving gains in the survival of childhood cancer: a call for collaborative action from SIOP, IPSO, PROS, WCC, CCI, St Jude Global, UICC and WHPCA.

The COVID-19 pandemic poses an unprecedented health crisis in all socio-economic regions across the globe. While the pandemic has had a profound impact on access to and delivery of health care by all services, it has been particularly disruptive for the care of patients with life-threatening noncommunicable diseases (NCDs) such as the treatment of children and young people with cancer. The reduction in child mortality from preventable causes over the last 50 years has seen childhood cancer emerge as a major unmet health care need. Whilst survival rates of 85% have been achieved in high income countries, this has not yet been translated into similar outcomes for children with cancer in resource-limited settings where survival averages 30%. Launched in 2018, by the World Health Organization (WHO), the Global Initiative for Childhood Cancer (GICC) is a pivotal effort by the international community to achieve at least 60% survival for children with cancer by 2030. The WHO GICC is already making an impact in many countries but the disruption of cancer care during the COVID-19 pandemic threatens to set back this global effort to improve the outcome for children with cancer, wherever they may live. As representatives of the global community committed to fostering the goals of the GICC, we applaud the WHO response to the COVID-19 pandemic, in particular we support the WHO's call to ensure the needs of patients with life threatening NCDs including cancer are not compromised during the pandemic. Here, as collaborative partners in the GICC, we highlight specific areas of focus that need to be addressed to ensure the immediate care of children and adolescents with cancer is not disrupted during the pandemic; and measures to sustain the development of cancer care so the long-term goals of the GICC are not lost during this global health crisis.

COVID-19 vaccine: Call for employees in international transportation industries and international travelers as the first priority in global distribution.

While countries are in a hurry to obtain SARS-CoV-2 vaccine, we are concerned with the availability of vaccine and whether a vaccine will be available to all in need. We predicted three possible scenarios for vaccine distributions and urge an international united action on the worldwide equitable access. In case the international community does not reach a consensus on how to distribute the vaccine to achieve worldwide equitable access, we call for a distribution plan that includes the employees in international transportation industries and international travelers to halt the disease transmission and promote the recovery of the global economy.

Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm.

The global COVID-19 outbreak is worrisome both for its high rate of spread, and the high case fatality rate reported by early studies and now in Italy. We report a new methodology, the Patient Information Based Algorithm (PIBA), for estimating the death rate of a disease in real-time using publicly available data collected during an outbreak. PIBA estimated the death rate based on data of the patients in Wuhan and then in other cities throughout China. The estimated days from hospital admission to death was 13 (standard deviation (SD), 6 days). The death rates based on PIBA were used to predict the daily numbers of deaths since the week of February 25, 2020, in China overall, Hubei province, Wuhan city, and the rest of the country except Hubei province. The death rate of COVID-19 ranges from 0.75% to 3% and may decrease in the future. The results showed that the real death numbers had fallen into the predicted ranges. In addition, using the preliminary data from China, the PIBA method was successfully used to estimate the death rate and predict the death numbers of the Korean population. In conclusion, PIBA can be used to efficiently estimate the death rate of a new infectious disease in real-time and to predict future deaths. The spread of 2019-nCoV and its case fatality rate may vary in regions with different climates and temperatures from Hubei and Wuhan. PIBA model can be built based on known information of early patients in different countries.