Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country

Country-based modelling of COVID-19 case fatality rate: A multiple regression analysis

BACKGROUND

The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection. The determinants of mortality on a global scale cannot be fully understood due to lack of information.

AIM

To identify key factors that may explain the variability in case lethality across countries.

METHODS

We identified 21 Potential risk factors for coronavirus disease 2019 (COVID-19) case fatality rate for all the countries with available data. We examined univariate relationships of each variable with case fatality rate (CFR), and all independent variables to identify candidate variables for our final multiple model. Multiple regression analysis technique was used to assess the strength of relationship.

RESULTS

The mean of COVID-19 mortality was 1.52 ± 1.72%. There was a statistically significant inverse correlation between health expenditure, and number of computed tomography scanners per 1 million with CFR, and significant direct correlation was found between literacy, and air pollution with CFR. This final model can predict approximately 97% of the changes in CFR.

CONCLUSION

The current study recommends some new predictors explaining affect mortality rate. Thus, it could help decision-makers develop health policies to fight COVID-19.

Risk of severe COVID-19 in unvaccinated patients during the period from wild-type to Omicron variant: real-world evidence from Japan

BACKGROUND

Many studies have reported that the Omicron variant is less pathogenic than the Delta variant and the wild-type. Epidemiological evidence regarding the risk of severe COVID-19 from the wild-type to the Omicron variant has been lacking.

METHODS

Study participants were COVID-19 patients aged 18 and older without previous COVID-19 infection who were notified to the Nara Prefecture Chuwa Public Health Center from January 2020 to March 2023, during the periods from the wild-type to the Omicron variant. The outcome variable was severe COVID-19 (i.e., ICU admission or COVID-19-related death). The explanatory variable was SARS-CoV-2 variant type or the number of COVID-19 vaccinations. Covariates included gender, age, risk factors for aggravation, and the number of general hospital beds per population. The generalized estimating equations of negative binomial regression models were used to estimate the adjusted incidence proportion (AIP) with 95% confidence interval (CI) for severe COVID-19.

RESULTS

Among 77,044 patients included in the analysis, 14,556 (18.9%) were unvaccinated and 520 (0.7%) developed severe COVID-19. Among unvaccinated patients, the risk of severe COVID-19 increased in the Alpha/Delta variants and decreased in the Omicron variant compared to the wild-type (AIP [95% CI] was 1.55 [1.06-2.27] in Alpha/Delta and 0.25 [0.15-0.40] in Omicron), but differed by age. Especially in patients aged ≥80, there was no significant difference in the risk of severe COVID-19 between the wild-type and the Omicron variant (AIP [95% CI] = 0.59 [0.27-1.29]). Regarding the preventive effect of vaccines, among all study participants, the number of vaccinations was significantly associated with the prevention of severe COVID-19, regardless of variant type. After stratified analyses by age, patients aged ≥80 remained a significant association for all variant types. On the other hand, the number of vaccinations had no association in Omicron BA.5 of patients aged 18-64.

CONCLUSIONS

Patients aged ≥80 had less reduction in risk of severe COVID-19 during the Omicron variant period, and a greater preventive effect of vaccines against severe COVID-19, compared to younger people. Our findings suggest that booster vaccination is effective and necessary for older people, especially aged ≥80.

Study of optimal vaccination strategies for early COVID-19 pandemic using an age-structured mathematical model: A case study of the USA

In this paper we study different vaccination strategies that could have been implemented for the early COVID-19 pandemic. We use a demographic epidemiological mathematical model based on differential equations in order to investigate the efficacy of a variety of vaccination strategies under limited vaccine supply. We use the number of deaths as the metric to measure the efficacy of each of these strategies. Finding the optimal strategy for the vaccination programs is a complex problem due to the large number of variables that affect the outcomes. The constructed mathematical model takes into account demographic risk factors such as age, comorbidity status and social contacts of the population. We perform simulations to assess the performance of more than three million vaccination strategies which vary depending on the vaccine priority of each group. This study focuses on the scenario corresponding to the early vaccination period in the USA, but can be extended to other countries. The results of this study show the importance of designing an optimal vaccination strategy in order to save human lives. The problem is extremely complex due to the large amount of factors, high dimensionality and nonlinearities. We found that for low/moderate transmission rates the optimal strategy prioritizes high transmission groups, but for high transmission rates, the optimal strategy focuses on groups with high CFRs. The results provide valuable information for the design of optimal vaccination programs. Moreover, the results help to design scientific vaccination guidelines for future pandemics.

A large-scale machine learning study of sociodemographic factors contributing to COVID-19 severity

Understanding sociodemographic factors behind COVID-19 severity relates to significant methodological difficulties, such as differences in testing policies and epidemics phase, as well as a large number of predictors that can potentially contribute to severity. To account for these difficulties, we assemble 115 predictors for more than 3,000 US counties and employ a well-defined COVID-19 severity measure derived from epidemiological dynamics modeling. We then use a number of advanced feature selection techniques from machine learning to determine which of these predictors significantly impact the disease severity. We obtain a surprisingly simple result, where only two variables are clearly and robustly selected-population density and proportion of African Americans. Possible causes behind this result are discussed. We argue that the approach may be useful whenever significant determinants of disease progression over diverse geographic regions should be selected from a large number of potentially important factors.

Dietary, comorbidity, and geo-economic data fusion for explainable COVID-19 mortality prediction

Many factors significantly influence the outcomes of infectious diseases such as COVID-19. A significant focus needs to be put on dietary habits as environmental factors since it has been deemed that imbalanced diets contribute to chronic diseases. However, not enough effort has been made in order to assess these relations. So far, studies in the field have shown that comorbid conditions influence the severity of COVID-19 symptoms in infected patients. Furthermore, COVID-19 has exhibited seasonal patterns in its spread; therefore, considering weather-related factors in the analysis of the mortality rates might introduce a more relevant explanation of the disease's progression. In this work, we provide an explainable analysis of the global risk factors for COVID-19 mortality on a national scale, considering dietary habits fused with data on past comorbidity prevalence and environmental factors such as seasonally averaged temperature geolocation, economic and development indices, undernourished and obesity rates. The innovation in this paper lies in the explainability of the obtained results and is equally essential in the data fusion methods and the broad context considered in the analysis. Apart from a country's age and gender distribution, which has already been proven to influence COVID-19 mortality rates, our empirical analysis shows that countries with imbalanced dietary habits generally tend to have higher COVID-19 mortality predictions. Ultimately, we show that the fusion of the dietary data set with the geo-economic variables provides more accurate modeling of the country-wise COVID-19 mortality rates with respect to considering only dietary habits, proving the hypothesis that fusing factors from different contexts contribute to a better descriptive analysis of the COVID-19 mortality rates.

The impact of geo-environmental factors on global COVID-19 transmission: A review of evidence and methodology

Studies on Coronavirus Disease 2019 (COVID-19) transmission indicate that geo-environmental factors have played a significant role in the global pandemic. However, there has not been a systematic review on the impact of geo-environmental factors on global COVID-19 transmission in the context of geography. As such, we reviewed 49 well-chosen studies to reveal the impact of geo-environmental factors (including the natural environment and human activity) on global COVID-19 transmission, and to inform critical intervention strategies that could mitigate the worldwide effects of the pandemic. Existing studies frequently mention the impact of climate factors (e.g., temperature and humidity); in contrast, a more decisive influence can be achieved by human activity, including human mobility, health factors, and non-pharmaceutical interventions (NPIs). The above results exhibit distinct spatiotemporal heterogeneity. The related analytical methodology consists of sensitivity analysis, mathematical modeling, and risk analysis. For future studies, we recommend highlighting geo-environmental interactions, developing geographically statistical models for multiple waves of the pandemic, and investigating NPIs and care patterns. We also propose four implications for practice to combat global COVID-19 transmission.

Research on Design of Emergency Science Popularization Information Visualization for Public Health Events-Taking “COVID-19”as an Example

This study explores the optimization method of emergency popular science information design elements in public health events, breaks through the traditional design with the designer as the subjective consciousness and proposes an emergency popular science information design method oriented by perceptual narrative. First, relevant research on public health events was carried out to screen out and analyze relevant narrative information elements and image elements, and narrative element divergence tree was established to show evaluation indicators. Second, relevant personnel were invited to evaluate the importance and kansei engineering, factor analysis and other methods were used to establish the correlation evaluation indicators of narrative elements. Finally, the optimization narrative elements of popular science information design were calculated with the fuzzy evaluation method to provide an effective auxiliary role for the visualization design of emergency popular science information. Taking “COVID-19 Event” as an example, the narrative design practice of emergency popular science elements was carried out. According to 313 effective questionnaires, the satisfaction of “COVID-19 event” popular science information elements that adopt the optimization method is relatively high, which verifies the feasibility of this method. The conclusion proves that the perceptual narrative design method can obtain the perceptual identity from the audience and plays a positive role in disseminating emergency popular science information.

Data Science in Healthcare: COVID-19 and Beyond

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning (ML), neural networks (NN) and artificial intelligence (AI), to create value, based on extracting knowledge and insights from available 'big' data [...].

One year of COVID-19 in Italy: are containment policies enough to shape the pandemic pattern?

A successful fight against COVID-19 greatly depends on citizens' adherence to the restrictive measures, which may not suffice alone. Making use of a containment index, data on sanctions, and Google's movement trends across Italian provinces, complemented by other sources, we investigate the extent to which compliance with the mobility limitations has affected the number of infections and deaths over time, for the period running from February 24, 2020 to February 23, 2021. We find proof of a deterrent effect on mobility given by the increase in sanction rate and positivity rate among the population. We also show how the pandemic dynamics have changed between the first and the second wave of the emergency. Lots of people could be spared by incorporating greater interventions and many more are at stake, despite the recent boost in vaccinations. Informing citizens about the effects and purposes of the restrictive measures has become increasingly important throughout the various phases of the pandemic.

A global country-level analysis of the relationship between obesity and COVID-19 cases and mortality

AIM

To assess the association of country-level obesity prevalence with COVID-19 case and mortality rates, to evaluate the impact of obesity prevalence on worldwide variation.

METHODS

Data on COVID-19 prevalence and mortality, country-specific governmental actions, socioeconomic, demographic, and healthcare capacity factors were extracted from publicly available sources. Multivariable negative binomial regression was used to assess the independent association of obesity with COVID-19 case and mortality rates.

RESULTS

Across 168 countries for which data were available, higher obesity prevalence was associated with increased COVID-19 mortality and prevalence rates. For every 1% increase in obesity prevalence, the mortality rate was increased by 8.3% (incidence rate ratio [IRR] 1.083, 95% confidence interval [CI] 1.048-1.119; P < 0.001) and the case rate was higher by 6.6% (IRR 1.066, 95% CI 1.035-1.099; P < 0.001). Additionally, higher median population age, greater female ratio, higher Human Development Index (HDI), lower population density, and lower hospital bed availability were all significantly associated with higher COVID-19 mortality rate. In addition, stricter governmental actions, higher HDI and lower mean annual temperature were significantly associated with higher COVID-19 case rate.

CONCLUSION

These findings demonstrate that obesity prevalence is a significant and potentially modifiable risk factor of increased COVID-19 national caseload and mortality. Future research to study whether weight loss improves COVID-19 outcomes is urgently required.

COVID-19 severity determinants inferred through ecological and epidemiological modeling

Understanding variations in the severity of infectious diseases is essential for planning proper mitigation strategies. Determinants of COVID-19 clinical severity are commonly assessed by transverse or longitudinal studies of the fatality counts. However, the fatality counts depend both on disease clinical severity and transmissibility, as more infected also lead to more deaths. Instead, we use epidemiological modeling to propose a disease severity measure that accounts for the underlying disease dynamics. The measure corresponds to the ratio of population-averaged mortality and recovery rates (m/r), is independent of the disease transmission dynamics (i.e., the basic reproduction number), and has a direct mechanistic interpretation. We use this measure to assess demographic, medical, meteorological, and environmental factors associated with the disease severity. For this, we employ an ecological regression study design and analyze different US states during the first disease outbreak. Principal Component Analysis, followed by univariate, and multivariate analyses based on machine learning techniques, is used for selecting important predictors. The usefulness of the introduced severity measure and the validity of the approach are confirmed by the fact that, without using prior knowledge from clinical studies, we recover the main significant predictors known to influence disease severity, in particular age, chronic diseases, and racial factors. Additionally, we identify long-term pollution exposure and population density as not widely recognized (though for the pollution previously hypothesized) significant predictors. The proposed measure is applicable for inferring severity determinants not only of COVID-19 but also of other infectious diseases, and the obtained results may aid a better understanding of the present and future epidemics. Our holistic, systematic investigation of disease severity at the human-environment intersection by epidemiological dynamical modeling and machine learning ecological regressions is aligned with the One Health approach. The obtained results emphasize a syndemic nature of COVID-19 risks.

Imaging of COVID-19: CT, MRI, and PET

Soon after reports of a novel coronavirus capable of causing severe pneumonia surfaced in late 2019, expeditious global spread of the Severe Acute Respiratory Distress Syndrome Coronavirus 2 (SARS-CoV-2) forced the World Health Organization to declare an international state of emergency. Although best known for causing symptoms of upper respiratory tract infection in mild cases and fulminant pneumonia in severe disease, Coronavirus Disease 2019 (COVID-19) has also been associated with gastrointestinal, neurologic, cardiac, and hematologic presentations. Despite concerns over poor specificity and undue radiation exposure, chest imaging nonetheless remains central to the initial diagnosis and monitoring of COVID-19 progression, as well as to the evaluation of complications. Classic features on chest CT include ground-glass and reticular opacities with or without superimposed consolidations, frequently presenting in a bilateral, peripheral, and posterior distribution. More recently, studies conducted with MRI have shown excellent concordance with chest CT in visualizing typical features of COVID-19 pneumonia. For patients in whom exposure to ionizing radiation should be avoided, particularly pregnant patients and children, pulmonary MRI may represent a suitable alternative to chest CT. Although PET imaging is not typically considered among first-line investigative modalities for the diagnosis of lower respiratory tract infections, numerous reports have noted incidental localization of radiotracer in parenchymal regions of COVID-19-associated pulmonary lesions. These findings are consistent with data from Middle East Respiratory Syndrome-CoV cohorts which suggested an ability for ¹⁸F-FDG PET to detect subclinical infection and lymphadenitis in subjects without overt clinical signs of infection. Though highly sensitive, use of PET/CT for primary detection of COVID-19 is constrained by poor specificity, as well as considerations of cost, radiation burden, and prolonged exposure times for imaging staff. Even still, decontamination of scanner bays is a time-consuming process, and proper ventilation of scanner suites may additionally require up to an hour of downtime to allow for sufficient air exchange. Yet, in patients who require nuclear medicine investigations for other clinical indications, PET imaging may yield the earliest detection of nascent infection in otherwise asymptomatic individuals. Especially for patients with concomitant malignancies and other states of immunocompromise, prompt recognition of infection and early initiation of supportive care is crucial to maximizing outcomes and improving survivability.

Cross-National Variations in COVID-19 Mortality: The Role of Diet, Obesity and Depression

Background: The COVID-19 pandemic has been characterized by wide variations in mortality across nations. Some of this variability may be explained by medical comorbidities such as obesity and depression, both of which are strongly correlated with dietary practices such as levels of sugar and seafood consumption. Methods: COVID-19 mortality indices for 156 countries were obtained from the Johns Hopkins University’s data aggregator. Correlations between these variables and (a) per capita consumption of sugar and seafood, and (b) country-wise prevalence of depression and obesity were examined. Results: Sugar consumption (r = 0.51, p < 0.001) and prevalence of obesity (r = 0.66, p < 0.001) and depression (r = 0.56, p < 0.001) were positively correlated with crude mortality rates, while seafood consumption was negatively correlated with the infection fatality rate (r = −0.28, p = 0.015). These effects were significant even after correcting for potential confounders. The associations with depression and obesity remained significant upon multivariate regression. Conclusions: Both obesity and depression, which are associated with inflammatory dysregulation, may be related to cross-national variations in COVID-19 mortality, while seafood consumption may be protective. These findings have implications in terms of protecting vulnerable individuals during the current pandemic.

Technological Advances in Ozone and Ozonized Water Spray Disinfection Devices

To control infectious diseases such as the severe acute respiratory syndrome coronavirus (Covid-19) that caused the current pandemic, disinfection measures are essential. Among building measures, disinfection chambers can help to decrease the transmission rate through the sanitizing capacity of the disinfectant used, which can thereby clean surfaces or humans. Out of existing biocides, ozone is considered one of the safest for humans, but one of the most powerful oxidizers, making the substance a better alternative as the biocidal solution in disinfection chambers. Analyses were carried out by using all patented documents related to disinfection chambers that used ozone as a disinfectant. A Derwent Innovation Index (DII) database search was undertaken to find these patents. Patent prospecting resulted in 620 patent documents that were divided into 134 patent families. There was no technology related to protective barriers for individuals, and the majority of patents in the retrieved data aimed at sterilizing medical devices and surfaces. Given that the specific Cooperative Patent Classification (CPC) code for ozone dissolved in liquid was used in the methodology search, but not included among the 10 most used codes in the patents, the use of ozonized water may be an innovative approach in the technology landscape of sterilization chambers.

Higher albumin levels on admission predict better prognosis in patients with confirmed COVID-19

BACKGROUND

Research surrounding COVID-19 (coronavirus disease 2019) is rapidly increasing, including the study of biomarkers for predicting outcomes. There is little data examining the correlation between serum albumin levels and COVID-19 disease severity. The purpose of this study is to evaluate whether admission albumin levels reliably predict outcomes in COVID-19 patients.

METHODS

We retrospectively reviewed 181 patients from two hospitals who had COVID-19 pneumonia confirmed by polymerase chain reaction (PCR) testing and radiologic imaging, who were hospitalized between March and July 2020. We recorded demographics, COVID-19 testing techniques, and day of admission labs. The outcomes recorded included the following: venous thromboembolism (VTE), acute respiratory distress syndrome (ARDS), intensive care unit (ICU) admission, discharge with new or higher home oxygen supplementation, readmission within 90 days, in-hospital mortality, and total adverse events. A multivariate modified Poisson regression analysis was then performed to determine significant predictors for increased adverse events in patients with COVID-19 pneumonia.

RESULTS

A total of 109 patients (60.2%) had hypoalbuminemia (albumin level < 3.3 g/dL). Patients with higher albumin levels on admission had a 72% decreased risk of developing venous thromboembolism (adjusted relative risk [RR]:0.28, 95% confidence interval [CI]:0.14-0.53, p<0.001) for every 1 g/dL increase of albumin. Moreover, higher albumin levels on admission were associated with a lower risk of developing ARDS (adjusted RR:0.73, 95% CI:0.55-0.98, p = 0.033), admission to the ICU (adjusted RR:0.64, 95% CI:0.45-0.93, p = 0.019), and were less likely to be readmitted within 90 days (adjusted RR:0.37, 95% CI:0.17-0.81, p = 0.012). Furthermore, higher albumin levels were associated with fewer total adverse events (adjusted RR:0.65, 95% CI:0.52-0.80, p<0.001).

CONCLUSIONS

Admission serum albumin levels appear to be a predictive biomarker for outcomes in COVID-19 patients. We found that higher albumin levels on admission were associated with significantly fewer adverse outcomes, including less VTE events, ARDS development, ICU admissions, and readmissions within 90 days. Screening patients may lead to early identification of patients at risk for developing in-hospital complications and improve optimization and preventative efforts in this cohort.