The age distribution of mortality from novel coronavirus disease (COVID-19) suggests no large difference of susceptibility by age

Unlocking the importance of perceived governance: The impact on COVID-19 in NUTS-2 European regions

In the immediate aftermath of the pandemic, governments implemented non-pharmaceutical interventions (NPIs). Previous literature suggests that NPI effectiveness is influenced by governance quality. The acceptance and perceived necessity of these measures by the public are crucial to their success, as NPIs cannot be easily enforced without public support. Does regional governance also play a role? This study examines the correlation between the quality of governance in European NUTS-2 regions and the spread of COVID-19. The findings indicate that overall perceived governance, and its perceived quality and corruption pillars, significantly impact the effectiveness of these interventions. This effect was pronounced during the first wave and then diminished in importance, disappearing before vaccines were available, suggesting that regional governance matters especially in the immediate aftermath of an exogenous shock.

Evaluating the frequency of neurological symptoms in COVID-19 patients: A cross-sectional study

Background and Aims

Due to the recent emergence of COVID-19, the exact pathology of this disease has not been determined. Therefore, this study evaluated the frequency of neurological symptoms in patients with COVID-19.

Methods

This cross-sectional study was conducted on 2200 in patients with COVID-19 who were selected from an educational hospital in Sanandaj, Iran, from April 2020 to March 2021. The research samples were selected by census, all patients with COVID-19 were admitted to the hospital. The data collection tool was a checklist of the studied variables (dizziness, headache, and impaired consciousness) prepared by the researchers based on the specialists' opinions. The researcher completed these checklists based on the patients' hospitalization records. The data were analyzed by descriptive and analytical statistical tests using SPSS Software Version 20. The quantitative variables were compared using the independent t-test. The χ ² test was also used to compare qualitative variables. A p Value of less than 0.05 was considered statistically significant.

Results

The mean age of the patients was 57.41 years old, of whom 53.1% were male. The average blood oxygen level of the patients was 88.10%, and most disease symptoms were related to shortness of breath and cough, with a frequency of 24.3%. In addition, 20.8% of patients needed hospitalization in intensive care unit. The highest frequency of central and peripheral nervous system manifestations was related to headache, ageusia (loss of sense of taste), hyposmia (A decreased sense of smell and anosmia (The complete loss of smell). Finally, 15.3% of patients died, and 84.7% recovered. The analytical findings showed a significant relationship between the disease outcome and patients' dizziness, consciousness disorder, seizure and ageusia. There was a significant relationship between gender and headache in patients. There was a significant difference between the mean age and oxygen level with central and peripheral nervous system manifestations (dizziness, headache, impaired consciousness, smell disorder) and the disease outcome in patients.

Conclusion

The pathophysiology of COVID-19 virus infection involving the central nervous system is not fully understood. Neurological symptoms of this virus include delirium, headache, decreased level of consciousness, and seizures. Identifying the symptoms and mechanisms of neurological complications of COVID-19 is necessary for proper screening and complete treatment because a patient infected by COVID-19 may not show respiratory failure signs but may be a carrier. A complete and accurate knowledge of the symptoms and complications of this infection for proper screening of patients to prevent transmission and spread of this disease is critically needed.

Impelling Factors for Contracting COVID-19 Among Surgical Professionals During the Pandemic: A Multinational Cohort Study

Background

Medical workers, including surgical professionals working in coronavirus disease 2019 (COVID-19) treating hospitals, were under enormous stress during the pandemic. This global study investigated factors endowing COVID-19 amongst surgical professionals and students.

Methods

This global cross-sectional survey was made live on February 18, 2021 and closed for analysis on March 13, 2021. It was freely shared on social and scientific media platforms and was sent via email groups and circulated through a personal network of authors. Chi-square test for independence, and binary logistic regression analysis were carried out on determining predictors of surgical professionals contracting COVID-19.

Results

This survey captured the response of 520 surgical professionals from 66 countries. Of the professionals, 92.5% (481/520) reported practising in hospitals managing COVID-19 patients. More than one-fourth (25.6%) of the respondents (133/520) reported suffering from COVID-19 which was more frequent in surgical professionals practising in public sector healthcare institutions (P = 0.001). Thirty-seven percent of those who reported never contracting COVID-19 (139/376) reported being still asked to practice self-isolation and wear a shield without the diagnosis (P = 0.001). Of those who did not contract COVID-19, 75.7% (283/376) were vaccinated (P < 0.001). Surgical professionals undergoing practice in the private sector (odds ratio (OR): 0.33; 95% confidence interval (CI): 0.14 - 0.77; P = 0.011) and receiving two doses of vaccine (OR: 0.55; 95% CI: 0.32 - 0.95; P = 0.031) were identified to enjoy decreased odds of contracting COVID-19. Only 6.9% of those who reported not contracting COVID-19 (26/376) were calculated to have the highest "overall composite level of harm" score (P < 0.001).

Conclusions

High prevalence of respondents got COVID-19, which was more frequent in participants working in public sector hospitals. Those who reported contracting COVID-19 were calculated to have the highest level of harm score. Self-isolation or shield, getting two doses of vaccines decreases the odds of contracting COVID-19.

Asymptomatic and Mild SARS-CoV-2 Infections in a Hungarian Outpatient Cohort in the First Year of the COVID-19 Pandemic

We aimed to estimate the proportion of the population infected with SARS-CoV-2 in the first year of the pandemic. The study population consisted of outpatient adults with mild or no COVID-19 symptoms and was divided into subpopulations with different levels of exposure. Among the subpopulation without known previous COVID-19 contacts, 4143 patients were investigated. Of the subpopulation with known COVID-19 contacts, 594 patients were investigated. IgG- and IgA-seroprevalence and RT-PCR positivity were determined in context with COVID-19 symptoms. Our results suggested no significant age-related differences between participants for IgG positivity but indicated that COVID-19 symptoms occurred most frequently in people aged between 20 and 29 years. Depending on the study population, 23.4-74.0% PCR-positive people (who were symptomless SARS-CoV-2 carriers at the time of the investigation) were identified. It was also observed that 72.7% of the patients remained seronegative for 30 days or more after their first PCR-positive results. This study hoped to contribute to the scientific understanding of the significance of asymptomatic and mild infections in the long persistence of the pandemic.

Infrared image method for possible COVID-19 detection through febrile and subfebrile people screening

This study proposed an infrared image-based method for febrile and subfebrile people screening to comply with the society need for alternative, quick response, and effective methods for COVID-19 contagious people screening. The methodology consisted of: (i) Developing a method based on facial infrared imaging for possible COVID-19 early detection in people with and without fever (subfebrile state); (ii) Using 1206 emergency room (ER) patients to develop an algorithm for general application of the method, and (iii) Testing the method and algorithm effectiveness in 2558 cases (RT-qPCR tested for COVID-19) from 227,261 workers evaluations in five different countries. Artificial intelligence was used through a convolutional neural network (CNN) to develop the algorithm that took facial infrared images as input and classified the tested individuals in three groups: fever (high risk), subfebrile (medium risk), and no fever (low risk). The results showed that suspicious and confirmed COVID-19 (+) cases characterized by temperatures below the 37.5 °C fever threshold were identified. Also, average forehead and eye temperatures greater than 37.5 °C were not enough to detect fever similarly to the proposed CNN algorithm. Most RT-qPCR confirmed COVID-19 (+) cases found in the 2558 cases sample (17 cases/89.5%) belonged to the CNN selected subfebrile group. The COVID-19 (+) main risk factor was to be in the subfebrile group, in comparison to age, diabetes, high blood pressure, smoking and others. In sum, the proposed method was shown to be a potentially important new tool for COVID-19 (+) people screening for air travel and public places in general.

Spontaneous, naturally occurring cancers in non-human primates as a translational model for cancer immunotherapy

The complexity of cancer immunotherapy (CIT) demands reliable preclinical models to successfully translate study findings to the clinics. Non-human primates (NHPs; here referring to rhesus and cynomolgus macaques) share broad similarities with humans including physiology, genetic homology, and importantly also immune cell populations, immune regulatory mechanisms, and protein targets for CIT. Furthermore, NHP naturally develop cancers such as colorectal and breast cancer with an incidence, pathology, and age pattern comparable to humans. Thus, these tumor-bearing monkeys (TBMs) have the potential to bridge the experimental gap between early preclinical cancer models and patients with human cancer.This review presents our current knowledge of NHP immunology, the incidence and features of naturally-occurring cancers in NHP, and recent TBM trials investigating CIT to provide a scientific rationale for this unique model for human cancer.

Parameter identifiability and optimal control of an SARS-CoV-2 model early in the pandemic

We fit an SARS-CoV-2 model to US data of COVID-19 cases and deaths. We conclude that the model is not structurally identifiable. We make the model identifiable by prefixing some of the parameters from external information. Practical identifiability of the model through Monte Carlo simulations reveals that two of the parameters may not be practically identifiable. With thus identified parameters, we set up an optimal control problem with social distancing and isolation as control variables. We investigate two scenarios: the controls are applied for the entire duration and the controls are applied only for the period of time. Our results show that if the controls are applied early in the epidemic, the reduction in the infected classes is at least an order of magnitude higher compared to when controls are applied with 2-week delay. Further, removing the controls before the pandemic ends leads to rebound of the infected classes.

Measuring impact of vaccination among wildlife: The case of bait vaccine campaigns for classical swine fever epidemic among wild boar in Japan

Understanding the impact of vaccination in a host population is essential to control infectious diseases. However, the impact of bait vaccination against wildlife diseases is difficult to evaluate. The vaccination history of host animals is generally not observable in wildlife, and it is difficult to distinguish immunity by vaccination from that caused by disease infection. For these reasons, the impact of bait vaccination against classical swine fever (CSF) in wild boar inhabiting Japan has not been evaluated accurately. In this study, we aimed to estimate the impact of the bait vaccination campaign by modelling the dynamics of CSF and the vaccination process among a Japanese wild boar population. The model was designed to estimate the impact of bait vaccination despite lack of data regarding the demography and movement of wild boar. Using our model, we solved the theoretical relationship between the impact of vaccination, the time-series change in the proportion of infected wild boar, and that of immunised wild boar. Using this derived relationship, the increase in antibody prevalence against CSF because of vaccine campaigns in 2019 was estimated to be 12.1 percentage points (95% confidence interval: 7.8–16.5). Referring to previous reports on the basic reproduction number (R₀) of CSF in wild boar living outside Japan, the amount of vaccine distribution required for CSF elimination by reducing the effective reproduction number under unity was also estimated. An approximate 1.6 (when R₀ = 1.5, target vaccination coverage is 33.3% of total population) to 2.9 (when R₀ = 2.5, target vaccination coverage is 60.0% of total population) times larger amount of vaccine distribution would be required than the total amount of vaccine distribution in four vaccination campaigns in 2019.

Vaccination of wildlife is important to control infectious diseases in animals. However, the impact of common vaccination of wildlife, bait vaccination, is difficult to evaluate owing to difficulty in obtaining the vaccination history at the individual level. Mathematical modelling can estimate the impact of vaccination; however, the demography and movement of hosts are required to describe disease dynamics. In this study, we aimed to estimate the impact of bait vaccination by modelling the dynamics of classical swine fever (CSF) and the vaccination among Japanese wild boar. The model was designed to estimate the impact of bait vaccination despite lack of data regarding the demography and movement of wild boar. Using our model, the increase in antibody prevalence because of vaccination in 2019 was estimated to be 12 percentage points. Furthermore, we estimated the amount of vaccine distribution required for CSF elimination by reducing the effective reproduction number under unity. Referring to previous reports on the basic reproduction number of CSF in wild boar living outside Japan, it was estimated that an approximate 1.6 to 2.9 times larger amount of vaccine distribution would be required than the total amount of vaccine distribution in four vaccination campaigns in 2019.

Measuring concerns about the COVID-19 vaccine among Japanese internet users through search queries

With the increasing availability of the COVID-19 vaccines, vaccination has been rapidly promoted globally as a countermeasure against the spread of COVID-19. In Japan, vaccination was first introduced in February 2021. However, the amount of concern towards vaccination differs between individuals, and topics of concern include adverse reactions and side effects. This study investigated attitudes toward vaccines or vaccination during the COVID-19 pandemic across different Japanese prefectures, using Yahoo! JAPAN search queries. We first defined a vaccine concern index (VCI) by aggregating the search counts of vaccine-related queries from Yahoo! JAPAN users before examining VCI across all Japanese prefectures, accounting for gender and age. Our results demonstrated that VCI tended to be lower in more populated areas, and VCI was higher in their 20s to 40s than older people, especially in female users. Furthermore, there was a significant positive correlation (Spearman's Rank correlation coefficient [Formula: see text] = 0.60, [Formula: see text]) between VCI and prefectural vaccination rate, suggesting that web searching of adverse vaccine reactions may precede actual vaccination. This could reflect the information-seeking behavior of individuals who are accepting of vaccinations.

Plan de seguro de salud: factor que más contribuye a las desigualdades en la mortalidad por COVID-19 en Colombia

Objetivo.

Cuantificar las desigualdades socioeconómicas en la mortalidad por COVID-19 en Colombia y evaluar en qué medida el tipo de seguro de salud, la carga de enfermedades concomitantes, la zona de residencia y el origen étnico explican estas desigualdades.

Métodos.

Se analizaron los datos de una cohorte retrospectiva de casos de COVID-19. Se estimó el índice relativo de desigualdad (IRD) y el índice de desigualdad basado en la pendiente (IDP) utilizando modelos de supervivencia con todos los participantes, y estratificándolos por edad y sexo. El porcentaje de reducción del IRD y el IDP se calculó después de ajustar con respecto a factores que podrían ser relevantes.

Resultados.

Se pusieron en evidencia desigualdades notables en toda la cohorte y en los subgrupos (edad y sexo). Las desigualdades fueron mayores en los adultos más jóvenes y disminuyeron de manera gradual con la edad, pasando de un IRD de 5,65 (intervalo de confianza de 95% [IC 95%] = 3,25-9,82) en los participantes menores de 25 años a un IRD de 1,49 (IC 95% = 1,41-1,58) en los mayores de 65 años. El tipo de seguro de salud fue el factor más importante, al cual se atribuyó 20% de las desigualdades relativas y 59% de las absolutas.

Conclusiones.

La mortalidad por COVID-19 en Colombia presenta importantes desigualdades socioeconómicas. El seguro de salud aparece como el factor que más contribuye a estas desigualdades, lo cual plantea retos al diseño de las estrategias de salud pública.

COVID-19 pandemic vaccination strategies of early 2021 based on behavioral differences between residents of Tokyo and Osaka, Japan

Background

During the fourth COVID-19 wave in Japan, marked differences became apparent in the scale of the epidemic between metropolitan Tokyo in eastern Japan and Osaka prefecture in western Japan.

Methods

Public epidemic data were analyzed, with performance of mathematical simulations using simplified SEIR models.

Results

The increase in the number of infected persons per 100,000 population during the fourth wave of expansion was greater in Osaka than in Tokyo. The basic reproduction number in Osaka was greater than in Tokyo. Particularly, the number of infected people in their 20 s increased during the fourth wave: The generation-specific reproduction number for people in their 20 s was higher than for people of other generations. Both Tokyo and Osaka were found to have strong correlation between the increase in the number of infected people and the average number of people using the main downtown stations at night. Simulations showed vaccination of people in their 60 s and older reduced the number of infected people among the high-risk elderly population in the fourth wave. However, age-specific vaccination of people in their 20 s reduced the number of infected people more than vaccination of people in their 60 s and older.

Conclusions

Differences in the epidemic between Tokyo and Osaka are explainable by different behaviors of the most socially active generation. When vaccine supplies are adequate, priority should be assigned to high-risk older adults, but if vaccine supplies are scarce, simulation results suggest consideration of vaccinating specific groups among whom the epidemic is spreading rapidly.

Health Insurance Scheme: Main Contributor to Inequalities in COVID-19 Mortality in Colombia

Objectives. To quantify socioeconomic inequalities in COVID-19 mortality in Colombia and to assess the extent to which type of health insurance, comorbidity burden, area of residence, and ethnicity account for such inequalities. Methods. We analyzed data from a retrospective cohort of COVID-19 cases. We estimated the relative and slope indices of inequality (RII and SII) using survival models for all participants and stratified them by age and gender. We calculated the percentage reduction in RII and SII after adjustment for potentially relevant factors. Results. We identified significant inequalities for the whole cohort and by subgroups (age and gender). Inequalities were higher among younger adults and gradually decreased with age, going from RII of 5.65 (95% confidence interval [CI] = 3.25, 9.82) in participants younger than 25 years to RII of 1.49 (95% CI = 1.41, 1.58) in those aged 65 years and older. Type of health insurance was the most important factor, accounting for 20% and 59% of the relative and absolute inequalities, respectively. Conclusions. Significant socioeconomic inequalities exist in COVID-19 mortality in Colombia. Health insurance appears to be the main contributor to those inequalities, posing challenges for the design of public health strategies. (Am J Public Health. 2022;112(S6):S586-S590. https://doi.org/10.2105/AJPH.2021.306637).

Numerical Simulation to Predict COVID-19 Cases in Punjab

Coronavirus disease 2019 is a novel disease caused by a newly identified virus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). India recorded its first case of COVID-19 on 30 January 2020. This work is an attempt to calculate the number of COVID-19 cases in Punjab by solving a partial differential equation using the modified cubic B-spline function and differential quadrature method. The real data of COVID-19 cases and Google Community Mobility Reports of Punjab districts were used to verify the numerical simulation of the model. The Google mobility data reflect the changes in social behavior in real time and therefore are an important factor in analyzing the spread of COVID-19 and the corresponding precautionary measures. To investigate the cross-border transmission of COVID-19 between the 23 districts of Punjab with an analysis of human activities as a factor, the 23 districts were divided into five regions. This paper is aimed at demonstrating the predictive ability of the model.

Cloaking the ACE2 receptor with salivary cationic proteins inhibits SARS-CoV-2 entry

Saliva contributes to the innate immune system, which suggests that it can prevent SARS-CoV-2 entry. We studied the ability of healthy salivary proteins to bind to angiotensin-converting enzyme 2 (ACE2) using biolayer interferometry and pull-down assays. Their effects on binding between the receptor-binding domain of the SARS-CoV-2 spike protein S1 (S1) and ACE2 were determined using an enzyme-linked immunosorbent assay. Saliva bound to ACE2 and disrupted the binding of S1 to ACE2 and four ACE2-binding salivary proteins were identified, including cationic histone H2A and neutrophil elastase, which inhibited the S1-ACE2 interaction. Calf thymus histone (ct-histone) also inhibited binding as effectively as histone H2A. The results of a cell-based infection assay indicated that ct-histone suppressed SARS-CoV-2 pseudoviral invasion into ACE2-expressing host cells. Manufactured polypeptides, such as ε-poly-L-lysine, also disrupted S1-ACE2 binding, indicating the importance of the cationic properties of salivary proteins in ACE2 binding. Overall, we demonstrated that positively-charged salivary proteins are a barrier against SARS-CoV-2 entry by cloaking the negatively-charged surface of ACE2, and provided a view that the cationic polypeptides represent a preventative and therapeutic treatment against COVID-19.

The COVID-19 pandemic's effects on voter turnout

The COVID-19 pandemic has increased the risk of participating in public events, among them elections. We assess whether the voter turnout in the 2020 local government elections in Italy was affected by the COVID-19 pandemic. We do so by exploiting the variation among municipalities in the intensity of the COVID-19 outbreak as measured by the mortality rate among the elderly. We find that a 1 percentage point increase in the elderly mortality rate decreased the voter turnout by 0.5 percentage points, with no gender differences in the behavioural response. The effect was especially strong in densely populated municipalities. We do not detect statistically significant differences in voter turnout among different levels of autonomy from the central government.

Anti-coronavirus vaccines will not accelerate the transition of humanity to a non-pandemic period, but the pandemic will take fewer victims

The vaccination rate worldwide has reached enormous proportions, and it is likely that at least 75% of the world's population will be vaccinated. The controversy is that, while people aged 65 and older suffer a significantly higher mortality rate from COVID-19, plans are being made to vaccinate young people under the age of 20. Equally thorny is the question of vaccinating people who already have antibodies to SARS-CoV-2, as well as B and T memory cells, because they contracted and survived the virus. The possible consequences of large-scale vaccination are difficult to predict, when some people do not have access to the vaccine at all and others have already received 3 doses of the vaccine. SARS-CoV-2 will circulate through the human population forever and continue to mutate, as viruses do. Therefore, in the coming years, the need to develop and use effective vaccines and medicines for the prevention and treatment of COVID-19 will remain urgent in view of the high mortality rate from this disease. To date, three vaccine platforms have been most used: adenoviral vector, inactivated, and mRNA. There is some concern about the side effects that occur after vaccination. Whether modern anti-coronavirus vaccines can raise the safety threshold, only time will answer. It is obvious that the pandemic will end, but the virus will remain in the human population, leaving behind invaluable experience and tens of millions of victims. This article is based on search retrieves in research articles devoted to COVID-19 mainly published in 2020-2021 and examines the possible consequences of the worldwide vaccination against SARS-CoV-2 and suggests that, while anti-coronavirus vaccines will not magically transport humanity to a non-pandemic world, they may greatly reduce the number of victims of the pandemic and help us learn how to live with COVID-19.

COVID-19 in Africa: An Explorative Cross-Sectional Analysis of Twenty-One African Countries From January to June 2020

INTRODUCTION

 Africa has surprisingly recorded better gains in containing the coronavirus spread than countries with the better health indices, such as the USA and UK. The low rate of coronavirus disease 2019 (COVID-19) cases and death in Africa represents a puzzle with different biological and social theories such as low COVID-19 testing capacity, substantial young population, few old people, favourable climate, genetic admixture, infectious disease antibodies, and sound community health care systems proposed. We aimed to understand the COVID-19 preventive measures in a group of twenty-one systematically selected African countries that may explain the low burden of COVID-19 in Africa.

METHODS

Data (COVID-19, health, socioeconomic, and demographics indices) of twenty-one systemically selected African countries were retrieved from the various official country and multilateral organization sources such as Worldbank, and the United nations development Programme (UNDP). The extracted data were analyzed in three large groups: international travel restrictions, physical and social distancing, and movement restrictions (lockdown measures; curfews, partial or/and national lockdowns). Data cleaning, analysis (including Pearson correlation), and visualization were done with Microsoft Excel and Graph Pad Prism version 9 (https://www.graphpad.com/).

RESULT

Southern Africa had the greatest number of cases and deaths within the period studied compared to East Africa, which was the least COVID-19 affected sub-region (in terms of COVID-19 cases and deaths). We observed that coronary artery disease death rate was highly correlated with COVID-19 death density (number of COVID-19 deaths/total population) and similarly observed a correlation between the number of cases and deaths and number of in-country arrivals, pandemic preparedness (health security index), COVID-19 containment, and health index (not correlated with deaths). Finally, we noted that the most effective preventive strategy was the 'use of a face mask'.

CONCLUSION

Africa had fewer COVID-19 cases and COVID-19 related deaths. Our data shows that the rapidity and stringency of COVID-19 preventive measures and government policies, and the low level of tourism in Africa compared to other countries (i.e., low COVID-19 seeding rate) may have been contributory to these favorable statistics. We hope these findings impact how the preparedness for pandemics can be enhanced to decrease the burden of preventable deaths and morbidity.

Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany

BACKGROUND

Despite the vaccination process in Germany, a large share of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests.

METHODS

We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type, metapopulation model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the regions (German counties) are initialized randomly with incidences between 75 and 150 weekly new cases per 100,000 inhabitants (red zones) or below (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20% of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario.

RESULTS

We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, the negative effect of less strict interventions can be overcome by high commuter testing rates. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable. If less strict interventions are in favor, substantially increased testing rates are needed to avoid overall higher infection dynamics.

CONCLUSIONS

Our results indicate that local containment of outbreaks and maintenance of low overall incidence is possible even in densely populated and highly connected regions such as Germany or Western Europe. While we demonstrate this on data from Germany, similar patterns of mobility likely exist in many countries and our results are, hence, generalizable to a certain extent.

Post-lockdown changes of age-specific susceptibility and its correlation with adherence to social distancing measures

Social distancing measures are effective in reducing overall community transmission but much remains unknown about how they have impacted finer-scale dynamics. In particular, much is unknown about how changes of contact patterns and other behaviors including adherence to social distancing, induced by these measures, may have impacted finer-scale transmission dynamics among different age groups. In this paper, we build a stochastic age-specific transmission model to systematically characterize the degree and variation of age-specific transmission dynamics, before and after lifting the lockdown in Georgia, USA. We perform Bayesian (missing-)data-augmentation model inference, leveraging reported age-specific case, seroprevalence and mortality data. We estimate that overall population-level transmissibility was reduced to 41.2% with 95% CI [39%, 43.8%] of the pre-lockdown level in about a week of the announcement of the shelter-in-place order. Although it subsequently increased after the lockdown was lifted, it only bounced back to 62% [58%, 67.2%] of the pre-lockdown level after about a month. We also find that during the lockdown susceptibility to infection increases with age. Specifically, relative to the oldest age group (> 65+), susceptibility for the youngest age group (0–17 years) is 0.13 [0.09, 0.18], and it increases to 0.53 [0.49, 0.59] for 18–44 and 0.75 [0.68, 0.82] for 45–64. More importantly, our results reveal clear changes of age-specific susceptibility (defined as average risk of getting infected during an infectious contact incorporating age-dependent behavioral factors) after the lockdown was lifted, with a trend largely consistent with reported age-specific adherence levels to social distancing and preventive measures. Specifically, the older groups (> 45) (with the highest levels of adherence) appear to have the most significant reductions of susceptibility (e.g., post-lockdown susceptibility reduced to 31.6% [29.3%, 34%] of the estimate before lifting the lockdown for the 6+ group). Finally, we find heterogeneity in case reporting among different age groups, with the lowest rate occurring among the 0–17 group (9.7% [6.4%, 19%]). Our results provide a more fundamental understanding of the impacts of stringent lockdown measures, and finer evidence that other social distancing and preventive measures may be effective in reducing SARS-CoV-2 transmission. These results may be exploited to guide more effective implementations of these measures in many current settings (with low vaccination rate globally and emerging variants) and in future potential outbreaks of novel pathogens.

Learning accurate personalized survival models for predicting hospital discharge and mortality of COVID-19 patients

Since it emerged in December of 2019, COVID-19 has placed a huge burden on medical care in countries throughout the world, as it led to a huge number of hospitalizations and mortalities. Many medical centers were overloaded, as their intensive care units and auxiliary protection resources proved insufficient, which made the effective allocation of medical resources an urgent matter. This study describes learned survival prediction models that could help medical professionals make effective decisions regarding patient triage and resource allocation. We created multiple data subsets from a publicly available COVID-19 epidemiological dataset to evaluate the effectiveness of various combinations of covariates—age, sex, geographic location, and chronic disease status—in learning survival models (here, “Individual Survival Distributions”; ISDs) for hospital discharge and also for death events. We then supplemented our datasets with demographic and economic information to obtain potentially more accurate survival models. Our extensive experiments compared several ISD models, using various measures. These results show that the “gradient boosting Cox machine” algorithm outperformed the competing techniques, in terms of these performance evaluation metrics, for predicting both an individual’s likelihood of hospital discharge and COVID-19 mortality. Our curated datasets and code base are available at our Github repository for reproducing the results reported in this paper and for supporting future research.

Outcome, Severity, and Risk of Mortality in Patients with COVID-19 and Chronic Underlying Diseases: A Prospective Study

Background: The novel coronavirus pandemic, severe acute respiratory syndrome CoV-2 (SARS COV-2), has become a global threat and rapidly spread worldwide. The COVID-19 pandemic has posed a number of challenges, the most notable of which is the management of patients with chronic underlying diseases. Objectives: The present study aimed to evaluate the risk of COVID-19 severity and mortality in patients with chronic underlying diseases. Methods: In this retrospective cohort study, the data on the disease severity and morality of confirmed COVID-19 patients admitted to Baharloo Hospital, Tehran, Iran, from February 2020 to March 2020 were analyzed and reported. Patients’ characteristics, including age, gender, and underlying diseases, were also considered. Results: The study encompassed 1244 patients with the mean age of 53.29 years, among whom there were 573 patients with at least one co-existing chronic disease. Hypertension, diabetes mellitus (DM), and ischemic heart disease (IHD) were the most common co-existing chronic diseases. The findings revealed that underlying diseases were significantly associated with disease mortality and severity. Conclusions: The findings showed that patients with comorbidities were significantly at higher risk of death and severe forms of COVID-19. In this regard, patients with underlying diseases should be of concern.

Age structure in SIRD models for the COVID-19 pandemic-A case study on Italy data and effects on mortality

The COVID-19 pandemic is bringing disruptive effects on the healthcare systems, economy and social life of countries all over the world. Even though the elder portion of the population is the most severely affected by the COVID-19 disease, the counter-measures introduced so far by governments took into little account the age structure, with restrictions that act uniformly on the population irrespectively of age. In this paper, we introduce a SIRD model with age classes for studying the impact on the epidemic evolution of lockdown policies applied heterogeneously on the different age groups of the population. The proposed model is then applied to age-stratified COVID-19 Italian data. The simulation results suggest that control measures focused to specific age groups may bring benefits in terms of reduction of the overall mortality rate.

Immunoglobulin G Antibodies to SARS-CoV-2 Among Healthcare Workers at a Tertiary Care Center in South India

Introduction

Healthcare workers (HCWs) are at risk of exposure to SARS-CoV-2. Seroprevalence in this group may offer insights into trends to monitor and revise strategies to prevent transmission.

Methods

A cross-sectional study was conducted in two phases among healthcare workers at a tertiary care center to detect IgG antibodies to SARS-CoV-2. Seropositivity was calculated during both phases, and possible associations were determined using regression analysis.

Results

A total of 382 and 168 HCWs took part in the two phases, respectively. IgG antibodies were detected in 13 of 382 (3.4%; 95% confidence interval (CI): 2%-5.7%) and 71 of 168 (42.3%) participants in the first and second phases, respectively. Receiving at least one dose of vaccine (p < 0.001) and age (p = 0.028) were factors associated with the presence of antibodies, while gender, job type, exposure to COVID-19 cases, and comorbidities were not associated with seropositivity.

Conclusion

Serosurveys among HCWs may help identify transmission patterns and redesign infection control practices in the healthcare setting.

A Continuous Markov-Chain Model for the Simulation of COVID-19 Epidemic Dynamics

Simple Summary

Predicting the spreading trend of the COVID-19 epidemic is one of the hot topics in the modeling field. In this study, we applied a continuous Markov-chain model to simulate the spread of the COVID-19 epidemic. The results of this study indicate that the herd immunity threshold should be significantly higher than 1 − 1/R₀. Taking the immunity waning effect into consideration, the model could predict an epidemic resurgence after the herd immunity threshold. Meanwhile, this Markov-chain approach could also forecast the epidemic distribution and predict the epidemic hotspots at different times. It is implied from our model that it is significantly challenging to eradicate SARS-CoV-2 in the short term. The actual epidemic development is consistent with our prediction. In the end, this method displayed great potential as an alternative approach to traditional compartment models.

Abstract

To address the urgent need to accurately predict the spreading trend of the COVID-19 epidemic, a continuous Markov-chain model was, for the first time, developed in this work to predict the spread of COVID-19 infection. A probability matrix of infection was first developed in this model based upon the contact frequency of individuals within the population, the individual’s characteristics, and other factors that can effectively reflect the epidemic’s temporal and spatial variation characteristics. The Markov-chain model was then extended to incorporate both the mutation effect of COVID-19 and the decaying effect of antibodies. The developed comprehensive Markov-chain model that integrates the aforementioned factors was finally tested by real data to predict the trend of the COVID-19 epidemic. The result shows that our model can effectively avoid the prediction dilemma that may exist with traditional ordinary differential equations model, such as the susceptible–infectious–recovered (SIR) model. Meanwhile, it can forecast the epidemic distribution and predict the epidemic hotspots geographically at different times. It is also demonstrated in our result that the influence of the population’s spatial and geographic distribution in a herd infection event is needed in the model for a better prediction of the epidemic trend. At the same time, our result indicates that no simple derivative relationship exists between the threshold of herd immunity and the virus basic reproduction number R₀. The threshold of herd immunity achieved through natural immunity is significantly higher than 1 − 1/R₀. These not only explain the theoretical misconceptions of herd immunity thresholds in herd immunity theory but also provide a guidance for predicting the optimal vaccination coverage. In addition, our model can predict the temporal and spatial distribution of infections in different epidemic waves. It is implied from our model that it is challenging to eradicate COVID-19 in the short term for a large population size and a wide spatial distribution. It is predicted that COVID-19 is likely to coexist with humans for a long time and that it will exhibit multipoint epidemic effects at a later stage. The statistical evidence is consistent with our prediction and strongly supports our modeling results.

Nationwide assessment of energy costs and policies to limit airborne infection risks in U.S. schools

Practices such as improved ventilation and air filtration are being considered by schools to reduce the transmission of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that causes the pandemic of coronavirus disease 2019 (COVID-19). Improved ventilation may significantly increase the energy cost of heating, ventilation, and air conditioning (HVAC), exacerbating financial challenges schools face amidst the worst pandemic in decades. This study evaluated HVAC energy costs for reducing COVID-19 airborne infection risks in 111,485 public and private schools in the U.S. to support decision-makings. The average annual HVAC energy cost to maintain the infection risk below 1% for the schools in the U.S. is estimated at $20.1 per square meter or $308.4 per capita with improved ventilation and air filtration, where the private schools have higher costs than the public schools on average. The cost could be reduced by adopting partial online learning. It was also found that additional cost to control infection risk with increased ventilation and air filtration is significantly lower for PK-5 schools than that for middle and high schools, indicating the possibility of remaining in-person instructions for PK-5 schools with necessary governmental assistance. Analyses of school HVAC energy costs to reduce airborne infection risks under different intervention scenarios provide important operational guidelines, financial implications, and policy insights for schools, community stakeholders, and policymakers to keep schools safe during the ongoing pandemic and improve preparedness for epidemics projected in the future.

First wave of SARS-COV2 in Europe: Study and typology of the 15 worst affected European countries

Since 11 March 2020 when officially declared a global pandemic, Covid-19 (or SARS-COV2) has turned out to be a multifaceted disease differently affecting countries and individuals. What makes certain countries more vulnerable than others has attracted the interest of scientists from various disciplines. This paper intends to compare the impact of demographic parameters, population health conditions and policy actions on prevalence and fatality levels of Covid-19 during the first 3 months since its declaration of global pandemic. A country-level exploratory analysis has been conducted in order to assess how demography, national health conditions and measures taken interact and condition the disease outcomes. Analysis relies on publicly available data on Covid-19 reported cases, deaths and number of persons tested. Those data are combined with demographic parameters (sex ratio, mean age, population density and life expectancy), health data (cardiovascular death rate, diabetes prevalence, share of smokers among males and females and number of hospital beds) and information about relative national policies aiming the management of the pandemic (lockdown timing and duration). Our analysis confirms the diversity of factors and the complexity of their interaction in explaining the propagation and fatality of the disease across Europe. Our findings question some well-established attitudes concerning the role of demographic variables and public health conditions in the spread of the disease.

Estimation of the Lethality Rate, Recovery Rate, and Case Fatality Ratio of Classical Swine Fever in Japanese Wild Boar: An Analysis of the Epidemics From September 2018 to March 2019

Understanding the morbidity and lethality of diseases is necessary to evaluate the effectiveness of countermeasure against the epidemics (e.g., vaccination). To estimate them, detailed data on host population dynamics are required; however, estimating the population size for wildlife is often difficult. We aimed to elucidate the morbidity and lethality of classical swine fever (CSF) currently highly prevalent in the wild boar population in Japan. To this end, we estimated lethality rate, recovery rate, and case fatality ratio (CFR) of CSF without detailed data on the population estimates of wild boar. A mathematical model was constructed to describe the CSF dynamics and population dynamics of wild boar. We fitted the model to the (i) results of the reverse transcription polymerase chain reaction (RT-PCR) test for the CSFV gene and the (ii) results of the enzyme-linked immunosorbent assay (ELISA) test for the antibody against CSFV in sampled wild boar. In the 280 wild boar sampled from September 2018 to March 2019 in the major CSF-affected area in Japan, the lethality rate and recovery rate of CSF per week were estimated as 0.165 (95% confidence interval: 0.081–0.250) and 0.004 (0–0.009), respectively. While the estimate of lethality rate of CSF was similar with the estimates in previous studies, the recovery rate was lower than those reported previously. CFR was estimated as 0.959 (0.904–0.981) using our estimate of recovery rate. This study is the first to estimate lethality rate of CSF from the dynamics of CSF epidemics in the wild boar population. Since the value of CFR is sensitive to the value of recovery rate, the accuracy in the estimate of recovery rate is a key for the accurate estimation of CFR. A long-term transmission experiment of moderately virulent strains may lead to more accurate estimation of the recovery rate and CFR of CSF.

Do Old Age and Comorbidity via Non-Communicable Diseases Matter for COVID-19 Mortality? A Path Analysis

This paper used Our World data for coronavirus disease-2019 (COVID-19) death count, test data, stringency, and transmission count and prepared a path model for COVID-19 deaths. We augmented the model with age structure-related variables and comorbidity via non-communicable diseases for 117 countries of the world for September 23, 2021, on a cross-section basis. A broad-based global quantitative study incorporating these two prominent channels with regional variation was unavailable in the existing literature. Old age and comorbidity were identified as two prime determinants of COVID-19 mortality. The path model showed that after controlling for these factors, one SD increase in the proportion of persons above 65, above 70, or of median age raised COVID-19 mortality by more than 0.12 SDs for 117 countries. The regional intensity of death is alarmingly high in South America, Europe, and North America compared with Oceania. After controlling for regions, the figure was raised to 0.213, which was even higher. For old age, the incremental coefficient was the highest for South America (0.564), and Europe (0.314), which were substantially higher than in Oceania. The comorbidity channel via non-communicable diseases illustrated that one SD increase in non-communicable disease intensity increased COVID-19 mortality by 0.132 for the whole sample. The regional figure for the non-communicable disease was 0.594 for South America and 0.358 for Europe compared with the benchmark region Oceania. The results were statistically significant at a 10% level of significance or above. This suggested that we should prioritize vaccinations for the elderly and people with comorbidity via non-communicable diseases like heart disease, cancer, chronic respiratory disease, and diabetes. Further attention should be given to South America and Europe, which are the worst affected regions of the world.

Statistical analysis of the Hungarian COVID-19 victims

With the wide spread of Coronavirus, most people who infected with the COVID-19, will recover without requiring special treatment. Whereas, elders and those with underlying medical problems are more likely to have serious illnesses, even be threatened with death. Many more disciplines try to find solutions and drive master plan to this global trouble. Consequently, by taking one particular population, Hungary, this study aims to explore a pattern of COVID-19 victims, who suffered from some underlying conditions. Age, gender, and underlying medical problems form the structure of the clustering. K-Means and two step clustering methods were applied for age-based and age-independent analysis. Grouping of the deaths in the form of two different scenarios may highlight some concepts of this deadly disease for public health professionals. Our result for clustering can forecast similar cases which are assigned to any cluster that it will be a serious cautious for the population.

In silico drug repositioning against human NRP1 to block SARS-CoV-2 host entry

Despite COVID-19 turned into a pandemic, no approved drug for the treatment or globally available vaccine is out yet. In such a global emergency, drug repurposing approach that bypasses a costly and long-time demanding drug discovery process is an effective way in search of finding drugs for the COVID-19 treatment. Recent studies showed that SARS-CoV-2 uses neuropilin-1 (NRP1) for host entry. Here we took advantage of structural information of the NRP1 in complex with C-terminal of spike (S) protein of SARS-CoV-2 to identify drugs that may inhibit NRP1 and S protein interaction. U.S. Food and Drug Administration (FDA) approved drugs were screened using docking simulations. Among top drugs, well-tolerated drugs were selected for further analysis. Molecular dynamics (MD) simulations of drugs-NRP1 complexes were run for 100 ns to assess the persistency of binding. MM/GBSA calculations from MD simulations showed that eltrombopag, glimepiride, sitagliptin, dutasteride, and ergotamine stably and strongly bind to NRP1. In silico Alanine scanning analysis revealed that Tyr²⁹⁷, Trp³⁰¹, and Tyr³⁵³ amino acids of NRP1 are critical for drug binding. Validating the effect of drugs analyzed in this paper by experimental studies and clinical trials will expedite the drug discovery process for COVID-19.

The burden of SARS-CoV-2 among healthcare workers across 16 hospitals of Kashmir, India-A seroepidemiological study

SARS-CoV-2 pandemic has greatly affected healthcare workers because of the high risk of getting infected. The present cross-sectional study measured SARS-CoV-2 antibody in healthcare workers of Kashmir, India.

METHODS

Serological testing to detect antibodies against nucleocapsid protein of SARS-CoV-2 was performed in 2003 healthcare workers who voluntarily participated in the study.

RESULTS

We report relatively high seropositivity of 26.8% (95% CI 24.8-28.8) for SARS-CoV-2in healthcare workers, nine months after the first case was detected in Kashmir. Most of the healthcare workers (71.7%) attributed infection to the workplace environment. Among healthcare workers who neither reported any prior symptom nor were they ever tested for infection by nasopharyngeal swab test, 25.5% were seropositive.

CONCLUSION

We advocate interval testing by nasopharyngeal swab test of all healthcare workers regardless of symptoms to limit the transmission of infection within healthcare settings.

Prognostic value of SARS-CoV-2 on patients undergoing cardiac surgery

Objective

To analyze Italian Cardiac Surgery experience during the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) identifying risk factors for overall mortality according to coronavirus disease 2019 (COVID‐19) status.

Methods

From February 20 to May 31, 2020, 1354 consecutive adult patients underwent cardiac surgery at 22 Italian Centers; 589 (43.5%), patients came from the red zone. Based on COVID‐19 status, 1306 (96.5%) were negative to SARS‐CoV‐2 (COVID‐N), and 48 (3.5%) were positive to SARS‐CoV‐2 (COVID‐P); among the COVID‐P 11 (22.9%) and 37 (77.1%) become positive, before and after surgery, respectively. Surgical procedures were as follows: 396 (29.2%) isolated coronary artery bypass grafting (CABG), 714 (52.7%) isolated non‐CABG procedures, 207 (15.3%) two associate procedures, and three or more procedures in 37 (2.7%). Heart failure was significantly predominant in group COVID‐N (10.4% vs. 2.5%, p = .01).

Results

Overall in‐hospital mortality was 1.6% (22 cases), being significantly higher in COVID‐P group (10 cases, 20.8% vs. 12, 0.9%, p < .001). Multivariable analysis identified COVID‐P condition as a predictor of in‐hospital mortality together with emergency status. In the COVID‐P subgroup, the multivariable analysis identified increasing age and low oxygen saturation at admission as risk factors for in‐hospital mortality.

Conclusion

As expected, SARS‐CoV‐2 infection, either before or soon after cardiac surgery significantly increases in‐hospital mortality. Moreover, among COVID‐19‐positive patients, older age and poor oxygenation upon admission seem to be associated with worse outcomes.

The healthy behaviours and COVID-19 mortality among Iranian women: a case-control study

BACKGROUND

Women are among the susceptible groups to Coronavirus disease-19 (COVID-19) in Ardabil, north-west of Iran, despite the current global status. The underlying causes of high incidence and fatality rate of women in Ardabil are not fully understood. Hence, this study aimed to investigate the healthy behaviours in women of Ardabil and its relationship with COVID-19 mortality.

METHODS

We conducted a case-control study to compare the adherence to health protocols and behaviours with respect to COVID-19 between the infected (261 patients) and healthy (515 persons) women. Health protocols and behaviours such as using mask, gloves, disinfectants, history of travelling and contacting, and attending various gatherings and places during the COVID-19 pandemic along with demographic variables were defined as independent variables, and COVID-19 death rate was defined as the dependent variable. Multivariable logistic regression methods were used to explore the risk factors associated with COVID-19 mortality.

RESULTS

Chi-square and Fisher tests showed significant differences between infected and healthy women in terms of history of contact and traveling (p < 0.05), wearing mask (p < 0.001), going to work place (p < 0.001), and attend public gatherings (p = 0.038). Multivariable logistic regression disclosed that the age group over 80 years: 8.97 times (95% CI 2.27-29.85), women with underlying chronic diseases: 4.14 times (95% CI 1.61-10.64), and obese women: 3.01 times (95% CI 1.04-6.03) were more likely to die from COVID-19 than other women.

CONCLUSION

Considering the high incidence and mortality rate in Ardabil women due to COVID-19 and the corresponding health behavioural factors, special emphasis should be given to the increase of women awareness on the importance of healthy behaviours, diet, and life-style.

Should we operate on all patients with COVID-19 and proximal femoral fractures? An analysis of thirty, sixty, and ninety day mortality rates based on patients' clinical presentation and comorbidity: a multicentric study in Northern Italy

PURPOSE

This study aims to evaluate 30-60-90-day mortality of operated proximal femur fractures (PFFs) suffering from COVID-19 and correlation with patients' clinical presentation and comorbidities.

METHODS

Between February 1, 2020, and December 31, 2020, patients with COVID-19 infection and surgically treated PFF were included. Patients' demographic characteristics, oxygen (O2) therapy, comorbidities, and AO type fracture were collected. Chi-square test or Fisher test and hazard ratio were used to assessing the correlation between mortality rate, patient characteristics, and COVID-19 status. Kaplan-Meyer curve was used to analyze 30-60-90-day mortality. Level of significance was set as p < 0.05.

RESULTS

Fifty-six patients (mean age of 82.7 ± 8.85 years) were included. Thirty-day mortality rate was 5%, which increased to 21% at 60 days and 90 days. Eleven patients died, eight due to AO type A-like and three due to AO type B-like fractures. No significant difference in mortality rate between patients with cardiopulmonary comorbidity or no cardiopulmonary comorbidity was found (p = 0.67); a significant difference in patients with chronic obstructive pulmonary disease (COPD) or history of pulmonary embolism (PE) and patients without COPD was found (p = 0.0021). A significant difference between asymptomatic/mild symptomatic COVID-19 status and symptomatic COVID-19 status was found (p = 0.0415); a significant difference was found for O2 therapy with < 4 L/min and O2 therapy ≥ 4 L/min (p = 0.0049).

CONCLUSION

Thirty-day mortality rate of COVID-19 infection and PFFs does not differ from mortality rate of non-COVID-19 PFFs. However, patients with pre-existing comorbidities and symptomatic COVID-19 infection requiring a high volume of O2 therapy have a higher incidence of 60-90-day mortality when surgically treated.

What does COVID-19 testing results really say? The real statistics concealed behind the accessible data

Accurate and comprehensive testing is crucial for practitioners to portray the pandemic. Without testing there is no data; yet, the exact number of infected people cannot be determined due to the lack of comprehensive testing. The number of seropositive for SARS-CoV-2 infection is obviously relative to the extent of testing. However, the true number of infections might be still far higher than the reported values. To compare the countries based on the number of seropositive for SARS-CoV-2 infection is misleading, as there may not be enough tests being carried out to properly monitor the outbreak. In this paper, we closely look through the COVID-19 testing results. Herein, we try to draw conclusions based on the reported data: first, the presence of a possible relationship between COVID-19 transition and patients' age will be assessed. Then, the COVID-19 case fatality rate (CFR) is compared with the age-demographic data for different countries. Based on the results, a method for estimating a lower bound (minimum) for the number of actual positive cases will be developed and validated. Results of this study have shown that CFR is a metric reflecting the spread of the virus, but is a factor of the extent of testing and does not necessarily show the real size of the outbreak. Moreover, no large difference in susceptibility by age has been found. The results suggest the similarity between the age distribution of COVID-19 and the population age-demographic is improving over the course of the pandemic. In addition, countries with lower CFRs have a more similar COVID-19 age distribution, which is a result of more comprehensive testing. Finally, a method for estimation of the real number of infected people based on the age distributions, reported CFRs, and the extent of testing will be developed and validated.

Animal reservoirs of SARS-CoV-2: calculable COVID-19 risk for older adults from animal to human transmission

The current COVID-19 pandemic, caused by the highly contagious respiratory pathogen SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has already claimed close to three million lives. SARS-CoV-2 is a zoonotic disease: it emerged from a bat reservoir and it can infect a number of agricultural and companion animal species. SARS-CoV-2 can cause respiratory and intestinal infections, and potentially systemic multi-organ disease, in both humans and animals. The risk for severe illness and death with COVID-19 significantly increases with age, with older adults at highest risk. To combat the pandemic and protect the most susceptible group of older adults, understanding the human-animal interface and its relevance to disease transmission is vitally important. Currently high infection numbers are being sustained via human-to-human transmission of SARS-CoV-2. Yet, identifying potential animal reservoirs and potential vectors of the disease will contribute to stronger risk assessment strategies. In this review, the current information about SARS-CoV-2 infection in animals and the potential spread of SARS-CoV-2 to humans through contact with domestic animals (including dogs, cats, ferrets, hamsters), agricultural animals (e.g., farmed minks), laboratory animals, wild animals (e.g., deer mice), and zoo animals (felines, non-human primates) are discussed with a special focus on reducing mortality in older adults.

Changes in Stroke Patients' Health-Seeking Behavior by COVID-19 Epidemic Regions: Data from the Korean Stroke Registry

Introduction

The coronavirus disease 2019 (COVID-19) pandemic has led to changes in stroke patients' healthcare use. This study evaluated changes in Korean stroke patients' health-seeking behaviors and stroke care services using data from the Korean Stroke Registry (KSR).

Methods

We reviewed data from patients with acute stroke and transient ischemic attack (TIA) during 2019 (before COVID-19 period) and 2020 (COVID-19 period). Outcomes included patient characteristics, time from stroke onset to hospital arrival, and in-hospital stroke pathways. Subgroup analyses were performed for an epidemic region (Daegu city and Gyeongsangbuk-do region, the D-G region).

Results

The study included 1,792 patients from the pre-COVID-19 period and 1,555 patients from the COVID-19 period who visited hospitals that contribute to the KSR. During the COVID-19 period, the D-G region had two-thirds the number of cases (vs. the pre-CO­VID-19 period) and a significant decrease in the proportion of patients with TIA (9.97%–2.91%). Unlike other regions, the median onset-to-door time increased significantly in the D-G region (361 min vs. 526.5 min, p = 0.016), and longer onset-to-door times were common for patients with mild symptoms and who were in their 60s or 70s. The number of patients who underwent intravenous thrombolysis also decreased during the COVID-19 period, although the treatment times were not significantly different between the 2 periods.

Discussion/Conclusion

Korean stroke patients in a CO­VID-19 epidemic region exhibited distinct changes in health-seeking behaviors. Appropriate triage system and public education regarding the importance of early treatment are needed during the COVID-19 pandemic.

Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

BACKGROUND

Prediction of mortality in patients with coronavirus disease 2019 (COVID-19) is a key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. This study aimed to identify the factors associated with COVID-19 mortality and to develop a nomogram for predicting mortality using clinical parameters and underlying diseases.

METHODS

This study was performed in 5,626 patients with confirmed COVID-19 between February 1 and April 30, 2020 in South Korea. A Cox proportional hazards model and logistic regression model were used to construct a nomogram for predicting 30-day and 60-day survival probabilities and overall mortality, respectively in the train set. Calibration and discrimination were performed to validate the nomograms in the test set.

RESULTS

Age ≥ 70 years, male, presence of fever and dyspnea at the time of COVID-19 diagnosis, and diabetes mellitus, cancer, or dementia as underling diseases were significantly related to 30-day and 60-day survival and mortality in COVID-19 patients. The nomogram showed good calibration for survival probabilities and mortality. In the train set, the areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively; the AUC for mortality of 0.959. In the test set, AUCs for 30-day and 60-day survival was 0.876 and 0.660, respectively, and that for mortality was 0.926. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/.

CONCLUSION

The prediction model could accurately predict COVID-19-related mortality; thus, it would be helpful for identifying the risk of mortality and establishing medical policies during the pandemic to improve the clinical outcomes.

Multiepitope Proteins for the Differential Detection of IgG Antibodies against RBD of the Spike Protein and Non-RBD Regions of SARS-CoV-2

The COVID-19 pandemic has exposed the extent of global connectivity and collective vulnerability to emerging diseases. From its suspected origins in Wuhan, China, it spread to all corners of the world in a matter of months. The absence of high-performance, rapid diagnostic methods that could identify asymptomatic carriers contributed to its worldwide transmission. Serological tests offer numerous benefits compared to other assay platforms to screen large populations. First-generation assays contain targets that represent proteins from SARS-CoV-2. While they could be quickly produced, each actually has a mixture of specific and non-specific epitopes that vary in their reactivity for antibodies. To generate the next generation of the assay, epitopes were identified in three SARS-Cov-2 proteins (S, N, and Orf3a) by SPOT synthesis analysis. After their similarity to other pathogen sequences was analyzed, 11 epitopes outside of the receptor-binding domain (RBD) of the spike protein that showed high reactivity and uniqueness to the virus. These were incorporated into a ß-barrel protein core to create a highly chimeric protein. Another de novo protein was designed that contained only epitopes in the RBD. In-house ELISAs suggest that both multiepitope proteins can serve as targets for high-performance diagnostic tests. Our approach to bioengineer chimeric proteins is highly amenable to other pathogens and immunological uses.

Should we operate on all patients with COVID-19 and proximal femoral fractures? An analysis of thirty, sixty, and ninety day mortality rates based on patients' clinical presentation and comorbidity: a multicentric study in Northern Italy

Purpose

This study aims to evaluate 30–60–90-day mortality of operated proximal femur fractures (PFFs) suffering from COVID-19 and correlation with patients’ clinical presentation and comorbidities.

Methods

Between February 1, 2020, and December 31, 2020, patients with COVID-19 infection and surgically treated PFF were included. Patients’ demographic characteristics, oxygen (O2) therapy, comorbidities, and AO type fracture were collected. Chi-square test or Fisher test and hazard ratio were used to assessing the correlation between mortality rate, patient characteristics, and COVID-19 status. Kaplan-Meyer curve was used to analyze 30–60–90-day mortality. Level of significance was set as p < 0.05.

Results

Fifty-six patients (mean age of 82.7 ± 8.85 years) were included. Thirty-day mortality rate was 5%, which increased to 21% at 60 days and 90 days. Eleven patients died, eight due to AO type A-like and three due to AO type B-like fractures. No significant difference in mortality rate between patients with cardiopulmonary comorbidity or no cardiopulmonary comorbidity was found (p = 0.67); a significant difference in patients with chronic obstructive pulmonary disease (COPD) or history of pulmonary embolism (PE) and patients without COPD was found (p = 0.0021). A significant difference between asymptomatic/mild symptomatic COVID-19 status and symptomatic COVID-19 status was found (p = 0.0415); a significant difference was found for O2 therapy with < 4 L/min and O2 therapy ≥ 4 L/min (p = 0.0049).

Conclusion

Thirty-day mortality rate of COVID-19 infection and PFFs does not differ from mortality rate of non-COVID-19 PFFs. However, patients with pre-existing comorbidities and symptomatic COVID-19 infection requiring a high volume of O2 therapy have a higher incidence of 60–90-day mortality when surgically treated.

Tracking Pandemic Severity Using Data on the Age Structure of Mortality: Lessons From the 1918 Influenza Pandemic in Michigan

Objectives. To test whether distortions in the age structure of mortality during the 1918 influenza pandemic in Michigan tracked the severity of the pandemic. Methods. We calculated monthly excess deaths during the period of 1918 to 1920 by using monthly data on all-cause deaths for the period of 1912 to 1920 in Michigan. Next, we measured distortions in the age distribution of deaths by using the Kuiper goodness-of-fit test statistic comparing the monthly distribution of deaths by age in 1918 to 1920 with the baseline distribution for the corresponding month for 1912 to 1917. Results. Monthly distortions in the age distribution of deaths were correlated with excess deaths for the period of 1918 to 1920 in Michigan (r = 0.83; P < .001). Conclusions. Distortions in the age distribution of deaths tracked variations in the severity of the 1918 influenza pandemic. Public Health Implications. It may be possible to track the severity of pandemic activity with age-at-death data by identifying distortions in the age distribution of deaths. Public health authorities should explore the application of this approach to tracking the COVID-19 pandemic in the absence of complete data coverage or accurate cause-of-death data.

Impact of natural selection on global patterns of genetic variation, and association with clinical phenotypes, at genes involved in SARS-CoV-2 infection

We investigated global patterns of genetic variation and signatures of natural selection at host genes relevant to SARS-CoV-2 infection (ACE2, TMPRSS2, DPP4, and LY6E). We analyzed novel data from 2,012 ethnically diverse Africans and 15,997 individuals of European and African ancestry with electronic health records, and integrated with global data from the 1000GP. At ACE2, we identified 41 non-synonymous variants that were rare in most populations, several of which impact protein function. However, three non-synonymous variants were common among Central African hunter-gatherers from Cameroon and are on haplotypes that exhibit signatures of positive selection. We identify strong signatures of selection impacting variation at regulatory regions influencing ACE2 expression in multiple African populations. At TMPRSS2, we identified 13 amino acid changes that are adaptive and specific to the human lineage. Genetic variants that are targets of natural selection are associated with clinical phenotypes common in patients with COVID-19.

Science's Response to CoVID-19

CoVID-19 is a multi-symptomatic disease which has made a global impact due to its ability to spread rapidly, and its relatively high mortality rate. Beyond the heroic efforts to develop vaccines, which we do not discuss herein, the response of scientists and clinicians to this complex problem has reflected the need to detect CoVID-19 rapidly, to diagnose patients likely to show adverse symptoms, and to treat severe and critical CoVID-19. Here we aim to encapsulate these varied and sometimes conflicting approaches and the resulting data in terms of chemistry and biology. In the process we highlight emerging concepts, and potential future applications that may arise out of this immense effort.

Impact of natural selection on global patterns of genetic variation, and association with clinical phenotypes, at genes involved in SARS-CoV-2 infection

We investigated global patterns of genetic variation and signatures of natural selection at host genes relevant to SARS-CoV-2 infection ( ACE2, TMPRSS2, DPP4 , and LY6E ). We analyzed novel data from 2,012 ethnically diverse Africans and 15,997 individuals of European and African ancestry with electronic health records, and integrated with global data from the 1000GP. At ACE2 , we identified 41 non-synonymous variants that were rare in most populations, several of which impact protein function. However, three non-synonymous variants were common among Central African hunter-gatherers from Cameroon and are on haplotypes that exhibit signatures of positive selection. We identify strong signatures of selection impacting variation at regulatory regions influencing ACE2 expression in multiple African populations. At TMPRSS2 , we identified 13 amino acid changes that are adaptive and specific to the human lineage. Genetic variants that are targets of natural selection are associated with clinical phenotypes common in patients with COVID-19.

Differential mutation profile of SARS-CoV-2 proteins across deceased and asymptomatic patients

BACKGROUND

The SARS-CoV-2 infection has spread at an alarming rate with many places showing multiple peaks in incidence. Present study analyzes a total of 332 SARS-CoV-2 genome sequences from 114 asymptomatic and 218 deceased patients from twenty-one different countries to assess the mutation profile therein in order to establish the correlation between the clinical status and the observed mutations.

METHODS

The mining of mutations was carried out using the GISAID CoVSurver (www.gisaid.org/epiflu-applications/covsurver-mutations-app) with the reference sequence 'hCoV-19/Wuhan/WIV04/2019' present in NCBI with Accession number NC-045512.2. The impact of the mutations on SARS-CoV-2 proteins mutation was predicted using PredictSNP1(loschmidt.chemi.muni.cz/predictsnp1) which is a meta-server integrating six predictor tools: SIFT, PhD-SNP, PolyPhen-1, PolyPhen-2, MAPP and SNAP. The iStable integrated server (predictor.nchu.edu.tw/iStable) was used to predict shifts in the protein stability due to mutations.

RESULTS

A total of 372 variants were observed in the 332 SARS-CoV-2 sequences with several variants present in multiple patients accounting for a total of 1596 incidences. Asymptomatic and deceased specific mutants constituted 32% and 62% of the repertoire respectively indicating their partial exclusivity. However, the most prevalent mutations were those present in both. Though some parts of the genome are more variable than others but there was clear difference between incidence and prevalence. Non-structural protein 3 (NSP3) with 68 variants had a total of only 105 incidences whereas Spike protein had 346 incidences with just 66 variants. Amongst the Deleterious variants, NSP3 had the highest incidence of 25 followed by NSP2 (16), ORF3a (14) and N (14). Spike protein had just 7 Deleterious variants out of 66.

CONCLUSION

Deceased patients have more Deleterious than Neutral variants as compared to the asymptomatic ones. Further, it appears that the Deleterious variants which decrease protein stability are more significant in pathogenicity of SARS-CoV-2.

Wearing a mask-For yourself or for others? Behavioral correlates of mask wearing among COVID-19 frontline workers

Human behavior can have effects on oneself and externalities on others. Mask wearing is such a behavior in the current pandemic. What motivates people to wear face masks in public when mask wearing is voluntary or not enforced? Which benefits should the policy makers rather emphasize in information campaigns-the reduced chances of getting the SARS-CoV-2 virus (benefits for oneself) or the reduced chances of transmitting the virus (benefits for others in the society)? In this paper, we link measured risk preferences and other-regarding preferences to mask wearing habits among 840 surveyed employees of two large Swiss hospitals. We find that the leading mask-wearing motivations change with age: While for older people, mask wearing habits are best explained by their self-regarding risk preferences, younger people are also motivated by other-regarding concerns. Our results are robust to different specifications including linear probability models, probit models and Lasso covariate selection models. Our findings thus allow drawing policy implications for effectively communicating public-health recommendations to frontline workers during the COVID-19 pandemic.

Effect of Race and Ethnicity on In-Hospital Mortality in Patients with COVID-19

Objective

To identify differences in short-term outcomes of patients with coronavirus disease 2019 (COVID-19) according to various racial/ethnic groups.

Design

Analysis of Cerner de-identified COVID-19 dataset.

Setting

A total of 62 health care facilities.

Participants

The cohort included 49,277 adult COVID-19 patients who were hospitalized from December 1, 2019 to November 13, 2020.

Main Outcome Measures

The primary outcome of interest was in-hospital mortality. The secondary outcome was non-routine discharge (discharge to destinations other than home, such as short-term hospitals or other facilities including intermediate care and skilled nursing homes).

Methods

We compared patients' age, gender, individual components of Charlson and Elixhauser comorbidities, medical complications, use of do-not-resuscitate, use of palliative care, and socioeconomic status between various racial and/or ethnic groups. We further compared the rates of in-hospital mortality and non-routine discharges between various racial and/or ethnic groups.

Results

Compared with White patients, in-hospital mortality was significantly higher among African American (OR 1.5; 95%CI:1.3-1.6, P<.001), Hispanic (OR1.4; 95%CI:1.3-1.6, P<.001), and Asian or Pacific Islander (OR 1.5; 95%CI: 1.1-1.9, P=.002) patients after adjustment for age and gender, Elixhauser comorbidities, do-not-resuscitate status, palliative care use, and socioeconomic status.

Conclusions

Our study found that, among hospitalized patients with COVID-2019, African American, Hispanic, and Asian or Pacific Islander patients had increased mortality compared with White patients after adjusting for sociodemographic factors, comorbidities, and do-not-resuscitate/palliative care status. Our findings add additional perspective to other recent studies.

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

Impact of COVID-19 on older adults and role of long-term care facilities during early stages of epidemic in Italy

Older adults are the main victims of the novel COVID-19 coronavirus outbreak and elderly in Long Term Care Facilities (LTCFs) are severely hit in terms of mortality. This paper presents a quantitative study of the impact of COVID-19 outbreak in Italy during first stages of the epidemic, focusing on the effects on mortality increase among older adults over 80 and its correlation with LTCFs. The study of growth patterns shows a power-law scaling regime for the first stage of the pandemic with an uneven behaviour among different regions as well as for the overall mortality increase according to the different impact of COVID-19. However, COVID-19 incidence rate does not fully explain the differences of mortality impact in older adults among different regions. We define a quantitative correlation between mortality in older adults and the number of people in LTCFs confirming the tremendous impact of COVID-19 on LTCFs. In addition a correlation between LTCFs and undiagnosed cases as well as effects of health system dysfunction is also observed. Our results confirm that LTCFs did not play a protective role on older adults during the pandemic, but the higher the number of elderly people living in LTCFs the greater the increase of both general and COVID-19 related mortality. We also observed that the handling of the crises in LTCFs hampered an efficient tracing of COVID-19 spread and promoted the increase of deaths not directly attributed to SARS-CoV-2.