Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China

Modelling the spreading of the SARS-CoV-2 in presence of the lockdown and quarantine measures by a kinetic-type reactions approach

We propose a realistic model for the evolution of the COVID-19 pandemic subject to the lockdown and quarantine measures, which takes into account the timedelay for recovery or death processes. The dynamic equations for the entire process are derived by adopting a kinetic-type reactions approach. More specifically, the lockdown and the quarantine measures are modelled by some kind of inhibitor reactions where susceptible and infected individuals can be trapped into inactive states. The dynamics for the recovered people is obtained by accounting people who are only traced back to hospitalized infected people. To get the evolution equation we take inspiration from the Michaelis Menten's enzyme-substrate reaction model (the so-called MM reaction) where the enzyme is associated to the available hospital beds, the substrate to the infected people, and the product to the recovered people, respectively. In other words, everything happens as if the hospitals beds act as a catalyzer in the hospital recovery process. Of course, in our case, the reverse MM reaction has no sense in our case and, consequently, the kinetic constant is equal to zero. Finally, the ordinary differential equations (ODEs) for people tested positive to COVID-19 is simply modelled by the following kinetic scheme $S+I\Rightarrow 2I$ with $I\Rightarrow R$ or $I\Rightarrow D$, with $S$, $I$, $R$ and $D$ denoting the compartments susceptible, infected, recovered and deceased people, respectively. The resulting kinetic-type equations provide the ODEs, for elementary reaction steps, describing the number of the infected people, the total number of the recovered people previously hospitalized, subject to the lockdown and the quarantine measure and the total number of deaths. The model foresees also the second wave of infection by coronavirus. The tests carried out on real data for Belgium, France and Germany confirmed the correctness of our model.

Risk factors for the deterioration of patients with mild COVID-19 admitted to a COVID-19 community treatment center

Background: The rapid increase in the number of coronavirus disease (COVID-19) patients led to the operation of COVID-19 facilities for patients with mild COVID-19 in South Korea. We analyzed the correlation and effect of risk factors associated with the worsening of patients with mild COVID-19 leading to their transfer from a community treatment center to a hospital.Methods: This retrospective cohort observational study included 1,208 COVID-19 patients with mild symptoms who were admitted to the Namsan Community Treatment Center between June 2020 and January 2021. A chi-square test was performed to examine the differences in the transfer rate by age, sex, nationality, presence of symptoms at admission, and season, and a multivariable logistic regression analysis was performed to examine the association of variables to the hospitalization rate.Results: Of the 1,208 patients, 212 (17.5%) were transferred to a hospital due to clinical deterioration. Increasing odds of hospital transfer were associated significantly with higher age and presentation in autumn, whereas sex and symptomatic illness at admission did not show a statistically significant association.Conclusion: The findings indicate the importance of the initial risk classification of COVID-19 patients based on thorough assessment and close monitoring, timely allocation of appropriate resources to high-risk groups that are likely to develop severe disease, and reduction of medical resource wastage and limiting of administrative force to ensure that patients receive the best treatment.

Online information and support seeking during COVID-19 lockdown in Wuhan: implications for health promotion

The lockdown that was deployed in Wuhan, China to combat the COVID-19 pandemic curbed the infection but also created great information challenges for people in social isolation. This resulted in surge in online health information seeking (OHIS) behaviors of the patients and their families. While the Internet has been widely used by Chinese public to access and search health information, there is relatively little research in the context of pandemic outbreaks, especially at the onset of a strong lockdown while many people were panicking. From a total of 10 908 ‘#COVID-19 Patient Seeking Help’ posts on Weibo in a period of 20 days when the lockdown policy was first initiated, we identified 1496 unique patients living in or with family in Wuhan, China. Using textual analysis, we explored OHIS behaviors at the onset of the pandemic. Many faced increased difficulties accessing offline healthcare services and such turned to social media for help and information. In particular, the findings highlight the following themes: ‘OHIS for medical treatment’, ‘OHIS to manage self-quarantine’, ‘OHIS for tangible support’ and ‘OHIS to navigate information discrepancy’. Overall, our findings provide important insights into health information seeking behaviors and the role of social media during a pandemic. Our findings also highlight the importance of considering people’s information need and challenges created due to the lockdown policies in the future pandemic communication and preparedness.

Sociodemographic and Policy Factors Associated with the Transmission of COVID-19: Analyzing Longitudinal Contact Tracing Data from a Northern Chinese City

To examine how sociodemographic characteristics and non-pharmaceutical interventions affect the transmission of COVID-19, we analyze patient profiles and contact tracing data from almost all cases in an outbreak in Shijiazhuang, China, from January to February 2021. Because of universal testing and digital tracing, the data are of high quality. Results from negative binomial models indicate that the counts of close contacts and secondary infections vary with the cases' age and occupation. Notably, cases under age 18 are causing an increased infection rate among their close contacts and leading to more within-neighborhood secondary infections than adults aged 18-49. Also, county-wide interventions and lockdown are found to be effective at containing the spread of COVID-19. These measures can reduce the number of close contacts that each case has and largely restrict the remaining infections to the case's neighborhood. These results suggest that transmission risks of COVID-19 are associated with the case's sociodemographic characteristics and can be reduced with interventions at the county level. Implications on mitigation measures and reopening plans are discussed.

Neutrophil subsets and their differential roles in viral respiratory diseases

Neutrophils play significant roles in immune homeostasis and as neutralizers of microbial infections. Recent evidence further suggests heterogeneity of neutrophil developmental and activation states that exert specialized effector functions during inflammatory disease conditions. Neutrophils can play multiple roles during viral infections, secreting inflammatory mediators and cytokines that contribute significantly to host defense and pathogenicity. However, their roles in viral immunity are not well understood. In this review, we present an overview of neutrophil heterogeneity and its impact on the course and severity of viral respiratory infectious diseases. We focus on the evidence demonstrating the crucial roles neutrophils play in the immune response toward respiratory infections, using influenza as a model. We further extend the understanding of neutrophil function with the studies pertaining to COVID-19 disease and its neutrophil-associated pathologies. Finally, we discuss the relevance of these results for future therapeutic options through targeting and regulating neutrophil-specific responses.

Cardiovascular Tropism and Sequelae of SARS-CoV-2 Infection

The extrapulmonary manifestation of coronavirus disease-19 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), became apparent early in the ongoing pandemic. It is now recognized that cells of the cardiovascular system are targets of SARS-CoV-2 infection and associated disease pathogenesis. While some details are emerging, much remains to be understood pertaining to the mechanistic basis by which SARS-CoV-2 contributes to acute and chronic manifestations of COVID-19. This knowledge has the potential to improve clinical management for the growing populations of patients impacted by COVID-19. Here, we review the epidemiology and pathophysiology of cardiovascular sequelae of COVID-19 and outline proposed disease mechanisms, including direct SARS-CoV-2 infection of major cardiovascular cell types and pathogenic effects of non-infectious viral particles and elicited inflammatory mediators. Finally, we identify the major outstanding questions in cardiovascular COVID-19 research.

A modeling study of the effect of social distancing policies on the early spread of coronavirus disease 2019: a case of South Korea

The social distancing policy is an effective way to prevent the spread of infectious diseases in the initial phase of their outbreak when medical evidence to support a particular course of treatment is deficient. While studies on the coronavirus disease 2019 (COVID-19) have mainly focused on the effects of specific measures (e.g., school and workplace closures and restrictions on movement), few investigated the characteristics of epidemic trends in response to the intensity of the policy and the amount of time required for policy measures to take effect. This study employs the SIRD (susceptible, infected, recovered, and deceased) model to analyze the COVID-19 epidemic trend according to the intensity of the social distancing policy in South Korea. The model reveals that the reproduction number began at 5.58 and fluctuated between 0.14 and 1.72 during the study period in accordance with different policy intensities. At the beginning of the social distancing policy, restrictions on public facility use were likely to have been effective in preventing the spread of COVID-19. When the intervention was relaxed, the transmission potential increased significantly. According to the reproduction number, social distancing policies prove to be effective after 13-19 days of implementation; however, as the pandemic progressed, this period extended from 13-14 to 18-19 days for the same effect. This suggests that governments need to consider not only the intensity of the social distancing policy, but also people's low responsiveness as the pandemic remains prevalent over time. It is also recommended they take preemptive action to ensure sufficient time for the policy to achieve its stated goal.

How do people get information for COVID‐19 according to age groups?

This study highlights how people get important information on COVID‐19 according to age groups by employing social network analysis for Twitter. First, people have different key players according to the age groups. For example, while universities and journals play a crucial role in the adults' networks, news media have a significant impact on the elderly's networks. Second, people have different characteristics of social network groups according to age. For example, people belong to small groups, and barely communicate with others across the groups in the teens' networks, whereas people in each group have strong communication networks with other groups in the elderly's networks. Third, this study shows that people utilise different domains to share COVID‐19 information according to age. For example, while twitter.com ranks first in the children, teens, and elderly's networks, cnn.com places first in the adults' networks.

People have different key players according to age

People have different characteristics of social network groups according to age

People utilise different domains to share COVID‐19 information according to age

Governments and policymakers should understand the characteristics of social networks for COVID‐19 according to age

Spread of COVID-19 Infection in Long-Term Care Facilities of Trieste (Italy) during the Pre-Vaccination Era, Integrating Findings of 41 Forensic Autopsies with Geriatric Comorbidity Index as a Valid Option for the Assessment of Strength of Causation

BACKGROUND

in 2020, a new form of coronavirus spread around the world starting from China. The older people were the population most affected by the virus worldwide, in particular in Italy where more than 90% of deaths were people over 65 years. In these people, the definition of the cause of death is tricky due to the presence of numerous comorbidities.

OBJECTIVE

to determine whether COVID-19 was the cause of death in a series of older adults residents of nursing care homes.

METHODS

41 autopsies were performed from May to June 2020. External examination, swabs, and macroscopic and microscopic examination were performed.

RESULTS

the case series consisted of nursing home guests; 15 men and 26 women, with a mean age of 87 years. The average number of comorbidities was 4. Based only on the autopsy results, the defined cause of death was acute respiratory failure due to diffuse alveolar damage (8%) or (31%) bronchopneumonia with one or more positive swabs for SARS-CoV-2. Acute cardiac failure with one or more positive swabs for SARS-CoV-2 was indicated as the cause of death in in symptomatic (37%) and asymptomatic (10%) patients. Few patients died for septic shock (three cases), malignant neoplastic diseases (two cases), and massive digestive bleeding (one case).

CONCLUSIONS

Data from post-mortem investigation were integrated with previously generated Geriatric Index of Comorbidity (GIC), resulting in four different degrees of probabilities: high (12%), intermediate (10%), low (59%), and none (19%), which define the level of strength of causation and the role of COVID-19 disease in determining death.

Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach

The outbreak of COVID-19 is a global problem today, and, to reduce infectious cases and increase recovered cases, it is relevant to estimate the future movement and pattern of the disease. To identify the hotspot for COVID-19 in Bangladesh, we performed a cluster analysis based on the hierarchical k-means approach. A well-known epidemiological model named "susceptible-infectious-recovered (SIR)" and an additive regression model named "Facebook PROPHET Procedure" were used to predict the future direction of COVID-19 using data from IEDCR. Here we compare the results of the optimized SIR model and a well-known machine learning algorithm (PROPHET algorithm) for the forecasting trend of the COVID-19 pandemic. The result of the cluster analysis demonstrates that Dhaka city is now a hotspot for the COVID-19 pandemic. The basic reproduction ratio value was 2.1, which indicates that the infection rate would be greater than the recovery rate. In terms of the SIR model, the result showed that the virus might be slightly under control only after August 2022. Furthermore, the PROPHET algorithm observed an altered result from SIR, implying that all confirmed, death, and recovered cases in Bangladesh are increasing on a daily basis. As a result, it appears that the PROPHET algorithm is appropriate for pandemic data with a growing trend. Based on the findings, the study recommended that the pandemic is not under control and ensured that if Bangladesh continues the current pattern of infectious rate, the spread of the pandemic in Bangladesh next year will increase.

A novel epidemiological scoring system for the prediction of mortality in COVID-19 patients

BACKGROUND

Most of the reported risk score models for coronavirus disease 2019 (COVID-19) mortality are based on the levels of inflammatory markers, comorbidities or various treatment modalities, and there is a paucity of risk score models based on clinical symptoms and comorbidities.

METHODS

To address this need, age, clinical symptoms and comorbidities were used to develop a COVID-19 scoring system (CSS) for early prediction of mortality in severe COVID-19 patients. The CSS was developed with scores ranging from 0 to 9. A higher score indicates higher risk with good discrimination quality presented by Mann Whitney U test and area under receiver operating characteristic curve (AUROC).

RESULTS

Patient age of ≥60 y, cough, breathlessness, diabetes and any other comorbidity (with or without diabetes) are significant and independent risk factors for non-survival among COVID-19 patients. The CSS showed good sensitivity and specificity (i.e. 74.1% and 78.5% at CSS≥5, respectively), with an overall diagnostic accuracy of 82.8%, which was close to the diagnostic accuracy detected in the validation cohort (81.9%). In the validation cohort, high (8-9), medium (5-7) and low (0-4) CSS groups had 54.80%, 28.60% and 6.5% observed mortality, respectively, which was very close to the predicted mortality (62.40%, 27.60% and 5.2%, respectively, by scoring cohort).

CONCLUSIONS

The CSS shows a positive relationship between a higher score and proportion of mortality and, as its validation showed, it is useful for the prediction of risk of mortality in COVID-19 patients at an early stage, so that referral for triage and admission can be predetermined even before admission to hospital.

Propagation dynamics and control policies of COVID-19 pandemic at early stages: Implications on future resurgence response

The spreading of novel coronavirus (SARS-CoV-2) has gravely impacted the world in the last year and a half. Understanding the spatial and temporal patterns of how it spreads at the early stage and the effectiveness of a governments' immediate response helps our society prepare for future COVID-19 waves or the next pandemic and contain it before the spreading gets out of control. In this article, a susceptible-exposed-infectious-removed model is used to model the city-to-city spreading patterns of the disease at the early stage of its emergence in China (from December 2019 to February 2020). Publicly available reported case numbers in 312 Chinese cities and between-city mobility data are leveraged to estimate key epidemiological characteristics, such as the transmission rate and the number of infectious people for each city. It is discovered that during any given time period, there are always only a few cities that are responsible for spreading the disease to other cities. We term these few cities as transmission centers. The spatial and temporal changes in transmission centers demonstrate predictable patterns. Moreover, rigorously designed experiments show that in controlling the disease spread in a city, non-pharmaceutical interventions (NPIs) implemented at transmission centers are more effective than the NPI implemented in the city itself. These findings have implications on the control of an infectious disease at the early stage of its spreading: implementing NPIs at transmission centers at early stages is effective in controlling the spread of infectious diseases.

T-Cell Subsets and Interleukin-10 Levels Are Predictors of Severity and Mortality in COVID-19: A Systematic Review and Meta-Analysis

Background

Many COVID-19 patients reveal a marked decrease in their lymphocyte counts, a condition that translates clinically into immunodepression and is common among these patients. Outcomes for infected patients vary depending on their lymphocytopenia status, especially their T-cell counts. Patients are more likely to recover when lymphocytopenia is resolved. When lymphocytopenia persists, severe complications can develop and often lead to death. Similarly, IL-10 concentration is elevated in severe COVID-19 cases and may be associated with the depression observed in T-cell counts. Accordingly, this systematic review and meta-analysis aims to analyze T-cell subsets and IL-10 levels among COVID-19 patients. Understanding the underlying mechanisms of the immunodepression observed in COVID-19, and its consequences, may enable early identification of disease severity and reduction of overall morbidity and mortality.

Methods

A systematic search was conducted covering PubMed MEDLINE, Scopus, Web of Science, and EBSCO databases for journal articles published from December 1, 2019 to March 14, 2021. In addition, we reviewed bibliographies of relevant reviews and the medRxiv preprint server for eligible studies. Our search covered published studies reporting laboratory parameters for T-cell subsets (CD4/CD8) and IL-10 among confirmed COVID-19 patients. Six authors carried out the process of data screening, extraction, and quality assessment independently. The DerSimonian-Laird random-effect model was performed for this meta-analysis, and the standardized mean difference (SMD) and 95% confidence interval (CI) were calculated for each parameter.

Results

A total of 52 studies from 11 countries across 3 continents were included in this study. Compared with mild and survivor COVID-19 cases, severe and non-survivor cases had lower counts of CD4/CD8 T-cells and higher levels of IL-10.

Conclusion

Our findings reveal that the level of CD4/CD8 T-cells and IL-10 are reliable predictors of severity and mortality in COVID-19 patients. The study protocol is registered with the International Prospective Register of Systematic Reviews (PROSPERO); registration number CRD42020218918.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020218918, identifier: CRD42020218918.

Seroreactivity of the Severe Acute Respiratory Syndrome Coronavirus 2 Recombinant S Protein, Receptor-Binding Domain, and Its Receptor-Binding Motif in COVID-19 Patients and Their Cross-Reactivity With Pre-COVID-19 Samples From Malaria-Endemic Areas

Despite the global interest and the unprecedented number of scientific studies triggered by the COVID-19 pandemic, few data are available from developing and low-income countries. In these regions, communities live under the threat of various transmissible diseases aside from COVID-19, including malaria. This study aims to determine the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroreactivity of antibodies from COVID-19 and pre-COVID-19 samples of individuals in Mali (West Africa). Blood samples from COVID-19 patients (n = 266) at Bamako Dermatology Hospital (HDB) and pre-COVID-19 donors (n = 283) from a previous malaria survey conducted in Dangassa village were tested by ELISA to assess IgG antibodies specific to the full-length spike (S) protein, the receptor-binding domain (RBD), and the receptor-binding motif (RBM436-507). Study participants were categorized by age, gender, treatment duration for COVID-19, and comorbidities. In addition, the cross-seroreactivity of samples from pre-COVID-19, malaria-positive patients against the three antigens was assessed. Recognition of the SARS-CoV-2 proteins by sera from COVID-19 patients was 80.5% for S, 71.1% for RBD, and 31.9% for RBM (p < 0.001). While antibody responses to S and RBD tended to be age-dependent, responses to RBM were not. Responses were not gender-dependent for any of the antigens. Higher antibody levels to S, RBD, and RBM at hospital entry were associated with shorter treatment durations, particularly for RBD (p < 0.01). In contrast, higher body weights negatively influenced the anti-S antibody response, and asthma and diabetes weakened the anti-RBM antibody responses. Although lower, a significant cross-reactive antibody response to S (21.9%), RBD (6.7%), and RBM (8.8%) was detected in the pre-COVID-19 and malaria samples. Cross-reactive antibody responses to RBM were mostly associated (p < 0.01) with the absence of current Plasmodium falciparum infection, warranting further study.

A Non-Uniform Continuous Cellular Automata for Analyzing and Predicting the Spreading Patterns of COVID-19

During the COVID-19 outbreak, modeling the spread of infectious diseases became a challenging research topic due to its rapid spread and high mortality rate. The main objective of a standard epidemiological model is to estimate the number of infected, suspected, and recovered from the illness by mathematical modeling. This model does not capture how the disease transmits between neighboring regions through interaction. A more general framework such as Cellular Automata (CA) is required to accommodate a more complex spatial interaction within the epidemiological model. The critical issue of modeling in the spread of diseases is how to reduce the prediction error. This research aims to formulate the influence of the interaction of a neighborhood on the spreading pattern of COVID-19 using a neighborhood frame model in a Cellular-Automata (CA) approach and obtain a predictive model for the COVID-19 spread with the error reduction to improve the model. We propose a non-uniform continuous CA (N-CCA) as our contribution to demonstrate the influence of interactions on the spread of COVID-19. The model has succeeded in demonstrating the influence of the interaction between regions on the COVID-19 spread, as represented by the coefficients obtained. These coefficients result from multiple regression models. The coefficient obtained represents the population’s behavior interacting with its neighborhood in a cell and influences the number of cases that occur the next day. The evaluation of the N-CCA model is conducted by root mean square error (RMSE) for the difference in the number of cases between prediction and real cases per cell in each region. This study demonstrates that this approach improves the prediction of accuracy for 14 days in the future using data points from the past 42 days, compared to a baseline model.

COVID-19 distributes socially in China: A Bayesian spatial analysis

Purpose

The ongoing coronavirus disease 2019 (COVID-19) epidemic increasingly threatens the public health security worldwide. We aimed to identify high-risk areas of COVID-19 and understand how socioeconomic factors are associated with the spatial distribution of COVID-19 in China, which may help other countries control the epidemic.

Methods

We analyzed the data of COVID-19 cases from 30 provinces in mainland China (outside of Hubei) from 16 January 2020 to 31 March 2020, considering the data of demographic, economic, health, and transportation factors. Global autocorrelation analysis and Bayesian spatial models were used to present the spatial pattern of COVID-19 and explore the relationship between COVID-19 risk and various factors.

Results

Global Moran’s I statistics of COVID-19 incidences was 0.31 (P<0.05). The areas with a high risk of COVID-19 were mainly located in the provinces around Hubei and the provinces with a high level of economic development. The relative risk of two socioeconomic factors, the per capita consumption expenditure of households and the proportion of the migrating population from Hubei, were 1.887 [95% confidence interval (CI): 1.469~2.399] and 1.099 (95% CI: 1.053~1.148), respectively. The two factors explained up to 78.2% out of 99.7% of structured spatial variations.

Conclusion

Our results suggested that COVID-19 risk was positively associated with the level of economic development and population movements. Blocking population movement and reducing local exposures are effective in preventing the local transmission of COVID-19.

Understanding the dynamics of pandemic models to support predictions of COVID-19 transmission: Parameter sensitivity analysis of the SIR-type model

Despite efforts made to model and predict COVID-19 transmission, large predictive uncertainty remains. Failure to understand the dynamics of the nonlinear pandemic prediction model is an important reason. To this end, local and multiple global sensitivity analysis approaches are synthetically applied to analyze the sensitivities of parameters and initial state variables and community size (N) in susceptible-infected-recovered (SIR) and its variant susceptible-exposed-infected-recovered (SEIR) models and basic reproduction number (R0), aiming to provide prior information for parameter estimation and suggestions for COVID-19 prevention and control measures. We found that N influences both the maximum number of actively infected cases and the date on which the maximum number of actively infected cases is reached. The high effect of N on maximum actively infected cases and peak date suggests the necessity of isolating the infected cases in a small community. The protection rate and average quarantined time are most sensitive to the infected populations, with a summation of their first-order sensitivity indices greater than 0.585, and their interactions are also substantial, being 0.389 and 0.334, respectively. The high sensitivities and interaction between the protection rate and average quarantined time suggest that protection and isolation measures should always be implemented in conjunction and started as early as possible. These findings provide insights into the predictability of the pandemic models by estimating influential parameters and suggest how to effectively prevent and control epidemic transmission.

Revealing Potential Bioactive Compounds and Mechanisms of Lithospermum erythrorhizon against COVID-19 via Network Pharmacology Study

Lithospermum erythrorhizon (LE) is known in Korean traditional medicine for its potent therapeutic effect and antiviral activity. Currently, coronavirus (COVID-19) disease is a developing global pandemic that can cause pneumonia. A precise study of the infection and molecular pathway of COVID-19 is therefore obviously important. The compounds of LE were identified from the Natural Product Activity and Species Source (NPASS) database and screened by SwissADME. The targets interacted with the compounds and were selected using the Similarity Ensemble Approach (SEA) and Swiss Target Prediction (STP) methods. PubChem was used to classify targets linked to COVID-19. The protein-protein interaction (PPI) networks and signaling pathways-targets-bioactive compounds (STB) networks were constructed by RPackage. Lastly, we performed the molecular docking test (MDT) to verify the binding affinity between significant complexes through AutoDock 1.5.6. The Natural Product Activity and Species Source (NPASS) revealed a total of 82 compounds from LE, which interacted with 1262 targets (SEA and STP), and 249 overlapping targets were identified. The 19 final overlapping targets from the 249 targets and 356 COVID-19 targets were ultimately selected. A bubble chart exhibited that inhibition of the MAPK signaling pathway could be a key mechanism of LE on COVID-19. The three key targets (RELA, TNF, and VEGFA) directly related to the MAPK signaling pathway, and methyl 4-prenyloxycinnamate, tormentic acid, and eugenol were related to each target and had the most stable binding affinity. The three bioactive effects on the three key targets might be synergistic effects to alleviate symptoms of COVID-19 infection. Overall, this study shows that LE can play a role in alleviating COVID-19 symptoms, revealing that the three components (bioactive compounds, targets, and mechanism) are the most significant elements of LE against COVID-19. However, the promising mechanism of LE on COVID-19 is only predicted on the basis of mining data; the efficacy of the chemical compounds and the affinity between compounds and the targets in experiment was ignored, which should be further substantiated through clinical trials.

COVID-19 Mortality and Vaccine Coverage - Hong Kong Special Administrative Region, China, January 6, 2022-March 21, 2022

On January 6, 2022, a cluster of COVID-19 cases* caused by the Omicron variant of SARS-CoV-2, the virus that causes COVID-19, was detected in Hong Kong Special Administrative Region, China (Hong Kong), resulting in the territory's fifth wave of COVID-19 cases (1). This wave peaked on March 4, 2022, with 8,764 COVID-19 cases per million population (2), resulting in a total of 1,049,959 cases and 5,906 COVID-19-associated deaths reported to the Hong Kong Department of Health during January 6-March 21, 2022.† Throughout this period, the COVID-19 mortality rate in Hong Kong (37.7 per million population) was among the highest reported worldwide since the COVID-19 pandemic began (3). Publicly available data on age-specific vaccination coverage in Hong Kong with a 2-dose primary vaccination series (with either Sinovac-CoronaVac [Sinovac], an inactivated COVID-19 viral vaccine, recommended for persons aged ≥3 years or BNT162b2 [Pfizer-BioNTech], an mRNA vaccine, for persons aged ≥5 years), as of December 23, 2021,§,¶ and COVID-19 mortality during January 6-March 21, 2022, were analyzed. By December 23, 2021, 67% of vaccine-eligible persons in Hong Kong had received ≥1 dose of a COVID-19 vaccine, 64% had received ≥2 doses, and 5% had received a booster dose. Among persons aged ≥60 years, these proportions were 52%, 49%, and 7%, respectively. Among those aged ≥60 years, vaccination coverage declined with age: 48% of persons aged 70-79 years had received ≥1 dose, 45% received ≥2 doses, and 7% had received a booster, and among those aged ≥80 years, 20%, 18%, and 2% had received ≥1 dose, ≥2 doses, and a booster dose, respectively. Among 5,906 COVID-19 deaths reported, 5,655 (96%) occurred in persons aged ≥60 years**; among these decedents, 3,970 (70%) were unvaccinated, 18% (1,023) had received 1 vaccine dose, and 12% (662) had received ≥2 doses. The overall rates of COVID-19-associated mortality among persons aged ≥60 years who were unvaccinated, who had received 1 COVID-19 vaccine dose, and who had received ≥2 vaccine doses were 10,076, 1,099, and 473 per million population, respectively; the risk for COVID-19-associated death among unvaccinated persons was 21.3 times that among recipients of 2-3 doses in this age group. The high overall mortality rate during the ongoing 2022 Hong Kong Omicron COVID-19 outbreak is being driven by deaths among unvaccinated persons aged ≥60 years. Efforts to identify and address gaps in age-specific vaccination coverage can help prevent high mortality from COVID-19, especially among persons aged ≥60 years.

Projecting the Pandemic Trajectory through Modeling the Transmission Dynamics of COVID-19

The course of the COVID-19 pandemic has given rise to many disease trends at various population scales, ranging from local to global. Understanding these trends and the epidemiological phenomena that lead to the changing dynamics associated with disease progression is critical for public health officials and the global community to rein in further spread of this and other virulent diseases. Classic epidemiological modeling based on dynamical systems are powerful tools used for modeling and understanding diseases, but often necessitate modifications to the classic compartmental models to reflect empirical observations. In this paper, we present a collection of extensions to the classic SIRS model to support public health decisions associated with viral pandemics. Specifically, we present models that reflect different levels of disease severity among infected individuals, capture the effect of vaccination on different population groups, capture the effect of different vaccines with different levels of effectiveness, and model the impact of a vaccine with varying number of doses. Further, our mathematical models support the investigation of a pandemic's trend under the emergence of new variants and the associated reduction in vaccine effectiveness. Our models are supported through numerical simulations, which we use to illustrate phenomena that have been observed in the COVID-19 pandemic. Our findings also confirm observations that the mild infectious group accounts for the majority of infected individuals, and that prompt immunization results in weaker pandemic waves across all levels of infection as well as a lower number of disease-caused deaths. Finally, using our models, we demonstrate that, when dealing with a single variant and having access to a highly effective vaccine, a three-dose vaccine has a strong ability to reduce the infectious population. However, when a new variant with higher transmissibility and lower vaccine efficiency emerges, it becomes the dominant circulating variant, as was observed in the recent emergence of the Omicron variant.

COVID-19 Mortality and Vaccine Coverage - Hong Kong Special Administrative Region, China, January 6, 2022-March 21, 2022

What is already known about this topic?

COVID-19 vaccines are important tools to protect populations from severe disease and death.

What is added by this report?

Among persons aged ≥60 years in Hong Kong, 49%, had received ≥2 doses of a COVID-19 vaccine, and vaccination coverage declined with age. During January-March 2022, reported COVID-19-associated deaths rose rapidly in Hong Kong. Among these deaths, 96% occurred in persons aged ≥60 years; within this age group, the risk for death was 20 times lower among those who were fully vaccinated compared with those who were unvaccinated.

What are the implications for public health practice?

Efforts to identify and address gaps in age-specific vaccination coverage can help prevent high mortality from COVID-19, especially in older adults.

The Effects of Migration and Limited Medical Resources of the Transmission of SARS-CoV-2 Model with Two Patches

The sudden outbreak of SARS-CoV-2 has caused the shortage of medical resources around the world, especially in developing countries and underdeveloped regions. With the continuous increase in the duration of this disease, the control of migration of humans between regions or countries has to be relaxed. Based on this, we propose a two-patches mathematical model to simulate the transmission of SARS-CoV-2 among two-patches, asymptomatic infected humans and symptomatic infected humans, where a half-saturated detection rate function is also introduced to describe the effect of medical resources. By applying the methods of linearization and constructing a suitable Lyapunov function, the local and global stability of the disease-free equilibrium of this model without migration is obtained. Further, the existence of forward/backward bifurcation is analyzed, which is caused by the limited medical resources. This means that the elimination or prevalence of the disease no longer depends on the basic reproduction number but is closely related to the initial state of asymptomatic and symptomatic infected humans and the supply of medical resources. Finally, the global dynamics of the full model are discussed, and some numerical simulations are carried to explain the main results and the effects of migration and supply of medical resources on the transmission of disease.

Highly coordinated nationwide massive travel restrictions are central to effective mitigation and control of COVID-19 outbreaks in China

COVID-19, the disease caused by the novel coronavirus 2019, has caused grave woes across the globe since it was first reported in the epicentre of Wuhan, Hubei, China, in December 2019. The spread of COVID-19 in China has been successfully curtailed by massive travel restrictions that rendered more than 900 million people housebound for more than two months since the lockdown of Wuhan, and elsewhere, on 23 January 2020. Here, we assess the impact of China's massive lockdowns and travel restrictions reflected by the changes in mobility patterns across and within provinces, before and during the lockdown period. We calibrate movement flow between provinces with an epidemiological compartment model to quantify the effectiveness of lockdowns and reductions in disease transmission. Our analysis demonstrates that the onset and phase of local community transmission in other provinces depends on the cumulative population outflow received from the epicentre Hubei. Moreover, we show that synchronous lockdowns and consequent reduced mobility lag a certain time to elicit an actual impact on suppressing the spread. Such highly coordinated nationwide lockdowns, applied via a top-down approach along with high levels of compliance from the bottom up, are central to mitigating and controlling early-stage outbreaks and averting a massive health crisis.

COVID-19 epidemic under the K-quarantine model: Network approach

The COVID-19 pandemic is still ongoing worldwide, and the damage it has caused is unprecedented. For prevention, South Korea has adopted a local quarantine strategy rather than a global lockdown. This approach not only minimizes economic damage but also efficiently prevents the spread of the disease. In this work, the spread of COVID-19 under local quarantine measures is modeled using the Susceptible-Exposed-Infected-Recovered model on complex networks. In this network approach, the links connected to infected and so isolated people are disconnected and then reinstated when they are released. These link dynamics leads to time-dependent reproduction number. Numerical simulations are performed on networks with reaction rates estimated from empirical data. The temporal pattern of the accumulated number of confirmed cases is then reproduced. The results show that a large number of asymptomatic infected patients are detected as they are quarantined together with infected patients. Additionally, possible consequences of the breakdowns of local quarantine measures and social distancing are considered.

The allocation of COVID-19 vaccines and antivirals against emerging SARS-CoV-2 variants of concern in East Asia and Pacific region: A modelling study

BACKGROUND

In view of emerging variants of concern (VOCs), we aimed to evaluate the impact of various allocation strategies of COVID-19 vaccines and antiviral such that the pandemic exit strategy could be tailored to risks and preferences of jurisdictions in the East Asia and Pacific region (EAP) to improve its efficiency and effectiveness.

METHODS

Vaccine efficacies were estimated from the titre distributions of 50% plaque reduction neutralization test (PRNT50), assuming that PRNT50 titres of primary vaccination decreased by 2-10 folds due to antibody waning and emergence of VOCs, and an additional dose of vaccine would increase PRNT50 titres by 3- or 9-fold. We then used an existing SARS-CoV-2 transmission model to assess the outcomes of vaccine allocation strategies with and without the use of antivirals for symptomatic patients in Japan, Hong Kong, and Vietnam.

FINDINGS

Increasing primary vaccination coverage was the most important contributing factor in reducing the total and peak number of COVID-19 hospitalisations, especially when population vaccine coverage or vaccine uptake among older adults was low. Providing antivirals to 50% of symptomatic infections only further reduced total and peak hospitalisations by 10-13%. The effectiveness of an additional dose of vaccine was highly dependent on the immune escape potential of VOCs and antibody waning, but less dependent on the boosting efficacy of the additional dose.

INTERPRETATION

Increasing primary vaccination coverage should be prioritised in the design of allocation strategies of COVID-19 vaccines and antivirals against emerging VOCs, such as Omicron, in the EAP region. Heterologous vaccination with any available vaccine as the additional dose could be considered when planning pandemic exit strategies tailored to the circumstances of EAP jurisdictions.

FUNDING

Health and Medical Research Fund, General Research Fund, AIR@InnoHK.

Assessing Socioeconomic Vulnerabilities Related to COVID-19 Risk in India: A State-Level Analysis

OBJECTIVE

There is a paucity of scientific analysis that has examined spatial heterogeneities in the socioeconomic vulnerabilities related to coronavirus disease 2019 (COVID-19) risk and potential mitigation strategies at the sub-national level in India. The present study examined the demographic, socioeconomic, and health system-related vulnerabilities shaping COVID-19 risk across 36 states and union territories in India.

METHODS

Using secondary data from the Ministry of Health and Family Welfare (MoHFW), Government of India; Census of India, 2011; National Family Health Survey, 2015-16; and various rounds of the National Sample Survey, we examined socioeconomic vulnerabilities associated with COVID-19 risk at the sub-national level in India from March 16, 2020, to May 3, 2020. Descriptive statistics, principal component analysis, and the negative binomial regression model were used to examine the predictors of COVID-19 risk in India.

RESULTS

There persist substantial heterogeneities in the COVID-19 risk across states and union territories in India. The underlying demographic, socioeconomic, and health infrastructure characteristics drive the vulnerabilities related to COVID-19 in India.

CONCLUSIONS

This study emphasizes that concerted socially inclusive policy action and sustained livelihood/economic support for the most vulnerable population groups is critical to mitigate the impact of the COVID-19 pandemic in India.

Senior-COVID-Rea Cohort Study: A Geriatric Prediction Model of 30-day Mortality in Patients Aged over 60 Years in ICU for Severe COVID-19

The SARS-COV2 pandemic induces tensions on health systems and ethical dilemmas. Practitioners need help tools to define patients not candidate for ICU admission. A multicentre observational study was performed to evaluate the impact of age and geriatric parameters on 30-day mortality in patients aged ≥60 years of age. Patients or next of kin were asked to answer a phone questionnaire assessing geriatric covariates 1 month before ICU admission. Among 290 screened patients, 231 were included between March 7 and May 7, 2020. In univariate, factors associated with lower 30-day survival were: age (per 10 years increase; OR 3.43, [95%CI: 2.13-5.53]), ≥3 CIRS-G grade ≥2 comorbidities (OR 2.49 [95%CI: 1.36-4.56]), impaired ADL, (OR 4.86 [95%CI: 2.44-9.72]), impaired IADL8 (OR 6.33 [95%CI: 3.31-12.10], p<0.001), frailty according to the Fried score (OR 4.33 [95%CI: 2.03-9.24]) or the CFS ≥5 (OR 3.79 [95%CI: 1.76-8.15]), 6-month fall history (OR 3.46 [95%CI: 1.58-7.63]). The final multivariate model included age (per 10 years increase; 2.94 [95%CI:1.78-5.04], p<0.001) and impaired IADL8 (OR 5.69 [95%CI: 2.90-11.47], p<0.001)). Considered as continuous variables, the model led to an AUC of 0.78 [95% CI: 0.72, 0.85]. Age and IADL8 provide independent prognostic factors for 30-day mortality in the considered population. Considering a risk of death exceeding 80% (82.6% [95%CI: 61.2% - 95.0%]), patients aged over 80 years with at least 1 IADL impairment appear as poor candidates for ICU admission.

Inferring age-specific differences in susceptibility to and infectiousness upon SARS-CoV-2 infection based on Belgian social contact data

Several important aspects related to SARS-CoV-2 transmission are not well known due to a lack of appropriate data. However, mathematical and computational tools can be used to extract part of this information from the available data, like some hidden age-related characteristics. In this paper, we present a method to investigate age-specific differences in transmission parameters related to susceptibility to and infectiousness upon contracting SARS-CoV-2 infection. More specifically, we use panel-based social contact data from diary-based surveys conducted in Belgium combined with the next generation principle to infer the relative incidence and we compare this to real-life incidence data. Comparing these two allows for the estimation of age-specific transmission parameters. Our analysis implies the susceptibility in children to be around half of the susceptibility in adults, and even lower for very young children (preschooler). However, the probability of adults and the elderly to contract the infection is decreasing throughout the vaccination campaign, thereby modifying the picture over time.

Effectiveness of Localized Lockdowns in the COVID-19 Pandemic

Nonpharmaceutical interventions, such as social distancing and lockdowns, have been essential to control of the coronavirus disease 2019 (COVID-19) pandemic. In particular, localized lockdowns in small geographic areas have become an important policy intervention for preventing viral spread in cases of resurgence. These localized lockdowns can result in lower social and economic costs compared with larger-scale suppression strategies. Using an integrated data set from Chile (March 3-June 15, 2020) and a novel synthetic control approach, we estimated the effect of localized lockdowns, disentangling its direct and indirect causal effects on transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our results showed that the effects of localized lockdowns are strongly modulated by their duration and are influenced by indirect effects from neighboring geographic areas. Our estimates suggest that extending localized lockdowns can slow down SARS-CoV-2 transmission; however, localized lockdowns on their own are insufficient to control pandemic growth in the presence of indirect effects from contiguous neighboring areas that do not have lockdowns. These results provide critical empirical evidence about the effectiveness of localized lockdowns in interconnected geographic areas.

Evaluation of Spike Protein Epitopes by Assessing the Dynamics of Humoral Immune Responses in Moderate COVID-19

The coronavirus disease 2019 (COVID-19) pandemic is caused by a novel coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spike protein (S) of SARS-CoV-2 is a major target for diagnosis and vaccine development because of its essential role in viral infection and host immunity. Currently, time-dependent responses of humoral immune system against various S protein epitopes are poorly understood. In this study, enzyme-linked immunosorbent assay (ELISA), peptide microarray, and antibody binding epitope mapping (AbMap) techniques were used to systematically analyze the dynamic changes of humoral immune responses against the S protein in a small cohort of moderate COVID-19 patients who were hospitalized for approximately two months after symptom onset. Recombinant truncated S proteins, target S peptides, and random peptides were used as antigens in the analyses. The assays demonstrated the dynamic IgM- and IgG recognition and reactivity against various S protein epitopes with patient-dependent patterns. Comprehensive analysis of epitope distribution along the spike gene sequence and spatial structure of the homotrimer S protein demonstrated that most IgM- and IgG-reactive peptides were clustered into similar genomic regions and were located at accessible domains. Seven S peptides were generally recognized by IgG antibodies derived from serum samples of all COVID-19 patients. The dynamic immune recognition signals from these seven S peptides were comparable to those of the entire S protein or truncated S1 protein. This suggested that the humoral immune system recognized few conserved S protein epitopes in most COVID-19 patients during the entire duration of humoral immune response after symptom onset. Furthermore, in this cohort, individual patients demonstrated stable immune recognition to certain S protein epitopes throughout their hospitalization period. Therefore, the dynamic characteristics of humoral immune responses to S protein have provided valuable information for accurate diagnosis and immunotherapy of COVID-19 patients.

What drives innovation? Lessons from COVID-19 R&D

This paper studies the global R&D effort to fight the deadliest diseases. We find: (1) the elasticity of R&D effort with respect to market size is about 1/2 in the cross-section of diseases; (2) given this elasticity, the R&D response to COVID-19 has been 4 to 26 times greater than that implied by its market size; (3) the aggregate short-term elasticity of science and innovation can be very large, as demonstrated by the aggregate flow of clinical trials increasing by 38% in 2020, with limited crowding out of trials for non-COVID diseases; and (4) public institutions and government-led incentives were a key driver of the COVID-19 R&D effort-with public research institutions accounting for 70 percent of all COVID-19 clinical trials globally. Overall, our work suggests that leveraging early-stage incentives, non-monetary incentives, and public institutions may be important for scaling up global innovation.

Risk Factors Associated with In-Hospital Mortality in Iranian Patients with COVID-19: Application of Machine Learning

Introduction: Predicting the mortality risk of COVID-19 patients based on patient’s physiological conditions and demographic characteristics can help optimize resource consumption along with the provision of effective medical services for patients. In the current study, we aimed to develop several machine learning models to forecast the mortality risk in COVID-19 patients, evaluate their performance, and select the model with the highest predictive power.

Material and methods: We conducted a retrospective analysis of the records belonging to COVID-19 patients admitted to one of the main hospitals of Qazvin located in the northwest of Iran over 12 months period. We selected 29 variables for developing machine learning models incorporating demographic factors, physical symptoms, comorbidities, and laboratory test results. The outcome variable was mortality as a binary variable. Logistic regression analysis was conducted to identify risk factors of in-hospital death.

Results: In prediction of mortality, Ensemble demonstrated the maximum values of accuracy (0.8071, 95%CI: 0.7787, 0.8356), F1-score (0.8121 95%CI: 0.7900, 0.8341), and AUROC (0.8079, 95%CI: 0.7800, 0.8358). Including fourteen top-scored features identified by maximum relevance minimum redundancy algorithm into the subset of predictors of ensemble classifier such as BUN level, shortness of breath, seizure, disease history, fever, gender, body pain, WBC, diarrhea, sore throat, blood oxygen level, muscular pain, lack of taste and history of drug (medication) use are sufficient for this classifier to reach to its best predictive power for prediction of mortality risk of COVID-19 patients.

Conclusions: Study findings revealed that old age, lower oxygen saturation level, underlying medical conditions, shortness of breath, seizure, fever, sore throat, and body pain, besides serum BUN, WBC, and CRP levels, were significantly associated with increased mortality risk of COVID-19 patients. Machine learning algorithms can help healthcare systems by predicting and reduction of the mortality risk of COVID-19 patients.

Seroprevalence of SARS-Cov-2 IgG antibodies in patients at a single center in Saudi Arabia

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has had a massive impact on public health as well as the economy. Understanding the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among undiagnosed individuals is important for developing an informed pandemic response.

OBJECTIVE

Investigate the prevalence of undiagnosed COVID-19 disease.

DESIGN

Cross-sectional.

SETTING

Tertiary care center in Madinah, Saudi Arabia.

SUBJECTS AND METHODS

All participants were on follow-up visits to various clinics and had not been previously diagnosed with COVID-19. Enzyme-linked immunosorbent assay was used to specifically assess the anti-spike IgG antibody seropositivity in serum samples. We associated the seropositivity rates of the participants with age, body mass index (BMI), nationality, blood groups, and sex with uni- and multivariate analyses.

MAIN OUTCOME MEASURES

Seropositivity for IgG anti-spike antibodies against SARS-CoV-2.

SAMPLE SIZE AND CHARACTERISTICS

527 subjects, with a median (interquartile percentiles) age of the 527 subjects was 34 (24-41).

RESULTS

Of the 527 samples, about one-fourth (n=124, 23.5%) were positive for anti-spike IgG antibody against SARS CoV-2. Age was associated with anti-spike IgG antibody positivity (P<.002). Participants >30 years were more likely to be seropositive (28-29%) than younger participants (15.4%). Additionally, seropositivity was associated with female gender (P<.001) and a higher BMI (P<.006). In the multivariate logistic regression, age >30, female gender and BMI >40 were associated with seropositivity.

CONCLUSION

The percentage of seropositive individuals reflects the high level of undiagnosed COVID-19 patients among the population. Our results will help in a better evaluation of the public health measures applied during the COVID-19 pandemic and any future public health crises.

LIMITATIONS

Sample size was small, single-center study and no rural areas were included.

CONFLICT OF INTEREST

None.

A COVID-19 mortality prediction model for Korean patients using nationwide Korean disease control and prevention agency database

The experience of the early nationwide COVID-19 pandemic in South Korea led to an early shortage of medical resources. For efficient resource allocation, accurate prediction of the prognosis or mortality of confirmed patients is essential. Therefore, the aim of this study was to develop an accurate model for predicting COVID-19 mortality using epidemiolocal and clinical variables and for identifying a high-risk group of confirmed patients. Clinical and epidemiolocal variables of 4049 patients with confirmed COVID-19 between January 20, 2020 and April 30, 2020 collected by the Korean Disease Control and Prevention Agency were used. Among the 4049 total confirmed patients, 223 patients died, while 3826 patients were released from isolation. Patients who had the following risk factors showed significantly higher risk scores: age over 60 years, male sex, difficulty breathing, diabetes, cancer, dementia, change of consciousness, and hospitalization in the intensive care unit. High accuracy was shown for both the development set (n = 2467) and the validation set (n = 1582), with AUCs of 0.96 and 0.97, respectively. The prediction model developed in this study based on clinical features and epidemiological factors could be used for screening high-risk groups of patients and for evidence-based allocation of medical resources.

Evolution of resistance to COVID-19 vaccination with dynamic social distancing

The greatest hope for a return to normalcy following the COVID-19 pandemic is worldwide vaccination. Yet, a relaxation of social distancing that allows increased transmissibility, coupled with selection pressure due to vaccination, will probably lead to the emergence of vaccine resistance. We analyse the evolutionary dynamics of COVID-19 in the presence of dynamic contact reduction and in response to vaccination. We use infection and vaccination data from six different countries. We show that under slow vaccination, resistance is very likely to appear even if social distancing is maintained. Under fast vaccination, the emergence of mutants can be prevented if social distancing is maintained during vaccination. We analyse multiple human factors that affect the evolutionary potential of the virus, including the extent of dynamic social distancing, vaccination campaigns, vaccine design, boosters and vaccine hesitancy. We provide guidelines for policies that aim to minimize the probability of emergence of vaccine-resistant variants.

Predicting and monitoring COVID-19 epidemic trends in India using sequence-to-sequence model and an adaptive SEIR model

In the year 2019, during the month of December, the first case of SARS-CoV-2 was reported in China. As per reports, the virus started spreading from a wet market in the Wuhan City. The person infected with the virus is diagnosed with cough and fever, and in some rare occasions, the person suffers from breathing inabilities. The highly contagious nature of this corona virus disease (COVID-19) caused the rapid outbreak of the disease around the world. India contracted the disease from China and reported its first case on January 30, 2020, in Kerala. Despite several counter measures taken by Government, India like other countries could not restrict the outbreak of the epidemic. However, it is believed that the strict policies adopted by the Indian Government have slowed the rate of the epidemic to a certain extent. This article proposes an adaptive SEIR disease model and a sequence-to-sequence (Seq2Seq) learning model to predict the future trend of COVID-19 outbreak in India and analyze the performance of these models. Optimization of hyper parameters using RMSProp is done to obtain an efficient model with lower convergence time. This article focuses on evaluating the performance of deep learning networks and epidemiological models in predicting a pandemic outbreak.

Modeling Vaccine Efficacy for COVID-19 Outbreak in New York City

Simple Summary

This article aims to study the COVID-19 data for New York City. We use both the daily number of second dose vaccination and the daily number of reported cases for New York City. This article provides a method to combine an epidemic model and such data. We explore the influence of vaccine efficacy on our results.

Abstract

In this article we study the efficacy of vaccination in epidemiological reconstructions of COVID-19 epidemics from reported cases data. Given an epidemiological model, we developed in previous studies a method that allowed the computation of an instantaneous transmission rate that produced an exact fit of reported cases data of the COVID-19 outbreak. In this article, we improve the method by incorporating vaccination data. More precisely, we develop a model in which vaccination is variable in its effectiveness. We develop a new technique to compute the transmission rate in this model, which produces an exact fit to reported cases data, while quantifying the efficacy of the vaccine and the daily number of vaccinated. We apply our method to the reported cases data and vaccination data of New York City.

Stochastic mathematical models for the spread of COVID-19: a novel epidemiological approach

In this paper, three stochastic mathematical models are developed for the spread of the coronavirus disease (COVID-19). These models take into account the known special characteristics of this disease such as the existence of infectious undetected cases and the different social and infectiousness conditions of infected people. In particular, they include a novel approach that considers the social structure, the fraction of detected cases over the real total infected cases, the influx of undetected infected people from outside the borders, as well as contact-tracing and quarantine period for travellers. Two of these models are discrete time-discrete state space models (one is simplified and the other is complete) while the third one is a continuous time-continuous state space stochastic integro-differential model obtained by a formal passing to the limit from the proposed simplified discrete model. From a numerical point of view, the particular case of Lebanon has been studied and its reported data have been used to estimate the complete discrete model parameters, which can be of interest in estimating the spread of COVID-19 in other countries. The obtained simulation results have shown a good agreement with the reported data. Moreover, a parameters' analysis is presented in order to better understand the role of some of the parameters. This may help policy makers in deciding on different social distancing measures.

Potential role of Drug Repositioning Strategy (DRS) for management of tauopathy

Tauopathy is a term that has been used to represent a pathological condition in which hyperphosphorylated tau protein aggregates in neurons and glia which results in neurodegeneration, synapse loss and dysfunction and cognitive impairments. Recently, drug repositioning strategy (DRS) becomes a promising field and an alternative approach to advancing new treatments from actually developed and FDA approved drugs for an indication other than the indication it was originally intended for. This paradigm provides an advantage because the safety of the candidate compound has already been established, which abolishes the need for further preclinical safety testing and thus substantially reduces the time and cost involved in progressing of clinical trials. In the present review, we focused on correlation between tauopathy and common diseases as type 2 diabetes mellitus and the global virus COVID-19 and how tau pathology can aggravate development of these diseases in addition to how these diseases can be a risk factor for development of tauopathy. Moreover, correlation between COVID-19 and type 2 diabetes mellitus was also discussed. Therefore, repositioning of a drug in the daily clinical practice of patients to manage or prevent two or more diseases at the same time with lower side effects and drug-drug interactions is a promising idea. This review concluded the results of pre-clinical and clinical studies applied on antidiabetics, COVID-19 medications, antihypertensives, antidepressants and cholesterol lowering drugs for possible drug repositioning for management of tauopathy.

Genomic epidemiology of SARS-CoV-2 under an elimination strategy in Hong Kong

Hong Kong employed a strategy of intermittent public health and social measures alongside increasingly stringent travel regulations to eliminate domestic SARS-CoV-2 transmission. By analyzing 1899 genome sequences (>18% of confirmed cases) from 23-January-2020 to 26-January-2021, we reveal the effects of fluctuating control measures on the evolution and epidemiology of SARS-CoV-2 lineages in Hong Kong. Despite numerous importations, only three introductions were responsible for 90% of locally-acquired cases. Community outbreaks were caused by novel introductions rather than a resurgence of circulating strains. Thus, local outbreak prevention requires strong border control and community surveillance, especially during periods of less stringent social restriction. Non-adherence to prolonged preventative measures may explain sustained local transmission observed during wave four in late 2020 and early 2021. We also found that, due to a tight transmission bottleneck, transmission of low-frequency single nucleotide variants between hosts is rare.

SARS-CoV-2 Dysregulates Neutrophil Degranulation and Reduces Lymphocyte Counts

SARS-CoV-2, the virus that causes COVID-19, has given rise to one of the largest pandemics, affecting millions worldwide. High neutrophil-to-lymphocyte ratios have been identified as an important correlate to poor recovery rates in severe COVID-19 patients. However, the mechanisms underlying this clinical outcome and the reasons for its correlation to poor prognosis are unclear. Furthermore, the mechanisms involved in healthy neutrophils acquiring a SARS-CoV-2-mediated detrimental role are yet to be fully understood. In this study, we isolated circulating neutrophils from healthy donors for treatment with supernates from infected epithelial cells and direct infection with SARS-CoV-2 in vitro. Infected epithelial cells induced a dysregulated degranulation of primary granules with a decrease in myeloperoxidase (MPO), but slight increase in neutrophil elastase release. Infection of neutrophils resulted in an impairment of both MPO and elastase release, even though CD16 receptor shedding was upregulated. Importantly, SARS-CoV-2-infected neutrophils had a direct effect on peripheral blood lymphocyte counts, with decreasing numbers of CD19+ B cells, CD8+ T cells, and CD4+ T cells. Together, this study highlights the independent role of neutrophils in contributing to the aberrant immune responses observed during SARS-CoV-2 infection that may be further dysregulated in the presence of other immune cells.

The COVID-19 pandemic: a new epoch and fresh challenges for cancer patients and caregivers-a descriptive cross-sectional study

PURPOSE

Cancer patients and their caregivers are overwhelmed with features of uncertainty, fear, shock, worry, anxiety, sadness, and grief. To add on to their misery, the COVID-19 pandemic has severely afflicted the cancer care delivery. The study was conducted to observe the challenges faced by cancer patients and their caregivers and to formulate strategies for oncological setups to overcome those challenges.

METHODS

After obtaining institutional ethical clearance, a descriptive cross-sectional study was conducted to observe the challenges faced by patients and their caregivers at the level of various domains (physical, logistic, psychological, socioeconomic, and spiritual) who visited the outpatient and inpatient department of cancer pain and palliative care unit. The results were expressed in absolute numbers.

RESULTS

Major challenges encountered were suffering from physical symptoms like pain, nausea, vomiting, dyspnea (90%), postponement of cancer treatment (80%), fear of contracting COVID infection due to hospital visit (93.5%), lack of accommodation (70%), and lack of spiritual clarity and hope (50%).

CONCLUSIONS

Major challenges faced by patients were in physical and psychological domains, and those by caregivers were in socioeconomic domains and handling physical symptoms of their patients. It is imperative to recognize and be cognizant of the challenges faced by cancer patients and their caregivers. Health care setups should formulate strategies to alleviate these challenges and provide holistic care to cancer patients. These strategies will hold in good stead for future pandemics also.

A sequential test to compare the real-time fatality rates of a disease among multiple groups with an application to COVID-19 data

Infectious diseases, such as the ongoing COVID-19 pandemic, pose a significant threat to public health globally. Fatality rate serves as a key indicator for the effectiveness of potential treatments or interventions. With limited time and understanding of novel emerging epidemics, comparisons of the fatality rates in real-time among different groups, say, divided by treatment, age, or area, have an important role to play in informing public health strategies. We propose a statistical test for the null hypothesis of equal real-time fatality rates across multiple groups during an ongoing epidemic. An elegant property of the proposed test statistic is that it converges to a Brownian motion under the null hypothesis, which allows one to develop a sequential testing approach for rejecting the null hypothesis at the earliest possible time when statistical evidence accumulates. This property is particularly important as scientists and clinicians are competing with time to identify possible treatments or effective interventions to combat the emerging epidemic. The method is widely applicable as it only requires the cumulative number of confirmed cases, deaths, and recoveries. A large-scale simulation study shows that the finite-sample performance of the proposed test is highly satisfactory. The proposed test is applied to compare the difference in disease severity among Wuhan, Hubei province (exclude Wuhan) and mainland China (exclude Hubei) from February to March 2020. The result suggests that the disease severity is potentially associated with the health care resource availability during the early phase of the COVID-19 pandemic in mainland China.

Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19

Estimating the changes of epidemiological parameters, such as instantaneous reproduction number, Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to estimate the instantaneous reproduction number Rt during emerging epidemics, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in describing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for making accurate and timely estimation for transmission dynamics based on reported data.

Assessing the Psychological Impact of the Pandemic COVID -19 in Uninfected High-Risk Population

Purpose

To assess the impact of the COVID-19 pandemic on the psyche of uninfected people with chronic diseases in the Elduim community, White Nile State, Sudan, during the COVID -19 pandemic.

Methods

We used a generalized anxiety disorder scale (GAD -7) and a patient health questionnaire (PHQ-9) for psychological assessment. The study included two hundred thirty-four participants; all participants with a chronic disease but not infected with COVID -19 were between 24 and 65 years of age. Residents of the study area were randomly selected. Descriptive statistics and a t-test were used for associations with a p-value of 0.05 or less.

Results

This study found that anxiety rated by GAD 7 was either mild (18, 7.7%), moderate (98, 41.9%), or severe (41, 17.5%) among participants. PHQ 9-rated depression showed 22 (9.4%) mild depression, most of them in participants aged 36–44 years. Participants with kidney disease showed major depression 11 (42.31%). Factors that significantly affected anxiety scores were age 24–35 years (P =0.002), university graduates (P < 0.000), married (P < 0.000), those with diabetes and hypertension (P =0.041), and urban residents (P < 0.023). Those who had secondary education were married and smoked were significantly more likely to have major depression than those with another educational status (p < 0.05).

Conclusion

COVID 19 pandemic had a significant impact on the psyche of uninfected people with chronic diseases in Sudan, and significant associated factors were identified. Unique interventions are strongly recommended to reduce the psychological impact of the COVID 19 pandemic.

SARS-CoV-2 epidemic after social and economic reopening in three U.S. states reveals shifts in age structure and clinical characteristics

State-level reopenings in late spring 2020 facilitated the resurgence of severe acute respiratory syndrome coronavirus 2 transmission. Here, we analyze age-structured case, hospitalization, and death time series from three states-Rhode Island, Massachusetts, and Pennsylvania-that had successful reopenings in May 2020 without summer waves of infection. Using 11 daily data streams, we show that from spring to summer, the epidemic shifted from an older to a younger age profile and that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals. Clinical case management improved from spring to summer, resulting in fewer critical care admissions and lower infection fatality rate. Attack rate estimates through 31 August 2020 are 6.2% [95% credible interval (CI), 5.7 to 6.8%] of the total population infected for Rhode Island, 6.7% (95% CI, 5.4 to 7.6%) in Massachusetts, and 2.7% (95% CI, 2.5 to 3.1%) in Pennsylvania.