Spatially resolved simulations of the spread of COVID-19 in three European countries

We explore the spatial and temporal spread of the novel SARS-CoV-2 virus under containment measures in three European countries based on fits to data of the early outbreak. Using data from Spain and Italy, we estimate an age dependent infection fatality ratio for SARS-CoV-2, as well as risks of hospitalization and intensive care admission. We use them in a model that simulates the dynamics of the virus using an age structured, spatially detailed agent based approach, that explicitly incorporates governmental interventions and changes in mobility and contact patterns occurred during the COVID-19 outbreak in each country. Our simulations reproduce several of the features of its spatio-temporal spread in the three countries studied. They show that containment measures combined with high density are responsible for the containment of cases within densely populated areas, and that spread to less densely populated areas occurred during the late stages of the first wave. The capability to reproduce observed features of the spatio-temporal dynamics of SARS-CoV-2 makes this model a potential candidate for forecasting the dynamics of SARS-CoV-2 in other settings, and we recommend its application in low and lower-middle income countries which remain understudied.     

Bayesian time-varying autoregressive models of COVID-19 epidemics

The COVID-19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time-dependent Poisson autoregressive models that include time-varying coefficients to estimate the effect of policy covariates on disease counts. The model is applied to the observed series of new positive cases in Italy and in the United States. The results suggest that our proposed models are capable of capturing nonlinear growth of disease counts. We also find that policy measures and, in particular, closure policies and the distribution of vaccines, lead to a significant reduction in disease counts in both countries.     

The effect of income support programs on job search,workplace mobility and COVID-19: International evidence

This study examines the effect of Income Support Programs (ISPs) on job search effort, work- place mobility, COVID-19 cases, and mortality growth rates. To identify ISPs’ causal effect, I use the variation in their introductions’ timing across countries and implement a difference-in-difference and multi-event analysis method. I find that ISPs led to a 4.4–8.29 percentage points reduction in workplace mobility and a 6.6–11.6 percentage points reduction in job search effort levels. They also caused a 21.8–47.7 and 17.1–29.7 percentage points reduction in the COVID-19 case growth rate and COVID-19 mortality growth rates, respectively. Using the event analysis estimates, I simulated the counterfactual job search effort, workplace mobility, and the number of COVID-19 cases and mortality without income support programs. The average global job search effort and workplace mobility without ISPs would have been 11.12 and 9.26 percent higher than the observed mean job search effort and workplace mobility. However, these would have come at the cost of 3.69 million and 166, 690 additional COVID-19 cases and mortality than the cases and deaths registered by May 15th.     

Patterns of Gustatory Recovery in Patients Affected by the COVID-19 Outbreak

Coronavirus disease 2019 (COVID-19) is a viral infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). From March 2020, several studies indicate that many subjects affected by mild-to-moderate COVID-19 presented olfactory/gustatory dysfunction (OD/GD) that appeared strongly correlated between them but not with the other symptoms suggestive of upper airway infectionIn order to evaluate patterns of gustatoy recovery, data from patients with confirmed COVID-19 were collected prospectively from 4 University Hospitals. At this relatively early point in the pandemic, the authors considered that subjective patterns of recovery of olfactory disfunction in COVID-19 patients are valuable for our patients, for hypothesis generation and treatment development.     

Immune response pattern across the asymptomatic,symptomatic and convalescent periods of COVID-19

We present an integrated analysis of urine and serum proteomics and clinical measurements in asymptomatic, mild/moderate, severe and convalescent cases of COVID-19. We identify the pattern of immune response during COVID-19 infection. The immune response is activated in asymptomatic infection, but is dysregulated in mild and severe COVID-19 patients. Our data suggest that the turning point depends on the function of myeloid cells and neutrophils. In addition, immune defects persist into the recovery stage, until 12 months after diagnosis. Moreover, disorders of cholesterol metabolism span the entire progression of the disease, starting from asymptomatic infection and lasting to recovery. Our data suggest that prolonged dysregulation of the immune response and cholesterol metabolism might be the pivotal causative agent of other potential sequelae. Our study provides a comprehensive understanding of COVID-19 immunopathogenesis, which is instructive for the development of early intervention strategies to ameliorate complex disease sequelae.     

Mobility restrictions and alcohol use during lockdown: “A still and dry pandemic for the many”?

Unexpected mobility disruptions during lockdown during the first wave of COVID-19 became ’tipping points’ with the potential to alter pre-pandemic routines sensitive to socialisation. This paper investigates the impact of lockdown exposure on alcohol consumption. We document two findings using information from the Google Mobility Report and longitudinal data from the Understanding Society survey (UKHLS) in the United Kingdom. First, we find a sharp reduction in both actual mobility and alcohol use (consistent with a ”still and dry pandemic for the many” hypothesis). However, we document an increase in alcohol use among heavy drinkers, implying a split behavioural response to COVID-19 mobility restrictions based on alcohol use prior to the pandemic. Second, using the predictions of the prevalence-response elasticity theory, we find that the pandemic’s reduction in social contacts is responsible for a 2.8 percentage point reduction in drinking among men.     

An analytical study of the factors that influence COVID-19 spread

The outbreak of the coronavirus disease 2019 (COVID-19) continues to constitute an international public health emergency. Seasonality is a long-recognized attribute of many viral infections of humans. Nevertheless, the relationship between environmental factors and the spread of infection, particularly for person-to-person communicable diseases, remains poorly understood. This study explores the relationship between environmental factors and the incidence of COVID-19 in 188 countries with reported COVID-19 cases as of April 13, 2020. Here we show that COVID-19 growth rates peaked in temperate zones in the Northern Hemisphere during the outbreak period, while they were lower in tropical zones. The relationships between COVID-19 and environmental factors were resistant to the potentially confounding effects of air pollution, sea level, and population. To prove the effect of those factors, study, and analysis of the prevalence of COVID-19 in Italy, Spain, and China was undertaken. A fuzzy logic system was designed to predict the effects of that variables on the rate of viral spread of COVID-19.     

NLRP3 inflammasome activation in COVID-19: an interlink between risk factors and disease severity

NLRP3 inflammasome is a critical immune component that plays a crucial role in mounting innate immune responses. The deleterious effects of inflammasome activation have been correlated with the COVID-19 disease severity. In the presence of several underlying disorders, the immune components of our bodies are dysregulated, creating conditions that could adversely affect us other than providing a required level of protection. In this review, we focused on the occurrence of NLRP3 inflammasome activation in response to SARS-COV-2 infection, dysregulation of NLRP3 activation events in the presence of several comorbidities, the contribution of activated NLRP3 inflammasome to the severity of COVID-19, and available therapeutics for the treatment of such NLRP3 inflammasome related diseases based on current knowledge. The primed state of immunity in individuals with comorbidities (risk factors) could accelerate many deaths and severe COVID-19 cases via activation of NLRP3 inflammasome and the release of downstream inflammatory molecules. Therefore, a detailed understanding of the host–pathogen interaction is needed to clarify the pathophysiology and select a potential therapeutic approach.     

Low serum calcium: a new,important indicator of COVID-19 patients from mild/moderate to severe/critical

Background: Coronavirus disease 2019 (COVID-19) virus is still spreading, finding out the initial hits of viral infection is important to minimize the mild/moderate population, prevent disease aggravation and organs dysfunction.Objective: We investigated COVID-19 patients with different serum calcium levels.Design: We checked the serum calcium level of the patients based on days after symptom onset as well as the severity of COVID-19. We also checked multiorgan injuries and immune cytokines level in their blood.Results: Both mild/moderate and severe critical cases we observed showed low calcium level in the early stage of viral infection, while the severe/critical cases showed significant lower calcium level than mild/moderate cases in the early stage. We also found that low calcium level related to severe/critical multiorgan injuries especially in the mild/moderate population. Proinflammatory cytokine IL-6 also correlated to calcium change in both mild/moderate and severe/critical cases.Conclusions: Our finding indicates that calcium balance is a primal hit of COVID-19 and a biomarker of clinical severity at the beginning of symptom onset. Calcium is closely associated with virus-associated multiple organ injuries and the increase in inflammatory cytokines. Our results provide a new, important indicator of COVID-19 patients from mild/moderate to severe/critical: serum calcium.     

Mass gathering events and the spread of infectious diseases: Evidence from the early growth phase of COVID-19

This paper studies the impact on reported coronavirus 2019 (COVID-19) cases and deaths in Spain resulting from large mass gatherings that occurred from March 6 to March 8, 2020. To study these outcomes, the geographic differences in the planned pre-pandemic major events that took place on these dates were exploited, which is a quasi-random source of variation for identification purposes. We collected daily and detailed information about the number of attendees at football (soccer) and basketball matches in addition to individuals participating in the Women’s Day marches across Spain, which we merged with daily data on reported COVID-19 cases and deaths at the provincial level. Our results reveal evidence of non-negligible COVID-19 cases related to the differences in the percentage of attendees at these major events from March 6 to March 8. In a typical province, approximately 31% of the average daily reported COVID-19 cases per 100,000 inhabitants between mid-March and early April 2020 can be explained by the participation rate in those major events. A back-of-the-envelope calculation suggests that this implies almost five million euros (169,000 euros/day) of additional economic cost in the health system of a typical province with one million inhabitants in the period under consideration. Several mechanisms behind the spread of COVID-19 are also examined.     

C-reactive protein as an early predictor of COVID-19 severity

BackgroundData for predicting severity of patients with COVID-19 infection are sparse and still under investigation. We retrospectively studied whether the admission serum C-reactive protein level (CRP) can serve as nearly predictor of disease severity during COVID-19 infection in comparison with other hematologic and inflammatory markers.MethodsWe included all consecutive patients who were admitted in Cheikh Khalifa International University Hospital, Casablanca, Morocco, between February to April 2020, with a confirmed diagnosis of COVID-19 infection using SARS-CoV-2 viral nucleic acid via RT-PCR. The complete blood count and serum CRP level were routinely measured on admission. All clinical and laboratory data of patients were collected and analyzed. The classification of the disease severity was in accordance with the clinical classification of the WHO interim guidance, and the management of patients were adapted to the national management guideline. We estimated receiver operating characteristic (ROC) curves of blood routine parameters as well as their association with COVID-19 disease severity.Results145 COVID-19 patients were included in the study. The median age (range) was 50 (32-63) years, and 75 (51.7%) were men. 101 patients were classified in the non-severe group and 44 patients in the severe group. Based on disease severity, significant differences were observed in the age, gender, comorbidities, and respiratory symptom. Similarly, the biological analysis found significant differences for the neutrophil count, lymphocyte count, eosinophil count, and CRP level. However, according to ROC curves of these laboratory biomarkers, the AUC of CRP at 0.872 was significantly higher than all other parameters. Further, CRP was independently associated with severity of COVID-19 disease (OR = 1.11, 95% IC (1.01-1.22) and or = 1.13, 95% IC (1.04-1.23)).ConclusionsThis study found that the CRP level at admission represent a simple and independent factor that can be useful for early detection of severity during COVID-19 and the easy guidance of primary care.     

Long-term predictions of current confirmed and dead cases of COVID-19 in China by the non-autonomous delayed epidemic models

In this paper, we make long-term predictions based on numbers of current confirmed cases, accumulative dead cases of COVID-19 in different regions in China by modeling approach. Firstly, we use the SIRD epidemic model (S-Susceptible, I-Infected, R-Recovered, D-Dead) which is a non-autonomous dynamic system with incubation time delay to study the evolution of the COVID-19 in Wuhan City, Hubei Province and China Mainland. According to the data in the early stage issued by the National Health Commission of China, we can accurately estimate the parameters of the model, and then accurately predict the evolution of the COVID-19 there. From the analysis of the issued data, we find that the cure rates in Wuhan City, Hubei Province and China Mainland are the approximately linear increasing functions of time t and their death rates are the piecewisely decreasing functions. These can be estimated by finite difference method. Secondly, we use the delayed SIRD epidemic model to study the evolution of the COVID-19 in the Hubei Province outside Wuhan City. We find that its cure rate is an approximately linear increasing function and its death rate is nearly a constant. Thirdly, we use the delayed SIR epidemic model (S-Susceptible, I-Infected, R-Removed) to predict those of Beijing, Shanghai, Zhejiang and Anhui Provinces. We find that their cure rates are the approximately linear increasing functions and their death rates are the small constants. The results indicate that it is possible to make accurate long-term predictions for numbers of current confirmed, accumulative dead cases of COVID-19 by modeling. In this paper the results indicate we can accurately obtain and predict the turning points, the end time and the maximum numbers of the current infected and dead cases of the COVID-19 in China. In spite of our simple method and small data, it is rather effective in the long-term prediction of the COVID-19.     

基于多源地理数据精细尺度的武汉市人居环境新型冠状病毒肺炎疫情传播风险评估

新型冠状病毒肺炎的迅速传播和扩散警示着疾病风险评估的重要性。但现有的风险评估方法受数据限制，缺少实时性和准确性。此外，多数研究以行政统计单元作为分析尺度，存在可变面元问题。为解决这些问题，耦合精细尺度下武汉市疫情数据及多源地理数据，基于随机森林算法构建社区尺度的市域疫情传播风险评估模型并进行了疫情风险制图。模型测试精度达到0.85，Kappa系数达到0.70。此外，本研究还建立基于随机森林算法的社区及场所尺度的"空间变量-感染风险"模型，评估了不同场所设施疫情传播的风险程度。研究表明，（1）武汉中心区域感染风险最高并呈现出向外围递减的趋势；（2）感染风险排名前五的一级场所类型分别为购物服务、医疗服务、金融服务、交通设施以及公共设施；（3）小学、中学的疫情传播风险较低，而高等院校传播风险较高；（4）社区尺度下的疫情风险程度，预测购物场所与交通场所是疫情传播风险最高的驱动因子。本研究基于精细尺度提出风险评估新方法，可为未来疾病风险评估提供新思路，为疫情防控提供决策支持，人民群众提供安全保障。     

MicroRNA-425-5p modulates osteoporosis by targeting annexin A2

The disease (COVID-19) novel coronavirus pandemic has so far infected millions resulting in the death of over a million people as of Oct 2020. More than 90% of those infected with COVID-19 show mild or no symptoms but the rest of the infected cases show severe symptoms resulting in significant mortality. Age has emerged as a major factor to predict the severity of the disease and mortality rates are significantly higher in elderly patients. Besides, patients with underlying conditions like Type 2 diabetes, cardiovascular diseases, hypertension, and cancer have an increased risk of severe disease and death due to COVID-19 infection. Obesity has emerged as a novel risk factor for hospitalization and death due to COVID-19. Several independent studies have observed that people with obesity are at a greater risk of severe disease and death due to COVID-19. Here we review the published data related to obesity and overweight to assess the possible risk and outcome in Covid-19 patients based on their body weight. Besides, we explore how the obese host provides a unique microenvironment for disease pathogenesis, resulting in increased severity of the disease and poor outcome.

     

Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level

The Islamic Republic of Iran reported its first COVID-19 cases by 19^th February 2020, since then it has become one of the most affected countries, with more than 73,000 cases and 4,585 deaths to this date. Spatial modeling could be used to approach an understanding of structural and sociodemographic factors that have impacted COVID-19 spread at a province-level in Iran. Therefore, in the present paper, we developed a spatial statistical approach to describe how COVID-19 cases are spatially distributed and to identify significant spatial clusters of cases and how socioeconomic and climatic features of Iranian provinces might predict the number of cases. The analyses are applied to cumulative cases of the disease from February 19^th to March 18^th. They correspond to obtaining maps associated with quartiles for rates of COVID-19 cases smoothed through a Bayesian technique and relative risks, the calculation of global (Moran’s I) and local indicators of spatial autocorrelation (LISA), both univariate and bivariate, to derive significant clustering, and the fit of a multivariate spatial lag model considering a set of variables potentially affecting the presence of the disease. We identified a cluster of provinces with significantly higher rates of COVID-19 cases around Tehran (p-value< 0.05), indicating that the COVID-19 spread within Iran was spatially correlated. Urbanized, highly connected provinces with older population structures and higher average temperatures were the most susceptible to present a higher number of COVID-19 cases (p-value < 0.05). Interestingly, literacy is a factor that is associated with a decrease in the number of cases (p-value < 0.05), which might be directly related to health literacy and compliance with public health measures. These features indicate that social distancing, protecting older adults, and vulnerable populations, as well as promoting health literacy, might be useful to reduce SARS-CoV-2 spread in Iran. One limitation of our analysis is that the most updated information we found concerning socioeconomic and climatic features is not for 2020, or even for a same year, so that the obtained associations should be interpreted with caution. Our approach could be applied to model COVID-19 outbreaks in other countries with similar characteristics or in case of an upturn in COVID-19 within Iran.     

中国和伊朗新型冠状病毒肺炎流行现状及比较分析

新型冠状病毒肺炎(Coronavirus disease 2019,COVID-19)已造成全球大流行,比较不同国家流行特征是了解该病流行规律的重要内容。为了推进我国提出的"一带一路"倡议,防止传染病跨境传播,并探讨COVID-19流行规律和不同国家传染病研究方法,本研究对中国和伊朗COVID-19流行特征和影响因素进行比较分析。根据世界卫生组织、国家卫生健康委员会及新闻媒体等提供的数据和信息,采用描述流行病学方法和Pearson相关进行分析,结果显示,截至2020年7月31日24时,中国累计报告确诊病例88122例,占全球报告0.50%,位居28位;同期伊朗确诊304204例,占全球1.73%,位居全球第9位。2020年4月22日伊朗报告累计确诊病例数首次超过中国,中国宁夏回族自治区、北京市、上海市、甘肃省、广东省分别报告有伊朗输入病例。COVID-19流行水平和严重程度伊朗高于中国:伊朗的发病率为371/10万、中国6.29/10万;伊朗的死亡率为20/10万、中国0.33/10万;伊朗的治愈率为86.63%、中国为92.18%;病死率两国水平接近;伊朗平均每日新增确诊病例数1395例,中国404例。中国疫情出现拐点的时间较伊朗早,每日新增确诊病例首次出现下降用时25d(伊朗42d)、每日新增治愈超过新增确诊用时31d(伊朗49d)。伊朗疫情在5月后出现持续反弹,2020年6月4日确诊病例数达反弹峰值(3574例),超过前期的3186例,且在多数时间新增治愈人数低于新增确诊人数。中国和伊朗每日新增确诊、新增治愈病例数趋势形态不同,中国新增治愈病例数趋势图整体延后新增确诊病例,而伊朗新增治愈与新增确诊病例数几乎同步。相关分析显示武汉每日新增确诊病例数在划分的两个阶段与预防控制措施得分均呈正相关(相关系数分别为0.44、0.53),而与当时武汉的气象因素平均气压(百帕)、平均2min风速(米/秒)、平均气温(℃)、平均相对湿度(百分率)、日照时数(时)无相关性;伊朗在疫情的第一阶段呈正相关,第二阶段为负相关。COVID-19在中国和伊朗的流行存在差异,对不同国家流行情况进行比较研究,有助于加深对COVID-19全球大流行的认识。     

Universal risk phenotype of US counties for flu-like transmission to improve county-specific COVID-19 incidence forecasts

The spread of a communicable disease is a complex spatio-temporal process shaped by the specific transmission mechanism, and diverse factors including the behavior, socio-economic and demographic properties of the host population. While the key factors shaping transmission of influenza and COVID-19 are beginning to be broadly understood, making precise forecasts on case count and mortality is still difficult. In this study we introduce the concept of a universal geospatial risk phenotype of individual US counties facilitating flu-like transmission mechanisms. We call this the Universal Influenza-like Transmission (UnIT) score, which is computed as an information-theoretic divergence of the local incidence time series from an high-risk process of epidemic initiation, inferred from almost a decade of flu season incidence data gleaned from the diagnostic history of nearly a third of the US population. Despite being computed from the past seasonal flu incidence records, the UnIT score emerges as the dominant factor explaining incidence trends for the COVID-19 pandemic over putative demographic and socio-economic factors. The predictive ability of the UnIT score is further demonstrated via county-specific weekly case count forecasts which consistently outperform the state of the art models throughout the time-line of the COVID-19 pandemic. This study demonstrates that knowledge of past epidemics may be used to chart the course of future ones, if transmission mechanisms are broadly similar, despite distinct disease processes and causative pathogens.     

Comparative analysis of antibody responses to SARS-CoV-2 in various populations

This paper aimed to analyze antibody responses to SARS-CoV-2 in various populations. Two hundred and six COVID-19 patients, 46 convalescent patients, and 270 healthy population were enrolled. Antibodies against nucleocapsid protein (N) and spike protein''s receptor-binding domain (RBD), and neutralizing antibody were detected. The results demonstrated both anti-N and anti-RBD antibodies could be detected in about 80% of COVID-19 patients and 90% of convalescent patients, while no antibodies could be detected in some convalescents and patients even after 14 days post-onset of symptoms. The level of anti-RBD antibody strongly correlated with the neutralizing activity of sera from these two cohorts. The titer of neutralizing antibody was lower in convalescents than that in active COVID-19 patients. In addition, the titer of neutralizing antibody was less than 1:80 in none of the severe COVID-19 patients, 18.8% in non-severe COVID-19 patients, and 32.6% in convalescents. The study suggests that the level of anti-RBD antibody is closely related to neutralization activity in COVID-19 patients and convalescents. Some SARS-CoV-2-infected cases trigger a weak antiviral immune response, and the level of neutralizing antibody may have a faster decay rate.     

Circulating cell-free DNA,peripheral lymphocyte subsets alterations and neutrophil lymphocyte ratio in assessment of COVID-19 severity

Cell destruction results in plasma accumulation of cell-free DNA (cfDNA). Dynamic changes in circulating lymphocytes are features of COVID-19. We aimed to investigate if cfDNA level can serve in stratification of COVID-19 patients, and if cfDNA level is associated with alterations in lymphocyte subsets and neutrophil-to-lymphocyte ratio (NLR). This cross-sectional comparative study enrolled 64 SARS-CoV-2-positive patients. Patients were subdivided to severe and non-severe groups. Plasma cfDNA concentration was determined by real-time quantitative PCR. Lymphocyte subsets were assessed by flow cytometry. There was significant increase in cfDNA among severe cases when compared with non-severe cases. cfDNA showed positive correlation with NLR and inverse correlation with T cell percentage. cfDNA positively correlated with ferritin and C-reactive protein. The output data of performed ROC curves to differentiate severe from non-severe cases revealed that cfDNA at cut-off ≥17.31 ng/µl and AUC of 0.96 yielded (93%) sensitivity and (73%) specificity. In summary, excessive release of cfDNA can serve as sensitive COVID-19 severity predictor. There is an association between cfDNA up-regulation and NLR up-regulation and T cell percentage down-regulation. cfDNA level can be used in stratification and personalized monitoring strategies in COVID-19 patients.