Epidemiological characteristics of COVID-19: a systematic review and meta-analysis

Survival of hospitalised COVID-19 patients in Hawassa, Ethiopia: a cohort study

The COVID-19 pandemic, caused by SARS-CoV-2, led to 622,119,701 reported cases and 6,546,118 deaths. Most studies on COVID-19 patients in hospitals are from high-income countries, lacking data for developing countries such as Ethiopia.This study assesses clinical features, demographics, and risk factors for in-hospital mortality in Hawassa, Ethiopia. The research cohort comprises 804 cases exhibiting clinical diagnoses and/or radiological findings and indicative of symptoms consistent with COVID-19 at Hawassa University Comprehensive Specialized Hospital from September 24, 2020, to November 26, 2021. In-hospital mortality rate was predicted using Cox regression. The median age was 45 years, with males making up 64.1% of the population. 173 (21.5%) fatalities occurred, with 125 (72.3%) among males. Male patients had higher mortality rates than females. Severe and critical cases were 24% and 21%. 49.1% had at least one comorbidity, with 12.6% having multiple. Common comorbidities were diabetes (15.9%) and hypertension (15.2%). The Cox regression in Ethiopian COVID-19 patients found that factors like gender, advanced age group, disease severity, symptoms upon admission, shortness of breath, sore throat, body weakness, hypertension, diabetes, multiple comorbidities, and prior health facility visits increased the risk of COVID-19 death, similar to high-income nations. However, in Ethiopia, COVID-19 patients were young and economically active. Patients with at least one symptom had reduced death risk. As a conclusion, COVID-19 in Ethiopia mainly affected the younger demographic, particularly economically active individuals. Early detection can reduce the risk of mortality. Prompt medical attention is essential, especially for individuals with comorbidities. Further research needed on diabetes and hypertension management to reduce mortality risk. Risk factors identified at admission play a crucial role in guiding clinical decisions for intensive monitoring and treatment. Broader risk indicators help prioritize patients for allocation of hospital resources, especially in regions with limited medical facilities. Government's focus on timely testing and strict adherence to regulations crucial for reducing economic impact.

Estimation of standardized real-time fatality rate for ongoing epidemics

BACKGROUND

The fatality rate is a crucial metric for guiding public health policies during an ongoing epidemic. For COVID-19, the age structure of the confirmed cases changes over time, bringing a substantial impact on the real-time estimation of fatality. A 'spurious decrease' in fatality rate can be caused by a shift in confirmed cases towards younger ages even if the fatalities remain unchanged across different ages.

METHODS

To address this issue, we propose a standardized real-time fatality rate estimator. A simulation study is conducted to evaluate the performance of the estimator. The proposed method is applied for real-time fatality rate estimation of COVID-19 in Germany from March 2020 to May 2022.

FINDINGS

The simulation results suggest that the proposed estimator can provide an accurate trend of disease fatality in all cases, while the existing estimator may convey a misleading signal of the actual situation when the changes in temporal age distribution take place. The application to Germany data shows that there was an increment in the fatality rate at the implementation of the 'live with COVID' strategy.

CONCLUSIONS

As many countries have chosen to coexist with the coronavirus, frequent examination of the fatality rate is of paramount importance.

Estimating hidden relationships in dynamical systems: Discovering drivers of infection rates of COVID-19

Discovering causal influences among internal variables is a fundamental goal of complex systems research. This paper presents a framework for uncovering hidden relationships from limited time-series data by combining methods from nonlinear estimation and information theory. The approach is based on two sequential steps: first, we reconstruct a more complete state of the underlying dynamical system, and second, we calculate mutual information between pairs of internal state variables to detail causal dependencies. Equipped with time-series data related to the spread of COVID-19 from the past three years, we apply this approach to identify the drivers of falling and rising infections during the three main waves of infection in the Chicago metropolitan region. The unscented Kalman filter nonlinear estimation algorithm is implemented on an established epidemiological model of COVID-19, which we refine to include isolation, masking, loss of immunity, and stochastic transition rates. Through the systematic study of mutual information between infection rate and various stochastic parameters, we find that increased mobility, decreased mask use, and loss of immunity post sickness played a key role in rising infections, while falling infections were controlled by masking and isolation.

Predicting subnational incidence of COVID-19 cases and deaths in EU countries

BACKGROUND

Recurring COVID-19 waves highlight the need for tools able to quantify transmission risk, and identify geographical areas at risk of outbreaks. Local outbreak risk depends on complex immunity patterns resulting from previous infections, vaccination, waning and immune escape, alongside other factors (population density, social contact patterns). Immunity patterns are spatially and demographically heterogeneous, and are challenging to capture in country-level forecast models.

METHODS

We used a spatiotemporal regression model to forecast subnational case and death counts and applied it to three EU countries as test cases: France, Czechia, and Italy. Cases in local regions arise from importations or local transmission. Our model produces age-stratified forecasts given age-stratified data, and links reported case counts to routinely collected covariates (e.g. test number, vaccine coverage). We assessed the predictive performance of our model up to four weeks ahead using proper scoring rules and compared it to the European COVID-19 Forecast Hub ensemble model. Using simulations, we evaluated the impact of variations in transmission on the forecasts. We developed an open-source RShiny App to visualise the forecasts and scenarios.

RESULTS

At a national level, the median relative difference between our median weekly case forecasts and the data up to four weeks ahead was 25% (IQR: 12-50%) over the prediction period. The accuracy decreased as the forecast horizon increased (on average 24% increase in the median ranked probability score per added week), while the accuracy of death forecasts was more stable. Beyond two weeks, the model generated a narrow range of likely transmission dynamics. The median national case forecasts showed similar accuracy to forecasts from the European COVID-19 Forecast Hub ensemble model, but the prediction interval was narrower in our model. Generating forecasts under alternative transmission scenarios was therefore key to capturing the range of possible short-term transmission dynamics.

DISCUSSION

Our model captures changes in local COVID-19 outbreak dynamics, and enables quantification of short-term transmission risk at a subnational level. The outputs of the model improve our ability to identify areas where outbreaks are most likely, and are available to a wide range of public health professionals through the Shiny App we developed.

Ocular manifestations of COVID-19: A systematic review of current evidence

Introduction

COVID-19 caused by SARS-CoV-2, commonly presents with symptoms such as fever and shortness of breath but can also affect other organs. There is growing evidence pointing to potential eye complications. In this article, we aim to systematically review the ocular manifestations of COVID-19.

Methods

We conducted a systematic review to explore the ocular manifestations of COVID-19. We searched online databases including PubMed, Embase, Scopus, and Web of Science up to September 4, 2023. After a two-stage screening process and applying inclusion/exclusion criteria, eligible articles were advanced to the data extraction phase. The PRISMA checklist and Newcastle-Ottawa Scale (NOS) were used for quality and bias risk assessments.

Results

We selected and extracted data from 42 articles. Most of the studies were cross-sectional (n = 33), with the highest number conducted in Turkey (n = 10). The most frequent ocular manifestation was conjunctivitis, reported in 24 articles, followed by photophobia, burning, chemosis, itching, and ocular pain. Most studies reported complete recovery from these manifestations; however, one study mentioned visual loss in two patients.

Conclusion

In general, ocular manifestations of COVID-19 appear to resolve either spontaneously or with supportive treatments. For more severe cases, both medical treatment and surgery have been employed, with the outcomes suggesting that complete recoveries are attainable.

A strategy for predicting waste production and planning recycling paths in e-logistics based on improved EMD-LSTM

With the rapid development of e-commerce, express delivery has been chosen and accepted by consumers, and a large number of express packages have resulted in serious waste of resources and environmental pollution. Because of the irregularity of online goods purchases by users in real life, logistics parks are unable to accurately judge the recycling needs of various regions. In order to solve this problem, we propose an improved empirical mode decomposition (IEMD) algorithm combined with a long-short-term memory (LSTM) network to deal with the addresses and categories in logistics data, analyze the distribution of recyclable logistics waste in the logistics park service area and in the express recycling station within the logistics park, judge the value of recyclable logistics waste, optimize the best path for recycling vehicles and improve the success rate of logistics waste recycling. In order to better research and verify the IEMD-LSTM prediction model, we model and simulate the algorithm behavior of the express waste packaging recycling prediction model system, and compare it with other classification methods through specific logistics data experiments. The prediction accuracy, stability and advantages of the four algorithms are analyzed and compared, and the application reliability of the algorithm proposed in this paper to the logistics waste recycling process is verified. The application in the actual express logistics packaging recycling case shows the feasibility and effectiveness of the waste recycling scheme proposed in this paper.

[Places of death of COVID-19 patients: an observational study based on evaluated death certificates from the city of Muenster, Germany (2021)]

INTRODUCTION

The places of death of COVID-19 patients have so far hardly been investigated in Germany.

METHODS

In a places of death study in Westphalia (Germany), statistical evaluations were carried out in the city of Muenster on the basis of all death certificates from 2021. Persons who had died with or from a COVID-19 infection were identified by medical information on cause of death and analyzed with descriptive statistical methods using SPSS.

RESULTS

A total of 4044 death certificates were evaluated, and 182 deceased COVID-19 patients were identified (4.5%). In 159 infected patients (3.9%), the viral infection was fatal, whereby the distribution of places of death was as follows: 88.1% in hospital (57.2% in the intensive care unit; 0.0% in the palliative care unit), 0.0% in hospice, 10.7% in nursing homes, 1.3% at home, and 0.0% in other places. All infected patients < 60 years and 75.4% of elderly patients ≥ 80 years died in hospital. Only two COVID-19 patients, both over 80 years old, died at home. COVID-19 deaths in nursing homes (17) affected mostly elderly female residents. Ten of these residents had received end-of-life care from a specialized outpatient palliative care team.

DISCUSSION

The majority of COVID-19 patients died in hospital. This can be explained by the rapid course of the disease with a high symptom burden and the frequent young age of the patients. Inpatient nursing facilities played a certain role as a place of death in local outbreaks. COVID-19 patients rarely died at home. Infection control measures may be one reason why no patients died in hospices or palliative care units.

COVID-19 and HIV: Clinical Outcomes among Hospitalized Patients in the United States

The concurrence of HIV and COVID-19 yields unique challenges and considerations for healthcare providers, patients living with HIV, and healthcare systems at-large. Persons living with HIV may face a higher risk of acquiring SARS-CoV-2 infection and experiencing worse clinical outcomes compared to those without. Notably, COVID-19 may have a disproportionate impact on historically disadvantaged populations, including African Americans and those stratified in a lower socio-economic status. Using the National Inpatient Sample (NIS) database, we compared patients with a diagnosis of both HIV and COVID-19 and those who exclusively had a diagnosis of COVID-19. The primary outcome was in-hospital mortality. Secondary outcomes were intubation rate and vasopressor use; acute MI, acute kidney injury (AKI); AKI requiring hemodialysis (HD); venous thromboembolism (VTE); septic shock and cardiac arrest; length of stay; financial burden on healthcare; and resource utilization. A total of 1,572,815 patients were included in this study; a COVID-19-positive sample that did not have HIV (n = 1,564,875, 99.4%) and another sample with HIV and COVID-19 (n = 7940, 0.56%). Patients with COVID-19 and HIV did not have a significant difference in mortality compared to COVID-19 alone (10.2% vs. 11.3%, respectively, p = 0.35); however, that patient cohort did have a significantly higher rate of AKI (33.6% vs. 28.6%, aOR: 1.26 [95% CI 1.13-1.41], p < 0.001). Given the complex interplay between HIV and COVID-19, more prospective studies investigating the factors such as the contribution of viral burden, CD4 cell count, and the details of patients' anti-retroviral therapeutic regimens should be pursued.

How to model the impact of vaccines for policymaking when the characteristics are uncertain: A case study in Thailand prior to the vaccine rollout during the COVID-19 pandemic

Thailand faced a dilemma of which groups to prioritise with a limited first tranche of COVID-19 vaccinations in early 2021, at a time when there was low incidence and low mortality in the country. A mathematical modelling analysis was performed to compare the potential short-term impact of allocating the available doses to either the high severity group (over 65-year-olds) or the high transmission group (aged 20-39). At the time of the analysis, there was uncertainty about the precise characteristics of the vaccines available, in terms of their potential impact on transmission and reductions to the severity of infection. As such, a range of vaccine characteristic scenarios, with differing levels of severity and transmission reductions were explored. Using the evidence available at the time regarding severity reduction of infection due to the vaccines, the model suggested that vaccinating high severity group should be the priority if reductions in deaths is the priority. Vaccinating this group was found to have a direct impact on reducing the number of deaths, while the incidence and hospitalisations remained unchanged. However, the model found that vaccinating the high transmission group with a vaccine with sufficiently high protection against infection (more than 70%) could provide enough herd effects to delay the expected epidemic peak, resulting in both case and death reductions in both target groups. The model explored a 12-month time horizon. These analyses helped to inform the vaccination strategy in Thailand throughout 2021 and can inform future modelling studies for policymaking when the characteristics of vaccines are uncertain.

Is elevated blood glucose at admission associated with poor outcomes in hospitalized COVID-19 patients?

Objective

Hyperglycemia has been suggested as a risk factor for poor outcomes in coronavirus disease 2019 (COVID-19). The aim of our work was to evaluate the association between blood glucose levels at admission (BGA) and disease outcomes in hospitalized COVID-19 patients.

Subjects and methods

Retrospective study including all adult COVID-19 patients admitted to a Portuguese hospital from March to August 2020 with BGA measurement. Subjects were categorized into two groups: BGA < 140 mg/dL and ≥ 140 mg/dL. Statistical analysis was performed using SPSSv26® (significance defined as p < 0.05).

Results

We included 202 patients: median age 74 (60-86) years; 43.1% female; 31.2% with diabetes. The median BGA was 130.5 (108-158) mg/dL. When compared to normoglycemic, patients with BGA ≥ 140 mg/dL were older (p = 0.013), more vaccinated for influenza (p = 0.025) and had more comorbidities (hypertension, heart failure and peripheral arterial disease, p < 0.05). The last group presented higher leucocyte and neutrophile count, higher procalcitonin and prothrombin time, and lower lymphocyte count. Concerning prognosis, BGA ≥ 140 mg/dL was associated with higher rates of mechanical ventilation requirement and intensive care unit admission (p < 0.001), shock (p = 0.011), in-hospital mortality (p = 0.022) and 30-day mortality (p = 0.037). Considering only non-diabetic patients (n = 139), those with hyperglycemia presented higher rates of severity indicators (polypnea, SatO2 ≤ 93% and PaO2/FiO2 ≤ 300) and an association with poor outcomes was also found, namely mechanical ventilation requirement and in-hospital/30-day mortality (p < 0.05).

Conclusion

Hyperglycemia at admission was associated with poor outcomes in COVID-19 patients, even in those without known pre-existing diabetes. Glycemic testing should be recommended for all COVID-19 patients.

Antiretroviral therapy abandonment among adolescents and young people with HIV/AIDS during COVID-19: A case-control study

OBJECTIVE

to identify the factors associated with antiretroviral therapy abandonment among adolescents and young people living with HIV/AIDS during the COVID-19 pandemic.

METHOD

a case-control study carried out between 2020 and 2021 in Maringá, Paraná. The cases corresponded to the following: adolescents and young people (aged from 10 to 24 years old) diagnosed with HIV/AIDS and who abandoned treatment, while the Control Group consisted of people with similar sociodemographic characteristics, diagnosed with HIV/AIDS and with no history of treatment abandonment. Pairing of the cases and controls was by convenience, with four controls for each case. The research instrument presented sociodemographic variables, clinical characteristics and others, whose association with treatment abandonment was analyzed by means of logistic regression.

RESULTS

a total of 27 cases and 109 controls were included in the study (1/4 ratio). The variable associated with an increased chance of abandonment was age close to 22.8 years old (ORadj: 1.47; 95% CI: 1.07-2.13; p=0.024). Sporadic condom use (ORadj: 0.22; 95% CI: 0.07-0.59; p=0.003) and having an opportunistic infection (OR: 0.31; 95% CI: 0.10-0.90; p=0.030) were protective factors.

CONCLUSION

age close to 23 years old at the last consultation was associated with antiretroviral therapy abandonment. The presence of opportunistic infections and condom use are determining factors for treatment continuity during COVID-19.

Antiviral Therapy of COVID-19

Since the beginning of the COVID-19 pandemic, the scientific community has focused on prophylactic vaccine development. In parallel, the experience of the pharmacotherapy of this disease has increased. Due to the declining protective capacity of vaccines against new strains, as well as increased knowledge about the structure and biology of the pathogen, control of the disease has shifted to the focus of antiviral drug development over the past year. Clinical data on safety and efficacy of antivirals acting at various stages of the virus life cycle has been published. In this review, we summarize mechanisms and clinical efficacy of antiviral therapy of COVID-19 with drugs based on plasma of convalescents, monoclonal antibodies, interferons, fusion inhibitors, nucleoside analogs, and protease inhibitors. The current status of the drugs described is also summarized in relation to the official clinical guidelines for the treatment of COVID-19. In addition, here we describe innovative drugs whose antiviral effect is provided by antisense oligonucleotides targeting the SARS-CoV-2 genome. Analysis of laboratory and clinical data suggests that current antivirals successfully combat broad spectra of emerging strains of SARS-CoV-2 providing reliable defense against COVID-19.

Facteurs de risque de la sévérité de la COVID-19 chez des patients tunisiens aux Urgences de Sousse, Tunisie

INTRODUCTION

Despite the spread of COVID-19 in Tunisia and its impact on people, health and economy, few studies have investigated the profile of COVID-19 Tunisian patients.

AIM

Determine the epidemiological, clinical, para-clinical and therapeutic characteristics patients and identify the associated factors of severity.

METHODS

This is a retrospective study, conducted among confirmed COVID-19 patients consulting the hospital emergency department. We collected Data using from the patients' computerized files. We performed Data entry and analysis using SPSS 22.

RESULTS

We included 375 patients. The average age was 66.7±11.43 years with a sex ratio of 1.6. The most frequent comorbidities were diabetes (100%), hypertension (64.5%), and chronic heart disease (25.9%). The most frequent clinical signs were dyspnea (75.2%), asthenia (66.9%), cough (66.7%) and fever (60.3%). The most frequent biological abnormalities were biological inflammatory syndrome (96%) and elevation of troponin (69.3%). CT scans revealed lung damage in 34.1% of patients. As for treatments, 91.7% received antibiotics, 89% received corticosteroids, 89.3% received anticoagulants, and 85.1% received ventilation (42.6% non-invasive ventilation and 1.9% were intubated). Risk factors of severity were age, chronic heart disease and hypertension.

CONCLUSION

Knowing the particularities of Tunisian patients will help to install recommendations to improve the process of care and prevention.

Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank

Analysis of host genetic components provides insights into the susceptibility and response to viral infection such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). To reveal genetic determinants of susceptibility to COVID-19 related mortality, we train a deep learning model to identify groups of genetic variants and their interactions that contribute to the COVID-19 related mortality risk using the UK Biobank data (28,097 affected cases and 1656 deaths). We refer to such groups of variants as super variants. We identify 15 super variants with various levels of significance as susceptibility loci for COVID-19 mortality. Specifically, we identify a super variant (odds ratio [OR] = 1.594, p = 5.47 × 10-9 ) on Chromosome 7 that consists of the minor allele of rs76398985, rs6943608, rs2052130, 7:150989011_CT_C, rs118033050, and rs12540488. We also discover a super variant (OR = 1.353, p = 2.87 × 10-8 ) on Chromosome 5 that contains rs12517344, rs72733036, rs190052994, rs34723029, rs72734818, 5:9305797_GTA_G, and rs180899355.

A Method of Estimating Time-to-Recovery for a Disease Caused by a Contagious Pathogen Such as SARS-CoV-2 Using a Time Series of Aggregated Case Reports

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak.

A deconvolution approach to modelling surges in COVID-19 cases and deaths

The COVID-19 pandemic continues to emphasize the importance of epidemiological modelling in guiding timely and systematic responses to public health threats. Nonetheless, the predictive qualities of these models remain limited by their underlying assumptions of the factors and determinants shaping national and regional disease landscapes. Here, we introduce epidemiological feature detection, a novel latent variable mixture modelling approach to extracting and parameterizing distinct and localized features of real-world trends in daily COVID-19 cases and deaths. In this approach, we combine methods of peak deconvolution that are commonly used in spectroscopy with the susceptible-infected-recovered-deceased model of disease transmission. We analyze the second wave of the COVID-19 pandemic in Israel, Canada, and Germany and find that the lag time between reported cases and deaths, which we term case-death latency, is closely correlated with adjusted case fatality rates across these countries. Our findings illustrate the spatiotemporal variability of both these disease metrics within and between different disease landscapes. They also highlight the complex relationship between case-death latency, adjusted case fatality rate, and COVID-19 management across various degrees of decentralized governments and administrative structures, which provides a retrospective framework for responding to future pandemics and disease outbreaks.

Community Mitigation Strategies, Mobility, and COVID-19 Incidence Across Three Waves in the United States in 2020

BACKGROUND

Summarizing the impact of community-based mitigation strategies and mobility on COVID-19 infections throughout the pandemic is critical for informing responses and future infectious disease outbreaks. Here, we employed time-series analyses to empirically investigate the relationships between mitigation strategies and mobility on COVID-19 incident cases across US states during the first three waves of infections.

METHODS

We linked data on daily COVID-19 incidence by US state from March to December 2020 with the stringency index, a well-known index capturing the strictness of mitigation strategies, and the trip ratio, which measures the ratio of the number of trips taken per day compared with the same day in 2019. We utilized multilevel models to determine the relative impacts of policy stringency and the trip ratio on COVID-19 cumulative incidence and the effective reproduction number. We stratified analyses by three waves of infections.

RESULTS

Every five-point increase in the stringency index was associated with 2.89% (95% confidence interval = 1.52, 4.26%) and 5.01% (3.02, 6.95%) reductions in COVID-19 incidence for the first and third waves, respectively. Reducing the number of trips taken by 50% compared with the same time in 2019 was associated with a 16.2% (-0.07, 35.2%) decline in COVID-19 incidence at the state level during the second wave and 19.3% (2.30, 39.0%) during the third wave.

CONCLUSIONS

Mitigation strategies and reductions in mobility are associated with marked health gains through the reduction of COVID-19 infections, but we estimate variable impacts depending on policy stringency and levels of adherence.

Time-dependent risk of COVID-19 death with overwhelmed health-care capacity in Japan, 2020-2022

BACKGROUND

It has been descriptively argued that the case fatality risk (CFR) of coronavirus disease (COVID-19) is elevated when medical services are overwhelmed. The relationship between CFR and pressure on health-care services should thus be epidemiologically explored to account for potential epidemiological biases. The purpose of the present study was to estimate the age-dependent CFR in Tokyo and Osaka over time, investigating the impact of caseload demand on the risk of death.

METHODS

We estimated the time-dependent CFR, accounting for time delay from diagnosis to death. To this end, we first determined the time distribution from diagnosis to death, allowing variations in the delay over time. We then assessed the age-dependent CFR in Tokyo and Osaka. In Osaka, the risk of intensive care unit (ICU) admission was also estimated.

RESULTS

The CFR was highest among individuals aged 80 years and older and during the first epidemic wave from February to June 2020, estimated as 25.4% (95% confidence interval [CI] 21.1 to 29.6) and 27.9% (95% CI 20.6 to 36.1) in Tokyo and Osaka, respectively. During the fourth wave of infection (caused by the Alpha variant) in Osaka the CFR among the 70s and ≥ 80s age groups was, respectively, 2.3 and 1.5 times greater than in Tokyo. Conversely, despite the surge in hospitalizations, the risk of ICU admission among those aged 80 and older in Osaka decreased. Such time-dependent variation in the CFR was not seen among younger patients < 70 years old. With the Omicron variant, the CFR among the 80s and older in Tokyo and Osaka was 3.2% (95% CI 3.0 to 3.5) and 2.9% (95% CI 2.7 to 3.1), respectively.

CONCLUSION

We found that without substantial control, the CFR can increase when a surge in cases occurs with an identifiable elevation in risk-especially among older people. Because active treatment options including admission to ICU cannot be offered to the elderly with an overwhelmed medical service, the CFR value can potentially double compared with that in other areas of health care under less pressure.

COVID-19 in one region of New Zealand: a descriptive epidemiological study

OBJECTIVE

To describe the epidemiology of COVID-19 in one region of New Zealand in the context of the national lockdown and provide a reference for comparing infection dynamics and control measures between SARS-Cov-2 strains.  Methods: Epidemiological linking and analysis of COVID-19 cases and their close contacts residing in the geographical area served by the Southern District Health Board (SDHB).  Results: From 13 March to 5 April 5 2020, 186 cases were laboratory-confirmed with wild-type Sars-Cov-2 in SDHB. Overall, 35·1% of cases were attributable to household transmission, 27·0% to non-household, 25·4% to overseas travel and 12·4% had no known epidemiological links. The highest secondary attack rate was observed in households during lockdown (15·3%, 95%CI 10·4-21·5). The mean serial interval in 50 exclusive infector-infectee pairs was 4·0 days (95%CI 3·2-4·7days), and the mean incubation period was 3.4 days (95%CI 2·7-4·2).

CONCLUSIONS

The SARS-CoV-2 incubation period may be shorter than early estimates that were limited by uncertainties in exposure history or small sample sizes.

IMPLICATIONS FOR PUBLIC HEALTH

The continuation of household transmission during lockdown highlights the need for effective home-based quarantine guidance. Our findings of a short incubation period highlight the need to contact trace and isolate as rapidly as possible.

Demographic comparison of the first, second and third waves of COVID-19 in a tertiary care hospital at Jaipur, India

Background

Coronavirus disease 2019 (COVID-19) infection in India demonstrated three peaks in India, with differences in presentation and outcome in all the three waves. The aim of the paper was to assess differences in the epidemiological, clinical features and outcomes of patients with COVID-19 presenting at a tertiary care hospital in the three waves at Jaipur, India.

Methods

This was a retrospective study conducted at a tertiary care hospital at Jaipur, India. Demographic, clinical features and outcomes were compared of confirmed COVID-19 cases admitted during the first wave (16-7-2020 to 31-1-2021), second wave (16-3-2021 to 6-5-2021) and third wave (1-1-22 to 20-2-22) of the outbreak.

Results

There were 1006 cases, 639 cases and 125 cases admitted during the three waves, respectively. The cases presenting in the second wave were significantly younger, with significantly higher prevalence of symptoms such as fever, cough, sore throat, nausea, vomiting, headache, muscle ache, loss of appetite and fatigue (P < 0.05). A significantly higher proportion of patients received Remdesivir in the second wave (P < 0.001). However, in the second wave, the use of low molecular weight heparin, plasma therapy, non-invasive and invasive ventilator were higher (P < 0.001). Co-morbid conditions were significantly higher in the admitted patients during the third wave (P < 0.05). Radiological scores were similar in second and third wave, significantly higher than the first wave. Lymphopenia and rise of inflammatory markers including C-reactive protein and interleukin-6 were more evident in the second wave (P < 0.001). The mean mortality, hospital stay and air-leak complications were also significantly higher in the second wave (P < 0.001).

Conclusions

The second wave was more vicious in terms of symptoms, inflammatory markers, radiology, complications, requirement of ventilation and mortality. Mutation in the virus, lack of immunity and vaccination at the time point of second wave could have been the possible causes. The ferocity of the second wave has important implications for the government to formulate task forces for effective management of such pandemics.

Variation analysis of SARS-CoV-2 complete sequences from Iran

Aim: SARS-CoV-2 is an emerging coronavirus that was discovered in China and rapidly spread throughout the world. The authors looked at nucleotide and amino acid variations in SARS-CoV-2 genomes, as well as phylogenetic and evolutionary events in viral genomes, in Iran. Materials & methods: All SARS-CoV-2 sequences that were publicly released between the start of the pandemic and 15 October 2021 were included. Results: The majority of mutations were found in vaccine target proteins, Spike and Nucleocapsid proteins, and nonstructural proteins. The majority of the viruses that circulated in the early stages of the pandemic belonged to the B.4 lineage. Conclusion: We discovered the prevalence of viral populations in Iran. As a result, tracking the virus’s variation in Iran and comparing it with a variety of nearby neighborhoods may reveal a pattern for future variant introductions.

A systems biology approach for investigating significantly expressed genes among COVID-19, hepatocellular carcinoma, and chronic hepatitis B

Background

Worldwide, COVID-19's death rate is about 2%, considering the incidence and mortality. However, the information on its complications in other organs, specifically the liver and its disorders, is limited in mild or severe cases. In this study, we aimed to computationally investigate the typical relationships between liver-related diseases [i.e., hepatocellular carcinoma (HCC), and chronic hepatitis B (CHB)] and COVID-19, considering the involved significant genes and their molecular mechanisms.

Methods

We investigated two GEO microarray datasets (GSE164805 and GSE58208) to identify differentially expressed genes (DEGs) among the generated four datasets for mild/severe COVID-19, HCC, and CHB. Then, the overlapping genes among them were identified for GO and KEGG enrichment analyses, protein-protein interaction network construction, hub genes determination, and their associations with immune cell infiltration.

Results

A total of 22 significant genes (i.e., ACTB, ATM, CDC42, DHX15, EPRS, GAPDH, HIF1A, HNRNPA1, HRAS, HSP90AB1, HSPA8, IL1B, JUN, POLR2B, PTPRC, RPS27A, SFRS1, SMARCA4, SRC, TNF, UBE2I, and VEGFA) were found to play essential roles among mild/severe COVID-19 associated with HCC and CHB. Moreover, the analysis of immune cell infiltration revealed that these genes are mostly positively correlated with tumor immune and inflammatory responses.

Conclusions

In summary, the current study demonstrated that 22 identified DEGs might play an essential role in understanding the associations between the mild/severe COVID-19 patients with HCC and CHB. So, the HCC and CHB patients involved in different types of COVID-19 can benefit from immune-based targets for therapeutic interventions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43042-022-00360-3.

Modeling Dynamic Responses to COVID-19 Epidemics: A Case Study in Thailand

Quantifying the effects of control measures during the emergence and recurrence of SARS-CoV-2 poses a challenge to understanding the dynamic responses in terms of effectiveness and the population’s reaction. This study aims to estimate and compare the non-pharmaceutical interventions applied in the first and second outbreaks of COVID-19 in Thailand. We formulated a dynamic model of transmission and control. For each outbreak, the time interval was divided into subintervals characterized by epidemic events. We used daily case report data to estimate the transmission rates, the quarantine rate, and its efficiency by the maximum likelihood method. The duration-specific control reproduction numbers were calculated. The model predicts that the reproduction number dropped by about 91% after the nationwide lockdown in the first wave. In the second wave, after a high number of cases had been reported, the reproduction number decreased to about 80% in the next phase, but the spread continued. The estimated value was below the threshold in the last phase. For both waves, successful control was mainly induced by decreased transmission rate, while the explicit quarantine measure showed less effectiveness. The relatively weak control measure estimated by the model may have implications for economic impact and the adaptation of people.

Coronavirus Disease 2019-induced Acute Exudative Polymorphous Vitelliform Maculopathy

Acute exudative polymorphous vitelliform maculopathy (AEPVM) is a rare entity characterized by acute multifocal macular detachment with polymorphous subretinal vitelliform deposits. The disease is a presumed retinal pigment epithelial dysfunction and is reported to occur with malignancies. We report a case of a 32-year-old otherwise healthy woman who presented with an acute bilateral visual disturbance a few days after testing positive for coronavirus disease 2019 (COVID-19). Her initial visual acuity was 6/6 in both eyes. Fundus examination revealed bilateral multifocal round yellowish subretinal deposits. Spectral-domain optical coherence tomography showed bilateral foveal serous retinal detachment with subretinal hyperreflective materials consistent with vitelliform deposits. Systemic workup to exclude malignancies and genetic diseases was unremarkable. The patient was observed without treatment, and the vitelliform materials gradually resolved over 18 months of follow-up. In our era of the global pandemic, AEPVM may be associated with COVID-19 infection.

Modelling the first wave of COVID-19 in India

Estimating the burden of COVID-19 in India is difficult because the extent to which cases and deaths have been undercounted is hard to assess. Here, we use a 9-component, age-stratified, contact-structured epidemiological compartmental model, which we call the INDSCI-SIM model, to analyse the first wave of COVID-19 spread in India. We use INDSCI-SIM, together with Bayesian methods, to obtain optimal fits to daily reported cases and deaths across the span of the first wave of the Indian pandemic, over the period Jan 30, 2020 to Feb 15, 2021. We account for lock-downs and other non-pharmaceutical interventions (NPIs), an overall increase in testing as a function of time, the under-counting of cases and deaths, and a range of age-specific infection-fatality ratios. We first use our model to describe data from all individual districts of the state of Karnataka, benchmarking our calculations using data from serological surveys. We then extend this approach to aggregated data for Karnataka state. We model the progress of the pandemic across the cities of Delhi, Mumbai, Pune, Bengaluru and Chennai, and then for India as a whole. We estimate that deaths were undercounted by a factor between 2 and 5 across the span of the first wave, converging on 2.2 as a representative multiplier that accounts for the urban-rural gradient. We also estimate an overall under-counting of cases by a factor of between 20 and 25 towards the end of the first wave. Our estimates of the infection fatality ratio (IFR) are in the range 0.05—0.15, broadly consistent with previous estimates but substantially lower than values that have been estimated for other LMIC countries. We find that approximately 35% of India had been infected overall by the end of the first wave, results broadly consistent with those from serosurveys. These results contribute to the understanding of the long-term trajectory of COVID-19 in India.

Making sense of publicly available epidemiological data for the COVID-19 pandemic in India presents multiple challenges, largely to do with the quality of the data. Here, we describe ways of addressing these questions by studying the data using a well-parameterised, detailed compartmental model together with Bayesian methods, alongside information derived from pan-India serological surveys. We focus on the first wave of the Indian pandemic, across the interval Jan 30, 2020 to Feb 15, 2021. We estimate that deaths were under-counted by a factor between 2 and 5 across the span of the first wave and that cases were under-counted by a factor of between 20 and 25 towards its end. We estimate an infection fatality ratio (IFR) in the range 0.05—0.15. We find that approximately 35% of India had been infected overall by the end of the first wave, a number that helps us better understand the context in which the second and later waves unfolded.

A Study on the COVID-19 Preventive Behaviors of Automobile Manufacturing Workers in South Korea

The present study used a cross-sectional, descriptive survey design to investigate the influencing factors of COVID-19-related infection prevention behaviors of workers in the automobile manufacturing sector. An online survey was conducted on 157 workers in the automobile manufacturing sector of a company in Korea. We analyzed the collected data using SPSS to test whether there were significant differences in COVID-19 risk perception, crisis communication, health literacy, and infection prevention behaviors according to the general characteristics of the participants. An independent sample t-test and a one-way analysis of variance (ANOVA) were performed. A Pearson’s correlation analysis was performed to identify the correlations among COVID-19 risk perception, crisis communication, health literacy, and infection prevention behaviors. Multiple regression analysis was performed to identify the influencing factors of COVID-19 infection prevention behaviors. The regression model was found to be significant, and the employment period at current job, COVID-19 prevention education, source of information, COVID-19 risk perception, crisis communication, and health literacy were also found to be significant. Among the demographic variables, employment period at current job of 5−10 years showed a higher level of infection prevention behaviors than that of <5 years. Moreover, the level of infection prevention behaviors was also significantly higher when COVID-19-related information was acquired through the KDCA/health center. Higher COVID-19 risk perception, crisis communication, and health literacy were associated with significantly higher levels of infection prevention behaviors. Therefore, based on the results, health managers need to develop programs and educate and improve information comprehension and crisis communication skills in order to promote workers’ infection prevention behaviors of emerging infectious diseases in an era of global change.

Comparison of epidemiological characteristics and transmissibility of different strains of COVID-19 based on the incidence data of all local outbreaks in China as of March 1, 2022

Background

The epidemiological characteristics and transmissibility of Coronavirus Disease 2019 (COVID-19) may undergo changes due to the mutation of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) strains. The purpose of this study is to compare the differences in the outbreaks of the different strains with regards to aspects such as epidemiological characteristics, transmissibility, and difficulties in prevention and control.

Methods

COVID-19 data from outbreaks of pre-Delta strains, the Delta variant and Omicron variant, were obtained from the Chinese Center for Disease Control and Prevention (CDC). Case data were collected from China's direct-reporting system, and the data concerning outbreaks were collected by on-site epidemiological investigators and collated by the authors of this paper. Indicators such as the effective reproduction number (R _eff), time-dependent reproduction number (R _t), rate of decrease in transmissibility (RDT), and duration from the illness onset date to the diagnosed date (D _ID )/reported date (D _IR ) were used to compare differences in transmissibility between pre-Delta strains, Delta variants and Omicron variants. Non-parametric tests (namely the Kruskal-Wallis H and Mean-Whitney U tests) were used to compare differences in epidemiological characteristics and transmissibility between outbreaks of different strains. P < 0.05 indicated that the difference was statistically significant.

Results

Mainland China has maintained a "dynamic zero-out strategy" since the first case was reported, and clusters of outbreaks have occurred intermittently. The strains causing outbreaks in mainland China have gone through three stages: the outbreak of pre-Delta strains, the outbreak of the Delta variant, and outbreaks involving the superposition of Delta and Omicron variant strains. Each outbreak of pre-Delta strains went through two stages: a rising stage and a falling stage, Each outbreak of the Delta variant and Omicron variant went through three stages: a rising stage, a platform stage and a falling stage. The maximum R _eff value of Omicron variant outbreaks was highest (median: 6.7; ranged from 5.3 to 8.0) and the differences were statistically significant. The RDT value of outbreaks involving pre-Delta strains was smallest (median: 91.4%; [IQR]: 87.30-94.27%), and the differences were statistically significant. The D _ID and D _IR for all strains was mostly in a range of 0-2 days, with more than 75%. The range of duration for outbreaks of pre-Delta strains was the largest (median: 20 days, ranging from 1 to 61 days), and the differences were statistically significant.

Conclusion

With the evolution of the virus, the transmissibility of the variants has increased. The transmissibility of the Omicron variant is higher than that of both the pre-Delta strains and the Delta variant, and is more difficult to suppress. These findings provide us with get a more clear and precise picture of the transmissibility of the different variants in the real world, in accordance with the findings of previous studies. R _eff is more suitable than R _t for assessing the transmissibility of the disease during an epidemic outbreak.

Evictions and tenant-landlord relationships during the 2020-2021 eviction moratorium in the US

This study provisionally examined the effects of the US eviction moratorium instituted in response to the Coronavirus Disease 2019 (COVID-19) pandemic. Three waves of data collected May 2020-April 2021 from a nationally representative sample of middle- and low-income US tenants (n = 3393 in Wave 1, n = 1311 in Wave 2, and 814 in Wave 3) were analyzed. Across three waves, 4.3% of tenants reported experiencing an eviction during the moratorium and 6%-23% of tenants reported delaying paying rent because of the moratorium. Multivariable analyses found that tenants who delayed paying their rent, were female, or had a history of mental illness or substance use disorder were more likely to report the eviction moratorium had a negative effect on their landlord relationship. COVID-19 infection was not predictive of eviction but tenants with a history of homelessness were more than nine times as likely to report an eviction than those without such a history. Together, these findings suggest the eviction moratorium may have had some unintended consequences on rent payments and tenant-landlord relationships that need to be considered with the end of the federal eviction moratorium.

Associations of symptom combinations with in-hospital mortality of coronavirus disease-2019 patients using South Korean National data

BACKGROUND

There are various risk factors for death in coronavirus disease-2019 (COVID-19) patients. The effects of symptoms on death have been investigated, but symptoms were considered individually, rather than in combination, as predictors. We examined the effects of symptom combinations on in-hospital mortality.

METHODS

Data from the Korea Disease Control and Prevention Agency were analyzed. A cohort of 5,153 patients confirmed with COVID-19 in South Korea was followed from hospitalization to death or discharge. An exploratory factor analysis was performed to identify symptom combinations, and the hazard ratios (HRs) of death were estimated using the Cox proportional hazard model.

RESULTS

Three sets of symptom factors were isolated for symptom combination. Factor 1 symptoms were cold-like symptoms, factor 2 were neurological and gastrointestinal symptoms, and factor 3 were more severe symptoms such as dyspnea and altered state of consciousness. Factor 1 (HR 1.14, 95% confidence interval [95% CI] 1.01-1.30) and factor 3 (HR 1.25, 95% CI 1.19-1.31) were associated with a higher risk for death, and factor 2 with a lower risk (HR 0.71, 95% CI 0.71-0.96).

CONCLUSIONS

The effect on in-hospital mortality differed according to symptom combination. The results are evidence of the effects of symptoms on COVID-19 mortality and may contribute to lowering the COVID-19 mortality rate. Further study is needed to identify the biological mechanisms underlying the effects of symptom combinations on mortality.

Time from symptom onset to severe COVID-19 and risk factors among patients in Southern Ethiopia: a survival analysis

Objective

This study aimed to assess the time to severe coronavirus disease 2019 (COVID-19) and risk factors among confirmed COVID-19 cases in Southern Ethiopia.

Method

This two-center retrospective cohort study involved patients with confirmed COVID-19 from 1 October 2020 to 30 September 2021. Kaplan–Meier graphs and log-rank tests were used to determine the pattern of COVID-19 severity among categories of variables. Bivariable and multivariable Cox proportional regression models were used to identify the risk factors of severe COVID-19.

Results

Four hundred thirteen patients with COVID-19 with a mean age of 41.9 ± 15.3 years were involved in the study. There were 194 severe cases (46.9.1%), including 77 (39.6%) deaths. The median time from symptom onset to severe COVID-19 was 8 days (interquartile range: 7–12 days). The risk factors for severe COVID-19 were age >65 (adjusted hazard ratio [AHR] = 2.65, 95% confidence interval [95%CI]: 1.02, 3.72), cough (AHR = 1.59, 95%CI: 1.39, 2.84), chest pain (AHR = 1.47, 95%CI: 1.34, 2.66), headache (AHR = 2.04, 95%CI: 1.43, 2.88), comorbidity (AHR = 1.3, 95%CI: 1.01, 2.04), asthma (AHR = 1.6. 95%CI: 1.04, 2.24), and symptom onset to admission more than 5 days (AHR = 0.48, 95%CI: 0.34, 0.68).

Conclusion

Patients with symptoms and comorbidities should be closely monitored.

Impact of Non-pharmaceutical Interventions on the Control of COVID-19 in Iran: A Mathematical Modeling Study

BACKGROUND

During the first months of the coronavirus disease 2019 (COVID-19) pandemic, Iran reported high numbers of infections and deaths. In the following months, the burden of this infection decreased significantly, possibly due to the impact of a package of interventions. We modeled the dynamics of COVID-19 infection in Iran to quantify the impacts of these interventions.

METHODS

We used a modified susceptible-exposed-infected-recovered (SEIR) model to model the COVID-19 epidemic in Iran, from January 21, 2020 to September 21, 2020. We estimated the 95% uncertainty intervals (UIs) using Markov chain Monte Carlo simulation. Under different scenarios, we assessed the effectiveness of non-pharmaceutical interventions (NPIs) including physical distancing measures and self-isolation. We also estimated the time-varying reproduction number (Rt ), using our mathematical model and epidemiologic data.

RESULTS

If no NPIs were applied, there could have been a cumulative number of 51 800 000 (95% UI: 1 910 000- 77 600 000) COVID-19 infections and 266 000 (95% UI: 119 000-476 000) deaths by September 21, 2020. If physical distancing interventions, such as school/border closures and self-isolation interventions had been introduced a week earlier than they were actually launched, 30.8% and 35.2% reduction in the number of deaths and infections respectively could have been achieved by September 21, 2020. The observed daily number of deaths showed that the Rt was one or more than one almost every day during the analysis period.

CONCLUSION

Our models suggest that the NPIs implemented in Iran between January 21, 2020 and September 21, 2020 had significant effects on the spread of the COVID-19 epidemic. Our study also showed that the timely implementation of NPIs showed a profound effect on further reductions in the numbers of infections and deaths. This highlights the importance of forecasting and early detection of future waves of infection and of the need for effective preparedness and response capabilities.

Incidence of Death and Its Predictors of COVID-19 in Amhara Region, Ethiopia: A Retrospective Follow Up Study

Background

Risk factors associated with COVID-19 incidence of death would aid to notify the most favorable management strategies, hang about undecided, Moreover, studies regarding this issue are limited in Ethiopia and no region-wise study is conducted. Hence, the study investigated the COVID-19 incidence of death and its predictors in the Amhara regional state, Ethiopia.

Methods

A facility-based retrospective survey was conducted at all Amhara regional state COVID-19 treatment centers from 13 March 2020, through 13 January 2022. Epidata version 3.1 and STATA version 14 were used for data entry and analysis, respectively. Linearized survey analysis in a stratified Cox regression model was fitted to identify independent risk factors. P-value with 95% CI for hazard ratio was used for testing the significance at alpha 0.05.

Results

A total of 28,533 study participants were analyzed in this study. Of these, 2873 (11.2%) died and 25,660 (88.8%) were recovered from COVID-19. The death rate was 11.78 per 1000 person-days of observation with a median survival time of 32 days with IQR [12, 44]. Patients with co-morbidities (AHR = 1.54: 95% CI: 1.51–1.55), patients with age <5-year (AHR = 1.69: 95% CI: 1.78–1.81) and patients with age 60+ years (AHR = 2.91: 95% CI: 1.79–3.99), patients with asymptomatic diseases condition (AHR =1.15: 95% CI: 1.01–1.19), and being male (AHR = 1.22: 95% CI: 1.18–1.27) were independent significant risk factors of death from COVID-19.

Conclusion

A relatively high incidence of death from COVID-19 was found in this study. Significant risk factors were identified as patients with age <5 years, patients with age 60+ Years, being male, patients having at least one comorbid condition, and patients with asymptomatic disease conditions. These factors should be taken into consideration for a strategy of quarantining and treating COVID-19 patients.

Impact of COVID-19 in Chronic Viral Hepatitis B Patients on Virological, Clinical, and Paraclinical Aspects

Background: Coronavirus disease 2019 (COVID-19) is caused by an infection in the respiratory tract leading to extrapulmonary manifestations, including dysregulation of the immune system and hepatic injury. Objectives: Given the high prevalence of viral hepatitis and a few studies carried out on severe acute respiratory syndrome coronavirus 2 and hepatitis B virus (HBV), this study investigated the impact of COVID-19 on chronic hepatitis B (CHB) patients in the northeast region of Iran. Methods: In this cross-sectional study, the blood samples were collected from 93 CHB patients registered in the Patient Detection Data Bank of Golestan University of Medical Sciences, Gorgan, Iran, and 62 healthy individuals as controls. Reverse transcription-polymerase chain reaction was adopted to detect COVID-19 infection in all the participants’ nasopharyngeal samples. All the participants were subjected to anti-hepatitis C virus, anti-hepatitis delta virus, and liver function tests. Then, HBV deoxyribonucleic acid load was detected in CHB patients. The collected data were analyzed by statistical tests using SPSS software (version 20). A P-value less than 0.05 was considered statistically significant. Results: In this study, 14% (13/93) and 32.25% (20/62) of CHB patients and control individuals were infected with COVID-19, respectively. The mean age of CHB patients was 39.69 ± 19.58 years, and 71% of them were female. The risk of developing COVID-19 in healthy controls was observed to be 2.3 times higher than in patients with CHB (0.95% confidence interval: 1.242 - 4.290). On the other hand, the mean values of aspartate aminotransferase, alanine aminotransferase, and alkaline phosphatase in CHB patients superinfected with COVID-19 were higher than other participants. Out of 35.4% of patients with viral hepatitis B that were taking antiviral drugs, only 5.4% had COVID-19. Conclusions: Although CHB infection did not predispose COVID-19 patients to more severe outcomes, the data of this study suggest that antiviral agents also decreased susceptibility to COVID-19 infection. Alternatively, careful assessment of hepatic manifestations and chronic viral hepatitis infections in COVID-19 patients can lead to more favorable health outcomes.

Testing and isolation to prevent overloaded healthcare facilities and reduce death rates in the SARS-CoV-2 pandemic in Italy

Background

During the first wave of COVID-19, hospital and intensive care unit beds got overwhelmed in Italy leading to an increased death burden. Based on data from Italian regions, we disentangled the impact of various factors contributing to the bottleneck situation of healthcare facilities, not well addressed in classical SEIR-like models. A particular emphasis was set on the undetected fraction (dark figure), on the dynamically changing hospital capacity, and on different testing, contact tracing, quarantine strategies.

Methods

We first estimated the dark figure for different Italian regions. Using parameter estimates from literature and, alternatively, with parameters derived from a fit to the initial phase of COVID-19 spread, the model was optimized to fit data (infected, hospitalized, ICU, dead) published by the Italian Civil Protection.

Results

We show that testing influenced the infection dynamics by isolation of newly detected cases and subsequent interruption of infection chains. The time-varying reproduction number (R t) in high testing regions decreased to <1 earlier compared to the low testing regions. While an early test and isolate (TI) scenario resulted in up to ~31% peak reduction of hospital occupancy, the late TI scenario resulted in an overwhelmed healthcare system.

Conclusions

An early TI strategy would have decreased the overall hospital usage drastically and, hence, death toll (∼34% reduction in Lombardia) and could have mitigated the lack of healthcare facilities in the course of the pandemic, but it would not have kept the hospitalization amount within the pre-pandemic hospital limit.

Time Trend in SARS-CoV-2 Seropositivity, Surveillance Detection- and Infection Fatality Ratio until Spring 2021 in the Tirschenreuth County-Results from a Population-Based Longitudinal Study in Germany

Herein, we provide results from a prospective population-based longitudinal follow-up (FU) SARS-CoV-2 serosurveillance study in Tirschenreuth, the county which was hit hardest in Germany in spring 2020 and early 2021. Of 4203 individuals aged 14 years or older enrolled at baseline (BL, June 2020), 3546 participated at FU1 (November 2020) and 3391 at FU2 (April 2021). Key metrics comprising standardized seroprevalence, surveillance detection ratio (SDR), infection fatality ratio (IFR) and success of the vaccination campaign were derived using the Roche N- and S-Elecsys anti-SARS-CoV-2 test together with a self-administered questionnaire. N-seropositivity at BL was 9.2% (1st wave). While we observed a low new seropositivity between BL and FU1 (0.9%), the combined 2nd and 3rd wave accounted for 6.1% new N-seropositives between FU1 and FU2 (ever seropositives at FU2: 15.4%). The SDR decreased from 5.4 (BL) to 1.1 (FU2) highlighting the success of massively increased testing in the population. The IFR based on a combination of serology and registration data resulted in 3.3% between November 2020 and April 2021 compared to 2.3% until June 2020. Although IFRs were consistently higher at FU2 compared to BL across age-groups, highest among individuals aged 70+ (18.3% versus 10.7%, respectively), observed differences were within statistical uncertainty bounds. While municipalities with senior care homes showed a higher IFR at BL (3.0% with senior care home vs. 0.7% w/o), this effect diminished at FU2 (3.4% vs. 2.9%). In April 2021 (FU2), vaccination rate in the elderly was high (>77.4%, age-group 80+).

German federal-state-wide seroprevalence study of 1st SARS-CoV-2 pandemic wave shows importance of long-term antibody test performance

Background

Reliable data on the adult SARS-CoV-2 infection fatality rate in Germany are still scarce. We performed a federal state-wide cross-sectional seroprevalence study named SaarCoPS, that is representative for the adult population including elderly individuals and nursing home residents in the Saarland.

Methods

Serum was collected from 2940 adults via stationary or mobile teams during the 1^st pandemic wave steady state period. We selected an antibody test system with maximal specificity, also excluding seroreversion effects due to a high longitudinal test performance. For the calculations of infection and fatality rates, we accounted for the delays of seroconversion and death after infection.

Results

Using a highly specific total antibody test detecting anti-SARS-CoV-2 responses over more than 180 days, we estimate an adult infection rate of 1.02% (95% CI: [0.64; 1.44]), an underreporting rate of 2.68-fold (95% CI: [1.68; 3.79]) and infection fatality rates of 2.09% (95% CI: (1.48; 3.32]) or 0.36% (95% CI: [0.25; 0.59]) in all adults including elderly individuals, or adults younger than 70 years, respectively.

Conclusion

The study highlights the importance of study design and test performance for seroprevalence studies, particularly when seroprevalences are low. Our results provide a valuable baseline for evaluation of future pandemic dynamics and impact of public health measures on virus spread and human health in comparison to neighbouring countries such as Luxembourg or France.

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

Coronavirus (COVID-19) has created an unprecedented global crisis because of its detrimental effect on the global economy and health. COVID-19 cases have been rapidly increasing, with no sign of stopping. As a result, test kits and accurate detection models are in short supply. Early identification of COVID-19 patients will help decrease the infection rate. Thus, developing an automatic algorithm that enables the early detection of COVID-19 is essential. Moreover, patient data are sensitive, and they must be protected to prevent malicious attackers from revealing information through model updates and reconstruction. In this study, we presented a higher privacy-preserving federated learning system for COVID-19 detection without sharing data among data owners. First, we constructed a federated learning system using chest X-ray images and symptom information. The purpose is to develop a decentralized model across multiple hospitals without sharing data. We found that adding the spatial pyramid pooling to a 2D convolutional neural network improves the accuracy of chest X-ray images. Second, we explored that the accuracy of federated learning for COVID-19 identification reduces significantly for non-independent and identically distributed (Non-IID) data. We then proposed a strategy to improve the model's accuracy on Non-IID data by increasing the total number of clients, parallelism (client-fraction), and computation per client. Finally, for our federated learning model, we applied a differential privacy stochastic gradient descent (DP-SGD) to improve the privacy of patient data. We also proposed a strategy to maintain the robustness of federated learning to ensure the security and accuracy of the model.

COVID-19 outbreak response at a nursing hospital in South Korea in the post-vaccination era, including an estimation of the effectiveness of the first shot of the Oxford-AstraZeneca COVID-19 vaccine (ChAdOx1-S)

Objectives

We descriptively reviewed a coronavirus disease 2019 (COVID-19) outbreak at a nursing hospital in Gyeonggi Province (South Korea) and assessed the effectiveness of the first dose of the Oxford-AstraZeneca vaccine in a real-world population.

Methods

The general process of the epidemiological investigation included a public health intervention. The relative risk (RR) of vaccinated and unvaccinated groups was calculated and compared to confirm the risk of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, and vaccine effectiveness was evaluated based on the calculated RR.

Results

The population at risk was confined to ward E among 8 wards of Hospital X, where the outbreak occurred. This population comprised 55 people, including 39 patients, 12 nurses, and 4 caregivers, and 19 cases were identified. The RR between the vaccinated and unvaccinated groups was 0.04, resulting in a vaccine effectiveness of 95.3%. The vaccination rate of the non-patients in ward E was the lowest in the entire hospital, whereas the overall vaccination rate of the combined patient and non-patient groups in ward E was the third lowest.

Conclusion

The first dose of the Oxford-AstraZeneca vaccine (ChAdOx1-S) was effective in preventing SARS-CoV-2 infection. To prevent COVID-19 outbreaks in medical facilities, it is important to prioritize the vaccination of healthcare providers.

Assessment of Factors Affecting Time to Recovery from COVID-19: A Retrospective Study in Ethiopia

Background. The average duration of recovery from COVID-19 and influencing factors, which would help inform optimal control strategies, remain unclear. Moreover, studies regarding this issue are limited in Ethiopia, and no region-wise studies were conducted. Hence, this study aimed to investigate the median recovery time from COVID-19, and its predictors among patients admitted to Amhara regional state COVID-19 treatment centers, Ethiopia. Methods. A facility-based retrospective follow-up study was conducted at Amhara regional state COVID-19 treatment centers from 13 March 2020 through 30 March 2021. Data were entered using EpiData version 3.1, and STATA version 14 was used for analysis. A Kaplan–Meier curve was used to estimate survival time, and the Cox regression model was fitted to identify independent predictors. $P$ value with 95% CI for the hazard ratio was used for testing the significance at alpha 0.05. Results. Six hundred twenty-two cases followed, and 540 observations developed an event at the end of the follow-up. The median time to recovery was 11 days with an interquartile range of 9–14 days. Most of the patients were recovered from COVID-19 between days seven and fourteen. In the first six days of admission, only 4.2% of cases had recovered, but by day 14, 73.8% had recovered. Patients without comorbid illness/s were faster to recover than their counterparts (AHR = 1.44 : 95% CI: 1.10, 1.91) and those who have signs and symptoms on admission (AHR = 0.42 : 95% CI: 0.30, 0.60) and old-aged (AHR = 0.988; 95% CI: 0.982, 0.994) took longer to recover. Conclusion. In conclusion, a relatively short median recovery time was found in this study. Significant predictors for delayed recovery from COVID-19 were older age, presence of symptoms at admission, and having at least one comorbid condition. These factors should be placed under consideration while developing a strategy for quarantining and treating COVID-19 patients.

Mechanisms and clinical evidence to support melatonin's use in severe COVID-19 patients to lower mortality

The fear of SARS-CoV-2 infection is due to its high mortality related to seasonal flu. To date, few medicines have been developed to significantly reduce the mortality of the severe COVID-19 patients, especially those requiring tracheal intubation. The severity and mortality of SARS-CoV-2 infection not only depend on the viral virulence, but are primarily determined by the cytokine storm and the destructive inflammation driven by the host immune reaction. Thus, to target the host immune response might be a better strategy to combat this pandemic. Melatonin is a molecule with multiple activities on a virus infection. These include that it downregulates the overreaction of innate immune response to suppress inflammation, promotes the adaptive immune reaction to enhance antibody formation, inhibits the entrance of the virus into the cell as well as limits its replication. These render it a potentially excellent candidate for treatment of the severe COVID-19 cases. Several clinical trials have confirmed that melatonin when added to the conventional therapy significantly reduces the mortality of the severe COVID-19 patients. The cost of melatonin is a small fraction of those medications approved by FDA for emergency use to treat COVID-19. Because of its self-administered, low cost and high safety margin, melatonin could be made available to every country in the world at an affordable cost. We recommend melatonin be used to treat severe COVID-19 patients with the intent of reducing mortality. If successful, it would make the SARS-CoV-2 pandemic less fearful and help to return life back to normalcy.

The Foegen effect: A mechanism by which facemasks contribute to the COVID-19 case fatality rate

Extensive evidence in the literature supports the mandatory use of facemasks to reduce the infection rate of severe acute respiratory syndrome coronavirus 2, which causes the coronavirus disease (COVID-19). However, the effect of mask use on the disease course remains controversial. This study aimed to determine whether mandatory mask use influenced the case fatality rate in Kansas, USA between August 1st and October 15th 2020.This study applied secondary data on case updates, mask mandates, and demographic status related to Kansas State, USA. A parallelization analysis based on county-level data was conducted on these data. Results were controlled by performing multiple sensitivity analyses and a negative control.A parallelization analysis based on county-level data showed that in Kansas, counties with mask mandate had significantly higher case fatality rates than counties without mask mandate, with a risk ratio of 1.85 (95% confidence interval [95% CI]: 1.51-2.10) for COVID-19-related deaths. Even after adjusting for the number of "protected persons," that is, the number of persons who were not infected in the mask-mandated group compared to the no-mask group, the risk ratio remained significantly high at 1.52 (95% CI: 1.24-1.72). By analyzing the excess mortality in Kansas, this study determines that over 95% of this effect can solely be attributed to COVID-19.These findings suggest that mask use might pose a yet unknown threat to the user instead of protecting them, making mask mandates a debatable epidemiologic intervention.The cause of this trend is explained herein using the "Foegen effect" theory; that is, deep re-inhalation of hypercondensed droplets or pure virions caught in facemasks as droplets can worsen prognosis and might be linked to long-term effects of COVID-19 infection. While the "Foegen effect" is proven in vivo in an animal model, further research is needed to fully understand it.

Clinical Data and Health Outcomes for HIV-Positive Patients Diagnosed With COVID-19

Introduction

As we care for patients during the coronavirus pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is important to learn and analyze the health outcomes for HIV-positive patients who have been infected with COVID-19. The clinical course and outcome of COVID-19 among patients with HIV-1 infection are still unknown and novel.

Methods

This is a retrospective cohort study of 34 HIV-positive patients who are diagnosed with COVID-19. The following basic demographic, clinical, and laboratory test information were collected for each patient: age, race/ethnicity, gender, CD4/viral load count before and after COVID-19 diagnosis, clinical symptoms, hospitalizations, antiretroviral medications, and comorbidities. These data were collected from the electronic health record (EHR) and recorded in the study database.

Results

The mean (interquartile range (IQR)) HIV viral load (RNA PCR) after COVID-19 infection was 37,170 (<20-167) copies/mL compared to 25,730 (<20-100) copies/mL before COVID-19 infection. The mean (IQR) CD4+ lymphocyte count prior to and after COVID-19 infection was 583 (101-1139) and 477 (167-821) cells/mm³, respectively. Hypertension (n = 20) was the most prevalent comorbidity found in the cohort of HIV-positive patients. Patients with HIV RNA < 20 copies/mL prior to and after COVID-19 infection were 27 (79.3%) and 17 (73.7%), respectively.

Conclusion

As the pandemic situation keeps on evolving, there will be new findings on how people living with HIV might be affected by SARS-CoV-2. Our findings highlight the importance of larger sample size studies to better understand the management of HIV-positive patients in a pandemic situation.

The impact of economic freedom on COVID-19 pandemic control: the moderating role of equality

BACKGROUND

The absence of pharmaceutical interventions made it particularly difficult to mitigate the first outbreak of coronavirus disease 2019 (COVID-19). The current study investigated how economic freedom and equality influenced the pandemic control process.

METHODS

In Study 1, we assessed the effect of economic freedom and equality on COVID-19 pandemic control from nations worldwide. We collected the cumulative number of confirmed cases over time to perform logistic curve fitting and obtain the speed at which the first wave of the pandemic was controlled, and partial correlation analysis and representational similarity analysis (RSA) were performed to assess the similarity between economic freedom and the speed of pandemic control. In Study 2, an evolutionary game model in which economic freedom affects the speed of pandemic control through optimization of the allocation of available resources was developed. In Study 3, we used experimental manipulation to elucidate the psychological mechanism relating economic freedom and resource allocation.

RESULTS

The economic freedom of nation could be used to positively predict the speed of pandemic control and the related similarity pattern. Equality was found to moderate the correlation and representational similarity between economic freedom and the speed of pandemic control. The evolutionary game model revealed a mechanism whereby economic freedom influences the speed of pandemic control through high resource availability. Furthermore, cooperation was found to be a possible psychological mechanism explaining how economic freedom increases resource availability.

CONCLUSIONS

Economic freedom has a positive effect on the control of the COVID-19 pandemic only among highly egalitarian nations. New interventions are needed to help countries heighten economic freedom and equality as they continue to battle COVID-19 and other collective threats.

Epidemic Risk Assessment by a Novel Communication Station Based Method

The COVID-19 pandemic has caused serious consequences in the last few months and trying to control it has been the most important objective. With effective prevention and control methods, the epidemic has been gradually under control in some countries and it is essential to ensure safe work resumption in the future. Although some approaches are proposed to measure people's healthy conditions, such as filling health information forms or evaluating people's travel records, they cannot provide a fine-grained assessment of the epidemic risk. In this paper, we propose a novel epidemic risk assessment method based on the granular data collected by the communication stations. We first compute the epidemic risk of these stations in different intervals by combining the number of infected persons and the way they pass through the station. Then, we calculate the personnel risk in different intervals according to the station trajectory of the queried person. This method could assess people's epidemic risk accurately and efficiently. We also conduct extensive simulations and the results verify the effectiveness of the proposed method.

Modeling the effect of the vaccination campaign on the COVID-19 pandemic

Population-wide vaccination is critical for containing the SARS-CoV-2 (COVID-19) pandemic when combined with restrictive and prevention measures. In this study we introduce SAIVR, a mathematical model able to forecast the COVID-19 epidemic evolution during the vaccination campaign. SAIVR extends the widely used Susceptible-Infectious-Removed (SIR) model by considering the Asymptomatic (A) and Vaccinated (V) compartments. The model contains several parameters and initial conditions that are estimated by employing a semi-supervised machine learning procedure. After training an unsupervised neural network to solve the SAIVR differential equations, a supervised framework then estimates the optimal conditions and parameters that best fit recent infectious curves of 27 countries. Instructed by these results, we performed an extensive study on the temporal evolution of the pandemic under varying values of roll-out daily rates, vaccine efficacy, and a broad range of societal vaccine hesitancy/denial levels. The concept of herd immunity is questioned by studying future scenarios which involve different vaccination efforts and more infectious COVID-19 variants.

Estimating the impact of influenza on the epidemiological dynamics of SARS-CoV-2

As in past pandemics, co-circulating pathogens may play a role in the epidemiology of coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In particular, experimental evidence indicates that influenza infection can up-regulate the expression of ACE2-the receptor of SARS-CoV-2 in human cells-and facilitate SARS-CoV-2 infection. Here we hypothesized that influenza impacted the epidemiology of SARS-CoV-2 during the early 2020 epidemic of COVID-19 in Europe. To test this hypothesis, we developed a population-based model of SARS-CoV-2 transmission and of COVID-19 mortality, which simultaneously incorporated the impact of non-pharmaceutical control measures and of influenza on the epidemiological dynamics of SARS-CoV-2. Using statistical inference methods based on iterated filtering, we confronted this model with mortality incidence data in four European countries (Belgium, Italy, Norway, and Spain) to systematically test a range of assumptions about the impact of influenza. We found consistent evidence for a 1.8-3.4-fold (uncertainty range across countries: 1.1 to 5.0) average population-level increase in SARS-CoV-2 transmission associated with influenza during the period of co-circulation. These estimates remained robust to a variety of alternative assumptions regarding the epidemiological traits of SARS-CoV-2 and the modeled impact of control measures. Although further confirmatory evidence is required, our results suggest that influenza could facilitate the spread and hamper effective control of SARS-CoV-2. More generally, they highlight the possible role of co-circulating pathogens in the epidemiology of COVID-19.

Biometric covariates and outcome in COVID-19 patients: are we looking close enough?

BACKGROUND

The impact of biometric covariates on risk for adverse outcomes of COVID-19 disease was assessed by numerous observational studies on unstratified cohorts, which show great heterogeneity. However, multilevel evaluations to find possible complex, e.g. non-monotonic multi-variate patterns reflecting mutual interference of parameters are missing. We used a more detailed, computational analysis to investigate the influence of biometric differences on mortality and disease evolution among severely ill COVID-19 patients.

METHODS

We analyzed a group of COVID-19 patients requiring Intensive care unit (ICU) treatment. For further analysis, the study group was segmented into six subgroups according to Body mass index (BMI) and age. To link the BMI/age derived subgroups with risk factors, we performed an enrichment analysis of diagnostic parameters and comorbidities. To suppress spurious patterns, multiple segmentations were analyzed and integrated into a consensus score for each analysis step.

RESULTS

We analyzed 81 COVID-19 patients, of whom 67 required mechanical ventilation (MV). Mean mortality was 35.8%. We found a complex, non-monotonic interaction between age, BMI and mortality. A subcohort of patients with younger age and intermediate BMI exhibited a strongly reduced mortality risk (p < 0.001), while differences in all other groups were not significant. Univariate impacts of BMI or age on mortality were missing. Comparing MV with non-MV patients, we found an enrichment of baseline CRP, PCT and D-Dimers within the MV group, but not when comparing survivors vs. non-survivors within the MV patient group.

CONCLUSIONS

The aim of this study was to get a more detailed insight into the influence of biometric covariates on the outcome of COVID-19 patients with high degree of severity. We found that survival in MV is affected by complex interactions of covariates differing to the reported covariates, which are hidden in generic, non-stratified studies on risk factors. Hence, our study suggests that a detailed, multivariate pattern analysis on larger patient cohorts reflecting the specific disease stages might reveal more specific patterns of risk factors supporting individually adapted treatment strategies.

Longer-term mortality following SARS-CoV-2 infection in people with severe mental illness: retrospective case-matched study

Persisting symptoms and dysfunction after SARS-CoV-2 infection have frequently been observed. However, information on the aftermath of COVID-19 is inadequate. We followed up people with severe mental illness (SMI) infected with SARS-CoV-2, and evaluated their longer-term mortality, using data from Cambridgeshire and Peterborough NHS Foundation Trust, UK. We examined the time course and duration of mortality risk from the point of diagnosis. After SARS-CoV-2 infection, people with SMI had a substantially higher risk of death (hazard ratio (HR) = 5.16, 95% confidence interval (CI) 1.56-17.03; P = 0.007) during the first 28 days and during the following 28-60 days (HR = 2.96, 95% CI 1.21-7.26; P = 0.018) than those without infection, but after 60 days the additional risk of death was no longer significant (HR = 2.33, 95% CI 0.83-6.53; P = 0.107).