Cumulative Infection Rate Research Articles

Estimating cumulative infection rate of COVID-19 after adjusting the dynamic zero-COVID policy in China

Estimating cumulative infection rate of COVID-19 after adjusting the dynamic zero-COVID policy in China

Infection Rates and Symptomatic Proportion of SARS-CoV-2 and Influenza in Pediatric Population, China, 2023.

We conducted a longitudinal cohort study of SARS-CoV-2 and influenza rates in childcare centers and schools in Wuxi, China, collecting 1,760 environmental samples and 9,214 throat swabs from 593 students (regardless of symptoms) in weekly collections during February-June 2023. We estimated a cumulative infection rate of 124.8 (74 episodes)/1,000 persons for SARS-CoV-2 and 128.2 (76 episodes)/1,000 persons for influenza. The highest SARS-CoV-2 infection rate was in persons 18 years of age, and for influenza, in children 4 years of age. The asymptomatic proportion of SARS-CoV-2 was 59.6% and 66.7% for influenza; SARS-CoV-2 symptomatic proportion was lower in 16-18-year-olds than in 4-6-year-olds. Only samples from frequently touched surface tested positive for SARS-CoV-2 (4/1,052) and influenza (1/1,052). We found asynchronous circulation patterns of SARS-CoV-2 and influenza, similar to trends in national sentinel surveillance. The results support vaccination among pediatric populations and other interventions, such as environmental disinfection in educational settings.

Rapid and extensive SARS-CoV-2 Omicron variant infection wave revealed by wastewater surveillance in Shenzhen following the lifting of a strict COVID-19 strategy

Wastewater-based epidemiology (WBE) has emerged as a promising tool for monitoring the spread of COVID-19, as SARS-CoV-2 can be shed in the faeces of infected individuals, even in the absence of symptoms. This study aimed to optimize a prediction model for estimating COVID-19 infection rates based on SARS-CoV-2 RNA concentrations in wastewater, and reveal the infection trends and variant diversification in Shenzhen, China following the lifting of a strict COVID-19 strategy. Faecal samples (n = 4337) from 1204 SARS-CoV-2 infected individuals hospitalized in a designated hospital were analysed to obtain Omicron variant-specific faecal shedding dynamics. Wastewater samples from 6 wastewater treatment plants (WWTPs) and 9 pump stations, covering 3.55 million people, were monitored for SARS-CoV-2 RNA concentrations and variant abundance. We found that the viral load in wastewater increased rapidly in December 2022 in the two districts, demonstrating a sharp peak in COVID-19 infections in late-December 2022, mainly caused by Omicron subvariants BA.5.2.48 and BF.7.14. The prediction model, based on the mass balance between total viral load in wastewater and individual faecal viral shedding, revealed a surge in the cumulative infection rate from <0.1 % to over 70 % within three weeks after the strict COVID-19 strategy was lifted. Additionally, 39 cryptic SARS-CoV-2 variants were identified in wastewater, in addition to those detected through clinical surveillance. These findings demonstrate the effectiveness of WBE in providing comprehensive and efficient assessments of COVID-19 infection rates and identifying cryptic variants, highlighting its potential for monitoring emerging pathogens with faecal shedding.

Effects of subcutaneous drain on wound dehiscence and infection in gynecological midline laparotomy: Secondary analysis of a Korean Gynecologic Oncology Group study (KGOG 4001)

ObjectiveTo identify the effects of subcutaneous drain insertion on wound dehiscence and infection in patients who underwent gynecological midline laparotomy. MethodsThis analysis identified the secondary endpoints of the KGOG 4001 study, a prospective, multicenter, non-blind, randomized controlled trial. Patients scheduled to undergo midline laparotomy for gynecological diseases and, with body mass index<35 kg/m2, were randomized (1:1) to treatment (with subcutaneous drain) and control (without subcutaneous drain) groups from February 2021 to December 2021. We compared the incidence rate of wound dehiscence 4 weeks post-surgery and the cumulative incidence rate of wound dehiscence and infection up to 4 weeks post-surgery between the two groups. ResultsOf 174 patients randomized to the treatment (n = 84) and control (n = 90) groups, 12 were excluded owing to loss to follow-up; finally, 162 patients (treatment, n = 79; control, n = 83) were included in intention-to-treat analysis. The frequency of cancer surgery (79.7 % vs. 77.1 %, p = 0.683), mean surgery time (227.7 vs. 226.7 min, p = 0.960), and mean wound length (24.2 vs. 24.3 cm, p = 0.933) were comparable between two groups. No significant differences were observed in the incidence rate of wound dehiscence 4 weeks post-surgery (1.3 % vs. 2.4 %, p > 0.999), cumulative incidence rate of wound dehiscence (8.9 % vs. 6.0 %, p = 0.491), and cumulative incidence rate of wound infection (1.3 % vs. 0.0 %, p = 0.488) up to 4 weeks post-surgery between the two groups. ConclusionSubcutaneous drain insertion is not associated with a significant improvement in the incidence of wound dehiscence and infection in patients who undergo gynecological midline laparotomy. Clinical trial registrationClinicalTrials.gov, NCT04643197.

Patients, healthcare providers, and general population preferences for hemodialysis vascular access: a discrete choice experiment.

A patient-centered dialysis treatment option requires an understanding of patient preferences for alternative vascular accesses and nephrologists often face difficulties when recommending vascular access to end-stage kidney disease (ESKD) patients. We aimed to quantify the relative importance of various vascular access characteristics to patients, healthcare providers and general population, and how they affect acceptability for patients and healthcare providers. In a discrete choice experiment, patients with maintenance hemodialysis (MHD), healthcare providers, and individuals from the general population were invited to respond to a series of hypothetical vascular access scenarios that differed in five attributes: cumulative patency, infection rate, thrombosis rate, cost, and time to maturation. We estimated the respondents' preference heterogeneity and relative importance of the attributes with a mixed logit model (MXL) and predicted the willingness to pay (WTP) of respondents via a multinomial logit model (MNL). Healthcare providers (n = 316) and the general population (n = 268) exhibited a favorable inclination toward longer cumulative patency, lower access infection rate and lower access thrombosis rate. In contrast, the patients (n = 253) showed a preference for a 3-year cumulative patency, 8% access infection rate, 35% access thrombosis rate and 1.5 access maturity time, with only the 3-year cumulative patency reaching statistical significance. Among the three respondent groups, the general population found cumulative patency less important than healthcare providers and patients did. Patients demonstrated the highest WTP for cumulative patency, indicating a willingness to pay an extra RMB$24,720(US$3,708) for each additional year of patency time. Patients and healthcare providers had a strong preference for vascular access with superior patency. While the general population preferred vascular access with lower thrombosis rates. These results indicate that most patients prefer autogenous arteriovenous fistula (AVF) as an appropriate choice for vascular access due to its superior patency and lower complications than other vascular access types.

Survival, cumulative hospital days and infectious complications in urgent-start PD compared with urgent-start HD.

Urgent-start peritoneal dialysis (PD) carries a similar efficacy and safety profile compared to urgent-start haemodialysis (HD) but is only sparsely applied due to resource issues and concerns of complication risks. Furthermore, few data exist on adverse outcomes associated with central venous catheter (CVC) insertions in urgent-start HD patients. Thus, we sought to compare patient and dialysis-related outcomes in patients undergoing urgent-start PD or HD. All patients initiating urgent-start PD in a tertiary research hospital in 2005-2018 were included in this retrospective, single-centre, comparative study and matched with urgent-start HD patients of similar age and chronic kidney disease aetiology. All urgent-start PDs were initiated within 72 h after catheter insertion, and urgent-start HDs were performed via a CVC. All analyses were performed at 3 months and at 1 year of follow-up, respectively. Thirty-three patients who commenced urgent-start PD and 58 matched urgent-start HD control patients were included. Altogether, 26 patients (29%; PD: 36%, HD 24%) died within the 1-year follow-up, and patient survival was similar at 3 months (hazard ratio (HR): 1.15, 95% confidence interval (CI): 0.35-3.81, p = 0.82) and at 1 year of follow-up (HR: 0.64, 95% CI: 0.30-1.39, p = 0.26) between the study groups. There were no differences in the total kidney replacement therapy (KRT)-related infection rate (p = 0.66) or cumulative first-year hospital care days (p = 0.43) between the treatment groups. Altogether, 139 CVCs were inserted during the 1-year follow-up. The number of CVCs per patient was associated with the emergence of blood culture-positive bacteraemia and increased cumulative first-year hospital care days. Patient survival, cumulative first-year hospital care days and total KRT-related infection rate at 3 months and 1-year follow-up are similar between urgent-start PD and urgent-start HD patients. Furthermore, CVC insertion rate is associated with incident blood culture-positive bacteraemia and increased cumulative first-year hospital care days.

SARS-CoV-2 seroprevalence in Nova Scotia blood donors.

SARS-CoV-2 seroprevalence monitors cumulative infection rates irrespective of case testing protocols. We aimed to describe Nova Scotia blood donor seroprevalence in relation to public health policy and reported data over the course of the COVID-19 pandemic (May 2020 to August 2022). Monthly random Nova Scotia blood donation samples (24,258 in total) were tested for SARS-CoV-2 infection antibodies (anti-nucleocapsid) from May 2020 to August 2022, and vaccination antibodies (anti-spike) from January 2021 to August 2022. Multivariable logistic regression for infection antibodies and vaccination antibodies separately with month, age, sex, and racialization identified independent predictors. The provincial nucleic acid amplification test (NAAT)-positive case rate over the pandemic was calculated from publicly available data. Anti-N seroprevalence was 3.8% in January 2022, increasing to 50.8% in August 2022. The general population COVID-19 case rate was 3.5% in January 2022, increasing to 12.5% in August 2022. The percentage of NAAT-positive samples in public health laboratories increased from 1% in November 2021 to a peak of 30.7% in April 2022 with decreasing numbers of tests performed. Higher proportions of younger donors as well as Black, Indigenous, and racialized blood donors were more likely to have infection antibodies (p < 0.01). Vaccination antibodies increased to 100% over 2021, initially in older donors (60+ years), and followed by progressively younger age groups. SARS-CoV-2 infection rates were relatively low in Nova Scotia until the more contagious Omicron variant dominated, after which about half of Nova Scotia donors had been infected despite most adults being vaccinated (although severity was much lower in vaccinated individuals). Most COVID-19 cases were detected by NAAT until Omicron arrived. When NAAT testing priorities focused on high-risk individuals, infection rates were better reflected by seroprevalence.

Global trends in COVID-19 incidence and case fatality rates (2019-2023): a retrospective analysis.

Analyzing and comparing COVID-19 infection and case-fatality rates across different regions can help improve our response to future pandemics. We used public data from the WHO to calculate and compare the COVID-19 infection and case-fatality rates in different continents and income levels from 2019 to 2023. The Global prevalence of COVID-19 increased from 0.011 to 0.098, while case fatality rates declined from 0.024 to 0.009. Europe reported the highest cumulative infection rate (0.326), with Africa showing the lowest (0.011). Conversely, Africa experienced the highest cumulative case fatality rates (0.020), with Oceania the lowest (0.002). Infection rates in Asia showed a steady increase in contrast to other continents which observed initial rises followed by decreases. A correlation between economic status and infection rates was identified; high-income countries had the highest cumulative infection rate (0.353) and lowest case fatality rate (0.006). Low-income countries showed low cumulative infection rates (0.006) but the highest case fatality rate (0.016). Initially, high and upper-middle-income countries experienced elevated initial infection and case fatality rates, which subsequently underwent significant reductions. COVID-19 rates varied significantly by continent and income level. Europe and the Americas faced surges in infections and low case fatality rates. In contrast, Africa experienced low infection rates and higher case fatality rates, with lower- and middle-income nations exceeding case fatality rates in high-income countries over time.

Problems of cattle health improvement and protection of population against viral diarrhoea under current conditions of animal husbandry

The article presents the dynamics of the prevalence of bovine viral diarrhoea virus (BVDV) in livestock enterprises of the Ural region in the period 2018-2023. Retrospective and operational analysis of monitoring data showed that the epizootic situation was consistently tense. The share of unfavourable livestock enterprises ranged from 18.2% to 33.3%. The cumulative infection rate in different age groups of cattle ranged from 2.3% to 23.3%. Clinical symptoms of acute course of BVDV-infection in examined cattle were registered in 10.9% of cases; persistent form of BVDV-infection - in 16.5% of cases; subclinical course of BVDV-infection and latent form - in 73.6% of cases. Phylogenetic analysis of BVDV isolates obtained from animal biosamples showed that 81.8% of isolates belong to the Cp-biotype of BVDV, 18.2% of isolates - to the Ncpbiotype of BVDV. It has been established that the intensity of pathogen eradication decreases in livestock enterprises when the regulations of specific prophylaxis are violated, as evidenced by the increase in the number of animals carrying BVDV by 4%; calves with diseases caused by BVDV infection - by 2.5%; cows and heifers with reproductive losses - by 5.7%.

A Silent Outbreak of Hepatitis E Virus (HEV) Infection or False-Positive Reaction of Anti-HEV IgM after COVID-19 Vaccination? Epidemiological Investigation of an Outbreak in a Korean Factory Complex in 2022.

To investigate a reported outbreak of presumed hepatitis E virus (HEV) infection in a Korean food manufacturing facility and to explore the association between anti-HEV immunoglobulin M (IgM) positivity and coronavirus disease 2019 (COVID-19) infection or vaccination. Twenty-four cases of anti-HEV IgM positivity were reported among 646 workers at the facility in 2022. An epidemiological investigation was conducted, comprising HEV-RNA testing of blood and environmental samples, analysis of group meal records, and an association between anti-HEV IgM positivity and confirmed COVID-19 infection or vaccination. All 24 patients were asymptomatic, with cases spread sporadically across the facility. HEV RNA was not detected in the serum or environmental samples. Four out of 340 meals (1.2%) showed a significantly higher proportion of anti-HEV positivity in each meal intake group than in the non-intake group on certain days. Although the cumulative rate of COVID-19 infection showed no difference, the anti-HEV IgM positive group showed significantly higher proportions of >2 doses of COVID-19 vaccination (83.3% vs 48.7%, p=0.021), vaccination within 90 days (45.8% vs 19.7%, p=0.008), and having the Moderna vaccine administered as the last vaccine (75.0% vs 14.5%, p<0.001) than those of the anti-HEV negative group. In four multivariable models, three or more COVID-19 vaccinations and the Moderna vaccine as the last vaccine were consistently associated with anti-HEV IgM positivity, while the specific day group meal intake was also a significant factor. This epidemiological investigation showed that anti-HEV IgM positivity may occur as a false-positive result related to COVID-vaccination over three times and use of the Moderna vaccine, although a portion of true HEV infection may not be excluded.

COVID on campus: An empirical analysis of COVID infection rates at U.S. colleges and universities

We provide an empirical analysis of the determinants of cumulative COVID infection rates at 1069 U.S. colleges and universities during the 2020–21 academic year. We propose that financially constrained educational institutions faced a trade-off between the reduction of COVID infection risks and an institution's educational, social, reputational, and financial goals. We find that cumulative infection rates are higher at wealthier institutions, measured by higher endowments per student or higher tuition rates. Institutions with lower enrollment yields in admissions also have higher COVID infection rates, perhaps reflecting the greater influence of student preferences on decision making at these institutions. Economies of scale in COVID mitigation emerge gradually over the course of the year. Finally, COVID infection rates do not differ significantly for otherwise similar public and private institutions in states with Democratic governors, but they are significantly higher for public institutions in states with Republican governors.

Optimizing the nucleic acid screening strategy to mitigate regional outbreaks of SARS-CoV-2 Omicron variant in China: a modeling study

BackgroundThe Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spreads rapidly and insidiously. Coronavirus disease 2019 (COVID-19) screening is an important means of blocking community transmission in China, but the costs associated with testing are high. Quarantine capacity and medical resources are also threatened. Therefore, we aimed to evaluate different screening strategies to balance outbreak control and consumption of resources.MethodsA community network of 2000 people, considering the heterogeneities of household size and age structure, was generated to reflect real contact networks, and a stochastic individual-based dynamic model was used to simulate SARS-CoV-2 transmission and assess different whole-area nucleic acid screening strategies. We designed a total of 87 screening strategies with different sampling methods, frequencies of screening, and timings of screening. The performance of these strategies was comprehensively evaluated by comparing the cumulative infection rates, the number of tests, and the quarantine capacity and consumption of medical resource, which were expressed as medians (95% uncertainty intervals, 95% UIs).ResultsTo implement COVID-19 nucleic acid testing for all people (Full Screening), if the screening frequency was four times/week, the cumulative infection rate could be reduced to 13% (95% UI: 1%, 51%), the miss rate decreased to 2% (95% UI: 0%, 22%), and the quarantine and medical resource consumption was lower than higher-frequency Full Screening or sampling screening. When the frequency of Full Screening increased from five to seven times/week (which resulted in a 2581 increase in the number of tests per positive case), the cumulative infection rate was only reduced by 2%. Screening all people weekly by splitting them equally into seven batches could reduce infection rates by 73% compared to once per week, which was similar to Full Screening four times/week. Full Screening had the highest number of tests per positive case, while the miss rate, number of tests per positive case, and hotel quarantine resource consumption in Household-based Sampling Screening scenarios were lower than Random Sampling Screening. The cumulative infection rate of Household-based Sampling Screening or Random Sampling Screening seven times/week was similar to that of Full Screening four times/week.ConclusionsIf hotel quarantine, hospital and shelter hospital capacity are seriously insufficient, to stop the spread of the virus as early as possible, high-frequency Full Screening would be necessary, but intermediate testing frequency may be more cost-effective in non-extreme situations. Screening in batches is recommended if the testing capacity is low. Household-based Sampling Screening is potentially a promising strategy to implement.Graphical

Compact living or policy inaction? Effects of urban density and lockdown on the COVID-19 outbreak in the US.

The coronavirus pandemic has reignited the debate over urban density. Popular media has been quick to blame density as a key contributor to rapid disease transmission, questioning whether compact cities are still a desirable planning goal. Past research on the density-pandemic connection have produced mixed results. This article offers a critical perspective on this debate by unpacking the effects of alternative measures of urban density, and examining the impacts of mandatory lockdowns and the stringency of other government restrictions on cumulative Covid-19 infection and mortality rates during the early phase of the pandemic in the US. Our results show a consistent positive effect of density on Covid-19 outcomes across urban areas during the first six months of the outbreak. However, we find modest variations in the density-pandemic relationship depending on how densities are measured. We also find relatively longer duration mandatory lockdowns to be associated with lower infection and mortality rates, and lockdown duration's effect to be relatively more pronounced in high-density urban areas. Moreover, we find that the timing of lockdown imposition and the stringency of the government's response additionally influence Covid-19 outcomes, and that the effects vary by urban density. We argue that the adverse impact of density on pandemics could be mitigated by adopting strict lockdowns and other stringent human mobility and interaction restriction policies in a spatially targeted manner. Our study helps to inform current and future government policies to contain the virus, and to make our cities more resilient against future shocks and threats.

Spatiotemporal changes of COVID-19 outbreak in Shanghai

Objective: To clarify the epidemiological characteristics and spatiotemporal clustering dynamics of COVID-19 in Shanghai in 2022. Methods: The COVID-19 data presented on the official websites of Municipal Health Commissions of Shanghai during March 1, 2022 and May 31, 2022 were collected for a spatial autocorrelation analysis by GeoDa software. A logistic growth model was used to fit the epidemic situation and make a comparison with the actual infection situation. Results: Pudong district had the highest number of symptomatic and asymptomatic infectants, accounting for 29.30% and 35.58% of the total infectants. Differences in cumulative attack rates and infection rates among 16 districts (P<0.001) were significant. The rates were significantly higher in Huangpu district than in other districts. The attack rate of COVID-19 from March 1, 2022 to May 31, 2022 had a global spatial positive correlation (P<0.05). Spatial distribution of COVID-19 attack rate was different at different periods. The global autocorrelation coefficient from March 16 to March 29, April 6 to April 12 and May 18 to May 24 had no statistical significance (P>0.05). Our local autocorrelation analysis showed that 22 high-high clustering areas were detected in eight periods.The high-risk hot-spot areas have experienced a "less-more-less" change process. The growth model fitting results were consistent with the actual infection situation. Conclusion: There was a clear spatiotemporal correlation in the distribution of COVID-19 in Shanghai. The comprehensive prevention and control measures of COVID-19 epidemic in Shanghai have effectively prohibited the growth of the epidemic, not only curbing the spatially spread of high-risk epidemic areas, but also reducing the risk of transmission to other cities.

The FBProphet forecasting model to evaluate the spread of COVID-19 pandemic: A machine learning approach

The pandemic due to the COVID-19 virus causes financial disruption almost in all the countries in the world. It breaks the supply chain management system, especially in the food, energy and industrial sectors. This COVID-19 pandemic hits the world over different periods of time in a year and continues from the year 2020. It is important to predict the severity of the infection rate and death rate to prepare and take necessary actions for the future. But it is still difficult to forecast the outbreaks for the long term with higher accuracy. Here, a machine learning (ML) algorithm, FBProphet, is employed to forecast COVID-19 outbreaks in Bangladesh as a time series forecasting. This model predicts the daily and cumulative infection and death rates with a high and low error rate. Due to the seasonality feature of the FBProphet model, it can predict the different waves of outbreaks with higher accuracy. Furthermore, cross-validation of the predicted results has been performed to ensure the accuracy of the results. The effort and the outcomes of this computational study will help the decision makers of this country to take necessary actions for the future which can save lives and prevent economic disruption.

Causal Modeling to Mitigate Selection Bias and Unmeasured Confounding in Internet-Based Epidemiology of COVID-19: Model Development and Validation.

BackgroundSelection bias and unmeasured confounding are fundamental problems in epidemiology that threaten study internal and external validity. These phenomena are particularly dangerous in internet-based public health surveillance, where traditional mitigation and adjustment methods are inapplicable, unavailable, or out of date. Recent theoretical advances in causal modeling can mitigate these threats, but these innovations have not been widely deployed in the epidemiological community.ObjectiveThe purpose of our paper is to demonstrate the practical utility of causal modeling to both detect unmeasured confounding and selection bias and guide model selection to minimize bias. We implemented this approach in an applied epidemiological study of the COVID-19 cumulative infection rate in the New York City (NYC) spring 2020 epidemic.MethodsWe collected primary data from Qualtrics surveys of Amazon Mechanical Turk (MTurk) crowd workers residing in New Jersey and New York State across 2 sampling periods: April 11-14 and May 8-11, 2020. The surveys queried the subjects on household health status and demographic characteristics. We constructed a set of possible causal models of household infection and survey selection mechanisms and ranked them by compatibility with the collected survey data. The most compatible causal model was then used to estimate the cumulative infection rate in each survey period.ResultsThere were 527 and 513 responses collected for the 2 periods, respectively. Response demographics were highly skewed toward a younger age in both survey periods. Despite the extremely strong relationship between age and COVID-19 symptoms, we recovered minimally biased estimates of the cumulative infection rate using only primary data and the most compatible causal model, with a relative bias of +3.8% and –1.9% from the reported cumulative infection rate for the first and second survey periods, respectively.ConclusionsWe successfully recovered accurate estimates of the cumulative infection rate from an internet-based crowdsourced sample despite considerable selection bias and unmeasured confounding in the primary data. This implementation demonstrates how simple applications of structural causal modeling can be effectively used to determine falsifiable model conditions, detect selection bias and confounding factors, and minimize estimate bias through model selection in a novel epidemiological context. As the disease and social dynamics of COVID-19 continue to evolve, public health surveillance protocols must continue to adapt; the emergence of Omicron variants and shift to at-home testing as recent challenges. Rigorous and transparent methods to develop, deploy, and diagnosis adapted surveillance protocols will be critical to their success.

Community Roots of COVID-19 Infection Rates Between Population Composition and Regional Systems in Romania.

This is an analysis of conditions favouring the cumulative COVID-19 infection rates between February 2020 and April 2021 in Romania, as an Eastern European society, at the local community level. What are the socio-demographic and location profiles of the local communities by considering their infection rates with SARS-COV-2 at the beginning of the pandemia as a dependent variable? This is the research question that structured the approach. The general hypothesis that is tested is that reported infections with the new coronavirus are higher in communities of higher social interactions. The theoretical model is tested by multiple regression analysis working on more than 2500 local communities, out of the 3200 local administrative units of the country. Data basis for testing the model are coming from the National Institute of Public Health and the National Institute of Statistics. Higher COVID infection rates are favoured by socio-human capital, the regional capital, migration abroad experience, and modernity at a local level. Other factors are captured by the cultural areas as subregions of historical regions of the country, formed by neighboured similar counties. Nuclei of higher infections with COVID-19 are located in developed communities around large cities, high modernity areas, and communities of high emigration abroad. Principles for health public policies are formulated at the end by considering the role of decentralisation, and better ways to do a rapid and good diagnosis at local levels. To our knowledge, this is one of the very few studies that address determinants of COVID-19 infections at the local community level for a whole country in Europe. New research questions are formulated as an outcome of conclusions. They could be answered only by supplementary multilevel research. Limitations of analysis are derived from the fact that we are using only ecological, spatially aggregated data, and not multilevel ones. Relations that were recorded to the community could not be transferred to the individual level.

The Association Between Exposure to COVID-19 and Mental Health Outcomes Among Healthcare Workers.

Due to the unprecedented impact of the COVID-19 pandemic on health care systems, there has been great interest in the mental wellbeing of healthcare workers. While most studies investigated mental health outcomes among frontline vs. non-frontline healthcare workers, little is known about the impact of various work-related variables. The present study aimed to examine the association between work-related [i.e., having contact with COVID-19 patients, being redeployed due to the pandemic and availability of sufficient personal protective equipment (PPE)] and subjective (i.e., worries about getting infected or infecting others) exposures and self-reported mental health outcomes (i.e., psychological distress, depressive symptoms, and posttraumatic stress symptoms). Between February and May 2021, 994 healthcare workers employed at a variety of healthcare settings in the Netherlands filled out an online survey as part of the COVID-19 HEalth caRe wOrkErS (HEROES) study. Mental health outcomes were measured using the General Health Questionnaire-12, the Patient Health Questionnaire-9, and the Primary Care PTSD Screen for DSM-5. Approximately 13% reported depressive symptoms, 37% experienced psychological distress, and 20% reported posttraumatic stress symptoms. Multilevel linear models consisted of three levels: individual (work-related and subjective exposures), healthcare center (aggregated redeployment and availability of sufficient PPE), and regional (cumulative COVID-19 infection and death rates). Worries about infection were associated with all three mental health outcomes, whereas insufficient PPE was associated with psychological distress and depressive symptoms. There were no differences in outcomes between healthcare centers or provinces with different COVID-19 infection and death rates. Our findings highlight the importance of adequate PPE provision and the subjective experience of the COVID-19 pandemic. These factors should be part of interventions aimed at mitigating adverse mental health outcomes among healthcare workers during the COVID-19 pandemic.

SARS-CoV-2 transmission, persistence of immunity, and estimates of Omicron's impact in South African population cohorts.

Understanding the build-up of immunity with successive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and the epidemiological conditions that favor rapidly expanding epidemics will help facilitate future pandemic control. We analyzed high-resolution infection and serology data from two longitudinal household cohorts in South Africa to reveal high cumulative infection rates and durable cross-protective immunity conferred by prior infection in the pre-Omicron era. Building on the history of past exposures to different SARS-CoV-2 variants and vaccination in the more representative urban cohort given South Africa’s high urbanization rate, we used mathematical models to explore the fitness advantage of the Omicron variant and its epidemic trajectory. Modelling suggests the Omicron wave likely infected a large fraction (44% - 81%) of the population, leaving a complex landscape of population immunity primed and boosted with antigenically distinct variants. We project that future SARS-CoV-2 resurgences are likely under a range of scenarios of viral characteristics, population contacts, and residual cross-protection.

The impact of the Coronavirus pandemic on New York City real estate: First evidence.

We investigate whether pandemic‐induced contagion disamenities and income effects arising due to COVID‐related unemployment adversely affected real estate prices of one‐ or two‐family owner‐occupied properties across New York City (NYC). First, ordinary least squares hedonic results indicate that greater COVID case numbers are concentrated in neighborhoods with lower‐valued properties. Second, we use a repeat‐sales approach for the period 2003–2020, and we find that both the possibility of contagion and pandemic‐induced income effects adversely impacted home sale prices. Estimates suggest sale prices fell by roughly $60,000 or around 8% in response to both of the following: 1000 additional infections per 100,000 residents and a 10‐percentage point increase in unemployment in a given Modified Zip Code Tabulation Area (MODZCTA). These price effects were more pronounced during the second wave of infections. On the basis of cumulative MODZCTA infection rates through 2020, the estimated COVID‐19 price discount ranged from approximately 1% to 50% in the most affected neighborhoods, and averaged 14%. The contagion effect intensified in the more affluent, but less densely populated NYC neighborhoods, while the income effect was more pronounced in the most densely populated neighborhoods with more rental properties and greater population shares of foreign‐born residents. This disparity implies the pandemic may have been correlated with a wider gap in housing wealth in NYC between homeowners in lower‐priced and higher‐priced neighborhoods.