Epidemic Peak Research Articles

Hospital Strain and COVID-19 Fatality - England, April 2020-March 2022.

During the coronavirus disease 2019 (COVID-19) pandemic, tremendous efforts have been made in countries to suppress epidemic peaks and strengthen hospital services to avoid hospital strain and ultimately reduce the risk of death from COVID-19. However, there is limited empirical evidence that hospital strain increases COVID-19 deaths. We found the risk of death from COVID-19 was linearly associated with the number of patients currently in hospitals, a measure of hospital strain, before the Omicron period. This risk could be increased by a maximum of 188.0%. These findings suggest that any (additional) effort to reduce hospital strain would be beneficial during early large COVID-19 outbreaks and possibly also others alike. During an Omicron outbreak, vigilance remains necessary to prevent excess deaths caused by hospital strain as happened in Hong Kong Special Administrative Region, China.

Mexico Distribution of COVID-19 in Mexico and obesity comorbidities

The COVID-19 pandemic, caused by the SARS-​CoV-2 virus, began in China in December 2019. The World Health Organization (WHO) declared it a pandemic on March 11, 2020. In Mexico, the first cases were registered in February 2020. On December 31st, 2021, three epidemic waves or peaks were registered, when some cases and deaths had a differentiated space-​time behavior in the national territory, in which comorbidities, as one of the socio-​spatial conditioning factors, also had an influence. The objective of this work is to analyze morbidity and mortality due to SARS-​CoV-2, caused by the COVID-19 disease, with obesity comorbidities: high blood pressure, diabetes, obesity, and cardiovascular disease, accumulated from the years 2020 and 2021. Methodology. For this research, a literature review was carried out on the role of comorbidities in deaths from COVID-19. The universe of study is Mexico. The spatial disaggregation scale is atthe level ofthe federal entity or state-owned, with 32 spots. The sources of information are the Daily Technical Communications of the COVID-19 from the Government of Mexico; the Ministry of Health; the General Directorate of Epidemiology (DGE); information from the National Institute of Statistics and Geography (INEG), as well as information from the Geosalud México site. The analysis methods are the implementation of a geographic database, at the state-​owned level, from which tables, maps and the generation of indicators (absolute values, percentages and ratio) were prepared, as well as thematic cartography using GIS software.

Socio-demographical Profile of 7285 SARS-Cov-2 Positive Early Cases; Comparison with National Four Epidemic Waves.

Background: Objectives were to investigate aspects of the COVID-19 epidemics via testing the individuals who were referred to Aramesh Medical Laboratory in Tehran and to integrate the molecular results with epidemiological data since the beginning of the epidemic. Methods: In this cross-sectional Study 77528 outpatients were referred to Aramesh Medical laboratory by physicians for the diagnosis of SARS-CoV-2 infection between March 2019 and May 2021. Viral acid nucleic extracted from nasal and throat specimens and subsequently amplified using Reverse Transcriptase Real-Time PCR. Laboratory data including Ct values compared with epidemic peaks of COVID-19 countrywide. Statistical Analysis was done by SPSS 21 Software. Results: 14312 (18.46%) tested positive.36.5% of the positive cases were in the 30 to 39 years old age group. The positive result rate was significantly different based on months, ranging from 6% to 28%, compatible with four recognized epidemic peaks encompassing the end of March through the first week of April (first epidemic peak), from June to July 2020 (second epidemic peak), October until mid of November 2020 (third epidemic wave) followed by the end of April to May 2021 (until the end period of study, in the middle of 4th peak). In 37.8% of cases, the Ct value was between 21 and 28. Two separate trends were seen for Ct ≤ 25 and Ct ≤ 20 for the first and fourth epidemic peaks, respectively. There was an association between the number of total monthly positive results and total deaths in the country, especially with the second to third peaks (in the course of summer 2020) and fourth epidemic peak. Conclusion: It might be useful to consider laboratory admission rates as an indicator for changes in the epidemic level in the country to continue the SARS-CoV-2 surveillance in accordance with public decision-makers.

COVID-19: A Comparative Study of Contagions Peaks in Cities from Europe and the Americas

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a group of viruses that provoke illnesses ranging from the common cold to more serious illnesses such as pneumonia. COVID-19 started in China and spread rapidly from a single city to an entire country in just 30 days and to the rest of the world in no more than 3 months. Several studies have tried to model the behavior of COVID-19 in diverse regions, based on differential equations of the SIR and stochastic SIR type, and their extensions. In this article, a statistical analysis of daily confirmed COVID-19 cases reported in eleven different cities in Europe and America is conducted. Log-linear models are proposed to model the rise or drop in the number of positive cases reported daily. A classification analysis of the estimated slopes is performed, allowing a comparison of the eleven cities at different epidemic peaks. By rescaling the curves, similar behaviors among rises and drops in different cities are found, independent of socioeconomic conditions, type of quarantine measures taken, whether more or less restrictive. The log-linear model appears to be suitable for modeling the incidence of COVID-19 both in rises and drops.

The Transmission Dynamics of a Compartmental Epidemic Model for COVID-19 with the Asymptomatic Population via Closed-Form Solutions.

Unlike previous viral diseases, COVID-19 has an "asymptomatic" group that has no symptoms but can still spread the disease to others at the same rate as symptomatic patients who are infected. In the literature, the mass action or standard incidence rates are considered for compartmental models with asymptomatic compartment for studying the transmission dynamics of COVID-19, but the quarantined adjusted incidence rate is not. To bridge this gap, we developed a Susceptible Asymptomatic Infectious Quarantined (SAIQ) model with a Quarantine-Adjusted (QA) incidence to investigate the emergence and containment of COVID-19. COVID-19 models are investigated using various methods, but only a few studies take into account closed-form solutions. The knowledge of closed-form solutions simplifies the construction of the various epidemic indicators that describe the epidemic phenomenon and makes the sensitivity analysis to variations in the data under consideration possible. The closed-form solutions of the systems of four nonlinear first-order ordinary differential equations (ODEs) are established. The Epidemic Peak (EP), Force of Infection (FOI) and Rate of Infection (ROI) are the important indicators for the control and prevention of disease. We examined these indicators using closed-form solutions and particular parameter values. Different disease control scenarios are thoroughly examined. The four scenarios to analyze COVID-19 propagation and containment are (i) lockdown, (ii) quarantine and other preventative measures, (iii) stabilizing the basic reproduction rate to a level where the pandemic can be contained and (iv) containing the epidemic through an appropriate combination of lockdown, quarantine and other preventative measures.

Neutralizing Antibody Responses Elicited by Inactivated Whole Virus and Genetic Vaccines against Dominant SARS-CoV-2 Variants during the Four Epidemic Peaks of COVID-19 in Colombia.

Several SARS-CoV-2 variants of concern (VOC) and interest (VOI) co-circulate in Colombia, and determining the neutralizing antibody (nAb) responses is useful to improve the efficacy of COVID-19 vaccination programs. Thus, nAb responses against SARS-CoV-2 isolates from the lineages B.1.111, P.1 (Gamma), B.1.621 (Mu), AY.25.1 (Delta), and BA.1 (Omicron), were evaluated in serum samples from immunologically naïve individuals between 9 and 13 weeks after receiving complete regimens of CoronaVac, BNT162b2, ChAdOx1, or Ad26.COV2.S, using microneutralization assays. An overall reduction of the nAb responses against Mu, Delta, and Omicron, relative to B.1.111 and Gamma was observed in sera from vaccinated individuals with BNT162b2, ChAdOx1, and Ad26.COV2.S. The seropositivity rate elicited by all the vaccines against B.1.111 and Gamma was 100%, while for Mu, Delta, and Omicron ranged between 32 to 87%, 65 to 96%, and 41 to 96%, respectively, depending on the vaccine tested. The significant reductions in the nAb responses against the last three dominant SARS-CoV-2 lineages in Colombia indicate that booster doses should be administered following complete vaccination schemes to increase the nAb titers against emerging SARS-CoV-2 lineages.

Burnout Among Chinese Anesthesiologists After the COVID-19 Pandemic Peak: A National Survey.

Evidence has shown that large-scale pandemics can have prolonged psychological impacts on health care professionals. The current study aimed to evaluate the prevalence of burnout after the coronavirus disease 2019 (COVID-19) epidemic peak and to explore the prolonged impact of COVID-19 on burnout among Chinese anesthesiologists. From August 2021 to October 2021, a nationwide cross-sectional survey was conducted. Burnout was measured using the Maslach Burnout Inventory-Human Service Survey. Basic demographic information, exposure to COVID-19, and perceived institutional support were documented. Validated tools measuring mental health status, including anxiety, depression, post-traumatic stress disorder (PTSD) and resilience, were also used to provide additional information on psychological distress. Of the 8850 anesthesiologists from the 218 institutions who were invited to participate, 6331 (74.93%) completed the surveys and were included in the analysis. A total of 52.7% (95% confidence interval [CI], 51.5-53.9) met the criteria for burnout. Depression, anxiety, and PTSD were positively associated with burnout. After pooled multivariate analysis adjusting for potential confounding factors, among the COVID-19 exposure parameters, redeployment outside normal professional boundaries remained associated with an increased risk of burnout (odds ratio, 0.84; 95% CI, 0.72-0.92; P = .039). Higher perceived institutional support and resilience could act as protective factors against burnout. The impact of the COVID-19 epidemic on the psychological well-being of anesthesiologists still exists more than 1 year after the outbreak. Building better institutional support and cultivating stronger resilience may be helpful future intervention measures.

Ribotyping of Clostridioides difficile in the LiberecRegional Hospital: a tertiary health care facility.

The ribotyping of Clostridioides difficile is one of the basic methods of molecular epidemiology for monitoring the spread of C. difficile infections. In the Czech Republic, this procedure is mainly available in university hospitals. The introduction of ribotyping in a tertiary health care facility such as Liberec Regional Hospital not only increases safety in the facility but also supports regional professional development. In our study, 556 stool samples collected between June 2017 and June 2018 were used for C. difficile infection screening, followed by cultivation, toxinotyping, and ribotyping of positive samples. The toxinotyping of 96 samples revealed that 44.8% of typed strains could produce toxins A and B encoded by tcdA and tcdB, respectively. The ribotyping of the same samples revealed two epidemic peaks, caused by the regionally most prevalent ribotype 176 (n = 30, 31.3). C. difficile infection incidence ranged between 5.5 and 4.2 cases per 10,000 patient-bed days. Molecular diagnostics and molecular epidemiology are the two most developing parts of clinical laboratories. The correct applications of molecular methods help ensure greater safety in hospitals.

Human metapneumovirus in hospitalized children with acute respiratory tract infections in Beijing, China.

This study aims to described the epidemiology and genotypic diversity of Human metapneumovirus (HMPV) and the impact of SARS-CoV-2 on the prevalence of HMPV in hospitalized children with Acute respiratory tract infections (ARTIs) in Beijing, China. From April 2018 to March 2019 and from September 2020 to August 2021, nasopharyngeal aspirates (NPAs) from hospitalized children with ARTIs in Beijing were collected and subjected to real-time polymerase chain reaction tests for HMPV. Then genotyping, detection of 15 common respiratory viruses and clinical characteristics were analyzed on HMPV positive samples. 7.9% (124/1572) enrolled pediatric patients were identified as having HMPV infection, and the majority of children under the age of 5 (78.2%, 92/124), From April 2018 to March 2019. The detection rate of HMPV in spring and winter is significantly higher than that in summer and autumn. The co-infection rate were 37.1% (46/124), the most common co-infected virus were parainfluenza virus type 3 (HPIV-3). The main diagnosis of HMPV infection was pneumonia (92.7%,115/124), most patient have cough and fever. Of 78 HMPV-positive specimens, A2b (82.1%,64/78) were the main epidemic subtypes. Hospitalized children with HMPV genotype A infection had a higher viral load compared to genotype B. During the COVID-19 outbreak, Among 232 samples, only 4 cases were HMPV-positive. After statistical test, the detection rate of HMPV during the COVID-19 pandemic has decreased significantly compared with that before the epidemic (p=0.001). HMPV is an important cause of ARTIs in children under 5years old. The epidemic peak is generally in winter and spring, and the A2b subtype is the most common. However, under the prevention and control of the COVID-19 pandemic, the HMPV infection of hospitalized children with ARTIs has decreased significantly.

Long-term benefits of nonpharmaceutical interventions for endemic infections are shaped by respiratory pathogen dynamics

COVID-19 nonpharmaceutical interventions (NPIs), including mask wearing, have proved highly effective at reducing the transmission of endemic infections. A key public health question is whether NPIs could continue to be implemented long term to reduce the ongoing burden from endemic pathogens. Here, we use epidemiological models to explore the impact of long-term NPIs on the dynamics of endemic infections. We find that the introduction of NPIs leads to a strong initial reduction in incidence, but this effect is transient: As susceptibility increases, epidemics return while NPIs are in place. For low R0 infections, these return epidemics are of reduced equilibrium incidence and epidemic peak size. For high R0 infections, return epidemics are of similar magnitude to pre-NPI outbreaks. Our results underline that managing ongoing susceptible buildup, e.g., with vaccination, remains an important long-term goal.

Optimal control strategies of SARS-CoV-2 Omicron supported by invasive and dynamic models

BackgroundThere is a raising concern of a higher infectious Omicron BA.2 variant and the latest BA.4, BA.5 variant, made it more difficult in the mitigation process against COVID-19 pandemic. Our study aimed to find optimal control strategies by transmission of dynamic model from novel invasion theory.MethodsBased on the public data sources from January 31 to May 31, 2022, in four cities (Nanjing, Shanghai, Shenzhen and Suzhou) of China. We segmented the theoretical curves into five phases based on the concept of biological invasion. Then, a spatial autocorrelation analysis was carried out by detecting the clustering of the studied areas. After that, we choose a mathematical model of COVID-19 based on system dynamics methodology to simulate numerous intervention measures scenarios. Finally, we have used publicly available migration data to calculate spillover risk.ResultsEpidemics in Shanghai and Shenzhen has gone through the entire invasion phases, whereas Nanjing and Suzhou were all ended in the establishment phase. The results indicated that Rt value and public health and social measures (PHSM)-index of the epidemics were a negative correlation in all cities, except Shenzhen. The intervention has come into effect in different phases of invasion in all studied cities. Until the May 31, most of the spillover risk in Shanghai remained above the spillover risk threshold (18.81–303.84) and the actual number of the spillovers (0.94–74.98) was also increasing along with the time. Shenzhen reported Omicron cases that was only above the spillover risk threshold (17.92) at the phase of outbreak, consistent with an actual partial spillover. In Nanjing and Suzhou, the actual number of reported cases did not exceed the spillover alert value.ConclusionsBiological invasion is positioned to contribute substantively to understanding the drivers and mechanisms of the COVID-19 spread and outbreaks. After evaluating the spillover risk of cities at each invasion phase, we found the dynamic zero-COVID strategy implemented in four cities successfully curb the disease epidemic peak of the Omicron variant, which was highly correlated to the way to perform public health and social measures in the early phases right after the invasion of the virus.Graphical

Spatiotemporal dynamics and potential ecological drivers of acute respiratory infectious diseases: an example of scarlet fever in Sichuan Province

ObjectScarlet fever is an acute respiratory infectious disease that endangers public health and imposes a huge economic burden. In this paper, we systematically studied its spatial and temporal evolution and explore its potential ecological drivers. The goal of this research is to provide a reference for analysis based on surveillance data of scarlet fever and other acute respiratory infectious illnesses, and offer suggestions for prevention and control.MethodThis research is based on a spatiotemporal multivariate model (Endemic-Epidemic model). Firstly, we described the epidemiology status of the scarlet fever epidemic in Sichuan Province from 2016 to 2019. Secondly, we used spatial autocorrelation analysis to understand the spatial pattern. Thirdly, we applied the endemic-epidemic model to analyze the spatiotemporal dynamics by quantitatively decomposing cases into endemic, autoregressive, and spatiotemporal components. Finally, we explored potential ecological drivers that could influence the spread of scarlet fever.ResultsFrom 2016 to 2019, the incidence of scarlet fever in Sichuan Province varied much among cities. In terms of temporal distribution, there were 1–2 epidemic peaks per year, and they were mainly concentrated from April to June and October to December. In terms of transmission, the endemic and temporal spread were predominant. Our findings imply that the school holiday could help to reduce the spread of scarlet fever, and a standard increase in Gross Domestic Product (GDP) was associated with 2.6 folds contributions to the epidemic among cities.ConclusionScarlet fever outbreaks are more susceptible to previous cases, as temporal spread accounted for major transmission in many areas in Sichuan Province. The school holidays and GDP can influence the spread of infectious diseases. Given that covariates could not fully explain heterogeneity, adding random effects was essential to improve accuracy. Paying attention to critical populations and hotspots, as well as understanding potential drivers, is recommended for acute respiratory infections such as scarlet fever. For example, our study reveals GDP is positively associated with spatial spread, indicating we should consider GDP as an important factor when analyzing the potential drivers of acute infectious disease.

Shifts in global mobility dictate the synchrony of SARS-CoV-2 epidemic waves.

Human mobility changed in unprecedented ways during the SARS-CoV-2 pandemic. In March and April 2020, when lockdowns and large travel restrictions began in most countries, global air-travel almost entirely halted (92% decrease in commercial global air travel in the months between February and April 2020). Initial recovery in global air travel started around July 2020 and subsequently nearly tripled between May and July 2021. Here, we aim to establish a preliminary link between global mobility patterns and the synchrony of SARS-CoV-2 epidemic waves across the world. We compare epidemic peaks and human global mobility in two time periods: November 2020 to February 2021 (when just over 70 million passengers travelled) and November 2021 to February 2022 (when more than 200 million passengers travelled). We calculate the time interval during which continental epidemic peaks occurred for both of these time periods, and we calculate the pairwise correlations of epidemic waves between all pairs of countries for the same time periods. We find that as air travel increases at the end of 2021, epidemic peaks around the world are more synchronous with one another, both globally and regionally. Continental epidemic peaks occur globally within a 20day interval at the end of 2021 compared with 73days at the end of 2020, and epidemic waves globally are more correlated with one another at the end of 2021. This suggests that the rebound in human mobility dictates the synchrony of global and regional epidemic waves. In line with theoretical work, we show that in a more connected world, epidemic dynamics are more synchronized.

Epidemiology of hand, foot, and mouth disease and the genetic characteristics of Coxsackievirus A16 in Taiyuan, Shanxi, China from 2010 to 2021.

Hand, foot, and mouth disease (HFMD) is a common childhood infectious disease caused by human enteroviruses (EV). This study aimed to describe the epidemiological features of HFMD and the genetic characteristics of Coxsackievirus A16 (CVA16) in Taiyuan, Shanxi, China, from 2010 to 2021. Descriptive epidemiological methods were used to analyze the time and population distribution of HFMD and the genetic characteristics of CVA16. Except being affected by the COVID-19 epidemic in 2020, HFMD epidemics were sporadic from January to March each year, and began to increase in April, with a major epidemic peak from May to August, which declined in September, followed by a secondary peak from October to December. The prevalence of EV infection was the highest in children aged one to five years (84.42%), whereas its incidence was very low in children under one year of age (5.48%). Enterovirus nucleic acid was detected by real-time reverse transcription polymerase chain reaction in 6641 clinical specimens collected from patients with HFMD from 2010 to 2021, and 4236 EV-positive specimens were detected, including 988 enterovirus A71 (EV-A71), 1488 CVA16, and 1760 other enteroviruses. CVA16 remains prevalent and has co-circulated with other EVs in Taiyuan from 2010 to 2021.A phylogenetic tree constructed based on the VP1 region showed that all CVA16 strains belonged to two different clades of the B1 genotype, B1a and B1b. They showed a nucleotide similarity of 86.5-100%, and an amino acid similarity of 96.9-100%. Overall, these findings add to the global genetic resources of CVA16, demonstrate the epidemiological characteristics of HFMD as well as the genetic features of CVA16 in Taiyuan City during 2010-2021, and provide supporting evidence for the prevention and control of HFMD.

Has the COVID-19 Pandemic Affected Community-Acquired Urinary Tract Infections in Children?

To evaluate whether there were any changes in the rates of urinary tract infection (UTI) and antibiotic resistance in pediatric patients during the pandemic period. Urine culture samples collected due to suspected UTI were searched retrospectively from our hospital database, and the patients with growth in urine culture were identified. They were divided into 2 groups as Group A (before COVID-19, March 11, 2019- March 11, 2020) and Group B (COVID-19 period, March 11, 2020- March 11, 2021). Also, COVID-19 period was divided into 3 subgroups (March 2020- June 2020: first epidemic peak, July 2020 - November 2020: normalization process, December 2020- March 2021: second epidemic peak). We adjusted the patient age as <1, 1-6 and 7-18 years. Age, gender, microorganism strain types, and their antibiotic resistance patterns were compared between the 2 groups Results: This cross-sectional study included 250 eligible patients (Group A, n=182 and Group B, n=68) with a mean age of 10.91 ± 5.58 years. The male/female ratio was higher in Group B than in Group A (p = .004). Incidence of UTIs was lower in the curfew and restriction periods due to epidemic peaks than normalization process (p = .001). The proportion of E.coli decreased from 80.2% to 61.8% during the pandemic period when compared to pre-pandemic period (p = .001). Group B had lower rates of resistance to ampicillin, fosfomycin and nitrofurantoin for E.coli than Group A (p = .001, p = .012 and p = .001, respectively). Also, Group B had higher rate of uncommon microorganisms and lower rate of resistance to nitrofurantoin for E.coli than Group A in patients aged 7-18 years (p = .003 and p = .023, respectively). Our study demonstrates that the ongoing COVID-19 pandemic process has caused alterations in community-acquired UTIs in children. More hygienic lifestyle may be considered as the main factor in this change.

Clinical outcomes associated with Mu variant infection during the third epidemic peak of COVID-19 in Colombia

BackgroundThe higher number of cases and deaths caused by COVID-19 in Colombia occurred during the third epidemic peak, where the Mu variant was associated with 50% of the cases. ObjectiveTo evaluate the association between the clinical outcome of COVID-19 with health conditions and SARS-CoV-2 lineages. MethodsIn this study, clinical metadata and SARS-CoV-2 lineages from 535 patients with different degrees of COVID-19 severity were obtained after the SARS-CoV-2 genomic surveillance in Colombia. Then, the associations between these variables were determined using a multidimensional unfolding analysis. ResultsAsymptomatic, symptomatic, severe, and deceased outcomes represented 15.2%, 29.7%, 7.3%, and 47.8% of the cases, respectively. Males tend to develop more serious COVID-19, and severe or fatal outcomes were typically observed in patients aged >60 years with comorbidities, including chronic obstructive pulmonary disease, heart disease, kidney disease, obesity, asthma, and smoking history. The SARS-CoV-2 Mu and Gamma variants dominated the third epidemic peak and accounted for most fatal cases with odd ratio values of 128.2 (CI 53.0-310.1) and 18.6 (CI 8.294-41.917). ConclusionThis study shows the high impact of SARS-CoV-2 lineages with higher prevalence on public health and the importance of monitoring COVID-19 risk factors to control the associated mortality.

Network structure indexes to forecast epidemic spreading in real-world complex networks

Complex networks are the preferential framework to model spreading dynamics in several real-world complex systems. Complex networks can describe the contacts between infectious individuals, responsible for disease spreading in real-world systems. Understanding how the network structure affects an epidemic outbreak is therefore of great importance to evaluate the vulnerability of a network and optimize disease control. Here we argue that the best network structure indexes (NSIs) to predict the disease spreading extent in real-world networks are based on the notion of network node distance rather than on network connectivity as commonly believed. We numerically simulated, via a type-SIR model, epidemic outbreaks spreading on 50 real-world networks. We then tested which NSIs, among 40, could a priori better predict the disease fate. We found that the “average normalized node closeness” and the “average node distance” are the best predictors of the initial spreading pace, whereas indexes of “topological complexity” of the network, are the best predictors of both the value of the epidemic peak and the final extent of the spreading. Furthermore, most of the commonly used NSIs are not reliable predictors of the disease spreading extent in real-world networks.

First year of the pandemic: Years of Life Lost due to COVID-19 in Serbia

Abstract Background The first case of COVID-19 in Serbia was reported on 6th March 2020. Since then, Serbia has registered several epidemic peaks, which have led to a considerable increase in premature mortality. Basic evaluation of COVID-19 premature mortality burden needs to include frequency of COVID-19 deaths among different age-groups. Methods All-cause mortality data disaggregated by age and sex, population estimates and remaining life expectancy for different age-groups were acquired from the Statistical Office of the Republic of Serbia. Years of Life Lost (YLL) due to COVID-19 were calculated for the period from March to December 2020. European Standard Population was used for calculating age-standardized mortality rates. We acknowledge the support from the BoCO-19 - The Burden of Disease due to COVID-19 project coordinated/led by Robert Koch Institute and supported by the WHO Regional Office for Europe. Results In 2020, there were 127,572 YLLs due to COVID-19, with 81,147 of YLLs (63.6%) attributable to men and 46,425 (36.4%) to women. Contribution of COVID-19 to the total all-cause YLL was also higher in men comparing to women: 11.39% and 7.80%, respectively. Three epidemic peaks were observed in 2020, together composing two thirds (65.6%) of total YLLs due to COVID-19. December was the month with the greatest burden, accounting for 45.8% of all YLLs due to COVID-19. Crude YLL rate for COVID-19 was 1849.1 per 100,000, or 1733.5 per 100,000 after standardization. Conclusions Registered COVID-19 deaths accounted for one tenth of total YLLs in Serbia in 2020, with men contributing almost twice as much to that number compared to women. On average, 12.32 YLLs originated from each registered COVID-19 death. Further studies need to assess the impact of the COVID-19 epidemic on avoidable mortality trends in Serbia. Key messages • COVID-19 deaths comprised one tenth of all-cause YLLs, with two-thirds of COVID-19 YLLs attributable to men. • To reduce premature mortality burden, epidemic peaks need to be prevented.

Impact of SARS-CoV-2 Infection during Pregnancy and Perinatal Outcome: Findings from Private Hospitals at Dhaka, Bangladesh

Introduction: The pandemic novel corona virus (SARS-CoV- 2) infects pregnant women and affects pregnancy outcome. The perinatal outcome of COVID positive pregnancies attended at private hospitals during the last epidemic peak at Dhaka is unknown. The purpose of the study was to evaluate perinatal outcome among COVID positive and COVID negative pregnant women who delivered in the 2 study hospitals at Dhaka during the last epidemic wave. Methods: This retrospective study was conducted in 2 eminent private medical college&amp; hospitals in Dhaka,namely Popular medical college &amp;hospital (PMCH) and Greenlife medical college hospital (GLMC) over a period of 3 months from July 2021 to September 2021. COVID positive and COVID negative pregnant women who delivered in the study hospitals during the study period were included in this study. Outcome variables were COVID related maternal morbidity mortality, rate of caesarean delivery, stillbirth, preterm birth, low birth weight, neonatal COVID-19 positivity, neonatal death. Results: This study included 60 COVID positive and 60 COVID negative pregnant cases. COVID positive pregnant women delivered at earlier gestational age (weeks) than COVID negative cases(34.46±3.58 vs 36.50±3.20; p=0.001) Among COVID infected pregnancies,severe disease was observed in 15%; O2 support was needed in 68.3% and ICU admission required in 16.7% cases.The caesarean delivery rate ((93.3% vs 80%; p-.032) and preterm delivery rate (63.3% vs 35%; p-.002) were significantly higher among COVID positive cases. About 8.3% COVID affected pregnancies ended up with stillbirth.Maternal death rate due to COVID related complications was 5%.Proportion of low birth weight (LBW) was significantly more in infected pregnancies (65% vs 35%; p 0.001).No newborn from COVID positive mother was tested positive by RT-PCR at 24 hours of age. Conclusion: SARS-CoV-2 infection during pregnancy cancause moderate to severe disease requiring ICU admission and maternal death in 5% among COVID positive cases .It gives rise to more still birth, preterm birth, increasing number of LUCSand LBW. However maternal to neonatal transmission is not evident at 24-hour RT-PCR testing. J Bangladesh Coll Phys Surg 2022; 40: 270-278

Impact of the first wave of the COVID-19 pandemic on birth rates in Europe: a time series analysis in 24 countries.

Did the first wave of the COVID-19 pandemic have an impact on monthly birth rates in Europe? Using datasets on live births per month in Europe, collected from the Human Fertility Database, we found a -14.1% decline in live births in January 2021 (i.e. 9-10 months after the epidemic peaks and first lockdowns), compared to the average number of live births in January 2018 and 2019. Previous pandemics in the 20th and 21st centuries have been associated with a decline in birth rates 9 months after their peak, and a rebound in births over time. Lockdowns were necessary to control the first wave of the COVID-19 pandemic and may have had an impact on subsequent birth rates. Monthly time series data on live births from January 2018 to March 2021 were extracted to provide a time-series analysis of birthrates during and after the first wave of the COVID-19 pandemic in 24 European countries. We conducted a random-effect generalized least squares regression to assess the seasonality of births from January 2018 to March 2021, and to identify potential differences in monthly live births after the first wave of the COVID-19 pandemic, considering the seasonality of births. To quantify these potential differences, we estimated the variation rate between the monthly live births observed during 2020 and 2021 and the mean of the 2018-2019 monthly live births in Europe. Factors potentially associated with a variation in monthly birth rates were assessed using univariable and multivariable generalized linear regressions. When considering the seasonality of births, January 2021 was the only month with a significant difference in live births. A drop of -14.1% was observed compared to the average number of live births in January 2018 and 2019. At the national level, this drop was observed 9-10 months after the epidemic peaks in 13 countries. The duration of lockdowns was the variable that had the stronger association with this decrease, whereas higher incomes per capita could be a factor limiting this decline. A rebound in births compared to the previous years occurred in March 2021 in 13 countries. Our data are based on national data, limiting the power in the multivariable models used and the identification of other potential factors contributing to a decrease or an increase in birth rates. In addition, we collected only live births up to April 2021, which precludes the identification of a difference in births seasonality in 2021. As with previous pandemics, the COVID-19 outbreak was associated with a decline in births 9 months after its first wave. This trend may be associated with the duration of the lockdowns. Although there was a rebound in births in the following months, it does not seem to compensate for this decline. The authors receive no external funding and have no conflict of interest to declare. N/A.