scholarly journals Az alapellátásban kezelt COVID–19-fertőzött gyermekek tünettani és epidemiológiai jellemzői

2021 ◽  
Vol 162 (44) ◽  
pp. 1751-1760
Author(s):  
Éva Karászi ◽  
Beáta Onozó ◽  
Adrienn Sütő ◽  
Katalin Kutas ◽  
Beáta Szalóczi ◽  
...  
Keyword(s):  
Respiratory Symptoms ◽  
Transmission Rate ◽  
Age Distribution ◽  
Age Groups ◽  
Mitigation Measures ◽  
Third Wave ◽  
Virus Variant ◽  
The Third ◽  
Household Transmission ◽  
Epidemiological Characteristics

Összefoglaló. Bevezetés: A COVID–19-pandémia kapcsán számos tanulmány vizsgálta a tünetek gyakoriságát és a járványterjedés jellemzőit gyermekkorban, kevés azonban az alapellátás adatait összefoglaló publikáció. Közleményünkben 12 házi gyermekorvosi praxis 545 SARS-CoV-2-fertőzött betegének adatait elemeztük a 2. (n = 293) és a 3. (n = 252) járványhullámban. Célkitűzés: A gyermekkori fertőzések tünettanának és epidemiológiai jellemzőinek összehasonlítása korcsoportok és járványhullámok között. Módszer: Valamennyi alapellátó praxis egységes retrospektív adatgyűjtést végzett ugyanazon paraméterek regisztrálásával. Eredmények: A 10 év alatti betegekben a láz, a nátha és a köhögés dominált (30–50%), míg a 10 év felettiekben magas arányban regisztráltunk általános tüneteket is (30–40% fejfájás, gyengeség, szaglászavar). A 2. hullámban a 11–18 évesek (68%), a 3. hullámban a 0–10 évesek (53%) voltak többségben. A 3. hullámban szignifikánsan emelkedett a légúti tünetek előfordulása, az általános tünetek gyakorisága jelentősen csökkent, és szignifikánsan nőtt a családon belüli expozíció aránya (36% vs. 58%) a 2. hullámmal összehasonlítva. A gyermekről családtagra történő továbbterjedés 24% és 16% volt a két járványhullámban, és mértékét az életkor befolyásolta. Megbeszélés: A klinikai kép az életkorral és a feltételezett vírusvariánssal mutatott összefüggést: 10 év alatt a légúti tünetek domináltak, 10 év felett szignifikánsan több általános tünetet regisztráltunk a 0–10 évesekhez képest. A 3. járványhullámban az alfa-variáns terjedésével gyakoribbá váltak a légúti tünetek, az iskolabezárások következtében megváltozott az életkori megoszlás, és megemelkedett a családi expozíció okozta fertőzések aránya. A fertőzés továbbadása háztartáson belül mindkét hullámban alacsony maradt. Következtetés: A COVID–19 klinikai megjelenését és terjedési jellemzőit jelentősen befolyásolta az érintett gyermekpopuláció életkori összetétele, a cirkuláló vírusvariáns és az aktuális korlátozó intézkedések. Orv Hetil. 2021; 162(44): 1751–1760. Summary. Introduction: During the COVID-19 pandemic, a large number of publications examined the frequency of symptoms and the mode of transmission in childhood but only a few community-based studies have been published. In our paper, 545 pediatric COVID-19 patients’ data were collected by 12 primary care pediatricians in the second (n = 293) and third (n = 252) waves of the pandemic. Objective: To compare the frequency of symptoms and household transmission in different age groups and between the two waves. Method: Patients’ data and disease characteristics were recorded retrospectively in the same manner by all participating pediatricians. Results: In patients of <10 years of age, fever, rhinorrhea and cough were registered the most frequently (30–50%), in contrast to patients of >10 years, where high frequency of general symptoms was found (30–40% headache, weakness, anosmia). In the third wave, the ratio of the age group 11–18 years declined from 68% to 47%, the frequency of respiratory symptoms increased significantly, while the ratio of general symptoms decreased. Household exposition was more frequent in the third wave (36% vs. 58%), while the transmission rate from children to family members was 24% and 16%, respectively, and it varied with age. Discussion: Clinical manifestation showed relation to age and virus variant: the older age associated with higher frequency of general symptoms and the spread of the alpha variant led to the predominance of respiratory symptoms over general complaints. Prolonged school closures affected the age distribution and increased the frequency of household exposition. Secondary household transmission remained low. Conclusion: Clinical and epidemiological characteristics of pediatric COVID-19 disease were highly influenced by age, dominant virus variant and mitigation measures. Orv Hetil. 2021; 162(44): 1751–1760.

scholarly journals Re-emergence of respiratory syncytial virus following the COVID-19 pandemic in the United States: a modeling study

2021 ◽  
Author(s):  
Zhe Zheng ◽  
Virginia E. Pitzer ◽  
Eugene D. Shapiro ◽  
Louis J. Bont ◽  
Daniel M. Weinberger
Keyword(s):  
New York ◽  
Respiratory Syncytial Virus ◽  
Transmission Rate ◽  
Age Distribution ◽  
Age Groups ◽  
Pediatric Intensive Care ◽  
The United States ◽  
Mitigation Measures ◽  
Hospital Capacity ◽  
Syncytial Virus

Importance: Respiratory syncytial virus (RSV) is a leading cause of hospitalizations in young children. RSV largely disappeared in 2020 due to precautions taken because of the COVID-19 pandemic. Projecting the timing and intensity of the re-emergence of RSV and the age groups affected is crucial for planning for the administration of prophylactic antibodies and anticipating hospital capacity. Objective: To project the potential timing and intensity of re-emergent RSV epidemics in different age groups. Design, Setting, Participants: Mathematical models were used to reproduce the annual RSV epidemics before the COVID-19 pandemic in New York and California. These models were modified to project the trajectory of RSV epidemics in 2020-2025 under different scenarios with varying stringency of mitigation measures for SARS-CoV-2: 1) constant low RSV transmission rate from March 2020 to March 2021; 2) an immediate decrease in RSV transmission in March 2020 followed by a gradual increase in transmission until April 2021; 3) a decrease in non-household contacts from April to July 2020. Simulations also evaluated factors likely to impact the re-emergence of RSV epidemics, including introduction of virus from out-of-state sources and decreased transplacentally acquired immunity in infants. Main Outcomes and Measures: The primary outcome of this study was defined as the predicted number of RSV hospitalizations each month in the entire population. Secondary outcomes included the age distribution of hospitalizations among children <5 years of age, incidence of any RSV infection, and incidence of RSV lower respiratory tract infection (LRI). Results: In the 2021-2022 RSV season, we expect that the lifting of mitigation measures and build-up of susceptibility will lead to a larger-than-normal RSV outbreak. We predict an earlier-than-usual onset in the upcoming RSV season if there is substantial external introduction of RSV. Among children 1-4 years of age, the incidence of RSV infections could be twice that of a typical RSV season, with infants <6 months of age having the greatest seasonal increase in the incidence of both severe RSV LRIs and hospitalizations. Conclusions and Relevance: Pediatric departments, including pediatric intensive care units, should be alert to large RSV outbreaks. Enhanced surveillance is required for both prophylaxis administration and hospital capacity management.

scholarly journals Dynamics of the COVID-19 epidemic in Ireland under mitigation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bernard Cazelles ◽  
Benjamin Nguyen-Van-Yen ◽  
Clara Champagne ◽  
Catherine Comiskey
Keyword(s):  
Transmission Rate ◽  
Vaccination Campaign ◽  
Virus Transmission ◽  
Reporting Rate ◽  
Mitigation Measures ◽  
Third Wave ◽  
The European Union ◽  
Hospital Data ◽  
Prevalence Survey ◽  
Epidemic Curve

Abstract Background In Ireland and across the European Union the COVID-19 epidemic waves, driven mainly by the emergence of new variants of the SARS-CoV-2 have continued their course, despite various interventions from governments. Public health interventions continue in their attempts to control the spread as they wait for the planned significant effect of vaccination. Methods To tackle this challenge and the observed non-stationary aspect of the epidemic we used a modified SEIR stochastic model with time-varying parameters, following Brownian process. This enabled us to reconstruct the temporal evolution of the transmission rate of COVID-19 with the non-specific hypothesis that it follows a basic stochastic process constrained by the available data. This model is coupled with Bayesian inference (particle Markov Chain Monte Carlo method) for parameter estimation and utilized mainly well-documented Irish hospital data. Results In Ireland, mitigation measures provided a 78–86% reduction in transmission during the first wave between March and May 2020. For the second wave in October 2020, our reduction estimation was around 20% while it was 70% for the third wave in January 2021. This third wave was partly due to the UK variant appearing in Ireland. In June 2020 we estimated that sero-prevalence was 2.0% (95% CI: 1.2–3.5%) in complete accordance with a sero-prevalence survey. By the end of April 2021, the sero-prevalence was greater than 17% due in part to the vaccination campaign. Finally we demonstrate that the available observed confirmed cases are not reliable for analysis owing to the fact that their reporting rate has as expected greatly evolved. Conclusion We provide the first estimations of the dynamics of the COVID-19 epidemic in Ireland and its key parameters. We also quantify the effects of mitigation measures on the virus transmission during and after mitigation for the three waves. Our results demonstrate that Ireland has significantly reduced transmission by employing mitigation measures, physical distancing and lockdown. This has to date avoided the saturation of healthcare infrastructures, flattened the epidemic curve and likely reduced mortality. However, as we await for a full roll out of a vaccination programme and as new variants potentially more transmissible and/or more infectious could continue to emerge and mitigation measures change silent transmission, challenges remain.

scholarly journals Social disparities in the first wave of COVID-19 infections in Germany: A county-scale explainable machine learning approach

2020 ◽  
Author(s):  
Gabriele Doblhammer ◽  
Constantin Reinke ◽  
Daniel Kreft
Keyword(s):  
Machine Learning ◽  
Age Distribution ◽  
Age Groups ◽  
High Mobility ◽  
Incidence Rates ◽  
Mitigation Measures ◽  
Outcome Variable ◽  
Gradient Boosting ◽  
Robert Koch Institute ◽  
Individual Level

AbstractBackgroundLittle is known about factors correlated with this geographic spread of the first wave of COVID-19 infections in Germany. Given the lack of individual-level socioeconomic information on COVID-19 cases, we resorted to an ecological study design, exploring regional correlates of COVID-19 diagnoses.Data and MethodWe used data from the Robert-Koch-Institute on COVID-19 diagnoses by sex, age (age groups: 0-4, 5-14, 15-34, 35-59, 60-79, 80+), county (NUTS3 region) differentiating five periods (initial phase: through 15 March; 1st lockdown period: 16 March to 31 March; 2nd lockdown period: from 1 April to 15 April; easing period: 16 April to 30 April; post-lockdown period: 1 May through 23 July). For each period we calculated age-standardized incidence of COVID-19 diagnoses on the county level, using the German age distribution from the year 2018. We characterized the regions by macro variables in nine domains: “Demography”, “Employment”, “Politics, religion, and education”, “Income”, “Settlement structure and environment”, “Health care”, “(structural) Poverty”, “Interrelationship with other regions”, and “Geography”. We trained gradient boosting models to predict the age-standardized incidence rates with the macro structures of the counties, and used SHAP values to characterize the 20 most prominent features in terms of negative/positive correlations with the outcome variable.ResultsThe change in the age-standardized incidence rates over time is reflected in the changing importance of features as indicated by the mean SHAP values for the five periods. The first COVID-19 wave started as a disease in wealthy rural counties in southern Germany, and ventured into poorer urban and agricultural counties during the course of the first wave. The negative social gradient became more pronounced from the 2nd lockdown period onwards, when wealthy counties appeared to be better protected. Population density per se does not appear to be a risk factor, and only in the post-lockdown period did connectedness become an important regional characteristic correlated with higher infections. Features related to economic and educational characteristics of the young population in a county played an important role at the beginning of the pandemic up to the 2nd lockdown phase, as did features related to the population living in nursing homes; those related to international migration and a large proportion of foreigners living in a county became important in the post-lockdown period.DiscussionIn the absence of individual level data, explainable machine learning methods based on regional data may help to better understand the changing nature of the drivers of the pandemic. High mobility of high SES groups may drive the pandemic at the beginning of waves, while mitigation measures and beliefs about the seriousness of the pandemic as well as the compliance with mitigation measures put lower SES groups at higher risks later on.

scholarly journals Gender-Age Distribution of Patients with COVID-19 at Different Stages of Epidemic in Moscow

2020 ◽  
pp. 27-35
Author(s):  
V. G. Akimkin ◽  
S. N. Kuzin ◽  
T. A. Semenenko ◽  
A. A. Ploskireva ◽  
D. V. Dubodelov ◽  
...  
Keyword(s):  
Age Distribution ◽  
Age Groups ◽  
Population Based ◽  
Epidemiological Surveillance ◽  
Age Group ◽  
Female Ratio ◽  
The World ◽  
Male Female Ratio ◽  
Male Cohort ◽  
Epidemiological Characteristics

The ongoing COVID-19 pandemic around the world and in Russia remains a major event of 2020. All over the world, research is being conducted to comprehensively study the patterns and manifestations of the epidemic  process. The main quantitative characteristics of SARS-CoV-2 transmission dynamics among the population, based on the data of official monitoring over the current situation, play an important role in the development of  the epidemiological surveillance system.The aim of this study is to explore the peculiarities of age-gender distribution of COVID-19 patients in Moscow.Material and methods. The data related to the epidemiological characteristics of age-gender structure of COVID-19 patients in Moscow between March 19, 2020 and April 15, 2020, at different stages of the  epidemic were retrospectively analyzed.Results and discussion. The mean age of COVID-19 patients in Moscow was 46,41±20,58 years. The gender ratio (male/female) among the patients was 52.7/47.3 %, wherein the indicators varied depending upon the  age. Male/female ratio in the age group “under 39” stood at 53.7/46.3 %, and “over 40 years of age” – at  39.3/60.7 %. The predominant age range among male cases was 19 to 39 years old – 35.4 %, while among female patients – 40–59 years (36.5 %). The age distribution of patients in Moscow is indicative of the fact that COVID-19 is a disease that primarily affects older age groups. The age structure of all COVID-19 cases during the observation period is characterized by predominance of adult patients over 19 years of age – 92,7 % (92,6–92,8 %), the share of patients aged 40–59 years is 35,7% (35,5–35,9 %). The differences in the age distribution in males and females are as follows: in the male cohort, the age groups 19–39 years old and 40–59 years old prevail – 35.4 % (35.1–35.7 %) and 34.9 % (34.6–35.2 %), respectively. The age group 40–59 years old – 36.5 % (36.3–36.8%) dominates in the female cohort. 

scholarly journals Nature and Magnitude of COVID-19 First Wave in Tamil Nadu, India

2021 ◽  
Vol 9 (4) ◽  
Author(s):  
P Devi Priya
Keyword(s):  
Tamil Nadu ◽  
Middle Age ◽  
Age Groups ◽  
The Elderly ◽  
Third Wave ◽  
Age Group ◽  
Group 13 ◽  
The Third ◽  
First Case ◽  
Second Wave

The first case of COVID-19 Tamil Nadu was confirmed on 7th March 2020 in Chennai after aninternational travel from Muscat. The specific objectives of the study were to analyze the trends inCOVID 19 in Tamil Nadu from March 2020 to January 2021 and examine the pattern of it sex-wiseamong the age groups. The risk of COVID-19 was accessed with the computation of positive testrate, prevalence rate and fatality rate. The prevalence rates were highest in August, with a slightdownfall in September 2020 in the state. The positivity test rate was high in June and July. Then itstarted declining and has been one percent in January. About 83 percent of the occurrence of thevirus was among the middle age group, 13 percent among the elderly and four percent among thechildren. Intensifying immunity boosters, personal and public hygiene, vaccination on a large scaleprobably would contain the second wave and prohibit the third wave for the survival of humanity

scholarly journals A Data Driven Analysis and Forecast of COVID-19 Dynamics during the Third Wave Using SIRD Model in Bangladesh

COVID ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 503-517
Author(s):  
Omar Faruk ◽  
Suman Kar
Keyword(s):  
Numerical Simulations ◽  
Basic Reproduction Number ◽  
Goodness Of Fit ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Transmission Dynamics ◽  
Third Wave ◽  
The Third ◽  
The Government ◽  
The Basic Reproduction Number

In this study, we developed a compartmental SIRD model to analyze and forecast the transmission dynamics of the COVID-19 pandemic in Bangladesh during the third wave caused by the Indian delta variant. With the help of the nonlinear system of differential equations, this model can analyze the trends and provide reliable predictions regarding how the epidemic would evolve. The basic reproduction number regarding the pandemic has been determined analytically. The parameters used in this model have been estimated by fitting our model to the reported data for the months of May, June, and July 2021 and the goodness of fit of the parameter’s value has been found by the respective regression coefficients. Further, we conducted a sensitivity analysis of the basic reproduction number and observed that decreasing the transmission rate is the most significant factor in disease prevention. Our proposed model’s appropriateness for the available COVID-19 data in Bangladesh has been demonstrated through numerical simulations. According to the numerical simulation, it is evident that a rise in the transmission rate leads to a significant increase in the infected number of the population. Numerical simulations have also been performed by using our proposed model to forecast the future transmission dynamics for COVID-19 over a longer period of time. Knowledge of these forecasts may help the government in adopting appropriate measures to prepare for unforeseen situations that may arise in Bangladesh as well as to minimize detrimental impacts during the outbreak.

scholarly journals Forecasting the scale of the COVID-19 epidemic in Kenya

2020 ◽  
Author(s):  
Samuel P. C. Brand ◽  
Rabia Aziza ◽  
Ivy K. Kombe ◽  
Charles N. Agoti ◽  
Joe Hilton ◽  
...  
Keyword(s):  
Human Mobility ◽  
Age Distribution ◽  
Age Groups ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Specific Rate ◽  
Epidemic Curve ◽  
Mobility Data ◽  
Modelling Framework ◽  
Epidemiological Characteristics

AbstractBackgroundThe first COVID-19 case in Kenya was confirmed on March 13th, 2020. Here, we provide forecasts for the potential incidence rate, and magnitude, of a COVID-19 epidemic in Kenya based on the observed growth rate and age distribution of confirmed COVID-19 cases observed in China, whilst accounting for the demographic and geographic dissimilarities between China and Kenya.MethodsWe developed a modelling framework to simulate SARS-CoV-2 transmission in Kenya, KenyaCoV. KenyaCoV was used to simulate SARS-CoV-2 transmission both within, and between, different Kenyan regions and age groups. KenyaCoV was parameterized using a combination of human mobility data between the defined regions, the recent 2019 Kenyan census, and estimates of age group social interaction rates specific to Kenya. Key epidemiological characteristics such as the basic reproductive number and the age-specific rate of developing COVID-19 symptoms after infection with SARS-CoV-2, were adapted for the Kenyan setting from a combination of published estimates and analysis of the age distribution of cases observed in the Chinese outbreak.ResultsWe find that if person-to-person transmission becomes established within Kenya, identifying the role of subclinical, and therefore largely undetected, infected individuals is critical to predicting and containing a very significant epidemic. Depending on the transmission scenario our reproductive number estimates for Kenya range from 1.78 (95% CI 1.44 −2.14) to 3.46 (95% CI 2.81-4.17). In scenarios where asymptomatic infected individuals are transmitting significantly, we expect a rapidly growing epidemic which cannot be contained only by case isolation. In these scenarios, there is potential for a very high percentage of the population becoming infected (median estimates: >80% over six months), and a significant epidemic of symptomatic COVID-19 cases. Exceptional social distancing measures can slow transmission, flattening the epidemic curve, but the risk of epidemic rebound after lifting restrictions is predicted to be high.

scholarly journals Narrowing gap in regional and age-specific excess mortality in the first year and a half of COVID-19 in Hungary

2022 ◽  
Author(s):  
Csaba G. Toth
Keyword(s):  
Mortality Rate ◽  
Excess Mortality ◽  
Age Groups ◽  
Vaccination Schedule ◽  
First Year ◽  
Third Wave ◽  
The Third ◽  
Seasonal Flu ◽  
Almost All ◽  
Second Wave

In the first year and a half of the pandemic, the excess mortality in Hungary was 28,400, which was 1,700 lower than the official statistics on COVID-19 deaths. This discrepancy can be partly explained by protective measures instated during the COVID-19 pandemic that decreased the intensity of the seasonal flu outbreak, which caused on average 3,000 deaths per year. Compared to the second wave of the COVID-19 pandemic, the third wave showed a reduction in the differences in excess mortality between age groups and regions. The excess mortality rate for people aged 75+ fell significantly in the third wave, partly due to the vaccination schedule and the absence of a normal flu season. For people aged 40-77, the excess mortality rate rose slightly in the third wave. Between regions, excess mortality was highest in Northern Hungary and Western Transdanubia, and much lower in Central Hungary, where the capital is located. The excess mortality rate for men was almost twice as high as that for women in almost all age groups.

scholarly journals Modelling COVID-19 Vaccine Breakthrough Infections in Highly Vaccinated Israel - the effects of waning immunity and third vaccination dose

2022 ◽  
Author(s):  
Anyin Feng ◽  
Uri Obolski ◽  
Lewi Stone ◽  
Daihai He
Keyword(s):  
Vaccine Efficacy ◽  
Transmission Rate ◽  
Age Groups ◽  
Mitigation Measures ◽  
Reproductive Number ◽  
Time Varying ◽  
Age Structured ◽  
Alpha Variant ◽  
Vaccine Distribution ◽  
Induced Immunity

In August 2021, a major wave of the SARS-CoV-2 Delta variant erupted in the highly vaccinated population of Israel. The Delta variant has a transmission advantage over the Alpha variant, and thus replaced it in approximately two months. The outbreak led to an unexpectedly large proportion of breakthrough infections (BTI)-- a phenomenon that received worldwide attention. The BTI proportion amongst cases in the age group of 60+ years reached levels as high as ~85% in August 2021. Most of the Israeli population, especially those 60+ age, received their second dose of the vaccination, four months before the invasion of the Delta variant. Hence, either the vaccine induced immunity dropped significantly or the Delta variant possesses immunity escaping abilities. In this work, we analyzed and model age-structured cases, vaccination coverage, and vaccine BTI data obtained from the Israeli Ministry of Health, to help understand the epidemiological factors involved in the outbreak. We propose a mathematical model which captures a multitude of factors, including age structure, the time varying vaccine efficacy, time varying transmission rate, BTIs, reduced susceptibility and infectivity of vaccinated individuals, protection duration of the vaccine induced immunity, and the vaccine distribution. We fitted our model to the cases among vaccinated and unvaccinated, for <60 and 60+ age groups, to address the aforementioned factors. We found that the transmission rate was driven by multiple factors including the invasion of Delta variant and the mitigation measures. Through a model reconstruction of the reproductive number R0(t), it was found that the peak transmission rate of the Delta variant was 1.96 times larger than the previous Alpha variant. The model estimated that the vaccine efficacy dropped significantly from >90% to ~40% over 6 months, and that the immunity protection duration has a peaked Gamma distribution (rather than exponential). We further performed model simulations quantifying the important role of the third vaccination booster dose in reducing the levels of breakthrough infections. This allowed us to explore "what if" scenarios should the booster not have been rolled out. Application of this framework upon invasion of new pathogens, or variants of concern, can help elucidate important factors in the outbreak dynamics and highlight potential routes of action to mitigate their spread.

Sign in / Sign up

Export Citation Format

Share Document