Number Of Infected People Research Articles

Türkiye'de COVID-19 Salgınının Yayılımını Öngörmek İçin Tahmin Modeli

This study aimed to forecast the future of the COVID-19 outbreak parameters such as spreading, case fatality, and case recovery values based on the publicly available epidemiological data for Turkey. We first performed different forecasting methods including Facebook's Prophet, ARIMA and Decision Tree. Based on the metrics of MAPE and MAE, Facebook's Prophet has the most effective forecasting model. Then, using Facebook's Prophet, we generated a forecast model for the evolution of the outbreak in Turkey fifteen-days-ahead. Based on the reported confirmed cases, the simulations suggest that the total number of infected people could reach 4328083 (with lower and upper bounds of 3854261 and 4888611, respectively) by April 23, 2021. Simulation forecast shows that death toll could reach 35656 with lower and upper bounds of 34806 and 36246, respectively. Besides, our findings suggest that although more than 86.38% growth in recovered cases might be possible, the future active cases will also significantly increase compared to the current active cases. This time series analysis indicates an increase trend of the COVID-19 outbreak in Turkey in the near future. Altogether, the present study highlights the importance of an efficient data-driven forecast model analysis for the simulation of the pandemic transmission and hence for further implementation of essential interventions for COVID-19 outbreak.

An Epidemiological Study of COVID-19 Cases in Al-Leith, Saudi Arabia.

Background and objectiveEver since the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first detected in December 2019, more than 0.550 million cases of coronavirus disease 2019 (COVID-19) have been reported in the kingdom of Saudi Arabia (KSA) and the number is on the rise. In this study, we aimed to perform an epidemiological analysis of COVID-19 cases in Al-Leith, KSA.MethodsA community-based descriptive study was carried out to assess the epidemiology of COVID-19 in Al-Leith, KSA. The relevant data were collected using a questionnaire designed for this study, which included questions on basic information and characteristics in addition to data on COVID-19. Data were analyzed using the SPSS Statistics software (IBM, Armonk, NY).ResultsThe significant findings were as follows: people aged between 26-40 years were more affected (55.7%) than other age groups, and more than half (74.1%) of COVID-19 patients were female; most of them were employed (85, 48,9%), and most of those got infected through direct contact (137, 78.7%) with other infected people. About 163 (93.7%) cases were asymptomatic, and 168 (96.6%) cases were notified to the health authorities. The findings also illustrated that 78 (44.8%) COVID-19 cases suffered from psychological impact due to infection; 83 (47.7%) cases had at least one case in the family before they became infected. The majority of cases (93.7%) suffered from symptomatic COVID-19. A considerable number of COVID-19 patients did not follow precautions before and after infection.ConclusionsThe study concluded that various age groups were susceptible to developing COVID-19, and direct contact was the main mode of transmission. Moreover, a considerable number of infected people did not adhere to precautionary measures.

Regional differences in the epidemic shock on the local labor market and its spread

AbstractFirst, using the sequential linear panel data‐estimation model and maps, we demonstrate the relocation of job‐posting places, especially for part‐time jobs. We use data on the number of job postings at the public employment security office in Japan. These changes in the posting places are clearly observable in the retail trade for part‐ and full‐time jobs. In contrast, we do not find discernible relocation in the information‐services industry. The impact of the COVID‐19 shock varies by geographic location. However, if people's mobility rate, the number of infected people, or the declaration of the state of emergency are controlled for, the number of job postings differs among areas. Therefore, second, this study explores the factors that account for the differences in the number of job offers among areas. We find that industrial diversity increases the number of job postings for part‐ and full‐time jobs in 2021, that is, one year after the first wave of the pandemic began. In contrast, industrial specialization has a negative impact in 2021. Higher rigidity of finance decreases the number of part‐time job postings immediately after the first wave of the pandemic because local governments cannot provide various support measures to create jobs. However, this financial effect disappears with time. Furthermore, higher financial capability positively affects part‐time job postings, although they are not efficient in creating full‐time jobs. The unemployment rate in 2015 negatively affects the regional differences in job postings after 2021. Regarding whether firms seek regions with rich human resources, the result is unexpectedly unclear.

Predicting Pandemic Curve Distribution Using Statistical Models

This article discusses the distribution of pandemic in the world and pandemic curve in Jordan and how the science of probability and statistics predict when active cases tend to zero by determining the shape of epidemic curve and relating it to a special probability distribution that has specific measures and properties. At the beginning of the outbreak of any virus in a society, reliable data describing it and its spread will be rare, hence researchers set up statistical models that have the ability to predict the spreads’ shape, where the prospected people hosting such viruses will go to and the likelihood of transmitting it to places they travel. Those models use known statistical measures that estimate the probability of disease transmission from infected people to others. In addition, the factors related to roads and people’s movement, taking into consideration, public health interventions, such as wearing masks, closing places of people’s aggregations like schools, universities mosques and churches and quarantine make difference in numbers of infected people. The fundamental differences between the “Spanish flu” that attacked the world a hundred years ago and “Coronavirus” the world facing since the beginning of the current year 2020 is the amount of huge data concluded from scientific studies and reports related to virology and epidemiology.

National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil.

In this paper, we investigate the influence of holidays and community mobility on the transmission rate and death count of COVID-19 in Brazil. We identify national holidays and hallmark holidays to assess their effect on disease reports of confirmed cases and deaths. First, we use a one-variate model with the number of infected people as input data to forecast the number of deaths. This simple model is compared with a more robust deep learning multi-variate model that uses mobility and transmission rates (, ) from a SEIRD model as input data. A principal components model of community mobility, generated by the principal component analysis (PCA) method, is added to improve the input features for the multi-variate model. The deep learning model architecture is an LSTM stacked layer combined with a dense layer to regress daily deaths caused by COVID-19. The multi-variate model incremented with engineered input features can enhance the forecast performance by up to 18.99% compared to the standard one-variate data-driven model.

Detection of COVID-19 Using Heart Rate and Blood Pressure: Lessons Learned from Patients with ARDS.

The world has been affected by COVID-19 coronavirus. At the time of this study, the number of infected people in the United States is the highest globally (31.2 million infections). Within the infected population, patients diagnosed with acute respiratory distress syndrome (ARDS) are in more life-threatening circumstances, resulting in severe respiratory system failure. Various studies have investigated the infections to COVID-19 and ARDS by monitoring laboratory metrics and symptoms. Unfortunately, these methods are merely limited to clinical settings, and symptom-based methods are shown to be ineffective. In contrast, vital signs (e.g., heart rate) have been utilized to early-detect different respiratory diseases in ubiquitous health monitoring. We posit that such biomarkers are informative in identifying ARDS patients infected with COVID-19. In this study, we investigate the behavior of COVID-19 on ARDS patients by utilizing simple vital signs. We analyze the long-term daily logs of blood pressure (BP) and heart rate (HR) associated with 150 ARDS patients admitted to five University of California academic health centers (containing 77,972 samples for each vital sign) to distinguish subjects with COVID-19 positive and negative test results. In addition to the statistical analysis, we develop a deep neural network model to extract features from the longitudinal data. Our deep learning model is able to achieve 0.81 area under the curve (AUC) to classify the vital signs of ARDS patients infected with COVID-19 versus other ARDS diagnosed patients. Since our proposed model uses only the BP and HR, it would be possible to review data prior to the first reported cases in the U.S. to validate the presence or absence of COVID-19 in our communities prior to January 2020. In addition, by utilizing wearable devices, and monitoring vital signs of subjects in everyday settings it is possible to early-detect COVID-19 without visiting a hospital or a care site.

Modeling the dengue fever transmission in a periodic environment

A mathematical model for dengue fever transmission is analyzed, which incorporates relevant biological and ecological factors: vertical transmission and seasonality in the interaction between the vector (Aedes aegypti females) and the host (human). The existence and uniqueness of a positive disease-free periodic solution is proved; the global stability of the disease-free solution and the effect of periodic migrations of mosquitoes carrying the virus on the transmission of dengue are analyzed utilizing the mathematical definition of the Basic Reproductive Number in periodic environments; finally, it is numerically corroborated with the help of the Basic Reproductive Number that dengue cannot invade the disease-free state if it is less than one and can invade if it is greater than one, however, in both threshold conditions when vertical transmission occurs, the number of infected people and carrier vectors rises, representing a mechanism for the persistence of dengue cases in a community throughout a natural year.

Spatial Analysis of the Sociodemographic Characteristics, Comorbidities, Hospitalization, Signs, and Symptoms Among Hospitalized Coronavirus Disease 2019 Cases in the State of Rio De Janeiro, Brazil.

After more than 1 year from the beginning of the pandemic, the coronavirus disease 2019 (COVID-19) has reached all continents. The number of infected people is still increasing, and Brazil is among the countries with the highest number of registered cases in the world. In this study, we investigated the profile of hospitalized COVID-19 cases and the eventual clusters of similar areas, using geographic information systems. The study was conducted using secondary data. Variables such as sociodemographic characteristics, comorbidities, hospitalization, signs, and symptoms among confirmed cases were considered for each microregion/city of the state of Rio de Janeiro. These proportions were used when calculating the Global Moran's I. The local indicator of spatial association was used to identify local clusters. A significant global spatial auto correlation was found in 28% of the variables. The presence of spatial autocorrelation indicates that the proportions of patients with COVID-19 according to these characteristics are spatially oriented. Moran maps reveal 2 clusters, 1 of high proportions and 1 of low proportions. Understanding the geographic patterns of COVID-19 may assist public health investigators, contributing to actions to prevent and control the pandemic in the state.

Forecasting Development of COVID-19 Epidemic in European Union Using Entropy-Randomized Approach

The paper is devoted to the forecasting of the COVID-19 epidemic by the novel method of randomized machine learning. This method is based on the idea of estimation of probability distributions of model parameters and noises on real data. Entropy-optimal distributions correspond to the state of maximum uncertainty which allows the resulting forecasts to be used as forecasts of the most "negative" scenario of the process under study. The resulting estimates of parameters and noises, which are probability distributions, must be generated, thus obtaining an ensemble of trajectories that considered to be analyzed by statistical methods. In this work, for the purposes of such an analysis, the mean and median trajectories over the ensemble are calculated, as well as the trajectory corresponding to the mean over distribution values of the model parameters. The proposed approach is used to predict the total number of infected people using a three-parameter logistic growth model. The conducted experiment is based on real COVID-19 epidemic data in several countries of the European Union. The main goal of the experiment is to demonstrate an entropy-randomized approach for predicting the epidemic process based on real data near the peak. The significant uncertainty contained in the available real data is modeled by an additive noise within 30%, which is used both at the training and predicting stages. To tune the hyperparameters of the model, the scheme is used to configure them according to a testing dataset with subsequent retraining of the model. It is shown that with the same datasets, the proposed approach makes it possible to predict the development of the epidemic more efficiently in comparison with the standard approach based on the least-squares method.

Case study of COVID 19 from August to end of December 2020 in Babylon, Iraq

Objectives: The aim of study is to study Case study of COVID 19 in Babylon Province in Iraq. Method: In January 2021 we started searching for the official source for the numbers of people infected with COVID19 within the borders of Babylon Governorate, where the Babylon Health Department was reviewed, and after that we were directed with official authorization to the Public Health Department in Babylon Governorate / Department of Communicable Diseases / Respiratory Diseases Unit (weekly periodic visit). Where the official numbers were obtained for the number of infected people and the number of deaths that occur due to complication with this infection, as well as the number of recovery cases from the date of 01/08/2020 to 01/01/2021. Results: related to the number of infected cases we see decrease the no. of infected with corona virus in Babylon Governorate from beginning the study to become lest at the end of 2020 with the significant decrease (P < 0.05) (The highest infected cases were notices in first month (1/8 – 31/8) while the lowest cases were seen in last month (1/12 – 31/12)) that may be due to: The main cause may be the most people may have gained herd immunity or population immunity, is the indirect protection from an infectious disease that happens when a population is have immunity developed through previous infection, that may refer to the end of first wave of this pandemic which start from the June. Conclusion: Knowing the pathogenesis of the virus and risk factors, increasing the experience of doctors and working cadres, increasing clinical capacity, providing specialized treatments, increasing the number of ICU beds, in addition to increasing the number of respiratory ventilators and increasing the number of tests.

Scenario prediction of public health emergencies using infectious disease dynamics model and dynamic Bayes

This study was aimed to discuss the predictive value of infectious disease dynamics model (IDD model) and dynamic Bayesian network (DBN) for scenario deduction of public health emergencies (PHEs). Based on the evolution law of PHEs and the meta-scenario representation of basic knowledge, this study established a DBN scenario deduction model for scenario deduction and evolution path analysis of PHEs. At the same time, based on the average field dynamics model of the SIR network, the dimensionality reduction process was performed to calculate the epidemic scale and epidemic time based on the IDD model, so as to determine the calculation methods of threshold value and epidemic time under emergency measures (quarantine). The Corona Virus Disease (COVID) epidemic was undertaken as an example to analyze the results of DBN scenario deduction, and the infectious disease dynamics model was used to analyze the number of reproductive numbers, peak arrival time, epidemic time, and latency time of the COVID epidemic. It was found that after the M1 measure was used to process the S1 state, the state probability and the probability of being true (T) were the highest, which were 91.05 and 90.21, respectively. In the sixth stage of the development of the epidemic, the epidemic had developed to level 5, the number of infected people was about 26, and the estimated loss was about 220 million yuan. The comprehensive cumulative foreground (CF) values of O1 ∼ O3 schemes were −1.34, −1.21, and −0.77, respectively, and the final CF values were −1.35, 0.01, and -0.08, respectively. The final CF value of O2 was significantly higher than the other two options. The household infection probability was the highest, which was 0.37 and 0.35 in Wuhan and China, respectively. Under the measures of home quarantine, the numbers of confirmed cases of COVID in China and Wuhan were 1.503 (95% confidential interval (CI) = 1.328 ∼ 1.518) and 1.729 (95% CI = 1.107 ∼ 1.264), respectively, showing good fits with the real data. On the 21st day after the quarantine measures were taken, the number of COVID across the country had an obvious peak, with the confirmed cases of 24495, and the model prediction value was 24085 (95% CI = 23988 ∼ 25056). The incubation period 1/q was shortened from 8 days to 3 days, and the number of confirmed cases showed an upward trend. The peak period of confirmed cases was advanced, shortening the overall epidemic time. It showed that the prediction results of scenario deduction based on DBN were basically consistent with the actual development scenario and development status of the epidemic. It could provide corresponding decisions for the prevention and control of COVID based on the relevant parameters of the infectious disease dynamic model, which verified the rationality and feasibility of the scenario deduction method proposed in this study.

Short-Term Prediction Methodology of COVID-19 Infection in South Korea

The purpose of this study is to predict the short-term trend of the COVID-19 pandemic and give insights into effective response strategies. Based on the basic SIR model, a compartment method for modeling the course of an epidemic, the short-term infection change ratio md, is derived. The number of infected people can be predicted using this ratio. We calculated different md values on a weekly basis. As we tested different combinations of md, the prediction from the combination of md based on a week and md based on 4 weeks was found to be statistically reliable. According to our regression analysis, our approach has an explanatory power of 96%. However, this method could only predict 1 week ahead of current data. Thus, we use LSTM, a deep learning method applied for time series data, to forecast the trend 4 weeks ahead. The forecasted trends show that the number of infected people in South Korea will reach its peak a week after the writing of this work and start to gradually decline after that.

Ethics and Benefits of Vaccination between Theory and Practice

In this short paper we intend to examine Hepatitis B, infection of the liver produced by the hepatic B virus (HBV), which is nowadays a major public health problem on earth, because of the high number of infected people, which are sources of infection for those around them and because of the multiple transmission possibilities of the virus. The objective of this study is to dynamic analyze some of the epidemiological aspects of acute viral hepatitis B, admitted in the Clinical Infectious Diseases Hospital of Brasov in accordance to the recommendations of vaccination against hepatitis B in the Annual Vaccination Schedule since 1990 and numerous campaigns of pro-vaccination made by health professionals for promoting the fact that the specific vaccination is the only method of preventing the infections and the campaigns against vaccination made by people outside the medical field about concerns or refuses of vaccination because of association with other morbid states.

Ethical Considerations In ICU Preparations For Viral Epidemics

"Intensive Care Units (ICUs) admit patients with the most severe forms of the diseases, viral ARDS included. Since the 2009 H1N1 influenza outbreak, ICU preparations and triage have been recommended. The novel COVID-19 clinical picture resembles influenza in terms of organ dysfunction which may start with hypoxemic breathing insufficiency and ultimately, a possibility of evolution towards multiple organ failure. Its current clinical picture is not new for intensivists. However, there are several important differences as far as we know now: there is overall human susceptibility to become infected and nobody can be specifically protected by vaccination. This fact led to large numbers of infected people all over the world, overwhelming medical systems. Almost 10% of COVID-19 infections would be qualified for ICU admittance and ventilatory support. The main ethical issues in ICUs, in time of viral epidemics are: lack of free ICU beds, free ventilator machines, pressures on providing aggressive treatments for people with low chances of survival, dealing with terminally ill patients, high degrees of burnout in the medical team, reporting errors and inadequate behavior of other medical staff, lack of adequate protective equipment, as well as lack of administrative support. All these facts lead to moral distress and high burnout incidences. Administrative preparations, prioritization and triage are important aspects to consider. In conclusion, we all see that the modern world is not prepared enough to face such challenges and from these situations we, all (health care professionals, providers, population and authorities) should learn important personal and professional lessons. "

University Extension and Education in/for Health: knowledge, prevention and control of syphilis

The university extension activities are a form of interaction between the university and the community, and based on it, different health promotion activities can be developed, as the sustainable development of vulnerable communities for example. Syphilis is a sexually transmitted infection that can present asymptomatic or symptomatic forms and has affected countless people around the world. In Brazil, the number of infected people has increased over the years, even with the SUS providing treatment, which is quite worrying due to congenital syphilis. With the growth of infected people in the country, especially in the state of Pernambuco, it is essential to fight syphilis through preventive actions. Teaching and learning strategies such as health education demonstrate the transforming role of university extension for undergraduates and society, aiming to contribute to the academic training of undergraduates through the exchange of knowledge. The activities to raise awareness about syphilis were held in a conversation circle during the Science and Technology Week, CECINE-UFPE/2019, and in the Parasitology course at UFPE. At the end of each exhibition, information leaflets and condoms were also distributed. At first, the students were shy when they saw illustrated panels with syphilitic lesions and condoms on the benches. However, as the interaction progressed, they were amazed by the epidemiology, exposing their doubts and prior knowledge on the subject. With each new action, the extension workers understood more about the social commitment and demonstrated more security. Thus, the project contributes to the academic training of undergraduates, providing the development of educational practices with students, who become agents for disseminating knowledge in the fight and prevention of syphilis in their social spaces.

Two cases of COVID-19 reinfection in Turkey

SARS-CoV-2 has affected the whole world while the number of cases is increasing rapidly. Several reinfection cases have been reported with the rise in the number of infected people. It is important to study these reinfection cases as the knowledge gained can be used in critical decisions (...)

EPIDEMIOLOGICAL TRENDS AND STRUCTURE OF COVID-19 PATIENTS IN THE REPUBLIC OF KHAKASIA IN 2020

Background. In March 2020, the number of COVID-19 cases in the world increased 13 times, and the number of countries affected by the virus tripled. During the year, all countries of the world were affected by the COVID-19 pandemic, but the epidemic process has population characteristics. Purpose. To estimate the morbidity and mortality rates of patients with COVID-19 in the Republic of Khakassia, the structure of patients by sex and age infected with SARS-CoV-2 for 2020 Materials and methods. The incidence and mortality rates of the general population of patients with COVID-19 for 2020 in the republic has been analysed. For statistical processing of the results obtained, the Statistica 8.0 software package was used. Results. The number of registered cases of COVID-19 in 2020 in the Republic of Khakassia was 17552. There are two conditional growth intervals in the number of infected people in March-August (first wave) and in September-December (second wave). During the 1st wave, the incidence rate increased from 0.2 to 180.4 per 100 thousand of the population. In December, the incidence exceeded the same indicator in Russia by 1.8 times (2646.1 per 100 thousand population). There were three peak values of the mortality rate: in April – 3.1%, in July – 1.8%, in December – 2.4%. The proportion of deaths increases with age. The highest mortality rate is recorded at the age of 50 years and older: the proportion of deaths in this group is 3.7 times higher than at the age of 50-59, 10.6 times more than in the group of 40-49 years and 74 times more than in the young age group (under 40 years old). Among the patients of Khakassia, the ratio between men and women was 0.99: 1.0. Conclusion. The study of the clinical and epidemiological features of the disease continues and is a priority in the development of world health.

Intelligent Mobile Coronavirus Recognition Centre Based on IEEE 802.15.4

<p class="0abstract">The novel coronavirus (COVID-19) has become widespread around the world. It started in Wuhan, China, and has since spread rapidly among people living in other countries. Hence, the World Health Organization has considered COVID-19 as a pandemic that threatens millions of people’s lives. Due to the high number of infected people, many hospitals have been facing critical issues in providing the required medical services. For instance, some clinical centers have been unable to provide one of the most important medical services, namely blood tests to determine whether an individual is infected with COVID-19. Therefore, it is important to present an alternative diagnosis option to prevent the further spread of COVID-19. In this paper, a proposed intelligent detection communication system (IDCS) is configured for distributed mobile clinical centers to control the pandemic. In addition, the intelligent system is integrated with the Zigbee communication protocol to build a mobile COVID-19 detection system. The proposed system was trained on X-ray COVID-19 lung images used to identify infected people. The Zigbee protocol and decision tree algorithm were used to design the IDCS. The results of the proposed system show high accuracy 94.69% and accept results according to the performance measurements.</p>

Short-term forecasting of daily infections, fatalities and recoveries about COVID-19 in Algeria using statistical models

BackgroundA viral disease due to a virus called SARS-Cov-2 spreads globally with a total of 34,627,141 infected people and 1,029,815 deaths. Algeria is an African country where 51,690, 1,741 and 36,282 are currently reported as infected, dead and recovered. A multivariate time series model has been used to model these variables and forecast their future scenarios for the next 20 days.ResultsThe results show that there will be a minimum of 63 and a maximum of 147 new infections in the next 20 days with their corresponding 95% confidence intervals of − 89 to 214 and 108–186, respectively. Deaths’ forecast shows that there will be 8 and 12 minimum and maximum numbers of deaths in the upcoming 20 days with their 95% confidence intervals of 1–17 and 4–20, respectively. Minimum and maximum numbers of recovered cases will be 40 and 142 with their corresponding 95% confidence intervals of − 106 to 185 and 44–239, respectively. The total number of infections, fatalities and recoveries in the next 20 days will be 1850, 186 and 1680, respectively.ConclusionThe results of this study suggest that the new infections are higher in number than recover cases, and therefore, the number of infected people may increase in future. This study can provide valuable information for policy makers including health and education departments.

Stochastic Assessment of the Development of the Epidemic in Local Population Clusters

A mathematical model is proposed for stochastic assessment of the intensity of infection with highly active viruses in places with a high concentration of the population — closed (shopping and sports centers) or open (celebrations, rallies) spaces. Using a heuristic algorithm, the number of infected people is calculated depending on the observance or violation of the selected parameters: safe distances, personal immunity and protection standards. The initial conditions contain the total number of individuals in the clusters, the initial number of virus carriers, and the size of the space. Dispositions are introduced into the model that simulate the dynamics of changes in the coordinates of individuals in clusters. Programming, numerical experiments and graphics are done in MATLAB. A sharp jump was found in the number of those who had been initiated in case of non-observance of social distancing and frequent change of dispositions. The algorithm by fairly simple means allows you to quickly assess situations that are classified as poorly predictable. Introduces control elements into the complex problem of the development of an epidemic in local population clusters. The model is open to additions and improvements and may be of interest to support decision-making by healthcare or administrative authorities.