Cases Of COVID-19 In China Research Articles

Uncertain SEIAR model for COVID-19 cases in China

The Susceptible-Exposed-Infectious-Asymptomatic-Removed (SEIAR) epidemic model is one of most frequently used epidemic models. As an application of uncertain differential equations to epidemiology, an uncertain SEIAR model is derived which considers the human uncertainty factors during the spread of an epidemic. The parameters in the uncertain epidemic model are estimated with the numbers of COVID-19 cases in China, and a prediction to the possible numbers of active cases is made based on the estimates.

Analysis and prediction of confirmed COVID-19 cases in China with uncertain time series

This paper presents an uncertain time series model to analyse and predict the evolution of confirmed COVID-19 cases in China, excluding imported cases. Compared with the results of the classical time series model, the uncertain time series model could better describe the COVID-19 epidemic by using an uncertain hypothesis test to filter out outliers. This improvement is reflected in the two observations. One is that the estimated variance of the disturbance term in the uncertain time series model is more appropriate and acceptable than that in the classical time series model, and the other is that the disturbance term of the classical time series model cannot be regarded as a random variable but as an uncertain variable.

Warmer weather unlikely to reduce the COVID-19 transmission: An ecological study in 202 locations in 8 countries

PurposeTo examine the association between meteorological factors (temperature, relative humidity, wind speed, and UV radiation) and transmission capacity of COVID-19. MethodsWe collected daily numbers of COVID-19 cases in 202 locations in 8 countries. We matched meteorological data from the NOAA National Centers for Environmental Information. We used a time-frequency approach to examine the possible association between meteorological conditions and basic reproductive number (R0) of COVID-19. We determined the correlations between meteorological factors and R0 of COVID-19 using multiple linear regression models and meta-analysis. We further validated our results using a susceptible-exposed-infectious-recovered (SEIR) metapopulation model to simulate the changes of daily cases of COVID-19 in China under different temperatures and relative humidity conditions. Principal resultsTemperature did not exhibit significant association with R0 of COVID-19 (meta p = 0.446). Also, relative humidity (meta p = 0.215), wind speed (meta p = 0.986), and ultraviolet (UV) radiation (meta p = 0.491) were not significantly associated with R0 either. The SEIR model in China showed that with a wide range of meteorological conditions, the number of COVID-19 confirmed cases would not change substantially. ConclusionsMeteorological conditions did not have statistically significant associations with the R0 of COVID-19. Warmer weather alone seems unlikely to reduce the COVID-19 transmission.

The role of Covid-19 for Chinese stock returns: evidence from a GARCHX model

ABSTRACT This paper examines the effect of Covid-19 pandemic on the Chinese stock market returns and their volatility using the generalized autoregressive conditionally heteroskedastic GARCHX model. The GARCHX model allows us to include Covid-19 information within the GARCH framework. The findings document that daily increases in total confirmed Covid-19 cases in China, measured as total daily deaths and cases, have a significant negative impact on stock returns, with the negative impact of the Covid-19 on stock returns being more pronounced when total deaths proxy the effect of this infectious disease. The results also document that Covid-19 has a positive and statistically significant effect on the volatility of these market returns. Overall, new evidence is offered that infectious diseases, such as Covid-19, can seriously impact market returns, as well as their volatility. The findings could be essential in understanding the implications of Covid-19 for the stock market in China.

Development and validation of a risk stratification model for screening suspected cases of COVID-19 in China

How to quickly identify high-risk populations is critical to epidemic control. We developed and validated a risk prediction model for screening SARS-CoV-2 infection in suspected cases with an epidemiological history. A total of 1019 patients, ≥13 years of age, who had an epidemiological history were enrolled from fever clinics between January 2020 and February 2020. Among 103 (10.11%) cases of COVID-19 were confirmed. Multivariable analysis summarized four features associated with increased risk of SARS-CoV-2 infection, summarized in the mnemonic COVID-19-REAL: radiological evidence of pneumonia (1 point), eosinophils < 0.005 × 109/L (1 point), age ≥ 32 years (2 points), and leukocytes < 6.05 × 109 /L (1 point). The area under the ROC curve for the training group was 0.863 (95% CI, 0.813 - 0.912). A cut-off value of less than 3 points for COVID-19-REAL was assigned to define the low-risk population. Only 10 (2.70%) of 371 patients were proved to be SARS-CoV-2 positive, with a negative predictive value of 0.973. External validation was similar. This study provides a simple, practical, and robust screening model, COVID-19-REAL, able to identify populations at high risk for SARS-CoV-2 infection.

Dissemination Strategies and Usage of Psychological Assistance Hotlines During the COVID-19 Outbreak in China

When the 2019 novel coronavirus disease (COVID-19) was rapidly spreading in China in early 2020, China’s National Health Commission quickly responded to the psychological crisis by issuing guidelines for establishing mental health intervention systems, including providing psychological assistance hotlines. However, recent critiques have emphasized China’s lack of pre-established mental health interventions which resulted in an inefficient response. This is the first empirical study to systematically examine mental health service use in China during the COVID-19 outbreak. The current study focused on the use of mental health hotlines in a Northern Chinese region. This region originally had a regional level hotline. During the outbreak, 12 out of its 16 sub-regional juridical areas started providing their own hotlines. Data regarding the regional level hotline and the 12 sub-regional level hotlines were obtained, including daily number of calls received, strategies for disseminating hotline services, and callers’ expressed concerns. Confirmed COVID-19 cases in China, in the region, and in each of the sub-regional juridical area were also recorded daily during China’s peak period of COVID-19. Analyses of these data revealed that the mental health hotlines tended to have low usage overall. Hotlines that merely provided their numbers to community centers and quarantine centers tended to receive few calls. Hotlines that encouraged individuals to advertise the service on personal social media accounts tended to receive more calls. The daily number of confirmed COVID-19 cases in the country was closely related the number of phone calls received at the regional hotline. Sub-regional hotline operators reported that a significant proportion of callers had concerns about contracting COVID-19, negative emotions from prolonged social isolation, and family conflicts while stay-at-home policies were implemented. It was also observed that the sub-regional level hotlines did not start until COVID-19 cases in the country started to decline. Overall, the psychological assistance hotlines provided during COVID-19 satisfied some mental health needs. However, consistent with recent commentaries, the hotline services were not established during the time that demand likely peaked. Future studies are warranted to determine the best strategies to improve the accessibility of mental health hotline services.

Situación epidemiológica del Covid-19 en Sudamérica

Currently the coronavirus (COVID-19) infection has become a public health problem worldwide. In December 2019, in the city of Wuhan, province of Hubei, China, the first cases of pneumonia of unknown etiology were reported, which incremented rapidly in other provinces of the country. Subsequently, SARS-CoV2 was identified as the causal agent and in mid-January 2020, the World Health Organization (WHO) reported over 280 confirmed cases of COVID-19 in China, Thailand, Japan and Korea. In South America, the first case of COVID-19 is made known on February 26, 2020 in the city of Sao Paulo, Brazil, identified as a male patient of 61 years of age from the region of Lombardy in Italy. Soon after there were other confirmed cases imported from the Asian and European continents in other south American countries.

Children and Coronavirus Disease 2019 (COVID-19)

Source: Castagnoli R, Votto M, Licari A, et al. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children and adolescents: a systematic review. JAMA Pediatr. 2020 22 April [published online ahead of print]; doi: 10.1001/jamapediatrics.2020.1467Investigators from multiple institutions in Italy conducted a systematic review to describe the clinical features of children ≤19 years old diagnosed with coronavirus 2 (SARS-CoV-2) infection. Articles eligible for inclusion in the review were published from December 1, 2019 to March 3, 2020. A systematic process was conducted to identify the articles and determine eligibility for inclusion. Only articles that included clinical data on patients 0–19 years old were analyzed. Review of the included articles focused on age, diagnostic tests, clinical features, therapeutic management, and prognosis.A total of 776 articles were reviewed, and 18 were included in the analysis. Included articles were published in February and March 2020. Seventeen articles were from China and included children with travel to Wuhan, China, or contact with affected family members; one study was from Singapore.The 18 articles included data on 1,065 children with SARS-CoV-2 infection; 444 cases were in children <10 years old, and 553 were in youths 10–19 years old. There were 2 reported cases in neonates, including a newborn with symptoms at 30 hours of life. All diagnoses were confirmed by a nasopharyngeal swab that tested positive for SARS-CoV-2. At least 3 children among the 1,065 cases were asymptomatic. When symptoms did occur, they typically were mild and most commonly included fever and cough, although a wide array of symptoms were reported. One 13-month-old child required aggressive treatment in an ICU, including mechanical ventilation, but recovered. Except for this case, no patient required supplemental oxygen or ventilation. Information on chest imaging (radiographs and/or CT) was included in 12 articles. Common findings were bronchial thickening and ground-glass opacities. There was one death reported in a child who was in the 10- to 19-year age range. Otherwise, all patients reportedly had a good prognosis.The authors conclude that this is only the first systematic review of pediatric cases of SARS-CoV-2 infection. Further epidemiologic and clinical studies are needed.Dr Brady has disclosed no financial relationship relevant to this commentary. This commentary does not contain a discussion of an unapproved/investigative use of a commercial product/device.Analysis of the first 2,572 US pediatric patients with SARS-CoV-2 infection revealed a median age of 11 years; 1,495 (58%) were 10–17 years; 398 were infants <1 year old. Among 184 cases with exposure information, 91% had exposure to a COVID-19 patient in the household or community.1Data on signs and symptoms were available for 291 (11%) of the pediatric cases. Among those with information, 56% of pediatric patients reported fever, 54% reported cough, and 13% reported shortness of breath. These findings were consistent with a report of pediatric COVID-19 cases in China.2Information on hospitalization was available for 745 (29%) of the pediatric cases; 147 (20%) were hospitalized (15 admitted to the ICU). Among 95 children <1 year old whose hospitalization status was known, 59 (62%) were hospitalized, including 5 admitted to an ICU. This suggestion of more severe disease in infants also was noted in a study of 2,143 pediatric COVID-19 cases in China.3Much is still unknown about the risks of COVID-19 during pregnancy and to the newborn infant. However, current evidence suggests low rates of peripartum transmission.4,5 An analysis of 38 pregnant women with COVID-19 infection in China showed no maternal deaths.4 Preterm births have been reported in women who tested positive for COVID-19 late in pregnancy.5A small number of children have developed a multisystem inflammatory syndrome temporally associated with COVID-19.6,7 These children fulfill full or partial criteria for Kawasaki disease and often require ICU care. Their SARS-CoV-2 PCR testing may be negative, and hence, serology may be useful to establish this association.The major symptoms of pandemic SARS-CoV-2 are fever, cough, and shortness of breath. For the latest updates, consult the CDC website.8New data on SARS-CoV-2 disease presentation and complications in infants and children are so rapidly accruing, journal articles undoubtedly will be superseded by newer reports—including case series and studies. All we can advise is: stay tuned.

The BRICS response to COVID-19

Since late 2019, SARS-CoV2 pandemic has spread worldwide. After several generations without a severe pandemic, the mixture of health and economic crisis has hit populations in all continents. The high degree of connectivity that States share, enabled by the current transportation and communication technologies, caused different regions of the world to be affected by coronavirus disease at almost the same time. Yet States responses to the SARS-CoV2 pandemic were not the same. This brief focuses on the BRICS countries individual, bilateral and collective responses to the international health crisis caused by coronavirus disease, from the early detection of COVID-19 cases in China in late December 2019 to the current surge of cases in Brazil.

Prediction of the Number of Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models.

The outbreak of a novel coronavirus (SARS-CoV-2) has caused a large number of residents in China to be infected with a highly contagious pneumonia recently. Despite active control measures taken by the Chinese government, the number of infected patients is still increasing day by day. At present, the changing trend of the epidemic is attracting the attention of everyone. Based on data from 21 January to 20 February 2020, six rolling grey Verhulst models were built using 7-, 8- and 9-day data sequences to predict the daily growth trend of the number of patients confirmed with COVID-19 infection in China. The results show that these six models consistently predict the S-shaped change characteristics of the cumulative number of confirmed patients, and the daily growth decreased day by day after 4 February. The predicted results obtained by different models are very approximate, with very high prediction accuracy. In the training stage, the maximum and minimum mean absolute percentage errors (MAPEs) are 4.74% and 1.80%, respectively; in the testing stage, the maximum and minimum MAPEs are 4.72% and 1.65%, respectively. This indicates that the predicted results show high robustness. If the number of clinically diagnosed cases in Wuhan City, Hubei Province, China, where COVID-19 was first detected, is not counted from 12 February, the cumulative number of confirmed COVID-19 cases in China will reach a maximum of 60,364–61,327 during 17–22 March; otherwise, the cumulative number of confirmed cases in China will be 78,817–79,780.

Association between regional selenium status and reported outcome of COVID-19 cases in China

Potentially relevant to the recent appearance of COVID-19 in China is the fact that there is a belt of selenium deficiency running from northeast to southwest in the country and, indeed, China has populations that have both the lowest and the highest selenium status in the world. A set of interesting studies published by the Beck laboratory in the 1990s showed that host selenium deficiency increased the virulence of RNA viruses such as coxsackievirus B3 and influenza A. Passage through a selenium-deficient animal that was unable to produce sufficient antioxidant selenoproteins for its own protection resulted in the virus mutating to a virulent form that caused more severe pathology. Those findings shed light on a human selenium-deficiency disease, a cardiomyopathy known as Keshan disease, named after the area in northeast China where it was endemic. The disease showed a seasonal variation, suggesting a viral cofactor that was later identified as coxsackievirus B3. When the population was supplemented with selenium, the incidence of Keshan disease decreased dramatically.

Modelling spreading of an infection using time series by a novel family of models; fitting the time series of the confirmed cases of COVID-19 in China

Abstract Introduction Time series models are one of the frequently used methods to describe the pattern of spreading an epidemic. Methods We presented a new family of time series models able to represent the cumulative number of individuals that contracted an infectious disease from the start to the end of the first wave of spreading. This family is flexible enough to model the propagation of almost all infectious diseases. After a general discussion on competent time series to model the outbreak of a communicable disease, we introduced the new family through one of its examples. Results We estimated the parameters of two samples of the novel family to model the spreading of COVID-19 in China. Discussion Our model does not work well when the decreasing trend of the rate of growth is absent because it is the main presumption of the model. In addition, since the information on the initial days is of the utmost importance for this model, one of the challenges about this model is modifying it to get qualified to model datasets that lack the information on the first days.

Estimation of the time-varying reproduction number of COVID-19 outbreak in China.

BackgroundThe 2019 novel coronavirus (COVID-19) outbreak in Wuhan, China has attracted world-wide attention. As of March 31, 2020, a total of 82,631 cases of COVID-19 in China were confirmed by the National Health Commission (NHC) of China.MethodsThree approaches, namely Poisson likelihood-based method (ML), exponential growth rate-based method (EGR) and stochastic Susceptible-Infected-Removed dynamic model-based method (SIR), were implemented to estimate the basic and controlled reproduction numbers.ResultsA total of 198 chains of transmission together with dates of symptoms onset and 139 dates of infections were identified among 14,829 confirmed cases outside Hubei Province as reported as of March 31, 2020. Based on this information, we found that the serial interval had an average of 4.60 days with a standard deviation of 5.55 days, the incubation period had an average of 8.00 days with a standard deviation of 4.75 days and the infectious period had an average of 13.96 days with a standard deviation of 5.20 days. The estimated controlled reproduction numbers, , produced by all three methods in all analyzed regions of China are significantly smaller compared with the basic reproduction numbers .ConclusionsThe controlled reproduction number in China is much lower than one in all regions of China by now. It fell below one within 30 days from the implementations of unprecedent containment measures, which indicates that the strong measures taken by China government was effective to contain the epidemic. Nonetheless, efforts are still needed in order to end the current epidemic as imported cases from overseas pose a high risk of a second outbreak.

How do healthcare workers in diagnostic imaging minimise risks but maximise performance during the COVID-19 pandemic?

How do healthcare workers in diagnostic imaging minimise risks but maximise performance during the COVID-19 pandemic?

Failure in initial stage containment of global COVID-19 epicenters.

With multiple virus epicenters, COVID‐19 has been declared a pandemic by the World Health Organization. Consequently, many countries have implemented different policies to manage this crisis including curfew and lockdown. However, the efficacy of individual policies remains unclear with respect to COVID‐19 case development. We analyzed available data on COVID‐19 cases of eight majorly affected countries, including China, Italy, Iran, Germany, France, Spain, South Korea, and Japan. Growth rates and doubling time of cases were calculated for the first 6 weeks after the initial cases were declared for each respective country and put into context with implemented policies. Although the growth rate of total confirmed COVID‐19 cases in China has decreased, those for Japan have remained constant. For European countries, the growth rate of COVID‐19 cases considerably increased during the second time interval. Interestingly, the rates for Germany, Spain, and France are the highest measured in the second interval and even surpass the numbers in Italy. Although the initial data in Asian countries are encouraging with respect to case development at the initial stage, the opposite is true for European countries. Based on our data, disease management in the 2 weeks following the first reported cases is of utmost importance.

Understanding epidemic data and statistics: A case study of COVID-19.

The 2019 novel‐coronavirus (COVID‐19) has affected 181 countries with approximately 1197405 confirmed cases (by 5th April). Understanding the transmission dynamics of the infection in each country which got affected on a daily basis and evaluating the effectiveness of control policies are critical for our further actions. To date, the statistics of COVID‐19 reported cases show that more than 80% of infected are mild cases of disease, around 14% of infected have severe complications, and about 5% are categorized as critical disease victims. Today's report (5th April 2020; daily updates in the prepared website) shows that the confirmed cases of COVID‐19 in the United States, Spain, Italy, and Germany are 308850, 126168, 124632, and 96092, respectively. Calculating the total case fatality rate (CFR) of Italy (4th April 2020), about 13.3% of confirmed cases have passed away. Compared with South Korea's rate of 1.8% (seven times lower than Italy) and China's 4% (69% lower than Italy), the CFR of Italy is too high. Some effective policies that yielded significant changes in the trend of cases were the lockdown policy in China, Italy, and Spain (the effect observed after some days), the shutdown of all nonessential companies in Hubei (the effect observed after 5 days), combined policy in South Korea, and reducing working hours in Iran.

Mathematical prediction of the time evolution of the COVID-19 pandemic in Italy by a Gauss error function and Monte Carlo simulations

In this paper are presented mathematical predictions on the evolution in time of the number of positive cases in Italy of the COVID-19 pandemic based on official data and on the use of a function of the type of a Gauss error function, with four parameters, as a cumulative distribution function. We have analyzed the available data for China and Italy. The evolution in time of the number of cumulative diagnosed positive cases of COVID-19 in China very well approximates a distribution of the type of the error function, that is, the integral of a normal, Gaussian distribution. We have then used such a function to study the potential evolution in time of the number of positive cases in Italy by performing a number of fits of the official data so far available. We then found a statistical prediction for the day in which the peak of the number of daily positive cases in Italy occurs, corresponding to the flex of the fit, that is, to the change in sign of its second derivative (i.e., the change from acceleration to deceleration), as well as of the day in which a substantial attenuation of such number of daily cases is reached. We have also analyzed the predictions of the cumulative number of fatalities in both China and Italy, obtaining consistent results. We have then performed 150 Monte Carlo simulations to have a more robust prediction of the day of the above-mentioned peak and of the day of the substantial decrease in the number of daily positive cases and fatalities. Although official data have been used, those predictions are obtained with a heuristic approach since they are based on a statistical approach and do not take into account either a number of relevant issues (such as number of daily nasopharyngeal swabs, medical, social distancing, virological and epidemiological) or models of contamination diffusion.

Distinguishing Epidemiological Curve of Novel Coronavirus Disease (COVID-19) Cases in Iraq: How It Does not Follow the Epidemic Curve of China

Governments around the world must respond quickly to control the COVID-19 pandemic. Because the numbers of COVID-19 cases in Iraq do not follow the cases in China, we highlight the significant role of government response and population demography, mainly how the quarantine and age structure of the Iraqi population explains the differences in COVID-19 cases or fatality cases compared to China, and eventually to Italy and the United Kingdom (UK). In this study, we used the reported cases of confirmed COVID-19 in Iraq from the World Health Organization (WHO) to estimate the epidemiological curve of COVID-19 cases in Iraq. Then we compared our estimates with the epidemiological curve of reported COVID-19 cases in China, Italy, and the UK. The epidemiological curve of COVID-19 cases in Iraq illustrates the risks of explosive growth in COVID-19 cases. When aligning four sets of the epidemiological curve, the curve trend of the COVID-19 cases has a different level between Iraq, China, Italy, and the UK (due to a difference in “quarantine start” dates), the epidemiological curve of COVID-19 in Iraq was low due to the quick response from the local government in applying the quarantine. The COVID-19 death cases in Iraq are lower than these countries as their older adults are the main percentage in their population demography for them. An investigation in the neighboring country of Iraq, Jordan, showed the relative similarity in the epidemiological curve for Iraq (because of the similarities in population demography and “quarantine initiation”). In conclusion, the adequate government's response in Iraq illustrated how the epidemiological curve of COVID-19 cases in Iraq does not follow the epidemiological curve in China. If the response to quarantine is as now into the end of this month, the COVID-19 cases in Iraq may reach approximately 1,540 by April 30th, 2020. Nevertheless, if the quarantine is broken, the cases could reach the numbers that are currently recorded in Italy and the UK. On the other hand, the COVID-19 cases may be less with an increase in weather temperature.

The effect of human mobility and control measures on the COVID-19 epidemic in China.

The ongoing COVID-19 outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions have been undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China.

In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination (), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.