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 +===== COVID-19 Forecasting Abstracts - Page 7 =====
  
 +[[oa_db:​covid19_forecasting_abstracts|Back to Table of Contents]]
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 +[[oa_db:​covid19_forecasting_abstracts_pg6|Page 6]]
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 +----------------------------------------------------------------------<​br>​
 +<​b>​[235] Title: </​b>​Mathematical Modeling of COVID-19 Control and Prevention Based on Immigration Population Data in China: Model Development and Validation.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-25<​br>​
 +<​b>​Publisher:​ </​b>​JMIR Public Health and Surveillance<​br>​
 +<​b>​Keywords:​ </b>, 2019-ncov, covid-19, epidemic control and prevention, epidemic risk time series model, incoming immigration population, new diagnoses per day<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​publichealth.jmir.org/​2020/​2/​e18638/">​https://​publichealth.jmir.org/​2020/​2/​e18638/</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: At the end of February 2020, the spread of coronavirus disease (COVID-19) in China had drastically slowed and appeared to be under control compared to the peak data in early February of that year. However, the outcomes of COVID-19 control and prevention measures varied between regions (ie, provinces and municipalities) in China; moreover, COVID-19 has become a global pandemic, and the spread of the disease has accelerated in countries outside China. OBJECTIVE: This study aimed to establish valid models to evaluate the effectiveness of COVID-19 control and prevention among various regions in China. These models also targeted regions with control and prevention problems by issuing immediate warnings. METHODS: We built a mathematical model, the Epidemic Risk Time Series Model, and used it to analyze two sets of data, including the daily COVID-19 incidence (ie, newly diagnosed cases) as well as the daily immigration population size. RESULTS: Based on the results of the model evaluation, some regions, such as Shanghai and Zhejiang, were successful in COVID-19 control and prevention, whereas other regions, such as Heilongjiang,​ yielded poor performance. The evaluation result was highly correlated with the basic reproduction number (R0) value, and the result was evaluated in a timely manner at the beginning of the disease outbreak. CONCLUSIONS:​ The Epidemic Risk Time Series Model was designed to evaluate the effectiveness of COVID-19 control and prevention in different regions in China based on analysis of immigration population data. Compared to other methods, such as R0, this model enabled more prompt issue of early warnings. This model can be generalized and applied to other countries to evaluate their COVID-19 control and prevention.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[236] Title: </​b>​Visualising COVID-19 Pandemic Risk through Network Connectedness.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-07-01<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Microbiology,​ coronavirus,​ infographics,​ pandemic network, pandemic preparedness,​ risk assessment, visualisation,​ Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1201971220303179">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1201971220303179</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +With the domestic and international spread of the COVID-19, much attention has been given to estimating pandemic risk. We propose the use of a novel application of a well-established scientific approach, network analysis, to provide a direct visualisation (the infographics in Figures 1 and 2) of the COVID-19 pandemic risk. By showing visually the degree of connectedness between different regions based on reported confirmed cases of COVID-19, we demonstrate that network analysis provides a relatively simple yet powerful way to estimate the pandemic risk.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[237] Title: </​b>​Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-18<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Environmental Research and Public Health<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ anfis, covid-19, sars-cov-2, forecasting,​ marine predators algorithm (mpa), Health Sciences, Physical Sciences, Medicine, Environmental Science<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​1660-4601/​17/​10/​3520">​https://​www.mdpi.com/​1660-4601/​17/​10/​3520</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The current pandemic of the new coronavirus,​ severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2),​ or COVID-19, has received wide attention by scholars and researchers. The vast increase in infected people is a significant challenge for each country and the international community in general. The prediction and forecasting of the number of infected people (so-called confirmed cases) is a critical issue that helps in understanding the fast spread of COVID-19. Therefore, in this article, we present an improved version of the ANFIS (adaptive neuro-fuzzy inference system) model to forecast the number of infected people in four countries, Italy, Iran, Korea, and the USA. The improved version of ANFIS is based on a new nature-inspired optimizer, called the marine predators algorithm (MPA). The MPA is utilized to optimize the ANFIS parameters, enhancing its forecasting performance. Official datasets of the four countries are used to evaluate the proposed MPA-ANFIS. Moreover, we compare MPA-ANFIS to several previous methods to evaluate its forecasting performance. Overall, the outcomes show that MPA-ANFIS outperforms all compared methods in almost all performance measures, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), and Coefficient of Determination( R 2 ). For instance, according to the results of the testing set, the R 2 of the proposed model is 96.48%, 98.59%, 98.74%, and 95.95% for Korea, Italy, Iran, and the USA, respectively. More so, the MAE is 60.31, 3951.94, 217.27, and 12,979, for Korea, Italy, Iran, and the USA, respectively.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[238] Title: </​b>​Estimation of the time-varying reproduction number of COVID-19 outbreak in China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-07-01<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Hygiene & Environmental Health<​br>​
 +<​b>​Keywords:​ </b>, , Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1438463920302133">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1438463920302133</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: The 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. METHODS: Three 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. RESULTS: A 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, Rc, produced by all three methods in all analyzed regions of China are significantly smaller compared with the basic reproduction numbers R0. CONCLUSIONS:​ The 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.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[239] Title: </​b>​Estimation of the basic reproduction number, average incubation time, asymptomatic infection rate, and case fatality rate for COVID-19: Meta-analysis and sensitivity analysis.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-09<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Medical Virology<​br>​
 +<​b>​Keywords:​ </​b>​Medical Microbiology,​ covid-19, asymptomatic infection rate, basic reproduction number, case fatality rate, error-contaminated data, incubation time, Health Sciences, Life Sciences, Medicine, Immunology and Microbiology<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1002/​jmv.26041">​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1002/​jmv.26041</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The coronavirus disease-2019 (COVID-19) has been found to be caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, comprehensive knowledge of COVID-19 remains incomplete and many important features are still unknown. This manuscript conducts a meta-analysis and a sensitivity study to answer the questions: What is the basic reproduction number? How long is the incubation time of the disease on average? What portion of infections are asymptomatic?​ And ultimately, what is the case fatality rate? Our studies estimate the basic reproduction number to be 3.15 with the 95% CI (2.41-3.90),​ the average incubation time to be 5.08 days with the 95% CI (4.77-5.39) (in day), the asymptomatic infection rate to be 46% with the 95% CI (18.48%-73.60%),​ and the case fatality rate to be 2.72% with 95% CI (1.29%-4.16%) where asymptomatic infections are accounted for.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[240] Title: </​b>​Estimation of the serial interval and basic reproduction number of COVID-19 in Qom, Iran, and three other countries: A data-driven analysis in the early phase of the outbreak.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-16<​br>​
 +<​b>​Publisher:​ </​b>​Transboundary & Emerging Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Veterinary Sciences, covid-19, coronavirus infections, basic reproduction number, disease outbreaks, pandemics, Veterinary, Health Sciences, Life Sciences, Immunology and Microbiology<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1111/​tbed.13656">​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1111/​tbed.13656</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The outbreak of COVID-19 was first reported from China, and on 19 February 2020, the first case was confirmed in Qom, Iran. The basic reproduction number (R0 ) of infection is variable in different populations and periods. This study aimed to estimate the R0 of COVID-19 in Qom, Iran, and compare it with that in other countries. For estimation of the serial interval, we used data of the 51 confirmed cases of COVID-19 and their 318 close contacts in Qom, Iran. The number of confirmed cases daily in the early phase of the outbreak and estimated serial interval were used for R0 estimation. We used the time-varying method as a method with the least bias to estimate R0 in Qom, Iran, and in China, Italy and South Korea. The serial interval was estimated with a gamma distribution,​ a mean of 4.55 days and a standard deviation of 3.30 days for the COVID-19 epidemic based on Qom data. The R0 in this study was estimated to be between 2 and 3 in Qom. Of the four countries studied, the lowest R0 was estimated in South Korea (1.5-2) and the highest in Iran (4-5). Sensitivity analyses demonstrated that R0 is sensitive to the applied mean generation time. To the best of the authors'​ knowledge, this study is the first to estimate R0 in Qom. To control the epidemic, the reproduction number should be reduced by decreasing the contact rate, decreasing the transmission probability and decreasing the duration of the infectious period.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[241] Title: </​b>​Estimating the undetected infections in the Covid-19 outbreak by harnessing capture-recapture methods.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-08-01<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Microbiology,​ chao's lower bound, covid-19, population heterogeneity,​ undetected cases, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1201971220304446">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1201971220304446</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +OBJECTIVES: A major open question, affecting the decisions of policy makers, is the estimation of the true number of Covid-19 infections. Most of them are undetected, because of a large number of asymptomatic cases. We provide an efficient, easy to compute and robust lower bound estimator for the number of undetected cases. METHODS: A modified version of the Chao estimator is proposed, based on the cumulative time-series distributions of cases and deaths. Heterogeneity has been addressed by assuming a geometrical distribution underlying the data generation process. An (approximated) analytical variance of the estimator has been derived to compute reliable confidence intervals at 95% level. RESULTS: A motivating application to the Austrian situation is provided and compared with an independent and representative study on prevalence of Covid-19 infection. Our estimates match well with the results from the independent prevalence study, but the capture-recapture estimate has less uncertainty involved as it is based on a larger sample size. Results from other European countries are mentioned in the discussion. The estimated ratio of the total estimated cases to the observed cases is around the value of 2.3 for all the analyzed countries. CONCLUSIONS:​ The proposed method answers to a fundamental open question: "How many undetected cases are going around?"​. CR methods provide a straightforward solution to shed light on undetected cases, incorporating heterogeneity that may arise in the probability of being detected.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[242] Title: </​b>​Estimation of the secondary attack rate of COVID-19 using proportional meta-analysis of nationwide contact tracing data in Taiwan.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Microbiology,​ Immunology and Infection<​br>​
 +<​b>​Keywords:​ </​b>​Immunology,​ bayesian, covid-19, meta-analysis,​ pandemic, secondary attack rate, Health Sciences, Life Sciences, Medicine, Immunology and Microbiology<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1684118220301432">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1684118220301432</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Crude secondary attack rate (SAR) of COVID-19 in Taiwan was 0.84% using nationwide contact-tracing data till April 8, 2020. The random-effect Bayesian metaanalysis yielded 95% credible intervals of 0.42%-1.69% and 0.08%-8.32%,​ respectively,​ for estimated SAR pooling from 15 case series and for predicted SAR in the future if pandemic continues.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[243] Title: </​b>​Will the COVID-19 pandemic slow down in the Northern hemisphere by the onset of summer? An epidemiological hypothesis.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-23<​br>​
 +<​b>​Publisher:​ </​b>​Infection<​br>​
 +<​b>​Keywords:​ </​b>​Clinical Sciences, covid-19, epidemiology,​ influenza, pandemic, respiratory viral infection, seasonality,​ Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​link.springer.com/​article/​10.1007/​s15010-020-01460-1">​https://​link.springer.com/​article/​10.1007/​s15010-020-01460-1</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The COVID-19 pandemic has affected most countries of the world. As corona viruses are highly prevalent in the cold season, the question remains whether or not the pandemic will improve with increasing temperatures in the Northern hemisphere. We use data from a primary care registry of almost 15,000 patients over 20 years to retrieve information on viral respiratory infection outbreaks. Our analysis suggests that the severity of the pandemic will be softened by the seasonal change to summer.<​br>​
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 +
 +</​html>​
oa_db/covid19_forecasting_abstracts_pg7.txt · Last modified: 2020/06/27 20:12 by bpwhite