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 +===== COVID-19 Forecasting Abstracts - Page 5 =====
  
 +[[oa_db:​covid19_forecasting_abstracts|Back to Table of Contents]]
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 +<​b>​[157] Title: </​b>​Weathering the pandemic: How the Caribbean Basin can use viral and environmental patterns to predict, prepare, and respond to COVID-19.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​3.35<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-02<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Medical Virology<​br>​
 +<​b>​Keywords:​ </​b>​Medical Microbiology,​ coronavirus,​ pandemic, seasonal incidence, Health Sciences, Life Sciences, Medicine, Immunology and Microbiology<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1002/​jmv.25864">​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1002/​jmv.25864</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The 2020 coronavirus pandemic is developing at different paces throughout the world. Some areas, like the Caribbean Basin, have yet to see the virus strike at full force. When it does, there is reasonable evidence to suggest the consequent COVID-19 outbreaks will overwhelm healthcare systems and economies. This is particularly concerning in the Caribbean as pandemics can have disproportionately higher mortality impacts on lower and middle-income countries. Preliminary observations from our team and others suggest that temperature and climatological factors could influence the spread of this novel coronavirus,​ making spatiotemporal predictions of its infectiousness possible. This review studies geographic and time-based distribution of known respiratory viruses in the Caribbean Basin in an attempt to foresee how the pandemic will develop in this region. This review is meant to aid in planning short- and long-term interventions to manage outbreaks at the international,​ national, and subnational levels in the region.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[158] Title: </​b>​Estimation of the Probability of Reinfection With COVID-19 by the Susceptible-Exposed-Infectious-Removed-Undetectable-Susceptible Model.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​3.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-13<​br>​
 +<​b>​Publisher:​ </​b>​JMIR Public Health and Surveillance<​br>​
 +<​b>​Keywords:​ </b>, covid-19, seirus, coronavirus,​ disease, infectious, math, model, outbreak, pandemic, reinfection<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​publichealth.jmir.org/​2020/​2/​e19097/">​https://​publichealth.jmir.org/​2020/​2/​e19097/</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: With the sensitivity of the polymerase chain reaction test used to detect the presence of the virus in the human host, the worldwide health community has been able to record a large number of the recovered population. OBJECTIVE: The aim of this study was to evaluate the probability of reinfection in the recovered class and the model equations, which exhibits the disease-free equilibrium state for the coronavirus disease. METHODS: The model differential equation was evaluated for the disease-free equilibrium for the case of reinfection as well as the existence and stability criteria for the disease, using the model proportions. This evaluation shows that the criteria for a local or worldwide asymptotic stability with a basic reproductive number (R0=0) were satisfied. Hence, there is a chance of no secondary reinfections from the recovered population, as the rate of incidence of the recovered population vanishes (ie, B=0). RESULTS: With a total of about 900,000 infected cases worldwide, numerical simulations for this study were carried out to complement the analytical results and investigate the effect that the implementation of quarantine and observation procedures has on the projection of further virus spread. CONCLUSIONS:​ As shown by the results, the proportion of the infected population, in the absence of a curative vaccination,​ will continue to grow worldwide; meanwhile, the recovery rate will continue slowly, which means that the ratio of infection rate to recovery rate will determine the death rate that is recorded. Most significant for this study is the rate of reinfection by the recovered population, which will decline to zero over time as the virus is cleared clinically from the system of the recovered class.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[159] Title: </​b>​Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​3.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-01<​br>​
 +<​b>​Publisher:​ </​b>​IEEE Access<​br>​
 +<​b>​Keywords:​ </b>, epidemic transmission,​ infection rate, mathematical model, novel coronavirus,​ prediction, removal rate<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​ieeexplore.ieee.org/​document/​9028194/">​https://​ieeexplore.ieee.org/​document/​9028194/</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The 2019 novel coronavirus (2019-nCoV) outbreak has been treated as a Public Health Emergency of International Concern by the World Health Organization. This work made an early prediction of the 2019-nCoV outbreak in China based on a simple mathematical model and limited epidemiological data. Combing characteristics of the historical epidemic, we found part of the released data is unreasonable. Through ruling out the unreasonable data, the model predictions exhibit that the number of the cumulative 2019-nCoV cases may reach 76,000 to 230,000, with a peak of the unrecovered infectives (22,​000-74,​000) occurring in late February to early March. After that, the infected cases will rapidly monotonically decrease until early May to late June, when the 2019-nCoV outbreak will fade out. Strong anti-epidemic measures may reduce the cumulative infected cases by 40%-49%. The improvement of medical care can also lead to about one-half transmission decrease and effectively shorten the duration of the 2019-nCoV.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[160] Title: </​b>​Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​3<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-31<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Environmental Research and Public Health<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ covid-19, new case, outbreak, predictor, social media, Health Sciences, Physical Sciences, Medicine, Environmental Science<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​1660-4601/​17/​7/​2365">​https://​www.mdpi.com/​1660-4601/​17/​7/​2365</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus,​ and pneumonia were collected from 31 December 2019 to 9 February 2020. The new suspected cases of COVID-19 data were collected from 20 January 2020 to 9 February 2020. We used the lagged series of SMSI to predict new suspected COVID-19 case numbers during this period. To avoid overfitting,​ five methods, namely subset selection, forward selection, lasso regression, ridge regression, and elastic net, were used to estimate coefficients. We selected the optimal method to predict new suspected COVID-19 case numbers from 20 January 2020 to 9 February 2020. We further validated the optimal method for new confirmed cases of COVID-19 from 31 December 2019 to 17 February 2020. The new suspected COVID-19 case numbers correlated significantly with the lagged series of SMSI. SMSI could be detected 6-9 days earlier than new suspected cases of COVID-19. The optimal method was the subset selection method, which had the lowest estimation error and a moderate number of predictors. The subset selection method also significantly correlated with the new confirmed COVID-19 cases after validation. SMSI findings on lag day 10 were significantly correlated with new confirmed COVID-19 cases. SMSI could be a significant predictor of the number of COVID-19 infections. SMSI could be an effective early predictor, which would enable governments'​ health departments to locate potential and high-risk outbreak areas.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[161] Title: </​b>​Modeling and prediction of COVID-19 pandemic using Gaussian mixture model.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​3<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-09-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos,​ Solitons & Fractals<​br>​
 +<​b>​Keywords:​ </​b>​Mathematical Sciences, covid-19, discrete cosine transform (dct), fourier decomposition method (fdm), gaussian mixture model (gmm), mathematical model, susceptible-infected-recovered (sir) model, Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920304215">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920304215</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +COVID-19 is caused by a novel coronavirus and has played havoc on many countries across the globe. A majority of the world population is now living in a restricted environment for more than a month with minimal economic activities, to prevent exposure to this highly infectious disease. Medical professionals are going through a stressful period while trying to save the larger population. In this paper, we develop two different models to capture the trend of a number of cases and also predict the cases in the days to come, so that appropriate preparations can be made to fight this disease. The first one is a mathematical model accounting for various parameters relating to the spread of the virus, while the second one is a non-parametric model based on the Fourier decomposition method (FDM), fitted on the available data. The study is performed for various countries, but detailed results are provided for the India, Italy, and United States of America (USA). The turnaround dates for the trend of infected cases are estimated. The end-dates are also predicted and are found to agree well with a very popular study based on the classic susceptible-infected-recovered (SIR) model. Worldwide, the total number of expected cases and deaths are 12.7 x 10(6) and 5.27 x 10(5), respectively,​ predicted with data as of 06-06-2020 and 95% confidence intervals. The proposed study produces promising results with the potential to serve as a good complement to existing methods for continuous predictive monitoring of the COVID-19 pandemic.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[162] Title: </b>A dynamic modeling tool for estimating healthcare demand from the COVID19 epidemic and evaluating population-wide interventions.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​2.95<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-07-01<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Microbiology,​ covid, capacity, hospital, intervention,​ model, social distancing, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1201971220303507">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1201971220303507</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +OBJECTIVES: Public health officials need tools to assist in anticipating the healthcare resources required to confront the SARS-COV-2 pandemic. We constructed a modeling tool to aid active public health officials to estimate healthcare demand from the pandemic in their jurisdictions and to evaluate the potential impact of population-wide social-distancing interventions. METHODS: The tool uses an SEIR compartmental model to project the pandemic'​s local spread. Users input case counts, healthcare resources, and select intervention strategies to evaluate. Outputs include the number of infections and deaths with and without intervention,​ and the demand for hospital and critical care beds and ventilators relative to existing capacity. We illustrate the tool using data from three regions of Chile. RESULTS: Our scenarios indicate a surge in COVID-19 patients could overwhelm Chilean hospitals by June, peaking in July or August at six to 50 times the current supply of beds and ventilators. A lockdown strategy or combination of case isolation, home quarantine, social distancing of individuals >70 years, and telework interventions may keep treatment demand below capacity. CONCLUSIONS:​ Aggressive interventions can avert substantial morbidity and mortality from COVID-19. Our tool permits rapid evaluation of locally-applicable policy scenarios and updating of results as new data become available.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[163] Title: </​b>​The basic reproduction number of SARS-CoV-2 in Wuhan is about to die out, how about the rest of the World?<​br><​br>​
 +<​b>​Altmetric Score: </​b>​2.95<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-19<​br>​
 +<​b>​Publisher:​ </​b>​Reviews in Medical Virology<​br>​
 +<​b>​Keywords:​ </​b>​Medical Microbiology,​ covid-19, sara-cov-2, pandemic, the basic reproduction number ( r0), Health Sciences, Life Sciences, Medicine, Immunology and Microbiology<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1002/​rmv.2111">​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1002/​rmv.2111</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The virologically confirmed cases of a new coronavirus disease (COVID-19) in the world are rapidly increasing, leading epidemiologists and mathematicians to construct transmission models that aim to predict the future course of the current pandemic. The transmissibility of a virus is measured by the basic reproduction number ( R0 ), which measures the average number of new cases generated per typical infectious case. This review highlights the articles reporting rigorous estimates and determinants of COVID-19 R0 for the most affected areas. Moreover, the mean of all estimated R0 with median and interquartile range is calculated. According to these articles, the basic reproduction number of the virus epicentre Wuhan has now declined below the important threshold value of 1.0 since the disease emerged. Ongoing modelling will inform the transmission rates seen in the new epicentres outside of China, including Italy, Iran and South Korea.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[164] Title: </​b>​An updated analysis of turning point, duration and attack rate of COVID-19 outbreaks in major Western countries with data of daily new cases.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​2.75<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-08-01<​br>​
 +<​b>​Publisher:​ </​b>​Data in Brief<​br>​
 +<​b>​Keywords:​ </b>, daily new cases, dynamic prediction, governments'​ interventions,​ segmentation,​ seven-day lag, statistical analysis<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2352340920307241">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2352340920307241</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +As coronavirus spreads around the world, the study of its effects is of great practical significance. We collated data on daily new cases of the COVID-19 outbreaks in the six Western countries of the Group of Seven and the dates of governments'​ interventions. We studied the periods before and after the dates of major governments'​ interventions integrally based on a segmented Poisson model. The relevant results are published in the paper of "​Predicting turning point, duration and attack rate of COVID - 19 outbreaks in major Western countries"​ [1]. Our method can be used to update prediction daily as COVID-19 outbreaks evolve. In this article, we illustrate an updated analysis with our method to facilitate reproducibility. Both datasets used and updated are provided.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[165] Title: </​b>​COVID-19 in Colombia endpoints. Are we different, like Europe?<​br><​br>​
 +<​b>​Altmetric Score: </​b>​2.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-01<​br>​
 +<​b>​Publisher:​ </​b>​Research in Social and Administrative Pharmacy<​br>​
 +<​b>​Keywords:​ </​b>​Public Health And Health Services, , Health Sciences, Pharmacology,​ Toxicology and Pharmaceutics,​ Life Sciences, Health Professions<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1551741120302874">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1551741120302874</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The infection by the new coronavirus (SARS-CoV-2) has taken the dimension of a pandemic, affecting more than 160 countries in a few weeks. In Colombia, despite the implementation of the rules established by the national government, exists an elevate concern both for mortality and for the limited capacity of the health system to respond effectively to the needs of patients infected. For Colombia, assuming a case fatality rate among people infected with SARS-CoV-2 of 0.6% (average data from the information reported for Latin American countries for March 18) (Table 1), the number of deaths, in one or two weeks, could be 16 and 243, respectively. These estimates differ markedly from those documented in countries such as Spain and Italy, in which COVID-19 case fatality rates exceed 8% (case of Italy) and from the percentage of patients who have required intensive care, which has ranged from 9% to 11% of patients in Mediterranean European countries. These differences could be explained due to: a) the percentage of the population at risk (individuals older than 60 years); b) a higher epidemiological exposure to viral respiratory infections associated with more frequent exposure to them, due to geographic and climatic conditions; c) less spread of the virus by location in the tropical zone; and d) earlier preventive measures to contain the spread of SARS-CoV-2 infection. Therefore, it is possible to establish that the situation in this country will be different from in European Mediterranean and that Colombia could have different endpoints from Spain and Italy.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[166] Title: </​b>​COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​2.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Microbiology,​ Immunology and Infection<​br>​
 +<​b>​Keywords:​ </​b>​Immunology,​ arima, covid-19 outbreak, forecasting,​ italian population, lock down, Health Sciences, Life Sciences, Medicine, Immunology and Microbiology<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1684118220300980">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1684118220300980</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: Till 31 March 2020, 105,792 COVID-19 cases were confirmed in Italy including 15,726 deaths which explains how worst the epidemic has affected the country. After the announcement of lockdown in Italy on 9 March 2020, situation was becoming stable since last days of March. In view of this, it is important to forecast the COVID-19 evaluation of Italy condition and the possible effects, if this lock down could continue for another 60 days. METHODS: COVID-19 infected patient data has extracted from the Italian Health Ministry website includes registered and recovered cases from mid February to end March. Adoption of seasonal ARIMA forecasting package with R statistical model was done. RESULTS: Predictions were done with 93.75% of accuracy for registered case models and 84.4% of accuracy for recovered case models. The forecasting of infected patients could be reach the value of 182,757, and recovered cases could be registered value of 81,635 at end of May. CONCLUSIONS:​ This study highlights the importance of country lockdown and self isolation in control the disease transmissibility among Italian population through data driven model analysis. Our findings suggest that nearly 35% decrement of registered cases and 66% growth of recovered cases will be possible.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[167] Title: </​b>​Using Machine Learning to Estimate Unobserved COVID-19 Infections in North America.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​2.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-07<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Bone & Joint Surgery, American Volume<​br>​
 +<​b>​Keywords:​ </​b>​Biomedical Engineering,​ , Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​journals.lww.com/​jbjsjournal/​Abstract/​9000/​Using_Machine_Learning_to_Estimate_Unobserved.99725.aspx">​https://​journals.lww.com/​jbjsjournal/​Abstract/​9000/​Using_Machine_Learning_to_Estimate_Unobserved.99725.aspx</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: The detection of coronavirus disease 2019 (COVID-19) cases remains a huge challenge. As of April 22, 2020, the COVID-19 pandemic continues to take its toll, with >2.6 million confirmed infections and >183,000 deaths. Dire projections are surfacing almost every day, and policymakers worldwide are using projections for critical decisions. Given this background, we modeled unobserved infections to examine the extent to which we might be grossly underestimating COVID-19 infections in North America. METHODS: We developed a machine-learning model to uncover hidden patterns based on reported cases and to predict potential infections. First, our model relied on dimensionality reduction to identify parameters that were key to uncovering hidden patterns. Next, our predictive analysis used an unbiased hierarchical Bayesian estimator approach to infer past infections from current fatalities. RESULTS: Our analysis indicates that, when we assumed a 13-day lag time from infection to death, the United States, as of April 22, 2020, likely had at least 1.3 million undetected infections. With a longer lag time-for example, 23 days-there could have been at least 1.7 million undetected infections. Given these assumptions,​ the number of undetected infections in Canada could have ranged from 60,000 to 80,000. Duarte'​s elegant unbiased estimator approach suggested that, as of April 22, 2020, the United States had up to >1.6 million undetected infections and Canada had at least 60,000 to 86,000 undetected infections. However, the Johns Hopkins University Center for Systems Science and Engineering data feed on April 22, 2020, reported only 840,476 and 41,650 confirmed cases for the United States and Canada, respectively. CONCLUSIONS:​ We have identified 2 key findings: (1) as of April 22, 2020, the United States may have had 1.5 to 2.029 times the number of reported infections and Canada may have had 1.44 to 2.06 times the number of reported infections and (2) even if we assume that the fatality and growth rates in the unobservable population (undetected infections) are similar to those in the observable population (confirmed infections),​ the number of undetected infections may be within ranges similar to those described above. In summary, 2 different approaches indicated similar ranges of undetected infections in North America. LEVEL OF EVIDENCE: Prognostic Level V. See Instructions for Authors for a complete description of levels of evidence.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[168] Title: </​b>​Determining the spatial effects of COVID-19 using the spatial panel data model.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​2.1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-08-01<​br>​
 +<​b>​Publisher:​ </​b>​Spatial Statistics<​br>​
 +<​b>​Keywords:​ </b>, covid-19, spatial effects, spatial panel data models<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2211675320300373">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2211675320300373</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +This study investigates the propagation power and effects of the coronavirus disease 2019 (COVID-19) in light of published data. We examine the factors affecting COVID-19 together with the spatial effects, and use spatial panel data models to determine the relationship among the variables including their spatial effects. Using spatial panel models, we analyse the relationship between confirmed cases of COVID-19, deaths thereof, and recovered cases due to treatment. We accordingly determine and include the spatial effects in this examination after establishing the appropriate model for COVID-19. The most efficient and consistent model is interpreted with direct and indirect spatial effects.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[169] Title: </​b>​Estimating the Size of a COVID-19 Epidemic from Surveillance Systems.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​2<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-10<​br>​
 +<​b>​Publisher:​ </​b>​Epidemiology<​br>​
 +<​b>​Keywords:​ </​b>​Statistics,​ , Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​journals.lww.com/​epidem/​FullText/​2020/​07000/​Estimating_the_Size_of_a_COVID_19_Epidemic_from.13.aspx">​https://​journals.lww.com/​epidem/​FullText/​2020/​07000/​Estimating_the_Size_of_a_COVID_19_Epidemic_from.13.aspx</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Public health policy makers in countries with Coronavirus Disease 2019 (COVID-19) outbreaks face the decision of when to switch from measures that seek to contain and eliminate the outbreak to those designed to mitigate its effects. Estimates of epidemic size are complicated by surveillance systems that cannot capture all cases, and by the need for timely estimates as the epidemic is ongoing. This article provides a Bayesian methodology to estimate outbreak size from one or more surveillance systems such as virologic testing of pneumonia cases or samples from a network of general practitioners.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[170] Title: </​b>​The SARS-CoV-2 seroprevalence is the key factor for deconfinement in France.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​2<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-01<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Infection<​br>​
 +<​b>​Keywords:​ </​b>​Clinical Sciences, sars-cov-2, deconfinement,​ seroprevalence,​ statistical model, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0163445320302425">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0163445320302425</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +A new virus, SARS-CoV-2, has spread world-wide since December 2019, probably affecting millions of people and killing thousands. Failure to anticipate the spread of the virus now seriously threatens many health systems. We have designed a model for predicting the evolution of the SARS-CoV-2 epidemic in France, which is based on seroprevalence and makes it possible to anticipate the deconfinement strategy.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[171] Title: </​b>​Estimation of COVID-19 prevalence in Italy, Spain, and France.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​2<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-08-01<​br>​
 +<​b>​Publisher:​ </​b>​Science of the Total Environment<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ arima, covid-19, forecasting,​ infection disease, pandemic, time series, Environmental Science, Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720323342">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720323342</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +At the end of December 2019, coronavirus disease 2019 (COVID-19) appeared in Wuhan city, China. As of April 15, 2020, >1.9 million COVID-19 cases were confirmed worldwide, including >120,000 deaths. There is an urgent need to monitor and predict COVID-19 prevalence to control this spread more effectively. Time series models are significant in predicting the impact of the COVID-19 outbreak and taking the necessary measures to respond to this crisis. In this study, Auto-Regressive Integrated Moving Average (ARIMA) models were developed to predict the epidemiological trend of COVID-19 prevalence of Italy, Spain, and France, the most affected countries of Europe. The prevalence data of COVID-19 from 21 February 2020 to 15 April 2020 were collected from the World Health Organization website. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) models with the lowest MAPE values (4.7520, 5.8486, and 5.6335) were selected as the best models for Italy, Spain, and France, respectively. This study shows that ARIMA models are suitable for predicting the prevalence of COVID-19 in the future. The results of the analysis can shed light on understanding the trends of the outbreak and give an idea of the epidemiological stage of these regions. Besides, the prediction of COVID-19 prevalence trends of Italy, Spain, and France can help take precautions and policy formulation for this epidemic in other countries.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[172] Title: </​b>​Estimating the prevalence and risk of COVID-19 among international travelers and evacuees of Wuhan through modeling and case reports.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​2<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-23<​br>​
 +<​b>​Publisher:​ </​b>​PLoS ONE<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ , Biochemistry,​ Genetics and Molecular Biology, Health Sciences, Life Sciences, Agricultural and Biological Sciences, Medicine<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​journals.plos.org/​plosone/​article?​id=10.1371/​journal.pone.0234955">​https://​journals.plos.org/​plosone/​article?​id=10.1371/​journal.pone.0234955</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Coronavirus disease 2019 (COVID-19) started in Wuhan, China and has spread through other provinces and countries through infected travelers. On January 23rd, 2020, China issued a quarantine and travel ban on Wuhan because travelers from Wuhan were thought to account for the majority of exported COVID-19 cases to other countries. Additionally,​ countries evacuated their citizens from Wuhan after institution of the travel ban. Together, these two populations account for the vast majority of the "total cases with travel history to China" as designated by the World Health Organization (WHO). The current study aims to assess the prevalence and risk of COVID-19 among international travelers and evacuees of Wuhan. We first used case reports from Japan, Singapore, and Korea to investigate the date of flights of infected travelers. We then used airline traveler data and the number of infected exported cases to correlate the cases with the number of travelers for multiple countries. Our findings suggest that the risk of COVID-19 infection is highest among Wuhan travelers between January 19th and 22nd, 2020, with an approximate infection rate of up to 1.3% among international travelers. We also observed that evacuee infection rates varied heavily between countries and propose that the timing of the evacuation and COVID-19 testing of asymptomatic evacuees played significant roles in the infection rates among evacuees. These findings suggest COVID-19 cases and infectivity are much higher than previous estimates, including numbers from the WHO and the literature, and that some estimates of the infectivity of COVID-19 may need re-assessment.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[173] Title: </​b>​Forecasting the cumulative number of COVID-19 deaths in China: a Boltzmann function-based modeling study.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.85<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-02<​br>​
 +<​b>​Publisher:​ </​b>​Infection control and hospital epidemiology (Online)<​br>​
 +<​b>​Keywords:​ </​b>​Medical And Health Sciences, , Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.cambridge.org/​core/​journals/​infection-control-and-hospital-epidemiology/​article/​forecasting-the-cumulative-number-of-covid19-deaths-in-china-a-boltzmann-functionbased-modeling-study/​95BEA41D8DE42B32F2E744C372E9030A">​https://​www.cambridge.org/​core/​journals/​infection-control-and-hospital-epidemiology/​article/​forecasting-the-cumulative-number-of-covid19-deaths-in-china-a-boltzmann-functionbased-modeling-study/​95BEA41D8DE42B32F2E744C372E9030A</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The COVID-19 outbreak is ongoing in China. Here, Boltzmann function-based analyses reveal the potential total numbers of COVID-19 deaths: 3,260 (95% confidence interval [CI], 3187-3394) in China; 110 (95% CI, 109-112) in Hubei Province; 3,174 (95% CI, 3095-3270) outside Hubei; 2,550 (95% CI, 2494-2621) in Wuhan City; and 617 (95% CI, 607-632) outside Wuhan.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[174] Title: </​b>​Prediction for the spread of COVID-19 in India and effectiveness of preventive measures.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.85<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-08-01<​br>​
 +<​b>​Publisher:​ </​b>​Science of the Total Environment<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ covid-19, curve fitting, lstm, prediction, recurrent neural network, Environmental Science, Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720322798">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720322798</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The spread of COVID-19 in the whole world has put the humanity at risk. The resources of some of the largest economies are stressed out due to the large infectivity and transmissibility of this disease. Due to the growing magnitude of number of cases and its subsequent stress on the administration and health professionals,​ some prediction methods would be required to predict the number of cases in future. In this paper, we have used data-driven estimation methods like long short-term memory (LSTM) and curve fitting for prediction of the number of COVID-19 cases in India 30 days ahead and effect of preventive measures like social isolation and lockdown on the spread of COVID-19. The prediction of various parameters (number of positive cases, number of recovered cases, etc.) obtained by the proposed method is accurate within a certain range and will be a beneficial tool for administrators and health officials.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[175] Title: </​b>​Early Transmission Dynamics of Novel Coronavirus (COVID-19) in Nigeria.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.85<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-28<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Environmental Research and Public Health<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ africa, covid-19, nigeria, coronavirus,​ importation,​ infectious diseases, reproduction number, travel, Health Sciences, Physical Sciences, Medicine, Environmental Science<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​1660-4601/​17/​9/​3054">​https://​www.mdpi.com/​1660-4601/​17/​9/​3054</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +On 31 December 2019, the World Health Organization (WHO) was notified of a novel coronavirus disease in China that was later named COVID-19. On 11 March 2020, the outbreak of COVID-19 was declared a pandemic. The first instance of the virus in Nigeria was documented on 27 February 2020. This study provides a preliminary epidemiological analysis of the first 45 days of COVID-19 outbreak in Nigeria. We estimated the early transmissibility via time-varying reproduction number based on the Bayesian method that incorporates uncertainty in the distribution of serial interval (time interval between symptoms onset in an infected individual and the infector), and adjusted for disease importation. By 11 April 2020, 318 confirmed cases and 10 deaths from COVID-19 have occurred in Nigeria. At day 45, the exponential growth rate was 0.07 (95% confidence interval (CI): 0.05-0.10) with a doubling time of 9.84 days (95% CI: 7.28-15.18). Separately for imported cases (travel-related) and local cases, the doubling time was 12.88 days and 2.86 days, respectively. Furthermore,​ we estimated the reproduction number for each day of the outbreak using a three-weekly window while adjusting for imported cases. The estimated reproduction number was 4.98 (95% CrI: 2.65-8.41) at day 22 (19 March 2020), peaking at 5.61 (95% credible interval (CrI): 3.83-7.88) at day 25 (22 March 2020). The median reproduction number over the study period was 2.71 and the latest value on 11 April 2020, was 1.42 (95% CrI: 1.26-1.58). These 45-day estimates suggested that cases of COVID-19 in Nigeria have been remarkably lower than expected and the preparedness to detect needs to be shifted to stop local transmission.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[176] Title: </​b>​Modeling Nigerian Covid-19 cases: A comparative analysis of models and estimators.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.85<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-09-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos,​ Solitons & Fractals<​br>​
 +<​b>​Keywords:​ </​b>​Mathematical Sciences, covid-19, curve estimation statistical models, estimators, forecast values, quartic linear regression model, Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920303118">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920303118</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +COVID-19 remains a major pandemic currently threatening all the countries of the world. In Nigeria, there were 1, 932 COVID-19 confirmed cases, 319 discharged cases and 58 deaths as of 30th April 2020. This paper, therefore, subjected the daily cumulative reported COVID-19 cases of these three variables to nine (9) curve estimation statistical models in simple, quadratic, cubic, and quartic forms. It further identified the best of the thirty-six (36) models and used the same for prediction and forecasting purposes. The data collected by the Nigeria Centre for Disease Control for sixty-four (64) days, two (2) months and three (3), were daily monitored and eventually analyzed. We identified the best models to be Quartic Linear Regression Model with an autocorrelated error of order 1 (AR(1)); and found the Ordinary Least Squares, Cochrane Orcutt, Hildreth-Lu,​ and Prais-Winsten and Least Absolute Deviation (LAD) estimators useful to estimate the models'​ parameters. Consequently,​ we recommended the daily cumulative forecast values of the LAD estimator for May and June 2020 with a 99% confidence level. The forecast values are alarming, and so, the Nigerian Government needs to hastily review her activities and interventions towards COVID-19 to provide some tactical and robust structures and measures to avert these challenges.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[177] Title: </b>A novel IDEA: The impact of serial interval on a modified-Incidence Decay and Exponential Adjustment (m-IDEA) model for projections of daily COVID-19 cases.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.85<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-01<​br>​
 +<​b>​Publisher:​ </​b>​Infectious Disease Modelling<​br>​
 +<​b>​Keywords:​ </b>, covid-19, communicable diseases/​epidemiology,​ forecasting/​methods,​ models, sars coronavirus-2,​ statistical<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2468042720300166">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2468042720300166</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The SARS-CoV-2 virus causes the disease COVID-19, and has caused high morbidity and mortality worldwide. Empirical models are useful tools to predict future trends of disease progression such as COVID-19 over the near-term. A modified Incidence Decay and Exponential Adjustment (m-IDEA) model was developed to predict the progression of infectious disease outbreaks. The modification allows for the production of precise daily estimates, which are critical during a pandemic of this scale for planning purposes. The m-IDEA model was employed using a range of serial intervals given the lack of knowledge on the true serial interval of COVID-19. Both deterministic and stochastic approaches were applied. Model fitting was accomplished through minimizing the sum-of-square differences between predicted and observed daily incidence case counts, and performance was retrospectively assessed. The performance of the m-IDEA for projection cases in the near-term was improved using shorter serial intervals (1-4 days) at early stages of the pandemic, and longer serial intervals at mid- to late-stages (5-9 days) thus far. This, coupled with epidemiological reports, suggests that the serial interval of COVID-19 might increase as the pandemic progresses, which is rather intuitive: Increasing serial intervals can be attributed to gradual increases in public health interventions such as facility closures, public caution and social distancing, thus increasing the time between transmission events. In most cases, the stochastic approach captured the majority of future reported incidence data, because it accounts for the uncertainty around the serial interval of COVID-19. As such, it is the preferred approach for using the m-IDEA during dynamic situation such as in the midst of a major pandemic.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[178] Title: </​b>​The COVID-19 Infection in Italy: A Statistical Study of an Abnormally Severe Disease.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.75<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-21<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Clinical Medicine<​br>​
 +<​b>​Keywords:​ </b>, covid-19, epidemic in italy, statistical forecast<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2077-0383/​9/​5/​1564">​https://​www.mdpi.com/​2077-0383/​9/​5/​1564</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +We statistically investigate the Coronavirus Disease 19 (COVID-19) pandemic, which became particularly invasive in Italy in March 2020. We show that the high apparent lethality or case fatality ratio (CFR) observed in Italy, as compared with other countries, is likely biased by a strong underestimation of the number of infection cases. To give a more realistic estimate of the lethality of COVID-19, we use the actual (March 2020) estimates of the infection fatality ratio (IFR) of the pandemic based on the minimum observed CFR and analyze data obtained from the Diamond Princess cruise ship, a good representation of a "​laboratory"​ case-study from an isolated system in which all the people have been tested. From such analyses, we derive more realistic estimates of the real extent of the infection as well as more accurate indicators of how fast the infection propagates. We then isolate the dominant factors causing the abnormal severity of the disease in Italy. Finally, we use the death count-the only data estimated to be reliable enough-to predict the total number of people infected and the interval of time when the infection in Italy could end.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[179] Title: </​b>​Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.7<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-29<​br>​
 +<​b>​Publisher:​ </​b>​Computational & Mathematical Methods in Medicine<​br>​
 +<​b>​Keywords:​ </​b>​Applied Mathematics,​ , Biochemistry,​ Genetics and Molecular Biology, Health Sciences, Life Sciences, Physical Sciences, Medicine, Mathematics,​ Immunology and Microbiology<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.hindawi.com/​journals/​cmmm/​2020/​5714714/">​https://​www.hindawi.com/​journals/​cmmm/​2020/​5714714/</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Coronavirus (COVID-19) is a highly infectious disease that has captured the attention of the worldwide public. Modeling of such diseases can be extremely important in the prediction of their impact. While classic, statistical,​ modeling can provide satisfactory models, it can also fail to comprehend the intricacies contained within the data. In this paper, authors use a publicly available dataset, containing information on infected, recovered, and deceased patients in 406 locations over 51 days (22nd January 2020 to 12th March 2020). This dataset, intended to be a time-series dataset, is transformed into a regression dataset and used in training a multilayer perceptron (MLP) artificial neural network (ANN). The aim of training is to achieve a worldwide model of the maximal number of patients across all locations in each time unit. Hyperparameters of the MLP are varied using a grid search algorithm, with a total of 5376 hyperparameter combinations. Using those combinations,​ a total of 48384 ANNs are trained (16128 for each patient group-deceased,​ recovered, and infected), and each model is evaluated using the coefficient of determination (R2). Cross-validation is performed using K-fold algorithm with 5-folds. Best models achieved consists of 4 hidden layers with 4 neurons in each of those layers, and use a ReLU activation function, with R2 scores of 0.98599 for confirmed, 0.99429 for deceased, and 0.97941 for recovered patient models. When cross-validation is performed, these scores drop to 0.94 for confirmed, 0.781 for recovered, and 0.986 for deceased patient models, showing high robustness of the deceased patient model, good robustness for confirmed, and low robustness for recovered patient model.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[180] Title: </​b>​Effective Reproductive Number estimation for initial stage of COVID-19 pandemic in Latin American Countries.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.6<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Microbiology,​ , Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S120197122030285X">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S120197122030285X</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +OBJECTIVES: The coronavirus disease 2019 (COVID-19) has become pandemic and turn in a challenge for Latin America. Understanding the dynamics of the epidemic is essential for decision making, and to reduce the health, economic, and social impacts of the pandemic. The present study aimed to estimate the effective reproductive number (Rt) of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-Cov2) infection during the first 10 days of the outbreak in seven Latin American countries with the highest incidence of cases as of March 23, 2020. Furthermore,​ we chose to compare the seven countries with Spain and Italy given their history with the virus. METHODS: Incidence data retrieved from the COVID-19 data repository by Johns Hopkins University were analyzed. The Rt was calculated for the first 10 days of the epidemic in Brazil, Ecuador, Chile, Colombia, Panama, Mexico, and Peru. Rt estimations were compared with Spain and Italy values for the same interval. RESULTS: The median Rt for the first 10 days of the COVID-19 epidemic were 2.90 (2.67-3.14) for Spain and 2.83 (2.7-2.96) for Italy. Latin American Rt estimations were higher in Ecuador (3.95(3.7-4.21)),​ Panama (3.95(3.7-4.21)),​ and Brazil (3.95(3.7-4.21)). The smallest one was observed in Peru (2.36(2.11-2.63)). All Latin American countries had Rt greater than 2. CONCLUSIONS:​ The initial stages of the COVID-19 epidemic in Latin America suggested a high Rt. Interventions such as domestic and international travel restrictions,​ educational institutions closure, social distancing, and intensified case surveillance should be adopted to prevent the collapse of the health systems.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[181] Title: </b>A model based study on the dynamics of COVID-19: Prediction and control.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.6<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-07-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos,​ Solitons & Fractals<​br>​
 +<​b>​Keywords:​ </​b>​Mathematical Sciences, bang-bang and singular control, basic reproduction number, short term prediction of covid-19, theoretical epidemiology,​ transcritical bifurcation,​ Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920302897">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920302897</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +As there is no vaccination and proper medicine for treatment, the recent pandemic caused by COVID-19 has drawn attention to the strategies of quarantine and other governmental measures, like lockdown, media coverage on social isolation, and improvement of public hygiene, etc to control the disease. The mathematical model can help when these intervention measures are the best strategies for disease control as well as how they might affect the disease dynamics. Motivated by this, in this article, we have formulated a mathematical model introducing a quarantine class and governmental intervention measures to mitigate disease transmission. We study a thorough dynamical behavior of the model in terms of the basic reproduction number. Further, we perform the sensitivity analysis of the essential reproduction number and found that reducing the contact of exposed and susceptible humans is the most critical factor in achieving disease control. To lessen the infected individuals as well as to minimize the cost of implementing government control measures, we formulate an optimal control problem, and optimal control is determined. Finally, we forecast a short-term trend of COVID-19 for the three highly affected states, Maharashtra,​ Delhi, and Tamil Nadu, in India, and it suggests that the first two states need further monitoring of control measures to reduce the contact of exposed and susceptible humans.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[182] Title: </​b>​Estimating the instant case fatality rate of COVID-19 in China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-08-01<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Microbiology,​ covid-19, case fatality rate, china, cure rate, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S120197122030271X">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S120197122030271X</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +OBJECTIVE: The outbreak of coronavirus disease 2019 (COVID-19) in China has been basically controlled. However, the global epidemic of COVID-19 is worsening. We established a method to estimate the instant case fatality rate (CFR) and cure rate of COVID-19 in China. METHODS: A total of 82 735 confirmed cases released officially by the Chinese authorities from December 8, 2019 to April 18, 2020 were collected. The estimated diagnosis dates of deaths and cured cases were calculated based on the median cure time or median death time of individual cases. Following this, the instant CFR was calculated according to the number of deaths and cured cases on the same estimated diagnosis date. RESULTS: In China, the instant CFR of COVID-19 was 3.8-14.6% from January 1 to January 17; it then declined gradually and stabilized at 5.7% in April. The average CFR in China was 6.1+/-2.9%, while the CFR was 1.0+/-0.4% in China except Hubei Province. The cure rate of COVID-19 was 93.9+/-2.9% in China, and stabilized at 94.3%, while it was 99.0+/-0.4% in China except Hubei Province. CONCLUSIONS:​ The instant CFR of COVID-19 in China overall was much higher than that in China except Hubei Province. The CFR of COVID-19 in China was underestimated.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[183] Title: </​b>​ONLINE FORECASTING OF COVID-19 CASES IN NIGERIA USING LIMITED DATA.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​Data in Brief<​br>​
 +<​b>​Keywords:​ </b>, analytic modeling, coronavirus covid-19, ensembles, nigeria ncdc, small data, timeseries forecasting<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2352340920305771">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2352340920305771</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The novel Coronavirus disease (COVID-19) was first identified in Wuhan, China in December 2019 but later spread to other parts of the world. The disease as at the point of writing this paper has been declared a pandemic by the World Health Organization (WHO). The application of mathematical models, artificial intelligence,​ big data, and similar methodologies are potential tools to predict the extent of the spread and effectiveness of containment strategies to stem the transmission of this disease. In societies with constrained data infrastructures,​ modeling and forecasting COVID-19 becomes an extremely difficult endeavor. Nonetheless,​ we propose an online forecasting mechanism that streams data from the Nigeria Center for Disease Control to update the parameters of an ensemble model which in turn provides updated COVID-19 forecasts every 24 hours. The ensemble combines an Auto-Regressive Integrated Moving Average model (ARIMA), Prophet - an additive regression model developed by Facebook, and a Holt-Winters Exponential Smoothing model combined with Generalized Autoregressive Conditional Heteroscedasticity (GARCH). The outcomes of these efforts are expected to provide academic thrust in guiding the policymakers in the deployment of containment strategies and/or assessment of containment interventions in stemming the spread of the disease in Nigeria.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[184] Title: </​b>​Modelling the evolution trajectory of COVID-19 in Wuhan, China: experience and suggestions.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​Public Health (Elsevier)<​br>​
 +<​b>​Keywords:​ </​b>​Public Health And Health Services, covid-19, intervention strategies, modelling, modified seir model, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0033350620301542">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0033350620301542</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +OBJECTIVES: In December 2019, a novel coronavirus disease (COVID-19) emerged in Wuhan city, China, which has subsequently led to a global pandemic. At the time of writing, COVID-19 in Wuhan appears to be in the final phase and under control. However, many other countries, especially the US, Italy and Spain, are still in the early phases and dealing with increasing cases every day. Therefore, this article aims to summarise and share the experience of controlling the spread of COVID-19 in Wuhan and provide effective suggestions to enable other countries to save lives. STUDY DESIGN: Data from the National Health Commission of China are used to investigate the evolution trajectory of COVID-19 in Wuhan and discuss the impacts of the intervention strategies. METHODS: A four-stage modified Susceptible-Exposed-Infectious-Removed (SEIR) model is presented. This model considers many influencing factors, including chunyun (the Spring festival), sealing off the city and constructing the Fangcang shelter hospitals. In addition, a novel method is proposed to address the abnormal data on 12-13 February as a result of changing diagnostic criteria. Four different scenarios are considered to capture different intervention measures in practice. The exposed population in Wuhan who moved out before sealing off the city have also been identified, and an analysis on where they had gone was performed using the Baidu Migration Index. RESULTS: The results demonstrate that the four-stage model was effective in forecasting the peak, size and duration of COVID-19. We found that the combined intervention measures are the only effective way to control the spread and not a single one of them can be omitted. We estimate that England will be another epicentre owing to its incorrect response at the initial stages of COVID-19. Fortunately,​ big data technology can help provide early warnings to new areas of the pandemic. CONCLUSIONS:​ The four-stage SEIR model was effective in capturing the evolution trajectory of COVID-19. Based on the model analysis, several effective suggestions are proposed to prevent and control the pandemic for countries that are still in the initial phases.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[185] Title: </​b>​An Efficient COVID-19 Prediction Model Validated with the Cases of China, Italy and Spain: Total or Partial Lockdowns?<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-20<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Clinical Medicine<​br>​
 +<​b>​Keywords:​ </b>, covid-19, china, france, germany, italy, sars-cov-2, spain, uk, verhulst, coronavirus,​ forecast, model, prediction<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2077-0383/​9/​5/​1547">​https://​www.mdpi.com/​2077-0383/​9/​5/​1547</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The present work develops an accurate prediction model of the COVID-19 pandemic, capable not only of fitting data with a high regression coefficient but also to predict the overall infections and the infection peak day as well. The model is based on the Verhulst equation, which has been used to fit the data of the COVID-19 spread in China, Italy, and Spain. This model has been used to predict both the infection peak day, and the total infected people in Italy and Spain. With this prediction model, the overall infections, the infection peak, and date can accurately be predicted one week before they occur. According to the study, the infection peak took place on 23 March in Italy, and on 29 March in Spain. Moreover, the influence of the total and partial lockdowns has been studied, without finding any meaningful difference in the disease spread. However, the infected population, and the rate of new infections at the start of the lockdown, seem to play an important role in the infection spread. The developed model is not only an important tool to predict the disease spread, but also gives some significant clues about the main factors that affect to the COVID-19 spread, and quantifies the effects of partial and total lockdowns as well.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[186] Title: </​b>​The Spread of the Covid-19 Pandemic in Time and Space.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-28<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Environmental Research and Public Health<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ lasso, contagion, corona virus, networks, spatial autoregressions,​ stochastic cycles, Health Sciences, Physical Sciences, Medicine, Environmental Science<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​1660-4601/​17/​11/​3827">​https://​www.mdpi.com/​1660-4601/​17/​11/​3827</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +As the COVID-19 pandemic has had a profound impact on public health and global economies in 2020; it is crucial to understand how it developed and spread in time and space. This paper contributes to the growing literature by considering the dynamics of country-wise growth rates of infection numbers. Low-order serial correlation of growth rates is predominantly negative with cycles of two to four days for most countries. The results of fitted spatial autoregressive models suggest that there is high degree of spillover between countries. Forecast variances of many countries, in particular those with a high absolute number of infections, can to a large extent be explained by structural innovations of other countries. A better understanding of the serial and spatial dynamics of the spread of the pandemic may contribute to an improved containment and risk management.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[187] Title: </​b>​Interventions as experiments:​ Connecting the dots in forecasting and overcoming pandemics, global warming, corruption, civil rights violations, misogyny, income inequality, and guns.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-09-01<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Business Research<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ covid-19, experiment, intervention,​ marketing, ultimate broadening, Social Sciences, Business, Management and Accounting<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0148296320303234">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0148296320303234</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +This essay applies the "​ultimate broadening of the concept of marketing"​ for designing and implementing interventions in public laws and policy, national and local regulations,​ and everyday lives of individuals. The ultimate broadening of the concept of marketing: Marketing is any activity, message, emotion, or behavior by someone, firm, organization,​ government, community, or brand executed consciously or nonconsciously that may stimulate an observable or non-observable activity, emotion, attitude, belief, or thought by someone else, group, organization,​ firm or community. The broadening definition applies to the current interventions by national and state/​provincial governments as well as healthcare facilities, medical science facilities, firms, and individuals to mitigate and eliminate the impact of the COVID-19 pandemic. Framing interventions as experiments is helpful in improving the quality of their designs, implementing them successfully,​ and validly interpreting their effectiveness. In January and February 2020, a few nations were exemplars for accurately forecasting the coming disaster of COVID-19 as a cause of illness and death and in designing/​implementing effective mitigating strategies: Denmark, Finland, Republic of Korea, New Zealand, Norway, and Vietnam. While the COVID-19 prevention intervention tests now being run for several promising vaccines are true experiments,​ the researchers analyzing the data from these interventions may need prompting to examine the efficacy of each vaccine tested by modeling demographic subgroups for the members in the treatment and placebo groups in the randomized control trials.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[188] Title: </​b>​The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.35<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​Data in Brief<​br>​
 +<​b>​Keywords:​ </b>, covid-19, coronavirus,​ death rate, estimation, normal distribution,​ piba, prediction<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2352340920305138">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2352340920305138</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were chosen as representatives from the perspective of the coronavirus outbreak in China. Data were copied and pasted onto Excel spreadsheets to perform data analysis. A new methodology,​ Patient Information Based Algorithm (PIBA) [1], has been adapted to process the data and used to estimate the death rate of COVID-19 in real-time. Assumption is that the number of days from inpatients to death fall into a pattern of normal distribution and the scores in normal distribution can be obtained by observing 33 death cases and analysing the data [2]. We selected 5 scores in normal distribution of these durations as lagging days, which will be used in the following estimation of death rate. We calculated each death rate on accumulative confirmed cases with each lagging day from the current data and then weighted every death rate with its corresponding possibility to obtain the total death rate on each day. While the trendline of these death rate curves meet the curve of current ratio between accumulative death cases and confirmed cases at some points in the near future, we considered that these intersections are within the range of real death rates. Six tables were presented to illustrate the PIBA method using data from China and South Korea. One figure on estimated rate of infection and patients in serious condition and retrospective estimation of initially occurring time of CORID-19 based on PIBA.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[189] Title: </​b>​Modeling the epidemic dynamics and control of COVID-19 outbreak in China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-11<​br>​
 +<​b>​Publisher:​ </​b>​Quantitative Biology<​br>​
 +<​b>​Keywords:​ </​b>​Bioinformatics,​ sars-cov-2, coronavirus disease 2019, epidemic model<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​link.springer.com/​article/​10.1007/​s40484-020-0199-0">​https://​link.springer.com/​article/​10.1007/​s40484-020-0199-0</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Background: The coronavirus disease 2019 (COVID-19) is rapidly spreading in China and more than 30 countries over last two months. COVID-19 has multiple characteristics distinct from other infectious diseases, including high infectivity during incubation, time delay between real dynamics and daily observed number of confirmed cases, and the intervention effects of implemented quarantine and control measures. Methods: We develop a Susceptible,​ Un-quanrantined infected, Quarantined infected, Confirmed infected (SUQC) model to characterize the dynamics of COVID-19 and explicitly parameterize the intervention effects of control measures, which is more suitable for analysis than other existing epidemic models. Results: The SUQC model is applied to the daily released data of the confirmed infections to analyze the outbreak of COVID-19 in Wuhan, Hubei (excluding Wuhan), China (excluding Hubei) and four first-tier cities of China. We found that, before January 30, 2020, all these regions except Beijing had a reproductive number R > 1, and after January 30, all regions had a reproductive number R < 1, indicating that the quarantine and control measures are effective in preventing the spread of COVID-19. The confirmation rate of Wuhan estimated by our model is 0.0643, substantially lower than that of Hubei excluding Wuhan (0.1914), and that of China excluding Hubei (0.2189), but it jumps to 0.3229 after February 12 when clinical evidence was adopted in new diagnosis guidelines. The number of unquarantined infected cases in Wuhan on February 12, 2020 is estimated to be 3,509 and declines to 334 on February 21, 2020. After fitting the model with data as of February 21, 2020, we predict that the end time of COVID-19 in Wuhan and Hubei is around late March, around mid March for China excluding Hubei, and before early March 2020 for the four tier-one cities. A total of 80,511 individuals are estimated to be infected in China, among which 49,510 are from Wuhan, 17,679 from Hubei (excluding Wuhan), and the rest 13,322 from other regions of China (excluding Hubei). Note that the estimates are from a deterministic ODE model and should be interpreted with some uncertainty. Conclusions:​ We suggest that rigorous quarantine and control measures should be kept before early March in Beijing, Shanghai, Guangzhou and Shenzhen, and before late March in Hubei. The model can also be useful to predict the trend of epidemic and provide quantitative guide for other countries at high risk of outbreak, such as South Korea, Japan, Italy and Iran. Supplementary Materials: The supplementary materials can be found online with this article at 10.1007/​s40484-020-0199-0.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[190] Title: </​b>​COVID-19 in Italy: impact of containment measures and prevalence estimates of infection in the general population.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-10<​br>​
 +<​b>​Publisher:​ </​b>​Acta bio-medica : Atenei Parmensis<​br>​
 +<​b>​Keywords:​ </b>, <br>
 +<​b>​DOI:​ </​b><​a href="​https://​www.mattioli1885journals.com/​index.php/​actabiomedica/​article/​view/​9511">​https://​www.mattioli1885journals.com/​index.php/​actabiomedica/​article/​view/​9511</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Since the beginning of the COVID-19 epidemic in Italy, the Italian Government implemented several restrictive measures to contain the spread of the infection. Data shows that, among these measures, the lockdown implemented as of 9 March had a positive impact, in particular the central and southern regions of Italy, while other actions appeared to be less effective. When the true prevalence of a disease is unknown, it is possible estimate it, based on mortality data and the assumptive case-fatality rate of the disease. Given these assumptions,​ the estimated period-prevalence of COVID-19 in Italy varies from 0.35% in Sicity to 13.3% in Lombardy.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[191] Title: </​b>​Naive Forecast for COVID-19 in Utah Based on the South Korea and Italy Models-the Fluctuation between Two Extremes.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-16<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Environmental Research and Public Health<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ covid-19, pandemic, predictive modeling, Health Sciences, Physical Sciences, Medicine, Environmental Science<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​1660-4601/​17/​8/​2750">​https://​www.mdpi.com/​1660-4601/​17/​8/​2750</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Differences in jurisdictional public health actions have played a significant role in the relative success of local communities in combating and containing the COVID-19 pandemic. We forecast the possible COVID-19 outbreak in one US state (Utah) by applying empirical data from South Korea and Italy, two countries that implemented disparate public health actions. Forecasts were created by aligning the start of the pandemic in Utah with that in South Korea and Italy, getting a short-run forecast based on actual daily rates of spread, and long-run forecast by employing a log-logistic model with four parameters. Applying the South Korea model, the epidemic peak in Utah is 169 cases/day, with epidemic resolution by the end of May. Applying the Italy model, new cases are forecast to exceed 200/day by mid-April, with the potential for 250 new cases a day at the epidemic peak, with the epidemic continuing through the end of August. We identify a 3-month variation in the likely length of the pandemic, a 1.5-fold difference in the number of daily infections at outbreak peak, and a 3-fold difference in the expected cumulative cases when applying the experience of two developed countries in handling this virus to the Utah context.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[192] Title: </​b>​Modelling spatial variations of coronavirus disease (COVID-19) in Africa.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-08-01<​br>​
 +<​b>​Publisher:​ </​b>​Science of the Total Environment<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ africa, covid-19 attributable deaths, covid-19 confirmed cases, spatial variation, system gmm, Environmental Science, Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720325158">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720325158</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Clinical and epidemiological evidence has been advanced for human-to-human transmission of the novel coronavirus rampaging the world since late 2019. Outliers in the human-to-human transmission are yet to be explored. In this study, we examined the spatial density and leaned statistical credence to the global debate. We constructed spatial variations of clusters that examined the nexus between COVID-19 attributable deaths and confirmed cases. We rely on publicly available data on confirmed cases and death across Africa to unravel the unobserved factors, that could be responsible for the spread of COVID-19. We relied on the dynamic system generalised method of moment estimation procedure and found a ~0.045 Covid19 deaths as a result of confirmed cases in Africa. We accounted for cross-sectional dependence and found a basis for the strict orthogonal relationship. Policy measures were discussed.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[193] Title: </​b>​Forecast and evaluation of COVID-19 spreading in USA with reduced-space Gaussian process regression.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-07-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos,​ Solitons & Fractals<​br>​
 +<​b>​Keywords:​ </​b>​Mathematical Sciences, covid-19, forecast, gaussian, usa, Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920303234">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920303234</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +In this report, we analyze historical and forecast infections for COVID-19 death based on Reduced-Space Gaussian Process Regression associated to chaotic Dynamical Systems with information obtained in 82 days with continuous learning, day by day, from January 21 (th) , 2020 to April 12 (th) . According last results, COVID-19 could be predicted with Gaussian models mean-field models can be meaning- fully used to gather a quantitative picture of the epidemic spreading, with infections, fatality and recovery rate. The forecast places the peak in USA around July 14 (th) 2020, with a peak number of 132,074 death with infected individuals of about 1,157,796 and a number of deaths at the end of the epidemics of about 132,800. Late on January, USA confirmed the first patient with COVID-19, who had recently traveled to China, however, an evaluation of states in USA have demonstrated a fatality rate in China (4%) is lower than New York (4.56%), but lower than Michigan (5.69%). Mean estimates and uncertainty bounds for both USA and his cities and other provinces have increased in the last three months, with focus on New York, New Jersey, Michigan, California, Massachusetts,​ ... (January e April 12 (th) ). Besides, we propose a Reduced-Space Gaussian Process Regression model predicts that the epidemic will reach saturation in USA on July 2020. Our findings suggest, new quarantine actions with more restrictions for containment strategies implemented in USA could be successfully,​ but in a late period, it could generate critical rate infections and death for the next 2 month.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[194] Title: </b>A mathematical model of the evolution and spread of pathogenic coronaviruses from natural host to human host.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-09-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos,​ Solitons & Fractals<​br>​
 +<​b>​Keywords:​ </​b>​Mathematical Sciences, allee effect, coronavirus,​ differential equation with piecewise constant arguments, local stability analysis, neimark-sacker bifurcation,​ Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920303301">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920303301</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Coronaviruses are highly transmissible and are pathogenic viruses of the 21st century worldwide. In general, these viruses are originated in bats or rodents. At the same time, the transmission of the infection to the human host is caused by domestic animals that represent in the habitat the intermediate host. In this study, we review the currently collected information about coronaviruses and establish a model of differential equations with piecewise constant arguments to discuss the spread of the infection from the natural host to the intermediate,​ and from them to the human host, while we focus on the potential spillover of bat-borne coronaviruses. The local stability of the positive equilibrium point of the model is considered via the Linearized Stability Theorem. Besides, we discuss global stability by employing an appropriate Lyapunov function. To analyze the outbreak in early detection, we incorporate the Allee effect at time t and obtain stability conditions for the dynamical behavior. Furthermore,​ it is shown that the model demonstrates the Neimark-Sacker Bifurcation. Finally, we conduct numerical simulations to support the theoretical findings.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[195] Title: </​b>​Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-10-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos,​ Solitons & Fractals<​br>​
 +<​b>​Keywords:​ </​b>​Mathematical Sciences, covid-19, deep learning, lstm, prediction, rnn, Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S096007792030415X">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S096007792030415X</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +In this paper, Deep Learning-based models are used for predicting the number of novel coronavirus (COVID-19) positive reported cases for 32 states and union territories of India. Recurrent neural network (RNN) based long-short term memory (LSTM) variants such as Deep LSTM, Convolutional LSTM and Bi-directional LSTM are applied on Indian dataset to predict the number of positive cases. LSTM model with minimum error is chosen for predicting daily and weekly cases. It is observed that the proposed method yields high accuracy for short term prediction with error less than 3% for daily predictions and less than 8% for weekly predictions. Indian states are categorised into different zones based on the spread of positive cases and daily growth rate for easy identification of novel coronavirus hot-spots. Preventive measures to reduce the spread in respective zones are also suggested. A website is created where the state-wise predictions are updated using the proposed model for authorities,​researchers and planners. This study can be applied by other countries for predicting COVID-19 cases at the state or national level.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[196] Title: </​b>​Approaches to Daily Monitoring of the SARS-CoV-2 Outbreak in Northern Italy.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-22<​br>​
 +<​b>​Publisher:​ </​b>​Frontiers in Public Health<​br>​
 +<​b>​Keywords:​ </b>, covid-19, epidemiology,​ infectious disease, outbreak analyses, public health<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.frontiersin.org/​articles/​10.3389/​fpubh.2020.00222/​full">​https://​www.frontiersin.org/​articles/​10.3389/​fpubh.2020.00222/​full</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Italy was the first European country affected by the Sars-Cov-2 pandemic, with the first autochthonous case identified on Feb 21st. Specific control measures restricting social contacts were introduced by the Italian government starting from the beginning of March. In the current study we analyzed public data from the four most affected Italian regions. We (i) estimated the time-varying reproduction number (R t ), the average number of secondary cases that each infected individual would infect at time t, to monitor the positive impact of restriction measures; (ii) applied the generalized logistic and the modified Richards models to describe the epidemic pattern and obtain short-term forecasts. We observed a monotonic decrease of R t over time in all regions, and the peak of incident cases ~2 weeks after the implementation of the first strict containment measures. Our results show that phenomenological approaches may be useful to monitor the epidemic growth in its initial phases and suggest that costly and disruptive public health controls might have had a positive impact in limiting the Sars-Cov-2 spread in Northern Italy.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +
 +</​html>​
oa_db/covid19_forecasting_abstracts_pg5.txt · Last modified: 2020/06/27 18:00 by bpwhite