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 +===== COVID-19 Forecasting Abstracts - Page 6 =====
  
 +[[oa_db:​covid19_forecasting_abstracts|Back to Table of Contents]]
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 +----------------------------------------------------------------------<​br>​
 +<​b>​[197] Title: </​b>​Coronavirus pandemic: A predictive analysis of the peak outbreak epidemic in South Africa, Turkey, and Brazil.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1.1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-09-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos,​ Solitons & Fractals<​br>​
 +<​b>​Keywords:​ </​b>​Mathematical Sciences, age-structured,​ basic reproduction number, covid-19, computational epidemiology,​ peak epidemic, sir model, Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920303702">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920303702</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +In this research, we are interested in predicting the epidemic peak outbreak of the Coronavirus in South Africa, Turkey, and Brazil. Until now, there is no known safe treatment, hence the immunity system of the individual has a crucial role in recovering from this contagious disease. In general, the aged individuals probably have the highest rate of mortality due to COVID-19. It is well known that this immunity system can be affected by the age of the individual, so it is wise to consider an age-structured SEIR system to model Coronavirus transmission. For the COVID-19 epidemic, the individuals in the incubation stage are capable of infecting the susceptible individuals. All the mentioned points are regarded in building the responsible predictive mathematical model. The investigated model allows us to predict the spread of COID-19 in South Africa, Turkey, and Brazil. The epidemic peak outbreak in these countries is considered, and the estimated time of the end of infection is regarded by the help of some numerical simulations. Further, the influence of the isolation of the infected persons on the spread of COVID-19 disease is investigated.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[198] Title: </​b>​Estimation of basic reproduction number for COVID-19 and the reasons for its differences.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-16<​br>​
 +<​b>​Publisher:​ </​b>​International journal of clinical practice<​br>​
 +<​b>​Keywords:​ </​b>​Clinical Sciences, , Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1111/​ijcp.13518">​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1111/​ijcp.13518</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The novel coronavirus pneumonia is an acute respiratory disease. In December 2019, this disease emerged in Wuhan, China. The Chinese government called it SARS-CoV-2 which was subsequently named COVID-19 by the World Health Organization (WHO) [1]. In January 2020, WHO confirmed it as a sustained human to human disease [2]. By March 2020, COVID-19 had been transmitted round the world rapidly and every day large number of new cases were registered. COVID-19 is a leaped type of coronavirus family such as Severe Acute Respiratory Syndrome (SARS) and the Middle East Respiratory Syndrome (MERS) that has been transmitted from wild animals to human [3].<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[199] Title: </​b>​Outbreak Trends of Coronavirus Disease-2019 in India: A Prediction.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-22<​br>​
 +<​b>​Publisher:​ </​b>​Disaster Medicine and Public Health Preparedness (Highwire)<​br>​
 +<​b>​Keywords:​ </b>, covid-19 epidemic trend, covid-19 outbreak in india, machine learning, predictive model, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.cambridge.org/​core/​journals/​disaster-medicine-and-public-health-preparedness/​article/​outbreak-trends-of-coronavirus-disease2019-in-india-a-prediction/​76090B13B7FDD2C96920A81CAF608264">​https://​www.cambridge.org/​core/​journals/​disaster-medicine-and-public-health-preparedness/​article/​outbreak-trends-of-coronavirus-disease2019-in-india-a-prediction/​76090B13B7FDD2C96920A81CAF608264</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +OBJECTIVE: The objective of this paper is to prepare the government and citizens of India to take or implement the control measures proactively to reduce the impact of coronavirus disease 2019 (COVID-19). METHOD: In this work, the COVID-19 outbreak in India has been predicted based on the pattern of China using a machine learning approach. The model is built to predict the number of confirmed cases, recovered cases, and death cases based on the data available between January 22, 2020, and April 3, 2020. The time series forecasting method is used for prediction models. RESULTS: The COVID-19 effects are predicted to be at peak between the third and fourth weeks of April 2020 in India. This outbreak is predicted to be controlled around the end of May 2020. The total number of predicted confirmed cases of COVID-19 might reach around 68 978, and the number of deaths due to COVID-19 are predicted to be 1557 around April 25, 2020, in India. If this outbreak is not controlled by the end of May 2020, then India will face a severe shortage of hospitals, and it will make this outbreak even worse. CONCLUSION: The COVID-19 pandemic may be controlled if the Government of India takes proactive steps to aggressively implement a lockdown in the country and extend it further. This presented epidemiological model is an effort to predict the future forecast of COVID-19 spread, based on the present scenario, so that the government can frame policy decisions, and necessary actions can be initiated.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[200] Title: </​b>​Epidemiology of Coronavirus COVID-19: Forecasting the Future Incidence in Different Countries.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-15<​br>​
 +<​b>​Publisher:​ </​b>​Healthcare<​br>​
 +<​b>​Keywords:​ </b>, covid-19, coronavirus,​ control strategies, dynamic time warping, epidemiology,​ forecasting,​ incidence, lead-lag effects, risk management<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2227-9032/​8/​2/​99">​https://​www.mdpi.com/​2227-9032/​8/​2/​99</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +This paper forecasts the future spread of COVID-19 by exploiting the identified lead-lag effects between different countries. Specifically,​ we first determine the past relation among nations with the aid of dynamic time warping. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences. Afterwards, the established framework utilizes information about the leading country to predict the Coronavirus spread of the following nation. The presented methodology is applied to confirmed Coronavirus cases from 1 January 2020 to 28 March 2020. Our results show that China leads all other countries in the range of 29 days for South Korea and 44 days for the United States. Finally, we predict a future collapse of the healthcare systems of the United Kingdom and Switzerland in case of our explosion scenario.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[201] Title: </​b>​Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-08-01<​br>​
 +<​b>​Publisher:​ </​b>​Science of the Total Environment<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ covid-19, humidity, india, temperature,​ us, weather, Environmental Science, Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720323779">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720323779</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The effect of weather on COVID-19 spread is poorly understood. Recently, few studies have claimed that warm weather can possibly slowdown the global pandemic, which has already affected over 1.6 million people worldwide. Clarification of such relationships in the worst affected country, the US, can be immensely beneficial to understand the role of weather in transmission of the disease in the highly populated countries, such as India. We collected the daily data of new cases in 50 US states between Jan 1-Apr 9, 2020 and also the corresponding weather information (i.e., temperature (T) and absolute humidity (AH)). Distribution modeling of new cases across AH and T, helped identify the narrow and vulnerable AH range. We validated the results for 10-day intervals against monthly observations,​ and also worldwide trends. The results were used to predict Indian regions which would be vulnerable to weather based spread in upcoming months of 2020. COVID-19 spread in the US is significant for states with 4 < AH < 6 g/m(3) and number of new cases > 10,000, irrespective of the chosen time intervals for study parameters. These trends are consistent with worldwide observations,​ but do not correlate well with India so far possibly due the total cases reported per interval < 10,000. The results clarify the relationship between weather parameters and COVID-19 spread. The vulnerable weather parameters will help classify the risky geographic areas in different countries. Specifically,​ with further reporting of new cases in India, prediction of states with high risk of weather based spread will be apparent.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[202] Title: </b>A simple model for COVID-19.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-01<​br>​
 +<​b>​Publisher:​ </​b>​Infectious Disease Modelling<​br>​
 +<​b>​Keywords:​ </b>, asymptomatic infections, covid-19, erlang distribution,​ mathematical model<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2468042720300129">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2468042720300129</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +An S L 1 L 2 I 1 I 2 A 1 A 2 R epidemic model is formulated that describes the spread of an epidemic in a population. The model incorporates an Erlang distribution of times of sojourn in incubating, symptomatically and asymptomatically infectious compartments. Basic properties of the model are explored, with focus on properties important in the context of current COVID-19 pandemic.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[203] Title: </​b>​Estimation of the Excess COVID-19 Cases in Seoul, South Korea by the Students Arriving from China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-29<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Environmental Research and Public Health<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ covid-19, korea, compliance, coronavirus,​ quarantine, simulation, Health Sciences, Physical Sciences, Medicine, Environmental Science<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​1660-4601/​17/​9/​3113">​https://​www.mdpi.com/​1660-4601/​17/​9/​3113</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Background: In March 2020, overall, 37,000 international students from China, a country at risk of the 2019-novel coronavirus (COVID-19) infection has arrived in Seoul, South Korea. Individuals from the country at risk of COVID-19 infection have been included in the Korean home-quarantine program, but the efficacy of the program is uncertain. Methods: To estimate the possible number of infected individuals within the large influx of international students from China, we used a deterministic compartmental model for epidemic and performed a simulation-based search of different rates of compliance with home-quarantine. Results: Under the home-quarantine program, the number of the infected individuals would reach 40-72 from 12 March-24 March with the arrival of 0.2% of pre-infectious individuals. Furthermore,​ the number of isolated individuals would peak at 40-64 from 13 March-27 March in Seoul, South Korea. Our findings indicated when incoming international students showed strict compliance with quarantine, epidemics by the international student from China were less likely to occur in Seoul, South Korea. Conclusions:​ To mitigate possible epidemics, additional efforts to improve the compliance of home-quarantine of the individuals from countries with the virus risk are warranted along with other containment policies.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[204] Title: </​b>​Estimation of Unreported Novel Coronavirus (SARS-CoV-2) Infections from Reported Deaths: A Susceptible-Exposed-Infectious-Recovered-Dead Model.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-05<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Clinical Medicine<​br>​
 +<​b>​Keywords:​ </b>, covid-19, epidemic model, epidemiology,​ novel coronavirus<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2077-0383/​9/​5/​1350">​https://​www.mdpi.com/​2077-0383/​9/​5/​1350</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +In the midst of the novel coronavirus (SARS-CoV-2) epidemic, examining reported case data could lead to biased speculations and conclusions. Indeed, estimation of unreported infections is crucial for a better understanding of the current emergency in China and in other countries. In this study, we aimed to estimate the unreported number of infections in China prior to the 23 January 2020 restrictions. To do this, we developed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model that estimated unreported infections from the reported number of deaths. Our approach relied on the fact that observed deaths were less likely to be affected by ascertainment biases than reported infections. Interestingly,​ we estimated that the basic reproductive number (R0) was 2.43 (95%CI = 2.42-2.44) at the beginning of the epidemic and that 92.9% (95%CI = 92.5%-93.1%) of total cases were not reported. Similarly, the proportion of unreported new infections by day ranged from 52.1% to 100%, with a total of 91.8% (95%CI = 91.6%-92.1%) of infections going unreported. Agreement between our estimates and those from previous studies proves that our approach is reliable for estimating the prevalence and incidence of undocumented SARS-CoV-2 infections. Once it has been tested on Chinese data, our model could be applied to other countries with different surveillance and testing policies.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[205] Title: </​b>​Estimates of the ongoing need for social distancing and control measures post-"​lockdown"​ from trajectories of COVID-19 cases and mortality.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​European Respiratory Journal<​br>​
 +<​b>​Keywords:​ </​b>​Medical And Health Sciences, , Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​erj.ersjournals.com/​content/​early/​2020/​05/​26/​13993003.01483-2020">​https://​erj.ersjournals.com/​content/​early/​2020/​05/​26/​13993003.01483-2020</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +By 21st May 2020, SARS-CoV-2 had caused more than 5 million cases of COVID-19 across more than 200 countries. Most countries with significant outbreaks have introduced social distancing or "​lockdown"​ measures to reduce viral transmission. So the key question now is when, how, and to what extent, these measures can be lifted.Publically available data, on daily numbers of newly-confirmed cases and mortality, were used to fit regression models estimating trajectories,​ doubling times and the reproduction number (R0) of the disease, before and under the control measures. These data ran up to 21st May 2020, and were sufficient for analysis in 89 countries.The estimates of R0, before lockdown, based on these data were broadly consistent with those previously published: between 2.0 and 3.7 in the countries with the largest number of cases available for analysis (USA, Italy, Spain, France and UK). There was little evidence to suggest that the restrictions had reduced R far below 1 in many places, with France having the most rapid reductions - R0 0.76 (95%CI 0.72-0.82), based on cases and 0.77 (95%CI 0.73-0.80) based on mortality.Intermittent lockdown has been proposed as a means of controlling the outbreak while allowing periods of increase freedom and economic activity. These data suggest that few countries could have even 1 week per month unrestricted without seeing resurgence of the epidemic. Similarly, restoring 20% of the activity that has been prevented by the lockdowns looks difficult to reconcile with preventing the resurgence of the disease in most countries.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[206] Title: </​b>​Dynamic variations of the COVID-19 disease at different quarantine strategies in Wuhan and mainland China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Infection and Public Health<​br>​
 +<​b>​Keywords:​ </​b>​Public Health And Health Services, coronavirus disease 2019 (covid-19), mainland china, quarantine strategies, seirq model, scenario analysis, wuhan, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1876034120304809">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1876034120304809</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: The Coronavirus Disease 2019 (COVID-19) firstly announced in Wuhan of Hubei province, China is rapidly spreading to all the other 31 provinces of China and to more than 140 countries. Quarantine strategies play the key role on the disease controlling and public health in the world with this pandemic of the COVID-19 defined by the World Health Organization. METHODS: In this study, a SEIRQ epidemic model was developed to explore the dynamic changes of COVID-19 in Wuhan and mainland China, from January 27, 2020 to March 5, 2020. Moreover, to investigate the effects of the quarantine strategies, two perspectives are employed from the different quarantine magnitudes and quarantine time points. RESULTS: The major results suggest that the COVID-19 variations are well captured by the epidemic model with very high accuracy in the cumulative confirmed cases, confirmed cases, cumulative recovered cases and cumulative death cases. The quarantine magnitudes in the susceptible individuals play larger roles on the disease control than the impacts of the quarantines of the exposed individuals and infectious individuals. For the quarantine time points, it shows that the early quarantine strategy is significantly important for the disease controlling. The time delayed quarantining will seriously increase the COVID-19 disease patients and prolongs the days of the disease extinction. CONCLUSIONS:​ Our model can simulate and predict the COVID-19 variations and the quarantine strategies are important for the disease controlling,​ especially at the early period of the disease outbreak. These conclusions provide important scientific information for the government policymaker in the disease control strategies.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[207] Title: </​b>​Two complementary model-based methods for calculating the risk of international spreading of a novel virus from the outbreak epicentre. The case of COVID-19.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-09<​br>​
 +<​b>​Publisher:​ </​b>​Epidemiology & Infection<​br>​
 +<​b>​Keywords:​ </​b>​Public Health And Health Services, coronavirus,​ mathematical modelling, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.cambridge.org/​core/​journals/​epidemiology-and-infection/​article/​two-complementary-modelbased-methods-for-calculating-the-risk-of-international-spreading-of-a-novel-virus-from-the-outbreak-epicentre-the-case-of-covid19/​F74CA499693806D3F62BF354A54A2D77">​https://​www.cambridge.org/​core/​journals/​epidemiology-and-infection/​article/​two-complementary-modelbased-methods-for-calculating-the-risk-of-international-spreading-of-a-novel-virus-from-the-outbreak-epicentre-the-case-of-covid19/​F74CA499693806D3F62BF354A54A2D77</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +We present two complementary model-based methods for calculating the risk of international spread of the novel coronavirus SARS-CoV-2 from the outbreak epicentre. One model aims to calculate the number of cases that would be exported from an endemic country to disease-free regions by travellers. The second model calculates the probability that an infected traveller will generate at least one secondary autochthonous case in the visited country. Although this paper focuses on the data from China, our methods can be adapted to calculate the risk of importation and subsequent outbreaks. We found an average R0 = 5.31 (ranging from 4.08 to 7.91) and a risk of spreading of 0.75 latent individuals per 1000 travellers. In addition, one infective traveller would be able to generate at least one secondary autochthonous case in the visited country with a probability of 23%.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[208] Title: </​b>​An improved mathematical prediction of the time evolution of the Covid-19 pandemic in Italy, with a Monte Carlo simulation and error analyses.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-15<​br>​
 +<​b>​Publisher:​ </​b>​The European Physical Journal Plus<​br>​
 +<​b>​Keywords:​ </b>, <br>
 +<​b>​DOI:​ </​b><​a href="​https://​link.springer.com/​article/​10.1140/​epjp/​s13360-020-00488-4">​https://​link.springer.com/​article/​10.1140/​epjp/​s13360-020-00488-4</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +We present an improved mathematical analysis of the time evolution of the Covid-19 pandemic in Italy and a statistical error analyses of its evolution, including a Monte Carlo simulation with a very large number of runs to evaluate the uncertainties in its evolution. A previous analysis was based on the assumption that the number of nasopharyngeal swabs would be constant. However, the number of daily swabs is now more than five times what it was when we did our previous analysis. Therefore, here we consider the time evolution of the ratio of the new daily cases to number of swabs, which is more representative of the evolution of the pandemic when the number of swabs is increasing or changing in time. We consider a number of possible distributions representing the evolution of the pandemic in Italy, and we test their prediction capability over a period of up to 6 weeks. The results show that a distribution of the type of Planck black body radiation law provides very good forecasting. The use of different distributions provides an independent possible estimate of the uncertainty. We then consider five possible trajectories for the number of daily swabs and we estimate the potential dates of a substantial reduction in the number of new daily cases. We then estimate the spread in a substantial reduction, below a certain threshold, of the daily cases per swab among the Italian regions. We finally perform a Monte Carlo simulation with 25,000 runs to evaluate a random uncertainty in the prediction of the date of a substantial reduction in the number of diagnosed daily cases per swab.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[209] Title: </b>A nonlinear epidemiological model considering asymptotic and quarantine classes for SARS CoV-2 virus.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-09-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos,​ Solitons & Fractals<​br>​
 +<​b>​Keywords:​ </​b>​Mathematical Sciences, 92-02, 92b99, lyapunov'​s function, prophylaxis,​ reproduction number, Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920303520">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920303520</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +In this article, we develop a mathematical model considering susceptible,​ exposed, infected, asymptotic, quarantine/​isolation and recovered classes as in case of COVID-19 disease. The facility of quarantine/​isolation have been provided to both exposed and infected classes. Asymptotic individuals either recovered without undergo treatment or moved to infected class after some duration. We have formulated the reproduction number for the proposed model. Elasticity and sensitivity analysis indicates that model is more sensitive towards the transmission rate from exposed to infected classes rather than transmission rate from susceptible to exposed class. Analysis of global stability for the proposed model is studied through Lyapunov'​s function.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[210] Title: </b>A novel mathematics model of covid-19 with fractional derivative. Stability and numerical analysis.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-09-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos,​ Solitons & Fractals<​br>​
 +<​b>​Keywords:​ </​b>​Mathematical Sciences, covid-19 model, lagrange polynomial, non-local operators, reproductivity numbers, Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920304045">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920304045</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +a mathematical model depicting the spread of covid-19 epidemic and implementation of population covid-19 intervention in Italy. The model has 8 components leading to system of 8 ordinary differential equations. In this paper, we investigate the model using the concept of fractional differential operator. A numerical method based on the Lagrange polynomial was used to solve the system equations depicting the spread of COVID-19. A detailed investigation of stability including reproductive number using the next generation matrix, and the Lyapunov were presented in detail. Numerical simulations are depicted for various fractional orders.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[211] Title: </​b>​Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-09-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos,​ Solitons & Fractals<​br>​
 +<​b>​Keywords:​ </​b>​Mathematical Sciences, arima, covid-19, forecasting,​ lstm, modeling, narnn, Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920304136">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920304136</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +In this study, confirmed COVID-19 cases of Denmark, Belgium, Germany, France, United Kingdom, Finland, Switzerland and Turkey were modeled with Auto-Regressive Integrated Moving Average (ARIMA), Nonlinear Autoregression Neural Network (NARNN) and Long-Short Term Memory (LSTM) approaches. Six model performance metric were used to select the most accurate model (MSE, PSNR, RMSE, NRMSE, MAPE and SMAPE). According to the results of the first step of the study, LSTM was found the most accurate model. In the second stage of the study, LSTM model was provided to make predictions in a 14-day perspective that is yet to be known. Results of the second step of the study shows that the total cumulative case increase rate is expected to decrease slightly in many countries.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[212] Title: </​b>​Serial interval and time-varying reproduction number estimation for COVID-19 in western Iran.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-07-01<​br>​
 +<​b>​Publisher:​ </​b>​New Microbes and New Infections<​br>​
 +<​b>​Keywords:​ </b>, coronavirus disease 2019, generation time, iran, serial interval, time-varying reproduction number<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2052297520300676">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2052297520300676</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +There is no report on the serial interval (SI) of coronavirus disease 2019 (COVID-19) in Iran, the present report aims to estimate the SI and time-varying R of COVID-19 in western Iran. In this study, there were 1477 confirmed, probable and suspected cases of severe acute respiratory syndrome coronavirus 2 for Kermanshah from 22 February to 9 April. The close contacts of the confirmed cases were identified using telephone follow up of patients and their contacts. The SI distribution was used as an alternative. We fitted different models using the clinical onset dates of patients with their close contact (infector-infectee). Also, we applied a '​serial interval from sample'​ approach as a Bayesian methodology for estimating reproduction number. From 22 February to 29 March, 247 COVID-19 cases were confirmed by RT-PCR. Close contact between 21 patients (21 infector-infectee pairs), including 12 primary cases and 21 secondary cases, was confirmed. The mean and standard deviation of the SI were estimated as 5.71 and 3.89 days. The R varied from 0.79 to 1.88 for a 7-day time-lapse and ranged from 0.92 to 1.64 for a 14-day time-lapse on raw data. Also, the R varied from 0.83 to 1.84 for 7-day time-lapse and from 0.95 to 1.54 for a 14-day time-lapse using moving average data, respectively. It can be concluded that the low reproduction number for COVID-19 in Kermanshah province is an indication of the effectiveness of preventive and interventive programmes such as quarantine and isolation. Consequently,​ continuing these preventive measures is highly recommended.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[213] Title: </​b>​Using the kalman filter with Arima for the COVID-19 pandemic dataset of Pakistan.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-08-01<​br>​
 +<​b>​Publisher:​ </​b>​Data in Brief<​br>​
 +<​b>​Keywords:​ </b>, arima model, covid-2019 pandemic, forecast, holt-winters'​ method, infection control, kalman filter, state space model, suttearima<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2352340920307484">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2352340920307484</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The current pandemic of the Novel Corona virus (COVID-19) has resulted in multifold challenges related to health, economy, and society, etc. for the entire world. Many mathematical epidemiological models have been tried for the available data of the COVID-19 pandemic with the core objective to observe the trend and trajectories of infected cases, recoveries, and deaths, etc. However, these models have their own assumptions and parameters and vary with regional demography. This article suggests the use of a more pragmatic approach of the Kalman filter with the Autoregressive Integrated Moving Average (ARIMA) models in order to obtain more precise forecasts for the figures of prevalence, active cases, recoveries, and deaths related to the COVID-19 outbreak in Pakistan.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[214] Title: </​b>​Preliminary estimation of the novel coronavirus disease (COVID-19) cases in Iran: A modelling analysis based on overseas cases and air travel data.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.75<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-01<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Microbiology,​ air travel data, ascertainment rate, covid-19, coronavirus disease 2019, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1201971220301387">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1201971220301387</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +As of March 1, 2020, Iran had reported 987 novel coronavirus disease (COVID-19) cases, including 54 associated deaths. At least six neighboring countries (Bahrain, Iraq, Kuwait, Oman, Afghanistan,​ and Pakistan) had reported imported COVID-19 cases from Iran. In this study, air travel data and the numbers of cases from Iran imported into other Middle Eastern countries were used to estimate the number of COVID-19 cases in Iran. It was estimated that the total number of cases in Iran was 16 533 (95% confidence interval: 5925-35 538) by February 25, 2020, before the UAE and other Gulf Cooperation Council countries suspended inbound and outbound flights from Iran.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[215] Title: </​b>​When will the battle against novel coronavirus end in Wuhan: A SEIR modeling analysis.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.75<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Global Health<​br>​
 +<​b>​Keywords:​ </b>, <br>
 +<​b>​DOI:​ </​b><​a href="​http://​jogh.org/​documents/​issue202001/​jogh-10-011002.pdf">​http://​jogh.org/​documents/​issue202001/​jogh-10-011002.pdf</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Background: Recent outbreak of 2019-nCoV in Wuhan raised serious public health concerns. By February 15, 2020 in Wuhan, the total number of confirmed infection cases has reached 37 914, and the number of deaths has reached 1123, accounting for 56.9% of the total confirmed cases and 73.7% of the total deaths in China. People are eager to know when the epidemic will be completely controlled and when people'​s work and life will be on the right track. Method: In this study we analyzed the epidemic dynamics and trend of 2019-nCoV in Wuhan by using the data after the closure of Wuhan city till February 12, 2020 based on the SEIR modeling method. Results: The optimal parameters were estimated as R0 = 1.44 (interquartile range: 1.40-1.47), TI = 14 (interquartile range = 14-14) and TE = 3.0 (interquartile range = 2.8-3.1). Based on these parameters, the number of infected individuals in Wuhan city may reach the peak around February 19 at about 47 000 people. Once entering March, the epidemic would gradually decline, and end around the late March. It is worth noting that the above prediction is based on the assumption that the number of susceptible population N = 200 000 will not increase. If the epidemic situation is not properly controlled, the peak of infected number can be further increased and the peak time will be a little postponed. It was expected that the epidemic would subside in early March, and disappear gradually towards the late March. Conclusions:​ The epidemic situation of 2019-nCoV in Wuhan was effectively controlled after the closure of the city, and the disease transmission index also decreased significantly. It is expected that the peak of epidemic situation would be reached in late February and end in March.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[216] Title: </​b>​Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.75<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos,​ Solitons & Fractals<​br>​
 +<​b>​Keywords:​ </​b>​Mathematical Sciences, attack rate, covid-19 pandemic, poisson regression, turning point, Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920302290">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920302290</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +In this paper, we employed a segmented Poisson model to analyze the available daily new cases data of the COVID-19 outbreaks in the six Western countries of the Group of Seven, namely, Canada, France, Germany, Italy, UK and USA. We incorporated the governments'​ interventions (stay-at-home advises/​orders,​ lockdowns, quarantines and social distancing) against COVID-19 into consideration. Our analysis allowed us to make a statistical prediction on the turning point (the time that the daily new cases peak), the duration (the period that the outbreak lasts) and the attack rate (the percentage of the total population that will be infected over the course of the outbreak) for these countries.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[217] Title: </​b>​Prediction of the COVID-19 spread in African countries and implications for prevention and control: A case study in South Africa, Egypt, Algeria, Nigeria, Senegal and Kenya.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.75<​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, parameter estimation, prediction, seir, scenario analysis, Environmental Science, Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720324761">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720324761</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +COVID-19 (Corona Virus Disease 2019) is globally spreading and the international cooperation is urgently required in joint prevention and control of the epidemic. Using the Maximum-Hasting (MH) parameter estimation method and the modified Susceptible Exposed Infectious Recovered (SEIR) model, the spread of the epidemic under three intervention scenarios (suppression,​ mitigation, mildness) is simulated and predicted in South Africa, Egypt, and Algeria, where the epidemic situations are severe. The studies are also conducted in Nigeria, Senegal and Kenya, where the epidemic situations are growing rapidly and the socio-economic are relatively under-developed,​ resulting in more difficulties in preventing the epidemic. Results indicated that the epidemic can be basically controlled in late April with strict control of scenario one, manifested by the circumstance in the South Africa and Senegal. Under moderate control of scenario two, the number of infected people will increase by 1.43-1.55 times of that in scenario one, the date of the epidemic being controlled will be delayed by about 10 days, and Algeria, Nigeria, and Kenya are in accordance with this situation. In the third scenario of weak control, the epidemic will be controlled by late May, the total number of infected cases will double that in scenario two, and Egypt is in line with this prediction. In the end, a series of epidemic controlling methods are proposed, including patient quarantine, close contact tracing, population movement control, government intervention,​ city and county epidemic risk level classification,​ and medical cooperation and the Chinese assistance.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[218] Title: </​b>​Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.75<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-07<​br>​
 +<​b>​Publisher:​ </​b>​Frontiers in Medicine<​br>​
 +<​b>​Keywords:​ </b>, covid-19, seir, basic reproduction number, epidemic prediction, novel coronavirus,​ secondary transmission<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.frontiersin.org/​articles/​10.3389/​fmed.2020.00171/​full">​https://​www.frontiersin.org/​articles/​10.3389/​fmed.2020.00171/​full</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Understanding the transmission dynamics of COVID-19 is crucial for evaluating its spread pattern, especially in metropolitan areas of China, as its spread could lead to secondary outbreaks. In addition, the experiences gained and lessons learned from China have the potential to provide evidence to support other metropolitan areas and large cities outside China with their emerging cases. We used data reported from January 24, 2020, to February 23, 2020, to fit a model of infection, estimate the likely number of infections in four high-risk metropolitan areas based on the number of cases reported, and increase the understanding of the COVID-19 spread pattern. Considering the effect of the official quarantine regulations and travel restrictions for China, which began January 23~24, 2020, we used the daily travel intensity index from the Baidu Maps app to roughly simulate the level of restrictions and estimate the proportion of the quarantined population. A group of SEIR model statistical parameters were estimated using Markov chain Monte Carlo (MCMC) methods and fitting on the basis of reported data. As a result, we estimated that the basic reproductive number, R 0, was 2.91 in Beijing, 2.78 in Shanghai, 2.02 in Guangzhou, and 1.75 in Shenzhen based on the data from January 24, 2020, to February 23, 2020. In addition, we inferred the prediction results and compared the results of different levels of parameters. For example, in Beijing, the predicted peak number of cases was 467 with a peak time of March 01, 2020; however, if the city were to implement different levels (strict, moderate, or weak) of travel restrictions or regulation measures, the estimation results showed that the transmission dynamics would change and that the peak number of cases would differ by between 54% and 209%. We concluded that public health interventions would reduce the risk of the spread of COVID-19 and that more rigorous control and prevention measures would effectively contain its further spread, and awareness of prevention should be enhanced when businesses and social activities return to normal before the end of the epidemic. Further, the experiences gained and lessons learned from China offer the potential to provide evidence supporting other metropolitan areas and big cities with their emerging cases outside China.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[219] Title: </​b>​[Analysis on epidemic situation and spatiotemporal changes of COVID-19 in Anhui].<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-02-27<​br>​
 +<​b>​Publisher:​ </​b>​Zhonghua yu fang yi xue za zhi Chinese journal of preventive medicine<​br>​
 +<​b>​Keywords:​ </b>, anhui province, covid-19, epidemiology,​ spatiotemporal distribution,​ Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​http://​journal.yiigle.com/​PartnerAdmin/​HttpError/​NotFound">​http://​journal.yiigle.com/​PartnerAdmin/​HttpError/​NotFound</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +We used the epidemic data of COVID-19 published on the official website of the municipal health commission in Anhui province. We mapped the spatiotemporal changes of confirmed cases, fitted the epidemic situation by the population growth curve at different stages and took statistical description and analysis of the epidemic situation in Anhui province. It was found that the cumulative incidence of COVID-19 was 156/100 000 by February 18, 2020 and the trend of COVID-19 epidemic declined after February 7, changing from J curve to S curve. The actual number of new cases began to decrease from February 2 to February 4 due to the time of case report and actual onset delayed by 3 to 5 days.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[220] Title: </​b>​[Study on assessing early epidemiological parameters of COVID-19 epidemic in China].<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-01<​br>​
 +<​b>​Publisher:​ </​b>​Zhonghua liu xing bing xue za zhi Zhonghua liuxingbingxue zazhi<​br>​
 +<​b>​Keywords:​ </b>, basic reproduction number, covid-19, generation interval, incubation period, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​http://​journal.yiigle.com/​PartnerAdmin/​HttpError/​NotFound">​http://​journal.yiigle.com/​PartnerAdmin/​HttpError/​NotFound</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Objective: To study the early dynamics of the epidemic of coronavirus disease (COVID-19) in China from 15 to 31 January, 2020, and estimate the corresponding epidemiological parameters (incubation period, generation interval and basic reproduction number) of the epidemic. Methods: By means of Weibull, Gamma and Lognormal distributions methods, we estimated the probability distribution of the incubation period and generation interval data obtained from the reported COVID-19 cases. Moreover, the AIC criterion was used to determine the optimal distribution. Considering the epidemic is ongoing, the exponential growth model was used to fit the incidence data of COVID-19 from 10 to 31 January, 2020, and exponential growth method, maximum likelihood method and SEIR model were used to estimate the basic reproduction number. Results: Early COVID-19 cases kept an increase in exponential growth manner before 26 January, 2020, then the increase trend became slower. The average incubation period was 5.01 (95%CI: 4.31-5.69) days; the average generation interval was 6.03 (95%CI: 5.20-6.91) days. The basic reproduction number was estimated to be 3.74 (95%CI: 3.63-3.87), 3.16 (95%CI: 2.90-3.43), and 3.91 (95%CI: 3.71-4.11) by three methods, respectively. Conclusions:​ The Gamma distribution fits both the generation interval and incubation period best, and the mean value of generation interval is 1.02 day longer than that of incubation period. The relatively high basic reproduction number indicates that the epidemic is still serious; Based on our analysis, the turning point of the epidemic would be seen on 26 January, the growth rate would be lower afterwards.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[221] Title: </​b>​[Fitting and forecasting the trend of COVID-19 by SEIR(+CAQ) dynamic model].<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-01<​br>​
 +<​b>​Publisher:​ </​b>​Zhonghua liu xing bing xue za zhi Zhonghua liuxingbingxue zazhi<​br>​
 +<​b>​Keywords:​ </b>, covid-19, epidemic forecasting,​ seir(+caq) dynamic model, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​http://​journal.yiigle.com/​PartnerAdmin/​HttpError/​NotFound">​http://​journal.yiigle.com/​PartnerAdmin/​HttpError/​NotFound</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Objectives: Fitting and forecasting the trend of COVID-19 epidemics. Methods: Based on SEIR dynamic model, considering the COVID-19 transmission mechanism, infection spectrum and prevention and control procedures, we developed SEIR(+CAQ) dynamic model to fit the frequencies of laboratory confirmed cases obtained from the government official websites. The data from January 20, 2020 to February 7, 2020 were used to fit the model, while the left data between February 8-12 were used to evaluate the quality of forecasting. Results: According to the cumulative number of confirmed cases between January 29 to February 7, the fitting bias of SEIR(+CAQ) model for overall China (except for cases of Hubei province), Hubei province (except for cases of Wuhan city) and Wuhan city was less than 5%. For the data of subsequent 5 days between February 8 to 12, which were not included in the model fitting, the prediction biases were less than 10%. Regardless of the cases diagnosed by clinical examines, the numbers of daily emerging cases of China (Hubei province not included), Hubei Province (Wuhan city not included) and Wuhan city reached the peak in the early February. Under the current strength of prevention and control, the total number of laboratory-confirmed cases in overall China will reach 80 417 till February 29, 2020, respectively. Conclusions:​ The proposed SEIR(+CAQ) dynamic model fits and forecasts the trend of novel coronavirus pneumonia well and provides evidence for decision making.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[222] Title: </​b>​Optimization Method for Forecasting Confirmed Cases of COVID-19 in China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-02<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Clinical Medicine<​br>​
 +<​b>​Keywords:​ </b>, covid-19, adaptive neuro-fuzzy inference system (anfis), flower pollination algorithm (fpa), forecasting,​ salp swarm algorithm (ssa)<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2077-0383/​9/​3/​674">​https://​www.mdpi.com/​2077-0383/​9/​3/​674</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +In December 2019, a novel coronavirus,​ called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS,​ is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination ( R 2 ), and computing time. Furthermore,​ we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[223] Title: </​b>​Phase- and epidemic region-adjusted estimation of the number of coronavirus disease 2019 cases in China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-31<​br>​
 +<​b>​Publisher:​ </​b>​Frontiers of Medicine<​br>​
 +<​b>​Keywords:​ </​b>​Medicine & Public Health, covid-19, china, seir model, estimate<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​link.springer.com/​article/​10.1007/​s11684-020-0768-7">​https://​link.springer.com/​article/​10.1007/​s11684-020-0768-7</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The outbreak of the coronavirus disease 2019 was first reported in Wuhan in December 2019 and gradually spread to other areas in China. After implementation of prevention and control measures, the estimation of the epidemic trend is needed. A phase- and region-adjusted SEIR model was applied for modeling and predicting the number of cases in Wuhan, Hubei Province and regions outside Hubei Province in China. The estimated number of infections could reach its peak in late February 2020 in Wuhan and Hubei Province, which is 55 303-84 520 and 83 944-129 312, respectively,​ while the epidemic peaks in regions outside Hubei Province in China could appear on February 13, 2020 with the estimated 13 035-19 108 cases. According to the estimation, the outbreak would abate in March and April all over China. Current estimation provided evidence for planned work resumption under stringent prevention and control in China to further support the fight against the epidemic. Nevertheless,​ there is still possibility of the second outbreak brought by the work resumption and population migration, especially from Hubei Province and high intensity cities outside Hubei Province. Strict prevention and control measures still need to be considered in the regions with high intensity of epidemic and densely-populated cities.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[224] Title: </​b>​Investigating the cases of novel coronavirus disease (COVID-19) in China using dynamic statistical techniques.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-01<​br>​
 +<​b>​Publisher:​ </​b>​Heliyon<​br>​
 +<​b>​Keywords:​ </b>, covid-19, cases of novel coronavirus,​ china, econometrics,​ economics, environmental economics, environmental science, health economics, modelling covid-19, novel coronavirus disease, public health<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2405844020305922">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2405844020305922</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The initial investigation by local hospital attributed the outbreak of the novel coronavirus disease (COVID-19) to pneumonia with unknown cause that appeared like the 2003 severe acute respiratory syndrome (SARS). The World Health Organization declared COVID-19 as public health emergency after it spread outside China to several countries. Thus, an assessment of the novel coronavirus disease (COVID-19) with novel estimation approaches is essential to the global debate. This study is the first to develop both time series and panel data models to construct conceptual tools that examine the nexus between death from COVID-19 and confirmed cases. We collected daily data on four health indicators namely deaths, confirmed cases, suspected cases, and recovered cases across 31 Provinces/​States in China. Due to the complexities of the COVID-19, we investigated the unobserved factors including environmental exposures accounting for the spread of the disease through human-to-human transmission. We used estimation methods capable of controlling for cross-sectional dependence, endogeneity,​ and unobserved heterogeneity. We predicted the impulse-response between confirmed cases of COVID-19 and COVID-19-attributable deaths. Our study revealed that the effect of confirmed cases on the novel coronavirus attributable deaths is heterogeneous across Provinces/​States in China. We found a linear relationship between COVID-19 attributable deaths and confirmed cases whereas a nonlinear relationship was confirmed for the nexus between recovery cases and confirmed cases. The empirical evidence revealed that an increase in confirmed cases by 1% increases coronavirus attributable deaths by ~0.10%-~1.71% (95% CI). Our empirical results confirmed the presence of unobserved heterogeneity and common factors that facilitates the novel coronavirus attributable deaths caused by increased levels of confirmed cases. Yet, the role of such a medium that facilitates the transmission of COVID-19 remains unclear. We highlight safety precaution and preventive measures to circumvent the human-to-human transmission.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[225] Title: </​b>​Prediction of COVID-19 transmission dynamics using a mathematical model considering behavior changes in Korea.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-13<​br>​
 +<​b>​Publisher:​ </​b>​Epidemiology and Health<​br>​
 +<​b>​Keywords:​ </b>, behavior changes, covid-19, mathematical model, model prediction, parameter estimation<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.e-epih.org/​journal/​view.php?​doi=10.4178/​epih.e2020026">​https://​www.e-epih.org/​journal/​view.php?​doi=10.4178/​epih.e2020026</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +OBJECTIVES: Since the report of the first confirmed case in Daegu on February 18, 2020, local transmission of coronavirus disease 2019 (COVID-19) in Korea has continued. In this study, we aimed to identify the pattern of local transmission of COVID-19 using mathematical modeling and predict the epidemic size and the timing of the end of the spread. METHODS: We modeled the COVID-19 outbreak in Korea by applying a mathematical model of transmission that factors in behavioral changes. We used the Korea Centers for Disease Control and Prevention data of daily confirmed cases in the country to estimate the nationwide and Daegu/​Gyeongbuk area-specific transmission rates as well as behavioral change parameters using a least-squares method. RESULTS: The number of transmissions per infected patient was estimated to be about 10 times higher in the Daegu/​Gyeongbuk area than the average of nationwide. Using these estimated parameters, our models predicts that about 13,800 cases will occur nationwide and 11,400 cases in the Daegu/​Gyeongbuk area until mid-June. CONCLUSIONS:​ We mathematically demonstrate that the relatively high per-capita rate of transmission and the low rate of changes in behavior have caused a large-scale transmission of COVID-19 in the Daegu/​Gyeongbuk area in Korea. Since the outbreak is expected to continue until May, non-pharmaceutical interventions that can be sustained over the long term are required.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[226] Title: </​b>​Estimating COVID-19 outbreak risk through air travel.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-05<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Travel Medicine<​br>​
 +<​b>​Keywords:​ </​b>​Clinical Sciences, branching process, mathematical model, outbreak resurgence, policy changes, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​http://​doi.org/​10.1093/​jtm/​taaa093">​http://​doi.org/​10.1093/​jtm/​taaa093</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: Substantial limitations have been imposed on passenger air travel to reduce transmission of SARS-CoV-2 between regions and countries. However, as case numbers decrease, air travel will gradually resume. We considered a future scenario in which case numbers are low and air travel returns to normal. Under that scenario, there will be a risk of outbreaks in locations worldwide due to imported cases. We estimated the risk of different locations acting as sources of future COVID-19 outbreaks elsewhere. METHODS: We use modelled global air travel data and population density estimates from locations worldwide to analyse the risk that 1364 airports are sources of future COVID-19 outbreaks. We use a probabilistic,​ branching-process based approach that considers the volume of air travelers between airports and the reproduction number at each location, accounting for local population density. RESULTS: Under the scenario we model, we identify airports in East Asia as having the highest risk of acting as sources of future outbreaks. Moreover, we investigate the locations most likely to cause outbreaks due to air travel in regions that are large and potentially vulnerable to outbreaks: India, Brazil and Africa. We find that outbreaks in India and Brazil are most likely to be seeded by individuals travelling from within those regions. We find that this is also true for less vulnerable regions, such as the United States, Europe, and China. However, outbreaks in Africa due to imported cases are instead most likely to be initiated by passengers travelling from outside the continent. CONCLUSIONS:​ Variation in flight volumes and destination population densities create a non-uniform distribution of the risk that different airports pose of acting as the source of an outbreak. Accurate quantification of the spatial distribution of outbreak risk can therefore facilitate optimal allocation of resources for effective targeting of public health interventions.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[227] Title: </​b>​Distribution of Patients at Risk for Complications Related to COVID-19 in the United States: Model Development Study.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-18<​br>​
 +<​b>​Publisher:​ </​b>​JMIR Public Health and Surveillance<​br>​
 +<​b>​Keywords:​ </b>, covid-19, chronic conditions, modeling, older adults<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​publichealth.jmir.org/​2020/​2/​e19606/">​https://​publichealth.jmir.org/​2020/​2/​e19606/</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: Coronavirus disease (COVID-19) has spread exponentially across the United States. Older adults with underlying health conditions are at an especially high risk of developing life-threatening complications if infected. Most intensive care unit (ICU) admissions and non-ICU hospitalizations have been among patients with at least one underlying health condition. OBJECTIVE: The aim of this study was to develop a model to estimate the risk status of the patients of a nationwide pharmacy chain in the United States, and to identify the geographic distribution of patients who have the highest risk of severe COVID-19 complications. METHODS: A risk model was developed using a training test split approach to identify patients who are at high risk of developing serious complications from COVID-19. Adult patients (aged >/=18 years) were identified from the Walgreens pharmacy electronic data warehouse. Patients were considered eligible to contribute data to the model if they had at least one prescription filled at a Walgreens location between October 27, 2019, and March 25, 2020. Risk parameters included age, whether the patient is being treated for a serious or chronic condition, and urban density classification. Parameters were differentially weighted based on their association with severe complications,​ as reported in earlier cases. An at-risk rate per 1000 people was calculated at the county level, and ArcMap was used to depict the rate of patients at high risk for severe complications from COVID-19. Real-time COVID-19 cases captured by the Johns Hopkins University Center for Systems Science and Engineering (CSSE) were layered in the risk map to show where cases exist relative to the high-risk populations. RESULTS: Of the 30,100,826 adults included in this study, the average age is 50 years, 15% have at least one specialty medication, and the average patient has 2 to 3 comorbidities. Nearly 28% of patients have the greatest risk score, and an additional 34.64% of patients are considered high-risk, with scores ranging from 8 to 10. Age accounts for 53% of a patient'​s total risk, followed by the number of comorbidities (29%); inferred chronic obstructive pulmonary disease, hypertension,​ or diabetes (15%); and urban density classification (5%). CONCLUSIONS:​ This risk model utilizes data from approximately 10% of the US population. Currently, this is the most comprehensive US model to estimate and depict the county-level prognosis of COVID-19 infection. This study shows that there are counties across the United States whose residents are at high risk of developing severe complications from COVID-19. Our county-level risk estimates may be used alongside other data sets to improve the accuracy of anticipated health care resource needs. The interactive map can also aid in proactive planning and preparations among employers that are deemed critical, such as pharmacies and grocery stores, to prevent the spread of COVID-19 within their facilities.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[228] Title: </​b>​Estimated Effect of COVID-19 Lockdown on Skin Tumor Size and Survival: An Exponential Growth Model.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-01<​br>​
 +<​b>​Publisher:​ </​b>​Actas Dermo-Sifiliográficas<​br>​
 +<​b>​Keywords:​ </​b>​Medical And Health Sciences, covid-19, covid-19 virus disease, carcinoma de celulas escamosas cutaneo, confinamiento,​ cutaneous squamous cell carcinoma, diagnostico precoz, early diagnosis, lockdown, melanoma, prognosis, pronostico, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0001731020301423">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0001731020301423</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND AND OBJECTIVES: Spain is in a situation of indefinite lockdown due to the ongoing coronavirus disease 2019 (COVID-19) pandemic. One of the consequences of this lockdown is delays in medical and surgical procedures for common diseases. The aim of this study was to model the impact on survival of tumor growth caused by such delays in patients with squamous cell carcinoma (SCC) and melanoma. MATERIAL AND METHODS: Multicenter,​ retrospective,​ observational cohort study. We constructed an exponential growth model for both SCC and melanoma to estimate tumor growth between patient-reported onset and surgical excision at different time points. RESULTS: Data from 200 patients with SCC of the head and neck and 1000 patients with cutaneous melanoma were included. An exponential growth curve was calculated for each tumor type and we estimated tumor size after 1, 2, and 3 months of potential surgical delay. The proportion of patients with T3 SCC (diameter >4cm or thickness >6mm) increased from 41.5% (83 patients) in the initial study group to an estimated 58.5%, 70.5%, and 72% after 1, 2, and 3 months of delay. Disease-specific survival at 2, 5, and 10 years in patients whose surgery was delayed by 3 months decreased by 6.2%, 8.2%, and 5.2%, respectively. The proportion of patients with ultrathick melanoma (>6mm) increased from 6.9% in the initial study group to 21.9%, 30.2%, and 30.2% at 1, 2, and 3 months. Five- and 10-year disease-specific survival both decreased by 14.4% in patients treated after a potential delay of 3 months. CONCLUSIONS:​ In the absence of adequate diagnosis and treatment of SCC and melanoma in the current lockdown situation in Spain, we can expect to see to a considerable increase in large and thick SCCs and melanomas. Efforts must be taken to encourage self-examination and facilitate access to dermatologists in order to prevent further delays.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[229] Title: </​b>​ARIMA modelling and forecasting of irregularly patterned COVID-19 outbreaks using Japanese and South Korean data.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-08-01<​br>​
 +<​b>​Publisher:​ </​b>​Data in Brief<​br>​
 +<​b>​Keywords:​ </b>, daily new cases, dynamic prediction, statistical analysis, stationarity<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2352340920306739">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2352340920306739</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The World Health Organization (WHO) upgraded the status of the coronavirus disease 2019 (COVID-19) outbreak from epidemic to global pandemic on March 11, 2020. Various mathematical and statistical models have been proposed to predict the spread of COVID-2019 [1]. We collated data on daily new confirmed cases of the COVID-19 outbreaks in Japan and South Korea from January 20, 2020 to April 26, 2020. Auto Regressive Integrated Moving Average (ARIMA) model were introduced to analyze two data sets and predict the daily new confirmed cases for the 7-day period from April 27, 2020 to May 3, 2020. Also, the forecasting results and both data sets are provided.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[230] Title: </b>A Simulation of a COVID-19 Epidemic Based on a Deterministic SEIR Model.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-28<​br>​
 +<​b>​Publisher:​ </​b>​Frontiers in Public Health<​br>​
 +<​b>​Keywords:​ </b>, covid-19, lombardy (italy), seir model, epidemic, infection fatality rate (ifr), lockdown, reproduction ratio (r0)<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.frontiersin.org/​articles/​10.3389/​fpubh.2020.00230/​full">​https://​www.frontiersin.org/​articles/​10.3389/​fpubh.2020.00230/​full</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +An epidemic disease caused by a new coronavirus has spread in Northern Italy with a strong contagion rate. We implement an SEIR model to compute the infected population and the number of casualties of this epidemic. The example may ideally regard the situation in the Italian Region of Lombardy, where the epidemic started on February 24, but by no means attempts to perform a rigorous case study in view of the lack of suitable data and the uncertainty of the different parameters, namely, the variation of the degree of home isolation and social distancing as a function of time, the initial number of exposed individuals and infected people, the incubation and infectious periods, and the fatality rate. First, we perform an analysis of the results of the model by varying the parameters and initial conditions (in order for the epidemic to start, there should be at least one exposed or one infectious human). Then, we consider the Lombardy case and calibrate the model with the number of dead individuals to date (May 5, 2020) and constrain the parameters on the basis of values reported in the literature. The peak occurs at day 37 (March 31) approximately,​ with a reproduction ratio R 0 of 3 initially, 1.36 at day 22, and 0.8 after day 35, indicating different degrees of lockdown. The predicted death toll is approximately 15,600 casualties, with 2.7 million infected individuals at the end of the epidemic. The incubation period providing a better fit to the dead individuals is 4.25 days, and the infectious period is 4 days, with a fatality rate of 0.00144/day [values based on the reported (official) number of casualties]. The infection fatality rate (IFR) is 0.57%, and it is 2.37% if twice the reported number of casualties is assumed. However, these rates depend on the initial number of exposed individuals. If approximately nine times more individuals are exposed, there are three times more infected people at the end of the epidemic and IFR = 0.47%. If we relax these constraints and use a wider range of lower and upper bounds for the incubation and infectious periods, we observe that a higher incubation period (13 vs. 4.25 days) gives the same IFR (0.6 vs. 0.57%), but nine times more exposed individuals in the first case. Other choices of the set of parameters also provide a good fit to the data, but some of the results may not be realistic. Therefore, an accurate determination of the fatality rate and characteristics of the epidemic is subject to knowledge of the precise bounds of the parameters. Besides the specific example, the analysis proposed in this work shows how isolation measures, social distancing, and knowledge of the diffusion conditions help us to understand the dynamics of the epidemic. Hence, it is important to quantify the process to verify the effectiveness of the lockdown.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[231] Title: </​b>​[Dynamic basic reproduction number based evaluation for current prevention and control of COVID-19 outbreak in China].<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-01<​br>​
 +<​b>​Publisher:​ </​b>​Zhonghua liu xing bing xue za zhi Zhonghua liuxingbingxue zazhi<​br>​
 +<​b>​Keywords:​ </b>, coronavirus disease, dynamic basic reproduction number, statistical prediction, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​http://​journal.yiigle.com/​PartnerAdmin/​HttpError/​NotFound">​http://​journal.yiigle.com/​PartnerAdmin/​HttpError/​NotFound</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Objective: To evaluate the current status of the prevention and control of coronavirus disease (COVID-19) outbreak in China, establish a predictive model to evaluate the effects of the current prevention and control strategies, and provide scientific information for decision-making departments. Methods: Based on the epidemic data of COVID-19 openly accessed from national health authorities,​ we estimated the dynamic basic reproduction number R(0)(t) to evaluate the effects of the current COVID-19 prevention and control strategies in all the provinces (municipalities and autonomous regions) as well as in Wuhan and the changes in infectivity of COVID-19 over time. Results: For the stability of the results, 24 provinces (municipality) with more than 100 confirmed COVID-19 cases were included in the analysis. At the beginning of the outbreak, the R(0)(t) showed unstable trend with big variances. As the strengthening of the prevention and control strategies, R(0)(t) began to show a downward trend in late January, and became stable in February. By the time of data analysis, 18 provinces (municipality) (75%) had the R(0)(t)s less than 1. The results could be used for the decision making to free population floating conditionally. Conclusions:​ Dynamic R(0)(t) is useful in the evaluation of the change in infectivity of COVID-19, the prevention and control strategies for the COVID-19 outbreak have shown preliminary effects, if continues, it is expected to control the COVID-19 outbreak in China in near future.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[232] Title: </​b>​SutteARIMA:​ Short-term forecasting method, a case: Covid-19 and stock market in Spain.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-08-01<​br>​
 +<​b>​Publisher:​ </​b>​Science of the Total Environment<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ covid-19, ibex, short-term forecast, suttearima, Environmental Science, Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720324001">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720324001</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +This study aimed to predict the short-term of confirmed cases of covid-19 and IBEX in Spain by using SutteARIMA method. Confirmed data of Covid-19 in Spanish was obtained from Worldometer and Spain Stock Market data (IBEX 35) was data obtained from Yahoo Finance. Data started from 12 February 2020-09 April 2020 (the date on Covid-19 was detected in Spain). The data from 12 February 2020-02 April 2020 using to fitting with data from 03 April 2020 - 09 April 2020. Based on the fitting data, we can conducted short-term forecast for 3 future period (10 April 2020 - 12 April 2020 for Covid-19 and 14 April 2020 - 16 April 2020 for IBEX). In this study, the SutteARIMA method will be used. For the evaluation of the forecasting methods, we applied forecasting accuracy measures, mean absolute percentage error (MAPE). Based on the results of ARIMA and SutteARIMA forecasting methods, it can be concluded that the SutteARIMA method is more suitable than ARIMA to calculate the daily forecasts of confirmed cases of Covid-19 and IBEX in Spain. The MAPE value of 0.036 (smaller than 0.03 compared to MAPE value of ARIMA) for confirmed cases of Covid-19 in Spain and was in the amount of 0.026 for IBEX stock. At the end of the analysis, this study used the SutteARIMA method, this study calculated daily forecasts of confirmed cases of Covid-19 in Spain from 10 April 2020 until 12 April 2020 i.e. 158925; 164390; and 169969 and Spain Stock Market from 14 April 2020 until 16 April 2020 i.e. 7000.61; 6930.61; and 6860.62.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[233] Title: </​b>​COVID-19 Trend Estimation in the Elderly Italian Region of Sardinia.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-24<​br>​
 +<​b>​Publisher:​ </​b>​Frontiers in Public Health<​br>​
 +<​b>​Keywords:​ </b>, covid-19, sird-model, coronavirus,​ pandemic, public health emergency<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.frontiersin.org/​articles/​10.3389/​fpubh.2020.00153/​full">​https://​www.frontiersin.org/​articles/​10.3389/​fpubh.2020.00153/​full</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +December 2019 saw a novel coronavirus (COVID-19) from China quickly spread globally. Currently, COVID-19, defined as the new pandemic by the World Health Organization (WHO), has reached over 750,000 confirmed cases worldwide. The virus began to spread in Italy from the 22nd February, and the number of related cases is still increasing. Furthermore,​ given that a relevant proportion of infected people need hospitalization in Intensive Care Units, this may be a crucial issue for National Healthcare System'​s capacity. WHO underlines the importance of specific disease regional estimates. Because of this, Italy aimed to put in place proportioned and controlled measures, and to guarantee adequate funding to both increase the number of ICU beds and increase production of personal protective equipment. Our aim is to investigate the current COVID-19 epidemiological context in Sardinia region (Italy) and to estimate the transmission parameters using a stochastic model to establish the number of infected, recovered, and deceased people expected. Based on available data from official Italian and regional sources, we describe the distribution of infected cases during the period between 2nd and 15th March 2020. To better reflect the actual spread of COVID-19 in Sardinia based on data from 15th March (first Sardinian declared outbreak), two Susceptible-Infectious-Recovered-Dead (SIRD) models have been developed, describing the best and worst scenarios. We believe that our findings represent a valid contribution to better understand the epidemiological context of COVID-19 in Sardinia. Our analysis can help health authorities and policymakers to address the right interventions to deal with the rapidly expanding health emergency.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[234] Title: </​b>​Imitation dynamics in the mitigation of the novel coronavirus disease (COVID-19) outbreak in Wuhan, China from 2019 to 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​0.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-01<​br>​
 +<​b>​Publisher:​ </​b>​Annals of Translational Medicine<​br>​
 +<​b>​Keywords:​ </b>, coronavirus disease 2019 (covid-19), final epidemic size, imitation game, mathematical modelling, reproduction number<​br>​
 +<​b>​DOI:​ </​b><​a href="​http://​atm.amegroups.com/​article/​view/​39932/​html">​http://​atm.amegroups.com/​article/​view/​39932/​html</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Background: The coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China on December 2019 in patients presenting with atypical pneumonia. Although '​city-lockdown'​ policy reduced the spatial spreading of the COVID-19, the city-level outbreaks within each city remain a major concern to be addressed. The local or regional level disease control mainly depends on individuals self-administered infection prevention actions. The contradiction between choice of taking infection prevention actions or not makes the elimination difficult under a voluntary acting scheme, and represents a clash between the optimal choice of action for the individual interest and group interests. Methods: We develop a compartmental epidemic model based on the classic susceptible-exposed-infectious-recovered model and use this to fit the data. Behavioral imitation through a game theoretical decision-making process is incorporated to study and project the dynamics of the COVID-19 outbreak in Wuhan, China. By varying the key model parameters, we explore the probable course of the outbreak in terms of size and timing under several public interventions in improving public awareness and sensitivity to the infection risk as well as their potential impact. Results: We estimate the basic reproduction number, R 0, to be 2.5 (95% CI: 2.4-2.7). Under the current most realistic setting, we estimate the peak size at 0.28 (95% CI: 0.24-0.32) infections per 1,000 population. In Wuhan, the final size of the outbreak is likely to infect 1.35% (95% CI: 1.00-2.12%) of the population. The outbreak will be most likely to peak in the first half of February and drop to daily incidences lower than 10 in June 2020. Increasing sensitivity to take infection prevention actions and the effectiveness of infection prevention measures are likely to mitigate the COVID-19 outbreak in Wuhan. Conclusions:​ Through an imitating social learning process, individual-level behavioral change on taking infection prevention actions have the potentials to significantly reduce the COVID-19 outbreak in terms of size and timing at city-level. Timely and substantially resources and supports for improving the willingness-to-act and conducts of self-administered infection prevention actions are recommended to reduce to the COVID-19 associated risks.<​br>​
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 +</​html>​
oa_db/covid19_forecasting_abstracts_pg6.txt · Last modified: 2020/06/27 20:10 by bpwhite