User Tools

Site Tools


oa_db:covid19_forecasting_abstracts_pg4

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

oa_db:covid19_forecasting_abstracts_pg4 [2020/06/27 17:57] (current)
bpwhite created
Line 1: Line 1:
 +===== COVID-19 Forecasting Abstracts - Page 4 =====
  
 +[[oa_db:​covid19_forecasting_abstracts|Back to Table of Contents]]
 +
 +[[oa_db:​covid19_forecasting_abstracts_pg3|Page 3]] | [[oa_db:​covid19_forecasting_abstracts_pg5|Page 5]]
 +
 +<​html>​
 +
 +----------------------------------------------------------------------<​br>​
 +<​b>​[116] Title: </​b>​Eco-epidemiological assessment of the COVID-19 epidemic in China, January-February 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​11.35<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-14<​br>​
 +<​b>​Publisher:​ </​b>​Global Health Action<​br>​
 +<​b>​Keywords:​ </b>, covid19, china, sars-cov-2, corona virus, weather, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.tandfonline.com/​doi/​full/​10.1080/​16549716.2020.1760490">​https://​www.tandfonline.com/​doi/​full/​10.1080/​16549716.2020.1760490</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Background: The outbreak of COVID-19 in China in early 2020 provides a rich data source for exploring the ecological determinants of this new infection, which may be of relevance as the pandemic develops.Objectives:​ Assessing the spread of the COVID-19 across China, in relation to associations between cases and ecological factors including population density, temperature,​ solar radiation and precipitation.Methods:​ Open-access COVID-19 case data include 18,069 geo-located cases in China during January and February 2020, which were mapped onto a 0.25 degrees latitude/​longitude grid together with population and weather data (temperature,​ solar radiation and precipitation). Of 15,539 grid cells, 559 (3.6%) contained at least one case, and these were used to construct a Poisson regression model of cell-weeks. Weather parameters were taken for the preceding week given the established 5-7 day incubation period for COVID-19. The dependent variable in the Poisson model was incident cases per cell-week and exposure was cell population, allowing for clustering of cells over weeks, to give incidence rate ratios.Results:​ The overall COVID-19 incidence rate in cells with confirmed cases was 0.12 per 1,000. There was a single confirmed case in 113/559 (20.2%) of cells, while two grid cells recorded over 1,000 confirmed cases. Weekly means of maximum daily temperature varied from -28.0 degrees C to 30.1 degrees C, minimum daily temperature from -42.4 degrees C to 23.0 degrees C, maximum solar radiation from 0.04 to 2.74 MJm(-2) and total precipitation from 0 to 72.6 mm. Adjusted incidence rate ratios suggested brighter, warmer and drier conditions were associated with lower incidence.Conclusion:​ Though not demonstrating cause and effect, there were appreciable associations between weather and COVID-19 incidence during the epidemic in China. This does not mean the pandemic will go away with summer weather but demonstrates the importance of using weather conditions in understanding and forecasting the spread of COVID-19.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[117] Title: </​b>​Projecting the Course of COVID-19 in Turkey: A Probabilistic Modeling Approach.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​11.05<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-01<​br>​
 +<​b>​Publisher:​ </​b>​Turkish Journal of Medical Sciences<​br>​
 +<​b>​Keywords:​ </​b>​Medical And Health Sciences, bayesian regression, covid-19, epidemiology,​ forecasting,​ pandemic, projection, turkey, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​http://​online.journals.tubitak.gov.tr/​openAcceptedDocument.htm?​fileID=1322808&​no=287251">​http://​online.journals.tubitak.gov.tr/​openAcceptedDocument.htm?​fileID=1322808&​no=287251</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND/​AIM:​ The COVID-19 Pandemic originated in Wuhan, China, in December 2019 and became one of the worst global health crises ever. While struggling with the unknown nature of this novel coronavirus,​ many researchers and groups attempted to project the progress of the pandemic using empirical or mechanistic models, each one having its drawbacks. The first confirmed cases were announced early in March, and since then, serious containment measures have taken place in Turkey. MATERIALS AND METHODS: Here, we present a different approach, a Bayesian negative binomial multilevel model with mixed effects, for the projection of the COVID-19 pandemic and apply this model to the Turkish case. The model source code is available at https://​github.com/​kansil/​covid-19. We predicted confirmed daily cases and cumulative numbers for June 6th to June 26th with 80%, 95% and 99% prediction intervals (PI). RESULTS: Our projections showed that if we continued to comply with measures and no drastic changes are seen in diagnosis or management protocols, the epidemic curve would tend to decrease in this time interval. Also, the predictive validity analysis suggests that proposed model projections should be in the 95% PI band for the first 12 days of the projections. CONCLUSION: We expect that drastic changes in the course of the COVID-19 in Turkey will cause the model to suffer in predictive validity, and this can be used to monitor the epidemic. We hope that the discussion on these projections and the limitations of the epidemiological forecasting will be beneficial to the medical community, and policy-makers.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[118] Title: </​b>​Excess Mortality Estimation During the COVID-19 Pandemic: Preliminary Data from Portugal.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​10.93<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​Acta Medica Portuguesa<​br>​
 +<​b>​Keywords:​ </​b>​Medical And Health Sciences, coronavirus,​ coronavirus infections, disease outbreaks, mortality, pandemics, portugal, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.actamedicaportuguesa.com/​revista/​index.php/​amp/​article/​view/​13928">​https://​www.actamedicaportuguesa.com/​revista/​index.php/​amp/​article/​view/​13928</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +INTRODUCTION:​ Portugal is experiencing the effects of the COVID-19 pandemic since March 2020. All-causes mortality in Portugal increased during March and April 2020 compared to previous years, but this increase is not explained by COVID-19 reported deaths. The aim of this study was to analyze and consider other criteria for estimating excessive all-cause mortality during the early COVID-19 pandemic period. MATERIAL AND METHODS: Public data was used to estimate excess mortality by age and region between March 1 and April 22, proposing baselines adjusted for the lockdown period. RESULTS: An excess mortality of 2400 to 4000 deaths was observed. Excess mortality was associated with older age groups (over age 65) [corrected]. DISCUSSION: The data suggests a ternary explanation for early excess mortality: COVID-19, non-identified COVID-19 and decrease in access to healthcare. The estimates have implications in terms of communication of non-pharmaceutical actions, for research, and to healthcare professionals. CONCLUSION: Despite the inherent uncertainty,​ the excess mortality occurred between March 1 and April 22 could be 3.5- to 5-fold higher than what can be explained by the official COVID-19 deaths [corrected].<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[119] Title: </​b>​Air transportation,​ population density and temperature predict the spread of COVID-19 in Brazil.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​10.45<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-03<​br>​
 +<​b>​Publisher:​ </​b>​PeerJ<​br>​
 +<​b>​Keywords:​ </b>, climate, coronavirus,​ health, pandemic<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​peerj.com/​articles/​9322/">​https://​peerj.com/​articles/​9322/</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +There is evidence that COVID-19, the disease caused by the betacoronavirus SARS-CoV-2, is sensitive to environmental conditions. However, such conditions often correlate with demographic and socioeconomic factors at larger spatial extents, which could confound this inference. We evaluated the effect of meteorological conditions (temperature,​ solar radiation, air humidity and precipitation) on 292 daily records of cumulative number of confirmed COVID-19 cases across the 27 Brazilian capital cities during the 1st month of the outbreak, while controlling for an indicator of the number of tests, the number of arriving flights, population density, proportion of elderly people and average income. Apart from increasing with time, the number of confirmed cases was mainly related to the number of arriving flights and population density, increasing with both factors. However, after accounting for these effects, the disease was shown to be temperature sensitive: there were more cases in colder cities and days, and cases accumulated faster at lower temperatures. Our best estimate indicates that a 1 degrees C increase in temperature has been associated with a decrease in confirmed cases of 8%. The quality of the data and unknowns limit the analysis, but the study reveals an urgent need to understand more about the environmental sensitivity of the disease to predict demands on health services in different regions and seasons.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[120] Title: </​b>​Case fatality risk of the first pandemic wave of novel coronavirus disease 2019 (COVID-19) in China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​9.93<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-01<​br>​
 +<​b>​Publisher:​ </​b>​Clinical Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Biological Sciences, china, novel coronavirus diseases 2019, case fatality risk, severe acute respiratory syndrome coronavirus 2, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​http://​fdslive.oup.com/​www.oup.com/​pdf/​production_in_progress.pdf">​http://​fdslive.oup.com/​www.oup.com/​pdf/​production_in_progress.pdf</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +OBJECTIVE: To assess the case fatality risk (CFR) of COVID-19 in mainland China, stratified by region and clinical category, and estimate key time-to-event intervals. METHODS: We collected individual information and aggregated data on COVID-19 cases from publicly available official sources from December 29, 2019 to April 17, 2020. We accounted for right-censoring to estimate the CFR and explored the risk factors for mortality. We fitted Weibull, gamma, and lognormal distributions to time-to-event data using maximum-likelihood estimation. RESULTS: We analyzed 82,719 laboratory-confirmed cases reported in mainland China, including 4,632 deaths, and 77,029 discharges. The estimated CFR was 5.65% (95%CI: 5.50%-5.81%) nationally, with highest estimate in Wuhan (7.71%), and lowest in provinces outside Hubei (0.86%). The fatality risk among critical patients was 3.6 times that of all patients, and 0.8-10.3 fold higher than that of mild-to-severe patients. Older age (OR 1.14 per year; 95%CI: 1.11-1.16), and being male (OR 1.83; 95%CI: 1.10-3.04) were risk factors for mortality. The time from symptom onset to first healthcare consultation,​ time from symptom onset to laboratory confirmation,​ and time from symptom onset to hospitalization were consistently longer for deceased patients than for those who recovered. CONCLUSIONS:​ Our CFR estimates based on laboratory-confirmed cases ascertained in mainland China suggest that COVID-19 is more severe than the 2009 H1N1 influenza pandemic in hospitalized patients, particularly in Wuhan. Our study provides a comprehensive picture of the severity of the first wave of the pandemic in China. Our estimates can help inform models and the global response to COVID-19.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[121] Title: </​b>​Inferring the number of COVID-19 cases from recently reported deaths.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​9.85<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-27<​br>​
 +<​b>​Publisher:​ </​b>​Wellcome Open Research<​br>​
 +<​b>​Keywords:​ </b>, sars-cov-2, covid-19, epidemics, estimation, modelling, outbreak, statistics<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​wellcomeopenresearch.org/​articles/​5-78/​v1">​https://​wellcomeopenresearch.org/​articles/​5-78/​v1</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +We estimate the number of COVID-19 cases from newly reported deaths in a population without previous reports. Our results suggest that by the time a single death occurs, hundreds to thousands of cases are likely to be present in that population. This suggests containment via contact tracing will be challenging at this point, and other response strategies should be considered. Our approach is implemented in a publicly available, user-friendly,​ online tool.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[122] Title: </​b>​Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​9.33<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-14<​br>​
 +<​b>​Publisher:​ </​b>​JMIR Public Health and Surveillance<​br>​
 +<​b>​Keywords:​ </b>, covid-19, google trends, lstm, coronavirus,​ incidence, linear regression, outbreak, pandemic, prediction, public health<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​publichealth.jmir.org/​2020/​2/​e18828/">​https://​publichealth.jmir.org/​2020/​2/​e18828/</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: The recent global outbreak of coronavirus disease (COVID-19) is affecting many countries worldwide. Iran is one of the top 10 most affected countries. Search engines provide useful data from populations,​ and these data might be useful to analyze epidemics. Utilizing data mining methods on electronic resources'​ data might provide a better insight into the COVID-19 outbreak to manage the health crisis in each country and worldwide. OBJECTIVE: This study aimed to predict the incidence of COVID-19 in Iran. METHODS: Data were obtained from the Google Trends website. Linear regression and long short-term memory (LSTM) models were used to estimate the number of positive COVID-19 cases. All models were evaluated using 10-fold cross-validation,​ and root mean square error (RMSE) was used as the performance metric. RESULTS: The linear regression model predicted the incidence with an RMSE of 7.562 (SD 6.492). The most effective factors besides previous day incidence included the search frequency of handwashing,​ hand sanitizer, and antiseptic topics. The RMSE of the LSTM model was 27.187 (SD 20.705). CONCLUSIONS:​ Data mining algorithms can be employed to predict trends of outbreaks. This prediction might support policymakers and health care managers to plan and allocate health care resources accordingly.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[123] Title: </​b>​Estimation of the Transmission Risk of the 2019-nCoV and Its Implication for Public Health Interventions.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​9<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-02-07<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Clinical Medicine<​br>​
 +<​b>​Keywords:​ </b>, seir model, coronavirus,​ infection management and control, mathematical model, travel restriction<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2077-0383/​9/​2/​462">​https://​www.mdpi.com/​2077-0383/​9/​2/​462</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Since the emergence of the first cases in Wuhan, China, the novel coronavirus (2019-nCoV) infection has been quickly spreading out to other provinces and neighboring countries. Estimation of the basic reproduction number by means of mathematical modeling can be helpful for determining the potential and severity of an outbreak and providing critical information for identifying the type of disease interventions and intensity. A deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals,​ and intervention measures. The estimations based on likelihood and model analysis show that the control reproduction number may be as high as 6.47 (95% CI 5.71-7.23). Sensitivity analyses show that interventions,​ such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction adopted by Wuhan on 2019-nCoV infection in Beijing being almost equivalent to increasing quarantine by a 100 thousand baseline value. It is essential to assess how the expensive, resource-intensive measures implemented by the Chinese authorities can contribute to the prevention and control of the 2019-nCoV infection, and how long they should be maintained. Under the most restrictive measures, the outbreak is expected to peak within two weeks (since 23 January 2020) with a significant low peak value. With travel restriction (no imported exposed individuals to Beijing), the number of infected individuals in seven days will decrease by 91.14% in Beijing, compared with the scenario of no travel restriction.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[124] Title: </​b>​Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​9<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-08-01<​br>​
 +<​b>​Publisher:​ </​b>​Infection,​ Genetics & Evolution<​br>​
 +<​b>​Keywords:​ </​b>​Genetics,​ 2008 msc: r181.2, covid-19, coronavirus,​ infectious kinetics, mers, sars, Biochemistry,​ Genetics and Molecular Biology, Health Sciences, Life Sciences, Agricultural and Biological Sciences, Medicine, Immunology and Microbiology<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1567134820301374">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1567134820301374</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The purpose of this paper is to reveal the spread rules of the three pneumonia: COVID-19, SARS and MERS. We compare the new spread characteristics of COVID-19 with those of SARS and MERS. By considering the growth rate and inhibition constant of infectious diseases, their propagation growth model is established. The parameters of the three coronavirus transmission growth models are obtained by nonlinear fitting. Parametric analysis shows that the growth rate of COVID-19 is about twice that of the SARS and MERS, and the COVID-19 doubling cycle is two to three days, suggesting that the number of COVID-19 patients would double in two to three days without human intervention. The infection inhibition constant in Hubei is two orders of magnitude lower than in other regions, which reasonably explains the situation of the COVID-19 outbreak in Hubei.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[125] Title: </​b>​Why is it difficult to accurately predict the COVID-19 epidemic?<​br><​br>​
 +<​b>​Altmetric Score: </​b>​8.95<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-01<​br>​
 +<​b>​Publisher:​ </​b>​Infectious Disease Modelling<​br>​
 +<​b>​Keywords:​ </b>, bayesian inference, covid-19 epidemic in wuhan, model selection, nonidentifiability,​ peak time of epidemic, quarantine, sir and seir models<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2468042720300075">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2468042720300075</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations. Using the Akaike Information Criterion (AIC) for model selection, we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data. This indicates that predictions using more complex models may not be more reliable compared to using a simpler model. We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23, 2020. We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic, and modeling the potential of a second outbreak after the return-to-work in the city.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[126] Title: </​b>​Outbreak dynamics of COVID-19 in China and the United States.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​8.65<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-27<​br>​
 +<​b>​Publisher:​ </​b>​Biomechanics & Modeling in Mechanobiology<​br>​
 +<​b>​Keywords:​ </​b>​Biological and Medical Physics, Biophysics, covid-19, coronavirus,​ epidemiology modeling, network model, seir model, Biochemistry,​ Genetics and Molecular Biology, Life Sciences, Physical Sciences, Mathematics,​ Engineering<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​link.springer.com/​article/​10.1007/​s10237-020-01332-5">​https://​link.springer.com/​article/​10.1007/​s10237-020-01332-5</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +On March 11, 2020, the World Health Organization declared the coronavirus disease 2019, COVID-19, a global pandemic. In an unprecedented collective effort, massive amounts of data are now being collected worldwide to estimate the immediate and long-term impact of this pandemic on the health system and the global economy. However, the precise timeline of the disease, its transmissibility,​ and the effect of mitigation strategies remain incompletely understood. Here we integrate a global network model with a local epidemic SEIR model to quantify the outbreak dynamics of COVID-19 in China and the United States. For the outbreak in China, in [Formula: see text] provinces, we found a latent period of 2.56 +/- 0.72 days, a contact period of 1.47 +/- 0.32 days, and an infectious period of 17.82 +/- 2.95 days. We postulate that the latent and infectious periods are disease-specific,​ whereas the contact period is behavior-specific and can vary between different provinces, states, or countries. For the early stages of the outbreak in the United States, in [Formula: see text] states, we adopted the disease-specific values from China and found a contact period of 3.38 +/- 0.69 days. Our network model predicts that-without the massive political mitigation strategies that are in place today-the United States would have faced a basic reproduction number of 5.30 +/- 0.95 and a nationwide peak of the outbreak on May 10, 2020 with 3 million infections. Our results demonstrate how mathematical modeling can help estimate outbreak dynamics and provide decision guidelines for successful outbreak control. We anticipate that our model will become a valuable tool to estimate the potential of vaccination and quantify the effect of relaxing political measures including total lockdown, shelter in place, and travel restrictions for low-risk subgroups of the population or for the population as a whole.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[127] Title: </​b>​Covid-19 Outbreak Progression in Italian Regions: Approaching the Peak by the End of March in Northern Italy and First Week of April in Southern Italy.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​8.26<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-27<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Environmental Research and Public Health<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ covid-19, italian regions, model, outbreak progression,​ peak, Health Sciences, Physical Sciences, Medicine, Environmental Science<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​1660-4601/​17/​9/​3025">​https://​www.mdpi.com/​1660-4601/​17/​9/​3025</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Epidemiological figures of the SARS-CoV-2 epidemic in Italy are higher than those observed in China. Our objective was to model the SARS-CoV-2 outbreak progression in Italian regions vs. Lombardy to assess the epidemic'​s progression. Our setting was Italy, and especially Lombardy, which is experiencing a heavy burden of SARS-CoV-2 infections. The peak of new daily cases of the epidemic has been reached on the 29th, while was delayed in Central and Southern Italian regions compared to Northern ones. In our models, we estimated the basic reproduction number (R0), which represents the average number of people that can be infected by a person who has already acquired the infection, both by fitting the exponential growth rate of the infection across a 1-month period and also by using day-by-day assessments based on single observations. We used the susceptible-exposed-infected-removed (SEIR) compartment model to predict the spreading of the pandemic in Italy. The two methods provide an agreement of values, although the first method based on exponential fit should provide a better estimation, being computed on the entire time series. Taking into account the growth rate of the infection across a 1-month period, each infected person in Lombardy has involved 4 other people (3.6 based on data of April 23rd) compared to a value of R0 = 2.68, as reported in the Chinese city of Wuhan. According to our model, Piedmont, Veneto, Emilia Romagna, Tuscany and Marche will reach an R0 value of up to 3.5. The R0 was 3.11 for Lazio and 3.14 for the Campania region, where the latter showed the highest value among the Southern Italian regions, followed by Apulia (3.11), Sicily (2.99), Abruzzo (3.0), Calabria (2.84), Basilicata (2.66), and Molise (2.6). The R0 value is decreased in Lombardy and the Northern regions, while it is increased in Central and Southern regions. The expected peak of the SEIR model is set at the end of March, at a national level, with Southern Italian regions reaching the peak in the first days of April. Regarding the strengths and limitations of this study, our model is based on assumptions that might not exactly correspond to the evolution of the epidemic. What we know about the SARS-CoV-2 epidemic is based on Chinese data that seems to be different than those from Italy; Lombardy is experiencing an evolution of the epidemic that seems unique inside Italy and Europe, probably due to demographic and environmental factors.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[128] Title: </​b>​Early transmission dynamics of COVID-19 in a southern hemisphere setting: Lima-Peru: February 29th-March 30th, 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​8.08<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-01<​br>​
 +<​b>​Publisher:​ </​b>​Infectious Disease Modelling<​br>​
 +<​b>​Keywords:​ </b>, covid-19, generalized growth model, reproduction number, sars-cov-2, short-term forecast, transmission potential<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2468042720300130">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2468042720300130</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The COVID-19 pandemic that emerged in Wuhan China has generated substantial morbidity and mortality impact around the world during the last four months. The daily trend in reported cases has been rapidly rising in Latin America since March 2020 with the great majority of the cases reported in Brazil followed by Peru as of April 15th, 2020. Although Peru implemented a range of social distancing measures soon after the confirmation of its first case on March 6th, 2020, the daily number of new COVID-19 cases continues to accumulate in this country. We assessed the early COVID-19 transmission dynamics and the effect of social distancing interventions in Lima, Peru. We estimated the reproduction number, R, during the early transmission phase in Lima from the daily series of imported and autochthonous cases by the date of symptoms onset as of March 30th, 2020. We also assessed the effect of social distancing interventions in Lima by generating short-term forecasts grounded on the early transmission dynamics before interventions were put in place. Prior to the implementation of the social distancing measures in Lima, the local incidence curve by the date of symptoms onset displays near exponential growth dynamics with the mean scaling of growth parameter, p, estimated at 0.9 (95%CI: 0.9,1.0) and the reproduction number at 2.3 (95% CI: 2.0, 2.5). Our analysis indicates that school closures and other social distancing interventions have helped slow down the spread of the novel coronavirus,​ with the nearly exponential growth trend shifting to an approximately linear growth trend soon after the broad scale social distancing interventions were put in place by the government. While the interventions appear to have slowed the transmission rate in Lima, the number of new COVID-19 cases continue to accumulate, highlighting the need to strengthen social distancing and active case finding efforts to mitigate disease transmission in the region.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[129] Title: </​b>​CIRD-F:​ Spread and Influence of COVID-19 in China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​8<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-07<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Shanghai Jiaotong University<​br>​
 +<​b>​Keywords:​ </​b>​Architecture,​ general, capital asset pricing model, coronavirus disease 2019 (covid-19), dummy variable, epidemic prediction model, negative feedback, General<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​link.springer.com/​article/​10.1007/​s12204-020-2168-1">​https://​link.springer.com/​article/​10.1007/​s12204-020-2168-1</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The outbreak of coronavirus disease 2019 (COVID-19) has been spreading rapidly in China and the Chinese government took a series of policies to control the epidemic. Therefore, it will be helpful to predict the tendency of the epidemic and analyze the influence of official policies. Existing models for prediction, such as cabin models and individual-based models, are either oversimplified or too meticulous, and the influence of the epidemic was studied much more than that of official policies. To predict the epidemic tendency, we consider four groups of people, and establish a propagation dynamics model. We also create a negative feedback to quantify the public vigilance to the epidemic. We evaluate the tendency of epidemic in Hubei and China except Hubei separately to predict the situation of the whole country. Experiments show that the epidemic will terminate around 17 March 2020 and the final number of cumulative infections will be about 78 191 (prediction interval, 74 872 to 82 474). By changing the parameters of the model accordingly,​ we demonstrate the control effect of the policies of the government on the epidemic situation, which can reduce about 68% possible infections. At the same time, we use the capital asset pricing model with dummy variable to evaluate the effects of the epidemic and official policies on the revenue of multiple industries.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[130] Title: </​b>​Mathematical modeling of COVID-19 fatality trends: Death kinetics law versus infection-to-death delay rule.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​8<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-07-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos,​ Solitons & Fractals<​br>​
 +<​b>​Keywords:​ </​b>​Mathematical Sciences, corona, optimization,​ pandemic, population kinetics, prediction, sars-cov-2, Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920302915">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920302915</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The COVID-19 pandemic has world-widely motivated numerous attempts to properly adjust classical epidemiological models, namely those of the SEIR-type, to the spreading characteristics of the novel Corona virus. In this context, the fundamental structure of the differential equations making up the SEIR models has remained largely unaltered-presuming that COVID-19 may be just "​another epidemic"​. We here take an alternative approach, by investigating the relevance of one key ingredient of the SEIR models, namely the death kinetics law. The latter is compared to an alternative approach, which we call infection-to-death delay rule. For that purpose, we check how well these two mathematical formulations are able to represent the publicly available country-specific data on recorded fatalities, across a selection of 57 different nations. Thereby, we consider that the model-governing parameters-namely,​ the death transmission coefficient for the death kinetics model, as well as the apparent fatality-to-case fraction and the characteristic fatal illness period for the infection-to-death delay rule-are time-invariant. For 55 out of the 57 countries, the infection-to-death delay rule turns out to represent the actual situation significantly more precisely than the classical death kinetics rule. We regard this as an important step towards making SEIR-approaches more fit for the COVID-19 spreading prediction challenge.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[131] Title: </​b>​Mathematic modeling of COVID-19 in the United States.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​7.83<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-30<​br>​
 +<​b>​Publisher:​ </​b>​Emerging Microbes & Infections<​br>​
 +<​b>​Keywords:​ </b>, covid-19, sars-cov-2, united states, epidemiology,​ modeling<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.tandfonline.com/​doi/​full/​10.1080/​22221751.2020.1760146">​https://​www.tandfonline.com/​doi/​full/​10.1080/​22221751.2020.1760146</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +COVID-19, the worst pandemic in 100 years, has rapidly spread to the entire world in 2 months since its early report in January 2020. Based on the publicly available data sources, we developed a simple mathematic modeling approach to track the outbreaks of COVID-19 in the US and three selected states: New York, Michigan and California. The same approach is applicable to other regions or countries. We hope our work can stimulate more effort in understanding how an outbreak is developing and how big a scope it can be and in what kind of time framework. Such information is critical for outbreak control, resource utilization and re-opening of the normal daily life to citizens in the affected community.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[132] Title: </​b>​The correlation between the spread of COVID-19 infections and weather variables in 30 Chinese provinces and the impact of Chinese government mitigation plans.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​7.76<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-01<​br>​
 +<​b>​Publisher:​ </​b>​European review for medical and pharmacological sciences<​br>​
 +<​b>​Keywords:​ </b>, <br>
 +<​b>​DOI:​ </​b><​a href="​https://​www.europeanreview.org/​article/​21042">​https://​www.europeanreview.org/​article/​21042</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +On February 1, 2020, China announced a novel coronavirus CoVID-19 outbreak to the public. CoVID-19 was classified as an epidemic by the World Health Organization (WHO). Although the disease was discovered and concentrated in Hubei Province, China, it was exported to all of the other Chinese provinces and spread globally. As of this writing, all plans have failed to contain the novel coronavirus disease, and it has continued to spread to the rest of the world. This study aimed to explore and interpret the effect of environmental and metrological variables on the spread of coronavirus disease in 30 provinces in China, as well as to investigate the impact of new China regulations and plans to mitigate further spread of infections. This article forecasts the size of the disease spreading based on time series forecasting. The growing size of CoVID-19 in China for the next 210 days is estimated by predicting the expected confirmed and recovered cases. The results revealed that weather conditions largely influence the spread of coronavirus in most of the Chinese provinces. This study has determined that increasing temperature and short-wave radiation would positively increase the number of confirmed cases, mortality rate, and recovered cases. The findings of this study agree with the results of our previous study.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[133] Title: </​b>​Application of the ARIMA model on the COVID-2019 epidemic dataset.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​7.6<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-01<​br>​
 +<​b>​Publisher:​ </​b>​Data in Brief<​br>​
 +<​b>​Keywords:​ </b>, arima model, covid-2019 epidemic, forecast, infection control<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2352340920302341">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2352340920302341</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective,​ case definition and data collection have to be maintained in real time.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[134] Title: </​b>​Predicting COVID-19 in China Using Hybrid AI Model.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​7.58<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-01<​br>​
 +<​b>​Publisher:​ </​b>​IEEE Transactions on Cybernetics<​br>​
 +<​b>​Keywords:​ </b>, <br>
 +<​b>​DOI:​ </​b><​a href="​https://​ieeexplore.ieee.org/​document/​9090302/">​https://​ieeexplore.ieee.org/​document/​9090302/</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The coronavirus disease 2019 (COVID-19) breaking out in late December 2019 is gradually being controlled in China, but it is still spreading rapidly in many other countries and regions worldwide. It is urgent to conduct prediction research on the development and spread of the epidemic. In this article, a hybrid artificial-intelligence (AI) model is proposed for COVID-19 prediction. First, as traditional epidemic models treat all individuals with coronavirus as having the same infection rate, an improved susceptible-infected (ISI) model is proposed to estimate the variety of the infection rates for analyzing the transmission laws and development trend. Second, considering the effects of prevention and control measures and the increase of the public'​s prevention awareness, the natural language processing (NLP) module and the long short-term memory (LSTM) network are embedded into the ISI model to build the hybrid AI model for COVID-19 prediction. The experimental results on the epidemic data of several typical provinces and cities in China show that individuals with coronavirus have a higher infection rate within the third to eighth days after they were infected, which is more in line with the actual transmission laws of the epidemic. Moreover, compared with the traditional epidemic models, the proposed hybrid AI model can significantly reduce the errors of the prediction results and obtain the mean absolute percentage errors (MAPEs) with 0.52%, 0.38%, 0.05%, and 0.86% for the next six days in Wuhan, Beijing, Shanghai, and countrywide,​ respectively.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[135] Title: </​b>​Epidemiological Characteristics and Forecast of COVID-19 Outbreak in the Republic of Kazakhstan.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​7.55<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-01<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Korean Medical Science<​br>​
 +<​b>​Keywords:​ </b>, covid-19, forecast modeling, kazakhstan, quarantine, workforce, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​jkms.org/​DOIx.php?​id=10.3346/​jkms.2020.35.e227">​https://​jkms.org/​DOIx.php?​id=10.3346/​jkms.2020.35.e227</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: Coronavirus disease 2019 (COVID-19) pandemic entered Kazakhstan on 13 March 2020 and quickly spread over its territory. This study aimed at reporting on the rates of COVID-19 in the country and at making prognoses on cases, deaths, and recoveries through predictive modeling. Also, we attempted to forecast the needs in professional workforce depending on implementation of quarantine measures. METHODS: We calculated both national and local incidence, mortality and case-fatality rates, and made forecast modeling via classic susceptible-exposed-infected-removed (SEIR) model. The Health Workforce Estimator tool was utilized for forecast modeling of health care workers capacity. RESULTS: The vast majority of symptomatic patients had mild disease manifestations and the proportion of moderate disease was around 10%. According to the SEIR model, there will be 156 thousand hospitalized patients due to severe illness and 15.47 thousand deaths at the peak of an outbreak if no measures are implemented. Besides, this will substantially increase the need in professional medical workforce. Still, 50% compliance with quarantine may possibly reduce the deaths up to 3.75 thousand cases and the number of hospitalized up to 9.31 thousand cases at the peak. CONCLUSION: The outcomes of our study could be of interest for policymakers as they help to forecast the trends of COVID-19 outbreak, the demands for professional workforce, and to estimate the consequences of quarantine measures.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[136] Title: </​b>​Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​7.33<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos,​ Solitons & Fractals<​br>​
 +<​b>​Keywords:​ </​b>​Mathematical Sciences, covid-19, corona virus, epidemic transmission,​ long short term memory (lstm) networks, machine learning, time series forecasting,​ Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920302642">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920302642</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +On March 11 (th) 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14 (th) day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[137] Title: </​b>​Modeling COVID-19 Latent Prevalence to Assess a Public Health Intervention at a State and Regional Scale: Retrospective Cohort Study.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​7.33<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-19<​br>​
 +<​b>​Publisher:​ </​b>​JMIR Public Health and Surveillance<​br>​
 +<​b>​Keywords:​ </b>, covid-19, sir model, detection probability,​ forecasting,​ latent prevalence, novel coronavirus 2019, pandemic, public health surveillance<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​publichealth.jmir.org/​2020/​2/​e19353/">​https://​publichealth.jmir.org/​2020/​2/​e19353/</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: Emergence of the coronavirus disease (COVID-19) caught the world off guard and unprepared, initiating a global pandemic. In the absence of evidence, individual communities had to take timely action to reduce the rate of disease spread and avoid overburdening their health care systems. Although a few predictive models have been published to guide these decisions, most have not taken into account spatial differences and have included assumptions that do not match the local realities. Access to reliable information that is adapted to local context is critical for policy makers to make informed decisions during a rapidly evolving pandemic. OBJECTIVE: The goal of this study was to develop an adapted susceptible-infected-removed (SIR) model to predict the trajectory of the COVID-19 pandemic in North Carolina and the Charlotte Metropolitan Region, and to incorporate the effect of a public health intervention to reduce disease spread while accounting for unique regional features and imperfect detection. METHODS: Three SIR models were fit to infection prevalence data from North Carolina and the greater Charlotte Region and then rigorously compared. One of these models (SIR-int) accounted for a stay-at-home intervention and imperfect detection of COVID-19 cases. We computed longitudinal total estimates of the susceptible,​ infected, and removed compartments of both populations,​ along with other pandemic characteristics such as the basic reproduction number. RESULTS: Prior to March 26, disease spread was rapid at the pandemic onset with the Charlotte Region doubling time of 2.56 days (95% CI 2.11-3.25) and in North Carolina 2.94 days (95% CI 2.33-4.00). Subsequently,​ disease spread significantly slowed with doubling times increased in the Charlotte Region to 4.70 days (95% CI 3.77-6.22) and in North Carolina to 4.01 days (95% CI 3.43-4.83). Reflecting spatial differences,​ this deceleration favored the greater Charlotte Region compared to North Carolina as a whole. A comparison of the efficacy of intervention,​ defined as 1 - the hazard ratio of infection, gave 0.25 for North Carolina and 0.43 for the Charlotte Region. In addition, early in the pandemic, the initial basic SIR model had good fit to the data; however, as the pandemic and local conditions evolved, the SIR-int model emerged as the model with better fit. CONCLUSIONS:​ Using local data and continuous attention to model adaptation, our findings have enabled policy makers, public health officials, and health systems to proactively plan capacity and evaluate the impact of a public health intervention. Our SIR-int model for estimated latent prevalence was reasonably flexible, highly accurate, and demonstrated efficacy of a stay-at-home order at both the state and regional level. Our results highlight the importance of incorporating local context into pandemic forecast modeling, as well as the need to remain vigilant and informed by the data as we enter into a critical period of the outbreak.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[138] Title: </​b>​Strengths and limitations of mathematical models in pandemicsthe case of COVID-19 in Chile.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​7.1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-28<​br>​
 +<​b>​Publisher:​ </​b>​Medwave<​br>​
 +<​b>​Keywords:​ </b>, covid-19, epidemiology,​ mathematical models, coronavirus<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.medwave.cl/​link.cgi/​Medwave/​Perspectivas/​Comentario/​7876.act">​https://​www.medwave.cl/​link.cgi/​Medwave/​Perspectivas/​Comentario/​7876.act</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +This short paper focuses on the role of mathematical models to analyze the impact of pandemics on health resources and the different trade-offs that may be included in them. There is a large body of literature suggesting that mathematical modeling may be helpful to estimate how much additional equipment and infrastructure are necessary to mitigate an increase in demand for health services during a large-scale outbreak of an infectious disease. I comment on the crucial role of these models with a special focus on their strengths and limitations.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[139] Title: </​b>​[Estimating the basic reproduction number of COVID-19 in Wuhan, China].<​br><​br>​
 +<​b>​Altmetric Score: </​b>​7<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-03<​br>​
 +<​b>​Publisher:​ </​b>​Zhonghua liu xing bing xue za zhi Zhonghua liuxingbingxue zazhi<​br>​
 +<​b>​Keywords:​ </b>, basic reproduction number, covid-19, transmission rate, 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: The number of confirmed and suspected cases of the COVID-19 in Hubei province is still increasing. However, the estimations of the basic reproduction number of COVID-19 varied greatly across studies. The objectives of this study are 1) to estimate the basic reproduction number (R(0)) of COVID-19 reflecting the infectiousness of the virus and 2) to assess the effectiveness of a range of controlling intervention. Methods: The reported number of daily confirmed cases from January 17 to February 8, 2020 in Hubei province were collected and used for model fit. Four methods, the exponential growth (EG), maximum likelihood estimation (ML), sequential Bayesian method (SB) and time dependent reproduction numbers (TD), were applied to estimate the R(0). Results: Among the four methods, the EG method fitted the data best. The estimated R(0) was 3.49 (95%CI: 3.42-3.58) by using EG method. The R(0) was estimated to be 2.95 (95%CI: 2.86-3.03) after taking control measures. Conclusions:​ In the early stage of the epidemic, it is appropriate to estimate R(0) using the EG method. Meanwhile, timely and effective control measures were warranted to further reduce the spread of COVID-19.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[140] Title: </​b>​icumonitoring.ch:​ a platform for short-term forecasting of intensive care unit occupancy during the COVID-19 epidemic in Switzerland.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​6.95<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-04<​br>​
 +<​b>​Publisher:​ </​b>​Swiss Medical Weekly<​br>​
 +<​b>​Keywords:​ </​b>​Clinical Sciences, , Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​smw.ch/​article/​doi/​smw.2020.20277">​https://​smw.ch/​article/​doi/​smw.2020.20277</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +In Switzerland,​ the COVID-19 epidemic is progressively slowing down owing to &​ldquo;​social distancing&​rdquo;​ measures introduced by the Federal Council on 16 March 2020. However, the gradual ease of these measures may initiate a second epidemic wave, the length and intensity of which are difficult to anticipate. In this context, hospitals must prepare for a potential increase in intensive care unit (ICU) admissions of patients with acute respiratory distress syndrome. Here, we introduce icumonitoring.ch,​ a platform providing hospital-level projections for ICU occupancy. We combined current data on the number of beds and ventilators with canton-level projections of COVID-19 cases from two S-E-I-R models. We disaggregated epidemic projection in each hospital in Switzerland for the number of COVID-19 cases, hospitalisations,​ hospitalisations in ICU, and ventilators in use. The platform is updated every 3-4 days and can incorporate projections from other modelling teams to inform decision makers with a range of epidemic scenarios for future hospital occupancy.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[141] Title: </​b>​Analysis and forecast of COVID-19 spreading in China, Italy and France.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​6.55<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos,​ Solitons & Fractals<​br>​
 +<​b>​Keywords:​ </​b>​Mathematical Sciences, covid-19, epidemic spreading, non linear fitting, population model, Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920301636">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920301636</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +In this note we analyze the temporal dynamics of the coronavirus disease 2019 outbreak in China, Italy and France in the time window 22 / 01 - 15 / 03 / 2020 . A first analysis of simple day-lag maps points to some universality in the epidemic spreading, suggesting that simple mean-field models can be meaningfully used to gather a quantitative picture of the epidemic spreading, and notably the height and time of the peak of confirmed infected individuals. The analysis of the same data within a simple susceptible-infected-recovered-deaths model indicates that the kinetic parameter that describes the rate of recovery seems to be the same, irrespective of the country, while the infection and death rates appear to be more variable. The model places the peak in Italy around March 21(st) 2020, with a peak number of infected individuals of about 26000 (not including recovered and dead) and a number of deaths at the end of the epidemics of about 18,000. Since the confirmed cases are believed to be between 10 and 20% of the real number of individuals who eventually get infected, the apparent mortality rate of COVID-19 falls between 4% and 8% in Italy, while it appears substantially lower, between 1% and 3% in China. Based on our calculations,​ we estimate that 2500 ventilation units should represent a fair figure for the peak requirement to be considered by health authorities in Italy for their strategic planning. Finally, a simulation of the effects of drastic containment measures on the outbreak in Italy indicates that a reduction of the infection rate indeed causes a quench of the epidemic peak. However, it is also seen that the infection rate needs to be cut down drastically and quickly to observe an appreciable decrease of the epidemic peak and mortality rate. This appears only possible through a concerted and disciplined,​ albeit painful, effort of the population as a whole.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[142] Title: </​b>​Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​5.9<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos,​ Solitons & Fractals<​br>​
 +<​b>​Keywords:​ </​b>​Mathematical Sciences, arima, covid-19, decision-making,​ forecasting,​ machine learning, time-series,​ Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920302538">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920302538</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The new Coronavirus (COVID-19) is an emerging disease responsible for infecting millions of people since the first notification until nowadays. Developing efficient short-term forecasting models allow forecasting the number of future cases. In this context, it is possible to develop strategic planning in the public health system to avoid deaths. In this paper, autoregressive integrated moving average (ARIMA), cubist regression (CUBIST), random forest (RF), ridge regression (RIDGE), support vector regression (SVR), and stacking-ensemble learning are evaluated in the task of time series forecasting with one, three, and six-days ahead the COVID-19 cumulative confirmed cases in ten Brazilian states with a high daily incidence. In the stacking-ensemble learning approach, the CUBIST regression, RF, RIDGE, and SVR models are adopted as base-learners and Gaussian process (GP) as meta-learner. The models'​ effectiveness is evaluated based on the improvement index, mean absolute error, and symmetric mean absolute percentage error criteria. In most of the cases, the SVR and stacking-ensemble learning reach a better performance regarding adopted criteria than compared models. In general, the developed models can generate accurate forecasting,​ achieving errors in a range of 0.87%-3.51%,​ 1.02%-5.63%,​ and 0.95%-6.90% in one, three, and six-days-ahead,​ respectively. The ranking of models, from the best to the worst regarding accuracy, in all scenarios is SVR, stacking-ensemble learning, ARIMA, CUBIST, RIDGE, and RF models. The use of evaluated models is recommended to forecasting and monitor the ongoing growth of COVID-19 cases, once these models can assist the managers in the decision-making support systems.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[143] Title: </​b>​Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​5.75<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-07-01<​br>​
 +<​b>​Publisher:​ </​b>​Science of the Total Environment<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ covid-19, coronavirus,​ death rate, inpatient, normal distribution,​ prediction, Environmental Science, Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720319070">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720319070</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The global COVID-19 outbreak is worrisome both for its high rate of spread, and the high case fatality rate reported by early studies and now in Italy. We report a new methodology,​ the Patient Information Based Algorithm (PIBA), for estimating the death rate of a disease in real-time using publicly available data collected during an outbreak. PIBA estimated the death rate based on data of the patients in Wuhan and then in other cities throughout China. The estimated days from hospital admission to death was 13 (standard deviation (SD), 6days). The death rates based on PIBA were used to predict the daily numbers of deaths since the week of February 25, 2020, in China overall, Hubei province, Wuhan city, and the rest of the country except Hubei province. The death rate of COVID-19 ranges from 0.75% to 3% and may decrease in the future. The results showed that the real death numbers had fallen into the predicted ranges. In addition, using the preliminary data from China, the PIBA method was successfully used to estimate the death rate and predict the death numbers of the Korean population. In conclusion, PIBA can be used to efficiently estimate the death rate of a new infectious disease in real-time and to predict future deaths. The spread of 2019-nCoV and its case fatality rate may vary in regions with different climates and temperatures from Hubei and Wuhan. PIBA model can be built based on known information of early patients in different countries.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[144] Title: </​b>​Lockdown,​ one, two, none, or smart. Modeling containing covid-19 infection. A conceptual model.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​5.45<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-08-01<​br>​
 +<​b>​Publisher:​ </​b>​Science of the Total Environment<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ covid 19, epidemic, lockdown, mathematical modeling, system dynamics, Environmental Science, Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720324347">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720324347</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +A mathematical model has been created with the Systems Dynamics methodology. It is based on a SIR model, with the addition of auxiliary and state variables that represent hospital capacity, contacts, contacts with infected, deaths, giving, as a result, a model of four stock variables. Similarly, using piecewise functions, it was possible to model the "​quarantines"​ or lockdowns, and the effectiveness of reduction in the contacts, Results show the decrease in infected people due to the quarantines. The model was simulated for a population of 100,000. The simulations show trends of infections that could occur in three different scenarios: A) one extended lockdown (60 days), B) two medium lockdowns of 30 days, with a 30-day smart lockdown space, and C) an initial 40-day lockdown and then a 30-day smart lockdown. All the lockdowns start on day 25 after the first reported infection. The model presents a compact structure of broad understanding and successful capture of a COVID-19 outbreak and therefore provides an overview to improve knowledge of outbreak trends and quarantine effectiveness in reducing infection.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[145] Title: </​b>​COVID-19:​ Development of a robust mathematical model and simulation package with consideration for ageing population and time delay for control action and resusceptibility.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​5.4<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-10-01<​br>​
 +<​b>​Publisher:​ </​b>​Physica D<br>
 +<​b>​Keywords:​ </​b>​Applied Mathematics,​ covid-19, coronavirus,​ mathematical modelling and simulation, resusceptibility,​ seirs, time delay, Physical Sciences, Physics and Astronomy<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0167278920302700">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0167278920302700</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The current global health emergency triggered by the pandemic COVID-19 is one of the greatest challenges we face in this generation. Computational simulations have played an important role to predict the development of the current pandemic. Such simulations enable early indications on the future projections of the pandemic and is useful to estimate the efficiency of control action in the battle against the SARS-CoV-2 virus. The SEIR model is a well-known method used in computational simulations of infectious viral diseases and it has been widely used to model other epidemics such as Ebola, SARS, MERS, and influenza A. This paper presents a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibility,​ where we can tune the exit conditions in the model to extend prediction on the current projections of the pandemic into three possible outcomes; death, recovery, and recovery with a possibility of resusceptibility. The model also considers specific information such as ageing factor of the population, time delay on the development of the pandemic due to control action measures, as well as resusceptibility with temporal immune response. Owing to huge variations in clinical symptoms exhibited by COVID-19, the proposed model aims to reflect better on the current scenario and case data reported, such that the spread of the disease and the efficiency of the control action taken can be better understood. The model is verified using two case studies based on the real-world data in South Korea and Northern Ireland.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[146] Title: </​b>​AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/​Multimodal Data.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​5.1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-18<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Medical Systems<​br>​
 +<​b>​Keywords:​ </​b>​Health Informatics,​ active learning, artificial intelligence,​ covid-19, cross-population train/test models, machine learning, multitudinal and multimodal data, Health Sciences, Physical Sciences, Computer Science, Health Professions,​ Medicine<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​link.springer.com/​article/​10.1007/​s10916-020-01562-1">​https://​link.springer.com/​article/​10.1007/​s10916-020-01562-1</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The novel coronavirus (COVID-19) outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible global threats. Beside clinical procedures and treatments, since Artificial Intelligence (AI) promises a new paradigm for healthcare, several different AI tools that are built upon Machine Learning (ML) algorithms are employed for analyzing data and decision-making processes. This means that AI-driven tools help identify COVID-19 outbreaks as well as forecast their nature of spread across the globe. However, unlike other healthcare issues, for COVID-19, to detect COVID-19, AI-driven tools are expected to have active learning-based cross-population train/test models that employs multitudinal and multimodal data, which is the primary purpose of the paper.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[147] Title: </​b>​Estimating the infection horizon of COVID-19 in eight countries with a data-driven approach.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​5.08<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos,​ Solitons & Fractals<​br>​
 +<​b>​Keywords:​ </​b>​Mathematical Sciences, covid-19, data-driven,​ imposed measures, infection horizon, Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920302423">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0960077920302423</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The COVID-19 pandemic has affected all countries of the world producing a substantial number of fatalities accompanied by a major disruption in their social, financial and educational organization. The strict disciplinary measures implemented by China were very effective and thus were subsequently adopted by most world countries to various degrees. The infection duration and number of infected persons are of critical importance for the battle against the pandemic. We use the quantitative landscape of the disease spreading in China as a benchmark and utilize infection data from eight countries to estimate the complete evolution of the infection in each of these countries. The analysis predicts successfully both the expected number of daily infections per country and, perhaps more importantly,​ the duration of the epidemic in each country. Our quantitative approach is based on a Gaussian spreading hypothesis that is shown to arise as a result of imposed measures in a simple dynamical infection model. This may have consequences and shed light in the efficiency of policies once the phenomenon is over.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[148] Title: </​b>​Epidemic curve and reproduction number of COVID-19 in Iran.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​5.08<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-18<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Travel Medicine<​br>​
 +<​b>​Keywords:​ </​b>​Clinical Sciences, sars-cov-2, isolation, mitigation, nonpharmaceutical interventions,​ pandemic, public health emergency of international concern, quarantine, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​http://​doi.org/​10.1093/​jtm/​taaa077">​http://​doi.org/​10.1093/​jtm/​taaa077</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +COVID-19 was first reported in Iran on 19 February, 2020. We estimated the initial basic reproduction number to be 4.86. With increasingly stringent public health measures, the effective reproduction number declined to below 1 after 2 months.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[149] Title: </​b>​Forecasting COVID-19-Associated Hospitalizations under Different Levels of Social Distancing in Lombardy and Emilia-Romagna,​ Northern Italy: Results from an Extended SEIR Compartmental Model.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​5.08<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-15<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Clinical Medicine<​br>​
 +<​b>​Keywords:​ </b>, covid-19, sars-cov-2, seir model, coronavirus,​ forecasting,​ lockdown, mathematical modelling, pandemic, public health intervention,​ resurgence<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2077-0383/​9/​5/​1492">​https://​www.mdpi.com/​2077-0383/​9/​5/​1492</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The outbreak of coronavirus disease 2019 (COVID-19) was identified in Wuhan, China, in December 2019. As of 17 April 2020, more than 2 million cases of COVID-19 have been reported worldwide. Northern Italy is one of the world'​s centers of active coronavirus cases. In this study, we predicted the spread of COVID-19 and its burden on hospital care under different conditions of social distancing in Lombardy and Emilia-Romagna,​ the two regions of Italy most affected by the epidemic. To do this, we used a Susceptible-Exposed-Infectious-Recovered (SEIR) deterministic model, which encompasses compartments relevant to public health interventions such as quarantine. A new compartment L was added to the model for isolated infected population, i.e., individuals tested positives that do not need hospital care. We found that in Lombardy restrictive containment measures should be prolonged at least until early July to avoid a resurgence of hospitalizations;​ on the other hand, in Emilia-Romagna the number of hospitalized cases could be kept under a reasonable amount with a higher contact rate. Our results suggest that territory-specific forecasts under different scenarios are crucial to enhance or take new containment measures during the epidemic.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[150] Title: </​b>​Preliminary estimates of the reproduction number of the coronavirus disease (COVID-19) outbreak in Republic of Korea and Italy by 5 March 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​4.75<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Microbiology,​ basic reproduction number, covid-19, coronavirus disease 2019, italy, republic of korea, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1201971220302599">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1201971220302599</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The novel coronavirus disease 2019 (COVID-19) outbreak has caused 6088 cases and 41 deaths in Republic of Korea, and 3144 cases and 107 death in Italy by 5 March 2020, respectively. We modelled the transmission process in the Republic of Korea and Italy with a stochastic model, and estimated the basic reproduction number R0 as 2.6 (95% CI: 2.3-2.9) or 3.2 (95% CI: 2.9-3.5) in the Republic of Korea, under the assumption that the exponential growth starting on 31 January or 5 February 2020, and 2.6 (95% CI: 2.3-2.9) or 3.3 (95% CI: 3.0-3.6) in Italy, under the assumption that the exponential growth starting on 5 February or 10 February 2020, respectively.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[151] Title: </​b>​Chaos theory applied to the outbreak of COVID-19: an ancillary approach to decision making in pandemic context.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​4.7<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-08<​br>​
 +<​b>​Publisher:​ </​b>​Epidemiology & Infection<​br>​
 +<​b>​Keywords:​ </​b>​Public Health And Health Services, coronavirus,​ epidemics, infectious disease control, mathematical modelling, pandemic, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.cambridge.org/​core/​journals/​epidemiology-and-infection/​article/​chaos-theory-applied-to-the-outbreak-of-covid19-an-ancillary-approach-to-decision-making-in-pandemic-context/​07A53305F996B2B9498F25606DB83B0B">​https://​www.cambridge.org/​core/​journals/​epidemiology-and-infection/​article/​chaos-theory-applied-to-the-outbreak-of-covid19-an-ancillary-approach-to-decision-making-in-pandemic-context/​07A53305F996B2B9498F25606DB83B0B</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +While predicting the course of an epidemic is difficult, predicting the course of a pandemic from an emerging virus is even more so. The validity of most predictive models relies on numerous parameters, involving biological and social characteristics often unknown or highly uncertain. Data of the COVID-19 epidemics in China, Japan, South Korea and Italy were used to build up deterministic models without strong assumptions. These models were then applied to other countries to identify the closest scenarios in order to foresee their coming behaviour. The models enabled to predict situations that were confirmed little by little, proving that these tools can be efficient and useful for decision making in a quickly evolving operational context.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[152] Title: </​b>​Estimation of Coronavirus Disease Case-Fatality Risk in Real Time.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​4.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-08-01<​br>​
 +<​b>​Publisher:​ </​b>​Emerging Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Medical Microbiology,​ 2019 novel coronavirus disease, covid-19, sars, sars-cov-2, coronavirus,​ coronavirus disease, respiratory infections, severe acute respiratory syndrome coronavirus 2, viruses, zoonoses, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​wwwnc.cdc.gov/​eid/​article/​26/​8/​20-1096_article">​https://​wwwnc.cdc.gov/​eid/​article/​26/​8/​20-1096_article</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +We ran a simulation comparing 3 methods to calculate case-fatality risk for coronavirus disease using parameters described in previous studies. Case-fatality risk calculated from these methods all are biased at the early stage of the epidemic. When comparing real-time case-fatality risk, the current trajectory of the epidemic should be considered.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[153] Title: </​b>​Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020).<​br><​br>​
 +<​b>​Altmetric Score: </​b>​4.4<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-07-01<​br>​
 +<​b>​Publisher:​ </​b>​Diabetes & Metabolic Syndrome: Clinical Research & Reviews<​br>​
 +<​b>​Keywords:​ </​b>​Clinical Sciences, coronavirus,​ correlation,​ death rates, india, regression, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1871402120300576">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1871402120300576</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +INTRODUCTION:​ and Aims: No valid treatment or preventative strategy has evolved till date to counter the SARS CoV 2 (Novel Coronavirus) epidemic that originated in China in late 2019 and have since wrought havoc on millions across the world with illness, socioeconomic recession and death. This analysis was aimed at tracing a trend related to death counts expected at the 5th and 6th week of the COVID-19 in India. MATERIAL AND METHODS: Validated database was used to procure global and Indian data related to coronavirus and related outcomes. Multiple regression and linear regression analyses were used interchangeably. Since the week 6 death count data was not correlated significantly with any of the chosen inputs, an auto-regression technique was employed to improve the predictive ability of the regression model. RESULTS: A linear regression analysis predicted average week 5 death count to be 211 with a 95% CI: 1.31-2.60). Similarly, week 6 death count, in spite of a strong correlation with input variables, did not pass the test of statistical significance. Using auto-regression technique and using week 5 death count as input the linear regression model predicted week 6 death count in India to be 467, while keeping at the back of our mind the risk of over-estimation by most of the risk-based models. CONCLUSION: According to our analysis, if situation continue in present state; projected death rate (n) is 211 and467 at the end of the 5th and 6th week from now, respectively.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[154] Title: </b>A primer on COVID-19 Mathematical Models.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​4<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-09<​br>​
 +<​b>​Publisher:​ </​b>​Obesity<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ covid-19, dynamic model, forecast, mathematical model, prediction, projection, statistical model, Biochemistry,​ Genetics and Molecular Biology, Health Sciences, Nursing, Life Sciences, Medicine<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1002/​oby.22881">​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1002/​oby.22881</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or COVID-19) disease has led to a wide-spread global pandemic (1). COVID-19 symptoms and mortality are disproportionately more severe in people with obesity and obesity related comorbidities (2, 3). This is of concern for the United States, where ~42% have obesity and of these, 85% have type 2 diabetes.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[155] Title: </​b>​Can we predict the occurrence of COVID-19 cases? Considerations using a simple model of growth.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​3.95<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-08-01<​br>​
 +<​b>​Publisher:​ </​b>​Science of the Total Environment<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ coronavirus,​ cumulative distribution function, pandemic, sars-cov-2, Environmental Science, Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720323512">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720323512</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +This study aimed to present a simple model to follow the evolution of the COVID-19 (CV-19) pandemic in different countries. The cumulative distribution function (CDF) and its first derivative were employed for this task. The simulations showed that it is almost impossible to predict based on the initial CV-19 cases (1st 2nd or 3rd weeks) how the pandemic will evolve. However, the results presented here revealed that this approach can be used as an alternative for the exponential growth model, traditionally employed as a prediction model, and serve as a valuable tool for investigating how protective measures are changing the evolution of the pandemic.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[156] Title: </​b>​[An epidemiological forecast of COVID-19 in Chile based on the generalized SEIR model and the concept of recovered].<​br><​br>​
 +<​b>​Altmetric Score: </​b>​3.85<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-19<​br>​
 +<​b>​Publisher:​ </​b>​Medwave<​br>​
 +<​b>​Keywords:​ </b>, epidemiology,​ mathematical model, public health, coronavirus<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.medwave.cl/​link.cgi/​Medwave/​Revisiones/​Analisis/​7898.act">​https://​www.medwave.cl/​link.cgi/​Medwave/​Revisiones/​Analisis/​7898.act</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The COVID-19 pandemic declared by the World Health Organization (WHO) has generated a wide-ranging debate regarding epidemiological forecasts and the global implications. With the data obtained from the Chilean Ministry of Health (MINSAL), a prospective study was carried out using the generalized SEIR model to estimate the course of COVID-19 in Chile. Three scenarios were estimated: Scenario 1 with official MINSAL data; scenario 2 with official MINSAL data and recovery criteria proposed by international organizations of health; and scenario 3 with official MINSAL data, recovery criteria proposed by international organizations of health, and without considering deaths in the total recovered. There are considerable differences between scenario 1 compared to 2 and 3 in the number of deaths, active patients, and duration of the disease. Scenario 3, considered the most adverse, estimates a total of 11,000 infected people, 1,151 deaths, and that the peak of the disease will occur in the first days of May. We concluded that the concept of recovered may be decisive for the epidemiological forecasts of COVID-19 in Chile.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +
 +</​html>​
oa_db/covid19_forecasting_abstracts_pg4.txt · Last modified: 2020/06/27 17:57 by bpwhite