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 +===== COVID-19 Forecasting Abstracts - Page 3 =====
  
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 +<​b>​[72] Title: </​b>​Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​55.7<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-01<​br>​
 +<​b>​Publisher:​ </​b>​Infectious Disease Modelling<​br>​
 +<​b>​Keywords:​ </b>, covid-19, china, coronavirus,​ phenomenological models, real-time forecasts<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2468042720300051">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2468042720300051</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The initial cluster of severe pneumonia cases that triggered the COVID-19 epidemic was identified in Wuhan, China in December 2019. While early cases of the disease were linked to a wet market, human-to-human transmission has driven the rapid spread of the virus throughout China. The Chinese government has implemented containment strategies of city-wide lockdowns, screening at airports and train stations, and isolation of suspected patients; however, the cumulative case count keeps growing every day. The ongoing outbreak presents a challenge for modelers, as limited data are available on the early growth trajectory, and the epidemiological characteristics of the novel coronavirus are yet to be fully elucidated. We use phenomenological models that have been validated during previous outbreaks to generate and assess short-term forecasts of the cumulative number of confirmed reported cases in Hubei province, the epicenter of the epidemic, and for the overall trajectory in China, excluding the province of Hubei. We collect daily reported cumulative confirmed cases for the 2019-nCoV outbreak for each Chinese province from the National Health Commission of China. Here, we provide 5, 10, and 15 day forecasts for five consecutive days, February 5th through February 9th, with quantified uncertainty based on a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model. Our most recent forecasts reported here, based on data up until February 9, 2020, largely agree across the three models presented and suggest an average range of 7409-7496 additional confirmed cases in Hubei and 1128-1929 additional cases in other provinces within the next five days. Models also predict an average total cumulative case count between 37,415 and 38,028 in Hubei and 11,​588-13,​499 in other provinces by February 24, 2020. Mean estimates and uncertainty bounds for both Hubei and other provinces have remained relatively stable in the last three reporting dates (February 7th - 9th). We also observe that each of the models predicts that the epidemic has reached saturation in both Hubei and other provinces. Our findings suggest that the containment strategies implemented in China are successfully reducing transmission and that the epidemic growth has slowed in recent days.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[73] Title: </​b>​2019 Novel coronavirus:​ where we are and what we know.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​54.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-02-18<​br>​
 +<​b>​Publisher:​ </​b>​Infection<​br>​
 +<​b>​Keywords:​ </​b>​General Practice / Family Medicine, 2019-ncov, covid-19, china, clinical guideline, coronavirus,​ epidemiology,​ literature review, literature survey, novel coronavirus,​ review, virology, wuhan, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​link.springer.com/​article/​10.1007/​s15010-020-01401-y">​https://​link.springer.com/​article/​10.1007/​s15010-020-01401-y</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +There is a current worldwide outbreak of a new type of coronavirus (2019-nCoV),​ which originated from Wuhan in China and has now spread to 17 other countries. Governments are under increased pressure to stop the outbreak spiraling into a global health emergency. At this stage, preparedness,​ transparency,​ and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak sites and from laboratories supporting the investigation. This paper aggregates and consolidates the virology, epidemiology,​ clinical management strategies from both English and Chinese literature, official news channels, and other official government documents. In addition, by fitting the number of infections with a single-term exponential model, we report that the infection is spreading at an exponential rate, with a doubling period of 1.8 days.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[74] Title: </b>A Systematic Review of COVID-19 Epidemiology Based on Current Evidence.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​52.75<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-31<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Clinical Medicine<​br>​
 +<​b>​Keywords:​ </b>, covid-19, sars-cov-2, basic reproduction number, epidemiology,​ incubation period, serial interval, severity<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2077-0383/​9/​4/​967">​https://​www.mdpi.com/​2077-0383/​9/​4/​967</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +As the novel coronavirus (SARS-CoV-2) continues to spread rapidly across the globe, we aimed to identify and summarize the existing evidence on epidemiological characteristics of SARS-CoV-2 and the effectiveness of control measures to inform policymakers and leaders in formulating management guidelines, and to provide directions for future research. We conducted a systematic review of the published literature and preprints on the coronavirus disease (COVID-19) outbreak following predefined eligibility criteria. Of 317 research articles generated from our initial search on PubMed and preprint archives on 21 February 2020, 41 met our inclusion criteria and were included in the review. Current evidence suggests that it takes about 3-7 days for the epidemic to double in size. Of 21 estimates for the basic reproduction number ranging from 1.9 to 6.5, 13 were between 2.0 and 3.0. The incubation period was estimated to be 4-6 days, whereas the serial interval was estimated to be 4-8 days. Though the true case fatality risk is yet unknown, current model-based estimates ranged from 0.3% to 1.4% for outside China. There is an urgent need for rigorous research focusing on the mitigation efforts to minimize the impact on society.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[75] Title: </​b>​First two months of the 2019 Coronavirus Disease (COVID-19) epidemic in China: real-time surveillance and evaluation with a second derivative model.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​48.95<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-02<​br>​
 +<​b>​Publisher:​ </​b>​Global Health Research and Policy<​br>​
 +<​b>​Keywords:​ </​b>​Medicine & Public Health, 2019-ncov, outbreak, covid-19, dynamic modeling, infectious disease epidemic, second derivative<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​ghrp.biomedcentral.com/​articles/​10.1186/​s41256-020-00137-4">​https://​ghrp.biomedcentral.com/​articles/​10.1186/​s41256-020-00137-4</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Background: Similar to outbreaks of many other infectious diseases, success in controlling the novel 2019 coronavirus infection requires a timely and accurate monitoring of the epidemic, particularly during its early period with rather limited data while the need for information increases explosively. Methods: In this study, we used a second derivative model to characterize the coronavirus epidemic in China with cumulatively diagnosed cases during the first 2 months. The analysis was further enhanced by an exponential model with a close-population assumption. This model was built with the data and used to assess the detection rate during the study period, considering the differences between the true infections, detectable and detected cases. Results: Results from the second derivative modeling suggest the coronavirus epidemic as nonlinear and chaotic in nature. Although it emerged gradually, the epidemic was highly responsive to massive interventions initiated on January 21, 2020, as indicated by results from both second derivative and exponential modeling analyses. The epidemic started to decelerate immediately after the massive actions. The results derived from our analysis signaled the decline of the epidemic 14 days before it eventually occurred on February 4, 2020. Study findings further signaled an accelerated decline in the epidemic starting in 14 days on February 18, 2020. Conclusions:​ The coronavirus epidemic appeared to be nonlinear and chaotic, and was responsive to effective interventions. The methods used in this study can be applied in surveillance to inform and encourage the general public, public health professionals,​ clinicians and decision-makers to take coordinative and collaborative efforts to control the epidemic.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[76] Title: </​b>​Reproductive number of the COVID-19 epidemic in Switzerland with a focus on the Cantons of Basel-Stadt and Basel-Landschaft.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​47.38<​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.2020.20271">​https://​smw.ch/​article/​doi/​smw.2020.20271</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The reproductive number in Switzerland was between 1.5 and 2 during the first third of March, and has consistently decreased to around 1. After the announcement of the latest strict measure on 20 March 2020, namely that gatherings of more than five people in public spaces are prohibited, the reproductive number dropped significantly below 1; the authors of this study estimate the reproductive number to be between 0.6 and 0.8 in the first third of April.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[77] Title: </​b>​Forecasting the novel coronavirus COVID-19.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​46.65<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-31<​br>​
 +<​b>​Publisher:​ </​b>​PLoS ONE<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ , Biochemistry,​ Genetics and Molecular Biology, Health Sciences, Life Sciences, Agricultural and Biological Sciences, Medicine<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​journals.plos.org/​plosone/​article?​id=10.1371/​journal.pone.0231236">​https://​journals.plos.org/​plosone/​article?​id=10.1371/​journal.pone.0231236</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +What will be the global impact of the novel coronavirus (COVID-19)? Answering this question requires accurate forecasting the spread of confirmed cases as well as analysis of the number of deaths and recoveries. Forecasting,​ however, requires ample historical data. At the same time, no prediction is certain as the future rarely repeats itself in the same way as the past. Moreover, forecasts are influenced by the reliability of the data, vested interests, and what variables are being predicted. Also, psychological factors play a significant role in how people perceive and react to the danger from the disease and the fear that it may affect them personally. This paper introduces an objective approach to predicting the continuation of the COVID-19 using a simple, but powerful method to do so. Assuming that the data used is reliable and that the future will continue to follow the past pattern of the disease, our forecasts suggest a continuing increase in the confirmed COVID-19 cases with sizable associated uncertainty. The risks are far from symmetric as underestimating its spread like a pandemic and not doing enough to contain it is much more severe than overspending and being over careful when it will not be needed. This paper describes the timeline of a live forecasting exercise with massive potential implications for planning and decision making and provides objective forecasts for the confirmed cases of COVID-19.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[78] Title: </​b>​Modelling the pandemic: attuning models to their contexts.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​44.7<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-21<​br>​
 +<​b>​Publisher:​ </​b>​BMJ Global Health Journal<​br>​
 +<​b>​Keywords:​ </b>, public health<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​gh.bmj.com/​lookup/​doi/​10.1136/​bmjgh-2020-002914">​https://​gh.bmj.com/​lookup/​doi/​10.1136/​bmjgh-2020-002914</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The evidence produced in mathematical models plays a key role in shaping policy decisions in pandemics. A key question is therefore how well pandemic models relate to their implementation contexts. Drawing on the cases of Ebola and influenza, we map how sociological and anthropological research contributes in the modelling of pandemics to consider lessons for COVID-19. We show how models detach from their implementation contexts through their connections with global narratives of pandemic response, and how sociological and anthropological research can help to locate models differently. This potentiates multiple models of pandemic response attuned to their emerging situations in an iterative and adaptive science. We propose a more open approach to the modelling of pandemics which envisages the model as an intervention of deliberation in situations of evolving uncertainty. This challenges the '​business-as-usual'​ of evidence-based approaches in global health by accentuating all science, within and beyond pandemics, as '​emergent'​ and '​adaptive'​.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[79] Title: </​b>​As COVID-19 cases, deaths and fatality rates surge in Italy, underlying causes require investigation.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​38.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-31<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Infection in Developing Countries<​br>​
 +<​b>​Keywords:​ </b>, covid-19, case fatality rates, immunity, italy, novel coronavirus,​ sars-cov-19,​ Health Sciences, Life Sciences, Medicine, Immunology and Microbiology<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.jidc.org/​index.php/​journal/​article/​view/​12734">​https://​www.jidc.org/​index.php/​journal/​article/​view/​12734</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +COVID-19 case fatalities surged during the month of March 2020 in Italy, reaching over 10,000 by 28 March 2020. This number exceeds the number of fatalities in China (3,301) recorded from January to March, even though the number of diagnosed cases was similar (85,000 Italy vs. 80,000 China). Case Fatality Rates (CFR) could be somewhat unreliable because the estimation of total case numbers is limited by several factors, including insufficient testing and limitations in test kits and materials, such as NP swabs and PPE for testers. Sero prevalence of SARS-CoV-2 antibodies may help in more accurate estimations of the total number of cases. Nevertheless,​ the disparity in the differences in the total number of fatalities between Italy and China suggests investigation into several factors, such as demographics,​ sociological interactions,​ availability of medical equipment (ICU beds and PPE), variants in immune proteins (e.g., HLA, IFNs), past immunity to related CoVs, and mutations in SARS-CoV-2, could impact survival of severe COVID-19 illness survival and the number of case fatalities.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[80] Title: </​b>​Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​36.8<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-12<​br>​
 +<​b>​Publisher:​ </​b>​Eurosurveillance<​br>​
 +<​b>​Keywords:​ </b>, baidu index, covid-19, google trends, internet surveillance,​ weibo index, coronavirus,​ Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.eurosurveillance.org/​content/​10.2807/​1560-7917.ES.2020.25.10.2000199">​https://​www.eurosurveillance.org/​content/​10.2807/​1560-7917.ES.2020.25.10.2000199</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The peak of Internet searches and social media data about the coronavirus disease 2019 (COVID-19) outbreak occurred 10-14 days earlier than the peak of daily incidences in China. Internet searches and social media data had high correlation with daily incidences, with the maximum r > 0.89 in all correlations. The lag correlations also showed a maximum correlation at 8-12 days for laboratory-confirmed cases and 6-8 days for suspected cases.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[81] Title: </​b>​1,​000,​000 cases of COVID-19 outside of China: The date predicted by a simple heuristic.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​35.75<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-11-01<​br>​
 +<​b>​Publisher:​ </​b>​Global Epidemiology<​br>​
 +<​b>​Keywords:​ </b>, <br>
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2590113320300079">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2590113320300079</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +We forecast 1,000,000 COVID-19 cases outside of China by March 30, 2020 based on a heuristic and WHO situation reports. We do not model the COVID-19 pandemic; we model only the number of cases. The proposed heuristic is based on a simple observation that the plot of the given data is well approximated by an exponential curve. The exponential curve is used for forecasting the growth of new cases. It has been tested for the last situation report of the last day. Its accuracy has been 1.29% for the last day added and predicted by the 57 previous WHO situation reports (the date 18 March 2020). Prediction, forecast, pandemic, COVID-19, coronavirus,​ exponential growth curve parameter, heuristic, epidemiology,​ extrapolation,​ abductive reasoning, WHO situa- tion report.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[82] Title: </​b>​Three months of COVID-19: A systematic review and meta-analysis.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​34.4<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-18<​br>​
 +<​b>​Publisher:​ </​b>​Reviews in Medical Virology<​br>​
 +<​b>​Keywords:​ </​b>​Medical Microbiology,​ ace-2, covid-19, sars-cov, mathematical modeling, pandemic, Health Sciences, Life Sciences, Medicine, Immunology and Microbiology<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1002/​rmv.2113">​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1002/​rmv.2113</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The pandemic of 2019 novel coronavirus (SARS-CoV-2019),​ reminiscent of the 2002-SARS-CoV outbreak, has completely isolated countries, disrupted health systems and partially paralyzed international trade and travel. In order to be better equipped to anticipate transmission of this virus to new regions, it is imperative to track the progress of the virus over time. This review analyses information on progression of the pandemic in the past 3 months and systematically discusses the characteristics of SARS-CoV-2019 virus including its epidemiologic,​ pathophysiologic,​ and clinical manifestations. Furthermore,​ the review also encompasses some recently proposed conceptual models that estimate the spread of this disease based on the basic reproductive number for better prevention and control procedures. Finally, we shed light on how the virus has endangered the global economy, impacting it both from the supply and demand side.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[83] Title: </​b>​Evaluation of the lockdowns for the SARS-CoV-2 epidemic in Italy and Spain after one month follow up.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​34.3<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-07-01<​br>​
 +<​b>​Publisher:​ </​b>​Science of the Total Environment<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ intensive care unit admissions, interrupted time-series,​ lockdown, mortality, sars-cov-2, Environmental Science, Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720320520">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720320520</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +From the end of February, the SARS-CoV-2 epidemic in Spain has been following the footsteps of that in Italy very closely. We have analyzed the trends of incident cases, deaths, and intensive care unit admissions (ICU) in both countries before and after their respective national lockdowns using an interrupted time-series design. Data was analyzed with quasi-Poisson regression using an interaction model to estimate the change in trends. After the first lockdown, incidence trends were considerably reduced in both countries. However, although the slopes have been flattened for all outcomes, the trends kept rising. During the second lockdown, implementing more restrictive measures for mobility, it has been a change in the trend slopes for both countries in daily incident cases and ICUs. This improvement indicates that the efforts overtaken are being successful in flattening the epidemic curve, and reinforcing the belief that we must hold on.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[84] Title: </​b>​Changes in testing rates could mask the novel coronavirus disease (COVID-19) growth rate.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​33.7<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-01<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Microbiology,​ basic reproduction number, covid-19, growth rate, models, statistical,​ Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1201971220302368">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1201971220302368</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Since the novel coronavirus disease (COVID-19) emerged in December 2019 in China, it has rapidly spread around the world, leading to one of the most significant pandemic events of recent history. Deriving reliable estimates of the COVID-19 epidemic growth rate is quite important to guide the timing and intensity of intervention strategies. Indeed, many studies have quantified the epidemic growth rate using time-series of reported cases during the early phase of the outbreak to estimate the basic reproduction number, R0. Using daily time series of COVID-19 incidence, we illustrate how epidemic curves of reported cases may not always reflect the true epidemic growth rate due to changes in testing rates, which could be influenced by limited diagnostic testing capacity during the early epidemic phase.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[85] Title: </​b>​Potential short-term outcome of an uncontrolled COVID-19 epidemic in Lombardy, Italy, February to March 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​33.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-26<​br>​
 +<​b>​Publisher:​ </​b>​Eurosurveillance<​br>​
 +<​b>​Keywords:​ </b>, covid-19, coronavirus,​ lombardy outbreak, sars-cov-2, modelling, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.eurosurveillance.org/​content/​10.2807/​1560-7917.ES.2020.25.12.2000293">​https://​www.eurosurveillance.org/​content/​10.2807/​1560-7917.ES.2020.25.12.2000293</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Sustained coronavirus disease (COVID-19) transmission is ongoing in Italy, with 7,375 reported cases and 366 deaths by 8 March 2020. We provide a model-based evaluation of patient records from Lombardy, predicting the impact of an uncontrolled epidemic on the healthcare system. It has the potential to cause more than 250,039 (95% credible interval (CrI): 147,​717-459,​890) cases within 3 weeks, including 37,194 (95% CrI: 22,​250-67,​632) patients requiring intensive care. Aggressive containment strategies are required.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[86] Title: </​b>​Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections. The case of China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​32.6<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-09-01<​br>​
 +<​b>​Publisher:​ </​b>​Communications in Nonlinear Science & Numerical Simulation<​br>​
 +<​b>​Keywords:​ </​b>​Applied Mathematics,​ covid-19, coronavirus,​ mathematical model, numerical simulation, pandemic, parameter estimation, sars-cov-2, theta-seihrd model, Mathematics,​ Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1007570420301350">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1007570420301350</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +In this paper we develop a mathematical model for the spread of the coronavirus disease 2019 (COVID-19). It is a new theta-SEIHRD model (not a SIR, SEIR or other general purpose model), which takes into account the known special characteristics of this disease, as the existence of infectious undetected cases and the different sanitary and infectiousness conditions of hospitalized people. In particular, it includes a novel approach that considers the fraction theta of detected cases over the real total infected cases, which allows to study the importance of this ratio on the impact of COVID-19. The model is also able to estimate the needs of beds in hospitals. It is complex enough to capture the most important effects, but also simple enough to allow an affordable identification of its parameters, using the data that authorities report on this pandemic. We study the particular case of China (including Chinese Mainland, Macao, Hong-Kong and Taiwan, as done by the World Health Organization in its reports on COVID-19), the country spreading the disease, and use its reported data to identify the model parameters, which can be of interest for estimating the spread of COVID-19 in other countries. We show a good agreement between the reported data and the estimations given by our model. We also study the behavior of the outputs returned by our model when considering incomplete reported data (by truncating them at some dates before and after the peak of daily reported cases). By comparing those results, we can estimate the error produced by the model when identifying the parameters at early stages of the pandemic. Finally, taking into account the advantages of the novelties introduced by our model, we study different scenarios to show how different values of the percentage of detected cases would have changed the global magnitude of COVID-19 in China, which can be of interest for policy makers.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[87] Title: </​b>​Temporal estimates of case-fatality rate for COVID-19 outbreaks in Canada and the United States.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​32.13<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-22<​br>​
 +<​b>​Publisher:​ </​b>​CMAJ<​br>​
 +<​b>​Keywords:​ </​b>​Medical And Health Sciences, , Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.cmaj.ca/​content/​192/​25/​E666">​https://​www.cmaj.ca/​content/​192/​25/​E666</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: Estimates of the casefatality rate (CFR) associated with coronavirus disease 2019 (COVID-19) vary widely in different population settings. We sought to estimate and compare the COVID-19 CFR in Canada and the United States while adjusting for 2 potential biases in crude CFR. METHODS: We used the daily incidence of confirmed COVID-19 cases and deaths in Canada and the US from Jan. 31 to Apr. 22, 2020. We applied a statistical method to minimize bias in the crude CFR by accounting for the survival interval as the lag time between disease onset and death, while considering reporting rates of COVID-19 cases less than 50% (95% confidence interval 10%-50%). RESULTS: Using data for confirmed cases in Canada, we estimated the crude CFR to be 4.9% on Apr. 22, 2020, and the adjusted CFR to be 5.5% (credible interval [CrI] 4.9%-6.4%). After we accounted for various reporting rates less than 50%, the adjusted CFR was estimated at 1.6% (CrI 0.7%-3.1%). The US crude CFR was estimated to be 5.4% on Apr. 20, 2020, with an adjusted CFR of 6.1% (CrI 5.4%-6.9%). With reporting rates of less than 50%, the adjusted CFR for the US was 1.78 (CrI 0.8%-3.6%). INTERPRETATION:​ Our estimates suggest that, if the reporting rate is less than 50%, the adjusted CFR of COVID-19 in Canada is likely to be less than 2%. The CFR estimates for the US were higher than those for Canada, but the adjusted CFR still remained below 2%. Quantification of case reporting can provide a more accurate measure of the virulence and disease burden of severe acute respiratory syndrome coronavirus 2.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[88] Title: </​b>​Risk Assessment of Novel Coronavirus COVID-19 Outbreaks Outside China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​30.98<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-02-19<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Clinical Medicine<​br>​
 +<​b>​Keywords:​ </b>, covid-19, branching process, compartmental model, interventions,​ novel coronavirus,​ outbreak, risk assessment, transmission,​ travel<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2077-0383/​9/​2/​571">​https://​www.mdpi.com/​2077-0383/​9/​2/​571</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +We developed a computational tool to assess the risks of novel coronavirus outbreaks outside of China. We estimate the dependence of the risk of a major outbreak in a country from imported cases on key parameters such as: (i) the evolution of the cumulative number of cases in mainland China outside the closed areas; (ii) the connectivity of the destination country with China, including baseline travel frequencies,​ the effect of travel restrictions,​ and the efficacy of entry screening at destination;​ and (iii) the efficacy of control measures in the destination country (expressed by the local reproduction number R loc ). We found that in countries with low connectivity to China but with relatively high R loc , the most beneficial control measure to reduce the risk of outbreaks is a further reduction in their importation number either by entry screening or travel restrictions. Countries with high connectivity but low R loc benefit the most from policies that further reduce R loc . Countries in the middle should consider a combination of such policies. Risk assessments were illustrated for selected groups of countries from America, Asia, and Europe. We investigated how their risks depend on those parameters, and how the risk is increasing in time as the number of cases in China is growing.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[89] Title: </​b>​Projected early spread of COVID-19 in Africa through 1 June 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​30.33<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-07<​br>​
 +<​b>​Publisher:​ </​b>​Eurosurveillance<​br>​
 +<​b>​Keywords:​ </b>, africa, covid-19, computer simulation, epidemics, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.eurosurveillance.org/​content/​10.2807/​1560-7917.ES.2020.25.18.2000543">​https://​www.eurosurveillance.org/​content/​10.2807/​1560-7917.ES.2020.25.18.2000543</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +For 45 African countries/​territories already reporting COVID-19 cases before 23 March 2020, we estimate the dates of reporting 1,000 and 10,000 cases. Assuming early epidemic trends without interventions,​ all 45 were likely to exceed 1,000 confirmed cases by the end of April 2020, with most exceeding 10,000 a few weeks later.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[90] Title: </​b>​Monitoring transmissibility and mortality of COVID-19 in Europe.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​30.05<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Microbiology,​ covid-19, control, effective reproduction numbers, europe, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S120197122030182X">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S120197122030182X</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +OBJECTIVES: As a global pandemic is inevitable, real-time monitoring of transmission is vital for containing the spread of COVID-19. The main objective of this study was to report the real-time effective reproduction numbers (R(t)) and case fatality rates (CFR) in Europe. METHODS: Data for this study were obtained mainly from the World Health Organization website, up to March 9, 2020. R(t) were estimated by exponential growth rate (EG) and time-dependent (TD) methods. '​R0'​ package in R was employed to estimate R(t) by fitting the existing epidemic curve. Both the naive CFR (nCFR) and adjusted CFR (aCFR) were estimated. RESULTS: With the EG method, R(t) was 3.27 (95% confidence interval (CI) 3.17-3.38) for Italy, 6.32 (95% CI 5.72-6.99) for France, 6.07 (95% CI 5.51-6.69) for Germany, and 5.08 (95% CI 4.51-5.74) for Spain. With the TD method, the R value for March 9 was 3.10 (95% CI 2.21-4.11) for Italy, 6.56 (95% CI 2.04-12.26) for France, 4.43 (95% CI 1.83-7.92) for Germany, and 3.95 (95% CI 0-10.19) for Spain. CONCLUSIONS:​ This study provides important findings on the early outbreak of COVID-19 in Europe. Due to the recent rapid increase in new cases of COVID-19, real-time monitoring of the transmissibility and mortality in Spain and France is a priority.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[91] Title: </​b>​Propagation analysis and prediction of the COVID-19.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​29.75<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-01<​br>​
 +<​b>​Publisher:​ </​b>​Infectious Disease Modelling<​br>​
 +<​b>​Keywords:​ </b>, covid-19, epidemic control, short-term forecast<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2468042720300087">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2468042720300087</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Based on the official data modeling, this paper studies the transmission process of the Corona Virus Disease 2019 (COVID-19). The error between the model and the official data curve is quite small. At the same time, it realized forward prediction and backward inference of the epidemic situation, and the relevant analysis help relevant countries to make decisions.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[92] Title: </​b>​The relatively young and rural population may limit the spread and severity of COVID-19 in Africa: a modelling study.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​28.95<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-25<​br>​
 +<​b>​Publisher:​ </​b>​BMJ Global Health Journal<​br>​
 +<​b>​Keywords:​ </b>, epidemiology,​ health policy<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​gh.bmj.com/​lookup/​doi/​10.1136/​bmjgh-2020-002699">​https://​gh.bmj.com/​lookup/​doi/​10.1136/​bmjgh-2020-002699</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +INTRODUCTION:​ A novel coronavirus disease 2019 (COVID-19) has spread to all regions of the world. There is great uncertainty regarding how countries'​ characteristics will affect the spread of the epidemic; to date, there are few studies that attempt to predict the spread of the epidemic in African countries. In this paper, we investigate the role of demographic patterns, urbanisation and comorbidities on the possible trajectories of COVID-19 in Ghana, Kenya and Senegal. METHODS: We use an augmented deterministic Susceptible-Infected-Recovered model to predict the true spread of the disease, under the containment measures taken so far. We disaggregate the infected compartment into asymptomatic,​ mildly symptomatic and severely symptomatic to match observed clinical development of COVID-19. We also account for age structures, urbanisation and comorbidities (HIV, tuberculosis,​ anaemia). RESULTS: In our baseline model, we project that the peak of active cases will occur in July, subject to the effectiveness of policy measures. When accounting for the urbanisation,​ and factoring in comorbidities,​ the peak may occur between 2 June and 17 June (Ghana), 22 July and 29 August (Kenya) and, finally, 28 May and 15 June (Senegal). Successful containment policies could lead to lower rates of severe infections. While most cases will be mild, we project in the absence of policies further containing the spread, that between 0.78% and 1.03%, 0.61% and 1.22%, and 0.60% and 0.84% of individuals in Ghana, Kenya and Senegal, respectively,​ may develop severe symptoms at the time of the peak of the epidemic. CONCLUSION: Compared with Europe, Africa'​s younger and rural population may modify the severity of the epidemic. The large youth population may lead to more infections but most of these infections will be asymptomatic or mild, and will probably go undetected. The higher prevalence of underlying conditions must be considered.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[93] Title: </​b>​Estimating the Unreported Number of Novel Coronavirus (2019-nCoV) Cases in China in the First Half of January 2020: A Data-Driven Modelling Analysis of the Early Outbreak.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​26.18<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-02-01<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Clinical Medicine<​br>​
 +<​b>​Keywords:​ </b>, china, modelling, novel coronavirus,​ outbreak, reproduction number, underreporting<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2077-0383/​9/​2/​388">​https://​www.mdpi.com/​2077-0383/​9/​2/​388</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: In December 2019, an outbreak of respiratory illness caused by a novel coronavirus (2019-nCoV) emerged in Wuhan, China and has swiftly spread to other parts of China and a number of foreign countries. The 2019-nCoV cases might have been under-reported roughly from 1 to 15 January 2020, and thus we estimated the number of unreported cases and the basic reproduction number, R0, of 2019-nCoV. METHODS: We modelled the epidemic curve of 2019-nCoV cases, in mainland China from 1 December 2019 to 24 January 2020 through the exponential growth. The number of unreported cases was determined by the maximum likelihood estimation. We used the serial intervals (SI) of infection caused by two other well-known coronaviruses (CoV), Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) CoVs, as approximations of the unknown SI for 2019-nCoV to estimate R0. RESULTS: We confirmed that the initial growth phase followed an exponential growth pattern. The under-reporting was likely to have resulted in 469 (95% CI: 403-540) unreported cases from 1 to 15 January 2020. The reporting rate after 17 January 2020 was likely to have increased 21-fold (95% CI: 18-25) in comparison to the situation from 1 to 17 January 2020 on average. We estimated the R0 of 2019-nCoV at 2.56 (95% CI: 2.49-2.63). CONCLUSION: The under-reporting was likely to have occurred during the first half of January 2020 and should be considered in future investigation.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[94] Title: </​b>​The effectiveness of quarantine and isolation determine the trend of the COVID-19 epidemics in the final phase of the current outbreak in China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​23.88<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Microbiology,​ coronavirus,​ mathematical model, multi-source data, seir model, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1201971220301375">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1201971220301375</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +OBJECTIVES: Since January 23rd 2020, stringent measures for controlling the novel coronavirus epidemics have been gradually enforced and strengthened in mainland China. The detection and diagnosis have been improved as well. However, the daily reported cases staying in a high level make the epidemics trend prediction difficult. METHODS: Since the traditional SEIR model does not evaluate the effectiveness of control strategies, a novel model in line with the current epidemics process and control measures was proposed, utilizing multisource datasets including cumulative number of reported, death, quarantined and suspected cases. RESULTS: Results show that the trend of the epidemics mainly depends on quarantined and suspected cases. The predicted cumulative numbers of quarantined and suspected cases nearly reached static states and their inflection points have already been achieved, with the epidemics peak coming soon. The estimated effective reproduction numbers using model-free and model-based methods are decreasing, as well as new infections, while new reported cases are increasing. Most infected cases have been quarantined or put in suspected class, which has been ignored in existing models. CONCLUSIONS:​ The uncertainty analyses reveal that the epidemics is still uncertain and it is important to continue enhancing the quarantine and isolation strategy and improving the detection rate in mainland China.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[95] Title: </​b>​Doubling Time of the COVID-19 Epidemic by Province, China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​23.08<​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, china, sars-cov-2, coronavirus,​ epidemiology,​ infectious disease transmission,​ respiratory infections, severe acute respiratory syndrome coronavirus 2, viruses, zoonoses, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​wwwnc.cdc.gov/​eid/​404.html?​aspxerrorpath=/​eid/​article/​26/​8/​20-0219_article">​https://​wwwnc.cdc.gov/​eid/​404.html?​aspxerrorpath=/​eid/​article/​26/​8/​20-0219_article</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +In China, the doubling time of the coronavirus disease epidemic by province increased during January 20-February 9, 2020. Doubling time estimates ranged from 1.4 (95% CI 1.2-2.0) days for Hunan Province to 3.1 (95% CI 2.1-4.8) days for Xinjiang Province. The estimate for Hubei Province was 2.5 (95% CI 2.4-2.6) days.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[96] Title: </​b>​Mathematical Modelling to Assess the Impact of Lockdown on COVID-19 Transmission in India: Model Development and Validation.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​23.05<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-07<​br>​
 +<​b>​Publisher:​ </​b>​JMIR Public Health and Surveillance<​br>​
 +<​b>​Keywords:​ </b>, coronavirus,​ covid-19, epidemic, mathematical modelling, pandemic, sars<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​publichealth.jmir.org/​2020/​2/​e19368/">​https://​publichealth.jmir.org/​2020/​2/​e19368/</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: The World Health Organization has declared the novel coronavirus disease (COVID-19) to be a public health emergency; at present, India is facing a major threat of community spread. We developed a mathematical model for investigating and predicting the effects of lockdown on future COVID-19 cases with a specific focus on India. OBJECTIVE: The objective of this work was to develop and validate a mathematical model and to assess the impact of various lockdown scenarios on COVID-19 transmission in India. METHODS: A model consisting of a framework of ordinary differential equations was developed by incorporating the actual reported cases in 14 countries. After validation, the model was applied to predict COVID-19 transmission in India for different intervention scenarios in terms of lockdown for 4, 14, 21, 42, and 60 days. We also assessed the situations of enhanced exposure due to aggregation of individuals in transit stations and shopping malls before the lockdown. RESULTS: The developed model is efficient in predicting the number of COVID-19 cases compared to the actual reported cases in 14 countries. For India, the model predicted marked reductions in cases for the intervention periods of 14 and 21 days of lockdown and significant reduction for 42 days of lockdown. Such intervention exceeding 42 days does not result in measurable improvement. Finally, for the scenario of "panic shopping"​ or situations where there is a sudden increase in the factors leading to higher exposure to infection, the model predicted an exponential transmission,​ resulting in failure of the considered intervention strategy. CONCLUSIONS:​ Implementation of a strict lockdown for a period of at least 21 days is expected to reduce the transmission of COVID-19. However, a further extension of up to 42 days is required to significantly reduce the transmission of COVID-19 in India. Any relaxation in the lockdown may lead to exponential transmission,​ resulting in a heavy burden on the health care system in the country.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[97] Title: </​b>​Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13-23, 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​22.75<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-02-22<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Clinical Medicine<​br>​
 +<​b>​Keywords:​ </b>, covid-19, china, coronavirus,​ phenomenological models, real-time forecasts, sub-epidemic model<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2077-0383/​9/​2/​596">​https://​www.mdpi.com/​2077-0383/​9/​2/​596</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The ongoing COVID-19 epidemic continues to spread within and outside of China, despite several social distancing measures implemented by the Chinese government. Limited epidemiological data are available, and recent changes in case definition and reporting further complicate our understanding of the impact of the epidemic, particularly in the epidemic'​s epicenter. Here we use previously validated phenomenological models to generate short-term forecasts of cumulative reported cases in Guangdong and Zhejiang, China. Using daily reported cumulative case data up until 13 February 2020 from the National Health Commission of China, we report 5- and 10-day ahead forecasts of cumulative case reports. Specifically,​ we generate forecasts using a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model, which have each been previously used to forecast outbreaks due to different infectious diseases. Forecasts from each of the models suggest the outbreaks may be nearing extinction in both Guangdong and Zhejiang; however, the sub-epidemic model predictions also include the potential for further sustained transmission,​ particularly in Zhejiang. Our 10-day forecasts across the three models predict an additional 65-81 cases (upper bounds: 169-507) in Guangdong and an additional 44-354 (upper bounds: 141-875) cases in Zhejiang by February 23, 2020. In the best-case scenario, current data suggest that transmission in both provinces is slowing down.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[98] Title: </​b>​Understanding Unreported Cases in the COVID-19 Epidemic Outbreak in Wuhan, China, and the Importance of Major Public Health Interventions.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​21.63<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-08<​br>​
 +<​b>​Publisher:​ </​b>​Biology<​br>​
 +<​b>​Keywords:​ </b>, corona virus, epidemic mathematical model, isolation, public closings, quarantine, reported and unreported cases<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2079-7737/​9/​3/​50">​https://​www.mdpi.com/​2079-7737/​9/​3/​50</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +We develop a mathematical model to provide epidemic predictions for the COVID-19 epidemic in Wuhan, China. We use reported case data up to 31 January 2020 from the Chinese Center for Disease Control and Prevention and the Wuhan Municipal Health Commission to parameterize the model. From the parameterized model, we identify the number of unreported cases. We then use the model to project the epidemic forward with varying levels of public health interventions. The model predictions emphasize the importance of major public health interventions in controlling COVID-19 epidemics.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[99] Title: </​b>​Early estimation of the case fatality rate of COVID-19 in mainland China: a data-driven analysis.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​20.88<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-02-01<​br>​
 +<​b>​Publisher:​ </​b>​Annals of Translational Medicine<​br>​
 +<​b>​Keywords:​ </b>, 2019 novel coronavirus,​ covid-19, case fatality rate (cfr), severe acute respiratory syndrome coronavirus-2 (sars-cov-2)<​br>​
 +<​b>​DOI:​ </​b><​a href="​http://​atm.amegroups.com/​article/​view/​36613/​html">​http://​atm.amegroups.com/​article/​view/​36613/​html</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Background: An ongoing outbreak of pneumonia caused by a novel coronavirus [severe acute respiratory syndrome coronavirus (SARS-CoV)-2],​ named COVID-19, hit a major city of China, Wuhan in December 2019 and subsequently spread to other provinces/​regions of China and overseas. Several studies have been done to estimate the basic reproduction number in the early phase of this outbreak, yet there are no reliable estimates of case fatality rate (CFR) for COVID-19 to date. Methods: In this study, we used a purely data-driven statistical method to estimate the CFR in the early phase of the COVID-19 outbreak. Daily numbers of laboratory-confirmed COVID-19 cases and deaths were collected from January 10 to February 3, 2020 and divided into three clusters: Wuhan city, other cities of Hubei province, and other provinces of mainland China. Simple linear regression model was applied to estimate the CFR from each cluster. Results: We estimated that CFR during the first weeks of the epidemic ranges from 0.15% (95% CI: 0.12-0.18%) in mainland China excluding Hubei through 1.41% (95% CI: 1.38-1.45%) in Hubei province excluding the city of Wuhan to 5.25% (95% CI: 4.98-5.51%) in Wuhan. Conclusions:​ Our early estimates suggest that the CFR of COVID-19 is lower than the previous coronavirus epidemics caused by SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV).<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[100] Title: </​b>​Estimation of effects of nationwide lockdown for containing coronavirus infection on worsening of glycosylated haemoglobin and increase in diabetes-related complications:​ A simulation model using multivariate regression analysis.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​19.25<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-07-01<​br>​
 +<​b>​Publisher:​ </​b>​Diabetes & Metabolic Syndrome: Clinical Research & Reviews<​br>​
 +<​b>​Keywords:​ </​b>​Clinical Sciences, complications,​ diabetes, disaster, lockdown, multivariate regression analysis, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1871402120300540">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1871402120300540</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +INTRODUCTION:​ and aims: To prevent the spread of coronavirus disease (COVID19) total lockdown is in place in India from March 24, 2020 for 21 days. In this study, we aim to assess the impact of the duration of the lockdown on glycaemic control and diabetes-related complications. MATERIALS AND METHODS: A systematic search was conducted using Cochrane library. A simulation model was created using glycemic data from previous disasters (taken as similar in impact to current lockdown) taking baseline HBA1c and diabetes-related complications data from India-specific database. A multivariate regression analysis was conducted to analyse the relationship between the duration of lockdown and glycaemic targets & diabetes-related complications. RESULTS: The predictive model was extremely robust (R2 = 0.99) and predicted outcomes for period of lockdown up to 90 days. The predicted increment in HBA1c from baseline at the end of 30 days and 45 days lockdown was projected as 2.26% & 3.68% respectively. Similarly, the annual predicted percentage increase in complication rates at the end of 30-day lockdown was 2.8% for non-proliferative diabetic retinopathy,​ 2.9% for proliferative diabetic retinopathy,​ 1.5% for retinal photocoagulation,​ 9.3% for microalbuminuria,​ 14.2% for proteinuria,​ 2.9% for peripheral neuropathy, 10.5% for lower extremity amputation, 0.9% for myocardial infarction, 0.5% for stroke and 0.5% for infections. CONCLUSION: The duration of lockdown is directly proportional to the worsening of glycaemic control and diabetes-related complications. Such increase in diabetes-related complications will put additional load on overburdened healthcare system, and also increase COVID19 infections in patients with such uncontrolled glycemia.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[101] Title: </​b>​Estimating the effects of asymptomatic and imported patients on COVID-19 epidemic using mathematical modeling.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​18<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-10<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Medical Virology<​br>​
 +<​b>​Keywords:​ </​b>​Medical Microbiology,​ computer modeling, coronavirus,​ epidemiology,​ Health Sciences, Life Sciences, Medicine, Immunology and Microbiology<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1002/​jmv.25939">​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1002/​jmv.25939</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The epidemic of Coronavirus Disease 2019 has been a serious threat to public health worldwide. Data from 23 January to 31 March at Jiangsu and Anhui provinces in China were collected. We developed an adjusted model with two novel features: the asymptomatic population and threshold behavior in recovery. Unbiased parameter estimation identified faithful model fitting. Our model predicted that the epidemic for asymptomatic patients (ASP) was similar in both provinces. The latent periods and outbreak sizes are extremely sensitive to strongly controlled interventions such as isolation and quarantine for both asymptomatic and imported cases. We predicted that ASP serve as a more severe factor with faster outbreaks and larger outbreak sizes compared with imported patients. Therefore, we argued that the currently strict interventions should be continuously implemented,​ and unraveling the asymptomatic pool is critically important before preventive strategy such as vaccines.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[102] Title: </​b>​[Model for a threshold of daily rate reduction of COVID-19 cases to avoid hospital collapse in Chile].<​br><​br>​
 +<​b>​Altmetric Score: </​b>​16.85<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-28<​br>​
 +<​b>​Publisher:​ </​b>​Medwave<​br>​
 +<​b>​Keywords:​ </b>, epidemiology,​ mathematics,​ models, coronavirus<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.medwave.cl/​link.cgi/​Medwave/​Revisiones/​Analisis/​7871.act">​https://​www.medwave.cl/​link.cgi/​Medwave/​Revisiones/​Analisis/​7871.act</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Using a mathematical model, we explore the problem of availability versus overdemand of critical hospital processes (e.g., critical beds) in the face of a steady epidemic expansion such as is occurring from the COVID-19 pandemic. In connection with the statistics of new cases per day, and the assumption of maximum quota, the dynamics associated with the variables number of hospitalized persons (critical occupants) and mortality in the system are explored. A parametric threshold condition is obtained, which involves a parameter associated with the minimum daily effort for not collapsing the system. To exemplify, we include some simulations for the case of Chile, based on a parameter of effort to be sustained with the purpose of lowering the daily infection rate.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[103] Title: </​b>​Number of COVID-19 cases in Chile at 120 days with data at 21/03/2020 and threshold of daily effort to flatten the epi-curve.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​16.1<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-31<​br>​
 +<​b>​Publisher:​ </​b>​Medwave<​br>​
 +<​b>​Keywords:​ </b>, epidemiology,​ mathematical models, coronavirus<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.medwave.cl/​link.cgi/​Medwave/​Revisiones/​Analisis/​7861.act">​https://​www.medwave.cl/​link.cgi/​Medwave/​Revisiones/​Analisis/​7861.act</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +We present a straightforward projection with data up to 21/03/2020 of the evolution of the number of COVID-19 cases per day in Chile using data from the Ministry of Health. Assuming an arithmetical growth in the second variation of the data, we present a cubic adjustment model in which we estimate over 100 000 cases at 120 days consistent with the data recorded to date. Furthermore,​ we use an exponential total case model to represent (using a parameter) the daily effort to reduce a high initial daily growth rate. We simulate this model with different numerical scenarios of feasibility and desired future prevalence.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[104] Title: </​b>​Optimal temperature zone for the dispersal of COVID-19.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​15.35<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-09-01<​br>​
 +<​b>​Publisher:​ </​b>​Science of the Total Environment<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ covid-19, dispersal, pandemic, sars-cov-2, temperature,​ Environmental Science, Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720330047">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720330047</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +It is essential to know the environmental parameters within which the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can survive to understand its global dispersal pattern. We found that 60.0% of the confirmed cases of coronavirus disease 2019 (COVID-19) occurred in places where the air temperature ranged from 5 degrees C to 15 degrees C, with a peak in cases at 11.54 degrees C. Moreover, approximately 73.8% of the confirmed cases were concentrated in regions with absolute humidity of 3g/m(3) to 10g/m(3). SARS-CoV-2 appears to be spreading toward higher latitudes. Our findings suggest that there is an optimal climatic zone in which the concentration of SARS-CoV-2 markedly increases in the ambient environment (including the surfaces of objects). These results strongly imply that the COVID-19 pandemic may spread cyclically and outbreaks may recur in large cities in the mid-latitudes in autumn 2020.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[105] Title: </​b>​Optimal temperature zone for the dispersal of COVID-19.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​15.35<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-09-01<​br>​
 +<​b>​Publisher:​ </​b>​Science of the Total Environment<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ covid-19, dispersal, pandemic, sars-cov-2, temperature,​ Environmental Science, Physical Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720330047">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0048969720330047</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +It is essential to know the environmental parameters within which the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can survive to understand its global dispersal pattern. We found that 60.0% of the confirmed cases of coronavirus disease 2019 (COVID-19) occurred in places where the air temperature ranged from 5 degrees C to 15 degrees C, with a peak in cases at 11.54 degrees C. Moreover, approximately 73.8% of the confirmed cases were concentrated in regions with absolute humidity of 3 g/m(3) to 10 g/m(3). SARS-CoV-2 appears to be spreading toward higher latitudes. Our findings suggest that there is an optimal climatic zone in which the concentration of SARS-CoV-2 markedly increases in the ambient environment (including the surfaces of objects). These results strongly imply that the COVID-19 pandemic may spread cyclically and outbreaks may recur in large cities in the mid-latitudes in autumn 2020.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[106] Title: </​b>​The time scale of asymptomatic transmission affects estimates of epidemic potential in the COVID-19 outbreak.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​15.2<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​Epidemics : The Journal of Infectious Disease Dynamics<​br>​
 +<​b>​Keywords:​ </​b>​Clinical Sciences, asymptomatic transmission,​ basic reproduction number, covid-19, coronavirus disease, sars-cov-2, Health Sciences, Life Sciences, Medicine, Immunology and Microbiology<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1755436520300190">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1755436520300190</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The role of asymptomatic carriers in transmission poses challenges for control of the COVID-19 pandemic. Study of asymptomatic transmission and implications for surveillance and disease burden are ongoing, but there has been little study of the implications of asymptomatic transmission on dynamics of disease. We use a mathematical framework to evaluate expected effects of asymptomatic transmission on the basic reproduction number R0 (i.e., the expected number of secondary cases generated by an average primary case in a fully susceptible population) and the fraction of new secondary cases attributable to asymptomatic individuals. If the generation-interval distribution of asymptomatic transmission differs from that of symptomatic transmission,​ then estimates of the basic reproduction number which do not explicitly account for asymptomatic cases may be systematically biased. Specifically,​ if asymptomatic cases have a shorter generation interval than symptomatic cases, R0 will be over-estimated,​ and if they have a longer generation interval, R0 will be under-estimated. Estimates of the realized proportion of asymptomatic transmission during the exponential phase also depend on asymptomatic generation intervals. Our analysis shows that understanding the temporal course of asymptomatic transmission can be important for assessing the importance of this route of transmission,​ and for disease dynamics. This provides an additional motivation for investigating both the importance and relative duration of asymptomatic transmission.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[107] Title: </​b>​How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​14.31<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-02<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Environmental Research and Public Health<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ big data, artificial intelligence,​ epidemiology,​ public health, viral outbreak, Health Sciences, Physical Sciences, Medicine, Environmental Science<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​1660-4601/​17/​9/​3176">​https://​www.mdpi.com/​1660-4601/​17/​9/​3176</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +SARS-CoV2 is a novel coronavirus,​ responsible for the COVID-19 pandemic declared by the World Health Organization. Thanks to the latest advancements in the field of molecular and computational techniques and information and communication technologies (ICTs), artificial intelligence (AI) and Big Data can help in handling the huge, unprecedented amount of data derived from public health surveillance,​ real-time epidemic outbreaks monitoring, trend now-casting/​forecasting,​ regular situation briefing and updating from governmental institutions and organisms, and health facility utilization information. The present review is aimed at overviewing the potential applications of AI and Big Data in the global effort to manage the pandemic.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[108] Title: </​b>​Impact of nonpharmacological interventions on COVID-19 transmission dynamics in India.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​13.95<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-01<​br>​
 +<​b>​Publisher:​ </​b>​Indian Journal of Public Health<​br>​
 +<​b>​Keywords:​ </​b>​Medical And Health Sciences, covid-19, india, reproduction number, severe acute respiratory syndrome coronavirus 2, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​http://​www.ijph.in/​text.asp?​2020/​64/​6/​142/​285626">​http://​www.ijph.in/​text.asp?​2020/​64/​6/​142/​285626</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Background: As of May 4, 2020, India has reported 42,836 confirmed cases and 1,389 deaths from COVID-19. India'​s multipronged response included nonpharmacological interventions (NPIs) like intensive case-based surveillance,​ expanding testing capacity, social distancing, health promotion, and progressive travel restrictions leading to a complete halt of international and domestic movements (lockdown). Objectives: We studied the impact of NPI on transmission dynamics of COVID-19 epidemic in India and estimated the minimum level of herd immunity required to halt it. Methods: We plotted time distribution,​ estimated basic (R0) and time-dependent effective (Rt) reproduction numbers using software R, and calculated doubling time, the growth rate for confirmed cases from January 30 to May 4, 2020. Herd immunity was estimated using the latest Rtvalue. Results: Time distribution showed a propagated epidemic with subexponential growth. Average growth rate, 21% in the beginning, reduced to 6% after an extended lockdown (May 3). Based on early transmission dynamics, R0was 2.38 (95% confidence interval [CI] =1.79-3.07). Early, unmitigated Rt= 2.51 (95% CI = 2.05-3.14) (March 15) reduced to 1.28 (95% CI = 1.22-1.32) and was 1.83 (95% CI = 1.71-1.93) at the end of lockdown Phase 1 (April 14) and 2 (May 3), respectively. Similarly, average early doubling time (4.3 days) (standard deviation [SD] = 1.86) increased to 5.4 days (SD = 1.03) and 10.9 days (SD = 2.19). Estimated minimum 621 million recoveries are required to halt COVID-19 spread if Rtremains below 2. Conclusion: India'​s early response, especially stringent lockdown, has slowed COVID-19 epidemic. Increased testing, intensive case-based surveillance and containment efforts, modulated movement restrictions while protecting the vulnerable population, and continuous monitoring of transmission dynamics should be a way forward in the absence of effective treatment, vaccine, and undetermined postinfection immunity.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[109] Title: </​b>​Distribution of the SARS-CoV-2 Pandemic and Its Monthly Forecast Based on Seasonal Climate Patterns.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​13.45<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-17<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Environmental Research and Public Health<​br>​
 +<​b>​Keywords:​ </​b>​Multidisciplinary,​ covid-19, sars-cov-2, air pollution, climatic zones, epidemic forecasting,​ pandemic geographical distribution,​ population median age, weather conditions, Health Sciences, Physical Sciences, Medicine, Environmental Science<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​1660-4601/​17/​10/​3493">​https://​www.mdpi.com/​1660-4601/​17/​10/​3493</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +This paper investigates whether the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) pandemic could have been favored by specific weather conditions and other factors. It is found that the 2020 winter weather in the region of Wuhan (Hubei, Central China)-where the virus first broke out in December and spread widely from January to February 2020-was strikingly similar to that of the Northern Italian provinces of Milan, Brescia and Bergamo, where the pandemic broke out from February to March. The statistical analysis was extended to cover the United States of America, which overtook Italy and China as the country with the highest number of confirmed COronaVIrus Disease 19 (COVID-19) cases, and then to the entire world. The found correlation patterns suggest that the COVID-19 lethality significantly worsens (4 times on average) under weather temperatures between 4 composite function C and 12 composite function C and relative humidity between 60% and 80%. Possible co-factors such as median population age and air pollution were also investigated suggesting an important influence of the former but not of the latter, at least, on a synoptic scale. Based on these results, specific isotherm world maps were generated to locate, month by month, the world regions that share similar temperature ranges. From February to March, the 4-12 composite function C isotherm zone extended mostly from Central China toward Iran, Turkey, West-Mediterranean Europe (Italy, Spain and France) up to the United State of America, optimally coinciding with the geographic regions most affected by the pandemic from February to March. It is predicted that in the spring, as the weather gets warm, the pandemic will likely worsen in northern regions (United Kingdom, Germany, East Europe, Russia and North America) while the situation will likely improve in the southern regions (Italy and Spain). However, in autumn, the pandemic could come back and affect the same regions again. The Tropical Zone and the entire Southern Hemisphere, but in restricted colder southern regions, could avoid a strong pandemic because of the sufficiently warm weather during the entire year and because of the lower median age of their population. Google-Earth-Pro interactive-maps covering the entire world are provided as supplementary files.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[110] Title: </b>A first estimation of the impact of public health actions against COVID-19 in Veneto (Italy).<​br><​br>​
 +<​b>​Altmetric Score: </​b>​13.18<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-04<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Epidemiology and Community Health (1978)<​br>​
 +<​b>​Keywords:​ </​b>​Public Health And Health Services, covid-19, italy, public health, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​jech.bmj.com/​lookup/​doi/​10.1136/​jech-2020-214209">​https://​jech.bmj.com/​lookup/​doi/​10.1136/​jech-2020-214209</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: Veneto is one of the first Italian regions where the COVID-19 outbreak started spreading. Containment measures were approved soon thereafter. The present study aims at providing a first look at the impact of the containment measures on the outbreak progression in the Veneto region, Italy. METHODS: A Bayesian changepoint analysis was used to identify the changing speed of the epidemic curve. Then, a piecewise polynomial model was considered to fit the data in the first period before the detected changepoint. In this time interval, that is, the weeks from 27 February to 12 March, a quadratic growth was identified by a generalised additive model (GAM). Finally, the model was used to generate the projection of the expected number of hospitalisations at 2 weeks based on the epidemic speed before the changepoint. Such estimates were then compared with the actual outbreak behaviour. RESULTS: The comparison between the observed and predicted hospitalisation curves highlights a slowdown on the total COVID-19 hospitalisations after the onset of containment measures. The estimated daily slowdown effect of the epidemic growth is estimated as 78 hospitalisations per day as of 27 March (95% CI 75 to 81). CONCLUSIONS:​ The containment strategies seem to have positively impacted the progression of the COVID-19 epidemic outbreak in Veneto.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[111] Title: </​b>​Preliminary prediction of the basic reproduction number of the Wuhan novel coronavirus 2019-nCoV.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​13<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-02-12<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Evidence-Based Medicine<​br>​
 +<​b>​Keywords:​ </​b>​Medical And Health Sciences, 2019 novel coronavirus (2019-ncov),​ basic reproduction number, epidemiology<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1111/​jebm.12376">​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1111/​jebm.12376</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +OBJECTIVES: To estimate the basic reproduction number of the Wuhan novel coronavirus (2019-nCoV). METHODS: Based on the susceptible-exposed-infected-removed (SEIR) compartment model and the assumption that the infectious cases with symptoms occurred before 26 January, 2020 are resulted from free propagation without intervention,​ we estimate the basic reproduction number of 2019-nCoV according to the reported confirmed cases and suspected cases, as well as the theoretical estimated number of infected cases by other research teams, together with some epidemiological determinants learned from the severe acute respiratory syndrome (SARS). RESULTS: The basic reproduction number fall between 2.8 and 3.3 by using the real-time reports on the number of 2019-nCoV-infected cases from People'​s Daily in China and fall between 3.2 and 3.9 on the basis of the predicted number of infected cases from international colleagues. CONCLUSIONS:​ The early transmission ability of 2019-nCoV is close to or slightly higher than SARS. It is a controllable disease with moderate to high transmissibility. Timely and effective control measures are needed to prevent the further transmissions.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[112] Title: </​b>​COVID-19 UK Lockdown Forecasts and R 0.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​12.8<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-29<​br>​
 +<​b>​Publisher:​ </​b>​Frontiers in Public Health<​br>​
 +<​b>​Keywords:​ </b>, bayesian, covid-19, nhs, r0, seir, uk, forecast, modelling<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.frontiersin.org/​articles/​10.3389/​fpubh.2020.00256/​full">​https://​www.frontiersin.org/​articles/​10.3389/​fpubh.2020.00256/​full</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Introduction:​ The first reported UK case of COVID-19 occurred on 30 January 2020. A lockdown from 24 March was partially relaxed on 10 May. One model to forecast disease spread depends on clinical parameters and transmission rates. Output includes the basic reproduction number R 0 and the log growth rate r in the exponential phase. Methods: Office for National Statistics data on deaths in England and Wales is used to estimate r. A likelihood for the transmission parameters is defined from a gaussian density for r using the mean and standard error of the estimate. Parameter samples from the Metropolis-Hastings algorithm lead to an estimate and credible interval for R 0 and forecasts for cases and deaths. Results: The UK initial log growth rate is r = 0.254 with s.e. 0.004. R 0 = 6.94 with 95% CI (6.52, 7.39). In a 12 week lockdown from 24 March with transmission parameters reduced throughout to 5% of their previous values, peaks of around 90,000 severely and 25,000 critically ill patients, and 44,000 cumulative deaths are expected by 16 June. With transmission rising from 5% in mid-April to reach 30%, 50,000 deaths and 475,000 active cases are expected in mid-June. Had such a lockdown begun on 17 March, around 30,000 (28,000, 32,000) fewer cumulative deaths would be expected by 9 June. Discussion: The R 0 estimate is compatible with some international estimates but over twice the value quoted by the UK government. An earlier lockdown could have saved many thousands of lives.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[113] Title: </​b>​[COVID19-Tracker:​ a shiny app to analise data on SARS-CoV-2 epidemic in Spain].<​br><​br>​
 +<​b>​Altmetric Score: </​b>​11.85<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-01<​br>​
 +<​b>​Publisher:​ </​b>​Gaceta Sanitaria<​br>​
 +<​b>​Keywords:​ </b>, data visualization,​ interficie web, poisson regression, regresion de poisson, sars-cov-2, visualizacion de datos, web browser<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0213911120300856">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0213911120300856</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Data visualization is an important tool for exploring and communicating findings in medical research, and specially in epidemiological surveillance. The COVID19-Tracker web application systematically produces daily updated data visualization and analysis of SARS-CoV-2 epidemic in Spain. It collects automatically daily data on COVID-19 diagnosed cases and mortality, from February 24(th), 2020 onwards. Three applications have already been developed: 1) to analyze data trends and estimating short-term projections;​ 2) to estimate the case fatality rate; and 3) to assess the effect of the lockdowns on the data trends. The application may help for a better understanding of the SARS-CoV-2 epidemic data in Spain.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[114] Title: </​b>​Predicting intervention effect for COVID-19 in Japan: state space modeling approach.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​11.75<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-01<​br>​
 +<​b>​Publisher:​ </​b>​BioScience Trends<​br>​
 +<​b>​Keywords:​ </b>, covid-19, sir model, epidemic peak<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.jstage.jst.go.jp/​article/​bst/​advpub/​0/​advpub_2020.03133/​_article">​https://​www.jstage.jst.go.jp/​article/​bst/​advpub/​0/​advpub_2020.03133/​_article</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Japan has observed a surge in the number of confirmed cases of the coronavirus disease (COVID-19) that has caused a serious impact on the society especially after the declaration of the state of emergency on April 7, 2020. This study analyzes the real time data from March 1 to April 22, 2020 by adopting a sophisticated statistical modeling based on the state space model combined with the well-known susceptible-infected-recovered (SIR) model. The model estimation and forecasting are conducted using the Bayesian methodology. The present study provides the parameter estimates of the unknown parameters that critically determine the epidemic process derived from the SIR model and prediction of the future transition of the infectious proportion including the size and timing of the epidemic peak with the prediction intervals that naturally accounts for the uncertainty. Even though the epidemic appears to be settling down during this intervention period, the prediction results under various scenarios using the data up to May 18 reveal that the temporary reduction in the infection rate would still result in a delayed the epidemic peak unless the long-term reproduction number is controlled.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[115] Title: </​b>​Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​11.68<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-13<​br>​
 +<​b>​Publisher:​ </​b>​JMIR Public Health and Surveillance<​br>​
 +<​b>​Keywords:​ </b>, arima models, covid-19, sars-cov2, coronavirus,​ forecast, prediction<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​publichealth.jmir.org/​2020/​2/​e19115/">​https://​publichealth.jmir.org/​2020/​2/​e19115/</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019. OBJECTIVE: The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months. METHODS: The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. RESULTS: The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland,​ Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands,​ Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively,​ which represents acceptable results. CONCLUSIONS:​ The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland,​ and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.<​br>​
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oa_db/covid19_forecasting_abstracts_pg3.txt · Last modified: 2020/06/27 17:55 by bpwhite