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 +===== COVID-19 Forecasting Abstracts - Page 2 =====
  
 +[[oa_db:​covid19_forecasting_abstracts|Back to Table of Contents]]
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 +<​b>​[33] Title: </​b>​Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​485.19<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-01<​br>​
 +<​b>​Publisher:​ </​b>​International Journal of Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Microbiology,​ basic reproduction number, novel coronavirus (2019-ncov),​ Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1201971220300539">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1201971220300539</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUNDS:​ An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia hit a major city in China, Wuhan, December 2019 and subsequently reached other provinces/​regions of China and other countries. We present estimates of the basic reproduction number, R0, of 2019-nCoV in the early phase of the outbreak. METHODS: Accounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. With the estimated intrinsic growth rate (gamma), we estimated R0 by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI. FINDINGS: The early outbreak data largely follows the exponential growth. We estimated that the mean R0 ranges from 2.24 (95%CI: 1.96-2.55) to 3.58 (95%CI: 2.89-4.39) associated with 8-fold to 2-fold increase in the reporting rate. We demonstrated that changes in reporting rate substantially affect estimates of R0. CONCLUSION: The mean estimate of R0 for the 2019-nCoV ranges from 2.24 to 3.58, and is significantly larger than 1. Our findings indicate the potential of 2019-nCoV to cause outbreaks.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[34] Title: </​b>​Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​433.61<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-02-06<​br>​
 +<​b>​Publisher:​ </​b>​Eurosurveillance<​br>​
 +<​b>​Keywords:​ </b>, 2019-ncov, wuhan, exposure, incubation period, novel coronavirus,​ symptom onset, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.eurosurveillance.org/​content/​10.2807/​1560-7917.ES.2020.25.5.2000062">​https://​www.eurosurveillance.org/​content/​10.2807/​1560-7917.ES.2020.25.5.2000062</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +A novel coronavirus (2019-nCoV) is causing an outbreak of viral pneumonia that started in Wuhan, China. Using the travel history and symptom onset of 88 confirmed cases that were detected outside Wuhan in the early outbreak phase, we estimate the mean incubation period to be 6.4 days (95% credible interval: 5.6-7.7), ranging from 2.1 to 11.1 days (2.5th to 97.5th percentile). These values should help inform 2019-nCoV case definitions and appropriate quarantine durations.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[35] Title: </​b>​Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​426.35<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-01<​br>​
 +<​b>​Publisher:​ </​b>​The Lancet Public Health<​br>​
 +<​b>​Keywords:​ </b>, <br>
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S246826672030133X">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S246826672030133X</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: Non-pharmaceutical interventions have been implemented to reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the UK. Projecting the size of an unmitigated epidemic and the potential effect of different control measures has been crucial to support evidence-based policy making during the early stages of the epidemic. This study assesses the potential impact of different control measures for mitigating the burden of COVID-19 in the UK. METHODS: We used a stochastic age-structured transmission model to explore a range of intervention scenarios, tracking 66.4 million people aggregated to 186 county-level administrative units in England, Wales, Scotland, and Northern Ireland. The four base interventions modelled were school closures, physical distancing, shielding of people aged 70 years or older, and self-isolation of symptomatic cases. We also modelled the combination of these interventions,​ as well as a programme of intensive interventions with phased lockdown-type restrictions that substantially limited contacts outside of the home for repeated periods. We simulated different triggers for the introduction of interventions,​ and estimated the impact of varying adherence to interventions across counties. For each scenario, we projected estimated new cases over time, patients requiring inpatient and critical care (ie, admission to the intensive care units [ICU]) treatment, and deaths, and compared the effect of each intervention on the basic reproduction number, R0. FINDINGS: We projected a median unmitigated burden of 23 million (95% prediction interval 13-30) clinical cases and 350 000 deaths (170 000-480 000) due to COVID-19 in the UK by December, 2021. We found that the four base interventions were each likely to decrease R0, but not sufficiently to prevent ICU demand from exceeding health service capacity. The combined intervention was more effective at reducing R0, but only lockdown periods were sufficient to bring R0 near or below 1; the most stringent lockdown scenario resulted in a projected 120 000 cases (46 000-700 000) and 50 000 deaths (9300-160 000). Intensive interventions with lockdown periods would need to be in place for a large proportion of the coming year to prevent health-care demand exceeding availability. INTERPRETATION:​ The characteristics of SARS-CoV-2 mean that extreme measures are probably required to bring the epidemic under control and to prevent very large numbers of deaths and an excess of demand on hospital beds, especially those in ICUs. FUNDING: Medical Research Council.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[36] Title: </​b>​Temperature,​ Humidity, and Latitude Analysis to Estimate Potential Spread and Seasonality of Coronavirus Disease 2019 (COVID-19).<​br><​br>​
 +<​b>​Altmetric Score: </​b>​394.78<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-11<​br>​
 +<​b>​Publisher:​ </​b>​JAMA Network Open<​br>​
 +<​b>​Keywords:​ </​b>​Human Movement And Sports Science, <br>
 +<​b>​DOI:​ </​b><​a href="​http://​doi.org/​10.1001/​jamanetworkopen.2020.11834">​http://​doi.org/​10.1001/​jamanetworkopen.2020.11834</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Importance: Coronavirus disease 2019 (COVID-19) infection has resulted in a global crisis. Investigating the potential association of climate and seasonality with the spread of this infection could aid in preventive and surveillance strategies. Objective: To examine the association of climate with the spread of COVID-19 infection. Design, Setting, and Participants:​ This cohort study examined climate data from 50 cities worldwide with and without substantial community spread of COVID-19. Eight cities with substantial spread of COVID-19 (Wuhan, China; Tokyo, Japan; Daegu, South Korea; Qom, Iran; Milan, Italy; Paris, France; Seattle, US; and Madrid, Spain) were compared with 42 cities that have not been affected or did not have substantial community spread. Data were collected from January to March 10, 2020. Main Outcomes and Measures: Substantial community transmission was defined as at least 10 reported deaths in a country as of March 10, 2020. Climate data (latitude, mean 2-m temperature,​ mean specific humidity, and mean relative humidity) were obtained from ERA-5 reanalysis. Results: The 8 cities with substantial community spread as of March 10, 2020, were located on a narrow band, roughly on the 30 degrees N to 50 degrees N corridor. They had consistently similar weather patterns, consisting of mean temperatures of between 5 and 11 degrees C, combined with low specific humidity (3-6 g/kg) and low absolute humidity (4-7 g/m3). There was a lack of substantial community establishment in expected locations based on proximity. For example, while Wuhan, China (30.8 degrees N) had 3136 deaths and 80757 cases, Moscow, Russia (56.0 degrees N), had 0 deaths and 10 cases and Hanoi, Vietnam (21.2 degrees N), had 0 deaths and 31 cases. Conclusions and Relevance: In this study, the distribution of substantial community outbreaks of COVID-19 along restricted latitude, temperature,​ and humidity measurements was consistent with the behavior of a seasonal respiratory virus. Using weather modeling, it may be possible to estimate the regions most likely to be at a higher risk of substantial community spread of COVID-19 in the upcoming weeks, allowing for concentration of public health efforts on surveillance and containment.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[37] Title: </​b>​Real-time tentative assessment of the epidemiological characteristics of novel coronavirus infections in Wuhan, China, as at 22 January 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​361.39<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-23<​br>​
 +<​b>​Publisher:​ </​b>​Eurosurveillance<​br>​
 +<​b>​Keywords:​ </b>, coronavirus,​ public health, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.eurosurveillance.org/​content/​10.2807/​1560-7917.ES.2020.25.3.2000044">​https://​www.eurosurveillance.org/​content/​10.2807/​1560-7917.ES.2020.25.3.2000044</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +A novel coronavirus (2019-nCoV) causing severe acute respiratory disease emerged recently in Wuhan, China. Information on reported cases strongly indicates human-to-human spread, and the most recent information is increasingly indicative of sustained human-to-human transmission. While the overall severity profile among cases may change as more mild cases are identified, we estimate a risk of fatality among hospitalised cases at 14% (95% confidence interval: 3.9-32%).<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[38] Title: </​b>​The Extent of Transmission of Novel Coronavirus in Wuhan, China, 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​361.35<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-24<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Clinical Medicine<​br>​
 +<​b>​Keywords:​ </b>, emerging infectious diseases, epidemiology,​ foreigner, importation,​ migration, travel<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2077-0383/​9/​2/​330">​https://​www.mdpi.com/​2077-0383/​9/​2/​330</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +A cluster of pneumonia cases linked to a novel coronavirus (2019-nCoV) was reported by China in late December 2019. Reported case incidence has now reached the hundreds, but this is likely an underestimate. As of 24 January 2020, with reports of thirteen exportation events, we estimate the cumulative incidence in China at 5502 cases (95% confidence interval: 3027, 9057). The most plausible number of infections is in the order of thousands, rather than hundreds, and there is a strong indication that untraced exposures other than the one in the epidemiologically linked seafood market in Wuhan have occurred.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[39] Title: </​b>​Communicating the Risk of Death from Novel Coronavirus Disease (COVID-19).<​br><​br>​
 +<​b>​Altmetric Score: </​b>​333.45<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-02-21<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Clinical Medicine<​br>​
 +<​b>​Keywords:​ </b>, emerging infectious diseases, fatality, statistical estimation, virulence, virus<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2077-0383/​9/​2/​580">​https://​www.mdpi.com/​2077-0383/​9/​2/​580</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +To understand the severity of infection for a given disease, it is common epidemiological practice to estimate the case fatality risk, defined as the risk of death among cases. However, there are three technical obstacles that should be addressed to appropriately measure this risk. First, division of the cumulative number of deaths by that of cases tends to underestimate the actual risk because deaths that will occur have not yet observed, and so the delay in time from illness onset to death must be addressed. Second, the observed dataset of reported cases represents only a proportion of all infected individuals and there can be a substantial number of asymptomatic and mildly infected individuals who are never diagnosed. Third, ascertainment bias and risk of death among all those infected would be smaller when estimated using shorter virus detection windows and less sensitive diagnostic laboratory tests. In the ongoing COVID-19 epidemic, health authorities must cope with the uncertainty in the risk of death from COVID-19, and high-risk individuals should be identified using approaches that can address the abovementioned three problems. Although COVID-19 involves mostly mild infections among the majority of the general population, the risk of death among young adults is higher than that of seasonal influenza, and elderly with underlying comorbidities require additional care.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[40] Title: </​b>​The Rate of Underascertainment of Novel Coronavirus (2019-nCoV) Infection: Estimation Using Japanese Passengers Data on Evacuation Flights.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​310.23<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-02-04<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Clinical Medicine<​br>​
 +<​b>​Keywords:​ </b>, ascertainment,​ diagnosis, epidemiology,​ importation,​ statistical inference, travel<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2077-0383/​9/​2/​419">​https://​www.mdpi.com/​2077-0383/​9/​2/​419</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +From 29 to 31 January 2020, a total of 565 Japanese citizens were evacuated from Wuhan, China on three chartered flights. All passengers were screened upon arrival in Japan for symptoms consistent with novel coronavirus (2019-nCoV) infection and tested for presence of the virus. Assuming that the mean detection window of the virus can be informed by the mean serial interval (estimated at 7.5 days), the ascertainment rate of infection was estimated at 9.2% (95% confidence interval: 5.0, 20.0). This indicates that the incidence of infection in Wuhan can be estimated at 20,767 infected individuals,​ including those with asymptomatic and mildly symptomatic infections. The infection fatality risk (IFR)-the actual risk of death among all infected individuals-is therefore 0.3% to 0.6%, which may be comparable to Asian influenza pandemic of 1957-1958.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[41] Title: </​b>​Population-Based Estimates of Chronic Conditions Affecting Risk for Complications from Coronavirus Disease, United States.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​246.53<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-08-01<​br>​
 +<​b>​Publisher:​ </​b>​Emerging Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Medical Microbiology,​ covid-19, sars-cov-2, united states, chronic conditions, complications,​ coronavirus disease, infection, population-based estimates, respiratory infections, risk factors, 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/​20-0679_article">​https://​wwwnc.cdc.gov/​eid/​404.html?​aspxerrorpath=/​eid/​article/​26/​8/​20-0679_article</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +We estimated that 45.4% of US adults are at increased risk for complications from coronavirus disease because of cardiovascular disease, diabetes, respiratory disease, hypertension,​ or cancer. Rates increased by age, from 19.8% for persons 18-29 years of age to 80.7% for persons >80 years of age, and varied by state, race/​ethnicity,​ health insurance status, and employment.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[42] Title: </​b>​Potential impact of seasonal forcing on a SARS-CoV-2 pandemic.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​217.35<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-16<​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.2020.20224">​https://​smw.ch/​article/​doi/​smw.2020.20224</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +A novel coronavirus (SARS-CoV-2) first detected in Wuhan, China, has spread rapidly since December 2019, causing more than 100,000 confirmed infections and 4000 fatalities (as of 10 March 2020). The outbreak has been declared a pandemic by the WHO on Mar 11, 2020. Here, we explore how seasonal variation in transmissibility could modulate a SARS-CoV-2 pandemic. Data from routine diagnostics show a strong and consistent seasonal variation of the four endemic coronaviruses (229E, HKU1, NL63, OC43) and we parameterise our model for SARS-CoV-2 using these data. The model allows for many subpopulations of different size with variable parameters. Simulations of different scenarios show that plausible parameters result in a small peak in early 2020 in temperate regions of the Northern Hemisphere and a larger peak in winter 2020/2021. Variation in transmission and migration rates can result in substantial variation in prevalence between regions. While the uncertainty in parameters is large, the scenarios we explore show that transient reductions in the incidence rate might be due to a combination of seasonal variation and infection control efforts but do not necessarily mean the epidemic is contained. Seasonal forcing on SARS-CoV-2 should thus be taken into account in the further monitoring of the global transmission. The likely aggregated effect of seasonal variation, infection control measures, and transmission rate variation is a prolonged pandemic wave with lower prevalence at any given time, thereby providing a window of opportunity for better preparation of health care systems.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[43] Title: </​b>​Estimating the burden of United States workers exposed to infection or disease: A key factor in containing risk of COVID-19 infection.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​204.88<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-28<​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.0232452">​https://​journals.plos.org/​plosone/​article?​id=10.1371/​journal.pone.0232452</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +INTRODUCTION:​ With the global spread of COVID-19, there is a compelling public health interest in quantifying who is at increased risk of contracting disease. Occupational characteristics,​ such as interfacing with the public and being in close quarters with other workers, not only put workers at high risk for disease, but also make them a nexus of disease transmission to the community. This can further be exacerbated through presenteeism,​ the term used to describe the act of coming to work despite being symptomatic for disease. Quantifying the number of workers who are frequently exposed to infection and disease in the workplace, and understanding which occupational groups they represent, can help to prompt public health risk response and management for COVID-19 in the workplace, and subsequent infectious disease outbreaks. METHODS: To estimate the number of United States workers frequently exposed to infection and disease in the workplace, national employment data (by Standard Occupational Classification) maintained by the Bureau of Labor Statistics (BLS) was merged with a BLS O*NET survey measure reporting how frequently workers in each occupation are exposed to infection or disease at work. This allowed us to estimate the number of United States workers, across all occupations,​ exposed to disease or infection at work more than once a month. RESULTS: Based on our analyses, approximately 10% (14.4 M) of United States workers are employed in occupations where exposure to disease or infection occurs at least once per week. Approximately 18.4% (26.7 M) of all United States workers are employed in occupations where exposure to disease or infection occurs at least once per month. While the majority of exposed workers are employed in healthcare sectors, other occupational sectors also have high proportions of exposed workers. These include protective service occupations (e.g. police officers, correctional officers, firefighters),​ office and administrative support occupations (e.g. couriers and messengers, patient service representatives),​ education occupations (e.g. preschool and daycare teachers), community and social services occupations (community health workers, social workers, counselors),​ and even construction and extraction occupations (e.g. plumbers, septic tank installers, elevator repair). CONCLUSIONS:​ The large number of persons employed in occupations with frequent exposure to infection and disease underscore the importance of all workplaces developing risk response plans for COVID-19. Given the proportion of the United States workforce exposed to disease or infection at work, this analysis also serves as an important reminder that the workplace is a key locus for public health interventions,​ which could protect both workers and the communities they serve.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[44] Title: </​b>​Backcalculating the Incidence of Infection with COVID-19 on the Diamond Princess.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​191.35<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-02-29<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Clinical Medicine<​br>​
 +<​b>​Keywords:​ </b>, emerging infectious diseases, forecasting,​ incidence, statistical estimation, virus<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2077-0383/​9/​3/​657">​https://​www.mdpi.com/​2077-0383/​9/​3/​657</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +To understand the time-dependent risk of infection on a cruise ship, the Diamond Princess, I estimated the incidence of infection with novel coronavirus (COVID-19). The epidemic curve of a total of 199 confirmed cases was drawn, classifying individuals into passengers with and without close contact and crew members. A backcalculation method was employed to estimate the incidence of infection. The peak time of infection was seen for the time period from 2 to 4 February 2020, and the incidence has abruptly declined afterwards. The estimated number of new infections among passengers without close contact was very small from 5 February on which a movement restriction policy was imposed. Without the intervention from 5 February, it was predicted that the cumulative incidence with and without close contact would have been as large as 1373 (95% CI: 570, 2176) and 766 (95% CI: 587, 946) cases, respectively,​ while these were kept to be 102 and 47 cases, respectively. Based on an analysis of illness onset data on board, the risk of infection among passengers without close contact was considered to be very limited. Movement restriction greatly reduced the number of infections from 5 February onwards.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[45] Title: </b>A novel cohort analysis approach to determining the case fatality rate of COVID-19 and other infectious diseases.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​185<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-06-15<​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.0233146">​https://​journals.plos.org/​plosone/​article?​id=10.1371/​journal.pone.0233146</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +As the Coronavirus contagion develops, it is increasingly important to understand the dynamics of the disease. Its severity is best described by two parameters: its ability to spread and its lethality. Here, we combine a mathematical model with a cohort analysis approach to determine the range of case fatality rates (CFR). We use a logistical function to describe the exponential growth and subsequent flattening of COVID-19 CFR that depends on three parameters: the final CFR (L), the CFR growth rate (k), and the onset-to-death interval (t0). Using the logistic model with specific parameters (L, k and t0), we calculate the number of deaths each day for each cohort. We build an objective function that minimizes the root mean square error between the actual and predicted values of cumulative deaths and run multiple simulations by altering the three parameters. Using all of these values, we find out which set of parameters returns the lowest error when compared to the number of actual deaths. We were able to predict the CFR much closer to reality at all stages of the viral outbreak compared to traditional methods. This model can be used far more effectively than current models to estimate the CFR during an outbreak, allowing for better planning. The model can also help us better understand the impact of individual interventions on the CFR. With much better data collection and labeling, we should be able to improve our predictive power even further.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[46] Title: </​b>​Risk for Transportation of Coronavirus Disease from Wuhan to Other Cities in China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​179.33<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-01<​br>​
 +<​b>​Publisher:​ </​b>​Emerging Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Medical Microbiology,​ 2019 novel coronavirus disease, covid-19, china, sars-cov-2, wuhan, coronavirus,​ epidemiology,​ importation,​ outbreak, severe acute respiratory syndrome coronavirus 2, viruses, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​wwwnc.cdc.gov/​eid/​404.html?​aspxerrorpath=/​eid/​article/​26/​5/​20-0146_article">​https://​wwwnc.cdc.gov/​eid/​404.html?​aspxerrorpath=/​eid/​article/​26/​5/​20-0146_article</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +On January 23, 2020, China quarantined Wuhan to contain coronavirus disease (COVID-19). We estimated the probability of transportation of COVID-19 from Wuhan to 369 other cities in China before the quarantine. Expected COVID-19 risk is >50% in 130 (95% CI 89-190) cities and >99% in the 4 largest metropolitan areas.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[47] Title: </​b>​Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​172.53<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-07-01<​br>​
 +<​b>​Publisher:​ </​b>​Lancet Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Clinical Sciences, , Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1473309920302309">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1473309920302309</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BACKGROUND: The coronavirus disease 2019 (COVID-19) epidemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2),​ began in Wuhan city, Hubei province, in December, 2019, and has spread throughout China. Understanding the evolving epidemiology and transmission dynamics of the outbreak beyond Hubei would provide timely information to guide intervention policy. METHODS: We collected individual information from official public sources on laboratory-confirmed cases reported outside Hubei in mainland China for the period of Jan 19 to Feb 17, 2020. We used the date of the fourth revision of the case definition (Jan 27) to divide the epidemic into two time periods (Dec 24 to Jan 27, and Jan 28 to Feb 17) as the date of symptom onset. We estimated trends in the demographic characteristics of cases and key time-to-event intervals. We used a Bayesian approach to estimate the dynamics of the net reproduction number (Rt) at the provincial level. FINDINGS: We collected data on 8579 cases from 30 provinces. The median age of cases was 44 years (33-56), with an increasing proportion of cases in younger age groups and in elderly people (ie, aged >64 years) as the epidemic progressed. The mean time from symptom onset to hospital admission decreased from 4.4 days (95% CI 0.0-14.0) for the period of Dec 24 to Jan 27, to 2.6 days (0.0-9.0) for the period of Jan 28 to Feb 17. The mean incubation period for the entire period was estimated at 5.2 days (1.8-12.4) and the mean serial interval at 5.1 days (1.3-11.6). The epidemic dynamics in provinces outside Hubei were highly variable but consistently included a mixture of case importations and local transmission. We estimated that the epidemic was self-sustained for less than 3 weeks, with mean Rt reaching peaks between 1.08 (95% CI 0.74-1.54) in Shenzhen city of Guangdong province and 1.71 (1.32-2.17) in Shandong province. In all the locations for which we had sufficient data coverage of Rt, Rt was estimated to be below the epidemic threshold (ie, <1) after Jan 30. INTERPRETATION:​ Our estimates of the incubation period and serial interval were similar, suggesting an early peak of infectiousness,​ with possible transmission before the onset of symptoms. Our results also indicate that, as the epidemic progressed, infectious individuals were isolated more quickly, thus shortening the window of transmission in the community. Overall, our findings indicate that strict containment measures, movement restrictions,​ and increased awareness of the population might have contributed to interrupt local transmission of SARS-CoV-2 outside Hubei province. FUNDING: National Science Fund for Distinguished Young Scholars, National Institute of General Medical Sciences, and European Commission Horizon 2020.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[48] Title: </​b>​Projecting the impact of the coronavirus disease-19 pandemic on childhood obesity in the United States: A microsimulation model.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​158.65<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-01<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Sport and Health Science<​br>​
 +<​b>​Keywords:​ </b>, covid-19, childhood obesity, coronavirus,​ microsimulation,​ physical activity<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S209525462030065X">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S209525462030065X</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +PURPOSE: The coronavirus disease-2019 (COVID-19) pandemic in the United States led to nationwide stay-at-home orders and school closures. Declines in energy expenditure resulting from canceled physical education classes and reduced physical activity may elevate childhood obesity risk. This study estimated the impact of COVID-19 on childhood obesity. METHODS: A microsimulation model simulated the trajectory of a nationally representative kindergarten cohort'​s body mass index z-scores and childhood obesity prevalence from April 2020 to March 2021 under the control scenario without COVID-19 and under the 4 alternative scenarios with COVID-19-Scenario 1: 2-month nationwide school closure in April and May 2020; Scenario 2: Scenario 1 followed by a 10% reduction in daily physical activity in the summer from June to August; Scenario 3: Scenario 2 followed by 2-month school closure in September and October; and Scenario 4: Scenario 3 followed by an additional 2-month school closure in November and December. RESULTS: Relative to the control scenario without COVID-19, Scenarios 1, 2, 3, and 4 were associated with an increase in the mean body mass index z-scores by 0.056 (95% confidence interval (95%CI): 0.055-0.056),​ 0.084 (95%CI: 0.084-0.085),​ 0.141 (95%CI: 0.140-0.142),​ and 0.198 (95%CI: 0.197-0.199),​ respectively,​ and an increase in childhood obesity prevalence by 0.640 (95%CI: 0.515-0.765),​ 0.972 (95%CI: 0.819-1.126),​ 1.676 (95%CI: 1.475-1.877),​ and 2.373 (95%CI: 2.135-2.612) percentage points, respectively. Compared to girls and non-Hispanic whites and Asians, the impact of COVID-19 on childhood obesity was modestly larger among boys and non-Hispanic blacks and Hispanics, respectively. CONCLUSION: Public health interventions are urgently called to promote an active lifestyle and engagement in physical activity among children to mitigate the adverse impact of COVID-19 on unhealthy weight gains and childhood obesity.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[49] Title: </​b>​Transmission potential of the novel coronavirus (COVID-19) onboard the diamond Princess Cruises Ship, 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​158.63<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-01<​br>​
 +<​b>​Publisher:​ </​b>​Infectious Disease Modelling<​br>​
 +<​b>​Keywords:​ </b>, confined settings, corona, cruise, epidemic, next generation matrix<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2468042720300063">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2468042720300063</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +An outbreak of COVID-19 developed aboard the Princess Cruises Ship during January-February 2020. Using mathematical modeling and time-series incidence data describing the trajectory of the outbreak among passengers and crew members, we characterize how the transmission potential varied over the course of the outbreak. Our estimate of the mean reproduction number in the confined setting reached values as high as ~11, which is higher than mean estimates reported from community-level transmission dynamics in China and Singapore (approximate range: 1.1-7). Our findings suggest that R t decreased substantially compared to values during the early phase after the Japanese government implemented an enhanced quarantine control. Most recent estimates of R t reached values largely below the epidemic threshold, indicating that a secondary outbreak of the novel coronavirus was unlikely to occur aboard the Diamond Princess Ship.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[50] Title: </​b>​Using Early Data to Estimate the Actual Infection Fatality Ratio from COVID-19 in France.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​153.93<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-08<​br>​
 +<​b>​Publisher:​ </​b>​Biology<​br>​
 +<​b>​Keywords:​ </b>, bayesian inference, covid-19, sir model, case fatality rate, infection fatality ratio, mechanistic-statistical model<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2079-7737/​9/​5/​97">​https://​www.mdpi.com/​2079-7737/​9/​5/​97</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The number of screening tests carried out in France and the methodology used to target the patients tested do not allow for a direct computation of the actual number of cases and the infection fatality ratio (IFR). The main objective of this work is to estimate the actual number of people infected with COVID-19 and to deduce the IFR during the observation window in France. We develop a `mechanistic-statistical'​ approach coupling a SIR epidemiological model describing the unobserved epidemiological dynamics, a probabilistic model describing the data acquisition process and a statistical inference method. The actual number of infected cases in France is probably higher than the observations:​ we find here a factor x8 (95%-CI: 5-12) which leads to an IFR in France of 0.5% (95%-CI: 0.3-0.8) based on hospital death counting data. Adjusting for the number of deaths in nursing homes, we obtain an IFR of 0.8% (95%-CI: 0.45-1.25). This IFR is consistent with previous findings in China (0.66%) and in the UK (0.9%) and lower than the value previously computed on the Diamond Princess cruse ship data (1.3%).<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[51] Title: </​b>​Accurate closed-form solution of the SIR epidemic model.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​150.75<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-07-01<​br>​
 +<​b>​Publisher:​ </​b>​Physica D<br>
 +<​b>​Keywords:​ </​b>​Applied Mathematics,​ , Physical Sciences, Physics and Astronomy<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0167278920302694">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0167278920302694</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +An accurate closed-form solution is obtained to the SIR Epidemic Model through the use of Asymptotic Approximants (Barlow et al., 2017). The solution is created by analytically continuing the divergent power series solution such that it matches the long-time asymptotic behavior of the epidemic model. The utility of the analytical form is demonstrated through its application to the COVID-19 pandemic.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[52] Title: </​b>​Early phylogenetic estimate of the effective reproduction number of SARS-CoV-2.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​132.3<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-03<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Medical Virology<​br>​
 +<​b>​Keywords:​ </​b>​Medical Microbiology,​ sars-cov-2, evolutionary dynamics, reproductive number, Health Sciences, Life Sciences, Medicine, Immunology and Microbiology<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1002/​jmv.25723">​https://​onlinelibrary.wiley.com/​doi/​abs/​10.1002/​jmv.25723</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +To reconstruct the evolutionary dynamics of the 2019 novel-coronavirus recently causing an outbreak in Wuhan, China, 52 SARS-CoV-2 genomes available on 4 February 2020 at Global Initiative on Sharing All Influenza Data were analyzed. The two models used to estimate the reproduction number (coalescent-based exponential growth and a birth-death skyline method) indicated an estimated mean evolutionary rate of 7.8 x 10(-4) subs/​site/​year (range, 1.1 x 10(-4) -15 x 10(-4) ) and a mean tMRCA of the tree root of 73 days. The estimated R value was 2.6 (range, 2.1-5.1), and increased from 0.8 to 2.4 in December 2019. The estimated mean doubling time of the epidemic was between 3.6 and 4.1 days. This study proves the usefulness of phylogeny in supporting the surveillance of emerging new infections even as the epidemic is growing.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[53] Title: </​b>​Herd Immunity: Understanding COVID-19.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​118.9<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-01<​br>​
 +<​b>​Publisher:​ </​b>​Immunity<​br>​
 +<​b>​Keywords:​ </​b>​Immunology,​ , Health Sciences, Life Sciences, Medicine, Immunology and Microbiology<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1074761320301709">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S1074761320301709</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated disease, COVID-19, has demonstrated the devastating impact of a novel, infectious pathogen on a susceptible population. Here, we explain the basic concepts of herd immunity and discuss its implications in the context of COVID-19.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[54] Title: </​b>​Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​112.81<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-02-14<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Clinical Medicine<​br>​
 +<​b>​Keywords:​ </b>, censoring, emerging infectious diseases, importation,​ migration, mortality, travel<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2077-0383/​9/​2/​523">​https://​www.mdpi.com/​2077-0383/​9/​2/​523</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The exported cases of 2019 novel coronavirus (COVID-19) infection that were confirmed outside China provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in mainland China. Knowledge of the cCFR is critical to characterize the severity and understand the pandemic potential of COVID-19 in the early stage of the epidemic. Using the exponential growth rate of the incidence, the present study statistically estimated the cCFR and the basic reproduction number-the average number of secondary cases generated by a single primary case in a naive population. We modeled epidemic growth either from a single index case with illness onset on 8 December, 2019 (Scenario 1), or using the growth rate fitted along with the other parameters (Scenario 2) based on data from 20 exported cases reported by 24 January 2020. The cumulative incidence in China by 24 January was estimated at 6924 cases (95% confidence interval [CI]: 4885, 9211) and 19,289 cases (95% CI: 10,901, 30,158), respectively. The latest estimated values of the cCFR were 5.3% (95% CI: 3.5%, 7.5%) for Scenario 1 and 8.4% (95% CI: 5.3%, 12.3%) for Scenario 2. The basic reproduction number was estimated to be 2.1 (95% CI: 2.0, 2.2) and 3.2 (95% CI: 2.7, 3.7) for Scenarios 1 and 2, respectively. Based on these results, we argued that the current COVID-19 epidemic has a substantial potential for causing a pandemic. The proposed approach provides insights in early risk assessment using publicly available data.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[55] Title: </​b>​Unmasking the Actual COVID-19 Case Count.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​106.41<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-01<​br>​
 +<​b>​Publisher:​ </​b>​Clinical Infectious Diseases<​br>​
 +<​b>​Keywords:​ </​b>​Biological Sciences, cdc's influenza-like illness report, aggregated prescription data, influenza-like illness, total case count, undocumented infection, 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>
 +This report presents a novel approach to estimate the number of COVID-19 cases, including undocumented infections, in the US, by combining CDC's influenza-like illness surveillance data with aggregated prescription data. We estimated that the cumulative number of COVID-19 cases in the US by April 4 was above 2.5 million.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[56] Title: </​b>​Estimated Demand for US Hospital Inpatient and Intensive Care Unit Beds for Patients With COVID-19 Based on Comparisons With Wuhan and Guangzhou, China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​104.47<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-06<​br>​
 +<​b>​Publisher:​ </​b>​JAMA Network Open<​br>​
 +<​b>​Keywords:​ </​b>​Human Movement And Sports Science, <br>
 +<​b>​DOI:​ </​b><​a href="​http://​doi.org/​10.1001/​jamanetworkopen.2020.8297">​http://​doi.org/​10.1001/​jamanetworkopen.2020.8297</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Importance: Sustained spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has happened in major US cities. Capacity needs in cities in China could inform the planning of local health care resources. Objectives: To describe and compare the intensive care unit (ICU) and inpatient bed needs for patients with coronavirus disease 2019 (COVID-19) in 2 cities in China to estimate the peak ICU bed needs in US cities if an outbreak equivalent to that in Wuhan occurs. Design, Setting, and Participants:​ This comparative effectiveness study analyzed the confirmed cases of COVID-19 in Wuhan and Guangzhou, China, from January 10 to February 29, 2020. Exposures: Timing of disease control measures relative to timing of SARS-CoV-2 community spread. Main Outcomes and Measures: Number of critical and severe patient-days and peak number of patients with critical and severe illness during the study period. Results: In Wuhan, strict disease control measures were implemented 6 weeks after sustained local transmission of SARS-CoV-2. Between January 10 and February 29, 2020, patients with COVID-19 accounted for a median (interquartile range) of 429 (25-1143) patients in the ICU and 1521 (111-7202) inpatients with serious illness each day. During the epidemic peak, 19425 patients (24.5 per 10000 adults) were hospitalized,​ 9689 (12.2 per 10000 adults) were considered in serious condition, and 2087 (2.6 per 10000 adults) needed critical care per day. In Guangzhou, strict disease control measures were implemented within 1 week of case importation. Between January 24 and February 29, COVID-19 accounted for a median (interquartile range) of 9 (7-12) patients in the ICU and 17 (15-26) inpatients with serious illness each day. During the epidemic peak, 15 patients were in critical condition and 38 were classified as having serious illness. The projected number of prevalent critically ill patients at the peak of a Wuhan-like outbreak in US cities was estimated to range from 2.2 to 4.4 per 10000 adults, depending on differences in age distribution and comorbidity (ie, hypertension) prevalence. Conclusions and Relevance: Even after the lockdown of Wuhan on January 23, the number of patients with serious COVID-19 illness continued to rise, exceeding local hospitalization and ICU capacities for at least a month. Plans are urgently needed to mitigate the consequences of COVID-19 outbreaks on the local health care systems in US cities.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[57] Title: </​b>​Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​99.5<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-01<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Thoracic Disease<​br>​
 +<​b>​Keywords:​ </b>, coronavirus disease 2019 (covid-19), susceptible-exposed-infectious-removed (seir), epidemic, modeling, severe acute respiratory syndrome coronavirus 2 (sars-cov-2)<​br>​
 +<​b>​DOI:​ </​b><​a href="​http://​jtd.amegroups.com/​article/​view/​36385/​html">​http://​jtd.amegroups.com/​article/​view/​36385/​html</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Background: The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic. Methods: We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic. Results: We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction. Conclusions:​ Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[58] Title: </​b>​Estimating the COVID-19 infection rate: Anatomy of an inference problem.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​98.7<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-01<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Econometrics<​br>​
 +<​b>​Keywords:​ </​b>​Econometrics,​ epidemiology,​ missing data, novel coronavirus,​ partial identification,​ Arts and Humanities, Physical Sciences, Economics, Econometrics and Finance, Social Sciences, Mathematics<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0304407620301676">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0304407620301676</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of cumulative population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative. This paper explains the logical problem of bounding these rates and reports illustrative findings, using data from Illinois, New York, and Italy. We combine the data with assumptions on the infection rate in the untested population and on the accuracy of the tests that appear credible in the current context. We find that the infection rate might be substantially higher than reported. We also find that the infection fatality rate in Illinois, New York, and Italy is substantially lower than reported.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[59] Title: </​b>​Phase-adjusted estimation of the number of Coronavirus Disease 2019 cases in Wuhan, China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​98.18<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-02-24<​br>​
 +<​b>​Publisher:​ </​b>​Cell Discovery<​br>​
 +<​b>​Keywords:​ </b>, autoimmunity,​ immunology<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.nature.com/​articles/​s41421-020-0148-0">​https://​www.nature.com/​articles/​s41421-020-0148-0</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +An outbreak of clusters of viral pneumonia due to a novel coronavirus (2019-nCoV/​SARS-CoV-2) happened in Wuhan, Hubei Province in China in December 2019. Since the outbreak, several groups reported estimated R 0 of Coronavirus Disease 2019 (COVID-19) and generated valuable prediction for the early phase of this outbreak. After implementation of strict prevention and control measures in China, new estimation is needed. An infectious disease dynamics SEIR (Susceptible,​ Exposed, Infectious, and Removed) model was applied to estimate the epidemic trend in Wuhan, China under two assumptions of R t . In the first assumption, R t was assumed to maintain over 1. The estimated number of infections would continue to increase throughout February without any indication of dropping with R t = 1.9, 2.6, or 3.1. The number of infections would reach 11,044, 70,258, and 227,989, respectively,​ by 29 February 2020. In the second assumption, R t was assumed to gradually decrease at different phases from high level of transmission (R t = 3.1, 2.6, and 1.9) to below 1 (R t = 0.9 or 0.5) owing to increasingly implemented public health intervention. Several phases were divided by the dates when various levels of prevention and control measures were taken in effect in Wuhan. The estimated number of infections would reach the peak in late February, which is 58,​077-84,​520 or 55,​869-81,​393. Whether or not the peak of the number of infections would occur in February 2020 may be an important index for evaluating the sufficiency of the current measures taken in China. Regardless of the occurrence of the peak, the currently strict measures in Wuhan should be continuously implemented and necessary strict public health measures should be applied in other locations in China with high number of COVID-19 cases, in order to reduce R t to an ideal level and control the infection.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[60] Title: </​b>​Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​96.37<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-30<​br>​
 +<​b>​Publisher:​ </​b>​Eurosurveillance<​br>​
 +<​b>​Keywords:​ </b>, covid-19, generation interval, incubation period, reproduction number, serial interval, Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.eurosurveillance.org/​content/​10.2807/​1560-7917.ES.2020.25.17.2000257">​https://​www.eurosurveillance.org/​content/​10.2807/​1560-7917.ES.2020.25.17.2000257</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +BackgroundEstimating key infectious disease parameters from the coronavirus disease (COVID-19) outbreak is essential for modelling studies and guiding intervention strategies.AimWe estimate the generation interval, serial interval, proportion of pre-symptomatic transmission and effective reproduction number of COVID-19. We illustrate that reproduction numbers calculated based on serial interval estimates can be biased.MethodsWe used outbreak data from clusters in Singapore and Tianjin, China to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network. From those estimates, we obtained the serial interval, proportions of pre-symptomatic transmission and reproduction numbers.ResultsThe mean generation interval was 5.20 days (95% credible interval (CrI): 3.78-6.78) for Singapore and 3.95 days (95% CrI: 3.01-4.91) for Tianjin. The proportion of pre-symptomatic transmission was 48% (95% CrI: 32-67) for Singapore and 62% (95% CrI: 50-76) for Tianjin. Reproduction number estimates based on the generation interval distribution were slightly higher than those based on the serial interval distribution. Sensitivity analyses showed that estimating these quantities from outbreak data requires detailed contact tracing information.ConclusionHigh estimates of the proportion of pre-symptomatic transmission imply that case finding and contact tracing need to be supplemented by physical distancing measures in order to control the COVID-19 outbreak. Notably, quarantine and other containment measures were already in place at the time of data collection, which may inflate the proportion of infections from pre-symptomatic individuals.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[61] Title: </​b>​Growth Rate and Acceleration Analysis of the COVID-19 Pandemic Reveals the Effect of Public Health Measures in Real Time.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​93.99<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-22<​br>​
 +<​b>​Publisher:​ </​b>​Frontiers in Medicine<​br>​
 +<​b>​Keywords:​ </b>, hidden markov model, coronavirus,​ growth curve analysis, mathematical modeling, moving regression, severe acute respiratory syndrome<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.frontiersin.org/​articles/​10.3389/​fmed.2020.00247/​full">​https://​www.frontiersin.org/​articles/​10.3389/​fmed.2020.00247/​full</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Background: Ending the COVID-19 pandemic is arguably one of the most prominent challenges in recent human history. Following closely the growth dynamics of the disease is one of the pillars toward achieving that goal. Objective: We aimed at developing a simple framework to facilitate the analysis of the growth rate (cases/day) and growth acceleration (cases/​day(2)) of COVID-19 cases in real-time. Methods: The framework was built using the Moving Regression (MR) technique and a Hidden Markov Model (HMM). The dynamics of the pandemic was initially modeled via combinations of four different growth stages: lagging (beginning of the outbreak), exponential (rapid growth), deceleration (growth decay), and stationary (near zero growth). A fifth growth behavior, namely linear growth (constant growth above zero), was further introduced to add more flexibility to the framework. An R Shiny application was developed, which can be accessed at https://​theguarani.com.br/​ or downloaded from https://​github.com/​adamtaiti/​SARS-CoV-2. The framework was applied to data from the European Center for Disease Prevention and Control (ECDC), which comprised 3,722,128 cases reported worldwide as of May 8th 2020. Results: We found that the impact of public health measures on the prevalence of COVID-19 could be perceived in seemingly real-time by monitoring growth acceleration curves. Restriction to human mobility produced detectable decline in growth acceleration within 1 week, deceleration within ~2 weeks and near-stationary growth within ~6 weeks. Countries exhibiting different permutations of the five growth stages indicated that the evolution of COVID-19 prevalence is more complex and dynamic than previously appreciated. Conclusions:​ These results corroborate that mass social isolation is a highly effective measure against the dissemination of SARS-CoV-2, as previously suggested. Apart from the analysis of prevalence partitioned by country, the proposed framework is easily applicable to city, state, region and arbitrary territory data, serving as an asset to monitor the local behavior of COVID-19 cases.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[62] Title: </​b>​The potential effects of widespread community transmission of SARS-CoV-2 infection in the World Health Organization African Region: a predictive model.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​93.55<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-25<​br>​
 +<​b>​Publisher:​ </​b>​BMJ Global Health Journal<​br>​
 +<​b>​Keywords:​ </b>, epidemiology,​ health systems, mathematical modelling<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​gh.bmj.com/​lookup/​doi/​10.1136/​bmjgh-2020-002647">​https://​gh.bmj.com/​lookup/​doi/​10.1136/​bmjgh-2020-002647</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been unprecedented in its speed and effects. Interruption of its transmission to prevent widespread community transmission is critical because its effects go beyond the number of COVID-19 cases and deaths and affect the health system capacity to provide other essential services. Highlighting the implications of such a situation, the predictions presented here are derived using a Markov chain model, with the transition states and country specific probabilities derived based on currently available knowledge. A risk of exposure, and vulnerability index are used to make the probabilities country specific. The results predict a high risk of exposure in states of small size, together with Algeria, South Africa and Cameroon. Nigeria will have the largest number of infections, followed by Algeria and South Africa. Mauritania would have the fewest cases, followed by Seychelles and Eritrea. Per capita, Mauritius, Seychelles and Equatorial Guinea would have the highest proportion of their population affected, while Niger, Mauritania and Chad would have the lowest. Of the World Health Organization'​s 1 billion population in Africa, 22% (16%-26%) will be infected in the first year, with 37 (29 - 44) million symptomatic cases and 150 078 (82 735-189 579) deaths. There will be an estimated 4.6 (3.6-5.5) million COVID-19 hospitalisations,​ of which 139 521 (81 876-167 044) would be severe cases requiring oxygen, and 89 043 (52 253-106 599) critical cases requiring breathing support. The needed mitigation measures would significantly strain health system capacities, particularly for secondary and tertiary services, while many cases may pass undetected in primary care facilities due to weak diagnostic capacity and non-specific symptoms. The effect of avoiding widespread and sustained community transmission of SARS-CoV-2 is significant,​ and most likely outweighs any costs of preventing such a scenario. Effective containment measures should be promoted in all countries to best manage the COVID-19 pandemic.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[63] Title: </​b>​An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov).<​br><​br>​
 +<​b>​Altmetric Score: </​b>​93.35<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-01-01<​br>​
 +<​b>​Publisher:​ </​b>​Infectious Disease Modelling<​br>​
 +<​b>​Keywords:​ </b>, basic reproduction number, effective daily reproduction ratio, emerging and reemerging pathogens, mathematical modeling, novel coronavirus<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S246804272030004X">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S246804272030004X</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The basic reproduction number of an infectious agent is the average number of infections one case can generate over the course of the infectious period, in a naive, uninfected population. It is well-known that the estimation of this number may vary due to several methodological issues, including different assumptions and choice of parameters, utilized models, used datasets and estimation period. With the spreading of the novel coronavirus (2019-nCoV) infection, the reproduction number has been found to vary, reflecting the dynamics of transmission of the coronavirus outbreak as well as the case reporting rate. Due to significant variations in the control strategies, which have been changing over time, and thanks to the introduction of detection technologies that have been rapidly improved, enabling to shorten the time from infection/​symptoms onset to diagnosis, leading to faster confirmation of the new coronavirus cases, our previous estimations on the transmission risk of the 2019-nCoV need to be revised. By using time-dependent contact and diagnose rates, we refit our previously proposed dynamics transmission model to the data available until January 29th(,) 2020 and re-estimated the effective daily reproduction ratio that better quantifies the evolution of the interventions. We estimated when the effective daily reproduction ratio has fallen below 1 and when the epidemics will peak. Our updated findings suggest that the best measure is persistent and strict self-isolation. The epidemics will continue to grow, and can peak soon with the peak time depending highly on the public health interventions practically implemented.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[64] Title: </​b>​Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​86.16<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-01<​br>​
 +<​b>​Publisher:​ </​b>​Chaos<​br>​
 +<​b>​Keywords:​ </​b>​Applied Mathematics,​ , Mathematics,​ Physical Sciences, Physics and Astronomy<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​aip.scitation.org/​doi/​10.1063/​5.0008834">​https://​aip.scitation.org/​doi/​10.1063/​5.0008834</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Despite the importance of having robust estimates of the time-asymptotic total number of infections, early estimates of COVID-19 show enormous fluctuations. Using COVID-19 data from different countries, we show that predictions are extremely sensitive to the reporting protocol and crucially depend on the last available data point before the maximum number of daily infections is reached. We propose a physical explanation for this sensitivity,​ using a susceptible-exposed-infected-recovered model, where the parameters are stochastically perturbed to simulate the difficulty in detecting patients, different confinement measures taken by different countries, as well as changes in the virus characteristics. Our results suggest that there are physical and statistical reasons to assign low confidence to statistical and dynamical fits, despite their apparently good statistical scores. These considerations are general and can be applied to other epidemics.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[65] Title: </​b>​Data-based analysis, modelling and forecasting of the COVID-19 outbreak.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​80.82<​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.0230405">​https://​journals.plos.org/​plosone/​article?​id=10.1371/​journal.pone.0230405</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Since the first suspected case of coronavirus disease-2019 (COVID-19) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the publicly available epidemiological data for Hubei, China from January 11 to February 10, 2020, we provide estimates of the main epidemiological parameters. In particular, we provide an estimation of the case fatality and case recovery ratios, along with their 90% confidence intervals as the outbreak evolves. On the basis of a Susceptible-Infectious-Recovered-Dead (SIDR) model, we provide estimations of the basic reproduction number (R0), and the per day infection mortality and recovery rates. By calibrating the parameters of the SIRD model to the reported data, we also attempt to forecast the evolution of the outbreak at the epicenter three weeks ahead, i.e. until February 29. As the number of infected individuals,​ especially of those with asymptomatic or mild courses, is suspected to be much higher than the official numbers, which can be considered only as a subset of the actual numbers of infected and recovered cases in the total population, we have repeated the calculations under a second scenario that considers twenty times the number of confirmed infected cases and forty times the number of recovered, leaving the number of deaths unchanged. Based on the reported data, the expected value of R0 as computed considering the period from the 11th of January until the 18th of January, using the official counts of confirmed cases was found to be approximately 4.6, while the one computed under the second scenario was found to be approximately 3.2. Thus, based on the SIRD simulations,​ the estimated average value of R0 was found to be approximately 2.6 based on confirmed cases and approximately 2 based on the second scenario. Our forecasting flashes a note of caution for the presently unfolding outbreak in China. Based on the official counts for confirmed cases, the simulations suggest that the cumulative number of infected could reach 180,000 (with a lower bound of 45,000) by February 29. Regarding the number of deaths, simulations forecast that on the basis of the up to the 10th of February reported data, the death toll might exceed 2,700 (as a lower bound) by February 29. Our analysis further reveals a significant decline of the case fatality ratio from January 26 to which various factors may have contributed,​ such as the severe control measures taken in Hubei, China (e.g. quarantine and hospitalization of infected individuals),​ but mainly because of the fact that the actual cumulative numbers of infected and recovered cases in the population most likely are much higher than the reported ones. Thus, in a scenario where we have taken twenty times the confirmed number of infected and forty times the confirmed number of recovered cases, the case fatality ratio is around approximately 0.15% in the total population. Importantly,​ based on this scenario, simulations suggest a slow down of the outbreak in Hubei at the end of February.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[66] Title: </​b>​Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​78.18<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-06<​br>​
 +<​b>​Publisher:​ </​b>​Frontiers in Medicine<​br>​
 +<​b>​Keywords:​ </b>, covid-19, italy, coronavirus,​ epidemics trend, prediction<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.frontiersin.org/​articles/​10.3389/​fmed.2020.00169/​full">​https://​www.frontiersin.org/​articles/​10.3389/​fmed.2020.00169/​full</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Background: Coronavirus Disease 2019 (COVID-19) is currently a global public health threat. Outside of China, Italy is one of the countries suffering the most with the COVID-19 epidemic. It is important to predict the epidemic trend of the COVID-19 epidemic in Italy to help develop public health strategies. Methods: We used time-series data of COVID-19 from Jan 22 2020 to Apr 02 2020. An infectious disease dynamic extended susceptible-infected-removed (eSIR) model, which covers the effects of different intervention measures in dissimilar periods, was applied to estimate the epidemic trend in Italy. The basic reproductive number was estimated using Markov Chain Monte Carlo methods and presented using the resulting posterior mean and 95% credible interval (CI). Hunan, with a similar total population number to Italy, was used as a comparative item. Results: In the eSIR model, we estimated that the mean of basic reproductive number for COVID-19 was 4.34 (95% CI, 3.04-6.00) in Italy and 3.16 (95% CI, 1.73-5.25) in Hunan. There would be a total of 182 051 infected cases (95%CI:116 114-274 378) under the current country blockade and the endpoint would be Aug 05 in Italy. Conclusion: Italy'​s current strict measures can efficaciously prevent the further spread of COVID-19 and should be maintained. Necessary strict public health measures should be implemented as soon as possible in other European countries with a high number of COVID-19 cases. The most effective strategy needs to be confirmed in further studies.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[67] Title: </​b>​Estimating number of cases and spread of coronavirus disease (COVID-19) using critical care admissions, United Kingdom, February to March 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​76.05<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-05-07<​br>​
 +<​b>​Publisher:​ </​b>​Eurosurveillance<​br>​
 +<​b>​Keywords:​ </b>, sars-cov-2, coronavirus disease 2019, intensive care unit, mathematical model, reproduction number, surveillance,​ Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.eurosurveillance.org/​content/​10.2807/​1560-7917.ES.2020.25.18.2000632">​https://​www.eurosurveillance.org/​content/​10.2807/​1560-7917.ES.2020.25.18.2000632</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +An exponential growth model was fitted to critical care admissions from two surveillance databases to determine likely coronavirus disease (COVID-19) case numbers, critical care admissions and epidemic growth in the United Kingdom before the national lockdown. We estimate, on 23 March, a median of 114,000 (95% credible interval (CrI): 78,​000-173,​000) new cases and 258 (95% CrI: 220-319) new critical care reports, with 527,000 (95% CrI: 362,​000-797,​000) cumulative cases since 16 February.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[68] Title: </​b>​Prediction of the Epidemic Peak of Coronavirus Disease in Japan, 2020.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​75.23<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-03-13<​br>​
 +<​b>​Publisher:​ </​b>​Journal of Clinical Medicine<​br>​
 +<​b>​Keywords:​ </b>, covid-19, seir compartmental model, basic reproduction number<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​www.mdpi.com/​2077-0383/​9/​3/​789">​https://​www.mdpi.com/​2077-0383/​9/​3/​789</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +The first case of coronavirus disease 2019 (COVID-19) in Japan was reported on 15 January 2020 and the number of reported cases has increased day by day. The purpose of this study is to give a prediction of the epidemic peak for COVID-19 in Japan by using the real-time data from 15 January to 29 February 2020. Taking into account the uncertainty due to the incomplete identification of infective population, we apply the well-known SEIR compartmental model for the prediction. By using a least-square-based method with Poisson noise, we estimate that the basic reproduction number for the epidemic in Japan is R 0 = 2 . 6 ( 95 % CI, 2 . 4 - 2 . 8 ) and the epidemic peak could possibly reach the early-middle summer. In addition, we obtain the following epidemiological insights: (1) the essential epidemic size is less likely to be affected by the rate of identification of the actual infective population; (2) the intervention has a positive effect on the delay of the epidemic peak; (3) intervention over a relatively long period is needed to effectively reduce the final epidemic size.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[69] Title: </​b>​Mathematical prediction of the time evolution of the COVID-19 pandemic in Italy by a Gauss error function and Monte Carlo simulations.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​71.08<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-15<​br>​
 +<​b>​Publisher:​ </​b>​The European Physical Journal Plus<​br>​
 +<​b>​Keywords:​ </b>, <br>
 +<​b>​DOI:​ </​b><​a href="​https://​link.springer.com/​article/​10.1140/​epjp/​s13360-020-00383-y">​https://​link.springer.com/​article/​10.1140/​epjp/​s13360-020-00383-y</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +In this paper are presented mathematical predictions on the evolution in time of the number of positive cases in Italy of the COVID-19 pandemic based on official data and on the use of a function of the type of a Gauss error function, with four parameters, as a cumulative distribution function. We have analyzed the available data for China and Italy. The evolution in time of the number of cumulative diagnosed positive cases of COVID-19 in China very well approximates a distribution of the type of the error function, that is, the integral of a normal, Gaussian distribution. We have then used such a function to study the potential evolution in time of the number of positive cases in Italy by performing a number of fits of the official data so far available. We then found a statistical prediction for the day in which the peak of the number of daily positive cases in Italy occurs, corresponding to the flex of the fit, that is, to the change in sign of its second derivative (i.e., the change from acceleration to deceleration),​ as well as of the day in which a substantial attenuation of such number of daily cases is reached. We have also analyzed the predictions of the cumulative number of fatalities in both China and Italy, obtaining consistent results. We have then performed 150 Monte Carlo simulations to have a more robust prediction of the day of the above-mentioned peak and of the day of the substantial decrease in the number of daily positive cases and fatalities. Although official data have been used, those predictions are obtained with a heuristic approach since they are based on a statistical approach and do not take into account either a number of relevant issues (such as number of daily nasopharyngeal swabs, medical, social distancing, virological and epidemiological) or models of contamination diffusion.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[70] Title: </​b>​Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​66.75<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-01<​br>​
 +<​b>​Publisher:​ </​b>​Medical Journal Armed Forces India<​br>​
 +<​b>​Keywords:​ </​b>​Medical And Health Sciences, covid-19, coronavirus,​ epidemiology,​ models, quarantine, sars-cov2, theoretical,​ Medicine, Health Sciences<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0377123720300605">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S0377123720300605</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Background: In India, the SARS-CoV2 COVID-19 epidemic has grown to 1,251 cases and 32 deaths as on 30 Mar 2020. The healthcare impact of the epidemic in India was studied with a stochastic mathematical model. Methods: A compartmental SEIR model was developed, in which the flow of individuals through compartments is modeled using a set of differential equations. Different scenarios were modeled with 1000 runs of Monte Carlo simulation each using MATLAB. Hospitalization,​ ICU requirements and deaths were modeled on SimVoi software. The impact of Non-Pharmacological Interventions (NPI) including social distancing and lockdown on checking the epidemic was estimated. Results: Uninterrupted epidemic in India would have resulted in over 364 million cases and 1.56 million deaths with peak by mid-July. As per the model, at growth rate of 1.15, India is likely to reach approximately 3 million cases by 25 May, implying 125,455 (+/-18,034) hospitalizations,​ 26,130 (+/-3,298) ICU admissions and 13,447 (+/-1,819) deaths. This would overwhelm India'​s healthcare system. The model shows that with immediate institution of NPIs, the epidemic might still be checked by mid-April 2020. It would then result in 241,974 (+/-33,735) total infections, 10,214 (+/-1,649) hospitalizations,​ 2,121 (+/-334) ICU admissions and 1,​081(+/​-169) deaths. Conclusion: At current growth rate of epidemic, India'​s healthcare resources will be overwhelmed by end-May. With the immediate institution of NPIs, total cases, hospitalizations,​ ICU requirements and deaths can be reduced by almost 90%.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +----------------------------------------------------------------------<​br>​
 +<​b>​[71] Title: </​b>​Forecasting the Impact of Coronavirus Disease During Delivery Hospitalization:​ An Aid for Resources Utilization.<​br><​br>​
 +<​b>​Altmetric Score: </​b>​65.98<​br>​
 +<​b>​Pub_Date:​ </​b>​2020-04-01<​br>​
 +<​b>​Publisher:​ </​b>​American Journal of Obstetrics & Gynecology MFM<​br>​
 +<​b>​Keywords:​ </b>, covid-19, coronavirus,​ forecasting,​ prediction model<​br>​
 +<​b>​DOI:​ </​b><​a href="​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2589933320300641">​https://​linkinghub.elsevier.com/​retrieve/​pii/​S2589933320300641</​a><​br><​br>​
 +<​b>​Abstract:​ </b>
 +Background: The ongoing Coronavirus disease (COVID-19) pandemic has severely impacted the United States. In cases of infectious disease outbreak, forecasting models are often developed for resources utilization. Pregnancy and delivery pose unique challenges, given the altered maternal immune system and the fact that the majority of American women choose to deliver in the hospital setting. Objectives: The aim of our study is to forecast the incidence of COVID-19 in general population and to forecast the overall incidence, severe cases, critical cases and fatal COVID-19 cases during delivery hospitalization in the United States. Study design: We use a phenomenological model with generalized logistic growth models to forecast the incidence of COVID-19 in the United States from 4/15/2020 - 12/31/2020. Incidence data from 3/1/2020 - 4/14/2020 were used to provide best-fit model solution. Subsequently,​ Monte-Carlo simulation was performed for each week from 3/1/2020 - 12/31/2020 to estimate the incidence of COVID-19 in delivery hospitalizations using the available data estimate. Results: From 3/1/2020 - 12/31/2020, our model forecasted a total of 860,475 cases of COVID-19 in general population across the United States. The cumulative incidence for COVID-19 during delivery hospitalization is anticipated to be 16,601 (95% CI, 9,711 - 23,491) cases. Among those, 3,308 (95% CI, 1,755 - 4,861) cases are expected to be severe, 681 (95% CI, 1324 - 1,038) critical and 52 (95% CI, 23 - 81) maternal mortality. Assuming similar baseline maternal mortality rate as the year of 2018, we projected an increase in maternal mortality rate in the US to at least 18.7 (95% CI, 18.0 - 19.5) deaths per 100,000 live birth as a direct result of COVID-19. Conclusions:​ COVID-19 infection in pregnant women is expected to severely impact obstetrical care. From 3/1/2020 - 12/31/2020, we project 3,308 severe and 681 critical cases, with about 52 COVID-19 related maternal mortalities during delivery hospitalization in the United States. These data might be helpful for counseling and resource allocation.<​br>​
 +----------------------------------------------------------------------<​br><​br>​
 +
 +</​html>​
oa_db/covid19_forecasting_abstracts_pg2.txt · Last modified: 2020/06/27 17:53 by bpwhite