Query: italy covid-19

title: COVID 19 and Italy: what next?
abstract: The spread of severe acute respiratory syndrome coronavirus 2 (SARS CoV 2) has already taken on pandemic proportions, affecting over 100 countries in a matter of weeks. A global response to prepare health systems worldwide is imperative. Although containment measures in China have reduced new cases by more than 90% this reduction is not the case elsewhere, and Italy has been particularly affected. There is now grave concern regarding the Italian national health system's capacity to effectively respond to the needs of patients who are infected and require intensive care for SARS CoV 2 pneumonia. The percentage of patients in intensive care reported daily in Italy between March 1 and March 11, 2020, has consistently been between 9% and 11% of patients who are actively infected. The number of patients infected since Feb 21 in Italy closely follows an exponential trend. If this trend continues for 1 more week, there will be 30 000 infected patients. Intensive care units will then be at maximum capacity; up to 4000 hospital beds will be needed by mid April, 2020. Our analysis might help political leaders and health authorities to allocate enough resources, including personnel, beds, and intensive care facilities, to manage the situation in the next few days and weeks. If the Italian outbreak follows a similar trend as in Hubei province, China, the number of newly infected patients could start to decrease within 3 4 days, departing from the exponential trend. However, this cannot currently be predicted because of differences between social distancing measures and the capacity to quickly build dedicated facilities in China.

title: Data Analysis for the COVID 19 early dynamics in Northern Italy. The effect of first restrictive measures
abstract: In a recent report we have collected some data about the COVID 19 epidemics in Northern Italy; in this follow up we analyze how these changed after the mild restrictive measures taken by the Government two weeks ago and the large campaign of public awareness developed in the meanwhile.

title: The COVID 19 social media infodemic
abstract: We address the diffusion of information about the COVID 19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID 19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin} 69pt} \begin{document}$R_0$\end{document}R0 for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform dependent numerical estimates of rumors' amplification.

title: COVID 19 Epidemic Editorial
abstract: COVID 19 is a novel form of coronavirus which has spread from its initial identification in Wuhan, China, and has been declared a pandemic by the World Health Organisation (WHO)  Coronaviruses are a large family of viruses that cause illnesses ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERSCoV) and Severe Acute Respiratory Syndrome (SARS CoV) Coronaviruses are zoonotic, meaning they are transmitted between animals and people. Several known coronaviruses are circulating in animals that have not yet infected humans  but it appears that COVID 19 has crossed species from bats to snakes  and pangolins then to humans, initially via the live animal 'wet markets' of Wuhan

title: A SIDARTHE Model of COVID 19 Epidemic in Italy
abstract: In late December 2019, a novel strand of Coronavirus (SARS CoV 2) causing a severe, potentially fatal respiratory syndrome (COVID 19) was identified in Wuhan, Hubei Province, China and is causing outbreaks in multiple world countries, soon becoming a pandemic. Italy has now become the most hit country outside of Asia: on March 16, 2020, the Italian Civil Protection documented a total of 27980 confirmed cases and 2158 deaths of people tested positive for SARS CoV 2. In the context of an emerging infectious disease outbreak, it is of paramount importance to predict the trend of the epidemic in order to plan an effective control strategy and to determine its impact. This paper proposes a new epidemic model that discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms. The distinction between diagnosed and non diagnosed is important because non diagnosed individuals are more likely to spread the infection than diagnosed ones, since the latter are typically isolated, and can explain misperceptions of the case fatality rate and of the seriousness of the epidemic phenomenon. Being able to predict the amount of patients that will develop life threatening symptoms is important since the disease frequently requires hospitalisation (and even Intensive Care Unit admission) and challenges the healthcare system capacity. We show how the basic reproduction number can be redefined in the new framework, thus capturing the potential for epidemic containment. Simulation results are compared with real data on the COVID 19 epidemic in Italy, to show the validity of the model and compare different possible predicted scenarios depending on the adopted countermeasures.

title: The reproductive number of COVID 19 is higher compared to SARS coronavirus
abstract: Our review found the average R0 for 2019 nCoV to be 3.28, which exceeds WHO estimates of 1.4 to 2.5.

title: Space time dependence of corona virus (COVID 19) outbreak
abstract: We analyse the data for the global corona virus (COVID 19) outbreak using the results of a previously studied Susceptible Infected Removed (SIR) model of epidemic spreading on Euclidean networks. We also directly study the correlation of the distance from the epicenter and the number of cases. An inverse square law is seen to exist approximately. The studies are made for China and the rest of the world separately.

title: How will country based mitigation measures influence the course of the COVID 19 epidemic?
abstract: Governments will not be able to minimise both deaths from coronavirus disease 2019 (COVID 19) and the economic impact of viral spread. Keeping mortality as low as possible will be the highest priority for individuals; hence governments must put in place measures to ameliorate the inevitable economic downturn. In our view, COVID 19 has developed into a pandemic, with small chains of transmission in many countries and large chains resulting in extensive spread in a few countries, such as Italy, Iran, South Korea, and Japan. Most countries are likely to have spread of COVID 19, at least in the early stages, before any mitigation measures have an impact. What has happened in China shows that quarantine, social distancing, and isolation of infected populations can contain the epidemic. This impact of the COVID 19 response in China is encouraging for the many countries where COVID 19 is beginning to spread. However, it is unclear whether other countries can implement the stringent measures China eventually adopted. Singapore and Hong Kong, both of which had severe acute respiratory syndrome (SARS) epidemics in 2002 03, provide hope and many lessons to other countries. In both places, COVID 19 has been managed well to date, despite early cases, by early government action and through social distancing measures taken by individuals. The course of an epidemic is defined by a series of key factors, some of which are poorly understood at present for COVID 19. The basic reproduction number (R0) which defines the mean number of secondary cases generated by one primary case when the population is largely susceptible to infection, determines the overall number of people who are likely to be infected, or more precisely the area under the epidemic curve. For an epidemic to take hold, the value of R0 must be greater than unity in value. A simple calculation gives the fraction likely to be infected without mitigation. This fraction is roughly 1 1/R0. With R0 values for COVID 19 in China around 2*5 in the early stages of the epidemic, we calculate that approximately 60% of the population would become infected. This is a very worst case scenario for a number of reasons. We are uncertain about transmission in children, some communities are remote and unlikely to be exposed, voluntary social distancing by individuals and communities will have an impact, and mitigation efforts, such as the measures put in place in China, greatly reduce transmission. As an epidemic progresses, the effective reproduction number (R) declines until it falls below unity in value when the epidemic peaks and then decays, either due to the exhaustion of people susceptible to infection or the impact of control measures. The speed of the initial spread of the epidemic, its doubling time, or the related serial interval (the mean time it takes for an infected person to pass on the infection to others) and the likely duration of the epidemic are determined by factors such as the length of time from infection to when a person is infectious to others and the mean duration of infectiousness. For the 2009 influenza A H1N1 pandemic, in most infected people these epidemiological quantities were short with a day or so to infectiousness and a few days of peak infectiousness to others. By contrast, for COVID 19, the serial interval is estimated at 4*4 7*5 days, which is more similar to SARS. First among the important unknowns about COVID 19 is the case fatality rate (CFR) which requires information on the denominator that defines the number infected. We are unaware of any completed large scale serology surveys to detect specific antibodies to COVID 19. Best estimates suggest a CFR for COVID 19 of about 0*3 1% which is higher than the order of 0*1% CFR for a moderate influenza A season. The second unknown is the whether infectiousness starts before onset of symptoms. The incubation period for COVID 19 is about 5 6 days. Combining this time with a similar length serial interval suggests there might be considerable presymptomatic infectiousness (appendix 1) For reference, influenza A has a presymptomatic infectiousness of about 1 2 days, whereas SARS had little or no presymptomatic infectiousness. There have been few clinical studies to measure COVID 19 viraemia and how it changes over time in individuals. In one study of 17 patients with COVID 19, peak viraemia seems to be at the end of the incubation period, pointing to the possibility that viraemia might be high enough to trigger transmission for 1 2 days before onset of Published Online March 6, 2020 https:/doi.org/10.1016/ S0140 6736(20)30567 5

title: A Note on the Evolution of Covid 19 in Italy
abstract: We employ methods largely exploited in Physics, in the analysis of the evolution of dynamical systems, to study the pattern of the Covid 19 infection in Italy. The techniques we employ are based on the use of logistic function and of its derivative, namely the Hubbert function. The latter is exploited to give a prediction on the number of infected per day. We also mention the possibility of taking advantage from other mathematical tools based e.g. on the Gompertz equation and make some comparison on the different predictive capabilities.

title: The Incubation Period of Coronavirus Disease 2019 (COVID 19) From Publicly Reported Confirmed Cases: Estimation and Application
abstract: A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS CoV 2) was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019  which has important implications for surveillance and control activities.  To estimate the length of the incubation period of COVID 19 and describe its public health implications.  Pooled analysis of confirmed COVID 19 cases reported between 4 January 2020 and 24 February 2020.  News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China.  Persons with confirmed SARS CoV 2 infection outside Hubei province, China.  Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization.  There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID 19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days) and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine.  Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases.  This work provides additional evidence for a median incubation period for COVID 19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS CoV 2, although longer monitoring periods might be justified in extreme cases.  U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.