cord-006203-wwpd26bx	2016	Caduff, Keck and MacPhail all write against more sensationalistic accounts of pandemic flu with their dramatic tropes of virus hunters and looming catastrophe, seeking rather to demystify and explain in these ethnographies of influenza research. The temporal modality, perhaps most familiar to readers of this journal from the concept of the experiment as a "machine for producing the future" (Rheinberger,1997, quoting the Nobel prize-winning molecular biologist François Jacob), is most explicitly indebted to classical studies of witchcraft, oracles and divination (Evans-Pritchard, 1963) to more contemporary examinations of risk and uncertainty in clinical practice, global health and everyday life. Thinking about regimes of anticipation can bring in conversations that have emerged in contemporary ethnography around the work of Elizabeth Povinelli and specifically her notions of social tense and "the future anterior" as a mode of late liberal governmentalitya gesture made by Caduff.
cord-015967-kqfyasmu	2015	For instance, hub individuals of such high-risk individuals help in maintaining sexually transmitted diseases (STDs) in different populations where majority belong to long-term monogamous relationships, whereas in case of SARS epidemic, a significant proportion of all infections are due to high risk connected individuals. Likewise, models for epidemic spread in static heavy-tailed networks have illustrated that with a degree distribution having moments resulted in lesser prevalence and/or termination for smaller rates of infection [14] . Generally, epidemic models consider contact networks to be static in nature, where all links are existent throughout the infection course. But, in cases like HIV, which spreads through a population over longer time scales, the course of infection spread is heavily dependent on the properties of the contact individuals. Likewise, for a wide range of scale-free networks, epidemic threshold is not existent, and infections with low spreading rate prevail over the entire population [10] .
cord-016387-ju4130bq	2005	From time to time, this steady drain on long life and good health was punctuated by great and terrifying epidemics-smallpox, typhus, influenza, and, most terrible of all, the plague, or the "black death." The causes of these periodic devastations, the contributing reasons to why they happened, were a mystery. After Fracastorius, the pathfinders on the road to health became numerous, but mention here will be made of only a handful of public health heroes: Paracelsus, John Graunt, Antoni van Leeuwenhoek, Bernardino Ramazzini, James Lind, Edward Jenner, Johann Peter Frank, John Snow, Ignaz Semmelweiss, and Louis Pasteur. Many others belong in their company: The great German pathologist Rudolph Virchow recognized that political action as well as rational science are necessary to initiate effective action to control public health problems; Edwin Chadwick and Lemuel Shattuck reported on the appalling sanitary conditions associated with the unacceptably high infant and child death rates that prevailed in 19 th century industrial towns; William Farr established vital statistics in England as a model for other nations to follow.
cord-018151-5su98uan	2019	Providing original studies of rats, mosquitoes, marmots, dogs and ''bushmeat'', which at different points in the history of modern medicine and public health have come to embody social and scientific concerns about infection, this volume aims to elucidate the impact of framing non-human animals as epidemic villains. Whether it is stray dogs as spreaders of rabies in colonial and contemporary India, bushmeat as the source of Ebola in West Africa, mosquitoes as vectors of malaria, dengue, Zika and yellow fever in the Global South, or rats and marmots as hosts of plague during the third pandemic, this volume shows framings of non-human animals to be entangled in local webs of signification and, at the same time, to be global agents of modern epidemic imaginaries.
cord-018761-vm86d4mj	2017	
cord-019114-934xczf3	2018	
cord-020544-kc52thr8	2019	However, if Dukoral is readily available and staff are properly trained in its use according to the guidelines that come with the vaccine, the COTS program PERMITS Dukoral''s use (ideally before an outbreak) in the following high-risk populations: refugee populations in which cholera is present, health care workers managing cholera cases, and communities in which the incidence rate is greater than 1 in 1000 annually." [2] Epidemiological Surveillance (specific to cholera)
cord-020610-hsw7dk4d	2019	Combined with a divergent political practice and lived experiences of the state, especially between Sierra Leone and Guinea, the working hypothesis drawn from my ethnographic observations in Macenta and related literature review is that part of the continuing episodes of hostility and social resistance manifested by Guinean communities regarding the adoption of the proposed control measures against the scourge of Ebola has its origins in the divergence between explanatory systems of the disease; on the one hand, biomedical explanatory systems, and, on the other hand, popular explanatory systems. By framing ''bushmeat'' hunting, as well as local burials, as the main persisting cultural practices among the ''forest people'' to explain (or to justify) the maintenance of the EVD transmission during the West African epidemic, the notion of culture that fuelled sensational news coverage has strongly stigmatised this ''patient zero'' community both globally and within Guinea, and has been employed to obscure the actual, political, economic and political-economic drivers of infectious disease patterns.
cord-024683-3v8i39rk	2020	Here we review recent development of potential approaches for epilepsy control during an epidemic and propose a new three-level management framework to address these challenges. Hence, the proposed new approaches for treatment such as structured letter therapy [41] for consultation on mental problem during COVID-19 epidemic can be easily deployed in App. These Apps are largely available online and have helped different groups of patients improving their mental and emotional health. The patient & family level focuses on self-management, including all six components mentioned above [32] and is facilitated by epilepsy-related Apps, while the community support level, consisting of general physicians and other local caregivers from the community, acts both as a threshold for hospitalization and an outpost for providing basic intervention, including education, adjusting AED doses, rehabilitation and mental health management.
cord-024746-ijlnefz3	2019	We present a contact-based model to study the spreading of epidemics by means of extending the dynamic message-passing approach to temporal networks. The shift in perspective from nodeto edge-centric quantities enables accurate modeling of Markovian susceptible-infected-recovered outbreaks on time-varying trees, i.e., temporal networks with a loop-free underlying topology. On arbitrary graphs, the proposed contact-based model incorporates potential structural and temporal heterogeneities of the contact network and improves analytic estimations with respect to the individual-based (node-centric) approach at a low computational and conceptual cost. Our comparison between MC simulations and analytic estimations from the CB and IB models followed a bottom-up approach: We looked at (i) epidemic trajectories of individual nodes, (ii) averaged trajectories given the same outbreak location, and (iii) the final outbreak size for a range of infection probabilities and with random initial condition.
cord-027757-zb4wxt85	2012	
cord-028048-0oqv2jom	2020	The objective of this study was to evaluate the performance of two methods using means and medians to establish thresholds using data from the Moroccan national influenza-like illness (ILI) syndromic surveillance system. Using three seasons of virologic ILI surveillance data (2014/2015 to 2016/2017), we used the MEM method to make calculations using the composite parameter recommended by WHO [20] ; this method estimates the proportion of laboratory-confirmed influenza ILI consultations among all outpatient consultations, or the product of weekly ILI consultations of total outpatient visits and weekly percentage of influenzapositive specimens among respiratory tests. Whichever method is used, analysis of surveillance data will provide information about seasonal thresholds and epidemic curves that may help health care personnel in the clinical management of respiratory illness after the start of influenza season.
cord-029245-ay15ybcm	2020	There is also an inescapable economic aspect to pandemics, in terms of both their dynamics (the way they spread and the reasons why they appear when and where they do), and their consequences, among which economic impacts loom large. (In 1918-19 the time it took Spanish flu to travel from one part of the world to another was measured in months.) All this leads to the conclusion that several features of the world we live in, such as high levels of economic integration and trade, widespread mass travel, and rapid modes of transport, make it much more vulnerable to a true pandemic. These and other features of the modern world also mean that the economic impact of an extensive epidemic is going to be much greater than was the case in, for example, 1968-69. Moreover, the early signs are that lockdowns may not have had such a dramatic effect on rates of infection and rapidity of spread during the first phase of this pandemic.
cord-048339-nzh87aux	2007	On the other hand, the model predicts that border screening for symptomatic infection, wearing a protective mask during travel, promoting early presentation of cases arising among arriving passengers and moderate reduction in travel volumes increase the delay only by a matter of days or weeks. In this paper we demonstrate how the delay to importation of an epidemic of pandemic strain influenza may be quantified in terms of the growing infection incidence in the source region, traveler volumes, border screening measures, travel duration, inflight transmission and the delay until an infected arrival initiates a chain of transmission that gathers momentum. For example, if R = 1.5, and we reduce the number of intending travelers from 400 to 10 per day, implement 100% flight-based quarantining, implement compulsory mask wearing during travel and presentation at 6 hours following symptom onset then there is a substantial probability (0.74) that the pandemic strain will never be imported (assuming the epidemic is confined to the source country).
cord-103418-deogedac	2010	title: Shift of percolation thresholds for epidemic spread between static and dynamic small-world networks The network was constructed as a 2-dimensional Watts-Strogatz model (500x500 square lattice with additional shortcuts), and the dynamics involved rewiring shortcuts in every time step of the epidemic spread. For both dynamic and static small-world we observe suppression of the average epidemic size dependence on network size in comparison with finite-size scaling known for regular lattice. Nonetheless, qualitatively the epidemic on dynamic small world behaves in the same way as on the static one for the given range of parameters (φ = 0.5 corresponds to every node in the network having on average two additional links). We have shown that introducing dynamics of the long-range links in a smallworld network significantly lowers an epidemic threshold in terms of probability of disease transmission, although the overall dependence on number of shortcuts stays the same.
cord-131667-zl5txjqx	2020	In this paper, we present EPIC30M, a large-scale epidemic corpus that contains 30 millions micro-blog posts, i.e., tweets crawled from Twitter, from year 2006 to 2020. Furthermore, a time-series analysis also suggests that some of the epidemics, i.e. 2010 Haiti Cholera and 2018 Kivu Ebola, show a surge in tweets before the respective start dates of the outbreaks, which signifies the importance of leveraging social media to conduct early signal detection. Through the time-series line plots, we observe that some of the epidemics, i.e. 2010 Haiti Cholera and 2018 Kivu Ebola, show a surge in tweets before the respective official start dates of the outbreaks, which signifies the importance of leveraging social media to conduct early signal detection. While early detection and warning systems for crisis events may reduce overall damage and negative impacts [31] , EPIC30M provides high volume and timely information that facilitate trend analysis and pattern recognition tasks for epidemic events.
cord-204796-zy1608lw	2020	In order for our simulations to be as realistic as possible it is important that we calibrate our model, introduce the proper time scale, choose the proper parameters and initial conditions, and, finally consider the adequate confinement strategies. Thus in Figure 11 we show the ratio of the second to the first epidemic peak, i.e. the one reached after the exit from lockdown to the one obtained during the confinement, as a function of the duration of the strict confinement, T 1 . For example, suppose that the confinement lasts 10 units of time in the model, or 50 days, (this situation corresponds to curve (c) in Figure 12 ), then any value of a 1 (the intermediate value of the infection rate of confined people) smaller than 1.8 would lead to a second peak lower than the first one.
cord-211511-56q57zwc	2020	Each of them is characterized by different regimes of the three social epidemics: in the refusal phase, people refused to accept reality despite the increasing numbers of deaths in other countries; in the suspended reality phase (started after the announcement of the first death in the country), people''s fear translated into anger about the looming feeling that things were about to change; finally, in the acceptance phase (started after the authorities imposed physical-distancing measures), people found a"new normal"for their daily activities. These change-points identify three phases, which are described next by dwelling on the peaks of the different language categories (days when their standardized fractions reached the maximum) and reporting the percentage increase at peak (the increase is compared to the average over the whole period of study, and its peak is denoted by ''max peak'' in Table 1 ).
cord-211611-c9w6235b	2020	We demonstrate that the epidemic renormalisation group approach to pandemics provides an effective and simple way to investigate the dynamics of disease transmission and spreading across different regions of the world. Our epidemic renormalisation group (eRG) approach is based * g.cacciapaglia@ipnl.in2p3.fr † sannino@cp3.sdu.dk upon a simpler set of equations, which can be extended in a straightforward way to include interactions between multiple regions of the world, without the need for powerful numerical simulations. Thus, the dictionary between the eRG equation for the epidemic strength α and the high-energy physics analog is It has been shown in [3] that α captures the essential information about the infected population within a sufficiently isolated region of the world. To quantitatively estimate the interaction between two regions of the world, we consider benchmark values for the parameters in the two beta functions using the results given in [3] .
cord-220618-segffkbn	2020	Disposing of a robust and comprehensive framework to classify the SARS-CoV-2 pandemic events reported across different countries not only can enhance early [19, 20] public and governmental responses in containing the spreading and/or better absorbing the impact of a rapidly emerging epidemic outbreak, but it can further provide new information to better understand real-world epidemics and to boost the forecasting power of existing models [21] [22] [23] [24] [25] [26] [27] [28] [29] . Moving to a polar representation, we classify the plumes'' form through a set of three geometric parameters yielding two complementary rating scales for the SARS-CoV-2 pandemic types: one according to their epidemic magnitude-labeled with roman numbers from I to X for increasing strengths-and measuring the "size" of a national outbreak, and a second one according to their intensity-labeled alphabetically from A to D for increasing speed-quantifying instead the damage inflicted on the population.
cord-222193-0b4o0ccp	2020	Based on the classical SIR model, we derive a simple modification for the dynamics of epidemics with a known incubation period of infection. We use the proposed model to analyze COVID-19 epidemic data in Armenia. Moreover, it is crucial to consider the final incubation period of the disease to construct a correct model for the COVID-19 case. In this study, we derive a system of integro-differential equations based on the rigorous master equation that adequately describes infection dynamics with an incubation period, e.g., COVID-19. In fact, the real data allows us to measure three main parameters: the exponential growth coefficient at the beginning of the epidemic; the minimum period of time, in which an infected person can transmit the infection; and the maximum period, when an infected person ceases to transmit the infection. In this paper, we introduced a version of SIR model for infection spreading with known incubation period. This model was applied to analyze the COVID-19 epidemic data in Armenia.
cord-234552-0pbg0ldm	2020	Specifically, [1] defines the discrete-time activity-driven adaptive-SIS model bound on the decay ratio of the infection probabilities of the nodes and proposes tractable optimization problems for optimal containment of the epidemic by minimizing the bound on the decay ratio. In this paper, we propose a new activity-driven and adaptive generalized SIS epidemic model, referred to as the A-SIYS epidemic, where we treat asymptomatic and symptomatic individuals as distinct infection states (see Section II for a formal definition and discussion). As a second contribution, we highlight a potential inaccuracy in the derivation of the upper bound on the decay ratio of the A-SIS epidemic model in [1] and obtain a counterpart of their result for a more general setting where nodes choose different numbers of other nodes to connect to (Section III). With the above definition in place, we now formally define the activity-driven and state-dependent evolution of the network or contact pattern and the epidemic states of individual nodes.
cord-238342-ecuex64m	2020	Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. Section 2 describes the proposed methodology called GROOMS+CMCM, followed by introduction of two key soft computing algorithms -BFGS-PNN and FRI which is adopted for forecasting some particular future trends as inputs to the MC model and generating fuzzy decision rules respectively. Being able to work with limited data, flexible in simulating input variables (hybrid deterministic and its counterpart), and informative outcomes coupled with fuzzy rules and risks, would be useful for experts making sound decision at the critical time.
cord-251581-8ubyveyt	2020	
cord-266898-f00628z4	2020	Processing statistical data of countries that have reached an epidemic peak has shown that this regular monitoring obeys a simple analytical regularity which allows us to answer the question: is this or that country that has already passed the threshold of the epidemic close to its peak or is still far from it? To achieve this goal, it is necessary to identify, evaluate and study the mentioned regular component of the error, using the statistics of those countries that have already reached a peak -the stationary level of the epidemic dynamics. This regular error component explains the reason for the failure of a priori mathematical modelling of probable epidemic events in different countries of the world. Processing statistical data of countries that have reached an epidemic peak has shown that this regular monitoring obeys a simple analytical regularity.
cord-267030-khzivbzy	2020	Spatial lifecourse epidemiology provides a new perspective to understand the course of epidemics, especially pandemics, and a new toolkit to predict the course of future epidemics on the basis of big data. The advanced spatial and digital technologies provide a new perspective to understand the transmission patterns of epidemics, especially pandemics, and a new toolkit to predict the progression of future epidemics on the basis of big data. Transparent, anonymous reporting of travel and contact history of a relatively large number of COVID-19 cases has been realized in China for the first time in the history of pandemics, thus opening a new avenue in the era of big data for more advanced, transdisciplinary approaches to refine results from mathematical prediction models and achieve a data-driven epidemic course of the COVID-19 in China (Kummitha, 2020) .
cord-270679-heg1h19l	2020	title: Perception-based influence factors of intention to adopt COVID-19 epidemic prevention in China OBJECTIVE: This work has attempted to examine the perception-based influence factors of individuals'' intention to adopt COVID-19 epidemic prevention in a modified behavioral framework. Therefore, there is a clear scope of identifying perception-based influence factors (PIFs) of individuals'' intention to adopt epidemic prevention (IAEP) during the outbreak of the COVID-19 epidemic. To sum up, first, governments'' guidelines on epidemic prevention, risk perception, epidemic knowledge, risk aversion, perceived behavioral control, subjective norms, and attitude towards epidemic prevention are suspected to be the drivers of individuals'' IAEP. A modified behavioral framework depicting the influence factors of individuals'' intention to adopt epidemic prevention. The core focus of this work was to examine the perception-based factors influencing the individuals'' intention to adopt COVID-19 epidemic prevention in a modified behavioral framework in terms of estimating the relevance as well as the relative importance of those factors.
cord-272031-o2hx667i	2015	Mortality in piglets less than two weeks old varied from 0 to 100 %, but it was usually lower than that described in outbreaks of diarrhoea caused by transmissible gastroenteritis virus (TGEV) which is another porcine coronavirus classically recognized as a cause of diarrhoea disease in swine. Although some reports have suggested that they could be associated with differences in the virulence of PEDV isolates, exhaustive challenge studies using pig adapted virus (not cell culture adapted isolates) in suckling piglets are needed to elucidate the role of the strain. The detection of PEDV specific antibodies is very useful, not for the investigation of diarrhoea outbreaks, but to determine whether an animal or a herd has previously been infected by this virus. Genetic characterization of porcine epidemic diarrhoea virus (PEDV) isolates from southern Vietnam during 2009-2010 outbreaks
cord-272744-j4q7pcfa	2018	Generally, epidemic spreading is considered to be a dynamic process in which the disease is transmitted from one individual to another via physical contact in peer-to-peer networks. Therefore, the effect of behavioral changes arises in three aspects [27] : (i) disease state of the individuals, e.g., vaccination [38] [39] [40] [41] [42] ; (ii) epidemic transmission and recovery rate [35, 43] ; (iii) topological structure of contact network, e.g., the adaptive process [44] [45] [46] [47] . Considering the two small peaks of information in Fig. 1 (b1) and (b2), we can also find the same relationship between the the two dynamic processes as that of two largest peaks, suggesting also the possible coupling effect of the awareness of epidemics and the infected cases of dengue. Inspired by the empirical results, we propose a network based nonlinear model to describe the interaction between epidemic spreading and information diffusion in this section.
cord-281437-cb3u1s7s	2019	The science of epidemiology has described patterns of disease in human populations, investigated the causes of those diseases, evaluated attempts to control them 7 and has been the foundation for public health responses to epidemic infections for over 100 years. The vulnerability of populations to outbreaks of zoonotic diseases such as Ebola, Middle East respiratory syndrome (MERS) and Nipah has increased, the rise and spread of drug-resistant infections, marked shifts in the ecology of known vectors (for example, the expanding range of Aedes mosquitoes) and massive amplification of transmission through globally connected, high-density urban areas (particularly relevant to Ebola, dengue, influenza and severe acute respiratory syndrome-related coronavirus SARS-CoV). Preparing for epidemics, therefore, requires global health, economic and political systems to be integrated just as much as infectious disease epidemiology, translational research and development, and community engagement.
cord-283485-xit6najq	2020	
cord-283793-ab1msb2m	2016	
cord-284220-55mckelv	2020	title: Estimation of a state of Corona 19 epidemic in August 2020 by multistage logistic model: a case of EU, USA, and World The article provides an estimate of the size and duration of the Covid-19 epidemic in August 2020 for the European Union (EU), the United States (US), and the World using a multistage logistical epidemiological model. The second is that at the beginning of the outbreak or at a new wave, the parameters of the models are not known (Keeling & Rohani, 2008) , or better they depend on the course of the epidemic. In the graph in Figure 4 , we can see that the trend in predicting the size of the epidemic and its duration was linear, then began to rise sharply at the end of June and reached its peak in mid-June with an estimate of 10 million final infections.
cord-288342-i37v602u	2015	Incorporating adaptive behavior into a model of disease spread can provide important insight into population health outcomes, as the activation of social distancing and other nonpharmaceutical interventions (NPIs) have been observed to have the ability to alter the course of an epidemic [50] [51] [52] . The authors studied their coupled "disease-behavior" model in well-mixed populations, in square lattice populations, in random network populations, and in SF network populations, and found that population structure acts as a "double-edged sword" for public health: it can promote high levels of voluntary vaccination and herd immunity given that the cost for vaccination is not too large, but small increases in the cost beyond a certain threshold would cause vaccination to plummet, and infections to rise, more dramatically than in well-mixed populations. The first mathematical models studied the adaptive dynamics of disease-behavior responses in the homogeneously mixed population, assuming that individuals interact with each other at the same contact rate, without restrictions on selecting potential partners.
cord-289003-vov6o1jx	2018	
cord-292026-cj43pn0f	2020	We (i) estimated the time-varying reproduction number (R(t)), the average number of secondary cases that each infected individual would infect at time t, to monitor the positive impact of restriction measures; (ii) applied the generalized logistic and the modified Richards models to describe the epidemic pattern and obtain short-term forecasts. Both models were fitted to data in order to characterize the pattern of the epidemic in its early phases, produce 5 days forecast of the number of new infections, and estimate the peak time and the final size of the epidemic curve. Estimated time trends and 5-day forecasts for daily COVID-19 deaths should theoretically follow, by ∼1-15 days, the trends of new cases, and are thus less informative for decision making, but are possibly less affected by testing and reporting variations (Figure 4 , results from the GLM model only).
cord-295534-bwa4wz94	2015	Porcine epidemic diarrhea virus (PEDV), a member of the genera Alphacoronavirus in the family Coronaviridae, causes acute diarrhea/vomiting, dehydration and high mortality in seronegative neonatal piglets. Porcine epidemic diarrhea virus (PEDV), a member of the genera Alphacoronavirus in the family Coronaviridae of the order Nidovirales, causes acute diarrhea, vomiting, dehydration and high mortality in neonatal piglets, resulting in significant economic losses. A recent study confirmed that PDCoV is enteropathogenic in pigs and acutely infects the small intestine, causing severe diarrhea and/or vomiting and atrophic enteritis, similar to the clinical signs of PEDV and TGEV infections (Jung et al., 2015) . Decreased activity of brush border membrane-bound digestive enzymes in small intestines from pigs experimentally infected with porcine epidemic diarrhea virus Isolation of porcine epidemic diarrhea virus in porcine cell cultures and experimental infection of pigs of different ages
cord-298872-gbi74g0n	2020	It is only necessary to collect the cumulative data of the daily infected over a week in some of the most important cities involved in the outbreak, to form a unique sequence of these numbers and then to calculate the first digit distribution. The main idea is to estimate an approximating function for the epidemic growth curve within a time horizon of Tf days, using only the first seven epidemic data points of fifty Italian cities, accounting for about the 30% of the population, considered as a unique sequence formed of 50x7 data-points, called 50_cities sequence. To classify various possible approximant curves we have calculated their Benford gof, showed in the Table 1 , together with the gof of the real Italian epidemic data, the logistic curve, of the cubic curve and of the 50_cities.
cord-299846-yx18oyv6	2020	
cord-301463-jzke8fop	2011	
cord-303030-8unrcb1f	2020	In this paper we discuss the different effects of these ingredients on the epidemic dynamics; the discussion is conducted with the help of two simple models, i.e. the classical SIR model and the recently introduced variant A-SIR (arXiv:2003.08720) which takes into account the presence of a large set of asymptomatic infectives. In the SIR model [1] [2] [3] [4] [5] , a population of constant size (this means the analysis is valid over a relatively short time-span, or we should consider new births and also deaths not due to the epidemic) is subdivided in three classes: Susceptibles, Infected (and by this also Infectives), and Removed. Acting on α or on β to get the same γ will produce different timescales for the dynamics; see Fig. 1 , in which we have used values of the parameters resulting from our fit of early data for the Northern Italy COVID-19 epidemic [7] .
cord-303651-fkdep6cp	2020	
cord-304925-9gvx3swf	2020	Abstract This paper uses the exploratory spatial data analysis and the geodetector method to analyze the spatial and temporal differentiation characteristics and the influencing factors of the COVID-19 (corona virus disease 2019) epidemic spread in mainland China based on the cumulative confirmed cases, average temperature, and socio-economic data. Thus, we selected the indicators reflecting the population distribution, population inflow from Wuhan, traffic accessibility, economic connection intensity, average temperature, and medical facilities conditions J o u r n a l P r e -p r o o f as the detection factors (Table 2) , and the epidemic spread rate as the detected factor to assess the formation mechanism for the spatial pattern of COVID-19 epidemic. Specifically, the influence of the population distribution (X1) on the spatial distribution of the epidemic spread rate was significantly different from the population inflow from Wuhan (X2), economic connection intensity (X4), and average temperature (X5), but not different from the traffic accessibility (X3) and medical facility conditions (X6).
cord-305327-hayhbs5u	2017	
cord-307945-wkz43axo	2020	Guidelines drafted by an expert group led by the French-speaking Association of Endocrine Surgery (AFCE) propose specific surgical management principles for thyroid, parathyroid, endocrine pancreas and adrenal surgery during and after the Covid-19 epidemic. Likewise, to meet their need for specific guidelines, the Frenchspeaking Association of Endocrine Surgery (AFCE) brought together a group of experts to propose principles for the surgical management of thyroid, parathyroid, endocrine pancreas and adrenal pathologies during the Covid-19 epidemic and afterwards, when surgical activity will be able to return gradually to its normal pattern. In the Covid-19 epidemic setting, its scheduling depends on the presence or absence of severe hypercalcemia, defined by a very high level of blood calcium > 3.5 mmol/l (140 mg/l) (17) , and/or the presence of clinical complications -acute pancreatitis secondary to HPT, brown tumor, calciphylaxis, fracture osteopenia, heart rhythm disorders (QT shortening on ECG, bradycardia with risk of asystole) with cardiac insufficiency (17) (18) (19) (20) .
cord-307946-1olapsmv	2020	
cord-309359-85xiqz2w	2015	
cord-313991-u2rkn5uh	2020	Within the framework of a two-component model of the COVID-19 epidemic, taking into account the special role of superspreaders, we consider the impact of the recovery factor and quarantine measures on the course of the epidemic, as well as the possibility of a second wave of morbidity. In its second part, we consider the impact of recovery processes on the dynamics of the epidemic in the framework of the two-component model, as well as the impact of the quarantine as a factor affecting the spread rate. In this phase, the spread rates are suppressed by quarantine measures with a factor of Q < 1, the relative number of the superspreaders reaches the endemic equilibrium value s 2 = 1 − γ/(Qg) and stops growing. After the quarantine is released, the number of the superspreaders returns to the former endemic equilibrium non-zero value, and a new wave of infected people appears among the sensitive.
cord-315885-iu5wg5ik	2013	title: Full-Length Genome Sequence of a Plaque-Cloned Virulent Porcine Epidemic Diarrhea Virus Isolate (USA/Iowa/18984/2013) from a Midwestern U.S. Swine Herd The complete genome sequence of PEDV strain USA/Iowa/18984/2013 was submitted to GenBank under the accession no. Complete genome sequence of porcine epidemic diarrhea virus strain USA/Colorado/2013 from the United States Complete genome sequence of porcine epidemic diarrhea virus strain AJ1102 isolated from a suckling piglet with acute diarrhea in China Complete genome sequence of a Chinese virulent porcine epidemic diarrhea virus strain Complete genome sequence of a recombinant porcine epidemic diarrhea virus strain from eastern China Complete genome sequence of a highly prevalent isolate of porcine epidemic diarrhea virus in south China Complete genome sequence of a variant porcine epidemic diarrhea virus strain isolated in central China Complete genome sequence of novel porcine epidemic diarrhea virus strain GD-1 in China
cord-317939-9x377kdv	2020	
cord-318004-r08k40ob	2017	
cord-329256-7njgmdd1	2011	
cord-331771-fhy98qt4	2021	
cord-332898-gi23un26	2020	
cord-335886-m0d72ntg	2020	
cord-341187-jqesw4e8	2020	
cord-345567-8d1076ge	2020	
cord-347349-caz5fwl1	2020	
cord-348658-fz5nfdf9	2020	The current study addressed whether prior experience with disease epidemics among the spine surgeon community had an impact on preparedness and response toward COVID-19. The current study addresses the role of prior infectious disease outbreaks on the preparedness, response, and impact of COVID-19 on spine surgeons across the world. In total, 902 spine surgeons responded to the survey, representing 91 distinct countries and 7 global regions (Africa, Asia, Australia, Europe, the Middle East, North America, and South America/Latin America Respondents overall reported a moderate to high level of concern regarding the COVID-19 outbreak, with a mean score of 3.7 ± 1.2 on a scale of one to five. Multivariate regression analysis, controlling for statistically significant demographic differences (geographic region, population, fellowship training, and practice breakdown), revealed that prior epidemic exposure was independently associated with an increase in respondents reporting personal health as a source of stress (OR 1.66; 95% CI 1.21-2.27; p = 0.0015), music as a coping strategy (OR 1.67; 95% CI 1.21-2.30; p < 0.001, and still performing elective spine surgery (OR 1.55; 95% CI 1.01-2.38; p = 0.0035).
cord-349421-qzgxe24c	2013	
cord-355291-fq0h895i	2020	In this study, we attempted to identify the role of E-government and COVID-19 word of mouth in terms of their direct impact on online social presence during the outbreak as well as their impacts mediated by epidemic protection and attitudes toward epidemic outbreaks. The study results revealed that the roles of E-government and COVID-19 word of mouth are positively related to online social presence during the outbreak. Epidemic protection and attitude toward epidemic outbreak were found to positively moderate the impact of the role of E-government and COVID-19 word of mouth on online social presence during the outbreak. We used five constructs (2019-nCoV-WOM, role of E-Govt, attitude toward epidemic outbreak, epidemic protection, and online social presence in the outbreak) with a conceptual multi-mediation model. Our study results revealed that attitude toward epidemic outbreak has a strong mediation effect between the role of E-Govt and online social presence during outbreaks, indicating that other governments and organizations can follow China''s safety model.
cord-355419-8txtk0b3	2020	In this paper, we utilize a discrete-time Markov chain approach and propose an epidemic model to describe virus propagation in the heterogeneous graph, which is used to represent individuals with intra social connections and mobility between individuals and common locations. Different from commonly used homogeneous mixing approaches [2, 3] , we give an analysis of epidemic spreading in population following a structured network with recurrent mobility pattern in this work. One widely used approach to analyse epidemic spreading in complex networks is metapopulation model, which divides the whole population into several geographical structured parts [13, 18] , and contacts among individuals in the same subpopulation are assumed to be well-mixed. In Section 2 , we give the formulation of epidemic model for virus spreading in networked population with recurrent mobility pattern, along with theoretical results of epidemic threshold. We formulate an epidemic model of virus propagating in networked population with recurrent mobility pattern between individuals and public areas.