Summary of your 'study carrel' ============================== This is a summary of your Distant Reader 'study carrel'. The Distant Reader harvested & cached your content into a collection/corpus. It then applied sets of natural language processing and text mining against the collection. The results of this process was reduced to a database file -- a 'study carrel'. The study carrel can then be queried, thus bringing light specific characteristics for your collection. These characteristics can help you summarize the collection as well as enumerate things you might want to investigate more closely. This report is a terse narrative report, and when processing is complete you will be linked to a more complete narrative report. Eric Lease Morgan Number of items in the collection; 'How big is my corpus?' ---------------------------------------------------------- 62 Average length of all items measured in words; "More or less, how big is each item?" ------------------------------------------------------------------------------------ 5796 Average readability score of all items (0 = difficult; 100 = easy) ------------------------------------------------------------------ 51 Top 50 statistically significant keywords; "What is my collection about?" ------------------------------------------------------------------------- 62 epidemic 9 disease 7 covid-19 5 model 5 SARS 5 Fig 4 network 4 PEDV 4 Health 4 Ebola 4 China 3 risk 3 porcine 3 pandemic 3 individual 3 datum 3 case 3 COVID-19 2 transmission 2 region 2 human 2 health 2 figure 2 SIR 2 Korea 1 wom 1 virus 1 surgery 1 store 1 social 1 sample 1 rumor 1 response 1 rat 1 quarantine 1 public 1 product 1 prior 1 prevention 1 plague 1 phase 1 ped 1 pcp 1 number 1 new 1 language 1 knowledge 1 italian 1 intervention 1 information Top 50 lemmatized nouns; "What is discussed?" --------------------------------------------- 3972 epidemic 1465 model 1413 disease 1094 time 1084 network 1078 case 967 outbreak 897 infection 820 number 802 population 780 virus 775 datum 772 health 703 individual 655 transmission 607 rate 579 risk 577 value 574 people 573 study 568 pandemic 560 % 537 analysis 533 information 515 contact 512 country 510 effect 469 result 439 system 439 impact 426 dynamic 405 node 401 probability 397 influenza 391 day 385 threshold 375 measure 370 method 369 factor 365 parameter 362 intervention 353 state 336 response 336 control 335 spread 334 research 334 region 325 process 323 approach 320 r Top 50 proper nouns; "What are the names of persons or places?" -------------------------------------------------------------- 450 al 399 COVID-19 373 PEDV 372 et 366 Fig 362 . 311 China 293 SARS 233 Ebola 212 Health 149 CoV-2 139 SIR 138 S 133 j 128 • 127 US 123 SC 113 k 107 A 106 Italy 105 Wuhan 98 Disease 97 Table 95 T 92 Africa 90 MC 88 t 88 SIS 85 World 82 March 79 Eq 78 United 76 South 74 PF 71 Korea 71 E 70 β 69 States 68 D 67 Hubei 63 M 63 Europe 62 n 62 WHO 60 Organization 60 J 60 Epidemic 60 Coronavirus 59 H1N1 58 − Top 50 personal pronouns nouns; "To whom are things referred?" ------------------------------------------------------------- 1966 we 1366 it 524 i 481 they 184 them 115 us 114 he 78 one 50 itself 39 themselves 36 you 15 she 15 him 10 me 5 's 4 oneself 3 s 2 ℝ 2 ourselves 2 o139 2 o103 2 her 1 β 1 yourself 1 u 1 himself 1 em Top 50 lemmatized verbs; "What do things do?" --------------------------------------------- 11291 be 2195 have 1044 use 514 show 506 spread 503 base 442 infect 430 do 397 consider 338 take 335 include 330 make 310 give 309 provide 307 increase 299 follow 294 become 282 find 276 identify 255 reduce 251 affect 248 see 242 describe 237 cause 232 lead 230 report 213 estimate 208 need 208 know 206 compare 205 relate 203 develop 201 understand 186 occur 186 obtain 182 indicate 181 require 178 assume 177 predict 176 represent 169 model 168 accord 167 remain 166 emerge 165 observe 160 allow 159 propose 153 depend 152 determine 151 apply Top 50 lemmatized adjectives and adverbs; "How are things described?" --------------------------------------------------------------------- 1238 not 736 also 717 more 703 other 679 such 621 social 601 different 559 - 515 first 510 high 432 infected 431 large 425 well 422 new 419 only 391 public 383 however 371 infectious 362 most 362 human 347 early 330 many 292 as 288 thus 280 small 263 long 263 covid-19 256 susceptible 252 very 251 low 244 same 242 global 234 medical 233 then 231 porcine 227 non 214 local 211 second 205 therefore 204 possible 199 important 196 even 193 available 189 out 185 severe 184 so 181 real 179 less 179 asymptomatic 176 spatial Top 50 lemmatized superlative adjectives; "How are things described to the extreme?" ------------------------------------------------------------------------- 116 most 66 least 62 good 43 large 28 high 24 Most 17 simple 13 late 13 bad 11 great 10 short 8 small 6 low 5 big 4 near 4 long 4 fast 4 early 3 strong 3 easy 2 severe 2 heavy 1 ϕ 1 young 1 wide 1 w(x 1 slight 1 risky 1 quick 1 poor 1 old 1 k→l 1 humanity-'for 1 few 1 deadly 1 crude 1 close 1 anthropologistsb 1 Least 1 -which 1 -therefore 1 -VHF 1 -20/2 Top 50 lemmatized superlative adverbs; "How do things do to the extreme?" ------------------------------------------------------------------------ 246 most 32 least 13 well 2 worst 1 highest Top 50 Internet domains; "What Webbed places are alluded to in this corpus?" ---------------------------------------------------------------------------- 16 doi.org 4 github.com 2 coronavirus.jhu.edu 1 www.worldometers.info 1 www.who.int 1 www.weibo.com 1 www.tianqi.com 1 www.nature.com 1 www.github.com 1 www.frontiersin.org 1 www.chinacdc.cn 1 www.aasv.org 1 theconversation.com 1 news.qq.com 1 creativecommons 1 creat Top 50 URLs; "What is hyperlinked from this corpus?" ---------------------------------------------------- 5 http://doi.org/10.1101/2020.08.31.20185165 4 http://doi.org/10.1101/2020.08.14.20174557 2 http://doi.org/10.1101/2020.08.14.20170878 2 http://doi.org/10.1101/2020.06.01.20118869 2 http://coronavirus.jhu.edu/map.html 1 http://www.worldometers.info/coronavirus/ 1 http://www.who.int 1 http://www.weibo.com/ 1 http://www.tianqi.com 1 http://www.nature.com/reprints 1 http://www.github.com/junhua/epic 1 http://www.frontiersin.org/articles/10.3389/fpubh 1 http://www.chinacdc.cn/en/ 1 http://www.aasv.org/pedv 1 http://theconversation.com/ 1 http://news.qq.com/zt2020/page/feiyan.htm 1 http://github.com/xinhuayu/returnepidemic/ 1 http://github.com/pcm-dpc/COVID-19/tree/master/dati-regioni 1 http://github.com/jihoo-kim/Data-Science-for-COVID-19 1 http://github.com/Jiasong-Duan/COVID-19-epidemic-trajectories 1 http://doi.org/10.1101/2020.02.26.20027797 1 http://doi.org/10.1016/j.chaos.2020.110016 1 http://doi.org/10 1 http://creativecommons 1 http://creat Top 50 email addresses; "Who are you gonna call?" ------------------------------------------------- 1 shylmath@hotmail.com 1 sannino@cp3.sdu.dk 1 ivan.bms.2011@gmail.com 1 giuseppe.gaeta@unimi.it 1 g.cacciapaglia@ipnl.in2p3.fr 1 dimaschko@gmx.net Top 50 positive assertions; "What sentences are in the shape of noun-verb-noun?" ------------------------------------------------------------------------------- 28 epidemic spread rate 8 epidemic spreading process 5 epidemic is not 5 epidemic is over 4 % were less 4 data are available 4 epidemic spreading probability 4 model is not 4 viruses do not 3 data are not 3 effect is positive 3 effects are more 3 epidemic are more 3 epidemic does not 3 epidemic is first 3 epidemic spreading dynamics 3 epidemic spreading significantly 3 individuals are equivalent 3 model described above 3 model does not 3 models are not 3 numbers are low 3 outbreak is smaller 3 people are more 3 rate was fastest 3 rate was higher 3 results are very 3 system is therefore 3 value was higher 2 % were willing 2 case reports epidemic 2 cases are asymptomatic 2 cases does not 2 contacts is available 2 contacts is mandatory 2 contacts is not 2 countries are not 2 countries is similar 2 data are insufficient 2 data made available 2 data using bootstrapping 2 disease is generally 2 disease was not 2 diseases affecting humans 2 diseases do not 2 diseases have high 2 effect is absent 2 effect is not 2 epidemic affects people 2 epidemic are different Top 50 negative assertions; "What sentences are in the shape of noun-verb-no|not-noun?" --------------------------------------------------------------------------------------- 2 data are not comparable 2 effect is not significant 2 viruses do not usually 1 analysis is not refined 1 analysis were not significant 1 countries are not immune 1 data are not convenient 1 data were not sufficient 1 disease was not previously 1 epidemic has not only 1 epidemic is no time 1 epidemic is not fully 1 epidemic is not over 1 epidemics have not always 1 individual has no previous 1 individuals have no chance 1 model does not really 1 model is not contact 1 model is not new 1 models are not crystal 1 models is not sufficient 1 networks are not static 1 networks is not homogeneous 1 networks is not only 1 outbreaks were no better 1 outbreaks were no more 1 pandemic are not yet 1 people do not just 1 people is not too 1 rate is not uniform 1 rate is not very 1 result has not yet 1 results showed no unique 1 system is not overstretched 1 times are not too 1 value is not important 1 virus are not fully 1 virus is not new 1 viruses do not continuously A rudimentary bibliography -------------------------- id = cord-270679-heg1h19l author = Ahmad, Munir title = Perception-based influence factors of intention to adopt COVID-19 epidemic prevention in China date = 2020-07-27 keywords = IAEP; epidemic; individual; prevention summary = 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. doi = 10.1016/j.envres.2020.109995 id = cord-211511-56q57zwc author = Aiello, Luca Maria title = How Epidemic Psychology Works on Social Media: Evolution of responses to the COVID-19 pandemic date = 2020-07-26 keywords = Strong; category; epidemic; language; phase summary = 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 ). doi = nan id = cord-299846-yx18oyv6 author = Amar, Patrick title = Pandæsim: An Epidemic Spreading Stochastic Simulator date = 2020-09-18 keywords = France; epidemic; number; region summary = doi = 10.3390/biology9090299 id = cord-307945-wkz43axo author = Baud, Grégory title = Endocrine surgery during and after the Covid-19 epidemic: Expert guidelines in France date = 2020-04-30 keywords = covid-19; epidemic; surgery summary = 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) . doi = 10.1016/j.jviscsurg.2020.04.018 id = cord-281437-cb3u1s7s author = Bedford, Juliet title = A new twenty-first century science for effective epidemic response date = 2019-11-06 keywords = Ebola; disease; epidemic; health; response summary = 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. doi = 10.1038/s41586-019-1717-y id = cord-220618-segffkbn author = Bonamassa, Ivan title = Geometric characterization of SARS-CoV-2 pandemic events date = 2020-07-20 keywords = Fig; SARS; epidemic summary = 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. doi = nan id = cord-018761-vm86d4mj author = Bradt, David A. title = Technical Annexes date = 2017-11-08 keywords = bias; case; disease; epidemic; sample summary = doi = 10.1007/978-3-319-69871-7_8 id = cord-020544-kc52thr8 author = Bradt, David A. title = Technical Annexes date = 2019-12-03 keywords = Health; case; disease; epidemic summary = 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) doi = 10.1007/978-3-030-04801-3_7 id = cord-289003-vov6o1jx author = Burdet, C. title = Need for integrative thinking to fight against emerging infectious diseases. Proceedings of the 5th seminar on emerging infectious diseases, March 22, 2016 – current trends and proposals date = 2018-02-28 keywords = Ebola; MERS; SARS; epidemic; human; risk summary = doi = 10.1016/j.respe.2017.08.001 id = cord-211611-c9w6235b author = Cacciapaglia, Giacomo title = Interplay of social distancing and border restrictions for pandemics (COVID-19) via the epidemic Renormalisation Group framework date = 2020-05-11 keywords = epidemic; region summary = 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] . doi = nan id = cord-048339-nzh87aux author = Caley, Peter title = The Waiting Time for Inter-Country Spread of Pandemic Influenza date = 2007-01-03 keywords = delay; epidemic; figure summary = 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). doi = 10.1371/journal.pone.0000143 id = cord-272031-o2hx667i author = Carvajal, Ana title = Porcine epidemic diarrhoea: new insights into an old disease date = 2015-09-29 keywords = PEDV; epidemic; ped; porcine summary = 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 doi = 10.1186/s40813-015-0007-9 id = cord-283793-ab1msb2m author = Chanchan, Li title = Modeling and analysis of epidemic spreading on community network with node's birth and death date = 2016-10-31 keywords = epidemic; network summary = doi = 10.1016/s1005-8885(16)60061-4 id = cord-024683-3v8i39rk author = Chen, Deng title = Epilepsy control during an epidemic: emerging approaches and a new management framework date = 2020-05-12 keywords = Apps; China; epidemic; epilepsy summary = 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. doi = 10.1186/s42494-020-00015-z id = cord-029245-ay15ybcm author = Davies, Stephen title = Pandemics and the consequences of COVID‐19 date = 2020-06-29 keywords = epidemic; pandemic summary = 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. doi = 10.1111/ecaf.12415 id = cord-313991-u2rkn5uh author = Dimaschko, J. title = Superspreading as a Regular Factor of the COVID-19 Pandemic: II. Quarantine Measures and the Second Wave date = 2020-08-16 keywords = epidemic; quarantine summary = 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. doi = 10.1101/2020.08.14.20174557 id = cord-298872-gbi74g0n author = FIORITI, V. title = Estimating the epidemic growth dynamics within the first week date = 2020-08-16 keywords = Benford; epidemic; italian summary = 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. doi = 10.1101/2020.08.14.20170878 id = cord-355419-8txtk0b3 author = Feng, Liang title = Epidemic in networked population with recurrent mobility pattern date = 2020-06-25 keywords = epidemic summary = 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. doi = 10.1016/j.chaos.2020.110016 id = cord-238342-ecuex64m author = Fong, Simon James title = Composite Monte Carlo Decision Making under High Uncertainty of Novel Coronavirus Epidemic Using Hybridized Deep Learning and Fuzzy Rule Induction date = 2020-03-22 keywords = Eqn; FRI; datum; epidemic; model summary = 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. doi = nan id = cord-317939-9x377kdv author = Fu, You-Lei title = Fuzzy Logic Programming and Adaptable Design of Medical Products for the COVID-19 Anti-epidemic Normalization date = 2020-09-16 keywords = anti; design; epidemic; product summary = doi = 10.1016/j.cmpb.2020.105762 id = cord-303030-8unrcb1f author = Gaeta, Giuseppe title = Social distancing versus early detection and contacts tracing in epidemic management date = 2020-07-16 keywords = COVID; Italy; SIR; epidemic summary = 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] . doi = 10.1016/j.chaos.2020.110074 id = cord-305327-hayhbs5u author = Gonzalez, Jean-Paul title = Global Spread of Hemorrhagic Fever Viruses: Predicting Pandemics date = 2017-09-19 keywords = Africa; Ebola; disease; epidemic; host; pandemic; risk; virus summary = doi = 10.1007/978-1-4939-6981-4_1 id = cord-027757-zb4wxt85 author = Hardiman, David title = The Influenza Epidemic of 1918 and the Adivasis of Western India date = 2012-03-09 keywords = Bombay; Dangs; Gujarat; India; adivasi; epidemic summary = doi = 10.1093/shm/hks015 id = cord-315885-iu5wg5ik author = Hoang, Hai 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 date = 2013-12-19 keywords = PEDV; epidemic summary = 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 doi = 10.1128/genomea.01049-13 id = cord-301463-jzke8fop author = Hollingsworth, T. Déirdre title = Mitigation Strategies for Pandemic Influenza A: Balancing Conflicting Policy Objectives date = 2011-02-10 keywords = epidemic; figure; intervention; pandemic summary = doi = 10.1371/journal.pcbi.1001076 id = cord-234552-0pbg0ldm author = Hota, Ashish R. title = A Generalized SIS Epidemic Model on Temporal Networks with Asymptomatic Carriers and Comments on Decay Ratio date = 2020-07-26 keywords = SIS; epidemic summary = 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. doi = nan id = cord-331771-fhy98qt4 author = Huang, He title = Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading date = 2021-01-01 keywords = epidemic; knowledge; rumor summary = doi = 10.1016/j.amc.2020.125536 id = cord-345567-8d1076ge author = Ivanov, Dmitry title = Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case date = 2020-03-24 keywords = Ivanov; disruption; epidemic summary = doi = 10.1016/j.tre.2020.101922 id = cord-267030-khzivbzy author = Jia, Peng title = Understanding the Epidemic Course in Order to Improve Epidemic Forecasting date = 2020-10-01 keywords = covid-19; epidemic summary = 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) . doi = 10.1029/2020gh000303 id = cord-295534-bwa4wz94 author = Jung, Kwonil title = Porcine epidemic diarrhea virus infection: Etiology, epidemiology, pathogenesis and immunoprophylaxis date = 2015-02-26 keywords = PEDV; epidemic; porcine summary = 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 doi = 10.1016/j.tvjl.2015.02.017 id = cord-024746-ijlnefz3 author = Koher, Andreas title = Contact-Based Model for Epidemic Spreading on Temporal Networks date = 2019-08-02 keywords = Fig; Networks; epidemic summary = 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. doi = 10.1103/physrevx.9.031017 id = cord-016387-ju4130bq author = Last, John title = A Brief History of Advances Toward Health date = 2005 keywords = Jenner; cause; disease; epidemic; health; public summary = 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. doi = 10.1007/0-387-24103-5_1 id = cord-329256-7njgmdd1 author = Leecaster, Molly title = Modeling the variations in pediatric respiratory syncytial virus seasonal epidemics date = 2011-04-21 keywords = RSV; epidemic; model summary = doi = 10.1186/1471-2334-11-105 id = cord-131667-zl5txjqx author = Liu, Junhua title = EPIC30M: An Epidemics Corpus Of Over 30 Million Relevant Tweets date = 2020-06-09 keywords = EPIC30; Twitter; epidemic summary = 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. doi = nan id = cord-018151-5su98uan author = Lynteris, Christos title = Introduction: Infectious Animals and Epidemic Blame date = 2019-10-12 keywords = Aedes; Health; animal; disease; epidemic; human; plague; rat summary = 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. doi = 10.1007/978-3-030-26795-7_1 id = cord-292026-cj43pn0f author = Moirano, Giovenale title = Approaches to Daily Monitoring of the SARS-CoV-2 Outbreak in Northern Italy date = 2020-05-22 keywords = March; epidemic summary = 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). doi = 10.3389/fpubh.2020.00222 id = cord-204796-zy1608lw author = Nakamura, G. title = Confinement strategies in a simple SIR model date = 2020-04-20 keywords = confinement; epidemic summary = 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. doi = nan id = cord-006203-wwpd26bx author = Nguyen, Vinh-Kim title = When the world catches cold: Thinking with influenza date = 2016-02-26 keywords = Keck; epidemic; flu summary = 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. doi = 10.1057/biosoc.2016.2 id = cord-266898-f00628z4 author = Nikitenkova, S. title = It''s the very time to learn a pandemic lesson: why have predictive techniques been ineffective when describing long-term events? date = 2020-06-03 keywords = datum; epidemic summary = 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. doi = 10.1101/2020.06.01.20118869 id = cord-103418-deogedac author = Ochab, J. K. title = Shift of percolation thresholds for epidemic spread between static and dynamic small-world networks date = 2010-11-12 keywords = epidemic; network summary = 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. doi = 10.1140/epjb/e2011-10975-6 id = cord-318004-r08k40ob author = Raina MacIntyre, C. title = Converging and emerging threats to health security date = 2017-11-27 keywords = Health; Organization; epidemic; new; risk summary = doi = 10.1007/s10669-017-9667-0 id = cord-028048-0oqv2jom author = Rguig, Ahmed title = Establishing seasonal and alert influenza thresholds in Morocco date = 2020-06-29 keywords = ILI; MEM; epidemic summary = 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. doi = 10.1186/s12889-020-09145-y id = cord-222193-0b4o0ccp author = Saakian, David B. title = A simple statistical physics model for the epidemic with incubation period date = 2020-04-13 keywords = SIR; epidemic summary = 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. doi = nan id = cord-349421-qzgxe24c author = Shang, Yilun title = Modeling epidemic spread with awareness and heterogeneous transmission rates in networks date = 2013-05-03 keywords = epidemic summary = doi = 10.1007/s10867-013-9318-8 id = cord-309359-85xiqz2w author = Song, Daesub title = Porcine epidemic diarrhea: a review of current epidemiology and available vaccines date = 2015-07-29 keywords = Korea; PEDV; epidemic; porcine summary = doi = 10.7774/cevr.2015.4.2.166 id = cord-251581-8ubyveyt author = Szymkowiak, Andrzej title = In-store epidemic behavior: scale development and validation date = 2020-05-04 keywords = SARS; consumer; covid-19; epidemic; store summary = doi = nan id = cord-015967-kqfyasmu author = Tagore, Somnath title = Epidemic Models: Their Spread, Analysis and Invasions in Scale-Free Networks date = 2015-03-20 keywords = epidemic; individual; infection; network summary = 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] . doi = 10.1007/978-3-319-15916-4_1 id = cord-303651-fkdep6cp author = Thompson, Robin N. title = Key questions for modelling COVID-19 exit strategies date = 2020-08-12 keywords = COVID-19; SARS; datum; epidemic; estimate; model; transmission summary = doi = 10.1098/rspb.2020.1405 id = cord-020610-hsw7dk4d author = Thys, Séverine title = Contesting the (Super)Natural Origins of Ebola in Macenta, Guinea: Biomedical and Popular Approaches date = 2019-10-12 keywords = EVD; Ebola; Guinea; Macenta; disease; epidemic; model summary = 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. doi = 10.1007/978-3-030-26795-7_7 id = cord-335886-m0d72ntg author = Tomie, Toshihisa title = Relations of parameters for describing the epidemic of COVID―19 by the Kermack―McKendrick model date = 2020-03-03 keywords = epidemic summary = doi = 10.1101/2020.02.26.20027797 id = cord-283485-xit6najq author = Van Damme, Wim title = The COVID-19 pandemic: diverse contexts; different epidemics—how and why? date = 2020-07-27 keywords = COVID-19; China; Health; SARS; disease; epidemic; transmission summary = doi = 10.1136/bmjgh-2020-003098 id = cord-288342-i37v602u author = Wang, Zhen title = Coupled disease–behavior dynamics on complex networks: A review date = 2015-07-08 keywords = Fig; behavior; disease; epidemic; individual; model; network summary = 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. doi = 10.1016/j.plrev.2015.07.006 id = cord-348658-fz5nfdf9 author = Weiner, Joseph A. title = Learning from the past: did experience with previous epidemics help mitigate the impact of COVID-19 among spine surgeons worldwide? date = 2020-06-04 keywords = COVID-19; GHSI; epidemic; prior summary = 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). doi = 10.1007/s00586-020-06477-6 id = cord-304925-9gvx3swf author = Xie, Zhixiang title = Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors date = 2020-07-14 keywords = Wuhan; covid-19; epidemic summary = 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). doi = 10.1016/j.scitotenv.2020.140929 id = cord-307946-1olapsmv author = Xu, Zhijie title = Primary Care Practitioners’ Barriers to and Experience of COVID-19 Epidemic Control in China: a Qualitative Study date = 2020-08-31 keywords = China; control; epidemic; pcp summary = doi = 10.1007/s11606-020-06107-3 id = cord-355291-fq0h895i author = Yasir, Ammar title = Modeling Impact of Word of Mouth and E-Government on Online Social Presence during COVID-19 Outbreak: A Multi-Mediation Approach date = 2020-04-24 keywords = Govt; epidemic; social; wom summary = 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. doi = 10.3390/ijerph17082954 id = cord-341187-jqesw4e8 author = Yu, Xinhua title = Modeling Return of the Epidemic: Impact of Population Structure, Asymptomatic Infection, Case Importation and Personal Contacts date = 2020-08-27 keywords = case; covid-19; epidemic summary = doi = 10.1016/j.tmaid.2020.101858 id = cord-347349-caz5fwl1 author = Yu, Xinhua title = Distinctive trajectories of COVID-19 epidemic by age and gender: a retrospective modeling of the epidemic in South Korea date = 2020-07-02 keywords = Korea; covid-19; epidemic summary = doi = 10.1016/j.ijid.2020.06.101 id = cord-019114-934xczf3 author = Zhan, Xiu-Xiu title = Epidemic dynamics on information-driven adaptive networks date = 2018-02-16 keywords = Fig; epidemic summary = doi = 10.1016/j.chaos.2018.02.010 id = cord-272744-j4q7pcfa author = Zhan, Xiu-Xiu title = Coupling dynamics of epidemic spreading and information diffusion on complex networks date = 2018-09-01 keywords = Fig; epidemic; information summary = 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. doi = 10.1016/j.amc.2018.03.050 id = cord-332898-gi23un26 author = Zhou, Lingyun title = CIRD-F: Spread and Influence of COVID-19 in China date = 2020-04-07 keywords = China; Hubei; epidemic summary = doi = 10.1007/s12204-020-2168-1 id = cord-284220-55mckelv author = batista, m. title = Estimation of a state of Corona 19 epidemic in August 2020 by multistage logistic model: a case of EU, USA, and World date = 2020-09-02 keywords = covid-19; epidemic summary = 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. doi = 10.1101/2020.08.31.20185165