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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
15 117-134 Species denotes Novel Coronavirus Tax:2697049
16 600-611 Species denotes coronavirus Tax:11118
17 775-781 Species denotes people Tax:9606
18 795-799 Species denotes Taro Tax:4460
19 20-28 Disease denotes COVID-19 MESH:C000657245
20 514-522 Disease denotes COVID-19 MESH:C000657245
21 1408-1416 Disease denotes COVID-19 MESH:C000657245
31 1789-1794 Species denotes human Tax:9606
32 2385-2391 Species denotes people Tax:9606
33 2624-2630 Species denotes people Tax:9606
34 3116-3122 Species denotes people Tax:9606
35 3154-3160 Species denotes people Tax:9606
36 1661-1669 Disease denotes COVID-19 MESH:C000657245
37 3163-3170 Disease denotes anxiety MESH:D001007
38 3288-3296 Disease denotes COVID-19 MESH:C000657245
39 3370-3388 Disease denotes systematic failure MESH:D006333
43 3725-3731 Species denotes people Tax:9606
44 4485-4503 Disease denotes infectious disease MESH:D003141
45 4562-4570 Disease denotes COVID-19 MESH:C000657245
51 5871-5877 Species denotes people Tax:9606
52 6073-6079 Species denotes people Tax:9606
53 5823-5831 Disease denotes COVID-19 MESH:C000657245
54 5974-5993 Disease denotes infectious diseases MESH:D003141
55 6039-6047 Disease denotes infected MESH:D007239
62 6653-6661 Disease denotes COVID-19 MESH:C000657245
63 6817-6825 Disease denotes Infected MESH:D007239
64 6836-6855 Disease denotes infectious diseases MESH:D003141
65 7509-7527 Disease denotes infectious disease MESH:D003141
66 7564-7572 Disease denotes infected MESH:D007239
67 7719-7727 Disease denotes Infected MESH:D007239
69 8690-8699 Disease denotes infection MESH:D007239
73 10094-10100 Species denotes SARS-2 Tax:2697049
74 9593-9601 Disease denotes COVID-19 MESH:C000657245
75 10137-10145 Disease denotes COVID-19 MESH:C000657245
77 12869-12876 Species denotes patient Tax:9606
79 13362-13368 Species denotes people Tax:9606
81 15427-15433 Species denotes people Tax:9606
86 15702-15708 Species denotes people Tax:9606
87 15673-15681 Disease denotes COVID-19 MESH:C000657245
88 37259-37268 Disease denotes infection MESH:D007239
92 18592-18598 Species denotes people Tax:9606
93 18642-18648 Species denotes people Tax:9606
95 19420-19428 Disease denotes infected MESH:D007239
98 20072-20078 Species denotes people Tax:9606
99 20051-20059 Disease denotes infected MESH:D007239
103 20371-20380 Disease denotes infection MESH:D007239
104 20447-20455 Disease denotes infected MESH:D007239
105 20477-20486 Disease denotes infection MESH:D007239
107 20687-20696 Disease denotes infection MESH:D007239
110 20771-20781 Disease denotes randomness MESH:C562757
111 20836-20845 Disease denotes infection MESH:D007239
113 21032-21041 Disease denotes infection MESH:D007239
116 22688-22694 Species denotes people Tax:9606
117 22994-23000 Species denotes people Tax:9606
119 23361-23371 Disease denotes infections MESH:D007239
121 23708-23717 Disease denotes infection MESH:D007239
124 25823-25832 Disease denotes infection MESH:D007239
125 25893-25902 Disease denotes infection MESH:D007239
134 28096-28102 Species denotes people Tax:9606
135 28157-28163 Species denotes people Tax:9606
136 28243-28249 Species denotes people Tax:9606
137 27613-27622 Disease denotes infection MESH:D007239
138 27730-27739 Disease denotes infection MESH:D007239
139 27793-27802 Disease denotes infection MESH:D007239
140 28103-28111 Disease denotes infected MESH:D007239
141 28164-28172 Disease denotes infected MESH:D007239
143 28608-28617 Disease denotes infection MESH:D007239
146 28806-28815 Disease denotes infection MESH:D007239
147 29158-29167 Disease denotes infection MESH:D007239
150 29465-29471 Species denotes people Tax:9606
151 29630-29636 Species denotes people Tax:9606
155 30585-30591 Species denotes people Tax:9606
156 30596-30604 Disease denotes infected MESH:D007239
157 30656-30669 Disease denotes low infection MESH:D007239
160 32121-32139 Disease denotes infectious disease MESH:D003141
161 32348-32356 Disease denotes COVID-19 MESH:C000657245
163 34517-34526 Disease denotes infection MESH:D007239
169 35356-35362 Species denotes people Tax:9606
170 34944-34964 Disease denotes lower new infections MESH:C000657245
171 35003-35013 Disease denotes infections MESH:D007239
172 35166-35174 Disease denotes infected MESH:D007239
173 35306-35315 Disease denotes infection MESH:D007239
176 35829-35837 Disease denotes COVID-19 MESH:C000657245
177 36033-36043 Disease denotes infections MESH:D007239
181 37787-37805 Disease denotes infectious disease MESH:D003141
184 38531-38535 Disease denotes SARS MESH:D045169
185 38559-38567 Disease denotes COVID-19 MESH:C000657245
187 39698-39707 Disease denotes infection MESH:D007239

LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T1 3163-3170 Phenotype denotes anxiety http://purl.obolibrary.org/obo/HP_0000739
T2 8418-8434 Phenotype denotes highly sensitive http://purl.obolibrary.org/obo/HP_0041092
T3 26381-26386 Phenotype denotes falls http://purl.obolibrary.org/obo/HP_0002527
T4 26621-26626 Phenotype denotes falls http://purl.obolibrary.org/obo/HP_0002527
T5 27814-27819 Phenotype denotes falls http://purl.obolibrary.org/obo/HP_0002527
T6 36989-37005 Phenotype denotes highly sensitive http://purl.obolibrary.org/obo/HP_0041092

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T9 0-2 Sentence denotes 1.
T10 3-15 Sentence denotes Introduction
T11 16-92 Sentence denotes The COVID-19 pandemic has attacked the whole world over the past few months.
T12 93-301 Sentence denotes The key features of the Novel Coronavirus, such as long incubation period, high infectiousness, and asymptomatic transmission, were not perceived at the beginning until they were gradually unveiled [1,2,3,4].
T13 302-553 Sentence denotes The WHO and governments keep disclosing epidemic information, but the disclosure is based on their own endowments, preferences, and perceptions, resulting in misleading information at least in the early stage of COVID-19 outbreak, such as “Masks work?
T14 554-647 Sentence denotes NO” (quoted from Scott Atlas, the White House coronavirus task force member), “This is a flu.
T15 648-846 Sentence denotes This is like a flu” (quoted from Donald Trump, the president of the US), and “There is some immune system variation with Asian people”(quoted form Taro Aso, the Deputy Prime Minister of Japan), etc.
T16 847-925 Sentence denotes This information failed to alert the public but let their guards down instead.
T17 926-1060 Sentence denotes Then, the high mortality rate and emergency announcements subsequently incited widespread fear and exacerbated the epidemic situation.
T18 1061-1186 Sentence denotes Theoretically, a systematic provision of timely and effective information from the government can mitigate the downsides [5].
T19 1187-1336 Sentence denotes However, in the real world, speed entails inaccuracy and cognitive uncertainty that keep government away from accomplishing such a tough mission [6].
T20 1337-1587 Sentence denotes Thus, in the early stage of an epidemic with strong externalities like COVID-19, the government’s choice between timeliness and effectiveness of intervention strategies raises a theoretical challenge for the management of urgent public health crisis.
T21 1588-1833 Sentence denotes The key to successfully contain the spread of an unexpected disease like COVID-19 is to understand the complicated two-way interaction between the dynamics of disease and those of information (and the human behavior response to information) [7].
T22 1834-2017 Sentence denotes Information might either amplify or diminish the public’s response to a risk event, depending on the transmission of risk information and public’s reactions at the time it occurs [8].
T23 2018-2235 Sentence denotes At the micro-level, one’s behavior depends on the epidemiological status of the disease, the individual’s knowledge about it (information accessed), misinformation, and the individual’s education and income level [9].
T24 2236-2469 Sentence denotes Along with the spread of disease in social life (physical level), information spreads in a virtual network, which brings the awareness of crisis for people [10,11,12], leading them to take preventive measures to stay healthy [13,14].
T25 2470-2595 Sentence denotes Therefore, the spread of disease facilitates the spread of information, which in turn inhibits the spread of disease [15,16].
T26 2596-2744 Sentence denotes However, on the other hand, people usually get illogical, fail to discern falsity, and disregard the truth during information dissemination [17,18].
T27 2745-2902 Sentence denotes Misleading information seems to have a natural disposition to resonate with public opinions, which causes spontaneous misrepresentation in transmission [19].
T28 2903-3057 Sentence denotes In addition, discussions on epidemic bring panic [20] and aggravate the harm of the epidemic [21], which will be further exaggerated by social media [22].
T29 3058-3228 Sentence denotes Meanwhile, increasing uncertainty about the disease makes people feel loss of control and boost people’s anxiety [23], usually accompanied by psychological distress [24].
T30 3229-3394 Sentence denotes Therefore, information is critical to fighting against the COVID-19-crisis [25,26], and improper information management strategy may lead to systematic failure [27].
T31 3395-3586 Sentence denotes The government, as the main governing body, is the most critical (information) node in the entire network, since it can intervene in information by “blocking” [28,29] and “disclosing” [5,30].
T32 3587-3829 Sentence denotes The minimal blocking implies a free and open information environment, which stimulates information to be widely diffused and induces more people to take self-protective measures [31], but “rumors” might also proliferate at the mean time [24].
T33 3830-3965 Sentence denotes Even if (under certain premises) some “rumors” are accurate [7,32,33], it might still interrupt the prevention efforts to the epidemic.
T34 3966-4247 Sentence denotes It is always believed that government should perform as a central node to disclose accurate and up-to-date information to the entire society, so as to keep the public away from untruthful information and prompt the public to make informed decisions about health protection [30,34].
T35 4248-4467 Sentence denotes However, in the real world, governments do face time constraints and the trade-off between being accurate and being up-to-date in terms of information disclosing, which is not considered in classical information theory.
T36 4468-4776 Sentence denotes Because a highly infectious disease caused by unknown viruses with great externality, such as COVID-19, spreads together with information of varying qualities (truthfulness, accuracy, etc.), it is highly probable that the disease has already contaminated the society before low-quality information is purged.
T37 4777-5029 Sentence denotes In this case, the government has no way to disclose accurate information in time, resulting in the loss of public trust and raising the doubt of the public on the governing capacity of the government, which will accelerate epidemic outbreak [35,36,37].
T38 5030-5251 Sentence denotes Therefore, governments need to not only decide when to inject information into the network, but also whether to follow the tenet that governments do not and should not block information spreading at any circumstance [38].
T39 5252-5403 Sentence denotes For information blocking, studies have been conducted in theoretical [39,40,41], in empirical [42,43], in case studies [43,44], and other perspectives.
T40 5404-5594 Sentence denotes These studies argued that, even if governments have the power to control information [45], they should not do that because free spread of information is essential to welfare-maximizing [38].
T41 5595-5783 Sentence denotes This argument is based on two underlying assumptions: (1) publishers are completely competitive to reach an equilibrium of disclosing accurate information; (2) there is no time constraint.
T42 5784-5938 Sentence denotes These two assumptions do not apply for COVID-19 because, in the age of Internet media, people are not incentive compatible to spread accurate information.
T43 5939-6250 Sentence denotes Moreover, such highly externalized infectious diseases caused by unknown viruses might have already infected a considerable amount of people before low-quality information is purified, so the government should not simply adhere to the tenet of not blocking information when facing an unknown health crisis [38].
T44 6251-6380 Sentence denotes As a result, we will discuss the complexity and diversity in information blocking and broaden current information control theory.
T45 6381-6601 Sentence denotes By the discussion so far, we notice that the successful containment of epidemic outbreak relies on the successful management on the information dissemination process, which, however, is hard to achieve in the real world.
T46 6602-6856 Sentence denotes To better understand the failure in containing the COVID-19 pandemic, we here construct an information-behavior bi-layer model by adding a parallel layer of information transmission to the classical SI (Susceptible-Infected) model of infectious diseases.
T47 6857-7009 Sentence denotes We intend to describe the effect of heterogeneous virtual information (at information layer) on heterogeneous nodes’ behaviors (at physical layer) [46].
T48 7010-7143 Sentence denotes The government, as the key node in information network, can influence the entire network through information disclosing and blocking.
T49 7144-7357 Sentence denotes Based on this, we assign values to key variables such as the medical awareness level of the virus and the public’s health awareness level and then conduct computer simulation experiments under different scenarios.
T50 7358-7451 Sentence denotes We reveal the pattern of government information intervention based on the simulation results.
T51 7452-7744 Sentence denotes In addition, we only focus on the emergence stage of the infectious disease, during which the recovery from the infected status, such as self-healing and cure of the disease, is omitted [47]; therefore, the base model is the SI model rather than the SIR (Susceptible-Infected-Recovery) model.
T52 7745-7930 Sentence denotes Based on the bi-layered network model, we explore two main themes: how disease spreads affect information spreads and how information affects the efficiency of controlling the epidemic.
T53 7931-8181 Sentence denotes We introduce the non-dualism of information and the heterogeneity of nodes’ behaviors into the epidemic model and conduct a simulation to reveal the information intervention dilemma faced by the government between information disclosing and blocking.
T54 8182-8363 Sentence denotes We find that governments face a trade-off between speed and accuracy in information disclosing; and the optimal strategy is contingent on varying conditions in information blocking.
T55 8364-8492 Sentence denotes The optimal combination of disclosing and blocking is highly sensitive to the government preference and its governance capacity.
T56 8493-8887 Sentence denotes Governments that are only responsible for the outcome of intervention will focus unilaterally on the accuracy at the expense of speed; a risk-averse government that intends to minimize the maximum infection rate under uncertain scenarios will impose a more restrictive blocking; and the most restrictive blocking strategy might be the best for governments with lower capability and credibility.
T57 8888-8967 Sentence denotes In summary, this paper makes several important contributions to the literature.
T58 8968-9131 Sentence denotes First, existing studies did not pay sufficient attention to the spread and evolution of rumors during a public crisis [48,49,50], which is considered in our study.
T59 9132-9394 Sentence denotes We expounded the impacts of information dissemination on epidemic evolution in scenarios with different levels of medical awareness of the virus, public health awareness, and government preferences and credibilities, which complements the research in this field.
T60 9395-9701 Sentence denotes Second, most current studies regard information and disease transmissions as simultaneously happened and jointly induced by the physical movement of an agent [46], while this is not the case during COVID-19 pandemic as the internet obviates the needs for physical contact in information transmissions [51].
T61 9702-9881 Sentence denotes Thus, in our paper, we separate the information and disease transmissions and investigate the impact of heterogeneous information on the individual behaviors and disease dynamics.
T62 9882-10051 Sentence denotes Third, unlike previous research on government information interventions with known risk [5,28,29,30], ours are on government information interventions with unknown risk.
T63 10052-10323 Sentence denotes The lack of prior knowledge on the Corona-SARS-2 is the most striking feature of the COVID-19 pandemic, which weakens the usefulness of government action and calls for a reassessment of government information intervention under a crisis environment with high uncertainty.
T64 10324-10456 Sentence denotes To this end, this paper demonstrates a couple of intervention dilemmas faced by government, which complements the existing theories.
T65 10458-10460 Sentence denotes 2.
T66 10461-10468 Sentence denotes Methods
T67 10470-10474 Sentence denotes 2.1.
T68 10475-10510 Sentence denotes The Model of Information Disclosing
T69 10511-10643 Sentence denotes The information dissemination system resp. behavioral response system is embedded in the information network resp. physical network.
T70 10644-10679 Sentence denotes Both networks are given as follows.
T71 10680-10858 Sentence denotes Information network: the network has (N+1) nodes, first N are individual nodes representing N individuals denoted as i,i=1,2,⋯N, and one government information node denoted as j.
T72 10859-11077 Sentence denotes The degree of an individual node i is denoted as yi, which obeys a power-law distribution, that is, Fyi∝yi−v, where F(·) is the CDF and yi satisfies ϵ≤1∕yi≤1, where ϵ is a small constant to avoid the degree to blow up.
T73 11078-11162 Sentence denotes Degree and degree distribution are concepts used in graph theory and network theory.
T74 11163-11265 Sentence denotes A graph (or network) consists of a number of vertices (nodes) and the edges (links) that connect them.
T75 11266-11361 Sentence denotes The number of edges (links) connected to each vertex (node) is the degree of the vertex (node).
T76 11362-11659 Sentence denotes The degree distribution is a general description of the number of degrees of vertices (nodes) in a graph (or network), and, for random graphs, the degree distribution is the probability distribution of the number of degrees of vertices in the graph, which usually assumes a power-law distribution.
T77 11660-11719 Sentence denotes Throughout the following analysis, we take v=−1 and ϵ=0.01.
T78 11720-11966 Sentence denotes The government node j (representing real-world government) discloses information to every individual node and can only obtain information from n1 (n1≪N) (The notation “≪” means that the number n1 must be far less than the number N.) random nodes.
T79 11967-12084 Sentence denotes The neighborhood of an individual node i is the set of all other nodes (including j) it connects with, denoted as Oi.
T80 12085-12247 Sentence denotes Physical network: the physical network has M nodes, including n2 “special” nodes defined as the “gathering spots”, which predisposes these nodes to this epidemic.
T81 12248-12543 Sentence denotes Mt denotes the distribution of locations of all N individuals during period t, and M0 is the initial distribution that can be viewed as the “home” for every individual (node), thus at the beginning of each period t the individuals move from M0 to Mt and return back to M0 at the end of period t.
T82 12544-12655 Sentence denotes Home coordinates M0 and gathering spots are randomly assigned and different from each other, so we have N+n2<M.
T83 12656-12883 Sentence denotes Suppose there are n3 random nodes, each with identical initial information ξ, who disseminate information at the outbreak of disease; n4 random nodes are initially affected by the public crisis, representing the “patient zero”.
T84 12884-12955 Sentence denotes Without loss of generality, we unitize the information between 0 and 1.
T85 12956-13026 Sentence denotes The rules for information dissemination in each period are as follows.
T86 13027-13035 Sentence denotes Stage i.
T87 13036-13083 Sentence denotes Individual nodes send information to neighbors.
T88 13084-13248 Sentence denotes Each node that has information at the beginning of each period sends its information to all its neighbors, so all (N+1) nodes might receive information from others.
T89 13249-13520 Sentence denotes As information is spontaneously [19], rapidly, and extensively [22] misrepresented during transmission, and most people do not send more accurate information than they receive [17,18], we assume that information gets distorted and misrepresented during each transmission.
T90 13521-13666 Sentence denotes Thus, the actual amount of information received is δxi due to information decay, where δ∼U(0,1), and we assume xi∈0,1 without loss of generality.
T91 13667-13676 Sentence denotes Stage ii.
T92 13677-13729 Sentence denotes Individual nodes receive information from neighbors.
T93 13730-13883 Sentence denotes Each node might have multiple information sources, and it merges the information from all its neighbors weighted by their degrees (and including itself).
T94 13884-14035 Sentence denotes Each individual updates its information based on Equation (1) at each period before the government intervenes:(1) xi,t+1=∑k∈Oiδxk,tyk+xi,tyi∑k∈Oiyk+yi.
T95 14036-14046 Sentence denotes Stage iii.
T96 14047-14103 Sentence denotes The government node censors and screens the information.
T97 14104-14479 Sentence denotes The government has a threshold XD once it receives information from individuals (otherwise, the government would not act in this stage), the government will screen out all individuals with above-threshold information at the beginning of current period, among whom the government pinpoints the nearest ones and takes the maximum amount of information they carry denoted as xd.
T98 14480-14489 Sentence denotes Stage iv.
T99 14490-14528 Sentence denotes Government node discloses information.
T100 14529-14734 Sentence denotes The government is not able to intervene until it censors and screens the information; thus, there is a lag between receiving information and disclosing, which as we can see in Figure 4e, increases with XD.
T101 14735-14899 Sentence denotes After the lag (otherwise, the government would not act in this stage), the government shall disclose xd to all nodes in each period with a weight of λ, where λ∈0,1.
T102 14900-14952 Sentence denotes The higher the λ, the more credible the information.
T103 14953-14961 Sentence denotes Stage v.
T104 14962-15004 Sentence denotes Individual nodes update information again.
T105 15005-15194 Sentence denotes The government intervention switches the updating rule to (2) xi,t+1=λxd+(1−λ)∑k∈Oiδxk,tyk+xi,tyi∑k∈Oiyk+yi, which is also the final amount of the information after government intervention.
T106 15195-15384 Sentence denotes In addition, we assume that the amount of information of initial information holders (those who have information in period 0) is constant, i.e., they do not apply for Equations (1) and (2).
T107 15385-15577 Sentence denotes In short, in the first period, only a few people disseminate information, which will be randomly decayed in each subsequent period, this process simulates the misrepresentation of information.
T108 15578-15696 Sentence denotes Twitter data show that there was a significant heterogeneity in the behavioral response to the COVID-19 epidemic [52].
T109 15697-16071 Sentence denotes Some people, once informed about the epidemic, wear a mask and practice social distance to not expose themselves to the virus—while others panicked, herded, and behaved irrationally because of bad news, exemplified by flocking to churches for psychological comfort [53], to supermarkets for daily supplies [54], and taking radical actions like repeated hospital visits [55].
T110 16072-16382 Sentence denotes Thus, in this paper, we group the population by susceptibleness to irrational behavior caused by information described by an exogenous parameter—individual threshold XI that distinguishes whether an individual is panic-prone or non panic-prone by comparing it with the amount of information the individual has.
T111 16383-16471 Sentence denotes An above-threshold (under-threshold) information denotes a (non) panic-prone individual.
T112 16472-16655 Sentence denotes For a panic-prone node, we assume its probability of going to gathering spots instead of maintaining the original trajectory is 1−x·,a, where x·,a is the amount of information it has.
T113 16656-16742 Sentence denotes For a non panic-prone node, we assume that its probability of not moving is r·,N=x·,a.
T114 16743-17194 Sentence denotes Thus, the behavioral routine is as follows (see Figure 1 for a simplified example): a node moves along with its path with a maximum radius d1, and the actual distance it moves obeys a uniform distribution in (0,d1); this node will randomly choose one of the gathering spots if intending to go to one in this period; every individual node follows this routine, then we have an evolving geographical distribution Mt of the population moving in period t.
T115 17195-17335 Sentence denotes The uninfected will contact everyone within the maximum infection radius d2 and there is a probability μ of being infected for each contact.
T116 17336-17640 Sentence denotes Throughout the simulation analysis, we focus on the impact of three key parameters, the initial information (ϵ), individual threshold (XI), and disclosing threshold (XD), which are the most important quantities to measure the impact of government intervention on the coupled information-disease dynamics.
T117 17641-17907 Sentence denotes The initial information is the source of all information, which denotes the medical awareness of the virus; the individual threshold is a parameter to distinguish the population by groups set above, the smaller it is, the higher the level of public health awareness.
T118 17908-18042 Sentence denotes Disclosing the threshold, chosen by the government, measures its relative priority to speed and accuracy in information dissemination.
T119 18043-18132 Sentence denotes One of the objectives of our experiment is to ascertain the optimal disclosing threshold.
T120 18133-18345 Sentence denotes Government prioritizes speed more as its threshold is lower, “0“ means that government discloses the information immediately upon receipt; “1” means that government only discloses completely accurate information.
T121 18347-18351 Sentence denotes 2.2.
T122 18352-18393 Sentence denotes The Experiments of Information Disclosing
T123 18394-18683 Sentence denotes The simulation steps will be: (Figure 2 brief overviews these steps):Generate a random information network and a random physical network, the former illustrates the information relationship between people, and the latter records the coordinates of people M0 and gathering spots on the map.
T124 18684-18746 Sentence denotes Assign values to initial information and individual threshold.
T125 18747-19013 Sentence denotes The initial information is the source of all information, which denotes the medical awareness of the virus; the individual threshold is a parameter to distinguish the population by groups set above; the smaller it is, the higher the level of public health awareness.
T126 19014-19056 Sentence denotes Assign values to the disclosing threshold.
T127 19057-19191 Sentence denotes The disclosing threshold, chosen by the government, measures its relative priority to speed and accuracy in information dissemination.
T128 19192-19281 Sentence denotes One of the objectives of our experiment is to ascertain the optimal disclosing threshold.
T129 19282-19342 Sentence denotes Government prioritizes speed more as its threshold is lower.
T130 19343-19435 Sentence denotes Generate random individual nodes with initial information and random initial infected nodes.
T131 19436-19529 Sentence denotes Enter period 1. (a) Each individual node with information sends out information to neighbors.
T132 19530-19616 Sentence denotes (b) Each individual node will update its information (weighted) based on Equation (1).
T133 19617-19783 Sentence denotes (c) The government initiates a censoring and screening and enters stage d after a lag period, only for the first time does it receive the above-threshold information.
T134 19784-19869 Sentence denotes If the government never receives above-threshold information, skip c, d, and go to e.
T135 19870-20015 Sentence denotes (d) Government discloses information to the public, which induces another round of information update for individual nodes based on Equation (2).
T136 20016-20209 Sentence denotes (e) The population is grouped into infected and healthy people by health status, and into panic-prone and non panic-prone by how much information one has compared with the individual threshold.
T137 20210-20356 Sentence denotes (f) Each individual node moves in a physical layer following the routine of the subgroup it is in with probability based on its final information.
T138 20357-20499 Sentence denotes (g) Reset the infection status of healthy individual within the transmission radius of an infected one according to the infection probability.
T139 20500-20596 Sentence denotes Return to step 5, initiate a new round for 50 times, that is, run the experiment for 50 periods.
T140 20597-20661 Sentence denotes The data show a stability after 40 periods, so we stopped at 50.
T141 20662-20726 Sentence denotes Output the final overall infection rate at the end of period 50.
T142 20727-20851 Sentence denotes Repeat steps 4–7 for 50 times to reduce the randomness, record the mean, and standard deviation of the final infection rate.
T143 20852-21093 Sentence denotes Reassign for the disclosing threshold discrete values that equally divide the interval 0,1 into 11 parts, and repeat steps 3–8 for each value, that is, 11 times, to find the final infection rates for different disclosing threshold scenarios.
T144 21094-21279 Sentence denotes Reassign for initial information a discrete array 0.4,0.6,0.8,1.0, and reassign for an individual threshold the same values reassigned for the disclosing threshold in the previous step.
T145 21280-21322 Sentence denotes Then, repeat steps 2–9, that is, 44 times.
T146 21323-21415 Sentence denotes Now, we have conducted an experiment with a full parameter space for each initial condition.
T147 21416-21597 Sentence denotes A total of 484 different conditions were simulated for 24,200 repetitions of the experiment, each lasts for 50 periods, which adds up to a total of 1,210,000 periods of experiments.
T148 21598-21681 Sentence denotes They essentially cover all possible scenarios under different external constraints.
T149 21682-21770 Sentence denotes Table 1 lists the definitions, values, and distributions of all parameters in the model.
T150 21772-21776 Sentence denotes 2.3.
T151 21777-21826 Sentence denotes The Model and Experiments of Information Blocking
T152 21827-21971 Sentence denotes We assume the government will suppress any transmission of information under XB; thus, XB=0 denotes the special case in the previous discussion.
T153 21972-22178 Sentence denotes Combining the blocking threshold with other initial conditions above, we have a new parameter space simulation with a total of 5324 different scenarios simulated and a total of 266,200 repeated experiments.
T154 22179-22250 Sentence denotes Each group lasts for 50 periods, for a total of 13,310,000 experiments.
T155 22252-22254 Sentence denotes 3.
T156 22255-22277 Sentence denotes Results and Discussion
T157 22279-22283 Sentence denotes 3.1.
T158 22284-22302 Sentence denotes Modeling Framework
T159 22303-22407 Sentence denotes Our model consists of two main systems: information dissemination system and behavioral response system.
T160 22408-22549 Sentence denotes In the information dissemination system, each individual sends (receives) information to (from) its neighbors through an information network.
T161 22550-22841 Sentence denotes Given that information will always be rapidly, extensively [22], and spontaneously [19] misrepresented during transmission, and that most people do not send more accurate information than they receive [17,18], we assume information gets distorted and misrepresented during each transmission.
T162 22842-22954 Sentence denotes In the behavioral response system, each individual makes a move according to its information (with probability).
T163 22955-23209 Sentence denotes Once informed about the epidemic, some people behave rationally such as practicing social distancing, while others behave irrationally such as flocking to churches [53], to supermarkets [54], and taking radical actions like repeated hospital visits [55].
T164 23210-23286 Sentence denotes The information dissemination system affects the behavioral response system.
T165 23287-23417 Sentence denotes The government might intervene in the information dissemination to reduce infections by either disclosing or blocking information.
T166 23418-23571 Sentence denotes For information disclosing, the government discloses information to all individuals to make them behave rationally (or at least not behave irrationally).
T167 23572-23723 Sentence denotes Obviously, the more accurate the information is and the earlier it is disclosed, the public can be better guided which lowers the final infection rate.
T168 23724-23855 Sentence denotes However, it takes time for government to censor and screen information before disclosing, which brings an accuracy-speed trade-off.
T169 23856-24195 Sentence denotes We use a disclosing threshold to measure the government’s preference on speed or accuracy; a higher threshold indicates a higher preference on accuracy: a threshold “1” means the government would not disclose any information unless it is completely accurate; while “0” indicates an immediate disclosure without any censoring and screening.
T170 24196-24304 Sentence denotes For information blocking, the government blocks less-accurate information transmissions between individuals.
T171 24305-24397 Sentence denotes Obviously, a stringent blocking leads to a transmission of information with higher accuracy.
T172 24398-24560 Sentence denotes However, blocking will slow down the overall information dissemination in the network, and then slow down the government’s censoring and screening of information.
T173 24561-24620 Sentence denotes Thus, there is a trade-off between disclosing and blocking.
T174 24621-24876 Sentence denotes We use a blocking threshold to measure the blocking stringency, the government would block any transmission of any under-threshold information: a threshold “0” means no blocking at all; while “1” means that government blocks all information transmissions.
T175 24877-24934 Sentence denotes We analyze both of the optimal thresholds for government.
T176 24935-25013 Sentence denotes More details on the settings of our model can be found in the Methods section.
T177 25014-25096 Sentence denotes In reality, the government has a great influence on the information dissemination.
T178 25097-25352 Sentence denotes Thus, in our model, we assume that the government node is the most critical one and the government-disclosed information highly outweighs individuals’ information (except for the further discussion of a government with low credibility in a later section).
T179 25354-25358 Sentence denotes 3.2.
T180 25359-25405 Sentence denotes Intervention Dilemma in Disclosing Information
T181 25406-25525 Sentence denotes In this part, we will discuss the speed-accuracy trade-off results and analyze the mechanism in information disclosing.
T182 25526-26005 Sentence denotes First, Figure 3 summarizes the results of the simulations with 44 different external constraints, and we find that there is seldom a single dominant disclosing threshold (the government’s preference on speed or accuracy), i.e., seeking either speed or accuracy alone will not result in the lowest infection rate, and the optimal strategy (corresponding to the lowest infection rate at the end of the last period) is somewhere in between, which implies a speed-accuracy trade-off.
T183 26006-26169 Sentence denotes Specifically, the optimal disclosing threshold lies between both ends in about 84.09% of the cases, and their distributions vary in different external constraints.
T184 26170-26843 Sentence denotes Figure 3a,b show that (1) if the initial information is 1 or 0.8 and the individual threshold is in [0,0.7], the optimal disclosing threshold has a 91.75% probability of being in the middle, and mostly (66.89%) falls within [0.6,0.8]; (2) if the initial information is 0.6 and the individual threshold is in [0,0.5], the government has a 93.00% probability of dealing with a trade-off (Figure 3c), and the optimal disclosing threshold mostly (77.78%) falls in [0.4,0.6]; (3) if the initial information is 0.4, the optimal disclosing threshold will almost certainly be greater than 0.4 (99.98%), but the distribution is too scattered to give a specific interval (Figure 3d).
T185 26844-26923 Sentence denotes The mode of optimal disclosing threshold is 0.8 but only with 20.30% frequency.
T186 26924-27375 Sentence denotes In addition, we can see that, if the virus is medically well-known (ξ≥0.8) and public health awareness is low (XI≥0.7), the government shall prioritize accuracy over speed; if the virus is medically medium-known (ξ=0.6) and public awareness is high (XI≤0.5), the government shall balance speed and accuracy, which almost equally signifies; and if the virus in medically less-known (ξ=0.4), the government shall probably prioritize accuracy over speed.
T187 27376-27465 Sentence denotes Second, we will dissect the underlying logic and mechanism of the government’s trade-off.
T188 27466-27651 Sentence denotes We take one of the curves in Figure 3a that is denoted by ξ=1 and XI=0.5, as an example, to find the relationship between disclosing the threshold infection rate, then we have Figure 4.
T189 27652-28370 Sentence denotes In all 550 (11×50) experiments, the disclosing threshold for the lowest final infection rate usually lies between 0.7 and 0.9, and the final infection rate first falls then rises as the disclosing threshold increases, with the inflection point being at 0.8 (Figure 4a); the amount of final information per capita and the duration of government intervention both increase monotonically with the disclosing threshold (Figure 4b,e); the number of people infected after government intervention, the number of people infected by a panic after government intervention and the number of uninfected people remaining at the time of government intervention all negatively correlate with the disclosing threshold (Figure 4c,d,f).
T190 28371-28623 Sentence denotes One of the fundamental reasons for the government to balance speed and accuracy is the precipitous fall in the marginal contribution of accuracy as the disclosing threshold exceeds a certain “point”, while speed hardly affects the final infection rate.
T191 28624-28703 Sentence denotes The following is a detailed analysis on the effects of both accuracy and speed.
T192 28704-29001 Sentence denotes With respect to accuracy, the effect comes from two perspectives: (1) accurate information lowers the infection from panic in a healthy panic-prone population (Figure 4d); (2) accurate information changes the behavior routine in the panic-prone population, which reduces the spread of the disease.
T193 29002-29257 Sentence denotes Both (1) and (2) are in play until the disclosing threshold exceeds 0.5 (XI in our example); after that, there is no longer a panic-prone group, neither is infection from panic, which explains the precipitous fall in the marginal contribution of accuracy.
T194 29258-29521 Sentence denotes When it comes to speed, the effect comes from two perspectives as well: (1) the time the government spends on censoring and screening information, which we call a lag; (2) the number of remaining uninfected people at the time of government disclosing information.
T195 29522-29682 Sentence denotes The more accurate information the government seeks, the longer the lag (Figure 4e) and the fewer uninfected people remain at the time of disclosing (Figure 4f).
T196 29683-29835 Sentence denotes Notice that both have roughly the same slope with respect to disclosing threshold, which explains a roughly constant marginal cost of pursuing accuracy.
T197 29836-30370 Sentence denotes The combination of constant marginal costs and abrupt fall in marginal benefits leads to an inflection point in disclosing threshold, which explains the heterogeneity in the distribution of optimal disclosing threshold: as disclosing threshold exceeds individual threshold, a sudden fall of benefits occurs, which theoretically makes the optimal disclosing threshold slightly greater than the individual threshold, which explains what we discussed above that the intervals in which the optimal disclosing threshold mostly lays differ.
T198 30371-30675 Sentence denotes In most cases, the government’s premature disclosing of inaccurate information will contaminate the overall network, while obsession with accuracy may have the government miss the disclosure window before too many people are infected, which is intolerable to government who requires a low infection rate.
T199 30676-30703 Sentence denotes Thus, there is a trade-off.
T200 30705-30709 Sentence denotes 3.3.
T201 30710-30754 Sentence denotes Intervention Dilemma in Blocking Information
T202 30755-30937 Sentence denotes We assume the government will block any transmission of under-the-blocking-threshold (XB) information between individuals; thus, XB=0 denotes the special case in previous discussion.
T203 30938-30975 Sentence denotes Other settings are the same as above.
T204 30976-31064 Sentence denotes In this part, we will discuss the optimal blocking strategies and analyze the mechanism.
T205 31065-31143 Sentence denotes As shown in Figure 5, the optimal blocking threshold varies from case to case.
T206 31144-31480 Sentence denotes Overall, a small blocking threshold ([0.1,0.3]) is necessarily (100%) not optimal; a strict blocking threshold (XB≥0.8) is usually (50.41%) optimal, experimental data show a value between 45% and 55% in most external conditions; but 0 is the optimal threshold still in 20.25% of cases, and usually (89.80%) occurs when ξ≥0.8 and XI≤0.7.
T207 31481-31565 Sentence denotes When the initial information is low (ξ≤0.6), not blocking is seldom (0.83%) optimal.
T208 31566-31615 Sentence denotes We have our key findings from the above analysis.
T209 31616-31866 Sentence denotes First, minor blocking is not an option for government because it is dominated by stricter blocking in a deteriorated or being deteriorated information environment and undermines the efficiency of information dissemination in a benevolent environment.
T210 31867-32140 Sentence denotes Second, in the age of the Internet, information is extremely interconnected and low-quality information is more easily disseminated, thus stricter information blocking might be an option worth considering in the early stages of an outbreak of an unknown infectious disease.
T211 32141-32435 Sentence denotes Finally, if the virus is well-known at the medical level, plus the public has a certain level of health awareness, free spread of information might improve the situation; while, otherwise, as in the case of COVID-19, governments should intervene in the spread of information in social networks.
T212 32436-32574 Sentence denotes From the simulation results, we can see that, in most cases, the optimal strategy will be either highly stringent blocking or free spread.
T213 32575-32734 Sentence denotes Blocking low-quality information not only increases the overall information of the whole population, but causes side effects under certain external conditions.
T214 32735-32795 Sentence denotes Thus, not blocking can be an optimal strategy in some cases.
T215 32796-32884 Sentence denotes In this section, we provide an in-depth analysis of the data and a mechanistic analysis.
T216 32885-33144 Sentence denotes Figure 5b reveals in general the optimal blocking threshold negatively correlates with initial information: as initial information drops from 1 to 0.4, the probability of optimal blocking threshold taking 0 will be 52.07%, 27.27%, 1.65%, and 0%, respectively.
T217 33145-33244 Sentence denotes Figure 5c shows a positive correlation between optimal blocking threshold and individual threshold.
T218 33245-33428 Sentence denotes Governments block information mainly by suppressing less-accurate information, but which, once implemented, will slow down the overall information dissemination in the network anyway.
T219 33429-33534 Sentence denotes Therefore, blocking can neither be too stringent nor too liberal, an optimal one usually lies in between.
T220 33535-33635 Sentence denotes However, mild blocking is necessarily not optimal as it fails to purify the information environment.
T221 33636-33792 Sentence denotes Furthermore, when the virus is well-known (ξ=1), especially when public health awareness is high (XI≤0.3), not blocking dominates most of the time (69.70%).
T222 33793-34004 Sentence denotes While stringent blocking (XB≥0.8) is necessarily not (0%) an optimal strategy because higher-quality information, which helps to slow the spread of the disease with high public health awareness, is also blocked.
T223 34005-34195 Sentence denotes Thus, when both the initial information and the level of public health awareness are at a high level, not blocking is optimal; otherwise, information that would not cause panic might do now.
T224 34196-34339 Sentence denotes In addition, when medical awareness of the virus declines, so does the proportion of valuable information, which necessitates blocking as well.
T225 34341-34345 Sentence denotes 3.4.
T226 34346-34399 Sentence denotes Optimal Intervention under Different Government Types
T227 34400-34532 Sentence denotes In previous sections, our study was based on the neutral government assumption that governments only seek the lowest infection rate.
T228 34533-34690 Sentence denotes However, in reality, a government is not a personalized organization pursuing social optimum because it is often checked by various inside and outside nodes.
T229 34691-34754 Sentence denotes In addition, government credibility makes a difference as well.
T230 34755-34866 Sentence denotes In this section, we will discuss the optimal strategy for non-neutral governments and low-credible governments.
T231 34867-35066 Sentence denotes An unaccountable government that evades responsibilities would only care for lower new infections after intervention rather than global infections, which digresses from the objective described above.
T232 35067-35192 Sentence denotes As shown in Figure 6a, the later the government discloses information, the less that will be newly infected after disclosure.
T233 35193-35389 Sentence denotes There are two underlying reasons: (1) late disclosed information will indeed be more accurate, which reduces the infection rate; and (2) there are less uninfected people at the time of disclosing.
T234 35390-35487 Sentence denotes Therefore, a blame-evading government would delay the disclosing to avoid being held accountable.
T235 35488-35689 Sentence denotes A conservative government that prefers the least error-prone strategy (minimizing maximum loss) rather than the optimal one (the loss minimization strategy) would block all the information (Figure 6b).
T236 35690-35929 Sentence denotes Since the optimal strategy would not be accessed until all external conditions are fully judged and scrutinized, which is not feasible for COVID-19, complete blocking would be optimal for such a government to avoid the worst case scenario.
T237 35930-36044 Sentence denotes Our experiment of 484 different scenarios manifests a complete blocking will never lead to the highest infections.
T238 36046-36048 Sentence denotes 4.
T239 36049-36060 Sentence denotes Conclusions
T240 36061-36458 Sentence denotes In this paper, we introduce the non-dualism (by non-dualism, we mean the information is neither absolutely accurate nor absolutely not but partially accurate) of information and the heterogeneity of nodes’ behaviors into the epidemic model and conduct a simulation to reveal the information intervention dilemma faced by the government and to explore the trade-offs among corresponding strategies.
T241 36459-36574 Sentence denotes Our experiments highlight that:For information disclosing, governments face a trade-off between speed and accuracy.
T242 36575-36724 Sentence denotes A better medical understanding of the virus and an inadequate public health awareness make accuracy outweigh speed; otherwise, a quick one is better.
T243 36725-36934 Sentence denotes For information blocking, the optimal strategy is contingent on varying conditions: no blocking is usually optimal for a well-known virus and a higher public health awareness; otherwise, blocking is preferred.
T244 36935-37065 Sentence denotes The optimal combination of disclosing and blocking is highly sensitive to the government preference and its governance capability.
T245 37066-37449 Sentence denotes A government that is only responsible for the outcome of intervention will focus unilaterally on accuracy at the expense of speed; a risk-averse government that intends to minimize the maximum infection rate in uncertain scenarios will impose a more restrictive blocking; and the most restrictive blocking strategy might be best for governments with lower capability and credibility.
T246 37450-37653 Sentence denotes These findings reveal the complexity in government decision-making about dissemination of disease information: neither allowing free flow of information nor disclosing it as early as possible is optimal.
T247 37654-37746 Sentence denotes Under extreme conditions, they are even harmful to the goal of controlling disease outbreak.
T248 37747-37992 Sentence denotes The interaction between information and infectious disease deepens our knowledge about public health crisis governance, enriches the existing theories in public economics and public management, and provides useful social and policy implications.
T249 37993-38065 Sentence denotes In reality, some governments are not as capable and credible as assumed.
T250 38066-38217 Sentence denotes A lower credibility will discount the effects of disclosing information or even annul it, which makes a total blocking optimal as shown in Figure 6c,d.
T251 38218-38386 Sentence denotes The bankruptcy of government credibility originates in two ways: (1) the government’s past mediocre performance; (2) the public’s inherent belief in “small government”.
T252 38387-38671 Sentence denotes Meanwhile, a similar experience in the past also affected government responses and effects, as we can see with the horrible painful memories of SARS inducing vigilance for COVID-19 in East Asia countries, while the U.S. and Europe were indifferent in the early stage of this pandemic.
T253 38672-38803 Sentence denotes In the preceding discussion, we relaxed one assumption at a time, whereas the government’s preferences are more complex in reality.
T254 38804-39093 Sentence denotes In a broader context, the government’s preferences (objective function) are affected by two things: the government’s perception and judgment of the epidemic (decision-making base), and the government’s priorities in different objectives (decision-making objectives); both change over time.
T255 39094-39131 Sentence denotes This paper also has some limitations.
T256 39132-39363 Sentence denotes For instance, our discussion focuses mainly on the theoretical mechanisms behind the joint spreading process of information and epidemic, and the proposed intervention strategies have not yet been analyzed with the real-world data.
T257 39364-39464 Sentence denotes One reason for the lack of empirical analysis is the complex set-up of the bi-layered network model.
T258 39465-39665 Sentence denotes The information dissemination network and the physical-layer contact network are not precisely observable in the real world, which makes it challenging for acquiring sufficient data for model fitting.
T259 39666-39905 Sentence denotes On the other hand, the observed infection and information dissemination process are often already intervened in by the government; therefore, it is hard to separate the net effect of government intervention from the ex-post spreading data.
T260 39906-39987 Sentence denotes Then, it is technically difficult to quantify the key parameters of intervention.
T261 39988-40260 Sentence denotes To this end, we believe more sophisticated empirical techniques have to be introduced for the implement data-oriented analysis of our model, such as the network reconstruction and the causal detection techniques, which forms a promising direction for future investigation.