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Controlling epidemic spread by social distancing: Do it well or not at all
© Maharaj and Kleczkowski; licensee BioMed Central Ltd. 2012
Received: 7 February 2012
Accepted: 5 June 2012
Published: 20 August 2012
Existing epidemiological models have largely tended to neglect the impact of individual behaviour on the dynamics of diseases. However, awareness of the presence of illness can cause people to change their behaviour by, for example, staying at home and avoiding social contacts. Such changes can be used to control epidemics but they exact an economic cost. Our aim is to study the costs and benefits of using individual-based social distancing undertaken by healthy individuals as a form of control.
Our model is a standard SIR model superimposed on a spatial network, without and with addition of small-world interactions. Disease spread is controlled by allowing susceptible individuals to temporarily reduce their social contacts in response to the presence of infection within their local neighbourhood. We ascribe an economic cost to the loss of social contacts, and weigh this against the economic benefit gained by reducing the impact of the epidemic. We study the sensitivity of the results to two key parameters, the individuals’ attitude to risk and the size of the awareness neighbourhood.
Depending on the characteristics of the epidemic and on the relative economic importance of making contacts versus avoiding infection, the optimal control is one of two extremes: either to adopt a highly cautious control, thereby suppressing the epidemic quickly by drastically reducing contacts as soon as disease is detected; or else to forego control and allow the epidemic to run its course. The worst outcome arises when control is attempted, but not cautiously enough to cause the epidemic to be suppressed. The next main result comes from comparing the size of the neighbourhood of which individuals are aware to that of the neighbourhood within which transmission can occur. The control works best when these sizes match and is particularly ineffective when the awareness neighbourhood is smaller than the infection neighbourhood. The results are robust with respect to inclusion of long-range, small-world links which destroy the spatial structure, regardless of whether individuals can or cannot control them. However, addition of many non-local links eventually makes control ineffective.
These results have implications for the design of control strategies using social distancing: a control that is too weak or based upon inaccurate knowledge, may give a worse outcome than doing nothing.
Network models have been successfully used to describe the spread of many infectious diseases, ranging from human [1–8] through animal [6, 9–11] to plant diseases . In these models, individuals are represented as nodes and potential contacts between individuals as edges of the underlying network. Much work has been devoted to studying how networks are assembled [13–15] and to analysing the effect of network topology on disease spread and potential control strategies . However, the structure of interactions between individuals is most commonly assumed to be fixed and so represents an ‘average’ behavioural pattern. Thus, conventional epidemiological models either treat human behaviour as external to the disease system or even ignore it completely [17, 18].
The structure of real-life networks is, however, far from static and often responds to epidemic spread at both individual and at population level. Thus, for example, governments often introduce control measures aimed at disrupting disease transmission either locally or on long-range links [19–21]. Targeted social distancing may be promoted by governments and public health bodies as a strategy for the control of epidemics, for example in the form of school or workplace closures. The effectiveness of such measures is typically assessed by macroeconomic cost-benefit analysis, often based upon large-scale Computational General Equilibrium models [5, 22–28].
Social distancing also arises spontaneously as individuals respond to news about disease spread, obtained from media reports, public announcements, rumours or individual experience . Recognising the importance of behavioural responses, epidemiological modellers have begun to consider transmission via adaptive networks [15, 30, 31], whereby the structure of the network is modified depending on the state of individuals. Thus, changes in contact network structure can arise as a result of human responses to disease, such as a reduction in social contacts. Such models have shown that social distancing can be effective at reducing the attack rate of an epidemic [28, 32] and that it is a plausible explanation for certain phenomena arising in real epidemics, such as multiple outbreaks or waves of infection [32, 33].
Existing studies have, however, tended to neglect the associated cost to both society and individuals resulting from the actions of individuals leading to severing social links. Social contacts are necessary for economic activity: employees must go to work, students must go to school, and traders and customers must interact in order for an economy to function. Social contacts are also important to individuals for non-monetary reasons, such as interactions with family and friends. Awareness of an epidemic therefore presents each individual with a private choice between investing in social contacts and risking infection, or reducing the contacts and losing the social or economic benefits . This can have a severe impact on both individual and societal well-being as it has been noted that for example ‘(..) fear of exposure can result in significant worker absenteeism’ .
In this paper we study the economic impact of social distancing as a disease control strategy undertaken by healthy individuals in response to either local information about the number of cases in their immediate neighbourhood or global information communicated by mass media. We take into account two factors: the economic cost of individuals falling ill, and the economic cost of reduction in social contacts. For simplicity we do not distinguish between contacts for different purposes, treating all contacts as if they have the same economic worth. Our model is based upon a standard SIR model superimposed upon a simple spatial network. Computer simulations are used to study the effect of allowing susceptible individuals to reduce their contacts when their surrounding neighbourhood contains infected individuals. One important parameter we use is the risk attitude, which models how strongly individuals respond to the awareness of a threat. Another important parameter is the awareness neighbourhood, which represents the size of the area that individuals are knowledgeable about. Our main result is that, in order for social distancing to be a cost-effective strategy, it must be applied with a highly cautious attitude to risk. We also see that the size of the awareness neighbourhood matters: for best results, this should be the same as the size of the neighbourhood within which disease transmission can occur. In particular, the control is most effective if the response is based on local knowledge – the result of mass media-induced panic is to reduce the effectiveness of the spontaneous control.
In this paper we consider two network models. The first one is a regular network which has the advantage of simplicity but is only a crude representation of real social networks which often display a mixture of short-range and long-range links. We therefore also consider a small-world model that is more realistic in this respect [13, 36, 37], with long-range links representing occasional travel of individuals. The main result of our paper is robust to the addition of even a relatively high number of long-range links, although if the network becomes very random, the optimal response is to refrain from any action. In this case, the highly cautious risk attitude leads to a limited but long epidemic causing large losses in contact numbers as individuals repeatedly respond to the threat.
The model of infection is based upon a standard SIR model superimposed upon a simple spatial network with neighbourhoods defined by Euclidean distance, without and with addition of long-range links. Each individual has an infection neighbourhood whose size, and hence the number of contacts made by that individual, depends locally on the infection pressure and therefore changes throughout the epidemic. The model includes an economic cost-benefit analysis which evaluates the overall benefits to the global population arising from local individual responses to infection levels.
Social distancing is introduced by allowing susceptible individuals to temporarily reduce the number of contacts they make in response to the presence of nearby infection. Thus, each susceptible individual can detect the current infection pressure amongst its neighbours within the awareness radius, r a . The local infection pressure, Θ, is the ratio of the number of infected individuals in radius r a to the total number of neighbours within that radius. For any given susceptible individual, Θmay take values ranging from 0, meaning that there are no infected neighbours in radius r a , to 1, meaning that all neighbours within radius r a are infected.
Lower values of αrepresent more cautious (more risk-averse) attitudes to risk, and result in a greater reduction of r i for a given Θ. For completeness, Figure 3 also illustrates ‘risk-neutral’ and ‘risk-seeking’ responses, represented by the upper two lines. The risk-neutral case is one in which susceptibles do not modify their behaviour, and is equivalent to no control being used: here , regardless of the infection pressure. The risk-seeking case represents a response in which susceptibles seek to increase their contacts as the infection pressure increases, modelled by the formula . Risk-seeking response was not considered in this study, though we note that such seemingly perverse behaviour was observed  in a virtual ‘epidemic’ in an online computer game.
For contact to occur between two individuals, each one must be within the contact neighbourhood of the other. At each time step, any two individuals a and b will make contact, provided that each is within the other’s contact radius, i.e., if d is the Euclidean distance between a and b, then i and j will make contact provided that d < r i (a) and d < r i (b). We assume that all individuals are aware of their own status (susceptible, infected, or recovered) and take this into account when deciding how to respond. Susceptible individuals reduce their contact neighbourhood because they know they are at risk of becoming infected, whereas infected and recovered individuals no longer have this risk and so do not reduce their contact neighbourhood.
The value of R ∞ (no control) is obtained by taking the mean of 20 simulation runs where control is not used. Similarly, R ∞ (control) is the mean of 20 runs with control.
Secondly, the control produces an economic loss by reducing the number of economically beneficial contacts taking place between pairs of individuals. To count the contacts taking place during an epidemic, we sum up the number of contacts that take place at each time step, over a fixed reference period. This reference period is chosen to be equal to 900 time steps which exceeds the duration of the longest epidemic in our sample (most epidemics were significantly shorter than 900 steps). We identify the economic benefits with the infection neighbourhood. This is equivalent to the assumptions that each economically significant contact is associated with an infection risk.
For any given , the value of contacts (no control) is fixed: each individual will make contact with all of its neighbours within radius , so a fixed number of contacts occurs at each time step, and contacts (no control) can be calculated by multiplying this number by the length of the reference period. The value of contacts (control) is obtained by taking the mean of 20 simulation runs with control.
In order to construct the small-world model [36, 37], we start with the local-spread model as described above. A fixed number of local links (representing a proportion of all local links) is selected and those connections are ‘rewired’ to a random location outside the interaction neighbourhood determined by , thus keeping the total number of links (local and long-range) constant. For simplicity we assume that the probability of passing an infection along any of the long-range links is the same as for local links.
As described above, in response to infection located in their awareness neighbourhood susceptible individuals reduce their number of local links. We assume that a similar behaviour governs small-world links. Thus, at each time step the number of active long-range links originating from a susceptible individual is proportional to (1−Θ α ), where Θrepresents the infection pressure within the awareness neighbourhood of this individual (as for local links). Infection can only pass along the long-range link if it is active and joins a susceptible and an infected individual. Finally, we assume that each active long-range link contributes to the calculation of the number of contacts in the same way as local links. Non-active long-range links do not contribute to the overall number of contacts.
Parameters of the model
0.01 − 1
probability of infection per contact (per time step)
0.01 − 1
probability of removal/recovery (per time step)
1 − 5
contact radius (baseline)
1 − 5, 10, ∞
0.01 − 1.0
0.01, 0.1, 1, 10
10 − 400
Initial number of foci (0.4% − 40%)
Effect of social distancing
Social distancing (characterised here by a relatively risk-averse attitude, α=0.25, see Figure 3) shifts the critical infectiousness p towards higher values but also generally increases the duration of the epidemic, Figure 5. For a given p, social distancing always decreases the final size of the epidemic (Figure 5a), but the effect on the duration depends on the value of p (Figure 5b). The different outcomes are indicated roughly by the four regions marked on the graphs, labelled A, B, C, and D, and with the approximate boundaries between them shown as vertical dotted lines in Figure 5 and in other figures below. For small values of p the disease is non-invasive in both cases, while both the final size and the duration of the epidemic are lower in presence of social distancing than without it (region A). In regions B and C, the epidemic without social distancing is invasive, but the behavioural changes render it non-invasive. However, in region B the duration of the epidemic is shortened by the distancing, whereas in region C it is longer. Finally, in region D the disease is invasive regardless of the social distancing, with slightly lower final size, but significantly longer duration. The approximate boundaries between regions are given by the transition between invasive and non-invasive disease for the case without control (boundary between A and B) and with control (boundary between C and D), respectively. In addition, the boundary between B and C is placed at the value of p where controlled and uncontrolled epidemics last approximately the same time, see Figure 5.
The economic impact of the reduction in the number of contacts can be offset by a reduction in disease cases caused by the social distancing. The two factors are weighed by c, the relative cost of social interactions versus cost of infection. A typical dependence of the net economic benefit on p for medium values of c is shown in Figure 6b; we will explore the dependence on c later in the paper.
Overall, we see that the effect of the control may be neutral, beneficial, or detrimental, with the outcome depending on the infectiousness of the disease, p. The effect is different in different regions A-D. In region A the control makes little difference as the disease is not invasive even in the absence of social distancing. R ∞ is small and the epidemic is of short duration. Using the control causes a very slight reduction in the number of contacts, and a correspondingly very small reduction in the net benefit.
As p increases past the uncontrolled epidemic threshold value, indicated by the boundary between regions A and B in the graphs, the social distancing leads to a positive net benefit. The control reduces R ∞ almost to zero, and greatly shortens the duration of the epidemic. Although the number of contacts is also somewhat reduced, this is more than compensated for by the large reduction in R ∞ , so that the net benefit is strongly positive.
As p increases further, region C, the control becomes ineffective, R ∞ rises towards the levels seen without control, and the duration of the epidemic increases beyond no-control levels. The picture is of a control that is too weak to suppress the epidemic and is merely slowing down the speed of its spread without reducing its final impact. Figure 6a shows a further downside: by prolonging the epidemic, the control prolongs the period during which social distancing is practised, thus greatly reducing the number of contacts. This in turn results in an overall negative benefit. Eventually, as p enters region D, we reach the worst case scenario for the control, where using the control gives a much worse result than doing nothing. As p increases further beyond this point, we return to a situation where, as in region A, use of the control makes little difference to the severity of the epidemic or its duration. However, unlike region A, in this case the epidemic is invasive despite the control. The number of contacts and the overall benefit both improve slightly from the worst case scenario, though remaining low.
An interesting observation from these graphs is the close juxtaposition of the best case scenario with the worst case, as shown by the steep transition from positive to negative benefit taking place within region C. This implies that if social distancing is to be used to control disease, it is very important to get the parameters right. If the infectiousness of the disease is underestimated even slightly, the actual outcome of using the control can be substantially worse than anticipated. We shall see later on, when we consider different attitudes to risk and different values of the awareness radius, that the importance of getting the parameters exactly right is a recurring theme in this work.
Effect of varying c
Importance of risk attitude
Another way of thinking about these results is in terms of the four regions, A, B, C, and D, identified earlier. With a highly risk-averse value of α, regions C and D vanish, and region B (corresponding to the diseases for which the distancing is beneficial) extends from the invasion threshold value of p all the way to p=1. Relaxing (increasing) α causes region B to narrow and regions C and D to emerge. As αis increased further, the boundary between regions B and C shifts to the left, meaning that the control is beneficial for a narrower range of diseases.
The process by which inclusion of long-range links breaks down the usefulness of the social distancing is, however, not trivial. Two contrasting cases need to be distinguished, corresponding to a very relaxed risk attitude (α∼1) and to a very strict response (α∼0), see Figure 10. Firstly, if individuals do not respond to the local infection load (large α), the epidemics are usually very quick (rightmost parts of Figure 10a,b) and infect most of the individuals in the population (Figure 10c,d). However, the number of contacts lost to social distancing is also small, resulting in the net benefit close to 0 (rightmost parts of Figure 10e,f).
As the individuals become more cautious in their response to the infection load, the duration of the epidemics goes up (Figure 10a) and the number of cases goes down (Figure 10c). In the second case, where the values of α are small, the behaviour depends crucially on the number of long-range links. For small number of such contacts, the epidemics are short (leftmost parts of Figure 10a) and stop after infecting a small number of individuals (leftmost parts of Figure 10c). As the number of long-range links increases to 30%, the duration of the epidemic increases (Figure 10b) and the number of cases goes up (Figure 10d) but remains short of infecting the whole population. In this case, epidemics progress slowly, infecting only few individuals at a time, but resulting in a massive reduction in the number of contacts over the whole period of their duration. Further addition of non-local links results in a faster epidemic affecting a large number of individuals (Figure 10b,d). For the particular choice of c in Figure 10 this results in a very similar value of the net benefit for 30% and 100% non-local links, although the balance will change when other values of c are selected (cf. Figure 7).
In this model we assume that the control is applied to both local and long-range links in the same way. If long-range links are excluded from the control, it becomes even less effective, but the results are qualitatively similar to those described above.
Importance of matching spatial scales: regular networks
The model introduces two spatial scales, the infection (and contact) neighbourhood and the awareness neighbourhood. The infection neighbourhood is associated with disease transmission and with economic benefits accruing from social contacts, whereas the role of the awareness neighbourhood is to provide an estimate of risks associated with infection. So far in the case of regular networks we have only considered cases in which the radius of the awareness neighbourhood r a is equal to that of the maximum contact radius . This corresponds to a situation when individuals base their decisions on the same social neighbourhood as the infection risk comes from. However, two other cases need to be considered. The individuals might be in contact potentially leading to infection with individuals whose status might be unknown to them. In our approach we simulate this situation by considering an awareness neighbourhood which is smaller than the infection neighbourhood. This results in underestimation of risks and leads to sub-optimal control of epidemic spread, as illustrated by in Figure 11a. The benefit is in this case a monotonically increasing function of α, leading to a maximum corresponding to a relaxed attitude (large α).
Importance of matching spatial scales: small-world networks
Within the assumptions of our model, we have seen that social distancing is most cost-effective as an epidemic control strategy if it is applied with a high degree of caution (i.e., a low risk attitude), and that the more infectious the disease, the greater the caution that is required. If a disease is very highly infectious, social distancing may have no effect, or may require an unfeasibly high degree of caution in order to be effective. In these cases, doing nothing will be a more cost-effective strategy than using social distancing, because the worst-case outcome arises if the control is applied, but the level of caution used is too weak. The steep threshold separating the best-case outcome from the worst-case in Figure 6b indicates that it is very important that the level of infectiousness of the disease is not underestimated. The similar, steep threshold in Figure 9 highlights the danger of having an overly relaxed attitude to risk when using the control. Hence the slogan in the title of this paper: if social distancing is being considered, do it well, or not at all.
We have also seen the importance of having a good match between the spatial area from which an individual draws information which will affect his/her behaviour, and the spatial area containing those other individuals who actually pose an infection risk. The control works best when these two areas are the same. If the individual has too little knowledge and can be infected by others who are outside of the awareness radius, the control does not work. If, on the other hand, the individual responds to infection cases which do not pose a realistic risk of infection (as might happen if, say, individuals in the UK change their behaviour in response to mass media reports of faraway epidemics in distant countries) the control is also inefficient.
Additional simulation runs were performed to check the robustness of our results. We looked at the results of varying the number of initial foci from 0.4% to 40% of the population (10-1000 infected individuals in population of 2500). As the number of foci is increased, the epidemic reaches more of the population and its duration is shortened. The control becomes less effective, with fewer contacts taking place during the reference period and a reduction in the economic benefit from using the control. However, the overall shape of the results does not change. We also checked potential effects of different size and boundary conditions by performing exploratory runs with different assumptions. Edge wrapping has the effect of slightly increasing R ∞ and reducing the duration of the epidemic, but the overall shape of the graphs remains the same. Varying the size of the system (ranging from 400-10000 individuals) has no effect on the results. We also varied the recovery rate, q, in the range from 0.1 to 0.9; with appropriate scaling of p the results are consistent with those shown above. Finally, the simulations for small-world networks (up to 30% of long-range links) and a random network suggest that our results are fairly robust with respect to making the network structure more realistic.
Care must be taken in interpreting our results and attempting to relate them to epidemics in the real world. The model relies on a number of assumptions which must be taken into account. First, the disease model is a simple SIR model. Further work needs to be done to investigate whether the results hold for other systems, such as SIS or SEIR models and for real-world social networks. Another major assumption concerns the way we deal with recovered and infected individuals: we have supposed that these individuals maintain full contact with others, and that only susceptible individuals change their behaviour. If we changed these assumptions, for example, by assuming that recovered individuals die and cannot therefore engage in economic contact, or by assuming that infected individuals are weakened and have fewer contacts than healthy individuals, then our results might differ. This, again, is a subject for future investigation. Our present conclusions can only be applied to mild diseases which do not greatly affect the behaviour of infected individuals, and do not typically result in death.
The model can be extended in a number of ways. Currently we assume that the population is constant and that recovered individuals cease to be infectious and cannot be infected again. These assumptions can be relaxed to include full recovery, births, deaths and other demographic processes as well as asymptomatic stages of the disease or indeed asymptomatic carriers. In our model we do not distinguish between different activities and hence combine day/night cycles into one time step. Similarly, we do not include variations in contacts between weekdays and weekends or schools/holidays. Finally, with increased availability of data it is possible to analyse existing social networks in unprecedented detail. Each of these extensions is possible in agent-based models like ours, but exceeds the scope of the paper.
In our approach we have made a simple assumption about the monetary value of each contact (represented by c) and we assumed that all contacts (whether for monetary or non-monetary reasons) have the same value. This is clearly only a first approximation and an obvious extension of our model (beyond the scope of the current paper) is to consider different monetary value for different contacts. More work is also needed on understanding how individuals value different aspects of their social and economic activities, in the light of a possibility of losing them due to disease control. For simplicity we have also assumed that the only difference between the long-range links and the local links is in the topology of interactions; otherwise the long range links are treated the same as short-range links, in terms of reduction of contacts in response to infection and their contribution to the individual well-being. This is again a simplifying assumption as the long-range links may be more likely to be broken in the presence of infection. Alternatively, individuals trying to get away from the epidemic foci might increase the propensity of long-range links, as documented in e.g. .
When long-range links are included in the network, there are three options for the control. Authorities can either block the travel or strongly advise against it and individuals might respond to the epidemic threat by completely refraining from such activities, in which case the model becomes purely local. At the other extreme, individuals might not have control over occasional long-range contacts and the changes in the behaviour will be purely local. Finally, a mixture of responses is possible, with different behaviour associated with different kinds of links. In our paper we considered all three cases, although for the last one we assumed that local and long-range links are treated in the same way.
Other areas for future work could involve extending the economic and psychological aspects of the model to capture further aspects of human behaviour [34, 45]. Economic models, for example, often include the phenomenon of time discounting, whereby losses or gains anticipated to take place in the long term are considered less important than those occurring in the short term. This phenomenon could be included in models of lengthy epidemics where discounting may play a role in the decision-making processes of both individuals and social planners. Another aspect we have not considered is the role of memory and habitual behaviour; in our model, individuals respond instantaneously to the current conditions and do not remember what has happened in the past. Other researchers  have incorporated memory into models of behavioural response to epidemics, and this is something that could usefully be added to our model.
The results of this paper raise the question of how individuals respond to the threat of disease in the real world. There is surprisingly limited quantitative data available about changes in social interactions during historical epidemics. Surveys and questionnaires can provide static snapshots of behaviour but only limited information about changes over time and in response to infection pressure. For obvious reasons, it is not possible to run experiments involving induced epidemics in the real world. A promising alternative which we are currently pursuing is to use virtual experiments based on agent-based simulations of epidemics in which human beings participate and control the responses of the agents .
We are grateful to David Cairns for providing assistance with the Condor tool.
- Small M, Tse C: Clustering model for transmission of the SARS virus: application to epidemic control and risk assessment. Physica A: Stat Mech its App. 2005, 351 (2-4): 499-511.View ArticleGoogle Scholar
- Kiss IZ, Green DM, Kao RR: Infectious disease control using contact tracing in random and scale-free networks. J R Soc, Interface / R Soc. 2006, 3 (6): 55-62.View ArticleGoogle Scholar
- Meyers L, Pourbohloul B, Newman M, Skowronski D, Brunham R: Network theory and, SARS: predicting outbreak diversity. J theor biol. 2005, 232: 71-81.View ArticlePubMedGoogle Scholar
- Ferguson NM, Cummings DAT, Cauchemez S, Fraser C, Riley S, Meeyai A, Iamsirithaworn S, Burke DS: Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature. 2005, 437: 209-214.View ArticlePubMedGoogle Scholar
- Cauchemez S, Bhattarai A, Marchbanks TL, Fagan RP, Ostroff S, Ferguson NM, Swerdlow D: Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza. Proc National Acad Sci USA. 2011, 108 (7): 2825-2830.View ArticleGoogle Scholar
- Vernon MC, Keeling MJ: Representing the UK’s cattle herd as static and dynamic networks. Proc R Soc B-Biol Sci. 2009, 276 (1656): 469-76.View ArticleGoogle Scholar
- Bansal S, Grenfell BT, Meyers LA: When individual behaviour matters: homogeneous and network models in epidemiology. J R Soc Interface. 2007, 4 (16): 879-891.View ArticlePubMedPubMed CentralGoogle Scholar
- Xia YC, Bjornstad ON, Grenfell BT: Measles metapopulation dynamics: A gravity model for epidemiological coupling and dynamics. Am Naturalist. 2004, 164 (2): 267-281.View ArticleGoogle Scholar
- Kao RR, Green DM, Johnson J, Kiss IZ: Disease dynamics over very different time-scales: foot-and-mouth disease and scrapie on the network of livestock movements in the UK. J R Soc, Interface / R Soc. 2007, 4 (16): 907-16.View ArticleGoogle Scholar
- Green DM, Kiss IZ, Kao RR: Modelling the initial spread of foot-and-mouth disease through animal movements. Proc Biol sci / R Soc. 2006, 273 (1602): 2729-35.View ArticleGoogle Scholar
- Dent JE, Kao RR, Kiss IZ, Hyder K, Arnold M: Contact structures in the poultry industry in Great Britain: exploring transmission routes for a potential avian influenza virus epidemic. BMC Veterinary Res. 2008, 4: 27-View ArticleGoogle Scholar
- Harwood TD, Xu X, Pautasso M, Jeger MJ, Shaw MW: Epidemiological risk assessment using linked network and grid based modelling: Phytophthora ramorum and Phytophthora kernoviae in the UK. Ecol Modell. 2009, 220 (23): 3353-3361.View ArticleGoogle Scholar
- Watts DJ, Strogatz SH: Collective dynamics of ’small-world’ networks. Nature. 1998, 393 (6684): 440-2.View ArticlePubMedGoogle Scholar
- Jackson MO: Social and economic networks. 2008, Princeton, NJ, USA: Princeton University PressGoogle Scholar
- Gross T, D’Lima CJD, Blasius B: Epidemic Dynamics on an Adaptive Network. Phys Rev Lett. 2006, 96: 20871-View ArticleGoogle Scholar
- Riley S: Large-scale spatial-transmission models of infectious disease. Sci (New York, N.Y.). 2007, 316 (5829): 1298-301.View ArticleGoogle Scholar
- Horan RD, Fenichel EP, Wolf CA, Gramig BM: Managing Infectious Animal Disease Systems. Ann Rev Resour Economics. 2010, 2: 101-124.View ArticleGoogle Scholar
- Gersovitz M, Hammer JS: Infectious diseases, public policy, and the marriage of economics and epidemiology. World Bank Res Observer. 2003, 18 (2): 129-157.View ArticleGoogle Scholar
- Kao RR: The role of mathematical modelling in the control of the 2001 FMD epidemic in the UK. Trends Microbiol. 2002, 10 (6): 279-286.View ArticlePubMedGoogle Scholar
- Ferguson NM, Donnelly CA, Anderson RM: Transmission intensity and impact of control policies on the foot and mouth epidemic in Great Britain. NATURE. 2001, 413 (6855): 542-548.View ArticlePubMedGoogle Scholar
- Fèvre EM, Bronsvoort BMDC, Hamilton Ka, Cleaveland S: Animal movements and the spread of infectious diseases. Trends microbiol. 2006, 14 (3): 125-31.View ArticlePubMedGoogle Scholar
- Hens N, Ayele GM, Goeyvaerts N, Aerts M, Mossong J, Edmunds JW, Beutels P: Estimating the impact of school closure on social mixing behaviour and the transmission of close contact infections in eight European countries. BMC infectious diseases. 2009, 9: 187-View ArticlePubMedPubMed CentralGoogle Scholar
- Miller JC, Danon L, O’Hagan JJ, Goldstein E, Lajous M, Lipsitch M: Student Behavior during a School Closure Caused by Pandemic Influenza A/H1N1. PLoS ONE. 2010, 5 (5): e10425-View ArticlePubMedPubMed CentralGoogle Scholar
- Lee BY, Brown ST, Cooley P, Potter MA, Wheaton WD, Voorhees RE, Stebbins S, Grefenstette JJ, Zimmer SM, Zimmerman RK, Assi TM, Bailey RR, Wagener DK, Burke DS: Simulating School Closure Strategies to Mitigate an Influenza Epidemic. J Public Health Manage PRACT. 2010, 16 (3): 252-261.View ArticleGoogle Scholar
- Eames KTD, Tilston NL, Edmunds WJ: The impact of school holidays on the social mixing patterns of school children. Epidemics. 2011, 3 (2): 103-8.View ArticlePubMedGoogle Scholar
- Kelso JK, Milne GJ, Kelly H: Simulation suggests that rapid activation of social distancing can arrest epidemic development due to a novel strain of influenza. BMC public health. 2009, 9: 117-View ArticlePubMedPubMed CentralGoogle Scholar
- Keogh-Brown MR, Smith RD, Edmunds JW, Beutels P: The macroeconomic impact of pandemic influenza: estimates from models of the United Kingdom, France, Belgium and The Netherlands. Eur J Health Econ. 2010, 11 (6): 543-54.View ArticlePubMedGoogle Scholar
- Glass RJ, Glass LM, Beyeler WE, Min HJ: Targeted Social Distancing Design for Pandemic Influenza. Emerging Infectious Diseases. 2006, 12 (11): 3017-3026.View ArticleGoogle Scholar
- Blendon RJ, Benson JM, DesRoches CM, Raleigh E, Taylor-Clark K: The Public’s Response to Severe Acute Respiratory Syndrome in Toronto and the United States. Clinical Infectious Diseases. 2004, 38: 925-931.View ArticlePubMedGoogle Scholar
- Funk S, Gilad E, Watkins C, Jansen V: The spread of awareness and its impact on epidemic outbreaks. Proc National Acad Sci. 2009, 106 (16): 6872-6877.View ArticleGoogle Scholar
- Zanette DH, Risau-Gusmán S: Infection Spreading in a Population with Evolving Contacts. J Biol Phys. 2008, 34: 135-148.View ArticlePubMedPubMed CentralGoogle Scholar
- Poletti P, Caprile B, Ajelli M, Pugliese A, Merler S: Spontaneous behavioural changes in response to epidemics. J Theor Biol. 2009, 260: 31-40.View ArticlePubMedGoogle Scholar
- Caley P, Philp DJ, McCracken K: Quantifying social distancing arising from pandemic influenza. J R Soc Interface. 2008, 5: 631-639.View ArticlePubMedGoogle Scholar
- Fenichel E, Castillo-Chavez C, Ceddia M, Chowell G, Gonzales Parra P, Hickling G, Holloway G, Horan R, Morin B, Perrings C, Springborn M, Velazquez L, Villalobos C: Adaptive human behavior in epidemiological models. Proceedings of the National Academy of Sciences. 2011, 108 (15): 6306-11.View ArticleGoogle Scholar
- About Pandemics. Washington DC: US Department of Health & Human Services, Retrieved 6th Dec 2012 from http://www.flu.gov/pandemic/about,
- Moore C, Newman MEJ: Epidemics and percolation in small-world networks. Phys Rev E. 2000, 61 (5): 5678-5682.View ArticleGoogle Scholar
- Dybiec B, Kleczkowski A, Gilligan CA: Controlling disease spread on networks with incomplete knowledge. Phys Rev E. 2004, 70 (6):Google Scholar
- Anderson R, May RM: Infectious diseases of humans: Dynamics and control. 1991, Berlin: Oxford University PressGoogle Scholar
- Vastag B: Virtual Worlds, Real Science: Epidemiologists, Social Scientists Flock to Online World. Sci News. 2007, 172 (17): 264-265.View ArticleGoogle Scholar
- Wilensky U: NetLogo. 1999, [http://ccl.northwestern.edu/netlogo/]Google Scholar
- Thain D, Tannenbaum T, Livny M: Distributed computing in practice: the Condor experience. Concurrency Pract Experience. 2005, 17 (2-4): 323-356.View ArticleGoogle Scholar
- Crawley MJ: The R book. 2007, Wiley-BlackwellView ArticleGoogle Scholar
- Cardy J, Grassberger P: Epidemic models and percolation. J Phys A: Math Gen. 1985, 18: L267-View ArticleGoogle Scholar
- Beutels P, Jia N, Zhou QY, Smith R, Cao WC, de Vlas SJ: The economic impact of SARS in Beijing, China. Trop med & int health. 2009, 14 (november): 85-91. [http://www.ncbi.nlm.nih.gov/pubmed/19508435]View ArticleGoogle Scholar
- Durham DP, Casman EA: Incorporating individual health-protective decisions into disease transmission models: a mathematical framework. J R Soc Interface. 2012, 9 (68): 562-70.View ArticlePubMedGoogle Scholar
- Sadique MZ, Edmunds WJ, Smith RD, Meerding WJ, de Zwart O, Brug J, Beutels P: Precautionary behavior in response to perceived threat of pandemic influenza. Emerging infectious diseases. 2007, 13 (9): 1307-13.View ArticlePubMedPubMed CentralGoogle Scholar
- Maharaj S, McCaldin T, Kleczkowski A: A participatory simulation model for studying attitudes to infection risk. Proceedings of the 2011 Summer Computer Simulation Conference (SCSC ’11). 2011, Society for Modeling and Simulation, Vista, CA, 8–13, Summer Computer Simulation Conference (SCSC ’11)Google Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/12/679/prepub
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