SOCIAL INTRACTION AMONG PEOPOLE INFLUENCING SPREADING FLUE VIRUS

PRO. ENRICE  JORDAN; PRO. HORDY MISHALINE

(Department of humanity and culture studies, University Philippines)

VOL.03 Issue 11

 

ABSTRACT

Targeted social distancing to mitigate pandemic influenza will be designed through simulation of influenza’s spread within area people social contact networks. We demonstrate this design for a stylized community representative of atiny low town within the U.S. The critical importance of kids and teenagers in transmission of influenza is first identified and targeted. For influenza as infectious as 1957-58 Asian flu (? 50% infected), closing schools and keeping children and teenagers reception reduced the attack rate by >90%. For more infectious strains, or transmission that’s less focused on the young, adults and also the work environment must even be targeted. The beginning of an influenza pandemic, effective vaccine and antiviral drugs might not be available to the final population (1,2). If the accompanying illness and death rates of the virus strain are high, how might a community reply to protect itself.

                                                    We describe how social contact network–focused mitigation may be designed. At the inspiration of the look process may be a network-based simulation model for the spread of influenza. We apply this model to a community of 10,000 persons connected within an overlapping, stylized, social network representative of atiny low US town. After a study of the unmitigated transmission of influenza within the community, we alter the frequency of contact within targeted groups and build combinations of strategies that may contain the Epidemic.      

                Finally, we show how infectivity of the strain and underlying structure of the infectious contact network influence the planning of Social distancing strategies. Within the absence of a vaccine and antiviral drugs, design for specific communities would defend against highly virulent influenza.

METHODS AND DESIGN PROCESS

First creates a certain social contact network during which persons are linked to others in a Very community. The common number of links per person within the group is additionally specified because cliques form or are imposed (e.g., seating in an exceeding classroom). This number is employed to construct a within-group network that may take various forms. We used fully connected, random, or ring networks for every group.

                                                          This process is repeated until the number of links within the group yields the desired average (each person will have a distinct number of links). The ring is made by first placing per- sons next to neighbors and linking them to create an entire circle. Additional links are then made to the next nearest neighbors symmetrically around the ring. Finally, links within a gaggle are given a mean frequency of contact per day. With this approach, a network will be built from the experience of community members to exhibit the clustered yet small-world characteristics (6) and overlapping quality of a structured community (7,8). Our network represented a stylized small US town and took advantage of the various backgrounds of the authors (1 of whom could be a teenager). The population of 10,000 conformed to the 2000 Census (9) and consisted of youngsters (65 years old, 12.5%). Households were Composed of families (adults with children or teenagers), adults, or older adults. All persons within each household were linked to every other with mean link contact frequencies of 6/day. one and all also belonged to 1 multiage relative (or neighborhood) group (mean membership 12.5, mean link contact frequency 1/day).

BEHAVIORAL RULE

Disease state transitions follow the explanation of influenza; infected asymptomatic persons continue interacting without behavioral changes. Because this final transition doesn’t influence the spread of the disease, we use pM = 0. Person-to-person transmission events are evaluated at the start of every period during which an individual is infectious. Assuming contact events are statistically independent, a coordinated universal time for every infectious person’s links within the contact network is chosen from an exponential distribution with a mean of the link’s contact frequency scaled by ID ? IR ? IA ? SP ? SA, where ID is that the infectivity of the disease,

 

IR is that the relative infectivity of the disease state, SP is that the susceptibility of individuals to the disease (here taken as 1.0), IA is that the relative infectivity of the person who is transmitting, and SA is that the relative susceptibility of the person receiving. If the coordinated universal time is a smaller amount than the amount during which the person is going to be in an infectious state (also chosen from an exponential distribution with the prescribed means; Figure 2), transmission is scheduled at the chosen time. Otherwise, transmission along that link does not occur during that period.

                             All transmission parameters and phone frequencies could also be modified in each of the states, likewise as varied among age classes by relative scaling factors like IR. In this way, disease representations and mitigation strategies are implemented. days) has an IR of 0.25 after which it increased to 1.0 for the primary part of the symptomatic period (mean 0.5 days), when viral shedding is maximum and coughing begins. IR is then reduced to 0.375 for the rest of the infectious symptomatic period (mean 1 day). For infectious asymptomatic persons, IR was set at 0.25 for a mean period of two.0 days, making these per- sons half as infective as those with symptoms. We chose pS as 0.5, pH as 0.5 for adults and older adults, and pH as 0.9 for kids and teenagers

When someone is within the symptomatic stay-home state, we reduce the frequency of all non-household connections by 90%. Because children and teenagers have closer contact with others and are less likely to clean hands or control coughs (16), they’re more infective and susceptible: IA and SA are 1.5 for youngsters, 1.25 for teenagers, and 1.0 for adults and older the chosen time. Otherwise, transmission along that link doesn’t occur during that period. All transmission parameters and phone frequencies could also be modified in each of the states, furthermore as varied among age classes by relative scaling factors like IR. In this way, disease representations and mitigation strategies are implemented.

               Most influenza-specific parameters used here reflect those of (10,11) The latent period may be a constant (0.75 days) followed by a variable period (mean 0.5 days). The pre-symptomatic period (mean 0.5 days) has an IR of 0.25 after which it increased to 1.0 for the primary a part of the symptomatic period (mean 0.5 days), when viral shedding is maximum and coughing begins. IR is then reduced to 0.375 for the rest of the infectious symptomatic period (mean 1 day). For infectious asymptomatic persons, IR was set at 0.25 for a mean period of two.0 days, making these per- sons half as infective as those with symptoms. We chose pS as 0.5, pH as 0.5 for adults and older adults and pH as 0.9 for kids and teenagers. When someone is within the symptomatic stay-home state, we reduce the frequency of all non household connections by 90%. Because children and teenagers have closer contact with others and are less likely to scrub hands or control coughs (16), they’re more infective and susceptible: IA and SA are both 1.5 for children, 1.25 for teenagers, and 1.0 for adults and older

 

 

RESULT

We first show the spread of influenza within our unmitigated base case defined with parameters specified above and with ID chosen to yield an infected attack rate ?50% to reflect the 1957–58 Asiatic flu pandemic (10). Unless otherwise noted, we report infected attack rates and check with them as simply attack rates instead of reporting the illness attack rate which is 1/2 this value (pS = 0.5). We then demonstrate the planning of effective local mitigation strategies for the bottom case that specialize in targeted social distancing.

           A number of these initial infections instigate others and grow into a pandemic. Unmitigated Base Case The sequence of infected Persons is represented as an expanding network of infectious transmissions (Figure 4). The common branching factor depends on the person’s social class and generation during the epidemic (Figure 5A). The maxi- mum value within the primary 10 generations is 2.05 (standard deviation [SD] 0.57) for kids, 2.09 (SD 0.72) for teenagers, 1.11 (SD 0.43) for adults, 0.81 (SD 0.47) for older adults, and 1.54 (SD 0.36) for the whole population. Variability (large SD, especially for specific age classes) reflects the heterogeneity inherent within community con- tact networks of this size (Figure 5B).

                                 On average, 78% (SD 2%) of kids and 71% (SD 3%) of teenagers become infected. Older adults, who contact children and teenagers only through the relations or neighborhoods and therefore the random overall network, are relatively isolated (attack rate 23% of older adults, SD 2%). Children and teenagers compose. Only 29% of the population yet they’re answerable for 59% (SD 4.5%) of infectious contacts, adults for 38% (SD 7.9%), and older adults for 3% (SD 0.6%) (Table 3). Approximately half of the infectious contacts of either children or teenagers are within the same social class (19%, SD 0.8% and 9%, SD 0.7%, respectively). Adults get influenza from children or teenagers at approximately the identical frequency (24%, SD 1.6%) as from other adults (26%, SD 5.9%). Older adults are equally likely to induce influenza from children or teenagers as from adults or older adults (2%, SD 0.3%). Transmission to children or teenagers from adults is 10% (SD 1.8%) and nearly none by older adults. Infections transmitted within each environment also are in keeping with other simulation studies (10–14). The utmost value of the branching factor (Figure 5) reflects the often-cited reproductive number, Ro.

                However, how Ro should be calculated from small-community data like ours is ambiguous (10, 11, and 14). To estimate Ro, we pooled results across 100 communities (simulations) to reflect a population of 1 million (Figure 5B). The utmost value of the majority ratio (new infections to old) within the primary 10 generations is 1.6, and that we choose it as our estimate of Ro. A Ro of 1.6 with an attack rate of fifty matches recent pandemic simulation results (10,14) and lies within the range (1.5–1.7) for the 1957–58 influenza pandemic (Figure 5B) (10). Base children and teenagers now spend longer reception, in neighborhoods, with friends, and in public spaces. We assume that faculty closure at a minimum doubles household contacts, however, if we assume that faculty closure doubles all link contact frequencies for youngsters or teenagers within their non-school groups, attack rates are increased by 18% (Table 2). Alternatively, we send all children and teenagers home after school closure to stay for the duration of the pandemic.

Results We first show the spread of influenza within our unmitigated base case defined with parameters specified above and with ID chosen to yield an infected attack rate ?50% to reflect the 1957–58 Asiatic flu pandemic (10). Unless otherwise noted, we report infected attack rates and check with them as simply attack rates instead of reporting the illness attack rate which is 1/2 this value (pS = 0.5). We then demonstrate the planning of effective local mitigation strategies for the bottom case that specialize in targeted social distancing a number of these initial infections instigate others and grow into a pandemic. Unmitigated Base Case The sequence of infected persons is represented as an expanding network of infectious transmissions (Figure 4). The common branching factor depends on the person’s social class and generation during the epidemic (Figure 5A).

             The maximum value within the primary 10 generations is 2.05(Standard deviation [SD] 0.57) for kids, 2.09 (SD 0.72) for teenagers, 1.11 (SD 0.43) for adults, 0.81 (SD 0.47) for older adults, and 1.54(SD 0.36) for the whole population. Variability (large SD, especially for specific age classes) reflects the heterogeneity inherent within Community contact networks of this size (Figure 5B). On average, 78% (SD 2%) of kids and 71% (SD 3%) of teenagers become infected, Older adults, who contact children and teenagers only through the relations or neighborhoods and therefore the random overall network, are relatively isolated (attack rate 23% of older adults, SD 2%). Children and teenagers compose only 29% of the population yet they’re answerable for 59% (SD 4.5%) of infectious contacts, adults for 38% (SD 7.9%), and older adults for 3% (SD 0.6%) (Table 3).  

                     Approximately half of the infectious contacts of either children or teenagers are within the same social class (19%, SD 0.8% and 9%, SD 0.7%, respectively). Adults get influenza from children or teenagers at approximately the identical frequency (24%, SD 1.6%) as from other adults (26%, SD 5.9%). Older adults are equally likely to induce influenza from children or teenagers as from adults or older adults (2%, SD 0.3%). Transmission to children or teenagers from adults is 10% (SD 1.8%) and nearly none by older adults. Infections transmitted within each environment also are in keeping with other simulation studies (10–14). The utmost value of the branching factor (Figure 5) reflects the often-cited reproductive number Ro.

                                       However, how Ro should be calculated from small-community data like ours is ambiguous (10, 11, 14). To estimate Ro, we pooled results across 100 communities (simulations) to reflect a population of 1 million (Figure 5B). The utmost value of the majority ratio (new infections to old) within the primary 10 generations is 1.6, and that we choose it as our estimate of Ro. A Ro of 1.6 with an attack rate of fifty matches recent pandemic simulation results (10,14) and lies within the range (1.5–1.7) for the 1957–58 influenza pandemic (Figure 5B) (10). Base Case–Targeted Social Distancing First, we examined closing schools. Although contacts in classes are removed, those altogether other groups may increase because children and teenagers now spend longer reception, in neighborhoods, with friends, and public spaces. We assume that faculty closure at a minimum doubles household contact However if we assume that faculty closure doubles all link contact frequencies for youngsters or teenagers within their non-school groups, attack rates are increased by 18% (Table 2).                

      Alternatively, we send all children and teenagers home after school Closure to stay for the duration of the pandemic. Now contact frequencies are reduced by 90% for all groups that contain only children or teenagers (classes and their random networks) and doubled, as before, for kids or teenagers in households. Within the extended family or neighborhood and therefore the random overall networks, child or teenager contact frequencies are reduced by 90%. Thus, although children and teenagers are restricted to the house,

          Adults and older adults move their day-to-day routines, except that they avoid children or teenagers who aren’t household members. Imposing this strategy the day lengthen above the bottom case and reach an element of ?1.8 at 40% compliance (Figure 7B). Other social distancing strategies are often considered. Because children outnumber teenagers and kids are more infective and susceptible, what happens if only children are distanced, while teenagers attend school and follow their usual routines? What if all sick persons remain reception when symptomatic? At 90% compliance, this strategy reduces the attack rate by 20%

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Address for correspondence: Robert J. Glass, National Infrastructure Simulation and Analysis Center, Sandia National Laboratories, Box 5800, Albuquerque, NM 87185-1138, USA; email: rjglass@sandia.gov

Robert J. Glass,* Laura M. Glass,† Walter E. Beyeler,* and H. Jason Min; Targeted Social Distancing Design for Pandemic Influenza. Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 12, No. 11, November 2006

 

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