Limits...
Multi-scale modeling for the transmission of influenza and the evaluation of interventions toward it.

Guo D, Li KC, Peters TR, Snively BM, Poehling KA, Zhou X - Sci Rep (2015)

Bottom Line: Therefore, their prediction results can hardly be explained by the mechanisms of epidemic spreading.Parameter sensitivity analysis showed that temperature influences the dynamic of epidemic significantly and system behavior analysis showed social network degree is a critical factor determining the size of an epidemic.Finally, multiple scenarios for vaccination and school closure strategies were simulated and their performance was analyzed.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA.

ABSTRACT
Mathematical modeling of influenza epidemic is important for analyzing the main cause of the epidemic and finding effective interventions towards it. The epidemic is a dynamic process. In this process, daily infections are caused by people's contacts, and the frequency of contacts can be mainly influenced by their cognition to the disease. The cognition is in turn influenced by daily illness attack rate, climate, and other environment factors. Few existing methods considered the dynamic process in their models. Therefore, their prediction results can hardly be explained by the mechanisms of epidemic spreading. In this paper, we developed a heterogeneous graph modeling approach (HGM) to describe the dynamic process of influenza virus transmission by taking advantage of our unique clinical data. We built social network of studied region and embedded an Agent-Based Model (ABM) in the HGM to describe the dynamic change of an epidemic. Our simulations have a good agreement with clinical data. Parameter sensitivity analysis showed that temperature influences the dynamic of epidemic significantly and system behavior analysis showed social network degree is a critical factor determining the size of an epidemic. Finally, multiple scenarios for vaccination and school closure strategies were simulated and their performance was analyzed.

No MeSH data available.


Related in: MedlinePlus

Comparison of interventions in response to three different sizes of epidemic.The baseline is R0 = 1.26. Another two severe epidemics (R0 = 1.9 and R0 = 2.6) were simulated as well. Red lines indicate the case of R0 = 1.26; green lines show intervention results when R0 = 1.9, and blue lines present the case when R0 = 2.6. (a) Vaccination. (b) School closure.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4355742&req=5

f5: Comparison of interventions in response to three different sizes of epidemic.The baseline is R0 = 1.26. Another two severe epidemics (R0 = 1.9 and R0 = 2.6) were simulated as well. Red lines indicate the case of R0 = 1.26; green lines show intervention results when R0 = 1.9, and blue lines present the case when R0 = 2.6. (a) Vaccination. (b) School closure.

Mentions: We also simulated two other severe epidemics (R0 = 1.9 and R0 = 2.6) by increasing the social network degree and exotic infections that come into the studied area by air traffic. Figure 5 presents the comparison of interventions in response to moderate and severe epidemics. In Figure 5(a), we modeled the efficiency of vaccination adopted in four targeted groups under the three epidemics. In Group 1, we assumed 80% of children in child care centers and schools were vaccinated; in Group 2, we assumed 80% of children were vaccinated; in Group 3, we assumed 80% of children and 80% of people in the workplace were vaccinated; and in Group 4, we assumed 80% of the whole population was vaccinated. The baseline was the officially reported coverage (46.27%, 35.53%, and 67.07% for persons 0.5–17 years, 18–64 years, and 65+ years of age, respectively). In a severe epidemic (R0 = 1.9 and R0 = 2.6), interventions on Group 1 helped reduce R0 from 2.6 to 1.96, or from 1.9 to 1.50. In both cases, the epidemics were still out of control. Such a strategy is not enough in a severe epidemic. If we enlarged the vaccination range to all children (Group 2), in the very severe epidemic (R0 = 2.6), R0 was reduced to 1.58 and the epidemic was still out of control, but this strategy prevented the epidemics of both R0 = 1.9 and 1.26 very well. These results suggested that vaccination is more suitable when the epidemic is not very severe. In Groups 3 and 4, 80% vaccination resulted in a sharp decrease of R0 in each simulated epidemic, while R0 had almost the same reduction pattern in both groups. These results indicated that a vaccination strategy targeting children and people in the workplace can mitigate an epidemic; urging mass vaccination of a whole population is not necessary.


Multi-scale modeling for the transmission of influenza and the evaluation of interventions toward it.

Guo D, Li KC, Peters TR, Snively BM, Poehling KA, Zhou X - Sci Rep (2015)

Comparison of interventions in response to three different sizes of epidemic.The baseline is R0 = 1.26. Another two severe epidemics (R0 = 1.9 and R0 = 2.6) were simulated as well. Red lines indicate the case of R0 = 1.26; green lines show intervention results when R0 = 1.9, and blue lines present the case when R0 = 2.6. (a) Vaccination. (b) School closure.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4355742&req=5

f5: Comparison of interventions in response to three different sizes of epidemic.The baseline is R0 = 1.26. Another two severe epidemics (R0 = 1.9 and R0 = 2.6) were simulated as well. Red lines indicate the case of R0 = 1.26; green lines show intervention results when R0 = 1.9, and blue lines present the case when R0 = 2.6. (a) Vaccination. (b) School closure.
Mentions: We also simulated two other severe epidemics (R0 = 1.9 and R0 = 2.6) by increasing the social network degree and exotic infections that come into the studied area by air traffic. Figure 5 presents the comparison of interventions in response to moderate and severe epidemics. In Figure 5(a), we modeled the efficiency of vaccination adopted in four targeted groups under the three epidemics. In Group 1, we assumed 80% of children in child care centers and schools were vaccinated; in Group 2, we assumed 80% of children were vaccinated; in Group 3, we assumed 80% of children and 80% of people in the workplace were vaccinated; and in Group 4, we assumed 80% of the whole population was vaccinated. The baseline was the officially reported coverage (46.27%, 35.53%, and 67.07% for persons 0.5–17 years, 18–64 years, and 65+ years of age, respectively). In a severe epidemic (R0 = 1.9 and R0 = 2.6), interventions on Group 1 helped reduce R0 from 2.6 to 1.96, or from 1.9 to 1.50. In both cases, the epidemics were still out of control. Such a strategy is not enough in a severe epidemic. If we enlarged the vaccination range to all children (Group 2), in the very severe epidemic (R0 = 2.6), R0 was reduced to 1.58 and the epidemic was still out of control, but this strategy prevented the epidemics of both R0 = 1.9 and 1.26 very well. These results suggested that vaccination is more suitable when the epidemic is not very severe. In Groups 3 and 4, 80% vaccination resulted in a sharp decrease of R0 in each simulated epidemic, while R0 had almost the same reduction pattern in both groups. These results indicated that a vaccination strategy targeting children and people in the workplace can mitigate an epidemic; urging mass vaccination of a whole population is not necessary.

Bottom Line: Therefore, their prediction results can hardly be explained by the mechanisms of epidemic spreading.Parameter sensitivity analysis showed that temperature influences the dynamic of epidemic significantly and system behavior analysis showed social network degree is a critical factor determining the size of an epidemic.Finally, multiple scenarios for vaccination and school closure strategies were simulated and their performance was analyzed.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA.

ABSTRACT
Mathematical modeling of influenza epidemic is important for analyzing the main cause of the epidemic and finding effective interventions towards it. The epidemic is a dynamic process. In this process, daily infections are caused by people's contacts, and the frequency of contacts can be mainly influenced by their cognition to the disease. The cognition is in turn influenced by daily illness attack rate, climate, and other environment factors. Few existing methods considered the dynamic process in their models. Therefore, their prediction results can hardly be explained by the mechanisms of epidemic spreading. In this paper, we developed a heterogeneous graph modeling approach (HGM) to describe the dynamic process of influenza virus transmission by taking advantage of our unique clinical data. We built social network of studied region and embedded an Agent-Based Model (ABM) in the HGM to describe the dynamic change of an epidemic. Our simulations have a good agreement with clinical data. Parameter sensitivity analysis showed that temperature influences the dynamic of epidemic significantly and system behavior analysis showed social network degree is a critical factor determining the size of an epidemic. Finally, multiple scenarios for vaccination and school closure strategies were simulated and their performance was analyzed.

No MeSH data available.


Related in: MedlinePlus