Computergestützte Epidemiologie
Mathematische Modellierung
Simulation
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QuickFlu

Application of anti-viral drugs against influenza has been widely discussed. Using the parameter values and concepts, published by Longini et al. (Am. J Epidemiol. 159 (2004): 623-33), we have created a JAVA applet which allows to calculate the average number of secondary infections created by an index case.
The presented program is meant as a quick visualization which quantifies the consequences of the application of anti-viral drugs on the spread of the disease. More elaborate features (which are not considered in this tool) concern the immunization of susceptible individuals as well as temporal delays in drug adminsitration and the implementation of other intervention strategies. The authors of this applet are currently implementing such features in an individual-based simulator which aims to allow the optimization of intervention strategies against influenza.

The attached JAVA applet can be regarded as the visualization of a "basic reproduction spending function". After starting the tool, you will see a visualization of what will happen in a fully unprotected population if influenza infection is introduced. Starting with a randomly chosen contact of an index case, it is shown how this contact is infected, becomes infectious and to how many people are infected by this person. The infectious person is either free of symptoms (asymptomatic), moderately sick (symptomatic) or severely sick an bedridden (at home). Move the sliders on the left hand side to change these fractions or to modify the contagiousness of the cases in the different stages (for a full explanation of the sliders, see below). The sliders on the right hand side allow to change short-term effects of the application of anti-viral drugs. The spread of infection can finally only be controlled if each case on average gives rise to less than one secondary case before becoming immune.

Disclaimer:
In no event, shall the University to Tübingen, or any person be liable for any loss, expense or damage, of any type or nature arising out of the use of, or inability to use this program (SimPox), including, but not limited to, claims or suits.

(uses JAVA)

Parameter explanations

  • Cases, Asympt. Fract. [%]
    Fraction of asymptomatic cases among all infected individuals
  • Cases, Inf. of Asympt. [%]
    Contagiousness of asymptomatic cases as compared to symptomatic cases
  • Cases, Circ. [% of Sympt]
    Fraction of symptomatic (sick) cases which do not stay at home in spite of their sickness
  • Contacts, Close [/day]
    Average number of close contacts (e. g. family) per day; only such encounters which are sufficiently close for transmission are counted as "contacts". asymptomatic, sick and bedridden cases are assumed to have the same number of close contacts per day
  • Contacts, Remote [/day]
    Average number of casual contacts per day; only such encounters which are sufficiently close for transmission are counted as "contacts". only asymptomatic and circulating sick cases have "remote contacts", whereas bed-ridden cases are restricted to "close contacts".
  • Antiviral Treatment of the Population, Treated [%]
    Fraction of the population with access to antiviral drugs
  • Antiviral Treatment of the Population, Compliance [%]
    Fraction of those with access to the drug who actually take the drug; the treated fraction of the population is given as the product of the fraction with access to the drug and the compliance
  • Antiviral Efficacy, Susceptibility [%]
    Reduction in susceptiblity by anti-viral treatment of the contact; a 30% reduction indicates that the susceptibility drops from 100% to 70% - the probability of being infected is reduced
  • Antiviral Efficacy, Infectiousness [%]
    Reduction in contagiousness by anti-viral treatment of the case; an 80% reduction indicates that the contagiousness drops from 100% to 20% -
  • Antiviral Efficacy, Disease [%]
    Reduction in the probability that a case becomes sick (mildly systematic or bedridden); a 60% reduction indicates that only 40% of those who would otherwise become sick actually become sick