The impact of case diagnosis coverage and diagnosis delays on the effectiveness of antiviral strategies in mitigating pandemic influenza A/H1N1 2009.
BACKGROUND
Neuraminidase inhibitors were used to reduce the transmission of pandemic influenza A/H1N1 2009 at the early stages of the 2009/2010 pandemic. Policies for diagnosis of influenza for the purposes of antiviral intervention differed markedly between and within countries, leading to differences in the timing and scale of antiviral usage.


RESULTS
The impact of the percentage of symptomatic infected individuals who were diagnosed, and of delays to diagnosis, for three antiviral intervention strategies (each with and without school closure) were determined using a simulation model of an Australian community. Epidemic characteristics were based on actual data from the A/H1N1 2009 pandemic including reproduction number, serial interval and age-specific infection rate profile. In the absence of intervention an illness attack rate (AR) of 24.5% was determined from an estimated R(0) of 1.5; this was reduced to 21%, 16.5% or 13% by treatment-only, treatment plus household prophylaxis, or treatment plus household plus extended prophylaxis antiviral interventions respectively, assuming that diagnosis occurred 24 hours after symptoms arose and that 50% of symptomatic cases were diagnosed. If diagnosis occurred without delay, ARs decreased to 17%, 12.2% or 8.8% respectively. If 90% of symptomatic cases were diagnosed (with a 24 hour delay), ARs decreased to 17.8%, 11.1% and 7.6%, respectively.


CONCLUSIONS
The ability to rapidly diagnose symptomatic cases and to diagnose a high proportion of cases was shown to improve the effectiveness of all three antiviral strategies. For epidemics with R(0)< = 1.5 our results suggest that when the case diagnosis coverage exceeds ∼70% the size of the antiviral stockpile required to implement the extended prophylactic strategy decreases. The addition of at least four weeks of school closure was found to further reduce cumulative and peak attack rates and the size of the required antiviral stockpile.
School of Computer Science and Software Engineering, University of Western Australia, Crawley, Australia. joel@csse.uwa.edu.au
Joel K
JK
Kelso
Nilimesh
N
Halder
George J
GJ
Milne
D000328:Adult; D017677:Age Distribution; D000368:Aged; D000465:Algorithms; D000998:Antiviral Agents / Q000627:therapeutic use*; D002648:Child; D002675:Child, Preschool; D006801:Humans; D015994:Incidence; D007223:Infant; D007231:Infant, Newborn; D053118:Influenza A Virus, H1N1 Subtype / Q000187:drug effects*; D007251:Influenza, Human / Q000175:diagnosis / Q000188:drug therapy* / Q000453:epidemiology; D008875:Middle Aged; D008954:Models, Biological; D017063:Outcome Assessment, Health Care / Q000379:methods; D058873:Pandemics / Q000517:prevention & control*; D012621:Seasons; D013997:Time Factors; D014904:Western Australia / Q000453:epidemiology; D055815:Young Adult
D000998:Antiviral Agents