title: Likely effectiveness of pharmaceutical and non-pharmaceutical interventions for mitigating influenza virus transmission in Mongolia.
abstract: OBJECTIVE
  To assess the likely benefit of the interventions under consideration for use in Mongolia during future influenza pandemics.
  
  
  METHODS
  A stochastic, compartmental patch model of susceptibility, exposure, infection and recovery was constructed to capture the key effects of several interventions--travel restrictions, school closure, generalized social distancing, quarantining of close contacts, treatment of cases with antivirals and prophylaxis of contacts--on the dynamics of influenza epidemics. The likely benefit and optimal timing and duration of each of these interventions were assessed using Latin-hypercube sampling techniques, averaging across many possible transmission and social mixing parameters.
  
  
  RESULTS
  Timely interventions could substantially alter the time-course and reduce the severity of pandemic influenza in Mongolia. In a moderate pandemic scenario, early social distancing measures decreased the mean attack rate from around 10% to 7-8%. Similarly, in a severe pandemic scenario such measures cut the mean attack rate from approximately 23% to 21%. In both moderate and severe pandemic scenarios, a suite of non-pharmaceutical interventions proved as effective as the targeted use of antivirals. Targeted antiviral campaigns generally appeared more effective in severe pandemic scenarios than in moderate pandemic scenarios.
  
  
  CONCLUSIONS
  A mathematical model of pandemic influenza transmission in Mongolia indicated that, to be successful, interventions to prevent transmission must be triggered when the first cases are detected in border regions. If social distancing measures are introduced at this stage and implemented over several weeks, they may have a notable mitigating impact. In low-income regions such as Mongolia, social distancing may be more effective than the large-scale use of antivirals.
authors.affiliation: Melbourne School of Population Health, University of Melbourne, Carlton, Victoria 3010, Australia. kbolton@unimelb.edu.au
authors.forename: K J
authors.initials: KJ
authors.lastname: Bolton
authors.forename: J M
authors.initials: JM
authors.lastname: McCaw
authors.forename: R
authors.initials: R
authors.lastname: Moss
authors.forename: R S
authors.initials: RS
authors.lastname: Morris
authors.forename: S
authors.initials: S
authors.lastname: Wang
authors.forename: A
authors.initials: A
authors.lastname: Burma
authors.forename: B
authors.initials: B
authors.lastname: Darma
authors.forename: D
authors.initials: D
authors.lastname: Narangerel
authors.forename: P
authors.initials: P
authors.lastname: Nymadawa
authors.forename: J
authors.initials: J
authors.lastname: McVernon
mesh_terms: D000998:Antiviral Agents / Q000627:therapeutic use*; D018890:Chemoprevention / Q000379:methods; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology / Q000517:prevention & control* / Q000635:transmission; D008962:Models, Theoretical; D008986:Mongolia / Q000453:epidemiology; D009975:Orthomyxoviridae / Q000187:drug effects / Q000472:pathogenicity; D058873:Pandemics / Q000517:prevention & control*; D010356:Patient Isolation; D018571:Sentinel Surveillance; D012934:Social Isolation; D013269:Stochastic Processes; D014195:Travel
chemical_list: D000998:Antiviral Agents