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Pandemic preparedness: What can epidemiological modelling offer policy? Nim Arinaminpathy Department of Zoology University of Oxford Talk plan Influenza: a background From today to emergence of a novel influenza virus Antiviral drugs for control of pandemic influenza Influenza RNA virus Clinical manifestations: Headache, sore throat, chills, fever, myalgia, anorexia, malaise Transmission By contact with respiratory droplets, generated by coughing or sneezing Infectiousness can start a day before symptoms and continue for 3 – 5 days after symptoms developing in adults The seasonal influenza burden Disease: 5 – 15% of population affected with upper respiratory tract infections in annual ‘flu season Estimated 3-4,000 annual deaths in UK caused by influenza infection (mainly elderly and immunocompromised) The Economy: Europe: flu accounts for ~10% of sick leave Costs US estimated $90bn a year Influenza family tree Orthomyxoviridae … Influenza From http://www.abc.net.au/health Type Subtype A B … H1N1 H3N2 C Pandemic and seasonal influenza Taken from www.en.influenza.pl Social and economic disruption Social and economic disruption H5N1: Future pandemic? Wild bird reservoir Poultry Humans Transmitted from bird to human by inhaling dried aerosolised faeces First major outbreak in 1997, Hong Kong Resurgence in 2003 has seen virus established in poultry in South-East Asia So far human-to-human spread is non-existent or very limited 387 human cases, 245 deaths to date Wide geographical spread, from S.E.Asia (inc. Indonesia, Viet Nam) to Africa (Nigeria, Egypt) However, H7N7 and N9N2 are also pandemic candidates Evolution and emergence of pandemic influenza Each human case is an opportunity for an avian virus to adapt for human transmission Antiviral drugs for pandemic control No vaccine for at least first 6 months Oseltamivir (Tamiflu) is main antiviral drug of choice UK stockpile: Currently enough for 25% of population Drugs intended mainly for treatment, not prophylaxis For all clinical cases How best to minimise epidemic size and impact with a limited stockpile? A simple compartmental model αλ IT γT RT S (1-α)λ IT I N IN γN RN T N , 0 1 A simple compartmental model αλ IT γT RT S (1-α)λ IN γN U (t ) RT (t ) IT (t ) M RN 1957 ‘Asian Flu’ pandemic 1100 Mortality data, 1957 England & Wales 1000 900 Number of deaths 800 700 600 500 30/11/57 22/02/58 400 300 200 100 0 0 20 40 60 80 100 Time (days) 120 140 160 180 200 1957 ‘Asian Flu’ pandemic 1100 Mortality data, 1957 England & Wales Best fit, basic model 1000 900 Number of deaths 800 700 600 500 400 300 200 100 0 0 20 40 60 80 100 Time (days) 120 140 160 180 200 1957 ‘Asian Flu’ pandemic 1100 Mortality data, 1957 England & Wales Best fit, basic model 30% antiviral coverage 1000 900 Number of deaths 800 700 600 500 400 300 200 100 0 0 20 40 60 80 100 Time (days) 120 140 160 180 200 1957 ‘Asian Flu’ pandemic 1100 Mortality data, 1957 England & Wales Best fit, basic model 30% antiviral coverage 70% antiviral coverage 1000 900 Number of deaths 800 CFR 0.16% 700 600 R0 1.65 25% stockpile exhausted 500 400 300 200 100 0 0 20 40 60 80 100 Time (days) 120 140 160 180 200 How many drugs are needed? 0.8 R = 3.0 0 0.7 ‘Secondary’ effect of mass antiviral treatment is to reduce the spread of infection in the community R = 2.0 0 Minimum required stockpile R = 1.5 0.6 0 0.5 0.4 Its strength depends on drug efficacy and disease transmissibility 0.3 0.2 0.1 0 0 0.2 0.4 0.6 AV coverage, 0.8 1 Antiviral programmes By shortening infectious period and reducing infectiousness, antiviral drugs can influence the course of infection Broadening and delaying epidemic peak Reducing numbers of cases If there is a risk-group for whom the drug has little protective effect, the stockpile is better deployed in the general population. Priority shifts to protection from infection rather than from illness. The ‘social element’ Potential wastage of drugs on the ‘worried well’ Personal stockpiles Non-compliance with treatment regime may lead to drug resistance Pressing ethical questions, eg distributive justice Conclusions Mathematical models can offer valuable insights into disease control Transmission dynamics are often fundamental to epidemic outcomes and effects of interventions …sometimes offering counterintuitive results! However models always entail simplifications, often about human behaviour (important factors) Effective pandemic preparedness could involve a synergy between such models and the social sciences