Pandemic influenza in Papua New Guinea: a modelling study comparison with pandemic spread in a developed country

Milne, George J; Baskaran, Pravin; Halder, Nilimesh; Karl, Stephan; Kelso, Joel

To achieve simulations for a particular basic reproduction number R0, βv was adjusted; details of the procedure for estimating βv and R0 are given in [20] . A pandemic with a basic reproduction number of 1.5 is used in this study, and corresponds to some estimations of the 2009 H1N1 pandemic [36] [37] [38] [39] and the 1957 and 1968 pandemics [40, 41] . Since it is virus intrinsic, we assumed that βv was similar in Madang and thus calculated R0 in the Madang and Madang-nnh models using the βv corresponding to an R0 of 1.5 in the Albany model. Details of parameter settings used in each model are given in Table A2 in the Supplementary Data File. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 F o r p e e r r e v i e w o n l y 9 A simulation algorithm, realised in the C++ programming language, manipulates the underlying demographic model and captures both population mobility and the timechanging infectivity profile of each individual. Each individual has their infectivity status denoted by one of the four (S,E,I,R) states at any time point during the duration of the simulated period. The simulation algorithm captures the state of the whole population twice per day, a daytime point-in-time snapshot and an evening snapshot, with individuals (possibly) moving locations between successive day or night periods, such as household to school or workplace for the day phase, returning to home for the night period. Individuals come into contact with other individuals on a one-to-one basis in each location, with possible influenza transmission then occurring.