Flahault A, Letrait S, Blin P, Hazout S, Ménarés J, Valleron A J
INSERM, Unité de Recherches Biomathématiques et Biostatistiques, Université Paris, France.
Stat Med. 1988 Nov;7(11):1147-55. doi: 10.1002/sim.4780071107.
The Rvachev-Baroyan-Longini model is a space-time predictive model of the spread of influenza epidemics. It has been applied to 128 cities of the USSR, and more recently, to forecasting the spread of the pandemic of 1968-1969 throughout 52 large cities. It is a deterministic, mass-action, space and time continuous model. The model has been applied to the simulation of the influenza epidemic of 1984-1985 in the 22 French Metropolitan districts and results are presented. Estimates of the parameters of the model were made using the French Communicable Diseases Network data. These parameters are the contact rate, a, (estimate = 0.55) which is the number of people with whom an infectious individual will make contact daily sufficient to pass infection and the infectious period, 1/b, estimated as 2.49 days. The mean annual railroad passenger traffic from district i to district j varies from 0 to 1,991,000 persons depending on the districts. The computer spread of the epidemic is presented on weekly maps. Results are also presented on district charts, giving the size of district epidemics and the time of peak of the epidemic. The precision of the computer fittings was judged satisfactory by the calculated size of peak differing from the real one by less than 100 per cent, in 17 out of 18 districts, and by the calculated time of peak differing from the observed by less than two weeks in 14 out of 18 districts. Although precision could be improved with more detailed information about passenger traffic, the French use of the model has been satisfactory.
Rvachev - Baroyan - Longini模型是流感疫情传播的时空预测模型。它已应用于苏联的128个城市，最近还用于预测1968 - 1969年大流行在52个大城市的传播情况。这是一个确定性的、质量作用的、时空连续的模型。该模型已应用于模拟1984 - 1985年法国22个大城市地区的流感疫情，并给出了结果。使用法国传染病网络数据对模型参数进行了估计。这些参数包括接触率a（估计值 = 0.55），即一个感染个体每天足以传播感染的接触人数，以及传染期1/b，估计为2.49天。从地区i到地区j的年均铁路客运量因地区而异，从0到199.1万人不等。疫情的计算机传播情况以每周地图呈现。地区图表也给出了结果，显示了地区疫情规模和疫情高峰时间。通过计算得出的高峰规模与实际高峰规模相差不到100%（18个地区中有17个），以及计算得出的高峰时间与观测时间相差不到两周（18个地区中有14个），判断计算机拟合的精度令人满意。尽管有更详细的客运信息可以提高精度，但法国对该模型的应用效果令人满意。
