Carrat Fabrice, Luong Julie, Lao Hervé, Sallé Anne-Violaine, Lajaunie Christian, Wackernagel Hans
Université Pierre et Marie Curie-Paris6, INSERM, UMR-S 707, Paris F-75012, France.
BMC Med. 2006 Oct 23;4:26. doi: 10.1186/1741-7015-4-26.
With an influenza pandemic seemingly imminent, we constructed a model simulating the spread of influenza within the community, in order to test the impact of various interventions.
The model includes an individual level, in which the risk of influenza virus infection and the dynamics of viral shedding are simulated according to age, treatment, and vaccination status; and a community level, in which meetings between individuals are simulated on randomly generated graphs. We used data on real pandemics to calibrate some parameters of the model. The reference scenario assumes no vaccination, no use of antiviral drugs, and no preexisting herd immunity. We explored the impact of interventions such as vaccination, treatment/prophylaxis with neuraminidase inhibitors, quarantine, and closure of schools or workplaces.
In the reference scenario, 57% of realizations lead to an explosive outbreak, lasting a mean of 82 days (standard deviation (SD) 12 days) and affecting 46.8% of the population on average. Interventions aimed at reducing the number of meetings, combined with measures reducing individual transmissibility, would be partly effective: coverage of 70% of affected households, with treatment of the index patient, prophylaxis of household contacts, and confinement to home of all household members, would reduce the probability of an outbreak by 52%, and the remaining outbreaks would be limited to 17% of the population (range 0.8%-25%). Reactive vaccination of 70% of the susceptible population would significantly reduce the frequency, size, and mean duration of outbreaks, but the benefit would depend markedly on the interval between identification of the first case and the beginning of mass vaccination. The epidemic would affect 4% of the population if vaccination started immediately, 17% if there was a 14-day delay, and 36% if there was a 28-day delay. Closing schools when the number of infections in the community exceeded 50 would be very effective, limiting the size of outbreaks to 10% of the population (range 0.9%-22%).
This flexible tool can help to determine the interventions most likely to contain an influenza pandemic. These results support the stockpiling of antiviral drugs and accelerated vaccine development.
鉴于流感大流行似乎迫在眉睫，我们构建了一个模型来模拟流感在社区内的传播，以测试各种干预措施的影响。
该模型包括个体层面，根据年龄、治疗情况和疫苗接种状况模拟流感病毒感染风险和病毒排出动态；以及社区层面，在随机生成的图表上模拟个体之间的接触情况。我们使用实际大流行的数据来校准模型的一些参数。参考情景假设不进行疫苗接种、不使用抗病毒药物且不存在预先存在的群体免疫。我们探讨了疫苗接种、使用神经氨酸酶抑制剂进行治疗/预防、隔离以及关闭学校或工作场所等干预措施的影响。
在参考情景中，57%的模拟结果导致爆发性疫情，平均持续82天（标准差（SD）12天），平均影响46.8%的人口。旨在减少接触次数的干预措施，结合降低个体传播性的措施，将部分有效：对70%受影响家庭进行覆盖，对索引患者进行治疗，对家庭接触者进行预防，并让所有家庭成员居家隔离，可将爆发概率降低52%，其余疫情将局限于17%的人口（范围为0.8% - 25%）。对70%易感人群进行反应性疫苗接种将显著降低疫情的频率、规模和平均持续时间，但益处将明显取决于首例病例识别与大规模疫苗接种开始之间的间隔。如果立即开始疫苗接种，疫情将影响4%的人口；如果延迟14天，将影响17%的人口；如果延迟28天，将影响36%的人口。当社区感染人数超过50时关闭学校将非常有效，可将疫情规模限制在10%的人口（范围为0.9% - 22%）。
这个灵活的工具有助于确定最有可能控制流感大流行的干预措施。这些结果支持储备抗病毒药物和加速疫苗研发。
