Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, United States of America.
WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, the University of Hong Kong, Hong Kong Special Administrative Region, China.
PLoS Comput Biol. 2020 Jun 15;16(6):e1007989. doi: 10.1371/journal.pcbi.1007989. eCollection 2020 Jun.
Influenza epidemics cause substantial morbidity and mortality every year worldwide. Currently, two influenza A subtypes, A(H1N1) and A(H3N2), and type B viruses co-circulate in humans and infection with one type/subtype could provide cross-protection against the others. However, it remains unclear how such ecologic competition via cross-immunity and antigenic mutations that allow immune escape impact influenza epidemic dynamics at the population level. Here we develop a comprehensive model-inference system and apply it to study the evolutionary and epidemiological dynamics of the three influenza types/subtypes in Hong Kong, a city of global public health significance for influenza epidemic and pandemic control. Utilizing long-term influenza surveillance data since 1998, we are able to estimate the strength of cross-immunity between each virus-pairs, the timing and frequency of punctuated changes in population immunity in response to antigenic mutations in influenza viruses, and key epidemiological parameters over the last 20 years including the 2009 pandemic. We find evidence of cross-immunity in all types/subtypes, with strongest cross-immunity from A(H1N1) against A(H3N2). Our results also suggest that A(H3N2) may undergo antigenic mutations in both summers and winters and thus monitoring the virus in both seasons may be important for vaccine development. Overall, our study reveals intricate epidemiological interactions and underscores the importance of simultaneous monitoring of population immunity, incidence rates, and viral genetic and antigenic changes.
流感疫情每年在全球范围内造成大量发病率和死亡率。目前，两种甲型流感亚型 A(H1N1)和 A(H3N2)以及 B 型病毒在人类中共同传播，感染一种类型/亚型可能对其他类型/亚型提供交叉保护。然而，目前尚不清楚通过交叉免疫和允许免疫逃逸的抗原突变进行这种生态竞争如何影响人群水平上的流感疫情动态。在这里，我们开发了一个全面的模型推断系统，并将其应用于研究香港三种流感类型/亚型的进化和流行病学动态，香港是流感疫情和大流行控制方面具有全球公共卫生意义的城市。利用自 1998 年以来的长期流感监测数据，我们能够估计每种病毒对之间的交叉免疫强度、针对流感病毒抗原突变的人群免疫中突发变化的时间和频率，以及过去 20 年包括 2009 年大流行在内的关键流行病学参数。我们在所有类型/亚型中都发现了交叉免疫的证据，其中 A(H1N1)对 A(H3N2)的交叉免疫最强。我们的结果还表明，A(H3N2)可能在夏季和冬季都发生抗原突变，因此在两个季节监测病毒对疫苗开发可能很重要。总的来说，我们的研究揭示了复杂的流行病学相互作用，并强调了同时监测人群免疫、发病率以及病毒遗传和抗原变化的重要性。
