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印度应对 COVID-19 的务实疫苗接种策略:基于数学建模的分析。

India's pragmatic vaccination strategy against COVID-19: a mathematical modelling-based analysis.

机构信息

Division of Epidemiology and Communicable Diseases (Clinical Studies, Projection & Policy Unit), Indian Council of Medical Research, New Delhi, India.

MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.

出版信息

BMJ Open. 2021 Jul 2;11(7):e048874. doi: 10.1136/bmjopen-2021-048874.

Abstract

OBJECTIVES

To investigate the impact of targeted vaccination strategies on morbidity and mortality due to COVID-19, as well as on the incidence of SARS-CoV-2, in India.

DESIGN

Mathematical modelling.

SETTINGS

Indian epidemic of COVID-19 and vulnerable population.

DATA SOURCES

Country-specific and age-segregated pattern of social contact, case fatality rate and demographic data obtained from peer-reviewed literature and public domain.

MODEL

An age-structured dynamical model describing SARS-CoV-2 transmission in India incorporating uncertainty in natural history parameters was constructed.

INTERVENTIONS

Comparison of different vaccine strategies by targeting priority groups such as keyworkers including healthcare professionals, individuals with comorbidities (24-60 years old) and all above 60.

MAIN OUTCOME MEASURES

Incidence reduction and averted deaths in different scenarios, assuming that the current restrictions are fully lifted as vaccination is implemented.

RESULTS

The priority groups together account for about 18% of India's population. An infection-preventing vaccine with 60% efficacy covering all these groups would reduce peak symptomatic incidence by 20.6% (95% uncertainty intervals (UI) 16.7-25.4) and cumulative mortality by 29.7% (95% CrI 25.8-33.8). A similar vaccine with ability to prevent symptoms (but not infection) will reduce peak incidence of symptomatic cases by 10.4% (95% CrI 8.4-13.0) and cumulative mortality by 32.9% (95% CrI 28.6-37.3). In the event of insufficient vaccine supply to cover all priority groups, model projections suggest that after keyworkers, vaccine strategy should prioritise all who are >60 and subsequently individuals with comorbidities. In settings with weakest transmission, such as sparsely populated rural areas, those with comorbidities should be prioritised after keyworkers.

CONCLUSIONS

An appropriately targeted vaccination strategy would witness substantial mitigation of impact of COVID-19 in a country like India with wide heterogeneity. 'Smart vaccination', based on public health considerations, rather than mass vaccination, appears prudent.

摘要

目的

研究针对 COVID-19 发病率和死亡率以及 SARS-CoV-2 发病率的目标疫苗接种策略对印度的影响。

设计

数学建模。

设置

印度 COVID-19 流行和脆弱人群。

数据来源

来自同行评议文献和公共领域的特定国家和年龄组社会接触模式、病死率和人口统计数据。

模型

构建了一个描述印度 SARS-CoV-2 传播的年龄结构动力学模型,该模型纳入了自然史参数不确定性。

干预措施

通过针对重点人群(如包括医护人员在内的关键工作者、有合并症的个体(24-60 岁)以及所有 60 岁以上的个体)实施疫苗接种,比较不同的疫苗接种策略。

主要结果测量

在假设当前限制随着疫苗接种的实施而完全解除的情况下,不同情景下的发病率降低和避免的死亡人数。

结果

重点人群约占印度人口的 18%。如果为所有这些人群接种一种具有 60%效力的抗感染疫苗,将使有症状的发病高峰减少 20.6%(95%置信区间 16.7-25.4),累计死亡人数减少 29.7%(95%CrI 25.8-33.8)。具有预防症状(但不预防感染)能力的类似疫苗将使有症状病例的发病高峰减少 10.4%(95%CrI 8.4-13.0),累计死亡人数减少 32.9%(95%CrI 28.6-37.3)。如果疫苗供应不足以覆盖所有重点人群,则模型预测表明,在关键工作者之后,疫苗接种策略应优先考虑所有 60 岁以上的人群,随后是有合并症的人群。在传播较弱的地区,如人口稀少的农村地区,在关键工作者之后,应优先考虑有合并症的人群。

结论

在印度这样一个具有广泛异质性的国家,实施有针对性的疫苗接种策略将显著减轻 COVID-19 的影响。基于公共卫生考虑的“智能接种”而非大规模接种似乎更为谨慎。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/951b/8257292/7bda359b9e93/bmjopen-2021-048874f01.jpg

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