Lessons from SARS-CoV-2 in India: A data-driven framework for pandemic resilience

被引:10
作者
Salvatore, Maxwell [1 ,2 ,3 ]
Purkayastha, Soumik [1 ]
Ganapathi, Lakshmi [4 ,5 ]
Bhattacharyya, Rupam [1 ]
Kundu, Ritoban [1 ]
Zimmermann, Lauren [1 ,2 ]
Ray, Debashree [6 ,7 ]
Hazra, Aditi [5 ,8 ]
Kleinsasser, Michael [1 ]
Solomon, Sunil [9 ]
Subbaraman, Ramnath [10 ,11 ]
Mukherjee, Bhramar [1 ,2 ,3 ]
机构
[1] Univ Michigan, Dept Biostat, Ann Arbor, MI 48109 USA
[2] Univ Michigan, Ctr Precis Hlth Data Sci, Ann Arbor, MI 48109 USA
[3] Univ Michigan, Dept Epidemiol, Ann Arbor, MI 48109 USA
[4] Boston Childrens Hosp, Div Infect Dis, Boston, MA USA
[5] Harvard Med Sch, Boston, MA 02115 USA
[6] Johns Hopkins Univ, Bloomberg Sch Publ Hlth, Dept Epidemiol, Baltimore, MD USA
[7] Johns Hopkins Univ, Bloomberg Sch Publ Hlth, Dept Biostat, Baltimore, MD USA
[8] Brigham & Womens Hosp, Dept Med, 75 Francis St, Boston, MA 02115 USA
[9] Johns Hopkins Univ, Sch Med, Dept Med, Baltimore, MD 21205 USA
[10] Tufts Univ, Dept Publ Hlth & Community Med, Sch Med, Boston, MA 02111 USA
[11] Tufts Univ, Ctr Global Publ Hlth, Sch Med, Boston, MA 02111 USA
关键词
NONPHARMACEUTICAL INTERVENTIONS; COVID-19; TRANSMISSION; POPULATION; INFECTION;
D O I
10.1126/sciadv.abp8621
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
India experienced a massive surge in SARS-CoV-2 infections and deaths during April to June 2021 despite having controlled the epidemic relatively well during 2020. Using counterfactual predictions from epidemiological disease transmission models, we produce evidence in support of how strengthening public health interventions early would have helped control transmission in the country and significantly reduced mortality during the second wave, even without harsh lockdowns. We argue that enhanced surveillance at district, state, and national levels and constant assessment of risk associated with increased transmission are critical for future pandemic responsiveness. Building on our retrospective analysis, we provide a tiered data-driven framework for timely escalation of future interventions as a tool for policy-makers.
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页数:17
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