COVID-19 and the flu: data simulations and computational modelling to guide public health strategies

被引:3
作者
Tunaligil, Verda [1 ,2 ]
Meral, Gulsen [3 ,4 ]
Dabak, Mustafa Resat [5 ]
Canbulat, Mehmet [6 ,7 ]
Demir, Siddika Semahat [8 ,9 ,10 ,11 ]
机构
[1] TR MoH Hlth Directorate Istanbul, SIMMERK Med Simulat Ctr, Div Publ Hlth, TR-34330 Istanbul, Turkey
[2] TR MoH Hlth Directorate Istanbul, Dept Emergency, Disaster Med Serv, TR-34330 Istanbul, Turkey
[3] Nutrigenet & Epigenet Assoc, Presidents Off, Istanbul, Turkey
[4] Nutrigenet & Epigenet Assoc, Dept Pediat, Istanbul, Turkey
[5] TR MoH Haseki Res & Training Hosp, Dept Family Med, Div Residency, Training Programs & Clin Practice Chieftaincy, Istanbul, Turkey
[6] Turkish Airlines, Dept Data Management, Istanbul, Turkey
[7] Robert Koch Inst, Dept Data Sci, Berlin, Germany
[8] Sci Heroes Assoc, Presidents Off, Istanbul, Turkey
[9] Sci Heroes Assoc, Dept Biomed, Istanbul, Turkey
[10] Sci Heroes Assoc, Dept Elect, Istanbul, Turkey
[11] Sci Heroes Assoc, Dept Comp Engn, Istanbul, Turkey
关键词
Artificial learning; coronavirus; influenza; SARS-CoV-2; social distancing; vaccination;
D O I
10.1093/fampra/cmab058
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Background: Pandemics threaten lives and economies. This article addresses the global threat of the anticipated overlap of COVID-19 with seasonal-influenza. Objectives: Scientific evidence based on simulation methodology is presented to reveal the impact of a dual outbreak, with scenarios intended for propagation analysis. This article aims at researchers, clinicians of family medicine, general practice and policy-makers worldwide. The implications for the clinical practice of primary health care are discussed. Current research is an effort to explore new directions in epidemiology and health services delivery. Methods: Projections consisted of machine learning, dynamic modelling algorithms and whole simulations. Input data consisted of global indicators of infectious diseases. Four simulations were run for '20% versus 60% flu-vaccinated populations' and '10 versus 20 personal contacts'. Outputs consisted of numerical values and mathematical graphs. Outputs consisted of numbers for 'never infected', 'vaccinated', 'infected/recovered', 'symptomatic/asymptomatic' and 'deceased' individuals. Peaks, percentages, R-0, durations are reported. Results: The best-case scenario was one with a higher flu-vaccination rate and fewer contacts. The reverse generated the worst outcomes, likely to disrupt the provision of vital community services. Both measures were proven effective; however, results demonstrated that 'increasing flu-vaccination rates' is a more powerful strategy than 'limiting social contacts'. Conclusions: Results support two affordable preventive measures: (i) to globally increase influenza-vaccination rates, (ii) to limit the number of personal contacts during outbreaks. The authors endorse changing practices and research incentives towards multidisciplinary collaborations. The urgency of the situation is a call for international health policy to promote interdisciplinary modern technologies in public health engineering.
引用
收藏
页码:9 / 15
页数:7
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