Numerical modelling of coronavirus pandemic in Peru

Q3 Mathematics

Epidemiologic Methods Pub Date : 2022-02-01 DOI:10.1515/em-2020-0026

C. Jiménez, M. Merma

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Abstract

Abstract Objectives The main objective of this research is to demonstrate the effectiveness of non-pharmaceutical interventions (social isolation and quarantine) and of vaccination. Methods The SIR epidemiological numerical model has been revised to obtain a new model (SAIRDQ), which involves additional variables: the population that died due to the disease (D), the isolated (A), quarantined population (Q) and the effect of vaccination. We have obtained the epidemiological parameters from the data, which are not constant during the evolution of the pandemic, using an iterative approximation method. Results Analysis of the data of infected and deceased suggest that the evolution of the coronavirus epidemic in Peru has arrived at the end of the second wave (around October 2021). We have simulated the effect of quarantine and vaccination, which are effective measures to reduce the impact of the pandemic. For a variable infection and isolation rate, due to the end of the quarantine, the death toll would be around 200 thousand; if the isolation and quarantine were relaxed since March 01, 2021, there could be more than 280 thousand deaths. Conclusions Without non-pharmaceutical interventions and vaccination, the number of deaths would be much higher than 280 thousand.

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秘鲁冠状病毒大流行的数值模拟

本研究的主要目的是证明非药物干预措施(社会隔离和检疫)和疫苗接种的有效性。方法对SIR流行病学数值模型进行修正，得到一个新模型(SAIRDQ)，该模型增加了因病死亡人群(D)、隔离人群(a)、隔离人群(Q)和疫苗接种效果等变量。我们使用迭代逼近法从数据中获得流行病学参数，这些参数在大流行的演变过程中不是恒定的。结果对感染病例和死亡病例数据的分析表明，秘鲁冠状病毒疫情的演变已经到达第二波结束(2021年10月左右)。我们模拟了隔离和接种疫苗的效果，这是减少大流行影响的有效措施。在感染和隔离率可变的情况下，由于隔离的结束，死亡人数将在20万人左右;如果从2021年3月1日起放松隔离检疫，可能会有超过28万人死亡。结论如果没有非药物干预和疫苗接种，死亡人数将远远高于28万人。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Epidemiologic Methods Mathematics-Applied Mathematics

CiteScore

2.10

自引率

0.00%

发文量

期刊介绍： Epidemiologic Methods (EM) seeks contributions comparable to those of the leading epidemiologic journals, but also invites papers that may be more technical or of greater length than what has traditionally been allowed by journals in epidemiology. Applications and examples with real data to illustrate methodology are strongly encouraged but not required. Topics. genetic epidemiology, infectious disease, pharmaco-epidemiology, ecologic studies, environmental exposures, screening, surveillance, social networks, comparative effectiveness, statistical modeling, causal inference, measurement error, study design, meta-analysis