Purpose

The main purpose of this study is to examine the role of the coronavirus disease 2019 (COVID-19) test on transmission data globally to reveal the fact that the actual picture of transmission history cannot be exposed if the countries do not perform the test adequately.

Design/methodology/approach

Using Our World in Data for 212 countries and areas and 162 time periods daily from December 31, 2019, to June 09, 2020, on an unbalanced panel framework, we have developed a panel-based path analysis model to explore the interdependence of various actors of COVID-19 cases of transmission across the globe. After controlling for per capita gross domestic product (GDP), age structure and government stringency, we explore the proposition that COVID-19 tests affect transmission positively. As an anecdote, we also explore the direct, indirect and total effects of different potential determinants of transmission cases worldwide and gather an idea about each factor's relative role in a structural equation framework.

Findings

Using the panel path model, we find that a 1 standard deviation change in the number of tests results in a 0.70 standard deviation change in total cases per million after controlling for several variables like per capita GDP, government stringency and age population (above 65).

Research limitations/implications

It is not possible to get balanced data of COVID-19 for all the countries for all the periods. Similarly, the socioeconomic, political and demographic variables used in the model are not observed daily, and they are only available on an annual basis.

Practical implications

Countries which cannot afford to carry out more tests are also the countries where transmission rates are suppressed downward and negatively manipulated.

Social implications

Cross country collaboration in terms of COVID-19 test instruments, vaccination and technology transfer are urgently required. This collaboration may be sought as an alternative to foreign development assistance.

Originality/value

This article provides an alternative approach to modeling COVID-19 transmission through the panel path model where the test is considered as an endogenous determinant of transmission, and the endogeneity has been channeled through per capita GDP, government stringency and age structure without using any regression-based modeling like pooled ordinary least squares (OLS), fixed-effects, two-stage least squares or generalized method of moments (GMM). Endogeneity has been handled without using any instruments.

中文翻译：

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2019 年冠状病毒病传播决定因素的面板路径分析方法：检测对确诊病例重要吗？

目的

本研究的主要目的是检查 2019 年冠状病毒病 (COVID-19) 测试对全球传播数据的作用，以揭示如果各国没有充分执行测试，就无法暴露传播历史的实际情况。

设计/方法/方法

从2019年12月31日至2020年6月9日，每天使用212个国家和地区、162个时间段的Our World in Data，在非平衡面板框架上，我们开发了基于面板的路径分析模型来探索不同参与者之间的相互依存关系全球传播的 COVID-19 病例数。在控制了人均国内生产总值 (GDP)、年龄结构和政府严格程度后，我们探讨了 COVID-19 测试对传播产生积极影响的命题。作为轶事，我们还探索了全球传播病例的不同潜在决定因素的直接、间接和总体影响，并收集了关于每个因素在结构方程框架中的相对作用的想法。

发现

使用面板路径模型，我们发现在控制了人均 GDP、政府严格性和年龄人口（65 岁以上）等几个变量后，测试数量的 1 个标准差变化会导致每百万总病例数的标准差变化为 0.70 .

研究限制/影响

不可能获得所有国家/地区所有时期的 COVID-19 平衡数据。同样，模型中使用的社会经济、政治和人口变量也不是每天观察到的，它们只能每年提供一次。

实际影响

无力进行更多检测的国家，也是传播率被向下压制和负面操纵的国家。

社会影响

迫切需要在 COVID-19 测试仪器、疫苗接种和技术转让方面进行跨国合作。可以寻求这种合作作为外国发展援助的替代方案。

原创性/价值

本文提供了一种通过面板路径模型对 COVID-19 传播进行建模的替代方法，该模型将测试视为传播的内源性决定因素，并且内生性已通过人均 GDP、政府严格程度和年龄结构进行引导，而未使用任何回归-基于建模，如池化普通最小二乘法 (OLS)、固定效应、两阶段最小二乘法或广义矩法 (GMM)。内生性已在不使用任何工具的情况下进行处理。