The socio-economic determinants of the coronavirus disease (COVID-19) pandemic

Stojkoski, Viktor; Utkovski, Zoran; Jolakoski, Petar; Tevdovski, Dragan; Kocarev, Ljupčo

doi:10.1101/2020.04.15.20066068

Cited by 112 publications

(139 citation statements)

References 77 publications

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“…Closely related to our study are The Economist (2020), Stojkoski et al (2020) and Castex et al (2020). The Economist's review uses a sample of U.S. states focusing on the case-fatality rate.…”

Section: Mortality Ratessupporting

confidence: 78%

Accounting for Global COVID-19 Diffusion Patterns, January–April 2020

et al. 2020

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Key factors in modeling a pandemic and guiding policy-making include mortality rates associated with infections; the ability of government policies, medical systems, and society to adapt to the changing dynamics of a pandemic; and institutional and demographic characteristics affecting citizens' perceptions and behavioral responses to stringent policies. This paper traces the crosscountry associations between COVID-19 mortality, policy interventions aimed at limiting social contact, and their interactions with institutional and demographic characteristics. We document that, with a lag, more stringent pandemic policies were associated with lower mortality growth rates. The association between stricter pandemic policies and lower future mortality growth is more pronounced in countries with a greater proportion of the elderly population and urban population, greater democratic freedoms, and larger international travel flows. Countries with greater policy stringency in place prior to the first death realized lower peak mortality rates and exhibited lower durations to the first mortality peak. In contrast, countries with higher initial mobility saw higher peak mortality rates in the first phase of the pandemic, and countries with a larger elderly population, a greater share of employees in vulnerable occupations, and a higher level of democracy took longer to reach their peak mortalities. Our results suggest that policy interventions are effective at slowing the geometric pattern of mortality growth, reducing the peak mortality, and shortening the duration to the first peak. We also shed light on the importance of institutional and demographic characteristics in guiding policy-making for future waves of the pandemic.

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Section: Mortality Ratessupporting

confidence: 78%

Accounting for Global COVID-19 Diffusion Patterns, January–April 2020

et al. 2020

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“…Our results support the WHO recommendations, to increase testing and track of COVID-19 cases in all countries, given its de nitive impact to reduce the CFR. In line with Stojkoski, et al (2020) we found that the countries expenditure on health as well as their development level is positively related to CFR, cases and deaths, which cannot be interpreted as causation, but it is indicating that developing countries a not tracking enough cases yet. In consequence, we claimed that there is an under identi cation of data given the positive correlation between cases and deaths and testing, meaning that testing is still reactive and with little identi cation of asymptomatic, which is also highlighted by the OECD (2020) and the WHO (2020).…”

Section: Discussionsupporting

confidence: 83%

The importance of COVID-19 testing to assess socioeconomic fatality drivers and true case fatality rate. Facing the pandemic or walking in the dark?

Ibarra-Armenta

Alarcon-Osuna

2020

Preprint

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Up to date, European and other developed countries became the centre of the pandemic. While the COVID-19 spread to developing countries and less developed regions, seems to be still very low. The Case Fatality Rate (CFR) differs a lot among countries, genetics, health systems, population characteristics as well as public health and social measures (lockdown measures) are believed to be the determinants of such diversity. Through an Ordinal Probit, Cross Section and Panel data models for 71 countries, it is shown that the nations applying more tests per million inhabitants are also those reporting more cases and deaths, yet greater testing helped to reduce CFR; while, health infrastructure and population health indicators could not be confirmed as drivers for CFR. The Stringency Index showed a negative correlation with the number of deaths. Our main finding is that, the pandemic concentration on developed nations is highly related with their ability and resources for tracking the pandemic. Three additional conclusions are drawn: first, the true CFR and its drivers at national levels cannot be estimated without increasing the number of tests per million inhabitants; second, there is an under-identification of cases and/or deaths and the countries applying more tests, are most clearly identifying the reality of the pandemic, while countries with less cases, are actually still walking in the dark; third lockdown measures have been effective at reducing the number of deaths.

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“…Although it was anticipated by several researchers initially that the density of the population had a positive correlation with the spread of COVID-19 [39,40,41,42,43]], some recent studies show that the assumption might not be completely evident [44,32]. Health expenditure by the government was identified as a significant determinant of deaths caused from COVID-19 [45,46], whereas topographical locations along with their climatic circumstances were detected as prominent factors for the diffusion of the virus [47]. Although the temperature has been observed to be a relevant factor of COVID-19 transmission according to the results of some studies [48,49,50], there are some counterarguments as well [51,52].…”

Section: Prediction Models and Their Componentsmentioning

confidence: 99%

4P Model for Dynamic Prediction of Covid-19 with statistical and machine learning approach: Social Dynamics, Government Roles and Pathogen Behavior on Bangladesh Perspective

Hasan

Ahmmed

Chowdhury

et al. 2020

Preprint

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Introduction: Around the world, scientists are racing hard to understand how Covid-19 epidemic is spreading and growing, thus trying to find ways to prevent it before medications come to pass. Many different models have been proposed so far correlating different factors. Some of them are too localized to indicate a general trend of the pandemic while some others have established transient correlations only.Methods: Hence, in this study, a 4P model has been proposed based on four probabilities (4P) which has been found to be true for all affected countries taking Bangladesh as a case. Efficiency scores have been estimated from survey analysis not only for governing authorities on managing the situation (P(G)) but also for the compliance of the citizens ((P(P)). Since the immune responses of all the people are not uniform to a specific pathogen, the probability of a person getting infected ((P(I)) after being exposed has also been estimated. And the vital one is the probability of Test Positivity ((P(T)) which is a strong indicator of how effectively the infected people are diagnosed and isolated from the rest of the group that affects the rate of growth.Results and Conclusion: All the four parameters have been fitted in a non-linear exponential model that partly updates itself periodically with everyday facts. Along with the model, all the four probabilistic parameters are engaged to train a recurrent neural network using Long-Short Term Memory neural network and the followed trial confirmed a ruling functionality of the 4Ps.

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The socio-economic determinants of the coronavirus disease (COVID-19) pandemic

Cited by 112 publications

References 77 publications

Accounting for Global COVID-19 Diffusion Patterns, January–April 2020

Accounting for Global COVID-19 Diffusion Patterns, January–April 2020

The importance of COVID-19 testing to assess socioeconomic fatality drivers and true case fatality rate. Facing the pandemic or walking in the dark?

4P Model for Dynamic Prediction of Covid-19 with statistical and machine learning approach: Social Dynamics, Government Roles and Pathogen Behavior on Bangladesh Perspective

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