key: cord-1043011-keaxietu
authors: Farseev, Aleksandr; Chu-Farseeva, Yu-Yi; Qi, Yang; Loo, Daron Benjamin
title: Understanding Economic and Health Factors Impacting the Spread of COVID-19 Disease
date: 2020-04-11
journal: nan
DOI: 10.1101/2020.04.10.20058222
sha: 28ab5d3a7d92867307e4b8b43caf522b3bddc9ae
doc_id: 1043011
cord_uid: keaxietu

The rapid spread of the Coronavirus 2019 disease (COVID-19) had drastically impacted life all over the world. While some economies are actively recovering from this pestilence, others are experiencing fast and consistent disease spread, compelling governments to impose social distancing measures that have put a halt on routines, especially in densely-populated areas. Aiming at bringing more light on key economic and public health factors affecting the disease spread, this initial study utilizes a quantitative statistical analysis based on the most recent publicly-available COVID-19 datasets. The study had shown and explained multiple significant relationships between the COVID-19 data and other country-level statistics. We have also identified and statistically profiled four major country-level clusters with relation to different aspects of COVID-19 development and country-level economic and health indicators. Specifically, this study has identified potential COVID-19 under-reporting traits as well as various economic factors that impact COVID-19 Diagnosis, Reporting, and Treatment. Based on the country clusters, we have also described the four disease development scenarios, which are tightly knit to country-level economic and public health factors. Finally, we have highlighted the potential limitation of reporting and measuring COVID-19 and provided recommendations on further in-depth quantitative research.

The rapid spread of COVID-19 has drastically impacted economies around the world. On 11 March 2020 the disease was officially classified as a pandemic and, as reported on 24 March 2020, it has infected 440, 093, and causing 19, 748 deaths worldwide, with the highest new case intensities in the USA, Spain, Germany, France, Switzerland, South Korea, United Kingdom (UK), and Hubei Province in China. In response to such a volatile situation in the world, governments and the scientific communities have been actively studying the underlying principles and possible reasons for the disease spread and progression. For example , Bai et.al. [1] have first discovered that COVID-19 could have been possibly transmitted by asymptomatic carriers, while Wu et.al. [2] conducted a large-scale study based on 72, 314 confirmed cases listing important actionable lessons for other societies to apply. Furthermore, the Computer Science community has analyzed the disease spread from a statistical point of view. Specifically, in [3] , the authors witnessed a potential association between COVID-19 mortality rates and health-care re-2 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 11, 2020 . . https://doi.org/10.1101 /2020 source availability, while Chen et.al. [4] discovered a strong statistical relationship between initial emigration from Wuhan City and the infection spread to other cities in China. Finally, Chinazzi et.al. [5] suggested that travel restrictions to COVID-19 affected areas could be not as effective, as many infected individuals "...have been traveling internationally without being detected..." and as such, sharper restrictive measures are necessary to take control of the outbreak.

Even though significant efforts have been made so far towards a proper understanding of the COVID-19 outbreak from multiple perspectives, due to the constantly evolving pandemic, emerging new information and data sets, and inaccessibility of public large-scale data, literature based off quantitative research on the outbreak is still relatively sparse. To the best of our knowledge, this study is one of the first attempts to build a more holistic view on the COVID-19 pandemic, which hopes to explain relationships between the disease spread and various economic and public health factors through quantitative analysis.

In this study, we have incorporated the "COVID19 Global Forecasting (Week 2)" dataset [6] that was released by the Kaggle 1 platform. The dataset includes daily updates of the COVID-19 confirmed cases and mortality rates for 173 countries reported by WHO between 22 January 2020 and 28 March 2020.

To study the relationships between COVID-19 spread and various economic factors, we merged the the original dataset with "Country Statistics -UNData" dataset [7] , "Pollution by Country for COVID19 Analysis" dataset [8] , and "The World Bank (Demographics)" dataset by cross-matching country names across data sets. We also merged the original dataset with the dataset obtained by parsing the "World Life Expectancy" database [9] website for obtaining in-1 https://www.kaggle.com/ 3 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted April 11, 2020 . . https://doi.org/10.1101 /2020 

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The copyright holder for this preprint this version posted April 11, 2020 . . https://doi.org/10.1101 /2020 due to COVID-19's long incubation period [11] , natural migration laws [5] and various government-imposed travel policies [5] , it is not feasible to draw the analysis based on actual daily registered case and fatality rates available in the original Kaggle dataset [6] , but rather necessary to perform an additional data pre-processing aiming at establishing holistic data characteristics reflecting the general worldwide COVID-19 spread tendencies. Keeping this in mind, we have performed the following data pre-processing steps:

• Dataset Combination: Original Dataset [6] was joined by performing Country matching to four auxiliary data sets [7, 8, 12, 9] as described in the next sections. Fifteen country names have been replaced with the naming notation used in the original dataset to perform the successful matching.

• Normalized Daily Spread Speed Estimation: In this study, we ana- • Sparse Data Indicator Filtering: As some of the data indicators in the merged dataset were found to contain a large number of missing values, which might affect further analysis, we excluded data indicators that con-5 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 11, 2020 . . https://doi.org/10.1101 /2020 tained more than 35% of missing values. After the sparse data indicator exclusion, the resulting dataset contained 116 data indicators.

To determine the relationship between the COVID-19 Spread Speed and other indicators, we applied Pearson Product-Moment Correlation [13] to 286 data samples (the countries and regions in the combined dataset) and 116 data indicators (whose data indicators that have remained after the Sparse Data Indicator Filtering step). We then filtered out all non-significant correlation values (α = 0.05) and presented the obtained results in a form of a correlation semi-matrix (see Figure 3 ). On the Figure, white circles denote a positive correlation, while the black circles mean that the correlation is negative. The size of the circle is proportional to the correlation strength (the larger circle -the stronger the correlation) and the absence of a circle in a cell means that there was no correlation found or the correlation is not significant.

Let's take a look at the first 28 lines of the correlation semi-matrix. CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 11, 2020 April 11, . . https://doi.org/10.1101 April 11, /2020 doi: medRxiv preprint 7 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 11, 2020 . . https://doi.org/10.1101 /2020 detailed analysis of the longitudinal properties of the COVID-19 measurement and test procedures, in this work, we would only like to highlight the importance and the influence of time-related measurement and testing arrangement aspects as well as to recommend future research along this direction.

Despite several single negative or positive correlations mentioned above, one might find many significant correlations neither between consequent measurements of the same metric (i.e. Confirmed Case Speed on Different Days) nor between different COVID-19 metrics (i.e. Confirmed Case v.s. Fatalities). These suggest that overall there is NO strong relation between Confirmed Case Speed and Fatality Speed within the 14 day-interval and, therefore, additional information sources, such as economic and health data indicators, are necessary for gaining a deeper insight into the COVID-19 Spread Speed trends.

Let's now move our attention to the last 39 data indicators in the lower part of the correlation semi-matrix where the Chronic Disease Death Rates and other Public Health-Related factor correlations are displayed. From the plot, it can be seen that there are multiple significant and consistent correlations that can be found mainly for Confirmed Case Speed measurements. For example, such indicators as Skin Cancer (91.7% 5-year survival rate [14] ), Prostate Cancer (98.6% 5-year survival rate [14] ), Ovary Cancer (46.5% 5-year survival rate [14] ), Breast Cancer (89.7% 5-year survival rate [14] ), and Bladder Cancer (77.3% 5-year survival rate [14] ) Death Rates were found to be significantly positively correlated with the COVID-19 Spread Speed.

In order to explain the above relationship, it is necessary to consider the factors related to country-level chronic disease data indicators. First, taking into consideration the corresponding 5-year survival rates (indicated in the brackets), one can notice that most of the diseases in the group are the cancers with a high general chance of patient survival (let's call them "high-survival cancers").

Generally, it is reasonable to assume that the countries exhibiting higher death rates for such "high-survival cancers" might experience overall difficulties in 8 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 11, 2020 . . https://doi.org/10.1101 /2020 proper and timely patient treatment. When facing COVID-19 pandemic, such countries might not be always well prepared for proper patient isolation and treatment as well, which is essential for COVID-19 disease spread control [15] .

Correspondingly, in such countries, the COVID-19 Spread Speed could be higher entailing the above-reported significant positive correlation.

Supporting the above findings, we would also like to highlight the strong positive correlation of Obesity Rates (especially for Female demographics) to COVID-19 Spread Speed: similarly to Wells et. al. [16] , our correlation semi-matrix visualisation uncovers that obesity is tightly knit to the countries' Gross Domestic Product (GDP 3 ) rates. Consequently, for the countries with lower GDP, the higher COVID-19 spread rates could be attributed to the poorer readiness of these countries to the COVID-19-associated risks.

At last, let's consider an alternative explanation of the reported correlations. From the correlation semi-matrix, one can find that such variables as Regardless of the actual reason behind the discovered relationships, it is likely that more developed economies are able to respond to COVID-19 out- 

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The copyright holder for this preprint this version posted April 11, 2020 . . https://doi.org/10.1101 /2020 break better as compared to the less developed ones. At the same time, such developed world populations might be more affected by unhealthy life habits and various other urban-living factors [18, 19] , which, in turn, would potentially entail higher chronic disease rates and, correspondingly, COVID-19 health predispositions. While in this study we are only witnessing existing relationships, further research is necessary in order to make more conclusive observations regarding the cause for these relationships.

In the previous Section, we discovered that multiple economic factors exhibit a strong relationship to the chronic diseases across the globe, and can be used to characterise the profiles of these countries with relation to the economic development stage, and ultimately, COVID-19 Spread Speed.

To gain further insight into the relationship between such economic factors and the COVID-19 disease spread, let's now discuss the actual correlations between the two groups of variables. Precisely, from the correlation semi-matrix, it can be seen that six economic attributes are strongly positively correlated to the COVID-19 Spread Speed. Below, we will discuss the possible reasons for the discovered relationships.

Expense (% of GDP) , and GDP Per Capita (in USD) strong positive correlations could be attributed to the higher ability of the countries with stronger health systems in performing timely patient assessment, diagnosis, and reporting of the disease. In contrast, countries with weakly-subsidised health systems, many, especially asymptomatic [20] , COVID-19 cases could remain unreported bringing the COVID-19 confirmed case statistics down and entailing the inverse correlation traits that we have discovered from the dataset. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 11, 2020 . . https://doi.org/10.1101 /2020 proportions of the migrant population who often travel abroad for business and personal purposes. Interestingly, both variables exhibit a strong positive correlation during the second week of the observed period (22 March 2020 -28 March 2020), which could be potentially explained by the travel restrictions imposed by the governments during that week, resulting in the situation when many migrants were rushing to return back to their countries of residence prior to border closures [21, 22] .

Finally, the strong positive correlation of the Services and Other Activities % of Gross Value Added (GVA 4 ) measurement can be attributed to the more intense human interaction rates in countries with larger population involved in the service sector of economics, making the risk of COVID-19 infection higher [23] .

Last but not least, let's discuss several other relationships that can be observed from the correlation plot. Spread Speed. As it was previously reported in the literature [24] , and was also observed in this current study, the cultural and behavioural factors, such as human interaction habits, or government-regulated factors, such as social distancing rules, could be of a much higher influence on the ability of the country government to manage the COVID-19 disease outbreak.

At the same time, the reader could observe that such variables as CO2 4 In economics, gross value added (GVA) is the measure of the value of goods and services produced in an area, industry or sector of an economy.

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Ratio also exhibit a strong negative correlation to the COVID-19 Spread Speed. While the latter can be easily hypothesized by the natural geographical sparsity of population introduced by the forested landscape and entailing a limited inter-human interaction, the former two cases require additional clarifications. For example, one can further observe that, evidently, CO2 Emission

Estimates metric is strongly positively correlated to the Lung Cancer Death Rate, which, in turn, is also negatively correlated to the COVID-19 Spread Speed. Taking into consideration that the two metrics might not be related directly to the disease spread speed (as COVID-19 disease gets "...transmitted between people through close contact and droplets" [25] and, thus, more depends on the inter-human close contacts), it is then reasonable to assume that, correspondingly, the two variables could also be positively correlated to the COVID-19 Fatality Speed as we could expect more patients with lung pre- As the above two indicators describe countries' economics strength [26] , the observed relationship can be easily explained as reflecting the countries' ability to manage the spiking of COVID-19 disease outbreak: countries with stronger economies and medical equipment reserve might be able to provide patients 12 .

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The copyright holder for this preprint this version posted April 11, 2020 . . https://doi.org/10.1101 /2020 with necessary care, as compared to the economies experiencing a shortage of resources.

In the previous sections, we have determined multiple economic and public health factors that are strongly and significantly correlated to the COVID-19 disease spread. We have also witnessed various governments and population behavioural traits possibly explaining the different scenarios of COVID-19 development around the world.

Even though the above-discovered findings bring more light into the approaches that governments have adopted to mitigate the crisis, it is still unclear what the exact differences are in these approaches as well as in the country profiles affected by the COVID-19 disease spread.

Aiming at answering this second research question of the study, we have adopted a Clustering Analysis technique [27] to study the groups of countries in our dataset that could be found by separating it based on the economic and public health factors. Specifically, we have adopted the "X-Means" clustering algorithm, that have been reported to be effective in determining the number of clusters in the dataset without necessarily having a prior assumption on the number of clusters [28] . The X-Means clustering was applied to the whole dataset and have determined the four country clusters listed below: 

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The copyright holder for this preprint this version posted April 11, 2020 . . https://doi.org/10.1101 /2020 We then adopted correlation analysis as it was described in Sectionc to uncover the statistical profiles for each of the clusters. Our findings are discussed immediately next.

From the correlation semi-matrix, it can be seen that the countries from the Cluster 1 are positively correlated to the COVID-19 Confirmed Case Speed on 16, 20, and 23 March 2020, while negatively correlated on 25 March 2020. It can also be noticed that these economies heavily rely on agriculture 5 , have high fertility and infant death rates 6 , skewed towards younger population 7 , and have higher death rates from indoor pollution as well as such well-treatable chronic diseases and cancers 8 . All the above correlations could characterize the countries from Cluster 1 as belonging to the category of developing countries, which could also be observed from the country participant list provided in the previous section. Therefore, the non-consistent correlations with the COVID-19 Confirmed Case Speed (three significant positive correlations and one significant negative correlation), could then be explained by possible testing and reporting issues that frequently occur when facing world-scale disease outbreaks [29] . Given the limited available data in our COVID-19 dataset regarding COVID-19 reporting procedures in different countries, in this work, we would like to highlight a 

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The copyright holder for this preprint this version posted April 11, 2020 . . https://doi.org/10.1101 /2020 possible under-reporting issue for the developing world and, consequently, suggest further in-depth research towards COVID-19 spread characteristics in the developing countries.

When looking at the correlation profile of the Country Cluster 2, a reader could immediately notice that the cluster is not associated with any significant COVID-19 correlations except for one positive correlation with COVID-19

Spread Speed on 17 March 2020. Furthermore, one can also find that other positive correlations of the cluster are arguably weak, having its spikes in population growth 9 , Obesity 10 and Diabetes 11 , various heart-related diseases 12 , and reproduction system cancers 13 .

From the observed relationships, we can clearly acknowledge the existence of a cluster consisting of countries with population overweight, and correspondingly, heart [30] and reproductive cancer problems [31] . As the countries in the cluster do not exhibit significant correlation to COVID-19-related data indicators and, therefore, are out of focus in this article, in this work, we would not be further elaborating on such an interesting finding. Having said that, we would like to bring the readers' attention to such an interesting relationship, which could be guide future research direction. 

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Country Cluster 3 is the largest and also the most diverse cluster that we have discovered as it includes most of the European Countries and all states of US that have experienced spikes in COVID-19 cases over the past several 

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The copyright holder for this preprint this version posted April 11, 2020 April 11, . . https://doi.org/10.1101 April 11, /2020 doi: medRxiv preprint diseases 22 .

Taking into consideration both the above-described traits, we could further hypothesise that the populations in countries from Cluster 3 might be also initially predisposed to COVID-19 infection. The latter assumption raises from the two known COVID-19 risk factors that are also to be found related to the countries from Cluster 3, namely older population demographics [32] and existing pre-conditions that could lead to, for example, Cytokine Storm [17] or other highly-lethal COVID-19 complications.

At last, we would like to mention that, based on our findings in the previous section, it is also reasonable to assume that more developed countries might be able to diagnose and report COVID-19 cases timely and at a necessary scale as compared to some developing economies, that, ultimately, may lead to the strong correlations of such countries to COVID-19 Spread Speed in our dataset.

As China is the only country in the Cluster 4 and its economic, population, and, for example, pollution statistics are commonly known, we will omit some strongly positively-correlated indicators in the data commentary.

At the same time, we would like to bring the readers' attention to the possible bias in some of the conclusions that we have drawn from our data. For example, it is easy to notice that such variable as Lung Disease Death Rate, 

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The copyright holder for this preprint this version posted April 11, 2020 . . https://doi.org/10.1101 /2020 specific population chronic diseases and the shift of the disease development timeline between China and other countries.

Last but not least, we would also like to highlight the importance of the proper alignment and synchronization of the data that comes from the regions with large territories and specific disease development timelines.

In this work, we have performed a preliminary statistical analysis aiming at understanding the relationship between various economic and public health factors and COVID-19 disease spread cadence. The study had shown and explained multiple significant relationships between the COVID-19 data and other country-level statistics. We have also identified and statistically profiled four major country-level clusters with relation to different aspects of COVID-19 development and country-level economic and health statistics. Finally, we have highlighted the limitations of our adopted data and approach, encouraging further larger-scale research along the direction.

In future works, we are aiming at establishing automotive Machine Learning and Statistical frameworks, that would be attempting to predict the future development of COVID-19 disease based on our COVID-19 dataset. We will be also extending the dataset with more dynamic and comprehensive data, such as medical resource availability, government-imposed control measure, and culturerelated aspects.

We declare no competing interests.

Aleksandr Farseev and Yu-Yi Chu-Farseeva conceived of the presented idea.

Aleksandr Farseev and Qi Yang developed the theory and performed the data 19 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted April 11, 2020 . . https://doi.org/10.1101 /2020 analysis. Yu-Yi Chu-Farseeva and Daron Benjamin Loo verified the analytical methods. Yu-Yi Chu-Farseeva encouraged Aleksandr Farseev to investigate public health-related aspects of the COVID-19 Disease Spread and Fatalities Speed and supervised the findings of this work. All authors discussed the results and contributed to the final manuscript. spread in regions around world, https://www.kaggle.com/c/ covid19-global-forecasting-week-2/data, accessed: 2020-03-29.

[7] Country statistics -undata dataset, https://www.kaggle.com/ sudalairajkumar/undata-country-profiles, accessed: 2020-03-29.

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The copyright holder for this preprint this version posted April 11, 2020 . . https://doi.org/10.1101 /2020 Tech. rep., World Health Organization (2020).

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km2. Population.in.Thousands..2017. Population.Density..per.km2..2017. Sex.ratio..Number.of.Males.per.100.Females

Female Life.Expectancy.at.Birth.Male International.Migrant.Stock.per.1000

Rate Colorectal.Cancer.Death.Rate Drug.Use.Death.Rate HIV.AIDS.Death.Rate Leukemia.Death.Rate Liever.Disease.Death.Rate Oral.Cancer.Death.Rate Ovary.Cancer.Death.Rate Pancreatic.Cancer.Death.Rate Prostate

km2. Population.in.Thousands..2017. Population.Density..per.km2..2017. Sex.ratio..Number.of.Males.per.100.Females

Rate Colorectal.Cancer.Death.Rate Drug.Use.Death.Rate HIV.AIDS.Death.Rate Leukemia.Death.Rate Liever.Disease.Death.Rate Oral.Cancer.Death.Rate Ovary.Cancer.Death.Rate Pancreatic.Cancer.Death.Rate Prostate

Characteristics of and important lessons from the coronavirus disease 2019 (covid-19) outbreak in china: summary of a report of 72 314 cases from the chinese center for disease control and prevention

Potential association between covid-19 mortality and health-care resource availability, The Lancet Global Health

Distribution of the covid-19 epidemic and correlation with population emigration from wuhan, china

The effect of travel restrictions on the spread of the 2019 novel coronavirus

Covid19 global forecasting

The incubation period of coronavirus disease 2019 (covid-19) from publicly reported confirmed cases: estimation and application

The world bank data

Eer cancer statistics review

Feasibility of controlling covid-19 outbreaks by isolation of cases and contacts, The Lancet Global Health

Associations of economic and gender inequality with global obesity prevalence: understanding the female excess

Covid-19: consider cytokine storm syndromes and immunosuppression

International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted

Stroke epidemiology: a review of population-based studies of incidence, prevalence, and case-fatality in the late 20th century

Differences in prevalence, awareness, treatment and control of hypertension between developing and developed countries

Clinical characteristics of 24 asymptomatic infections with covid-19 screened among close contacts in nanjing, china

Coronavirus: Travel restrictions, border shutdowns by country

Coronavirus disease 2019 (covid-19): situation report

Estimation of country-level basic reproductive ratios for novel coronavirus (covid-19) using synthetic contact matrices, medRxiv

Spatiotemporal spread of the 2014 outbreak of ebola virus disease in liberia and the effectiveness of non-pharmaceutical interventions: a computational modelling analysis

Rational use of personal protective equipment for coronavirus disease (covid-19): interim guidance

Financial development and international trade: Is there a link?

Spatiotemporal clustering analysis and its determinants of hand, foot and mouth disease in hunan, china

X-means: Extending k-means with efficient estimation of the number of clusters

Accountability in the public health care systems: A developing economy perspective

Obesity and heart disease

The association of obesity and cervical cancer screening: a systematic review and metaanalysis

Clinical predictors of mortality due to covid-19 based on an analysis of data of 150 patients from wuhan, china

International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity

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