The macroecology of the COVID-19 pandemic in the Anthropocene.

Severe acute respiratory syndrome coronavirus 2, the virus that causes coronavirus disease 2019 (COVID-19), has expanded rapidly throughout the world. Thus, it is important to understand how global factors linked with the functioning of the Anthropocene are responsible for the COVID-19 outbreak. We tested hypotheses that the number of COVID-19 cases, number of deaths and growth rate of recorded infections: (1) are positively associated with population density as well as (2) proportion of the human population living in urban areas as a proxies of interpersonal contact rate, (3) age of the population in a given country as an indication of that population's susceptibility to COVID-19; (4) net migration rate and (5) number of tourists as proxies of infection pressure, and negatively associated with (5) gross domestic product which is a proxy of health care quality. Data at the country level were compiled from publicly available databases and analysed with gradient boosting regression trees after controlling for confounding factors (e.g. geographic location). We found a positive association between the number of COVID-19 cases in a given country and gross domestic product, number of tourists, and geographic longitude. The number of deaths was positively associated with gross domestic product, number of tourists in a country, and geographic longitude. The effects of gross domestic product and number of tourists were non-linear, with clear thresholds above which the number of COVID-19 cases and deaths increased rapidly. The growth rate of COVID-19 cases was positively linked to the number of tourists and gross domestic product. The growth rate of COVID-19 cases was negatively associated with the mean age of the population and geographic longitude. Growth was slower in less urbanised countries. This study demonstrates that the characteristics of the human population and high mobility, but not population density, may help explain the global spread of the virus. In addition, geography, possibly via climate, may play a role in the pandemic. The unexpected positive and strong association between gross domestic product and number of cases, deaths, and growth rate suggests that COVID-19 may be a new civilisation disease affecting rich economies.

1. Introduction

Macroecology is the study of broad-scale ecological patterns and processes [1]. Few ecologists, however, study the influence of the environment on humans, including the effects of biotic, abiotic, and social conditions on the population growth, economy, and health of our own species [2,3]. The emerging discipline of human macroecology [3] has an interesting duality [2]. The Homo sapiens is one of the most powerful species to inhabit the Earth [2] and is now a major geological and environmental force, as important as, or more important than, natural forces [4]. Thus, it has been suggested that the Earth is in the epoch called Anthropocene [4,5]. However, humans are subject to the same biological laws as any other organism. One of the most important areas of macroecology in the human context is disease ecology [6,7]. Humans, as hosts, exhibit three specific macroecological patterns: (1) humans spreading geographically disperse pathogens and parasites, (2) humans visiting or settling in new areas encounter new organisms, including new pathogens, and new alternative hosts for existing pathogens and parasites; (3) increased human population density and frequency of contact substantially influence the ecology of disease [2]. Thus, understanding how the spread of diseases is related to environmental and socioeconomic factors requires a global perspective [8].

New infectious diseases determine changes in mortality in populations of all organisms, including humans [9,10]. Among many viral diseases in humans, those caused by coronaviruses are especially troublesome [11]. Coronaviruses are a large family of viruses that usually cause disease in wild animals, but several of them, probably including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have made the jump to humans [11]. New viruses may be a threat to health systems and economies, and may even cause pandemics [12].

Coronavirus disease (COVID-19), caused by the SARS-CoV-2 virus, has been present since mid-December 2019. The first case of coronavirus was probably earlier (on 17 November, according to government data reported in the South China Morning Post), but until December, Chinese officials did not know that they had a new type of virus [13]. The World Health Organization officially recognised this disease on 11 March 2020 as a global pandemic [14]. In December and January, the incidence was limited primarily to the city of Wuhan in central China, but as early as mid-January, the virus quickly spread throughout China. On 13 January 2020, the first case outside China was confirmed. On 24 January, the first case was reported in Europe. In the second half of February, outbreaks with hundreds of patients erupted in South Korea, Italy, and Iran. On 20 June, the number of infected people worldwide reached over 8,385,440, of which 450,686 died [15]. Coronavirus-infected patients were registered on all continents, except Antarctica.

The COVID-19 pandemic will probably have numerous effects on the functioning of the human population, and, consequently, vast ecological consequences for human-affected ecosystems (e.g. bans to wildlife trade and increased poaching) [16]. It is thus urgent to recognise the factors responsible for the spread of this pathogen among human societies. This novel virus is unaffected by any immunity that people may have to older strains and can, therefore, spread extremely rapidly and infect very large numbers of humans in a short period of time. Typically, the SARS-CoV-2 virus is transmitted from infected individuals through the air by coughs or sneezes, creating aerosols containing the virus or by contact with contaminated surfaces, where the virus can survive for hours to days at a time [17]. Therefore, population density should positively correlate with the number of infections, deaths, and growth rate of infection cases. Higher population density increases the number of contacts among individuals and thus may mediate the transmission of pathogens [18,19]. The highest human population density occurs in urban areas. Towns and cities are also the usual areas of numerous social contact [20]. The high density of cars, buildings, and factories increases environmental pollution in urban areas compared with rural ones. This imposes additional stress on the immune system [21]. Thus, it may be expected that pandemics are most common in urbanised countries.

Disease spread increases with the exchange of people between human populations. In the globalisation era, people increasingly change their location [21,22]. International travel has connected the world in the past century, and this mobility facilitates coronavirus transmission, allowing regional epidemics to become worldwide pandemics within a matter of weeks or even days. The mass movement of large numbers of people creates new opportunities for the spread and establishment of common or novel infectious diseases [23,24]. Thus, one may predict that a higher number of tourists and the net immigration rate should be positively associated with COVID-19 cases.

Models predict that children can transmit different types of viruses [25,26]. The higher frequency of disease incidence among children and young adults than that in the older population is mainly attributable to a low level of immunity in these age groups due to lower past exposure to infectious diseases [27]. However, studies on H1N1 swine flu cases during the late spring and summer of 2009 in various countries showed a substantial age shift in local transmission cases, with adults mainly responsible for seeding unaffected regions and children most frequently driving community outbreaks [28]. A low number of acute courses of COVID-19 cases in young people indicates that young people may be vectors of COVID-19 for additional transmission. Thus, it may be expected that the number of cases may be higher in countries with lower average life spans. On the other hand, older people have a weaker immune response and poorer general health, and are affected the most by COVID-19 and other viruses [29]. Thus, one may expect that the number of deaths will be the highest in countries with a high average life span.

In addition, other socioeconomic factors may be associated with the prevalence of pathogens. Marginal and disadvantaged people with low socioeconomic status are generally more vulnerable during a pandemic outbreak of disease [30]. Limited access to the media, lack of adequate resources for precautionary activities, lower literacy rates, inadequate access to health services, and crowded accommodations make people more prone to be affected by the pandemic [31]. Gross domestic product (GDP) is a commonly used indicator of socioeconomic variables [32]. For example, GDP correlates positively with the healthcare system and the probability of survival of people with dangerous diseases such as cancer [33]. Hence, it is expected that the number of cases, deaths, and rates of infection growth should be negatively associated with GDP.

In this paper, we aim to determine which global factors are associated with the early pandemic of COVID-19. We tested the hypotheses that the number of infections, deaths, and the rate of growth in the number of COVID-19 infections are:

Positively associated with human population density.

Positively associated with the proportion of the population living in urban areas.

Negatively associated with the median age of the human population. However, the number of deaths should be positively associated with the median age of the population.

Positively associated with the number of tourists visiting a given country

Positively associated with the net migration rate (proportion of immigrants) in a given country.

Negatively associated with gross domestic product.

We tested these hypotheses by including variables that are inevitably related to pandemic spread, such as number of days since the start of the pandemic in a given country, global locality (geographic coordinates of the centroid of each country).

2. Methods

2.1. Data

We used publicly available databases. Data on COVID-19 were downloaded from the European Centre for Disease Prevention and Control (https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide) on 12 April 2020.

Data of socioeconomic variables in each country where COVID-19 infections were reported were derived from the United Nations Population Division available via Worldmeters (https://www.worldometers.info/world-population/population-by-country/), downloaded on 18 March 2020.

Data on the number of tourists were obtained from IndexMundi (https://www.indexmundi.com/facts/indicators/ST.INT.ARVL/rankings).

Moreover, data on geographic coordinates of country centroids was downloaded on 18 March and 25 May 2020 from WorldMap (https://worldmap.harvard.edu/data/geonode:country_centroids_az8).

Data were compiled and analysed in R Environment [34] with the set of packages in ‘tidyverse’ [35]. All data and codes are available in the Supplementary material.

2.2. Data analysis

We analysed three response variables: 1) the number of COVID-19 cases, 2) the number of deaths due to the infection, and 3) growth rate of the infection cases. The growth rate of infection was determined by fitting the exponential growth curve for data in each country. The explanatory variables were: human population density (Dens), the proportion of the population living in urban areas (Urban), median age of the population (Age), number of tourists visiting a country (Tour), net migrations rate (Mig; negative value if emigration prevails, positive if immigration prevails), gross domestic product in millions of US dollars (GDP), time in days since the first case recorded in a given country (Time), geographic longitude (Lon), and latitude (Lat) of a country centroid.

Gradient boosting regression trees (GBRTs) [36] implemented in ‘h2o’ package version 3.30.0.1 [37] were used to analyse the relationships between the explanatory variables and dependent variables. Gradient boosting regression trees are efficient machine learning algorithms that have been proven successful across many domains and are among the leading machine learning algorithms [38-40]. Boosting improves model accuracy by searching for many rough prediction rules rather than the single most accurate prediction rule [39,40]. Gradient boosting regression trees generate a final model that is more robust than a single regression tree model and enables curvilinear functions to be modelled [41]. Another advantage of this method is that it copes with collinearity among variables [41], which was the case in our dataset (Fig 1). Gradient boosting regression trees calculate the relative importance of explanatory variables [40,41] in the predictive model rather than P-values, which have been criticised [42,43].

Fig 1

Correlations among explanatory variables used in the analyses.

Only statistically significant associations are shown. The width of the lines indicates the strength of the correlation. Explanation of variable codes: Age = the median age of the population in a given country; Dens = human population density; GDP = gross domestic product; Lat = geographic latitude of the country centroid; Lon = geographic longitude of the country centroid; Mig = net migration rate; Time = number of days since the start of the pandemic in a given country; Tour = number of tourists in a given country; Urban = the proportion of the human population living in urban areas.

The GBRTs are prone to overfitting, but this can be solved by tuning the parameters [40]. The settings of the GBRTs model were tuned by searching for the optimal set of parameters minimising the mean squared error [40]. The tuning parameters were found via function ‘h2o.grid’ by running the model with different values for the parameters [40]. They were: maximum tree depth (values: 1, 3, 5), fewest allowed (weighted) observations in a leaf (values: 1, 5, 10), learning rate (values: 0.001, 0.01, 0.1), scale the learning rate by this factor after each tree (values: 0.99, 1), row sample rate per tree (values: 0.5, 0.75, 1), and column sample rate (values: 0.8, 0.9, 1).

The model was fitted to the training data (70% of data, randomly selected) with 10-fold cross validation [40]. For the number of cases and deaths, we used the Poisson distribution and for the growth rate, we used the Gaussian distribution. We used natural logarithm transformation (variables: Dens, GDP, Time, Tour) because gradient boosting regression may produce biased results in the presence of outliers [44]. The fitted model was then used to make predictions on the test data. Finally, the performance of each model was assessed on the test dataset. The R2 between the predicted and actual data was used as a measure of performance.

To visualise the results, we used individual conditional expectation (ICE) plots in ‘pdp’ R package [45], a tool for visualising the model estimated by any supervised learning algorithm and Friedman’s partial dependency plots [36]. Partial dependence plot (PDP) highlights the average partial relationship between a set of explanatory variables and the predicted response variable [40]. Individual conditional expectation plots highlight the variation in the fitted values across the range of an explanatory variable, suggesting where and to what extent heterogeneities may exist. The ICE plots disaggregate this average by displaying the estimated functional relationship for each observation [46]. We interpreted the results with an importance above 1%.

3. Results

3.1. Number of COVID-19 cases

The GBRT analysis revealed that all examined variables had a non-zero impact on the number of cases (Fig 2). However, only three variables, GDP, Tour, and Lon, had an importance above 1% (Fig 2). The number of cases positively correlated with GDP, but in a nonlinear manner (Fig 3A). The number of cases increased after the GDP reached 60 billion US dollars (Fig 3A). The number of cases also increased rapidly if the number of tourists in a country exceeded 20 million (Fig 3B). The number of cases increased with the geographic longitude from Asia to Europe (Fig 3C). Gradient-boosted regression trees built on trained data explained 81% of the variation in the test data.

Fig 2

Decomposition of the variation associated with explanatory variables into independent components using gradient boosting regression trees.

The importance of variables in gradient boosting regression trees explaining the number of COVID-19 cases, number of deaths, and growth rate of COVID-19 cases. Explanatory variables that had the importance of the dependent variables above 1% are given in red. Explanation of variable codes: see Fig 1.

Fig 3

Centred individual conditional expectation plots of the predicted number of COVID-19 cases by a) number of tourists, b) gross domestic product, and c) geographic longitude. The lines show the difference in prediction compared with the prediction with the respective value of the explanatory variables at their observed minimum. The red line is the averaged marginal functional estimate from the gradient boosting regression trees. Rug plots inside the bottom of the plots show the distribution of data, in deciles, of the variable on the X-axis.

3.2. Number of deaths

The GBRT analysis revealed that all examined variables had a non-zero impact on the number of deaths (Fig 2). However, only four variables, Tour, Cases, GDP, and Lon, had an importance above 1% (Fig 2). The number of deaths increased rapidly if the number of tourists in a country exceeded 30 million (Fig 4A). The number of deaths was positively associated with the number of COVID-19 cases (Fig 4B). The number of deaths increased slightly after the GDP reached 400 billion US dollars (Fig 4C). The number of cases decreased with increasing geographic longitude (Fig 4D). Gradient-boosted regression trees built on trained data explained 92% of the variation in the test data.

Fig 4

Individual conditional expectation plots of the predicted number of deaths by a) number of toursits, b) number of COVID-19 cases, c) gross domestic product, and d) geographic longitude. For explanations, see Fig 3.

3.3. Growth in the number of COVID-19 cases

The GBRT analysis revealed that all examined variables had a non-zero impact on the growth rate of COVID-19 cases (Fig 2). The growth rate accelerated with time (Fig 5A). Gross domestic product increased growth rate starting from the values of about 2 billion US dollars, then accelerated if it exceeded 400 billion US dollars (Fig 5B). The growth rate decreased with increasing geographic longitude (Fig 5C). The non-linear effect of population density was found on the growth rate (Fig 5D). The population density with values ranging roughly between 50 and 500 persons per square kilometre decreased the growth rate (Fig 5D). The growth rate of COVID-19 cases decreased with the median age of the country population (Fig 5E). The growth rate changed non-linearly with geographic latitude (Fig 5F). It was elevated between both tropics (Fig 5F). Also, in the northern hemisphere, the countries located above 50°N had a slower growth rate than countries located more to the south (Fig 5F). The growth rate also increased non-linearly with the number of tourists (Fig 5G). A non-linear effect of the migration rate was found (Fig 5H). Generally, countries with net emigration rates close to zero had higher growth rates in the number of COVID-19 cases than countries with both excess immigrants and emigrants (Fig 5G). Finally, the growth rate decreased in countries with a lower proportion of population living in urbanised areas but increased in highly urbanised territories. Gradient-boosted regression trees built on trained data explained 22% of the variation in the test data.

Fig 5

Individual conditional expectation plots of the predicted number of deaths by a) number of days since the start of the pandemic in a given country, b) gross domestic product, c) geographic longitude, d) human population density, e) median population age, f) geographic latitude, g) number of tourists, h) migration rate, and i) proportion of human population living in urbanised areas. For further explanations, see Fig 3.

4. Discussion

Our macro-ecological approach revealed the impact of several variables shaping the pattern of the COVID-19 pandemic on a global scale. One of our most interesting findings was that we did not find evidence of a positive association between population density and infection numbers and deaths. This contradicts our expectations, which were based on theory and earlier findings in other diseases [19,47]. It may be that population density plays a role at lower spatial scales [48]. In addition, human population density in investigated countries is likely to be so high that diseases can easily disperse among people. However, we observed a weak non-linear effect of human population density on growth rate. This effect is also in contradiction to our expectations because the growth rate was low at moderate human densities. This is difficult to explain and possibly other factors not investigated in this study, but linked with population density may obscure this effect.

We found that there is a positive association between the number of tourists visiting a given country and the number of infections, deaths, and growth rate of COVID-19 cases, which is in agreement with our expectations. This indicates that breaking geographical barriers may be a crucial step in colonising new areas and hosts. In ecological terms, the spread of SARS-CoV-2 resembles an invasion of an alien species after new geographical areas have been colonised, because of its impact on native ecosystems [49,50]. Overall, the effect was non-linear and the number of tourists had an impact if the number of tourists visiting a given country was high, usually above 20 million. This is also analogous to the invasion process where so-called ‘propagule pressure’ and continuous colonisations are key triggers of the invasion [50-52]. Global travel has increased in overall number, but there has also been a shift in areas visited by travellers, especially in Asia [53]. The role of tourism in the spread of diseases was reported in previous studies [54]. Early on, the spatial distribution of COVID-19 cases in China was well explained by human mobility data [55]. Thus, it may be that some regulations regarding tourism, such as limited visits to countries with a high prevalence of diseases or quarantine for people returning from them, may indeed be a solution worth considering in this pandemic and possibly also in future ones. Nevertheless, the role of tourism in the spread of the virus should be investigated thoroughly in future studies because it was one of the most important predictors in our models.

We did not find any impact of the net migration rate on the number of COVID-19 cases and deaths, except for a weak, non-linear association with growth rate of COVID-19 cases. In the latter case, the growth rate was the highest in countries with migration rates close to zero. It is possible that the latter effect involves some biological factors. For example, increased genetic diversity in societies with migrants may be a barrier for pathogens [56], decreasing the chances of virus transmission. However, the net migration rate close to zero may also indicate that immigration and emigration are balanced and this effect may be inseparable from the total isolation. It is important to note that migration substantially differs from touristic trips and is associated with many formal requirements, including health, in some host countries [57,58]. Furthermore, migration is usually a singular event in the life of an individual. Tourism, on the other hand, is linked with much higher mobility, visiting crowded places, and frequent changes of location [57].

Unexpectedly, the gross domestic product was positively related to the number of infections, deaths, and growth rate of the number of virus infections. Worldwide analysis indicated that there is a direct positive relationship between GDP and total health expenditure [59]. There is a positive significant relationship between total health expenditure and increased life expectancy [60]. Moreover, a cohort-based study showed that levels of GDP at the time of death were strongly inversely associated with all-cause mortality, especially among women [61]. However, there is also evidence that higher GDP is linked with morbid behaviours responsible for the occurrence of diseases. Rising income has been strongly associated with higher consumption of unhealthy commodities within countries and over time [62]. In consequence, wealthy, market-liberal countries have more overweight citizens [63] and there is increasing evidence that obesity is an independent risk factor for severe illness and death from COVID-19 [64]. Of course, this relationship may be mutual. Past pandemics, such as the 1918 influenza pandemic, have had a strong negative impact on socioeconomy and gross domestic product [65]. The strong positive association between COVID-19 and gross domestic product indicates that pandemics may strongly affect developed economies, which is in line with the opinions of some experts [66,67].

It is believed that pandemics can be characterized as having low mortality of infected people, high infectivity, a long period of contagiousness, and a lack of natural immunity of the population, and the disease does not destroy its host. Harmless symptoms contribute to neglect of the disease. Coronavirus disease seems to have these characteristics, except for the relatively high mortality among older people [68], mostly due to the prevalence of chronic diseases in older people [69]. However, we did not find an association between median age and number of cases and deaths. We found a relatively weak negative association between growth rate of COVID-19 cases and median age of the population. One possible but risky explanation is that younger people are vectors of the virus, which would be in line with findings for other diseases [35,36]. On the other hand, it was quickly identified that older people are the most endangered group and special care was devoted to older people in the health systems [68]. Thus, actions undertaken by countries could limit the spread of the virus via older people. In addition, older people are usually less mobile with a limited number of social contacts [70], which may explain why viruses may spread slowly in older societies.

We noted the potential impact of urban areas on the growth rate of infection cases. Urban areas are associated with high population density and high levels of social interaction, but also with stress and pollution [20]. This may promote the spread of viruses. Studies on influenza in the United Kingdom in 1918 indicated that death rates varied markedly with urbanisation, with 30% –40% higher rates in cities and towns than in rural areas [71]. However, Wood et al. [72] found that urbanisation was generally associated with lower burdens for many diseases, a pattern that could arise from increased access to sanitation and healthcare in cities and increased investment in healthcare. Thus, it seems that urban areas may have contradictory effects on transmission according to disease type.

We also found an effect of geographic location on infection rate, mortality, and infection rate. The number of COVID-19 cases and deaths but not growth rate were positively related to geographic longitude. This may be explained by some theoretical studies [73] that found that crossing geographical barriers is a major factor in spreading diseases. However, the decreasing growth rate of the number of cases may reflect the known phenomenon that pandemic spread is the highest in the place of origin and decreases with distance [74].

The growth rate of COVID-19 cases was non-linearly associated with geographic latitude. Geographic position is usually linked to the local climate. Our finding is similar to recent reports [75], with emerging evidence suggesting that weather conditions may influence the transmission of SARS-CoV-2, dry conditions appearing to boost the spread [76]. This phenomenon may manifest itself through two mechanisms: the stability of the virus and the effect of the weather on the host. However, reports indicate that the weather effect is minimal, and all estimates are subject to significant biases, reinforcing the need for robust public health measures [76]. On the other hand, the number of contacts among people may also be affected by climate. People born in a warmer climate are much more social than those coming from cold regions [77]. This may create opposing forces on the spread of the virus. We believe that further models that include more precise geolocation of infection data and local climatic and local human population density are highly warranted.

Not surprisingly, the growth rate of COVID-19 cases was positively associated with time. This variable is usually the most important factor in predicting the number of infections and diseases [78,79]. However, this variable is especially important if there is a time lag between incidence and healthcare system response with possible consequences for virus spread dynamics in space [80,81].

4.1. Study limitations

Our study has certain limitations that must be taken into consideration. Our analysis is based on data from the early stages of the pandemic. Repeated analyses after several weeks may yield different results. For example, different variables may play a role in different pandemic stages [82]. Moreover, our study is, of course, correlative. Thus, associations between explanatory variables and dependent variables should be treated with caution. Moreover, our analyses are based on ‘big data’, which is known to have caveats [83]. For example, the positive association between GDP and the number of COVID-19 cases may result from better diagnostics and a large number of performed tests in rich countries. Moreover, GDP is associated with many other variables and real-world phenomena [3]. Thus, this association should be interpreted with caution. Finally, our explanatory variables were correlated with each other. However, the values of correlation were moderate and the GBRTs were more robust in multicollinearity situations than ordinary least squares regression and produce reliable estimates that were straightforward to interpret in partial dependency plots. Nevertheless, only experimental tests of our hypotheses on non-human organisms would result in cause-effect relationships. However, studies on a global scale rarely, if at all, are experiments.

4.2. Conclusions

The COVID-19 pandemic prompted the need to identify the important components in the disease spread for better projections of global-scale pandemics. Several factors, such as anthropogenic environmental changes, human demography, international travel, and microbial adaptation, probably have contributed to the disease with which the global community is currently challenged. Unfortunately, epidemics seem to be idiosyncratic, which makes prediction much harder. However, if pathogen spread is a result of understood intrinsic processes, the relationships can be incorporated into pandemic predictions and healthcare response and delivery. This would require political agreement and cooperation in the exchange of information and open access to all data on diseases. Moreover, a multidisciplinary and macroscale approach [2] is needed, both in research and policymaking to better control and monitor the spread of diseases. Last but not least, the Anthropocene was proposed to delineate the epoch of significant human impact on Earth's ecosystems (e.g. climate change) [4,5,84]. The COVID-19 pandemic shows that the impact may be altered by a virus and raises the question of whether human impact is longstanding. Nevertheless, the positive correlation between infection number, deaths, and gross domestic product suggests that COVID-19 may be a new civilisation disease.

Covid_19–contains all the data used in analyses.

(XLSX)

Click here for additional data file.

Covid_19_codes–contains codes to reproduce the results.

Codes used data from the file Covid_19.

(R)

Click here for additional data file.

22 May 2020

PONE-D-20-08769

The macro-ecology of COVID-19 pandemics in Anthropocene

PLOS ONE

Dear Dr. Skórka,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jul 06 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Abdallah M. Samy, PhD

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that you have reported significance probabilities of 0 in places. Since p=0 is not strictly possible, please correct this to a more appropriate limit, eg 'p<0.0001'.

3. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper considers the factors that are correlated with country level variation in the number of cases, number of deaths and growth rate of COVID-19 infections. It is a timely paper on an interesting topic that synthesizes data from a variety of sources. The data that the authors have gotten together alone will be of value to researchers. However, I find that in the current form it contains a somewhat confusing admixture of results. There are also some minor issues with the prose.

Major Issue

The primary issue I struggled with was interpreting the statistical results. The results from univariate models, multivariate models and hierarchical partitioning differed greatly. The authors place the most emphasis on the latter results (e.g., the four variables that come up in both the cases and deaths panel of figure three are the ones that are mentioned in the abstract). However, I think the majority of readers will be most familiar with the methods shown in Table 1 and Table 2. I found the differences between these three sets of results confusing, and I did not find that the authors made a good case for why the results from Fig. 3 are more informative than the multivariate models (save the obvious fact that more variables show statistically significant effects). For example, age and number of tourists showed no significant effects in Table 2, but show up as relatively important in Figure 3. GDP is probably is the single most important predictor to include in models of number of deaths based on hierarchical partitioning (Fig. 3), but isn’t anywhere near being significant in multivariate models of number of deaths (Table 2). The fact that the relative importance of predictors changes so much from Table 1, to Table 2 to Figure 3 is also disconcerting. It implies that the results the authors discuss are not very robust.

At the very least, the authors will need to make a clear case for why hierarchical partitioning is the most useful and informative method for these analyses. I also wonder if some other modelling framework might be more informative. For example, gams can be implemented using the mgcv package (wood and wood, 2015), and boosted regression trees can easily be implemented using gbm (Ridgeway 2013). Both methods would allow for the discovery of nonlinear relationship between response and predictor variables (which also relates to a more minor issue, below), and the latter method is also robust to the use of collinear predictors with complex interaction effects. In fact the way the gbm calculates relative influence scores is very similar to the logic of hierarchical partitioning, and I believe more readers would be familiar with the method.

Minor Issues

1. It does not appear that the methods the authors use would allow them to detect nonlinear effects. For example, they found no influence of human population density on number or rate of infections. If it exhibits a threshold effect rather than a linear effect, it might be difficult to detect with methods that assume a linear relationship. For example, the authors speculate that the countries included had high enough population densities that COVID-19 can always easily spread in them (Page 6, lines 215-216). To me this implies that most of the countries included are above some key threshold. If there are at least a few countries below the threshold, a marginal plot from a gbm model or a gam plot would show this pattern clearly.

The fact that GDP is overall positively correlated with number of deaths and infections is also puzzling. I found the authors' discussion of this result on page seven interesting. However, I wonder if this might be a case where a nonlinear relationship occurs that might be easier to explain (for example, international trade and travel and thus risk increases up to some threshold of GDP, but then further increases in per capita GDP actually do slightly lower death rates).

2. I am not sure most readers will know what the authors mean by macroecology. It is used in the title but never defined. I assume the authors mean macroecology sensu Brown et al. (2014) and Burnsdie et al. (2012), but I’m not entirely sure. If this is what the authors intend, these or some other studies clarifying the relevance of the term to their work should at least be mentioned.

3. There are also a lot of minor issues with the writing (mainly typos). To illustrate this I am going to give examples, from the first few pages, but this list is nowhere near comprehensive.

Page 1, line 12: “Covid-19 has expanded” or “The COVID-19 virus has expanded” would be ok. “The COVID-19 has expanded” doesn’t quite make sense.

Page 2, line 43: Should be “those caused by”

Page 2 line 46: missing “economy” should be plural (“economies”)

Page 2 lines 47-50: Need a citation

Page 2 line 52: “the incidence occurred mainly in the city of Wuhan” is a bit unclear. Maybe the authors are trying to say “cases occurred mainly in the city of Wuhan” or “incidence was limited primarily to the city of Wuhan”

Page 2 line 54: “the first case in another country outside China” If the cases were in another country, of course they were outside China. I think “the first case outside of China was confirmed” would be much clearer.

Page 3 line 84” Models predict that most children are responsible for transmission of the virus.” I am fairly certain that most children have yet to encounter the virus, much less transmit it. Are the authors trying to say that the virus is primarily transmitted by children? Even if this is what the authors intended to convey, I am not really sure that’s true given that the great majority of confirmed cases are in adults.

Page 3, lines 106-122: Every single one of these starts with “The number of infections, deaths, and the rate of growth in the number of COVID-19 infections are”. This section would be much easier to read if that phrase only occurred once at the beginning of the section (around line 107). For example.

“We tested the hypotheses that the number of infections, deaths, and the rate of growth in the number of COVID-19 infections are:

1. Positively associated with human population density.

2. Positively associated with the proportion of the population living in urban areas.”

3. ect."

Cited:

Brown, James H., Joseph R. Burger, William R. Burnside, Michael Chang, Ana D. Davidson, Trevor S. Fristoe, Marcus J. Hamilton et al. "Macroecology meets macroeconomics: Resource scarcity and global sustainability." Ecological engineering 65 (2014): 24-32.

Burnside, William R., James H. Brown, Oskar Burger, Marcus J. Hamilton, Melanie Moses, and Luis MA Bettencourt. "Human macroecology: Linking pattern and process in big‐picture human ecology." Biological Reviews 87, no. 1 (2012): 194-208.

Ridgeway, Greg, Maintainer Harry Southworth, and Suggests RUnit. "Package ‘gbm’." Viitattu 10, no. 2013 (2013): 40.

Wood, S., & Wood, M. S. (2015). Package ‘mgcv’. R package version, 1, 29.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

2 Jul 2020

Dear Editor,

I would like to submit our revised manuscript PONE-D-20-08769R1 “The macro-ecology of the COVID-19 pandemic in the Anthropocene” by Piotr Skórka, Beata Grzywacz, Dawid Moroń, Magdalena Lenda.

We are grateful for all critical points that helped us to improve our paper. We did our best to incorporate all critical points in the revised version. First, we changed statistical analysis into gradient boosting regression as was suggested by the reviewer. We also added newer data that enabled us to increase sample size and receive a better picture of the pandemics. A new analysis produced less results that are easier for interpretation. We however had to change discussion as fewer variables appeared to be meaningful in analyses. We also better described the theoretical background of our paper by defining explicitly the term “macro-ecology”. We also corrected all minor issues. Moreover, the entire text was linguistically corrected by a native English-man familiar with scientific writing.

We believe that our revised manuscript meets scientific criteria required for publication in PloS One.

With kind regards

on behalf of the authors,

Piotr Skórka

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper considers the factors that are correlated with country level variation in the number of cases, number of deaths and growth rate of COVID-19 infections. It is a timely paper on an interesting topic that synthesizes data from a variety of sources. The data that the authors have gotten together alone will be of value to researchers. However, I find that in the current form it contains a somewhat confusing admixture of results. There are also some minor issues with the prose.

RESPONSE: Thank you for your assessment of our work. We did our best to incorporate all points and suggestions into the revised manuscript.

Major Issue

The primary issue I struggled with was interpreting the statistical results. The results from univariate models, multivariate models and hierarchical partitioning differed greatly. The authors place the most emphasis on the latter results (e.g., the four variables that come up in both the cases and deaths panel of figure three are the ones that are mentioned in the abstract). However, I think the majority of readers will be most familiar with the methods shown in Table 1 and Table 2. I found the differences between these three sets of results confusing, and I did not find that the authors made a good case for why the results from Fig. 3 are more informative than the multivariate models (save the obvious fact that more variables show statistically significant effects). For example, age and number of tourists showed no significant effects in Table 2, but show up as relatively important in Figure 3. GDP is probably is the single most important predictor to include in models of number of deaths based on hierarchical partitioning (Fig. 3), but isn’t anywhere near being significant in multivariate models of number of deaths (Table 2). The fact that the relative importance of predictors changes so much from Table 1, to Table 2 to Figure 3 is also disconcerting. It implies that the results the authors discuss are not very robust.

At the very least, the authors will need to make a clear case for why hierarchical partitioning is the most useful and informative method for these analyses. I also wonder if some other modelling framework might be more informative. For example, gams can be implemented using the mgcv package (wood and wood, 2015), and boosted regression trees can easily be implemented using gbm (Ridgeway 2013). Both methods would allow for the discovery of nonlinear relationship between response and predictor variables (which also relates to a more minor issue, below), and the latter method is also robust to the use of collinear predictors with complex interaction effects. In fact the way the gbm calculates relative influence scores is very similar to the logic of hierarchical partitioning, and I believe more readers would be familiar with the method.

RESPONSE: We are grateful for these comments and suggestions. Indeed, results of these three analyses differed greatly. We believe this is a result of positive correlation among variables and it is known that multiple regression may produce biased estimates (despite variance inflation factors were acceptable). We really like the idea of using gradient boosting regression (gbm) proposed by the referee. We had not used this method before. Indeed, this method copes with collinearity among predictors by producing importance scores and partial dependency plots for each variable. Also, as it was mentioned by the Reviewer the method allows to identify nonlinear relationships among variables. Therefore, we decided to use the gradient boosting machine learning technique to analyse results. First of all, we decided to add more newer data (gathered on 12th April) on COVID-19. This was done to enlarge sample size (larger number of countries of data) and get better estimates of the pandemic growth rates (but still with exponential mode). We used h2o.gbm function from h2o package (LeDell et al. 2020) because it enabled better visualization of results than ‘gbm` package by easier production of ice plots (individual conditional expectation plots). We searched for optimal parameters to build regression trees in this method. The use of advantage of gradient boosting regression was that it produced one set of results for each dependent variable. Moreover, it omits problems with P-values which use is being criticized very often. Results were slightly different, we identified a lower number of important variables, however the most interesting results, e.g. positive effect of growth domestic product and number of tourists in a country on the COVID-19 spread, remained.

Erin LeDell, Navdeep Gill, Spencer Aiello, Anqi Fu, Arno Candel, Cliff Click, Tom Kraljevic, Tomas Nykodym, Patrick Aboyoun, Michal Kurka and Michal Malohlava (2020). h2o: R Interface for the 'H2O' Scalable Machine Learning Platform. R package version 3.30.0.1. https://CRAN.R-project.org/package=h2o

Minor Issues

1. It does not appear that the methods the authors use would allow them to detect nonlinear effects. For example, they found no influence of human population density on number or rate of infections. If it exhibits a threshold effect rather than a linear effect, it might be difficult to detect with methods that assume a linear relationship. For example, the authors speculate that the countries included had high enough population densities that COVID-19 can always easily spread in them (Page 6, lines 215-216). To me this implies that most of the countries included are above some key threshold. If there are at least a few countries below the threshold, a marginal plot from a gbm model or a gam plot would show this pattern clearly.

RESPONSE: We agree. However, in ‘gbm’ models the effect of population density was identified as unimportant (which was somehow a surprise to us).

The fact that GDP is overall positively correlated with number of deaths and infections is also puzzling. I found the authors' discussion of this result on page seven interesting. However, I wonder if this might be a case where a nonlinear relationship occurs that might be easier to explain (for example, international trade and travel and thus risk increases up to some threshold of GDP, but then further increases in per capita GDP actually do slightly lower death rates).

RESPONSE: We agree. The new analysis revealed exactly what was said by the Reviewer. We included these explanations also in the revised version of our manuscript.

2. I am not sure most readers will know what the authors mean by macroecology. It is used in the title but never defined. I assume the authors mean macroecology sensu Brown et al. (2014) and Burnsdie et al. (2012), but I’m not entirely sure. If this is what the authors intend, these or some other studies clarifying the relevance of the term to their work should at least be mentioned.

RESPONSE: Thank you for these interesting works. We wrote a paragraph about human macroecology in Introduction and cited the abovementioned publications.

3. There are also a lot of minor issues with the writing (mainly typos). To illustrate this I am going to give examples, from the first few pages, but this list is nowhere near comprehensive.

RESPONSE: We apologize for these mistakes. The revised version of the manuscript was linguistically corrected by native English-man from Wiley Authors Service. We hope the revised version if free of such problems.

Page 1, line 12: “Covid-19 has expanded” or “The COVID-19 virus has expanded” would be ok. “The COVID-19 has expanded” doesn’t quite make sense.

RESPONSE: We changed this sentence to be more specific: “The SARS-CoV-2 coronavirus, causing coronavirus disease 2019 (COVID-19), has expanded…”

Page 2, line 43: Should be “those caused by”

RESPONSE: Corrected.

Page 2 line 46: missing “economy” should be plural (“economies”)

RESPONSE: Corrected.

Page 2 lines 47-50: Need a citation

RESPONSE: Citation added.

Page 2 line 52: “the incidence occurred mainly in the city of Wuhan” is a bit unclear. Maybe the authors are trying to say “cases occurred mainly in the city of Wuhan” or “incidence was limited primarily to the city of Wuhan”

RESPONSE: We clarified this sentence.

Page 2 line 54: “the first case in another country outside China” If the cases were in another country, of course they were outside China. I think “the first case outside of China was confirmed” would be much clearer.

RESPONSE: Changed as suggested.

Page 3 line 84” Models predict that most children are responsible for transmission of the virus.” I am fairly certain that most children have yet to encounter the virus, much less transmit it. Are the authors trying to say that the virus is primarily transmitted by children? Even if this is what the authors intended to convey, I am not really sure that’s true given that the great majority of confirmed cases are in adults.

RESPONSE: We were not clear in this sentence. We meant that generally viruses (not COVID-19) are transmitted by children (there is a good body of literature on this). We corrected these sentences.

Page 3, lines 106-122: Every single one of these starts with “The number of infections, deaths, and the rate of growth in the number of COVID-19 infections are”. This section would be much easier to read if that phrase only occurred once at the beginning of the section (around line 107). For example.

“We tested the hypotheses that the number of infections, deaths, and the rate of growth in the number of COVID-19 infections are:

1. Positively associated with human population density.

2. Positively associated with the proportion of the population living in urban areas.”

3. ect."

RESPONSE: We agree and corrected as suggested. Thank you.

Cited:

Brown, James H., Joseph R. Burger, William R. Burnside, Michael Chang, Ana D. Davidson, Trevor S. Fristoe, Marcus J. Hamilton et al. "Macroecology meets macroeconomics: Resource scarcity and global sustainability." Ecological Engineering 65 (2014): 24-32.

Burnside, William R., James H. Brown, Oskar Burger, Marcus J. Hamilton, Melanie Moses, and Luis MA Bettencourt. "Human macroecology: Linking pattern and process in big‐picture human ecology." Biological Reviews 87, no. 1 (2012): 194-208.

Ridgeway, Greg, Maintainer Harry Southworth, and Suggests RUnit. "Package ‘gbm’." Viitattu 10, no. 2013 (2013): 40.

Wood, S., & Wood, M. S. (2015). Package ‘mgcv’. R package version, 1, 29.

Thank you.

Submitted filename: Response.docx

Click here for additional data file.

16 Jul 2020

The macroecology of the COVID-19 pandemic in the Anthropocene

PONE-D-20-08769R1

Dear Dr. Skórka,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Abdallah M. Samy, PhD

Academic Editor

PLOS ONE

22 Jul 2020

PONE-D-20-08769R1

The macroecology of the COVID-19 pandemic in the Anthropocene

Dear Dr. Skórka:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Abdallah M. Samy

Academic Editor

PLOS ONE