LitCovid-sentences  

PMC:7510993 / 2005-33695 JSONTXT 10 Projects

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Id Subject Object Predicate Lexical cue
T13 0-12 Sentence denotes Introduction
T14 13-302 Sentence denotes The spread of infectious diseases through host–pathogen interaction is fundamentally underpinned by macroecological and biogeographical processes [1, 2]; key processes include virus origination, dispersal, and evolutional diversification through local transmissions in human societies [3].
T15 303-484 Sentence denotes Since December 2019, coronavirus disease 2019 (COVID-19), caused by sudden acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has quickly spread worldwide from Wuhan, China [4].
T16 485-809 Sentence denotes The disease transmission geography of COVID-19 was highly heterogeneous; some countries (e.g., Japan) had cases from the earliest stage of this pandemic, but their increase in the number of new cases was relatively moderate, whereas others (e.g., EU nations and the USA) experienced later but substantial COVID-19 outbreaks.
T17 810-930 Sentence denotes To predict infection risk on the global scale, the forces driving the COVID-19 outbreak patterns must be identified [5].
T18 931-1108 Sentence denotes Additionally, capturing region-specific factors influencing the outbreak progress is critically important for improving long-term control measures against this ongoing pandemic.
T19 1109-1211 Sentence denotes Infectious diseases due to respiratory viruses are empirically characterized by a seasonal nature [6].
T20 1212-1581 Sentence denotes Moriyama et al. [7] described a framework to better understand the mechanisms of virus transmission; air temperature, absolute/relative humidity, and sunlight are jointly associated with virus viability/stability and host defense, and thereby human-to-human transmission of COVID-19 is promoted by contact rates along with host susceptibility (or immunity) to COVID-19.
T21 1582-1834 Sentence denotes From this viewpoint, several research groups have focused on relevant factors separately and quickly examined the role of climate [8–10], international mobility linked to human contact [11, 12], and community-based host susceptibility to COVID-19 [13].
T22 1835-1986 Sentence denotes However, these analyses were inconclusive, and the relative importance of these factors in promoting the disease expansion of COVID-19 remains unclear.
T23 1987-2387 Sentence denotes This study assessed multiple potential drivers of the COVID-19 spread, by conducting an analysis of time-series data on the number of confirmed COVID-19 cases from December 2019 through June 2020, as well as on country/region-specific variables, e.g. socioeconomic conditions and screening effort (number of SARS-CoV-2 PCR tests conducted), that could potentially affect the number of COVID-19 cases.
T24 2388-2542 Sentence denotes Specifically, we explored the roles of climate, international mobility, and region-specific conditions in the disease expansion by controlling covariates.
T25 2543-3054 Sentence denotes In this analysis, we evaluated the relative importance of climate (temperature and precipitation relevant to habitat suitability for SARS-CoV-2), region-specific COVID-19 susceptibility (BCG vaccination factors, malaria incidence, and the relative proportion of citizens aged over 65 years in the population, as these were hypothesized to be linked with host susceptibility to COVID-19), and human mobility (international travel) in shaping the current geographical patterns of COVID-19 spread around the world.
T26 3056-3077 Sentence denotes Materials and methods
T27 3079-3091 Sentence denotes Data sources
T28 3092-3205 Sentence denotes We compiled geographic data on the number of reported COVID-19 cases per day from December 2019 to June 30, 2020.
T29 3206-3366 Sentence denotes We collected the numbers of COVID-19 cases for 1,020 countries/regions from various sources (see S1 Appendix for a list of data sources for the COVID-19 cases).
T30 3367-3527 Sentence denotes We then calculated the length of time (in days) since the onset of COVID-19 spread as defined by the date of the first confirmed case in each country or region.
T31 3528-3784 Sentence denotes We also examined the number of SARS-CoV-2 PCR tests conducted based on data published by the World Health Organization (WHO) (https://ourworldindata.org/covid-testing) to assess the influence of sampling effort on the number of confirmed cases of COVID-19.
T32 3785-3857 Sentence denotes For each country or region, we compiled several environmental variables.
T33 3858-4041 Sentence denotes For mapping cases of COVID-19, the longitude and latitude of the largest city and area for each country or region were extracted from GADM maps and data (https://gadm.org/index.html).
T34 4042-4377 Sentence denotes Based on the geocoordinates of the cities, we collected the climatic data of mean precipitation (mm month–1) and temperature (°C) from January to June (WorldClim) using WorldClim version 2.1 climate data (https://www.worldclim.org/data/worldclim21.html) at a resolution of 2.5 arc-minutes grid cells that contained a country or region.
T35 4378-4648 Sentence denotes Regarding international travel linked to the disease transmission, we compiled the average annual number of foreign visitors (per year) for individual countries/regions from data published by the World Tourism Organization (https://www.e-unwto.org/toc/unwtotfb/current).
T36 4649-4772 Sentence denotes We then calculated the relative amount of foreign visitors per population of each country or region to use in the analysis.
T37 4773-5055 Sentence denotes Regarding region-specific host susceptibility to COVID-19, we collected data on the following three epidemiologic properties: the proportion of the population aged over 65 years, the malaria incidence (per year), and information regarding bacillus Calmette–Guérin (BCG) vaccination.
T38 5056-5282 Sentence denotes We included these attributes in our analyses based on the assumptions that BCG vaccination and/or recurrent treatment with anti-malarial medications could be associated with providing some protection against COVID-19 [13, 14].
T39 5283-6208 Sentence denotes We compiled BCG data from the WHO (https://www.who.int/malaria/data/en/) and (https://apps.who.int/gho/data/view.main.80500?lang=en) and the BCG Atlas Team (http://www.bcgatlas.org/) on the following five attributes: i) the number of years since BCG vaccination was started (BCG_year); ii) the present situation regarding BCG vaccination (BCG_type), split into all vaccinated, partly vaccinated, vaccinated once in the past, or never vaccinated; iii) the relative frequency of post-1980 (i.e., the past 40 years) BCG vaccination for people aged less than 1 year old (BCG_rate); iv) the number of BCG vaccinations (MultipleBCG), describing countries as never having vaccinated their citizens with BCG, vaccinated their citizens with BCG only once, vaccinated their citizens with BCG multiple times in the past, or currently vaccinate their citizens with BCG multiple times; and v) tuberculosis cases per 1 million people (TB).
T40 6209-6266 Sentence denotes These BCG-related variables are strongly intercorrelated.
T41 6267-6582 Sentence denotes Therefore, we reduced the dimensions of these variables (BCG_year, BCG_type, BCG_rate, MultipleBCG, and TB) by extracting the first axis of the PCA analysis: the score of the PCA 1 axis was negatively correlated with the five variables, so the PCA 1 score multiplied by –1 was defined as the BCG vaccination effect.
T42 6583-6646 Sentence denotes We also compiled socioeconomic data for each country or region.
T43 6647-7005 Sentence denotes The population size, population density (per km2) (Gridded Population of the World GPW, v4.; https://sedac.ciesin.columbia.edu/data/collection/gpw-v4), gross domestic product (GDP in US dollars), and GDP per person were obtained from national census data (World Development Indicators; https://datacatalog.worldbank.org/dataset/world-development-indicators).
T44 7007-7027 Sentence denotes Statistical analyses
T45 7028-7245 Sentence denotes The monthly pattern for the cumulative number of COVID-19 cases in each country/region was visualized in relation to the geography, biome type, and climate (mean temperature and annual precipitation) of that location.
T46 7246-7483 Sentence denotes In addition, the pattern of increasing COVID-19 case numbers was evaluated based on country type, with individual countries being classified into four types defined by the number of COVID-19 cases per week and the date of outbreak onset.
T47 7484-7892 Sentence denotes To ensure the robustness of our results, we investigated the relationship between various environmental variables (climate, host susceptibility to COVID-19, international human mobility, and socioeconomic factors) and the number of COVID-19 cases (per 1 million population) using the two different approaches: conventional multiple linear regression and random forest, which is a machine-learning model [15].
T48 7893-8040 Sentence denotes We separately modeled the cumulative number of COVID-19 cases (per 1 million population) in successive periods from December 2019 to June 30, 2020.
T49 8041-8639 Sentence denotes In the multiple regression analysis, we set the log-scaled cumulative number of COVID-19 cases within a period as the response variable and the climatic factors (mean temperature, squared mean temperature, and log-scaled monthly precipitation), socioeconomic conditions (log-scaled population density and GDP per person), international human mobility (the relative amount of foreign visitors per population) and region-specific COVID-19 susceptibility (the percentage of people aged ≥ 65 years, the log-scaled relative incidence of malaria, and the BCG vaccination effect) as explanatory variables.
T50 8640-8903 Sentence denotes To control for country/region-specific observation biases, we included the length of time (measured in days) since the first confirmed COVID-19 case in each country/region and the number of COVID-19 tests conducted (as a measure of sampling effort) as covariates.
T51 8904-9189 Sentence denotes In addition, we applied the trend surface method to take spatial autocorrelation into account as a covariate; we added the first eigenvector of the geo-distance matrix among the countries or regions, which was computed using the geocoordinates of the largest city, as a covariate [16].
T52 9190-9289 Sentence denotes The explanatory power of the model was evaluated by the adjusted coefficient of determination (R2).
T53 9290-9533 Sentence denotes We also calculated the relative importance of each explanatory variable in a regression model according to its partial coefficient of determination and determined the predominant variables that explained the variance in the response variables.
T54 9534-9616 Sentence denotes The statistical significance of each variable was determined by conducting F-test.
T55 9617-9732 Sentence denotes All the explanatory variables were standardized to have a mean of zero and a variance of one before these analyses.
T56 9733-9826 Sentence denotes The explanatory factors of the regression model were compared between the four country types.
T57 9827-9946 Sentence denotes In the random forest model, we used the same set of response and explanatory variables, as well as the same covariates.
T58 9947-10026 Sentence denotes In each run of the random forest analysis, we generated 1,000 regression trees.
T59 10027-10116 Sentence denotes The model performance was evaluated by the proportion of variance explained by the model.
T60 10117-10264 Sentence denotes We evaluated the relative importance of each explanatory variable based on the increase in the mean squared error when the variable was permutated.
T61 10265-10400 Sentence denotes Before these analyses, we tested the collinearity between the explanatory variables by calculating the variance inflation factor (VIF).
T62 10401-10556 Sentence denotes For the study period, the largest VIF value was 8.56, and the VIF at June 30, 2020 was 8.56, indicating the absence of multicollinearity in the regression.
T63 10557-10797 Sentence denotes To confirm the testing effort bias on the number of confirmed cases, we conducted an additional analysis that accounted for the number of conducted tests (i.e., sampling efforts) in individual countries/regions, as a covariate in the model.
T64 10798-10986 Sentence denotes Note that this analysis was applied to the data from 128/828 countries/regions, because testing data for many countries is currently unavailable (https://ourworldindata.org/covid-testing).
T65 10987-11199 Sentence denotes All analyses were performed with the R environment for statistical computing [17]; the ‘sf’ package was used for graphics artworks [18] and the ‘randomForest’ package was used for the random forest analysis [19].
T66 11201-11223 Sentence denotes Results and discussion
T67 11224-11395 Sentence denotes COVID-19 (as measured by the number of cases per 1 million population) spread rapidly across the globe after it first appeared in Wuhan, China in December, 2019 (Li et al.
T68 11396-11534 Sentence denotes 2020) (Fig 1; S1 Video), but the outbreak appears to have occurred in particular climates around 8°C and 26°C or biomes (Fig 2; S2 Video).
T69 11535-11710 Sentence denotes Moreover, the patterns of increasing number of COVID-19 cases per week varied among the countries that are characterized by different COVID-19 spread dates (Fig 3 and S1 Fig).
T70 11711-11827 Sentence denotes Fig 1 Geographical distribution of COVID-19 cases (per 1 million population) for 1,020 countries/regions worldwide.
T71 11828-12103 Sentence denotes (A–F) Monthly patterns for the cumulative number of COVID-19 cases on January 31, 2020 (A), February 29, 2020 (B), March 31, 2020 (C), April 30, 2020 (D), May 31, 2020 (E), and June 30, 2020 (F) based on the cumulative number of day-to-day COVID-19 cases since December 2019.
T72 12104-12117 Sentence denotes See S3 Video.
T73 12118-12219 Sentence denotes The map was prepared using shapefile reprinted from a freely available database (GADM; www.gadm.org).
T74 12220-12357 Sentence denotes Fig 2 The distribution of COVID-19 cases across biome types based on the relationship between mean temperature and annual precipitation.
T75 12358-12663 Sentence denotes Biome classification is based on the scheme by Whittaker [20]. (TR) tropical rain forest; (TS) tropical seasonal forest/savanna; (TE) temperate rain forest; (SD) subtropical desert; (TD) temperate deciduous forest; (WS) woodland/shrubland; (TG) temperate grassland/desert; (BF) boreal forest, (TU) tundra.
T76 12664-13092 Sentence denotes Colors indicate the number of COVID-19 cases (per 1 million population) and also contours of climatic regions with ≥1000 cases per 1 million population. (A–F) Monthly patterns for the cumulative number of COVID-19 cases on January 31, 2020 (A), February 29, 2020 (B), March 31, 2020 (C), April 30, 2020 (D), May 31, 2020 (E), and June 30, 2020 (F) based on the cumulative number of day-to-day COVID-19 cases since December 2019.
T77 13093-13140 Sentence denotes Arrows indicate the location of Wuhan in China.
T78 13141-13154 Sentence denotes See S4 Video.
T79 13155-13270 Sentence denotes Fig 3 Patterns for the cumulative number of COVID-19 cases (per 1 million population) in relation to country type.
T80 13271-14091 Sentence denotes Based on the pattern of increasing COFVID-19 case numbers, individual countries were classified into four types (A–D): (A) Type A, countries that had a peak in the number of COVID-19 cases per week before the middle of April and had more than 1,000 COVID-19 cases per 1 million population; (B) type B, countries that exhibited an increase in the number of COVID-19 cases per week after the middle of June and had more than 1,000 COVID-19 cases per 1 million population; (C) type C, countries that had a peak in the number of COVID-19 cases per week before the middle of April and had less than 1,000 COVID-19 cases per 1 million population; (D) type D, countries that exhibited an increase in the number of COVID-19 cases per week after the middle of June and had less than 1,000 COVID-19 cases per 1 million population.
T81 14092-14193 Sentence denotes The map was prepared using shapefile reprinted from a freely available database (GADM; www.gadm.org).
T82 14194-14599 Sentence denotes Although the COVID-19 case numbers may not be suitable for conducting epidemiological analyses, such as modelling the disease growth dynamics, the available COVID-19 case data can be still informative for the implementation of containment and/or suppression measures because the number of the confirmed cases is directly linked to the consumption of medical resources for combatting the COVID-19 pandemic.
T83 14600-14920 Sentence denotes Here, we observed that the cumulative number of the COVID-19 cases (per 1 million population) according to the disease spread progression was significantly correlated with variables related to climate, international human mobility, and host susceptibility to COVID-19, at successive periods since December, 2019 (Fig 4).
T84 14921-15144 Sentence denotes Fig 4 Standardized regression coefficients and the partial coefficient of determination (r2) of each explanatory factor in the regression model explaining the cumulative number of COVID-19 cases (per 1 million population).
T85 15145-15319 Sentence denotes (A–F) Values for the period from December 2019 to January 31, 2020 (A), February 29, 2020 (B), March 31, 2020 (C), April 30, 2020 (D), May 31, 2020 (E), or June 30, 2020 (F).
T86 15320-15836 Sentence denotes Temp, mean temperature; Temp2, squared mean temperature; Prec, mean monthly precipitation; Pop dens, population density; Visitor, relative amount of foreign visitors per population; GDP, gross domestic product per person; BCG, BCG vaccination effect as defined by the first PCA axis summarizing five variables related to BCG vaccination (see the Methods section for details); Malaria, relative malaria incidence; Age, relative proportion of the population aged ≥65 years; First cases, number of days from case onset.
T87 15837-15906 Sentence denotes The regressions were conducted using ordinary least squares analyses.
T88 15907-15975 Sentence denotes Vertical lines represent the 95% confidence intervals of parameters.
T89 15976-16053 Sentence denotes Closed symbols indicate the significance of explanatory variables (p < 0.05).
T90 16054-16128 Sentence denotes The coefficient of determination (R2) for the overall model is also shown.
T91 16129-16500 Sentence denotes A nonlinear modeling analysis was also conducted using the random forest method with the same set of response and explanatory variables and the same covariates; the results of this parallel analysis are shown in S2 Fig. The explanatory power, i.e., coefficient of determination (R2), of the model as the COVID-19 pandemic progressed, reaching >70% in April 2020 (Fig 5A).
T92 16501-16668 Sentence denotes The number of days from case onset had some explanatory power (> 20%) in January, 2020, but this factor quickly lost its influence as the pandemic progressed (Fig 5B).
T93 16669-16851 Sentence denotes As the influence of this factor waned, other variables (related to climate, human mobility, and host susceptibility to COVID-19) exhibited the increasing explanatory powers (Fig 5B).
T94 16852-16985 Sentence denotes After April 2020, the explanatory power of variables related to human mobility and host susceptibility to COVID-19 rapidly decreased.
T95 16986-17070 Sentence denotes After this, the explanatory power of human population and climate factors increased.
T96 17071-17223 Sentence denotes These results demonstrate that the impact of virus dispersability between/within regions was predominant in the beginning stage of the pandemic (Fig 5).
T97 17224-17414 Sentence denotes Fig 5 Coefficients of determination (adjusted R2) of the regression model explaining the cumulative number of COVID-19 cases (per 1 million population) from December, 2019 to June 30, 2020.
T98 17415-17570 Sentence denotes (A) Overall coefficient of determination of the regression model; (B) coefficient of partial determination (r2) for each explanatory variable in the model.
T99 17571-17716 Sentence denotes The results shown are based on data starting from January, 2020, because the number of cases in December 2019 was insufficient for this analysis.
T100 17717-17887 Sentence denotes The standardized regression coefficients of the model greatly changed (from non-significant to significant) over the period from December, 2019 to April 12, 2020 (Fig 6).
T101 17888-18090 Sentence denotes After February, 2020, the mean temperature was negatively correlated with the cumulative number of COVID-19 cases, whereas the mean precipitation was positively correlated with these values (Fig 6A–6C).
T102 18091-18282 Sentence denotes After March, 2020, relative amount of foreign visitors per population and GDP per person were predominantly positively correlated with the cumulative number of COVID-19 cases (Fig 6E and 6F).
T103 18283-18477 Sentence denotes In contrast, since February or March 2020, the BCG vaccination factors and malaria incidence were consistently negatively correlated with the cumulative number of COVID-19 cases (Fig 6G and 6H).
T104 18478-18586 Sentence denotes Population density was slightly positively correlated with the cumulative number of COVID-19 cases (Fig 6D).
T105 18587-18768 Sentence denotes The relative proportion of the population aged ≥65 years was also positively correlated with these values, except for a temporary period where it was negatively correlated (Fig 6I).
T106 18769-19024 Sentence denotes This shift from positive to negative correlation reflects the initial spread of COVID-19 in developed countries with relatively older population and the later (after May 2020) spread of COVID-19 in developing countries with relatively younger populations.
T107 19025-19188 Sentence denotes In the early stage of COVID-19 spread, the number of days from case onset was strongly positively correlated with the cumulative number of COVID-19 cases (Fig 6J).
T108 19189-19387 Sentence denotes Fig 6 Time-series pattern of the standardized regression coefficients of the model explaining the cumulative number of COVID-19 cases (per 1 million population) from December 2019 to June 30, 2020.
T109 19388-19456 Sentence denotes Vertical lines represent the 95% confidence intervals of parameters.
T110 19457-19603 Sentence denotes The results are based on data starting from January 2020 because the number of COVID-19 cases in December 2019 was insufficient for this analysis.
T111 19604-19729 Sentence denotes The results of the random forest model were generally consistent with those of the linear multiple regression model (S2 Fig).
T112 19730-20051 Sentence denotes The relative importance of the variables related to human mobility and host susceptibility to COVID-19 (elderly population, BCG vaccination effect, and malaria incidence) became predominant over time, whereas the relative importance of population density and the number of days from case onset decreased after March 2020.
T113 20052-20395 Sentence denotes Moreover, additional analyses, which included the number of conducted COVID-19 tests as a covariate, revealed very similar patterns of regression coefficients, and their explanatory power (S3 Fig), i.e., the roles of climate, international human mobility, and host susceptibility to COVID-19, became more pronounced as the pandemic progressed.
T114 20396-20577 Sentence denotes Therefore, the nonlinearity of epidemic and region-specific testing bias had no serious influence on identifying the environmental drivers shaping the present COVID-19 distribution.
T115 20578-20823 Sentence denotes This study generally supports the findings of several recent reports, which found that climate [8–10], international human mobility [11, 12], and community-based host susceptibility to COVID-19 [13] jointly contributed to the spread of COVID-19.
T116 20824-20990 Sentence denotes Notably, the explanatory power of these drivers substantially increased as the pandemic progressed, indicating a deterministic expansion of COVID-19 around the world.
T117 20991-21112 Sentence denotes Cross-border human mobility, which has been facilitated by globalization [21], clearly accelerated the COVID-19 pandemic.
T118 21113-21251 Sentence denotes This finding is in line with a report by Coelho et al. [12], which emphasized the role of the air transportation network in this pandemic.
T119 21252-21592 Sentence denotes In addition, region-specific COVID-19 susceptibility, which was approximated here by BCG vaccination factors, malaria incidence (because COVID-19 susceptibility may be linked to anti-malarial drug use), and the proportion of the population aged over 65 years, explained a substantial part of the variance in COVID-19 case numbers worldwide.
T120 21593-21732 Sentence denotes This data support the findings by Sala et al. [13] that there is a significant correlation between BCG vaccination and COVID-19 prevalence.
T121 21733-21807 Sentence denotes Notably, these correlation patterns may change as the pandemic progresses.
T122 21808-22173 Sentence denotes For example, while the COVID-19 case numbers (per 1 million population) exhibited a relatively robust correlation with malaria incidence, their correlation with the BCG vaccination effect weakened after April 2020, potentially as a result of the recent spread of COVID-19 into more countries with a BCG vaccination program (e.g., Japan, Russia, Turkey, and Brazil).
T123 22174-22481 Sentence denotes Our analysis using the regression model, which comprehensively accounted for climate, international human mobility, region-specific COVID-19 susceptibility, and socioeconomic conditions, revealed that climate suitability remains an important driver shaping the current distribution of COVID-19 cases [5, 9].
T124 22482-22777 Sentence denotes Although human mobility and host susceptibility to COVID-19 were found to be the main drivers in the spread of COVID-19, the uneven distribution of COVID-19 cases across biome types (Fig 2 and S2 and S4 Videos) suggests that the pandemic may be partially shaped by biogeographical patterns [22].
T125 22778-22944 Sentence denotes However, until the pandemic has lasted a full year, it will not be possible to draw reliable conclusions on the relationship between abiotic factors and COVID-19 [7].
T126 22945-23175 Sentence denotes Our predictive model does not account for variables relevant to local-scale factors that are associated with community infection or containment/suppression measures implemented against the epidemic in individual countries/regions.
T127 23176-23349 Sentence denotes Consequently, the model has residuals (Fig 7), i.e., deviations in the observed number of COVID-19 cases that reflect the influence of local-scale drivers on disease spread.
T128 23350-23883 Sentence denotes Positive deviations in the number of COVID-19 cases may indicate more serious local-scale cluster infections, e.g., in some prefectures in Japan or in parts of South East Asia, Africa, and South America, than predicted by the macro-scale driver-based model, whereas negative deviations in the number of COVID-19 cases indicate the influence of distributional disequilibrium of COVID-19 cases (because SARS-CoV-2 has only recently reached an area, e.g., Africa) or suggest the effectiveness of the present control measures in an area.
T129 23884-24076 Sentence denotes Fig 7 Residual pattern of the regression model predicting the number of COVID-19 cases (per 1 million population) for 1,020 countries/regions across the globe and for 47 prefectures in Japan.
T130 24077-24178 Sentence denotes The map was prepared using shapefile reprinted from a freely available database (GADM; www.gadm.org).
T131 24179-24402 Sentence denotes There is still a distributional disequilibrium in the global prevalence of infections; the number of confirmed COVID-19 cases changes daily, and the trajectories among countries or regions differ largely (Fig 3 and S1 Fig).
T132 24403-24486 Sentence denotes The drivers of COVID-19 case numbers indicate a country-specific pattern (Table 1).
T133 24487-24560 Sentence denotes Table 1 Drivers of the COVID-19 spread in relation to the country types.
T134 24561-24668 Sentence denotes Country types were defined by the patterns of COVID-19 spread (cases per 1 million population) (see Fig 3).
T135 24669-25358 Sentence denotes Type A, countries that had a peak in the number of COVID-19 cases per week before the middle of April and had more than 1,000 COVID-19 cases per 1 million population; type B, countries that exhibited an increase in the number of COVID-19 cases per week after the middle of June and had more than 1,000 COVID-19 cases per 1 million population; type C, countries that had a peak in the number of COVID-19 cases per week before the middle of April and had less than 1,000 COVID-19 cases per 1 million population; and type D, countries that exhibited an increase in the number of COVID-19 cases per week after the middle of June and had less than 1,000 COVID-19 cases per 1 million population.
T136 25359-25483 Sentence denotes The statistical significance of differences between the country types was tested by a Bonferroni’s multiple comparison test.
T137 25484-25582 Sentence denotes Different letters indicate the values that are significantly different (p < 0.05) from each other.
T138 25583-25621 Sentence denotes Factor Type A Type B Type C Type D
T139 25622-25709 Sentence denotes Mean annual temperature 11.1 (±3.88) a 14.6 (±8.87) b 18.5 (±7.96) c 21.4 (±6.81) d
T140 25710-25793 Sentence denotes Mean annual precipitation 865 (±368) a 806 (±541) a 1250 (±629) b 1290 (±869) b
T141 25794-25863 Sentence denotes Population density 485 (±1060) 342 (±1400) 391 (±1500) 164 (±243)
T142 25864-25956 Sentence denotes Relative frequency of visitors 154 (±329) a 36.1 (±65.4) b 73.8 (±97.4) b 16.4 (±27.2) b
T143 25957-26041 Sentence denotes GDP per person 50200 (±21500) a 18500 (±18300) b 22200 (±18100) b 5690 (±5430) c
T144 26042-26132 Sentence denotes BCG vaccination effect -1.37 (±1.42) a 0.752 (±1.37) b 0.467 (±1.51) b 0.88 (±0.694) b
T145 26133-26250 Sentence denotes Relative frequency of people infected by malaria 0.163 (±1.26) a 2180 (±14200) a 2950 (±31800) a 40100 (±85000) b
T146 26251-26597 Sentence denotes Relative frequency of people ≥ 65 years old 18.9 (±3.17) a 11.6 (±4.92) b 14.8 (±6.51) c 7.33 (±4.5) d The type A countries, with more than 1,000 COVID-19 cases per million in which the infection peaked before mid-April, were mostly the developed countries that had predominant cross-border human mobility in relatively cool and dry climates.
T147 26598-26782 Sentence denotes The type B countries, with more than 1,000 COVID-19 cases per million in which the infection spread peaked after mid-June, were quasi-developed countries with BCG vaccination programs.
T148 26783-27030 Sentence denotes The type C countries, with less than 1,000 COVID-19 cases per million in which the infection peaked before mid-April, were countries with high temperature and humidity that are characterized by lower cross-border mobility and more BCG vaccination.
T149 27031-27306 Sentence denotes The type D countries, with less than 1,000 COVID-19 cases per million in which the infection spread peaked after mid-June, were mostly tropical developing countries with lower population density, less cross-border mobility, higher malaria incidence, and less BCG vaccination.
T150 27307-27486 Sentence denotes These country-specific factors indicate that the COVID-19 spread is not simply driven by specific environmental variables, and the underlying mechanisms are complicated (Table 1).
T151 27487-27608 Sentence denotes Therefore, evaluating the drivers of the COVID-19 spread at the present phase of disease expansion is a challenging task.
T152 27609-27737 Sentence denotes The absence of population-wide testing for COVID-19 makes it difficult to investigate the growth dynamics of COVID-19 infection.
T153 27738-27836 Sentence denotes The case data include a selection bias due to surveillance focusing mainly on symptomatic persons.
T154 27837-28138 Sentence denotes In particular, the availability of a reverse transcription polymerase chain reaction (PCR) test to identify COVID-19 cases, e.g. the number of PCR tests conducted per population, varies greatly among countries with different medical/public-health conditions (https://ourworldindata.org/covid-testing).
T155 28139-28299 Sentence denotes Therefore, the true number of the COVID-19 patients and the dynamics of the disease spread are obscured behind the prevalence of asymptomatic carriers [23, 24].
T156 28300-28491 Sentence denotes Nevertheless, our findings demonstrate that the COVID-19 pandemic is deterministically driven by climate suitability, cross-border human mobility, and region-specific COVID-19 susceptibility.
T157 28492-28774 Sentence denotes The present results, based on mapping the spread of COVID-19 and identifying multiple drivers of the outbreak trajectory, contribute to a better understanding of the disease transmission risk and may inform the application of appropriate preventative measures against this pandemic.
T158 28776-28798 Sentence denotes Supporting information
T159 28799-28902 Sentence denotes S1 Fig The distribution of four country types classified based on the COVID-19 outbreak across biomes.
T160 28903-29838 Sentence denotes Type A, countries that had a peak in the number of COVID-19 cases per week before the middle of April and had more than 1,000 COVID-19 cases per 1 million population; type B, countries that exhibited an increase in the number of COVID-19 cases per week after the middle of June and had more than 1,000 COVID-19 cases per 1 million population; type C, countries that had a peak in of the number of COVID-19 cases per week before the middle of April and had less than 1,000 COVID-19 cases per 1 million population; and type D, countries that exhibited an increase in the number of COVID-19 cases per week after the middle of June and had more than 1,000 COVID-19 cases per 1 million population. (TR) tropical rain forest; (TS) tropical seasonal forest/savanna; (TE) temperate rain forest; (SD) subtropical desert; (TD) temperate deciduous forest; (WS) woodland/shrubland; (TG) temperate grassland/desert; (BF) boreal forest; (TU) tundra.
T161 29839-29844 Sentence denotes (TIF)
T162 29845-29881 Sentence denotes Click here for additional data file.
T163 29882-30040 Sentence denotes S2 Fig Relative importance of explanatory factors in the random forest models explaining the geographical pattern of the cumulative number of COVID-19 cases.
T164 30041-30524 Sentence denotes Temp, mean temperature; Prec, mean monthly precipitation; Pop dens, population density; Visitor, relative amount of foreign visitors per population; GDP, gross domestic product per person; BCG, BCG vaccination effect as defined by the first PCA axis summarizing five variables related to BCG vaccination (see the Methods section for details); Malaria, relative malaria incidence; Age, relative proportion of the population aged ≥65 years; First cases, number of days from case onset.
T165 30525-30530 Sentence denotes (TIF)
T166 30531-30567 Sentence denotes Click here for additional data file.
T167 30568-30698 Sentence denotes S3 Fig Results of additional analyses using the number of conducted COVID-19 tests (sampling effort) as a covariate in the model.
T168 30699-31205 Sentence denotes Temp, mean temperature; Prec, mean monthly precipitation; Pop dens, population density; Visitor, relative amount of foreign visitors per population; GDP, gross domestic product per person; BCG, BCG vaccination effect as defined by the first PCA axis summarizing five variables related to BCG vaccination (see the Methods section for details); Malaria, relative malaria incidence; Age, relative proportion of the population aged ≥65 years; First cases, number of days from case onset; Test, number of tests.
T169 31206-31211 Sentence denotes (TIF)
T170 31212-31248 Sentence denotes Click here for additional data file.
T171 31249-31314 Sentence denotes S1 Appendix List of data sources for the COVID-19 cases numbers.
T172 31315-31321 Sentence denotes (DOCX)
T173 31322-31358 Sentence denotes Click here for additional data file.
T174 31359-31398 Sentence denotes S1 Video https://youtu.be/ZIDMtbek-48.
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T176 31405-31441 Sentence denotes Click here for additional data file.
T177 31442-31481 Sentence denotes S2 Video https://youtu.be/KlnpUY51D3k.
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T179 31488-31524 Sentence denotes Click here for additional data file.
T180 31525-31564 Sentence denotes S3 Video https://youtu.be/UQViOcMFhNk.
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T182 31571-31607 Sentence denotes Click here for additional data file.
T183 31608-31647 Sentence denotes S4 Video https://youtu.be/3DpjGoTrk-E.
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T185 31654-31690 Sentence denotes Click here for additional data file.