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LitCovid-sentences  

PMC:7510993 JSONTXT 17 Projects

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Id Subject Object Predicate Lexical cue
T1 0-40 Sentence denotes Multiple drivers of the COVID-19 spread:
T2 41-117 Sentence denotes The roles of climate, international mobility, and region-specific conditions
T3 118-144 Sentence denotes Drivers of COVID-19 spread
T4 146-154 Sentence denotes Abstract
T5 155-274 Sentence denotes Following its initial appearance in December 2019, coronavirus disease 2019 (COVID-19) quickly spread around the globe.
T6 275-791 Sentence denotes Here, we evaluated the role of climate (temperature and precipitation), region-specific COVID-19 susceptibility (BCG vaccination factors, malaria incidence, and percentage of the population aged over 65 years), and human mobility (relative amounts of international visitors) in shaping the geographical patterns of COVID-19 case numbers across 1,020 countries/regions, and examined the sequential shift that occurred from December 2019 to June 30, 2020 in multiple drivers of the cumulative number of COVID-19 cases.
T7 792-897 Sentence denotes Our regression model adequately explains the cumulative COVID-19 case numbers (per 1 million population).
T8 898-1013 Sentence denotes As the COVID-19 spread progressed, the explanatory power (R2) of the model increased, reaching > 70% in April 2020.
T9 1014-1275 Sentence denotes Climate, host mobility, and host susceptibility to COVID-19 largely explained the variance among COVID-19 case numbers across locations; the relative importance of host mobility and that of host susceptibility to COVID-19 were both greater than that of climate.
T10 1276-1553 Sentence denotes Notably, the relative importance of these factors changed over time; the number of days from outbreak onset drove COVID-19 spread in the early stage, then human mobility accelerated the pandemic, and lastly climate (temperature) propelled the phase following disease expansion.
T11 1554-1731 Sentence denotes Our findings demonstrate that the COVID-19 pandemic is deterministically driven by climate suitability, cross-border human mobility, and region-specific COVID-19 susceptibility.
T12 1732-2003 Sentence denotes The identification of these multiple drivers of the COVID-19 outbreak trajectory, based on mapping the spread of COVID-19, will contribute to a better understanding of the COVID-19 disease transmission risk and inform long-term preventative measures against this disease.
T13 2005-2017 Sentence denotes Introduction
T14 2018-2307 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 2308-2489 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 2490-2814 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 2815-2935 Sentence denotes To predict infection risk on the global scale, the forces driving the COVID-19 outbreak patterns must be identified [5].
T18 2936-3113 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 3114-3216 Sentence denotes Infectious diseases due to respiratory viruses are empirically characterized by a seasonal nature [6].
T20 3217-3586 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 3587-3839 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 3840-3991 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 3992-4392 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 4393-4547 Sentence denotes Specifically, we explored the roles of climate, international mobility, and region-specific conditions in the disease expansion by controlling covariates.
T25 4548-5059 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 5061-5082 Sentence denotes Materials and methods
T27 5084-5096 Sentence denotes Data sources
T28 5097-5210 Sentence denotes We compiled geographic data on the number of reported COVID-19 cases per day from December 2019 to June 30, 2020.
T29 5211-5371 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 5372-5532 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 5533-5789 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 5790-5862 Sentence denotes For each country or region, we compiled several environmental variables.
T33 5863-6046 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 6047-6382 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 6383-6653 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 6654-6777 Sentence denotes We then calculated the relative amount of foreign visitors per population of each country or region to use in the analysis.
T37 6778-7060 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 7061-7287 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 7288-8213 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 8214-8271 Sentence denotes These BCG-related variables are strongly intercorrelated.
T41 8272-8587 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 8588-8651 Sentence denotes We also compiled socioeconomic data for each country or region.
T43 8652-9010 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 9012-9032 Sentence denotes Statistical analyses
T45 9033-9250 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 9251-9488 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 9489-9897 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 9898-10045 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 10046-10644 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 10645-10908 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 10909-11194 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 11195-11294 Sentence denotes The explanatory power of the model was evaluated by the adjusted coefficient of determination (R2).
T53 11295-11538 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 11539-11621 Sentence denotes The statistical significance of each variable was determined by conducting F-test.
T55 11622-11737 Sentence denotes All the explanatory variables were standardized to have a mean of zero and a variance of one before these analyses.
T56 11738-11831 Sentence denotes The explanatory factors of the regression model were compared between the four country types.
T57 11832-11951 Sentence denotes In the random forest model, we used the same set of response and explanatory variables, as well as the same covariates.
T58 11952-12031 Sentence denotes In each run of the random forest analysis, we generated 1,000 regression trees.
T59 12032-12121 Sentence denotes The model performance was evaluated by the proportion of variance explained by the model.
T60 12122-12269 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 12270-12405 Sentence denotes Before these analyses, we tested the collinearity between the explanatory variables by calculating the variance inflation factor (VIF).
T62 12406-12561 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 12562-12802 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 12803-12991 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 12992-13204 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 13206-13228 Sentence denotes Results and discussion
T67 13229-13400 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 13401-13539 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 13540-13715 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 13716-13832 Sentence denotes Fig 1 Geographical distribution of COVID-19 cases (per 1 million population) for 1,020 countries/regions worldwide.
T71 13833-14108 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 14109-14122 Sentence denotes See S3 Video.
T73 14123-14224 Sentence denotes The map was prepared using shapefile reprinted from a freely available database (GADM; www.gadm.org).
T74 14225-14362 Sentence denotes Fig 2 The distribution of COVID-19 cases across biome types based on the relationship between mean temperature and annual precipitation.
T75 14363-14668 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 14669-15097 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 15098-15145 Sentence denotes Arrows indicate the location of Wuhan in China.
T78 15146-15159 Sentence denotes See S4 Video.
T79 15160-15275 Sentence denotes Fig 3 Patterns for the cumulative number of COVID-19 cases (per 1 million population) in relation to country type.
T80 15276-16096 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 16097-16198 Sentence denotes The map was prepared using shapefile reprinted from a freely available database (GADM; www.gadm.org).
T82 16199-16604 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 16605-16925 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 16926-17149 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 17150-17324 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 17325-17841 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 17842-17911 Sentence denotes The regressions were conducted using ordinary least squares analyses.
T88 17912-17980 Sentence denotes Vertical lines represent the 95% confidence intervals of parameters.
T89 17981-18058 Sentence denotes Closed symbols indicate the significance of explanatory variables (p < 0.05).
T90 18059-18133 Sentence denotes The coefficient of determination (R2) for the overall model is also shown.
T91 18134-18505 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 18506-18673 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 18674-18856 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 18857-18990 Sentence denotes After April 2020, the explanatory power of variables related to human mobility and host susceptibility to COVID-19 rapidly decreased.
T95 18991-19075 Sentence denotes After this, the explanatory power of human population and climate factors increased.
T96 19076-19228 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 19229-19419 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 19420-19575 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 19576-19721 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 19722-19892 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 19893-20095 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 20096-20287 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 20288-20482 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 20483-20591 Sentence denotes Population density was slightly positively correlated with the cumulative number of COVID-19 cases (Fig 6D).
T105 20592-20773 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 20774-21029 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 21030-21193 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 21194-21392 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 21393-21461 Sentence denotes Vertical lines represent the 95% confidence intervals of parameters.
T110 21462-21608 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 21609-21734 Sentence denotes The results of the random forest model were generally consistent with those of the linear multiple regression model (S2 Fig).
T112 21735-22056 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 22057-22400 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 22401-22582 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 22583-22828 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 22829-22995 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 22996-23117 Sentence denotes Cross-border human mobility, which has been facilitated by globalization [21], clearly accelerated the COVID-19 pandemic.
T118 23118-23256 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 23257-23597 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 23598-23737 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 23738-23812 Sentence denotes Notably, these correlation patterns may change as the pandemic progresses.
T122 23813-24178 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 24179-24486 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 24487-24782 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 24783-24949 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 24950-25180 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 25181-25354 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 25355-25888 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 25889-26081 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 26082-26183 Sentence denotes The map was prepared using shapefile reprinted from a freely available database (GADM; www.gadm.org).
T131 26184-26407 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 26408-26491 Sentence denotes The drivers of COVID-19 case numbers indicate a country-specific pattern (Table 1).
T133 26492-26565 Sentence denotes Table 1 Drivers of the COVID-19 spread in relation to the country types.
T134 26566-26673 Sentence denotes Country types were defined by the patterns of COVID-19 spread (cases per 1 million population) (see Fig 3).
T135 26674-27363 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 27364-27488 Sentence denotes The statistical significance of differences between the country types was tested by a Bonferroni’s multiple comparison test.
T137 27489-27587 Sentence denotes Different letters indicate the values that are significantly different (p < 0.05) from each other.
T138 27588-27626 Sentence denotes Factor Type A Type B Type C Type D
T139 27627-27714 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 27715-27798 Sentence denotes Mean annual precipitation 865 (±368) a 806 (±541) a 1250 (±629) b 1290 (±869) b
T141 27799-27868 Sentence denotes Population density 485 (±1060) 342 (±1400) 391 (±1500) 164 (±243)
T142 27869-27961 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 27962-28046 Sentence denotes GDP per person 50200 (±21500) a 18500 (±18300) b 22200 (±18100) b 5690 (±5430) c
T144 28047-28137 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 28138-28255 Sentence denotes Relative frequency of people infected by malaria 0.163 (±1.26) a 2180 (±14200) a 2950 (±31800) a 40100 (±85000) b
T146 28256-28602 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 28603-28787 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 28788-29035 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 29036-29311 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 29312-29491 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 29492-29613 Sentence denotes Therefore, evaluating the drivers of the COVID-19 spread at the present phase of disease expansion is a challenging task.
T152 29614-29742 Sentence denotes The absence of population-wide testing for COVID-19 makes it difficult to investigate the growth dynamics of COVID-19 infection.
T153 29743-29841 Sentence denotes The case data include a selection bias due to surveillance focusing mainly on symptomatic persons.
T154 29842-30143 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 30144-30304 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 30305-30496 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 30497-30779 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 30781-30803 Sentence denotes Supporting information
T159 30804-30907 Sentence denotes S1 Fig The distribution of four country types classified based on the COVID-19 outbreak across biomes.
T160 30908-31843 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 31844-31849 Sentence denotes (TIF)
T162 31850-31886 Sentence denotes Click here for additional data file.
T163 31887-32045 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 32046-32529 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 32530-32535 Sentence denotes (TIF)
T166 32536-32572 Sentence denotes Click here for additional data file.
T167 32573-32703 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 32704-33210 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 33211-33216 Sentence denotes (TIF)
T170 33217-33253 Sentence denotes Click here for additional data file.
T171 33254-33319 Sentence denotes S1 Appendix List of data sources for the COVID-19 cases numbers.
T172 33320-33326 Sentence denotes (DOCX)
T173 33327-33363 Sentence denotes Click here for additional data file.
T174 33364-33403 Sentence denotes S1 Video https://youtu.be/ZIDMtbek-48.
T175 33404-33409 Sentence denotes (TXT)
T176 33410-33446 Sentence denotes Click here for additional data file.
T177 33447-33486 Sentence denotes S2 Video https://youtu.be/KlnpUY51D3k.
T178 33487-33492 Sentence denotes (TXT)
T179 33493-33529 Sentence denotes Click here for additional data file.
T180 33530-33569 Sentence denotes S3 Video https://youtu.be/UQViOcMFhNk.
T181 33570-33575 Sentence denotes (TXT)
T182 33576-33612 Sentence denotes Click here for additional data file.
T183 33613-33652 Sentence denotes S4 Video https://youtu.be/3DpjGoTrk-E.
T184 33653-33658 Sentence denotes (TXT)
T185 33659-33695 Sentence denotes Click here for additional data file.
T186 33697-33771 Sentence denotes We are grateful to the Kubota-lab technical staff for the data management.