PMC:7510993 / 13206-30779 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7510993","sourcedb":"PMC","sourceid":"7510993","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7510993","text":"Results and discussion\nCOVID-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. 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). 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).\nFig 1 Geographical distribution of COVID-19 cases (per 1 million population) for 1,020 countries/regions worldwide.\n(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. See S3 Video. The map was prepared using shapefile reprinted from a freely available database (GADM; www.gadm.org).\nFig 2 The distribution of COVID-19 cases across biome types based on the relationship between mean temperature and annual precipitation.\nBiome 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. 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. Arrows indicate the location of Wuhan in China. See S4 Video.\nFig 3 Patterns for the cumulative number of COVID-19 cases (per 1 million population) in relation to country type.\nBased 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. The map was prepared using shapefile reprinted from a freely available database (GADM; www.gadm.org). 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. 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).\nFig 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).\n(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). 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. The regressions were conducted using ordinary least squares analyses. Vertical lines represent the 95% confidence intervals of parameters. Closed symbols indicate the significance of explanatory variables (p \u003c 0.05). The coefficient of determination (R2) for the overall model is also shown. 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 \u003e70% in April 2020 (Fig 5A). The number of days from case onset had some explanatory power (\u003e 20%) in January, 2020, but this factor quickly lost its influence as the pandemic progressed (Fig 5B). 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). After April 2020, the explanatory power of variables related to human mobility and host susceptibility to COVID-19 rapidly decreased. After this, the explanatory power of human population and climate factors increased. These results demonstrate that the impact of virus dispersability between/within regions was predominant in the beginning stage of the pandemic (Fig 5).\nFig 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.\n(A) Overall coefficient of determination of the regression model; (B) coefficient of partial determination (r2) for each explanatory variable in the model. The results shown are based on data starting from January, 2020, because the number of cases in December 2019 was insufficient for this analysis. 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). 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). 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). 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). Population density was slightly positively correlated with the cumulative number of COVID-19 cases (Fig 6D). 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). 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. 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).\nFig 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.\nVertical lines represent the 95% confidence intervals of parameters. 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. The results of the random forest model were generally consistent with those of the linear multiple regression model (S2 Fig). 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. 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. 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.\nThis 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. Notably, the explanatory power of these drivers substantially increased as the pandemic progressed, indicating a deterministic expansion of COVID-19 around the world.\nCross-border human mobility, which has been facilitated by globalization [21], clearly accelerated the COVID-19 pandemic. 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. 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. This data support the findings by Sala et al. [13] that there is a significant correlation between BCG vaccination and COVID-19 prevalence. Notably, these correlation patterns may change as the pandemic progresses. 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).\nOur 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]. 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]. 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].\nOur 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. 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. 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.\nFig 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.\nThe map was prepared using shapefile reprinted from a freely available database (GADM; www.gadm.org). 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). The drivers of COVID-19 case numbers indicate a country-specific pattern (Table 1).\nTable 1 Drivers of the COVID-19 spread in relation to the country types.\nCountry types were defined by the patterns of COVID-19 spread (cases per 1 million population) (see Fig 3). 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. The statistical significance of differences between the country types was tested by a Bonferroni’s multiple comparison test. Different letters indicate the values that are significantly different (p \u003c 0.05) from each other.\nFactor Type A Type B Type C Type D\nMean annual temperature 11.1 (±3.88) a 14.6 (±8.87) b 18.5 (±7.96) c 21.4 (±6.81) d\nMean annual precipitation 865 (±368) a 806 (±541) a 1250 (±629) b 1290 (±869) b\nPopulation density 485 (±1060) 342 (±1400) 391 (±1500) 164 (±243)\nRelative frequency of visitors 154 (±329) a 36.1 (±65.4) b 73.8 (±97.4) b 16.4 (±27.2) b\nGDP per person 50200 (±21500) a 18500 (±18300) b 22200 (±18100) b 5690 (±5430) c\nBCG vaccination effect -1.37 (±1.42) a 0.752 (±1.37) b 0.467 (±1.51) b 0.88 (±0.694) b\nRelative frequency of people infected by malaria 0.163 (±1.26) a 2180 (±14200) a 2950 (±31800) a 40100 (±85000) b\nRelative 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. 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. 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. 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. 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). Therefore, evaluating the drivers of the COVID-19 spread at the present phase of disease expansion is a challenging task.\nThe absence of population-wide testing for COVID-19 makes it difficult to investigate the growth dynamics of COVID-19 infection. The case data include a selection bias due to surveillance focusing mainly on symptomatic persons. 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). 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]. 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. 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 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