key: cord-0315562-ka9sdu6r authors: Zhang, Feifei; Chase-Topping, Margo; Guo, Chuan-Guo; Woolhouse, Mark E.J. title: Predictors of human-infective RNA virus discovery in the United States, China and Africa, an ecological study date: 2021-09-15 journal: bioRxiv DOI: 10.1101/2021.09.13.460031 sha: c0f73c6bbc7538cde3968b7d6f0601ae3714b0a7 doc_id: 315562 cord_uid: ka9sdu6r Background The variation in the pathogen type as well as the spatial heterogeneity of predictors make the generality of any associations with pathogen discovery debatable. Our previous work confirmed that the association of a group of predictors differed across different types of RNA viruses, yet there have been no previous comparisons of the specific predictors for RNA virus discovery in different regions. The aim of the current study was to close the gap by investigating whether predictors of discovery rates within three regions—the United States, China and Africa—differ from one another and from those at the global level. Methods Based on a comprehensive list of human-infective RNA viruses, we collated published data on first discovery of each species in each region. We used a Poisson boosted regression tree (BRT) model to examine the relationship between virus discovery and 33 predictors representing climate, socio-economics, land use, and biodiversity across each region separately. The discovery probability in three regions in 2010–2019 was mapped using the fitted models and historical predictors. Results The numbers of human-infective virus species discovered in the United States, China and Africa up to 2019 were 95, 80 and 107 respectively, with China lagging behind the other two regions. In each region, discoveries were clustered in hotspots. BRT modelling suggested that in all three regions RNA virus discovery was best predicted by land use and socio- economic variables, followed by climatic variables and biodiversity, though the relative importance of these predictors varied by region. Map of virus discovery probability in 2010– 2019 indicated several new hotspots outside historical high-risk areas. Most new virus species since 2010 in each region (6/6 in the United States, 19/19 in China, 12/19 in Africa) were discovered in high risk areas as predicted by our model. Conclusions The drivers of spatiotemporal variation in virus discovery rates vary in different regions of the world. Within regions virus discovery is driven mainly by land-use and socio- economic variables; climate and biodiversity variables are consistently less important predictors than at a global scale. Potential new discovery hotspots in 2010–2019 are identified. Results from the study could guide active surveillance for new human-infective viruses in local high risk areas. Funding Darwin Trust of Edinburgh; European Union. are highly spatially heterogeneous, making the generality of any associations with discovery 58 debatable. For example, the United States, China, and Africa have experienced different rates 59 region separately, following codes from our previous study (Zhang et al., 2020 ) and one 140 previous paper (Allen et al., 2017). As a tree-based learning method, BRT model can 141 automatically capture complex relationships and interactions between variables, and also can 142 well account for spatial autocorrelation within the data (Crase et al., 2012). We compared 143 Moran's I values of the raw virus data and the model residuals to estimate the ability of the 144 BRT model to account for spatial autocorrelation (Cliff et al., 1981) . In order to minimise the 145 effect of spatial uncertainty of virus discovery data, we performed 1000 times bootstrap 146 resampling for those discovery locations reported as polygons. We assumed each grid cell in 147 the polygon has the equal chance to be selected, and for each virus record we selected one 148 grid cell randomly from the polygon for each subsample. A ratio of 1:2 for presence to 149 absence constituted each subsample, i.e., for each grid cell with virus discovery, two grid 150 cells with no discovery were randomly selected from 'virus discovery free' areas at all time 151 points within the region. Take the United States as an example, each subsample included 95 152 grid cells with virus discovery and 190 with no virus discovery. We then matched the virus 153 data with all predictors (using the nearest decade for time-varying predictors). We assumed 154 that the virus count in any given grid cell in each decade followed a Poisson distribution, and 155 we calculated the virus discovery count in each grid cell by decade as the response variable. 156 All BRT models were fitted in R v. 3.6.3, using packages dismo and gbm. BRT models optimal model as well as the mean optimal number of trees across 1000 replicate models for 165 all three regions were summarised in Table 3S . 166 By fitting 1000 replicate BRT models, the relative contribution plots and partial dependence 167 plots with 95% quantiles were plotted. We defined variables with a relative contribution 168 greater than the mean (3.03%) as influential predictors in all three regions (Shearer et al., calculated from 50 rounds of ten-fold cross-validation, by following methods from our 178 previous paper (Zhang et al., 2020) . For the ten-fold cross-validation, we selected 50 data sets 179 randomly from the 1000 bootstrapped subsamples. We took the first data set and divided into 180 ten subsets. For each round of ten-fold cross-validation, the unique combinations of 9 subsets 181 constituted the training sets and were used to fit models, and the remaining one was used as a 182 test set to evaluate the predictive performance of the model. We repeat the same process as 183 above for the remaining 49 data sets. One intraclass correlation coefficient (ICC) was 184 calculated from each round of validation and the median with 95% quantiles across all 50 185 rounds was calculated. The ICC varies between 0 and 1, with an ICC of less than 0.40 186 representing a poor model, 0.40-0.59 representing a fair model, 0.60-0.74 representing a 187 good model, and 0.75-1 representing an excellent model (Cicchetti, 1994) . 188 Exploratory subgroup analyses (distinguishing viruses firstly discovered in regions and those 189 that had been discovered elsewhere in the world) were performed. We used the same BRT 190 modelling approach as we described above, and relative contribution of each predictor was 191 calculated for each subgroup. We were unable to perform subgroup analysis for China 192 because only 9 human-infective RNA viruses have been firstly discovered in it, and BRT 193 model cannot be fitted to a sample as small as 9. 194 The R software, version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria) was 195 used for all statistical analyses. All maps were visualised by using ArcGIS Desktop 10.5.1 196 (Environmental Systems Research Institute). 197 The numbers of human-infective virus species discovered in the United States, China and 199 Africa up to October 2019 were 95, 80 and 107 respectively (Table 1S) The discovery curves for the United States and Africa have seen a broadly similar pattern, 221 with China lagging behind these two regions (Figure 3 ). In comparison with the world, the 222 median time lag of the virus discovery was 0 [interquartile range (IQR): 2.5], 12 (IQR: 29.5) 223 and 2 (IQR: 10.5) years in the United States, China and Africa, respectively ( Figure 1S) 3.8%, diurnal temperature range: 3.3%) were identified as important predictors for 245 discriminating between locations with and without virus discovery ( Figure 4B) . GDP, 246 urbanized land, university count, vegetation, GDP growth, maximum precipitation, 247 population growth, and urbanization of secondary land presented a positive trend over narrow 248 ranges at lower levels; pasture, cropland, precipitation change, and diurnal temperature range 249 had non-monotonic/ negative impacts, with highest risks at lower values ( Figure 3S) . 250 In Africa, ten variables including two socio-economic variables (GDP growth: 21.2%, GDP: 251 13.0%), seven predictors related to land use (urbanized land: 9.4%, growth of cropland area: 252 5.6%, urbanization of cropland: 5.5%, growth of urbanized land: 5.1%, urbanization of 253 pasture: 3.8%, vegetation, 3.7%, cropland: 3.2%), and one biodiversity variable (mammal 254 species richness: 3.1%) were identified as important predictors for discriminating between 255 locations with and without virus discovery ( Figure 4C ). All important predictors presented a 256 positive trend over narrow ranges at lower positive values, except mammal species over a 257 large range ( Figure 4S) . 258 Our BRT models reduced Moran's I value below 0.15 in all three regions (Figure 5S ), 264 suggesting that BRT models with 33 predictors have adequately accounted for spatial 265 autocorrelations in the raw virus data in all three regions. The model validation statistics for 266 each region are shown in Table 4S . Combining these measures, our BRT model predictions 267 range from fair to good (Cicchetti, 1994) . 268 In all three regions, human-infective RNA virus discovery was best predicted by land use and 269 socio-economic variables, followed by climatic variables and biodiversity (Figure 5 Based on our subgroup analysis (distinguishing viruses firstly discovered in regions and those 305 that had been discovered elsewhere in the world), discoveries of human-infective RNA 306 viruses firstly discovered from either United States or Africa were better predicted by 307 climatic and biodiversity variables, while discoveries of viruses that had been discovered 308 from elsewhere in the world were better predicted by socio-economic variables ( Figure 9S) . In all three regions, GDP and/or GDP growth were identified as important predictors for virus 326 discovery, especially in Africa where GDP and GDP growth were identified as the leading 327 predictors. This is consistent with our previous analysis that GDP and GDP growth play a 328 major role in discovering viruses (Zhang et al., 2020) . In general, sufficient economic, human 329 and material resources, the availability of advanced infrastructure and technology, and greater 330 research capabilities in the relative higher-income areas enable the virus discovery 331 (Rosenberg et al., 2013) . That this effect applied both within one continent and within single 332 countries such as the United States and China suggested that most virus discoveries were 333 likely passive, i.e. the viruses were detected when they arrived in a location with the 334 resources to detect them. This is plausible because in all regions in our study, human-335 transmissible viruses accounted for the larger proportion, and our previous analysis suggested 336 richer areas were more likely to first capture transmissible viruses (e.g. Influenza virus, 337 Rhinovirus, Rabies lyssavirus, Measles morbillivirus, Mumps orthorubulavirus, Rubella virus, 338 and Norwalk virus) capable of spreading to multiple areas (Zhang et al., 2020) . Temporally, in China the rate of discovery increased after economic growth accelerated in the 1980s 340 ( Figure 3) . We note in publications describing first virus discoveries that most historical 341 virus discoveries in Africa received support from the United States and Europe, and this may 342 explain why Africa saw an increased number of virus discoveries after 1950-30 years 343 earlier than China (Figure 3) . Notably, in China the relative contribution of GDP growth to 344 virus discovery was not as substantial as that in Africa. In contrast, university count was 345 found to be associated with virus discovery, suggesting virus discovery likely being a 346 this, e.g. the underlying relationships between urbanization and economic growth as well as 361 population growth, but we can't untangle these from this study. 362 Consistent with our previous work, population growth was identified as a less prominent 363 predictors (Zhang et al., 2020) . In China, population growth-though with greater influence 364 than other regions-contributed less than urbanized land and three other land types on virus 365 discoveries. This reinforces our previous interpretation that urbanization brings larger 366 changes on human living environment than human population size/growth, and therefore may 367 have influenced virus discovery more greatly. percentiles of discovery probability within each region. Further, 35% (13/37) of those viruses 387 discovered in high-risk areas since 2010 were discovered at the potential new hotspots where 388 there had not been any virus discoveries in the past. 389 Our subgroup analyses suggested in both the United States and Africa, discoveries of viruses 390 firstly discovered in regions were more likely to be associated with climatic and biodiversity 391 variables while discoveries of viruses had been discovered elsewhere in the world were more 392 likely to be associated with socio-economic variables. This is plausible, again because after a 393 novel virus was discovered elsewhere in the world, it is usually areas with a higher socio-394 economic level firstly capture the virus in the local region. 395 This study had limitations. First, one common problem for data collected from literature 396 review is the time lag between virus discovery and publication, in which case the virus data 397 are likely to be matched to covariates in later decades. Second, we acknowledge that it is 398 possible we have not identified the earliest report for some well-known viruses such as 399 yellow fever virus, measles virus, especially in the post-vaccination era. Third, we were 400 unable to identify robust and comprehensive data for all three regions on virus discovery 401 effort, although we interpret GDP and university count as being an indirect measure of 402 resources available for this activity. 403 The study adds to our previous study (Zhang et al., 2020) in several ways. First, we firstly 404 construct data sets of human-infective RNA virus discovery reflecting the viral richness in 405 three broad regions of the world. Second, we reduced the heterogeneity of the predictors by 406 focusing on regions, including those predictors reflecting the research effort. Research effort 407 is less variable within restricted regions and therefore has less effect on virus detection. 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