Task 7 - Help us understand how geography affects virality

Are there geographic variations in the rate of COVID-19 spread?

The clear relationship between COVID - 19 growth rate and climate suggests that seasonal climatic variation may affect the spatial spread and severity of COVID - 19 outbreaks ( 14 ) , as observed for other virus - caused diseases ( 3 , 6 ) . We thus displayed potential seasonal changes in 5 Covid - 19 growth rates by projecting our best model of r 50 in relation to temperature under the average temperature conditions of the current ( March ) and next ( June and September ) months ( Fig . 1D - E ) . 15 SARS - CoV2 shows a substantial rate of undocumented infections that could facilitate the spread of the disease ( 21 ) . This may affect our analyses , which are based on the number of confirmed positive cases ( 8 , 9 ) . In most countries , reported positives largely refer to tested individuals showing Covid - 19 symptoms that require hospitalization . Therefore , even though our models cannot capture the ( unknown ) dynamics of undocumented infections , they provide key 20 information on the geographical variation in the risk of occurrence of symptomatic SARS - CoV2 infections .

The models used to estimate COVID - 19 growth rate on different countries showed an average R 2 of 0 . 96 ( SD = 0 . 03 ) , varying from 0 . 83 to 0 . 99 , indicating an overall excellent performance on estimating growth rates . Only one out of the 44 countries ( i . e . , exponential growth phase for at least ten days after country had 100 confirmed cases ) did not show an R 2 > 0 . 8 for model fitting , and , therefore , we removed this country from the following analysis . The geographical patterns in the growth rates of COVID - 19 cases do not show a clear trend , at least in terms of latitudinal variation , that would suggest a climatic effect at macroecological scale ( Fig . 2A ) . We build one model including only climate and socioeconomic variables , which explained only 19 % of the variation on growth rates with a significant ( p < 0 . 025 ) and negative coefficient for annual population growth rate . This model did not have spatial autocorrelation in the residuals . When we added country centrality ( i . e . country importance in global transportation network ) as a predictor , the R 2 increased to 34 . 5 % .

After prewhitening the COVID - 19 incidence time series by the ARIMA model fitted on the outbound traffic time series , we calculated the cross - correlation coefficients between daily outbound traffic volume and COVID - 19 incidence for each province . There were great geographical variations in time lags and correlation coefficients ( Figure 2A - 2B ) . K - means clustering analysis identified 3 latent clusters of provinces according to time lag and correlation coefficients ( Figure 2C ) . The estimated time lags between traffic volume and COVID - 19 incidence were < 1 week in 42 % of provinces , 1 week in 39 % of provinces , and 2 - 3 weeks in 19 % of provinces .

The mean rate ( r ) of expansion of Phase 1 countries is 0 . 238 , with standard deviation ( σ ) 0 . 0340 . We plot 1σ and 2σ variations about the function 2500 ( ) = 120 . 4 × 0 . 238 . There is a 95 % probability that India ' s case rate will fall between the outer two lines , assuming it COVID - 19 expands at the same rate as Phase 1 countries . Existing case data is plotted with red markers .

Host - pathogen interaction dynamics can be significantly affected by environmental conditions , either directly , via e . g . improved pathogen transmission rates , or indirectly , by affecting host susceptibility to pathogen attacks ( 1 ) . In the case of directly transmitted diseases , such as human influenza , multiple environmental parameters such as local temperatures and humidity impact on 5 virus survival and transmission , with significant consequences for the seasonal and geographic patterns of outbreaks ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) . A recently discovered coronavirus , SARS - CoV - 2 , is the aethiological agent of a pandemic disease , Covid - 19 , causing severe pneumonia outbreaks at the global scale ( 7 ) . Covid - 19 cases are now reported in about 170 countries and regions worldwide ( 8 ) . Three months after the discovery of SARS - CoV - 2 , the global pattern and the early dynamics of Covid - 10 19 outbreaks seem highly variable . Some countries have been experiencing limited growth and spread of Covid - 19 cases , while others are suffering widespread community transmission and nearly exponential growth of infections ( 8 ) . Understanding the drivers of early growth rates is pivotal to predict progresses of disease outbreaks in the absence of containment measures ( 9 , 10 ) , yet no study has so far assessed the role of environmental variation in the worldwide growth of 15 Covid - 19 cases . Given the impact of environmental conditions on the transmission of many pathogens , we tested the hypothesis that the severity of Covid - 19 outbreaks across the globe is affected by spatial variation of key environmental factors , such as temperature , air humidity ( 5 , ( 11 ) ( 12 ) ( 13 ) ( 14 ) ( 15 ) , and pollution [ fine particulate matter ( 16 ) ; see methods ] . We then evaluated if this could help to illustrate global variation in the risk of severe Covid - 19 outbreaks in the coming months . 20 Relying on a publicly available global dataset ( 8 ) , we computed the daily growth rates r of confirmed Covid - 19 cases ( Covid - 19 growth rate hereafter ) for 121 countries / regions ( see the Methods section ) . We limited our measure of epidemics growth rate to the first 5 days after . CC - BY - NC - ND 4 . 0 International license It is made available under a author / funder , who has granted medRxiv a license to display the preprint in perpetuity .

Are there geographic variations in the mortality rate of COVID-19?

To correct for false inflation of mortality rate , mortality analysis was restricted to only those countries reporting more than 100 COVID - 19 cases . Comparison of means was done with Mann Whitney U test . A binary logistic regression was run with COVID - 19 incidence as dependent variable and the following two variables : malaria free country and BCG vaccination coverage ≤ 95 % . Analysis of incidence and mortality of COVID - 19 with malaria incidence and BCG coverage was done separately for different geographical location of the countries . Finally , case fatality rate of COVID - 19 was analysed in comparison to malaria incidence with non - linear regression .

The copyright holder for this preprint . https : / / doi . org / 10 . 1101 / 2020 . 03 . 25 . 20043679 doi : medRxiv preprint Hokkaido . Fig . 1 suggests that Hokkaido was not or incompletely exposed to S type and the current epidemic is due to the L type . Mathematical modeling has shown that undocumented infections of SARS - CoV - 2 were the infection source for most documented cases . 7 Because children infected with SARS - CoV - 2 are asymptomatic 8 or mildly symptomatic , 4 Europe has become the center of the pandemic . But why mortality rates vary from country to country remains enigmatic . We arranged influenza epidemic curves of European countries in descending order of COVID - 19 mortality ( Supplementary Figs . 1 and 2 ) . There seems to be a pattern in the curve related to COVID - 19 case fatality rates ( CFR ) . Depending on the likelihood of S - type SRS - CoV - 2 transmission , a scoring system was developed to model the mortality - related curve patterns . As shown in Fig . 3 , this score correlates with the SARS - CoV - 2 CFR in each country , implying that the extent of S type SARS - CoV - 2 transmission determines the severity of the current infection . This scoring system is useful as a country - specific COVID - 19 severity risk score . As shown in Fig . 4 , the risk score reveals geographic effects on the spread of the

Generalized linear models with a negative binomial distribution of errors described well most COVID - 19 growth curves from our final dataset of 36 countries ( Fig . 1 ) . We found substantial variation in infection rates ( glm coefficients ) across countries , which ranged between 1 . 22x10 - 07 ( Denmark ) and 3 . 29x10 - 06 ( United States ; Fig . 1 ) . Mortality rates ranged between 0 . 17 ( South Africa ) and 9 . 25 ( Indonesia ; Fig . 2 ) .

COVID - 19 ( Coronavirus Disease - 2019 ) , a disease caused by the coronavirus SARS - CoV - 2 ( Severe Acute Respiratory Syndrome - Coronavirus - 2 ) , has emerged as a rapidly spreading communicable disease affecting more than 100 countries across the globe at present . The disease is primarily spread through large respiratory droplets , though the possibility of other routes of transmission cannot be ruled out , as the virus has been found in stool and urine of affected individuals [ 1 ] . The disease severity has varied from mild self - limiting flu - like illness to fulminant pneumonia , respiratory failure and death . There are regional variations in the mortality rates and these estimates are rapidly changing as more data are becoming available . There were 95 , 333 confirmed cases of COVID - 19 worldwide with a mortality rate of 3 . 4 % according to the situation report of World Health Organisation on March 5 , 2020 [ 2 ] . However , a much lower mortality of 1 . 4 % has been reported in analysis of data of 1099 patients with laboratory - confirmed COVID - 19 from 552 hospitals in mainland China [ 3 ] . Considering that the number of unreported and unconfirmed cases is likely to be much higher than the reported cases , the actual mortality may be less than 1 % , which is similar to that of severe seasonal influenza [ 4 ] . India has 39confirmed cases till 10th March , 2020 and contact surveillance of these cases is going on . The understanding of epidemiological characteristics of this infection is evolving on a daily basis as the disease is spreading to different parts of the globe .

Our study shows important variation in infection and mortality rates across countries , which was primarily explained by socio - economic factors . Importantly , our findings reveal that low - income country receiving large numbers of imported goods and international visitors are likely to exhibit higher COVID - 19 infection and mortality rates . International aid agencies could use this information to help mitigate the consequences of the current pandemic in the most vulnerable countries ( Bedoya & Dolinger , 2020 ) . Tables Table 1 : Confidence Intervals ( CI ) for model - averaged coefficients and sum of Akaike weights considering the set of best - fitting models ( ΔAIC AIC ≤ 2 ) . All models where linear multiple regressions containing either infection rate or mortality rate as response variables ( see methods for details ) . Predictor variables in bold fonts represent those where CI do not contain zero .

Is there any evidence to suggest geographic based virus mutations?