key: cord-0430046-72prlliz authors: Oshinubi, K.; Rachdi, M.; Demongeot, J. title: Analysis of daily reproduction rates of COVID-19 using Current Health Expenditure as Gross Domestic Product percentage (CHE/GDP) across countries date: 2021-08-29 journal: nan DOI: 10.1101/2021.08.27.21262737 sha: 4cb50b99144b5a6a6cf2047609976183a179f2df doc_id: 430046 cord_uid: 72prlliz (1) Background: Impact and severity of coronavirus pandemic on health infrastructure vary across countries. We examine the role percentage health expenditure plays in various countries in terms of their preparedness and see how countries improved their public health policy in the first and second wave of the coronavirus pandemic; (2) Methods: We considered the infectious period during the first and second wave of 195 countries with their Current Health Expenditure as Gross Domestic Product percentage (CHE/GDP). Exponential model was used to calculate the slope of the regression line while the ARIMA model was used to calculate the initial autocorrelation slope and also to forecast new cases for both waves. The relationship between epidemiologic and CHE/GDP data was used for processing ordinary least square multivariate modeling and classifying countries into different groups using PC analysis, K-means and Hierarchical clustering; (3) Results: Results show that some countries with high CHE/GDP improved their public health strategy against virus during the second wave of the pandemic; and (4) Conclusions: Results revealed that countries who spend more on health infrastructure improved in the tackling of the pandemic in the second wave as they were worst hit in the first wave. This research will help countries to decide on how to increase their CHE/GDP in order to tackle properly other pandemic waves of the present Covid-19 outbreak and future diseases that may occur. We are also opening up a debate on the crucial role socio-economic determinants play during the exponential phase of the pandemic modelling. The variables used for this research are seven in total. The maximum basic reproduction number R0 49 for first and second waves is chosen during the exponential phase of all countries considered. The 50 exponential and autocorrelation slopes are calculated using 100 days from the start of a wave 51 depending on the date a particular country recorded their first case between February and August 52 2020 while also 100 days was used to calculate for the second wave between October 15 2020 to 53 January 22 2021 for all countries considered. The opposite of initial autocorrelation slope was 54 averaged on six days. CHE/GDP was collated from World Bank data [1] . The The exponential model is given as y = ae bx , where y is the daily number of new cases, x is the number 61 of days, b is the slope and a is a constant, and the log format can be written as log y = log a + bx. ARIMA modelling has been introduced by N. Wiener for prediction and forecasting [5] . Its 63 parametric approach assumes that the underlying stationary stochastic process of the COVID-19 64 new daily cases N(t) can be described by a small number of parameters using the autoregressive ARIMA model N(t) = Σi=1,s a(i) N(i) + W(t), where W is a random residue whose variance is to 66 minimize. The autocorrelation analysis is done by calculating the correlation A(k) between the 67 N(t)'s and the N(t − k)'s (t belonging to a moving time window) by using the formula , (1) 69 where E denotes the expectation and σ the standard deviation. The autocorrelation function A allows 70 examining the serial dependence of the N(t)'s. We used ARIMA form of (6, 1, 0), which we think is 71 the best for the modelling. Principal component analysis (PCA) also helps to cluster data points and it is also one of dimension 80 reduction techniques because each variable has a different dimension. It allows us to summarize and 81 All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted August 29, 2021. ; https://doi.org/10.1101/2021.08.27.21262737 doi: medRxiv preprint (c) (d) perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted August 29, 2021. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted August 29, 2021. ; https://doi.org/10.1101/2021.08.27.21262737 doi: medRxiv preprint which makes sense, as these quantities are both related to the initial exponential growth of an 325 epidemic wave. For the first wave of all countries, Figure 6 shows the same positive correlation as 326 Figure 5a between the exponential regression slope and CHE/GDP. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in Estimation of Daily Reproduction 404 Rates in COVID-19 Outbreak Extrapolation, Interpolation, and Smoothing of Stationary Time Series Scikit-learn: Machine Learning in Python Temperature decreases spread parameters of the 416 new covid-19 cases dynamics Inverted covariate effects for 418 mutated 2nd vs 1st wave Covid-19: high temperature spread biased for young Computations of the transmission rates in SI epidemic 421 model applied to COVID-19 data in mainland China Towards unified and real-time analyses of outbreaks 423 at country-level during pandemics Counter-Intuitive Geoclimatic and Demographic Correlations of COVID-19 Spread Rates Factors 428 associated with spatial heterogeneity of Covid-19 in France: a nationwide ecological study. The 429 Lancet Public Health A robust phenomenological approach to investigate 431 COVID-19 data for France perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted August 29, 2021. ; https://doi.org/10.1101/2021.08.27.21262737 doi: medRxiv preprint All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted August 29, 2021. ; https://doi.org/10.1101/2021.08.27.21262737 doi: medRxiv preprint