Vaccine efficacy for COVID-19 outbreak in New York City

This article aims to study the COVID-19 data for New York City. We use both the daily number of second does vaccination and the daily number of reported cases for New York City. This article provides a method to combine an epidemic model and such data. We explore the influence of vaccine efficacy on our results.

Modelling of COVID-19 pandemic vis-a-vis some socioeconomic factors

The impacts of COVID-19 outbreak on socio-economic status of countries across the globe cannot be overemphasized as we examine the role it played in various countries. A lot of people were out of jobs, many households were careful of their spending and a greater social fracture of the population in fourteen different countries has emerged. We considered periods of infection spread during the first and second wave in Organization for Economic Co-operation and Development (OECD) countries and countries in Africa, that is developed and developing countries alongside their social-economic data. We established a mathematical and statistical relationship between Theil and Gini index, then we studied the relationship between the data from epidemiology and socio-economic determinants using several machine learning and deep learning methods. High correlations were observed between some of the socio-economic and epidemiologic parameters and we predicted three of the socio-economic variables in order to validate our results. These result shows a sharp difference between the first and second wave of the pandemic confirming the real dynamics of the spread of the outbreak in several countries and ways by which it was mitigated.

Functional Data Analysis: Transition from Daily Observation of COVID-19 Prevalence in France to Functional Curves

In this paper we use the technique of functional data analysis to model daily hospitalized, deceased, ICU cases and return home patient numbers along the COVID-19 outbreak, considered as functional data across different departments in France while our response variables are numbers of vaccinations, deaths, infected, recovered and tests in France. These sets of data were considered before and after vaccination started in France. We used some smoothing techniques to smooth our data set, then analysis based on functional principal components method was performed, clustering using k-means techniques was done to understand the dynamics of the pandemic in different French departments according to their geographical location on France map and we also performed canonical correlations analysis between variables. Finally, we made some predictions to assess the accuracy of the method using functional linear regression models.

Analysis of daily reproduction rates of COVID-19 using Current Health Expenditure as Gross Domestic Product percentage (CHE/GDP) across countries

(1) BackgroundImpact 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) MethodsWe 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) ResultsResults show that some countries with high CHE/GDP improved their public health strategy against virus during the second wave of the pandemic; (4) ConclusionsResults 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.

Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait

The first COVID 19 case of Kuwait was announced on 24th February, 2020 and the daily new cases increases exponentially since then until May, 2020 when the first wave started to decline. The same exponential dynamics has been observed between January and March, 2021. The forecast of new cases and death recorded daily is crucial so that health experts and citizens can be guided in order to avoid escalation of the pandemic. We propose a deterministic method to predict the basic reproduction number Ro of first and second wave of COVID-19 cases in Kuwait and also to forecast the daily new cases and death of the pandemic in the country. Forecasting has been done using ARIMA model, Exponential smoothing model, Holts method, Prophet forecasting model and machine learning models like log-linear, polynomial and support vector regressions. The results presented aligned with other methods used to predict Ro in first and second waves and the forecasting clearly shows the trend of the pandemic in Kuwait. The deterministic prediction of Ro is a good forecasting tool available during the exponential phase of the contagion, which shows an increasing trend during the beginning of the first and second waves of the pandemic in Kuwait.

What can we learn from COVID-19 data by using epidemic models with unidentified infectious cases?

BackgroundThe COVID-19 epidemic, which started in late December 2019 and rapidly spread throughout the world, was accompanied by an unprecedented release of reported case data. Our objective is to propose a fresh look at this data by coupling a phenomenological description to the epidemiological dynamics. MethodsWe use a phenomenological model to describe and regularize the data. This model can be matched by a single mathematical model reproducing the epidemiological dynamics with a time-dependent transmission rate. We provide a method to compute this transmission rate and reconstruct the changes in the social interactions between people as well as changes in host-pathogen interactions. This method is applied to the cumulative case data of 8 different geographic areas. FindingsWe reconstruct the transmission rate from the data, therefore we are in position to understand the contribution of the dynamical effects of social interactions (contacts between individuals) and the contribution of the dynamics of the epidemic. We deduce from the comparison of several instantaneous reproduction numbers that the social effects are the most important in the dynamic of COVID-19. We obtain an instantaneous reproduction number that stays below 3.5 from early beginning of the epidemic. ConclusionThe instantaneous reproduction number staying below 3.5 implies that it is sufficient to vaccinate 71% of the population in each state or country considered in our study. Therefore assuming the vaccines will remain efficient against the new variants, and to be more confident it is sufficient to vaccinate 75 - 80% to get rid of COVID-19 in each state or country. FundingThis research was funded by the Agence Nationale de la Recherche in France (Project name: MPCUII (PM) and (QG))

Estimation of Daily Reproduction Rates in COVID-19 Outbreak.

(1) BackgroundThe estimation of daily reproduction rates throughout the infectivity period is rarely considered and only their sum Ro is calculated to quantify the level of virulence of an infectious agent; (2) MethodsWe give the equation of the discrete dynamics of epidemic growth and we obtain an estimation of the daily reproduction rates, by using a technique of deconvolution of the series of observed new cases of Covid-19; (3) ResultsWe give both simulation results as well as estimations for several countries for the Covid-19 outbreak; (4) ConclusionsWe discuss the role of the noise on the precision of the estimation and we open on perspectives of forecasting methods to predict the distribution of daily reproduction rates along the infectivity period.

SI epidemic model applied to COVID-19 data in mainland China

The article is devoted to the parameters identification in the SI model. We consider several methods, starting with an exponential fit of the early cumulative data of Sars-CoV2 in mainland China. The present methodology provides a way to compute the parameters at the early stage of the epidemic. Next, we establish an identifiability result. Then we use the Bernoulli-Verhulst model as a phenomenological model to fit the data and derive some results on the parameters identification. The last part of the paper is devoted to some numerical algorithms to fit a daily piecewise constant rate of transmission.

Factors associated with the spatial heterogeneity of COVID-19 in France: a nationwide ecological study

Like in many countries and regions, spread of the COVID 19 pandemic has exhibited important spatial heterogeneity across France, one of the most affected countries so far. To better understand factors associated with incidence, mortality and lethality heterogeneity across the 96 administrative departments of metropolitan France, we thus conducted a geoepidemiological analysis based on publicly available data, using hierarchical ascendant classification (HAC) on principal component analysis (PCA) of multidimensional variables, and multivariate analyses with generalized additive models (GAM). Our results confirm a marked spatial heterogeneity of in-hospital COVID 19 incidence and mortality, following the North East / South West diffusion of the epidemic. The delay elapsed between the first COVID-19 associated death and the onset of the national lockdown on March 17th, 2020, appeared positively associated with in-hospital incidence, mortality and lethality. Mortality was also strongly associated with incidence. Mortality and lethality rates were significantly higher in departments with older population, but they were not significantly associated with the number of intensive-care beds available in 2018. We did not find any significant association between incidence, mortality or lethality rates and incidence of new chloroquine and hydroxychloroquine dispensations in pharmacies either, nor between COVID 19 incidence and climate, nor between economic indicators and in-hospital COVID 19 incidence or mortality. This ecological study highlights the impact of population age structure, epidemic spread and transmission mitigation policies in COVID-19 morbidity or mortality heterogeneity.

Summer COVID-19 third wave: faster high altitude spread suggests high UV adaptation

We present spread parameters for first and second waves of the COVID-19 pandemy for USA states, and third wave for 32 regions (19 countries and 13 states of the USA) detected beginning of August 2020. USA first/second wave spreads increase/decrease with population density, are uncorrelated with temperature and median population age. Pooling all 32 regions, third wave spread is slower than for first wave, similar to second wave, and increases with mean altitude (second wave slopes decrease above 900m). Apparently, viruses adapted in spring (second wave) to high temperatures and infecting the young, and in summer (third) waves for spread at altitudes above 1000m. Third wave slopes are not correlated to temperature, so patterns with elevation presumably indicate resistance to relatively high UV regimes. Environmental trends of the COVID-19 pandemy change at incredible rates, making predictions based on classical epidemiological knowledge particularly uncertain.

Inverted covariate effects for mutated 2nd vs 1st waveCovid-19: high temperature spread biased for young

(1) BackgroundHere, we characterize COVID-19 2nd waves, following a study presenting negative associations between 1st wave COVID-19 spread parameters and temperature; (2) MethodsVisual examinations of daily increase in confirmed COVID-19 cases in 124 countries, determined 1st and 2ndwaves in 28 countries; (3) Results1st wave spread rate increases with country mean elevation, temperature, time since wave onset, and median age. Spread rates decrease above 1000m, indicating high UV decrease spread rate. For 2nd waves, associations are opposite: viruses adapted to high temperature and to infect young populations. Earliest 2nd waves started April 5-7 at mutagenic high elevations (Armenia, Algeria). 2nd waves occurred also at warm-to-cold season transition (Argentina, Chile). Spread decreases in most (77%) countries. Death-to-total case ratios decrease during the 2ndwave, also when comparing with the same period for countries where the 1st wave is ongoing. In countries with late 1st wave onset, spread rates fit better 2nd than 1st wave-temperature patterns; In countries with ageing populations (examples: Japan, Sweden, Ukraine), 2nd waves only adapted to spread at higher temperatures, not to infect children. (4) Conclusions1st wave viruses evolved towards lower spread and mortality. 2nd wave mutant COVID-19 strain(s) adapted to higher temperature, infecting children and replace (also in cold conditions) 1st wave COVID-19 strains. Counterintuitively, low spread strains replace high spread strains, rendering prognostics and extrapolations uncertain.