Peak Date Research Articles

The Evolution Characteristics of Daily-Scale Silk Road Pattern and Its Relationship with Summer Temperature in the Yangtze River Valley

By employing multi-reanalysis daily datasets and station data, this study focuses on the evolution characteristics of the daily-scale Silk Road pattern (SRP) and its effect on summer temperatures in the Yangtze River Valley (YRV). The results manifest that the evolution characteristics of positive- and negative-phase SRP (referred to SRP+ and SRP−) exhibit marked distinctions. The anomaly centers of SRP+ over West Central Asia (WCA) and Mongolia emerge firstly, vanishing simultaneously one week after peak date; however, the Far East (FE) anomaly centers can persist for a longer period. The SRP− starts with the WCA and FE centers, with a rapid decline in the strength of the WCA center and preservation of other anomaly centers after its peak. In the vertical direction, daily-scale SRP mainly concentrates in the mid-to-upper troposphere. Baroclinicity accounts for its early development and barotropic instability process favors the maintenance. Moreover, the SRP+ (SRP−) is inextricably linked to heat wave (cool summer) processes in the YRV. Concretely, before the onset of SRP+ events, an anomalous anticyclone and significant negative vorticities over East Asia related to SRP+ favor the zonal advance between the South Asia high (SAH) and western Pacific subtropical high (WPSH), inducing local descents over YRV area. The sinking adiabatic warming and clear-sky radiation warming can be considered as the possible causes for the YRV heat waves. The adiabatic cooling with the local ascents leads to more total cloud cover (positive precipitation anomalies) and less solar radiation incident to surface of the YRV, inducing the cool summer process during SRP−.

The effects of continentality, marine nature and the recirculation of air masses on pollen concentration: Olea in a Mediterranean coastal enclave

Olea pollen concentrations have been studied in relation to the typology of air masses, pollen grain sources and marine nature during advections in a coastal enclave in the south-eastern Iberian Peninsula. Since Spain is the world's leading olive producer, and olive growing extends throughout the Mediterranean basin, this location is ideal for the study of long-distance transport events (LTD) during the main pollen season (MPS). The air masses were classified using the calculation of 48-h back trajectories at 250, 500 and 750 m above ground level using the HYSPLIT model. After that, the frequency of LDT events from Africa and Europe was found to be 8.7% of the MPS days. In contrast, regional air masses were found in 38.6% of the MPS days. This was reflected in pollen concentrations, with significantly higher concentrations (p-value <0.05) on days with regional air masses compared to days with European air masses. Regarding the source areas, the importance of nearby sources with intense olive cultivation was confirmed (i.e., Andalusia). This proximity was relevant beyond the attenuations observed when the advections acquired a marine nature as the air mass back trajectories moved over the sea (p-value <0.001). The review of air mass typologies, source areas and pollen concentrations resulted in establishing peak dates and the detection of LDT associated with these peak dates. Distortions in the typical path of each air mass explained alterations in pollen concentrations on consecutive days. The recirculation and loops of the air mass back trajectories varied the pollen load that every type of air mass could originally contain.

Modeling the underestimation of COVID-19 infection

Estimation of the undocumented cases of COVID-19 is critical for understanding the epidemic potential of the disease and informing pandemic response. The COVID-19 pandemic originated from a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a virus similar to severe acute respiratory syndrome (SARS) that was formerly identified in 2003. The contagiousness, dynamics of the pathogen, and mobility of the general population incurred the occurrence of underestimation of infection (i.e., the unidentified cases and the gap with the identified cases) that was potentially substantial in magnitude, which was supposed to connect with subsequent cyclical outbreaks in practice. We employed a Susceptible-Infected-Removed-Contained (SIR-C) mathematical model to infer critical epidemiological characteristics associated with COVID-19, then asymptotically simulated the peak sizes and peak dates of the identified and unidentified cases, the underestimation, and the dynamics of the gap. The simulation outcomes indicated that unidentified peak dates in practice could predate the reported peak dates for a variable period of weeks or months. In comparison, the saturation sizes of infection remained at commensurate levels. The curve of the initial exponential-like outbreak for the undocumented cases would flatten when the gap between concurrent identified cases and unidentified cases decreased. The rate of non-pharmaceutical containment could impact the trend of disease transmission ceteris paribus, and the greater the rate the larger reduction of infections. When the rate reached a certain level of threshold, the undocumented curve would shift from flattening effect to decaying effect. A similar trend was observed when it applied to the rate of pharmaceutical containment measures ceteris paribus. The results were sensitive to the duration of infection (DOI), it manifested that greater values of DOI were associated with greater peak sizes and greater peak dates for both documented and undocumented cases. Conditional on assumptions, calibration of DOI from 8 days to 18 days would increase the unidentified peak size by nearly 56% and the peak date by almost 18 days.

Wind Variations in the Mesosphere and Lower Thermosphere Near 60°S Latitude During the 2019 Antarctic Sudden Stratospheric Warming

AbstractSudden stratospheric warmings (SSWs) could act as an important mediator in the vertical coupling of atmospheric regions and dramatic variations in the mesosphere and lower thermosphere (MLT) in response to SSWs have been documented. However, due to rare occurrences, SSWs in the Southern Hemisphere (SH) and their impacts on the MLT dynamics are not well understood. This study presents an analysis of MLT winds at ∼80–98 km altitudes measured by meteor radars located at Tierra del Fuego (53.7°S, 67.7°W), King Edward Point (54.3°S, 36.5°W) and King Sejong Station (62.2°S, 58.8°W) near 60°S latitude during the Antarctic winter. Eastward zonal winds from these stations are observed to decrease significantly near the peak date of the 2019 Antarctic SSW, and both zonal and meridional winds in 2019 exhibit considerable differences to the mean winds averaged over other non‐SSW years. A quasi 6‐day oscillation is observed at all three radar locations, being consistent with the presence of the westward propagating zonal wave‐1 planetary wave. The vertical wavelength of this wave is estimated to be ∼55 km, and the enhancement of the wave amplitude during this SSW is noticeable. Evidence of the interaction between the 6‐day wave and the semidiurnal diurnal tide is provided, which suggests a possible mechanism for SSWs to impact the upper atmosphere. This study reports the large‐scale variations in winds in the MLT region at SH midlatitudes to high latitudes in a key dynamic but largely unexplored latitudinal band in response to the 2019 Antarctic SSW.

Studying the social determinants of COVID-19 in a data vacuum.

Race‐based and other demographic information on COVID‐19 patients is not being collected consistently across provinces in Canada. Therefore, whether the burden of COVID‐19 is falling disproportionately on the shoulders of particular demographic groups is relatively unknown. In this article, we first provide an overview of the available geographic and demographic data related to COVID‐19. We then make creative use of these existing data to fill the vacuum and identify key demographic risk factors for COVID‐19 across Canada's health regions. Drawing on COVID‐19 counts and tabular census data, we examine the association between communities’ demographic composition and the number of COVID‐19 infections. COVID‐19 infections are higher in communities with larger shares of Black and low‐income residents. Our approach offers a way for researchers and policymakers to use existing data to identify communities nationwide that are vulnerable to the pandemic in the absence of more detailed demographic and more granular geographic data.

A systematic review of the effects of temperature and precipitation on pollen concentrations and season timing, and implications for human health.

Climate and weather directly impact plant phenology, affecting airborne pollen. The objective of this systematic review is to examine the impacts of meteorological variables on airborne pollen concentrations and pollen season timing. Using PRISMA methodology, we reviewed literature that assessed whether there was a relationship between local temperature and precipitation and measured airborne pollen. The search strategy included terms related to pollen, trends or measurements, and season timing. For inclusion, studies must have conducted a correlation analysis of at least 5 years of airborne pollen data to local meteorological data and report quantitative results. Data from peer-reviewed articles were extracted on the correlations between seven pollen indicators (main pollen season start date, end date, peak date, and length, annual pollen integral, average daily pollen concentration, and peak pollen concentration), and two meteorological variables (temperature and precipitation). Ninety-three articles were included in the analysis out of 9,679 articles screened. Overall, warmer temperatures correlated with earlier and longer pollen seasons and higher pollen concentrations. Precipitation had varying effects on pollen concentration and pollen season timing indicators. Increased precipitation may have a short-term effect causing low pollen concentrations potentially due to "wash out" effect. Long-term effects of precipitation varied for trees and weeds and had a positive correlation with grass pollen levels. With increases in temperature due to climate change, pollen seasons for some taxa in some regions may start earlier, last longer, and be more intense, which may be associated with adverse health impacts, as pollen exposure has well-known health effects in sensitized individuals.

Natural versus induced estrus indicators of Holstein heifers determined by an automated activity monitoring system

The objective of this study was to determine the association between estrus induction and estrus indicators, as well as the association between estrus indicators and subsequent fertility. A total of 609 Holstein heifers were fitted with an ear tag automated activity monitor (AAM; SCR by Allflex) and each estrus event was categorized as natural or induced. An induced estrus event was defined as an estrus occurring within 2 to 5 d of PRID removal and/or prostaglandin administration. Most heifers received sex-sorted semen for the first AI and conventional semen for subsequent AIs, with 6 opportunities to become pregnant. Pregnancy was diagnosed by ultrasound at 30 d and confirmed at 45 d post-AI. The AAM recorded the date and time of onset, peak and end of high activity, heat index (a measure of estrus strength), maximum activity change, maximum rumination change, and duration of high activity. Data from 1,269 estrus events were included in the analyses, 881 from natural and 388 from induced estrus events. In total, 70% of heifers started high activity between 20:00 and 08:00 and 73% of heifers hit peak activity between 02:00 to 14:00. Heat index (82 vs. 80, p = 0.09) and rumination change (-49 vs. -46 min/d, p = 0.06) tended to be greater for induced vs. natural estrus. There was no difference in pregnancy at 30 d (68 vs. 67%, p = 0.79) or 45 d (64 vs. 63%, p = 0.82) between the induced and natural estrus groups. However, for pregnancy at 30 d post-AI using conventional semen, natural estrus events tended to have greater pregnancy per AI (P/AI) than induced estrus events (72 vs. 63%; p = 0.09). For pregnancy at 45 d post-AI, induced estrus with sex-sorted semen tended to have greater pregnancy per AI compared to induced estrus with conventional semen (68 vs. 58%; p = 0.09). There was no difference in pregnancy loss between estrus types (p = 0.38). When comparing estrus events that resulted in pregnant or non-pregnant diagnoses at 30 d post-AI, the only difference in estrus indicators was for estrus events resulting in pregnancy to have a greater rumination change (-50 vs. -46 min/d; p = 0.04). Overall, the induction of estrus had no negative effects on estrus indicators, as measured by an AAM, or on fertility compared with natural estrus in dairy heifers.

Effectiveness of potential antiviral treatments in COVID-19 transmission control: a modelling study

BackgroundNovel coronavirus disease 2019 (COVID-19) causes an immense disease burden. Although public health countermeasures effectively controlled the epidemic in China, non-pharmaceutical interventions can neither be maintained indefinitely nor conveniently implemented globally. Vaccination is mainly used to prevent COVID-19, and most current antiviral treatment evaluations focus on clinical efficacy. Therefore, we conducted population-based simulations to assess antiviral treatment effectiveness among different age groups based on its clinical efficacy.MethodsWe collected COVID-19 data of Wuhan City from published literature and established a database (from 2 December 2019 to 16 March 2020). We developed an age-specific model to evaluate the effectiveness of antiviral treatment in patients with COVID-19. Efficacy was divided into three types: (1) viral activity reduction, reflected as transmission rate decrease [reduction was set as v (0–0.8) to simulate hypothetical antiviral treatments]; (2) reduction in the duration time from symptom onset to patient recovery/removal, reflected as a 1/γ decrease (reduction was set as 1–3 days to simulate hypothetical or real-life antiviral treatments, and the time of asymptomatic was reduced by the same proportion); (3) fatality rate reduction in severely ill patients (fc) [reduction (z) was set as 0.3 to simulate real-life antiviral treatments]. The population was divided into four age groups (groups 1, 2, 3 and 4), which included those aged ≤ 14; 15–44; 45–64; and ≥ 65 years, respectively. Evaluation indices were based on outbreak duration, cumulative number of cases, total attack rate (TAR), peak date, number of peak cases, and case fatality rate (f).ResultsComparing the simulation results of combination and single medication therapy s, all four age groups showed better results with combination medication. When 1/γ = 2 and v = 0.4, age group 2 had the highest TAR reduction rate (98.48%, 56.01–0.85%). When 1/γ = 2, z = 0.3, and v = 0.1, age group 1 had the highest reduction rate of f (83.08%, 0.71–0.12%).ConclusionsAntiviral treatments are more effective in COVID-19 transmission control than in mortality reduction. Overall, antiviral treatments were more effective in younger age groups, while older age groups showed higher COVID-19 prevalence and mortality. Therefore, physicians should pay more attention to prevention of viral spread and patients deaths when providing antiviral treatments to patients of older age groups.

Statistical modeling of the novel COVID-19 epidemic in Iraq

Abstract Objectives This study aimed to apply three of the most important nonlinear growth models (Gompertz, Richards, and Weibull) to study the daily cumulative number of COVID-19 cases in Iraq during the period from 13th of March, 2020 to 22nd of July, 2020. Methods Using the nonlinear least squares method, the three growth models were estimated in addition to calculating some related measures in this study using the “nonlinear regression” tool available in Minitab-17, and the initial values of the parameters were deduced from the transformation to the simple linear regression equation. Comparison of these models was made using some statistics (F-test, AIC, BIC, AICc and WIC). Results The results indicate that the Weibull model is the best adequate model for studying the cumulative daily number of COVID-19 cases in Iraq according to some criteria such as having the highest F and lowest values for RMSE, bias, MAE, AIC, BIC, AICc and WIC with no any violations of the assumptions for the model’s residuals (independent, normal distribution and homogeneity variance). The overall model test and tests of the estimated parameters showed that the Weibull model was statistically significant for describing the study data. Conclusions From the Weibull model predictions, the number of cumulative confirmed cases of novel coronavirus in Iraq will increase by a range of 101,396 (95% PI: 99,989 to 102,923) to 114,907 (95% PI: 112,251 to 117,566) in the next 24 days (23rd of July to 15th of August 15, 2020). From the inflection points in the Weibull curve, the peak date when the growth rate will be maximum, is 7th of July, 2020, and at this time the daily cumulative cases become 67,338. Using the nonlinear least squares method, the models were estimated and some related measures were calculated in this study using the “nonlinear regression” tool available in Minitab-17, and the initial values of the parameters were obtained from the transformation to the simple linear regression model.

Pre-reproductive movements of potamodromous cyprinids in the Iberian Peninsula: when environmental variability meets semipermeable barriers

This study aims to describe pre-reproductive movements of Luciobarbus bocagei and Pseudochondrostoma duriense in a regulated canyon-stretch of the Tormes River (Spain), with high environmental variability, semipermeable barriers, and fishway retrofitting actions. The main objectives were to identify peak migration dates and environmental drivers, test ensemble-learning techniques to model fish migration and propose adaptive management measures. To achieve this, fish movements were 5-year monitored in a stepped fishway and Survival Analysis and Random Forest techniques were used for data analysis and modelling. Results showed that migration occurred in May–July, a wider period than the one previously reported in the literature. Movements were triggered by the increase in water temperature and photoperiod, and were strongly affected by the hydraulic river scenario (water levels and discharge) at the semipermeable barriers. Random Forest was able to include the effect of each barrier and predict accurately timing and number of migrants, classifying and ranking the importance of variables. Moreover, developed models allowed to assess fishway retrofitting actions and to predict positive effects in fish number under new, scheduled and variable environmental flow scenarios. Long-term monitoring together with ensemble-learning methods can allow the definition of cost-effective adaptive management strategies to ensure endemic fish conservation.

Integrating Causal and Evolutionary Analysis of Life-History Evolution: Arrival Date in a Long-Distant Migrant

In migratory species, the timing of arrival at the breeding grounds is a life-history trait with major fitness consequences. The optimal arrival date varies from year-to-year, and animals use cues to adjust their arrival dates to match this annual variation. However, which cues they use to time their arrival and whether these cues actually predict the annual optimal arrival date is largely unknown. Here, we integrate causal and evolutionary analysis by identifying the environmental variables used by a migratory songbird to time its arrival dates and testing whether these environmental variables also predicted the optimal time to arrive. We used 11 years of male arrival data of a pied flycatcher population. Specifically, we tested whether temperature and normalized difference vegetation index (NDVI) values from their breeding grounds in the Netherlands and from their wintering grounds in Ivory Coast explained the variation in arrival date, and whether these variables correlated with the position of the annual fitness peak at the breeding grounds. We found that temperature and NDVI, both from the wintering and the breeding grounds, explained the annual variation in arrival date, but did not correlate with the optimal arrival date. We explore three alternative explanations for this lack of correlation. Firstly, the date of the fitness peak may have been incorrectly estimated because a potentially important component of fitness (i.e., migration date dependent mortality en route or directly upon arrival) could not be measured. Secondly, we focused on male timing but the fitness landscape is also likely to be shaped by female timing. Finally, the correlation has recently disappeared because climate change disrupted the predictive value of the cues that the birds use to time their migration. In the latter case, birds may adapt by altering their sensitivity to temperature and NDVI.

A Bayesian Mixture Model for Predicting the COVID-19 Related Mortality in the United States.

ABSTRACTAn outbreak of SARS-CoV-2 has led to a global pandemic affecting virtually every country. As of August 31, 2020, globally, there have been approximately 25,500,000 confirmed cases and 850,000 deaths; in the United States (50 states plus District of Columbia), there have been more than 6,000,000 confirmed cases and 183,000 deaths. We propose a Bayesian mixture model to predict and monitor COVID-19 mortality across the United States. The model captures skewed unimodal (prolonged recovery) or multimodal (multiple surges) curves. The results show that across all states, the first peak dates of mortality varied between April 4, 2020 for Alaska and June 18, 2020 for Arkansas. As of August 31, 2020, 31 states had a clear bimodal curve showing a strong second surge. The peak date for a second surge ranged from July 1, 2020 for Virginia to September 12, 2020 for Hawaii. The first peak for the United States occurred about April 16, 2020—dominated by New York and New Jersey—and a second peak on August 6, 2020—dominated by California, Texas, and Florida. Reliable models for predicting the COVID-19 pandemic are essential to informing resource allocation and intervention strategies. A Bayesian mixture model was able to more accurately predict the shape of the mortality curves across the United States than other models, including the timing of multiple peaks. However, given the dynamic nature of the pandemic, it is important that the results be updated regularly to identify and better monitor future waves, and characterize the epidemiology of the pandemic.

Zooplankton variability in the Strait of Georgia, Canada, and relationships with the marine survivals of Chinook and Coho salmon.

The Strait of Georgia, Canada, has complex interactions among natural and human pressures that confound understanding of changes in this system. We report on the interannual variability in biomass of 12 zooplankton taxonomic groups in the deep (bottom depths greater than 50 m) central and northern Strait of Georgia from 1996 to 2018, and their relationships with 10 physical variables. Total zooplankton biomass was dominated (76%) by large-sized crustaceans (euphausiids, large and medium size calanoid copepods, amphipods). The annual anomaly of total zooplankton biomass was highest in the late 1990s, lowest in the mid-2000s, and generally above its climatological (1996-2010) average after 2011, although many individual groups had different patterns. Two latent trends (derived from dynamic factor analyses) described the variability of annual biomass anomalies underlying all zooplankton groups: a U-shaped trend with its minimum in the mid-2000s, and a declining trend from 2001 to 2011. Two latent trends also described the physical variables. The variability represented by these four latent trends clustered into two periods: 1996-2006, with generally declining zooplankton biomass and increasing salinities, and 2007-2018, with increasing zooplankton biomass and decreasing salinities. ARIMA modelling showed sea surface salinity at Entrance Island in the middle Strait of Georgia, the Pacific Decadal Oscillation, and the peak date of the spring phytoplankton bloom were significantly related to the two latent zooplankton trends. ARIMA models comparing zooplankton and physical variables with the marine survivals of four salmon populations which enter the Strait as juveniles (Chinook: Cowichan River, Puntledge River, Harrison River; Coho: Big Qualicum River) all included zooplankton groups consistent with known salmon prey; prominent among the physical variables were sea surface salinity and variables representing the flow from the Fraser River. These regressions explained (adjR2) 38 to 85% of the annual variability in marine survival rates of these salmon populations over the study time period. Although sea temperature was important in some relationships between zooplankton biomass and salmon marine survival, salinity was a more frequent and more important variable, consistent with its influence on the hydrodynamics of the Strait of Georgia system.

Predicting the spread of COVID-19 in Italy using machine learning: Do socio-economic factors matter?

We exploit the provincial variability of COVID-19 cases registered in Italy to select the territorial predictors of the pandemic. Absent an established theoretical diffusion model, we apply machine learning to isolate, among 77 potential predictors, those that minimize the out-of-sample prediction error. We first estimate the model considering cumulative cases registered before the containment measures displayed their effects (i.e. at the peak of the epidemic in March 2020), then cases registered between the peak date and when containment measures were relaxed in early June. In the first estimate, the results highlight the dominance of factors related to the intensity and interactions of economic activities. In the second, the relevance of these variables is highly reduced, suggesting mitigation of the pandemic following the lockdown of the economy. Finally, by considering cases at onset of the “second wave”, we confirm that the territorial distribution of the epidemic is associated with economic factors.

Rainfall decrease and red deer rutting behaviour: Weaker and delayed rutting activity though higher opportunity for sexual selection.

In the last decades, climate change has caused an increase in mean temperatures and a reduction in average rainfall in southern Europe, which is expected to reduce resource availability for herbivores. Resource availability can influence animals' physical condition and population growth. However, much less is known on its effects on reproductive performance and sexual selection. In this study, we assessed the impact of three environmental factors related to climate change (rainfall, temperature and vegetation index) on Iberian red deer Cervus elaphus hispanicus reproductive timing and sexual behaviour, and their effects on the opportunity for sexual selection in the population. We measured rutting phenology as rut peak date, the intensity of male rutting activity as roaring rate, and the opportunity for sexual selection from the distribution of females among harem holding males in Doñana Biological Reserve (Southwest Spain), from data of daily observations collected during the rut over a period of 25 years. For this study period, we found a trend for less raining and hence poorer environmental conditions, which associated with delayed rutting season and decreased rutting intensity, but that appeared to favour a higher degree of polygyny and opportunity for sexual selection, all these relationships being modulated by population density and sex ratio. This study highlights how climate change (mainly rainfall reduction in this area) can alter the conditions for mating and the opportunity for sexual selection in a large terrestrial mammal.

COVID-19 within a large UK prison with a high number of vulnerable adults, march to june 2020: An outbreak investigation and screening event

ObjectiveTo describe the public health response to COVID-19 within a large prison with a high number of clinically vulnerable residents. DesignAn outbreak investigation was undertaken among all residents and staff. A screening event involved nose and throat swab samples from residents and staff, examined by polymerase chain reaction (PCR). An electronic questionnaire regarding risk factors was distributed to staff. Results58 residents out of 1,156 (5.0%, 95% CI: 3.8 – 6.3) and 129 staff out of 510 (25.3%, 95% CI: 21.5 – 29.1) displayed COVID-19 symptoms, including six and eight confirmed, respectively. Residents reported cough symptoms with no fever (29.3%), followed by a cough and fever (15.5%). 62.1% of symptomatic residents were 50 years or older, placing them in the group at risk for severe COVID-19 disease. Wing I had the highest attack rate (12.5%). 1,063 individuals were swabbed during the 5-day screening event, and all had negative swab results. ConclusionThe findings were consistent with the hypothesis of a propagating outbreak with decreasing incidence since the peak date of onset. COVID-19 transmission within a high-risk setting was quickly contained, and an explosive outbreak was prevented through a multi-agency public health response.

Распространение уханьского коронавируса (SARS-CoV-2) в России: макроэкономическая производственная функция с учетом мировой цены на нефть марки «Брент»

The new Wuhan coronavirus, named by virologists SARS-CoV-2, has become widespread all over the world since spring 2020 and has led to significant human and economic losses. In this regard, predicting the spread of the Wuhan coronavirus by studying the laws of its dynamics is an urgent social and macroeconomic problem. We checked the accuracy of the econometric forecasts performance for the autumn-spring phase of the Wuhan coronavirus spread in Russia, which we made earlier on the basis of the Gaussian quadratic exponent [3, 4]. Average forecast errors for October 15, 2021 – March 20, 2021 ranged from 10% to 16%. The Gaussian quadratic exponents studied by us for 5 months indicate the landmarks (laws) of the dynamics of the Wuhan coronavirus spread in Russia in the form of forecast corridors with average errors of 10-16%. Moreover, one of the studied functions accurately predicted the peak daily population (30.2 thousand people) on November 30, i.e. 24 days before reaching the actual peak on December 24 (29.9 thousand people).) And another function predicted the peak date for the day (December 23) before the actual peak date (December 24), although its projected peak daily population (48 thousand people) was 18 thousand people higher than the actual one (30 thousand people). We also offered an analytical modification of the macroeconomic production function of Russia in regard to the Brent crude oil price by considering the average annual level of use of fixed assets as a piecewise linear function of the number of people hospitalized with symptoms of severe acute respiratory syndrome (SARS). This number is an increasing function of the daily number of Russian citizens infected with the Wuhan coronavirus. In addition, we conducted an econometric study of the macroeconomic production function of Russia in regard to the Brent crude oil price for the pre-coronavirus years (1990-2019). The results of the study showed that the coefficients of the production function for 2018 and 2019 are almost unchanged compared to 2016 and 2017. This indicates a certain stabilization of the process of expanded reproduction of the Russian national economy in the pre-coronavirus period of 2016-2019. That happened after a period of economic recession in 2015-2016, accompanied by a decrease in the coefficient of neutral technological progress and an increase in the GDP elasticity to fixed assets, along with a fairly stable dependence on the world oil price. The investigated production function has a good predictive power: the values of the arithmetic mean error of ex-post forecast range from 1 to 7%, and the mean error for 19 years ahead is 4.5%. Dmitri Medvedev noted in his article [6, p. 22]: «This coronavirus pandemic is a unique event as it directly affects all aspects of our lives. In this difficult period, transparency is the main prerequisite for survival. It is important to share scientific and practical information, skills and various technological solutions». We hope that the results of our research will contribute to the study, analysis and understanding of a new research object – the Wuhan coronavirus – and may be used by public authorities, medical and economic research institutes to predict human and economic losses due to the Corona-virus and to develop effective measures to minimize them.

Muslim-majority Countries Have Fewer COVID-19 Cases and Deaths: A Cross-country Analysis of 165 Countries During the 3 Global Peak Dates in 2020-2021

Objective To determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for anydisparities. Methods A cross-country panel analysis of the total number of new COVID-19 cases per million for 165 countries was conducted from May 1, 2020 to July 1, 2021. Regression models of the total number of COVID-19 cases and deaths per million were created for the 3 global peak dates of July 31, 2020 and January 7 and April 29, 2021. Results The number of daily new COVID-19 cases per million was signficantly less in Muslim-majority countries (N = 49) than non-Muslim countries (N=116), SD 1.57E-1, p-value &lt;0.001 from May 1, 2020 to July 1, 2021. Total number of cases per million of Muslim-majority countries was significantly less on July 31st: 0.089, p-value &lt;0.001; January 7th: SD 0.012, p-value 0.04; April 29th: SD 0.009, p-value &lt;0.01. Total number of deaths per million of Muslim-majority countries was also significantly less on July 31st: 0.510, p-value 0.009; January 7th: SD 0.090, p-value &lt;0.001; April 29th: SD 0.065, p-value 0.03. Discussion The data suggests a relationship between Islamic practices and the incidence of COVID-19 and COVID-19 related deaths. This study explored how that Muslims’ practice of tahara (purity or cleanliness) is similar to many COVID-19 containment measures and tawakkul (trust in Allah) helps them remain resilient and hopeful during difficult unpredictable times, such as living through a pandemic. Conclusion It is hoped that this paper brings awareness to the positive practices of the Islamic faith as it relates to COVID-19, and to population and individual health. Research should be conducted with Muslims in Muslim-majority and non-Muslim countries to further study the relationship between Islam and health.

Short-term forecast in the early stage of the COVID-19 outbreak in Italy. Application of a weighted and cumulative average daily growth rate to an exponential decay model.

To estimate the size of the novel coronavirus (COVID-19) outbreak in the early stage in Italy, this paper introduces the cumulated and weighted average daily growth rate (WR) to evaluate an epidemic curve. On the basis of an exponential decay model (EDM), we provide estimations of the WR in four-time intervals from February 27 to April 07, 2020. By calibrating the parameters of the EDM to the reported data in Hubei Province of China, we also attempt to forecast the evolution of the outbreak. We compare the EDM applied to WR and the Gompertz model, which is based on exponential decay and is often used to estimate cumulative events. Specifically, we assess the performance of each model to short-term forecast of the epidemic, and to predict the final epidemic size.Based on the official counts for confirmed cases, the model applied to data from February 27 until the 17th of March estimate that the cumulative number of infected in Italy could reach 131,280 (with a credibility interval 71,415-263,501) by April 25 (credibility interval April 12 to May 3). With the data available until the 24st of March the peak date should be reached on May 3 (April 23 to May 23) with 197,179 cumulative infections expected (130,033–315,269); with data available until the 31st of March the peak should be reached on May 4 (April 25 to May 18) with 202,210 cumulative infections expected (155.235–270,737); with data available until the 07st of April the peak should be reached on May 3 (April 26 to May 11) with 191,586 (160,861-232,023) cumulative infections expected. Based on the average mean absolute percentage error (MAPE), cumulated infections forecasts provided by the EDM applied to WR performed better across all scenarios than the Gompertz model.An exponential decay model applied to the cumulated and weighted average daily growth rate appears to be useful in estimating the number of cases and peak of the COVID-19 outbreak in Italy and the model was more reliable in the exponential growth phase.

THE SCALE OF THE SPREAD OF COVID-19 IN GEORGIA AND EFFECTIVENESS OF PREVENTIVE MEASURES IMPLIMENTED BY THE GOVERNMENT – WHAT DO MODELS SAY?

. In this article we present forecasts of the spread of COVID-19 virus, obtained by econometric and machine learning methods. Furthermore, by employing modelling method, we estimate effectiveness of preventive measures implemented by the government. Each of the models discussed in this article is modelling different characteristics of the COVID-19 epidemic’s trajectory: peak and end date, number of daily infections over different forecasting horizons, total number of infection cases. All these provide quite clear picture to the interested reader of the future threats posed by COVID-19. In terms of existing models and data, our research indicates that phenomenological models do well in forecasting the trend, duration and total infections of the COVID- 19 epidemic, but make serious mistakes in forecasting the number of daily infections. Machine learning models, deliver more accurate short –term forecast of daily infections, but due to data limitations, they struggle to make long-term forecasts. Compartmental models are the best choice for modelling the measures implemented by the government for preventing the spread of COVID-19 and determining optimal level of restrictions. These models show that until achieving herd immunity (i.e. without any epidemiological or government implemented measures), approximate number of people infected with COVID-19 would be 3 million, but due to preventive measures, expected total number of infections has reduced to several thousand (1555-3189) people. This unequivocally indicates the effectiveness of the preventive measures.