Weather variability and transmissibility of COVID-19: a time series analysis based on effective reproductive number.

COVID-19 is causing a significant burden on medical and healthcare resources globally due to high numbers of hospitalisations and deaths recorded as the pandemic continues. This research aims to assess the effects of climate factors (i.e., daily average temperature and average relative humidity) on effective reproductive number of COVID-19 outbreak in Wuhan, China during the early stage of the outbreak. Our research showed that effective reproductive number of COVID-19 will increase by 7.6% (95% Confidence Interval: 5.4% ~ 9.8%) per 1°C drop in mean temperature at prior moving average of 0-8 days lag in Wuhan, China. Our results indicate temperature was negatively associated with COVID-19 transmissibility during early stages of the outbreak in Wuhan, suggesting temperature is likely to effect COVID-19 transmission. These results suggest increased precautions should be taken in the colder seasons to reduce COVID-19 transmission in the future, based on past success in controlling the pandemic in Wuhan, China.

How the effective reproductive number impacts global stock markets

The pandemic caused by the novel coronavirus COVID-19 has impact the economies of countries across the world. In a short period of time, researchers have begun to analyse the effect of the pandemic on global stock markets. Although the most known measurements of COVID-19 are the number of new cases and deaths, there are more robust indicators. In particular, the effective reproductive number is one of the most important indicators to analyse the pandemic which indicates the degree to which the spread is under control. In this paper, we assess the impact that the Effective Reproductive Number (Rt) has on 26 countries around the world (32 stock market indexes) comparing the performance of various forms of Generalized AutoRegressive Conditional Heteroskedasticity models. The results demonstrate that of the 32 stock markets analysed, 37.5% had a negative effect with respect to Rt and only in 12.5% of the cases was the effect of the variation of Rt positive. This implies that in more than a third of the stock markets analysed as the pandemic progressed uncontrolled the result was a decrease in the value of the market index. The 11 of the 26 countries analysed had a negative and significant effect (Brazil, Germany, Indonesia, Israel, Italy, Japan, Russia, South Korea, Sweden, Taiwan, and United States). Findings suggest that the Effective Reproductive Number volatility had a significant impact on 10 of the 26 countries analysed (38.5%) (Australia, Brazil, Canada, China, India, Italy, Mexico, Russia, Singapore and United Kingdom). © 2023 John Wiley & Sons Ltd.

How the effective reproductive number impacts global stock markets

The pandemic caused by the novel coronavirus COVID-19 has impact the economies of countries across the world. In a short period of time, researchers have begun to analyse the effect of the pandemic on global stock markets. Although the most known measurements of COVID-19 are the number of new cases and deaths, there are more robust indicators. In particular, the effective reproductive number is one of the most important indicators to analyse the pandemic which indicates the degree to which the spread is under control. In this paper, we assess the impact that the Effective Reproductive Number (Rt) has on 26 countries around the world (32 stock market indexes) comparing the performance of various forms of Generalized AutoRegressive Conditional Heteroskedasticity models. The results demonstrate that of the 32 stock markets analysed, 37.5% had a negative effect with respect to Rt and only in 12.5% of the cases was the effect of the variation of Rt positive. This implies that in more than a third of the stock markets analysed as the pandemic progressed uncontrolled the result was a decrease in the value of the market index. The 11 of the 26 countries analysed had a negative and significant effect (Brazil, Germany, Indonesia, Israel, Italy, Japan, Russia, South Korea, Sweden, Taiwan, and United States). Findings suggest that the Effective Reproductive Number volatility had a significant impact on 10 of the 26 countries analysed (38.5%) (Australia, Brazil, Canada, China, India, Italy, Mexico, Russia, Singapore and United Kingdom). © 2023 John Wiley & Sons Ltd.

Impact of COVID-19 on electricity energy consumption: A quantitative analysis on electricity

In addition to the tremendous loss of life due to coronavirus disease 2019 (COVID-19), the pandemic created challenges for the energy system, as strict confinement measures such as lockdown and social distancing compelled by governments worldwide resulted in a significant reduction in energy demand. In this study, a novel, quantitative and uncomplex method for estimating the energy consumption loss due to the pandemic, which was derived from epidemiological data in the beginning stages, is provided;the method bonds a data-driven prediction (LSTM network) of energy consumption due to COVID-19 to an econometric model (ARDL) so that the long- and short-term impact can be synthesized with adequate statistical validation. The results show that energy loss is statistically correlated with the time-changing effective reproductive number (Rt) of the disease, which can be viewed as quantifying confinement intensity and the severity of the earlier stages of the pandemic. We detected a 1.62% decrease in electricity consumption loss caused by each percent decrease in Rt on average. We verify our method by applying it to Germany and 5 U.S. states with various social features and discuss implications and universality. Our results bridge the knowledge gap between key energy and epidemiological parameters and provide policymakers with a more precise estimate of the pandemic’s impact on electricity demand so that strategies can be formulated to minimize losses caused by similar crises.

Regression Models for Understanding COVID-19 Epidemic Dynamics With Incomplete Data.

Modeling infectious disease dynamics has been critical throughout the COVID-19 pandemic. Of particular interest are the incidence, prevalence, and effective reproductive number (Rt). Estimating these quantities is challenging due to under-ascertainment, unreliable reporting, and time lags between infection, onset, and testing. We propose a Multilevel Epidemic Regression Model to Account for Incomplete Data (MERMAID) to jointly estimate Rt, ascertainment rates, incidence, and prevalence over time in one or multiple regions. Specifically, MERMAID allows for a flexible regression model of Rt that can incorporate geographic and time-varying covariates. To account for under-ascertainment, we (a) model the ascertainment probability over time as a function of testing metrics and (b) jointly model data on confirmed infections and population-based serological surveys. To account for delays between infection, onset, and reporting, we model stochastic lag times as missing data, and develop an EM algorithm to estimate the model parameters. We evaluate the performance of MERMAID in simulation studies, and assess its robustness by conducting sensitivity analyses in a range of scenarios of model misspecifications. We apply the proposed method to analyze COVID-19 daily confirmed infection counts, PCR testing data, and serological survey data across the United States. Based on our model, we estimate an overall COVID-19 prevalence of 12.5% (ranging from 2.4% in Maine to 20.2% in New York) and an overall ascertainment rate of 45.5% (ranging from 22.5% in New York to 81.3% in Rhode Island) in the United States from March to December 2020. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Different Transmission Dynamics of Coronavirus Disease 2019 (COVID-19) and Influenza Suggest the Relative Efficiency of Isolation/Quarantine and Social Distancing Against COVID-19 in China.

BACKGROUND: Nonpharmaceutical interventions (NPIs) against coronavirus disease 2019 (COVID-19) are vital to reducing transmission risks. However, the relative efficiency of social distancing against COVID-19 remains controversial, since social distancing and isolation/quarantine were implemented almost at the same time in China. METHODS: In this study, surveillance data of COVID-19 and seasonal influenza in 2018-2020 were used to quantify the relative efficiency of NPIs against COVID-19 in China, since isolation/quarantine was not used for the influenza epidemics. Given that the relative age-dependent susceptibility to influenza and COVID-19 may vary, an age-structured susceptible/infected/recovered model was built to explore the efficiency of social distancing against COVID-19 under different population susceptibility scenarios. RESULTS: The mean effective reproductive number, Rt, of COVID-19 before NPIs was 2.12 (95% confidence interval [CI], 2.02-2.21). By 11 March 2020, the overall reduction in Rt of COVID-19 was 66.1% (95% CI, 60.1-71.2%). In the epidemiological year 2019-20, influenza transmissibility was reduced by 34.6% (95% CI, 31.3-38.2%) compared with transmissibility in epidemiological year 2018-19. Under the observed contact pattern changes in China, social distancing had similar efficiency against COVID-19 in 3 different scenarios. By assuming the same efficiency of social distancing against seasonal influenza and COVID-19 transmission, isolation/quarantine and social distancing could lead to 48.1% (95% CI, 35.4-58.1%) and 34.6% (95% CI, 31.3-38.2%) reductions of the transmissibility of COVID-19, respectively. CONCLUSIONS: Though isolation/quarantine is more effective than social distancing, given that the typical basic reproductive number of COVID-19 is 2-3, isolation/quarantine alone could not contain the COVID-19 pandemic effectively in China.

A simulated measurement for COVID-19 pandemic using the effective reproductive number on an empirical portion of population: epidemiological models.

COVID-19 as a global pandemic has had an unprecedented impact on the entire world. Projecting the future spread of the virus in relation to its characteristics for a specific suite of countries against a temporal trend can provide public health guidance to governments and organizations. Therefore, this paper presented an epidemiological comparison of the traditional SEIR model with an extended and modified version of the same model by splitting the infected compartment into asymptomatic mild and symptomatic severe. We then exposed our derived layered model into two distinct case studies with variations in mitigation strategies and non-pharmaceutical interventions (NPIs) as a matter of benchmarking and comparison. We focused on exploring the United Arab Emirates (a small yet urban centre (where clear sequential stages NPIs were implemented). Further, we concentrated on extending the models by utilizing the effective reproductive number (R t) estimated against time, a more realistic than the static R 0, to assess the potential impact of NPIs within each case study. Compared to the traditional SEIR model, the results supported the modified model as being more sensitive in terms of peaks of simulated cases and flattening determinations.

Estimating the Prevalence of Asymptomatic COVID-19 Cases and Their Contribution in Transmission - Using Henan Province, China, as an Example.

Background: Novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), is now sweeping across the world. A substantial proportion of infections only lead to mild symptoms or are asymptomatic, but the proportion and infectivity of asymptomatic infections remains unknown. In this paper, we proposed a model to estimate the proportion and infectivity of asymptomatic cases, using COVID-19 in Henan Province, China, as an example. Methods: We extended the conventional susceptible-exposed-infectious-recovered model by including asymptomatic, unconfirmed symptomatic, and quarantined cases. Based on this model, we used daily reported COVID-19 cases from January 21 to February 26, 2020, in Henan Province to estimate the proportion and infectivity of asymptomatic cases, as well as the change of effective reproductive number, R t . Results: The proportion of asymptomatic cases among COVID-19 infected individuals was 42% and the infectivity was 10% that of symptomatic ones. The basic reproductive number R 0 = 2.73, and R t dropped below 1 on January 31 under a series of measures. Conclusion: The spread of the COVID-19 epidemic was rapid in the early stage, with a large number of asymptomatic infected individuals having relatively low infectivity. However, it was quickly brought under control with national measures.

Novel approach for Monte Carlo simulation of the new COVID-19 spread dynamics.

A Monte Carlo simulation in a novel approach is used for studying the problem of the outbreak and spread dynamics of the new COVID-19 pandemic in this work. In particular, our goal was to generate epidemiological data based on natural mechanism of transmission of this disease assuming random interactions of a large-finite number of individuals in very short distance ranges. In the simulation we also take into account the stochastic character of the individuals in a finite population and given densities of people. On the other hand, we include in the simulation the appropriate statistical distributions for the parameters characterizing this disease. An important outcome of our work, besides the generated epidemic curves, is the methodology of determining the effective reproductive number during the main part of the daily new cases of the epidemic. Since this quantity constitutes a fundamental parameter of the SIR-based epidemic models, we also studied how it is affected by small variations of the incubation time and the crucial distance distributions, and furthermore, by the degree of quarantine measures. In addition, we compare our qualitative results with those of selected real epidemiological data.

Transmission dynamics of SARS-CoV-2 on the Diamond Princess uncovered using viral genome sequence analysis.

An outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occurred aboard the Diamond Princess cruise ship between her January 20 departure and late February 2020. Here, we used phylodynamic analyses to investigate the transmission dynamics of SARS-CoV-2 during the outbreak. Using a Bayesian coalescent-based method, the estimated mean nucleotide substitution rate of 240 SARS-CoV-2 whole-genome sequences was approximately 7.13 × 10-4 substitutions per site per year. Population dynamics and the effective reproductive number (Re) of SARS-CoV-2 infections were estimated using a Bayesian framework. The estimated origin of the outbreak was January 21, 2020. The infection spread substantially before quarantine on February 5. The Re peaked at 6.06 on February 4 and gradually declined to 1.51, suggesting that transmission continued slowly even after quarantine. These findings highlight the high transmissibility of SARS-CoV-2 and the need for effective measures to control outbreaks in confined settings.

Mathematical modeling of COVID-19 infection dynamics in Ghana: Impact evaluation of integrated government and individual level interventions.

The raging COVID-19 pandemic is arguably the most important threat to global health presently. Although there Although there is currently a a a vaccine, preventive measures have been proposed to reduce the spread of infection but the efficacy of these interventions, and their likely impact on the number of COVID-19 infections is unknown. In this study, we proposed the SEIQHRS model (susceptible-exposed-infectious-quarantine-hospitalized-recovered-susceptible) model that predicts the trajectory of the epidemic to help plan an effective control strategy for COVID-19 in Ghana. We provided a short-term forecast of the early phase of the epidemic trajectory in Ghana using the generalized growth model. We estimated the effective basic Reproductive number Re in real-time using three different estimation procedures and simulated worse case epidemic scenarios and the impact of integrated individual and government interventions on the epidemic in the long term using compartmental models. The maximum likelihood estimates of Re and the corresponding 95% confidence interval was 2.04 [95% CI: 1.82-2.27; 12th March-7th April 2020]. The Re estimate using the exponential growth method was 2.11 [95% CI: 2.00-2.24] within the same period. The Re estimate using time-dependent (TD) method showed a gradual decline of the Effective Reproductive Number since March 12, 2020 when the first 2 index cases were recorded but the rate of transmission remains high (TD: Re = 2.52; 95% CI: [1.87-3.49]). The current estimate of Re based on the TD method is 1.74 [95% CI: 1.41-2.10; (13th May 2020)] but with comprehensive integrated government and individual level interventions, the Re could reduce to 0.5 which is an indication of the epidemic dying out in the general population. Our results showed that enhanced government and individual-level interventions and the intensity of media coverage could have a substantial effect on suppressing transmission of new COVID-19 cases and reduced death rates in Ghana until such a time that a potent vaccine or drug is discovered.

Is Nigeria really on top of COVID-19? Message from effective reproduction number.

Following the importation of coronavirus disease (COVID-19) into Nigeria on 27 February 2020 and then the outbreak, the question is: How do we anticipate the progression of the ongoing epidemic following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population.In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of COVID-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R0) - this also enables us to estimate the initial daily transmission rate (ß0). We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on COVID-19 symptomatic and death cases from 27 February 2020 and 7 May 2020.In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 2.29. Most importantly, the R(t) is strictly greater than one from 13 April till 7 May 2020. The R0 is estimated to be 2.42 with credible interval: (2.37-2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below 1. Also, the estimated fraction of reported symptomatic cases is between 10 and 50%.Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

Linking key intervention timing to rapid decline of the COVID-19 effective reproductive number to quantify lessons from mainland China.

Effective reproductive numbers (Rt) were calculated from data on the COVID-19 outbreak in China and linked to dates in 2020 when different interventions were enacted. From a maximum of 3.98 before the lockdown in Wuhan City, the values of Rt declined to below 1 by the second week of February, after the construction of hospitals dedicated to COVID-19 patients. The Rt continued to decline following additional measures in line with the policy of "early detection, early report, early quarantine, and early treatment." The results provide quantitative evaluations of how intervention measures and their timings succeeded, from which lessons can be learned by other countries dealing with future outbreaks.

Transmission dynamics of the COVID-19 outbreak and effectiveness of government interventions: A data-driven analysis.

Using the parameterized susceptible-exposed-infectious-recovered model, we simulated the spread dynamics of coronavirus disease 2019 (COVID-19) outbreak and impact of different control measures, conducted the sensitivity analysis to identify the key factor, plotted the trend curve of effective reproductive number (R), and performed data fitting after the simulation. By simulation and data fitting, the model showed the peak existing confirmed cases of 59 769 arriving on 15 February 2020, with the coefficient of determination close to 1 and the fitting bias 3.02%, suggesting high precision of the data-fitting results. More rigorous government control policies were associated with a slower increase in the infected population. Isolation and protective procedures would be less effective as more cases accrue, so the optimization of the treatment plan and the development of specific drugs would be of more importance. There was an upward trend of R in the beginning, followed by a downward trend, a temporary rebound, and another continuous decline. The feature of high infectiousness for severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) led to an upward trend, and government measures contributed to the temporary rebound and declines. The declines of R could be exploited as strong evidence for the effectiveness of the interventions. Evidence from the four-phase stringent measures showed that it was significant to ensure early detection, early isolation, early treatment, adequate medical supplies, patients' being admitted to designated hospitals, and comprehensive therapeutic strategy. Collaborative efforts are required to combat the novel coronavirus, focusing on both persistent strict domestic interventions and vigilance against exogenous imported cases.

Effectiveness of Containment Measures to Control the Spread of COVID-19 in North Africa.

OBJECTIVES: Since the first case of severe acute respiratory syndrome coronavirus-2, identified in December 2019, in Wuhan in China, the number of cases rapidly increased into a pandemic. Governments worldwide have adopted different strategies and measures to interrupt the transmission of coronavirus disease 2019 (COVID-19). The main objective was to report and evaluate the effectiveness of the adopted measures in North Africa countries. METHODS: In these countries, the effective reproductive number R(t), the naïve case fatality rate, and the adjusted case fatality rate were estimated and compared on different dates. RESULTS: The obtained results show that the early strict application of containment measures and confinement could help contain the spread of the epidemic and maintain the number of deaths low. CONCLUSIONS: These measures might be useful for other countries that are experiencing the start of local COVID-19 outbreaks. They could also serve to halt the spread of new epidemics or pandemics.