How does Covid-19 affect global equity markets?

This study applies OLS,panel regression and Granger causality test to investigate the impact of the Coronavirus disease 2019(Covid-19)outbreak on the global equity markets during the early stage of the pandemic.We find that the Covid-19 outbreak has a significant negative impact on the overall equity index return of the eight economies even at 0.1%significance level.Furthermore,the pandemic has a more significant impact on the European countries than on the East Asian economies.The results have three main implications.Firstly,policy makers should react fast to mitigate the impact of a crisis.Secondly,investors should be aware of an outbreak of disease or other risks and adjust their investments accordingly.Furthermore,the Covid-19 outbreak results in a shift of power from the west to the east.

COVID-19: Africa’s Challenge and the Need for a Paradigm Shift on the Use of Ventilators

Background: The December 2019 Chinese epidemic of Corona Virus Disease [COVID-19], which erupted in Wuhan, South China, was declared a pandemic, by the World Health Organization [WHO], on 12th January 2020. The worldwide spread from China was rapid, but Africa was the last port-of-call. Her first diagnosed case was two months after China’s, on 14th February, 2020 in Egypt. The morbidity and mortality rates have, however, remained lower in Africa than in the developed world, and analysts believe that it was more of a temporary respite, since Africa’s poor health infrastructure will become her eventual albatross. Methodology: Data were collected on COVID-19 and records of the socio-economic capacity of Africa by accessing the relevant previous and current peer-reviewed publications from multiple search engines on internet. The data were, then, collated and comparatively analyzed. Results: The available data revealed that Africa had, mostly, the milder forms of COVID-19, and so, morbidity and mortality were low. Her shrinking elderly population and hot climate were believed to be contributory, but lately, as the pandemic spread, the role of these factors was not exactly predictive. Being low on healthcare infrastructure, Africa could tenaciously leverage on the supportive and preventive measures prescribed by WHO, while the world awaited a vaccine. The role of ventilators in the care of critically ill patients, also, came under scrutiny as some workers were questioning the underlying pathology, and advocating a paradigm shift from high-tech positive end expiratory pressure ventilation to plasmapheresis and packed cell transfusion. Conclusion: Africa faces a huge challenge with COVID-19, but the predicted heavy mortalities may be reduced by some local confounding factors, control of spread and re-focusing of critical care away from the expensive and unavailable ventilators.

Suspected Close Contacts as the Pilot Indicator of the Growth Trend of Confirmed Population During the COVID-19 Pandemic: A Simulation Approach

Regarding to the actual situation of the new coronavirus disease 2019 epidemic,social factors should be taken into account and the increasing growth trend of confirmed populations needs to be explained.A proper model needs to be established,not only to simulate the epidemic,but also to evaluate the future epidemic situation and find a pilot indicator for the outbreak.The original susceptible-infectious-recover model is modified into the susceptible-infectious-quarantine-confirm-recover combined with social factors(SIDCRL)model,which combines the natural transmission with social factors such as external interventions and isolation.The numerical simulation method is used to imitate the change curve of the cumulative number of the confirmed cases and the number of cured patients.Furthermore,we investigate the relationship between the suspected close contacts(SCC)and the final outcome of the growth trend of confirmed cases with a simulation approach.This article selects four representative countries,that is,China,South Korea,Italy,and the United States,and gives separate numerical simulations.The simulation results of the model fit the actual situation of the epidemic development and reasonable predictions are made.In addition,it is analyzed that the increasing number of SCC contributes to the epidemic outbreak and the prediction of the United States based on the population of the SCC highlights the importance of external intervention and active prevention measures.The simulation of the model verifies its reliability and stresses that observable variable SCC can be taken as a pilot indicator of the coronavirus disease 2019 pandemic.

Dynamic variations in COVID-19 with the SARS-CoV-2 Omicron variant in Kazakhstan and Pakistan

Background The ongoing coronavirus disease 2019(COVID-19)pandemic caused by the severe acute respiratory syndrome-coronavirus 2(SARS-CoV-2)and the Omicron variant presents a formidable challenge for control and prevention worldwide,especially for low-and middle-income countries(LMICs).Hence,taking Kazakhstan and Pakistan as examples,this study aims to explore COVID-19 transmission with the Omicron variant at different contact,quarantine and test rates.Methods A disease dynamic model was applied,the population was segmented,and three time stages for Omicron transmission were established:the initial outbreak,a period of stabilization,and a second outbreak.The impact of population contact,quarantine and testing on the disease are analyzed in five scenarios to analysis their impacts on the disease.Four statistical metrics are employed to quantify the model’s performance,including the correlation coefficient(CC),normalized absolute error,normalized root mean square error and distance between indices of simulation and observation(DISO).Results Our model has high performance in simulating COVID-19 transmission in Kazakhstan and Pakistan with high CC values greater than 0.9 and DISO values less than 0.5.Compared with the present measures(baseline),decreasing(increasing)the contact rates or increasing(decreasing)the quarantined rates can reduce(increase)the peak values of daily new cases and forward(delay)the peak value times(decreasing 842 and forward 2 days for Kazakhstan).The impact of the test rates on the disease are weak.When the start time of stage Ⅱ is 6 days,the daily new cases are more than 8 and 5 times the rate for Kazakhstan and Pakistan,respectively(29,573 vs.3259;7398 vs.1108).The impact of the start times of stageⅢon the disease are contradictory to those of stageⅡ.Conclusions For the two LMICs,Kazakhstan and Pakistan,stronger control and prevention measures can be more effective in combating COVID-19.Therefore,to reduce Omicron transmission,strict management of population movement should be employed.Moreover,the timely application of these strategies also plays a key role in disease control.

Forecasting COVID-19 situation in Bangladesh

Forecasting the COVID‐19 confirmed cases,deaths,and recoveries demands time to know the severity of the novel coronavirus.This research aims to predict all types of COVID‐19 cases(verified people,deaths,and recoveries)from the deadliest 3rd wave data of the COVID‐19 pandemic in Bangladesh.We used the official website of the Directorate General of Health Services as our data source.To identify and predict the upcoming trends of the COVID‐19 situation of Bangladesh,we fit the Auto‐Regressive Integrated Moving Average(ARIMA)model on the data from Mar.01,2021 to Jul.31,2021.The finding of the ARIMA model(forecast model)reveals that infected,deaths,and recoveries number will have experienced exponential growth in Bangladesh to October 2021.Our model reports that confirmed cases and deaths will escalate by four times,and the recoveries will improve by five times at a later point in October 2021 if the trend of the three scenarios of COVID‐19 from March to July lasts.The prediction of the COVID‐19 scenario for the next three months is very frightening in Bangladesh,so the strategic planner and field‐level personnel need to search for suitable policies and strategies and adopt these for controlling the mass transmission of the virus.

Power-law growth of the COVID-19 fatality incidents in Europe

We report on the dynamic scaling of the diffusion growth phase of the COVID-19 epidemic in Europe.During this initial diffusion stage,the European countries implemented unprecedented mitigation polices to delay and suppress the disease contagion,although not in a uniform way or timing.Despite this diversity,we find that the reported fatality cases grow following a power law in all European countries we studied.The difference among countries is the value of the power-law exponent 3.5<α<8.0.This common attribute can prove a practical diagnostic tool,allowing reasonable predictions for the growth rate from very early data at a country level.We propose a model for the disease-causing interactions,based on a mechanism of human decisions and risk taking in interpersonal associations.The model describes the observed statistical distribution and contributes to the discussion on basic assumptions for homogeneous mixing or for a network perspective in epidemiological studies of COVID-19.