Trends in managing COVID-19 from an emerging infectious disease to a common respiratory infectious disease: What are the subsequent impacts on and new challenges for healthcare systems?

Targeting the 9 countries with the highest cumulative number of newly confirmed cases in the past year, we analyzed the case fatality ratio (CFR) among newly confirmed cases and the vaccination rate (two or more doses of vaccine per 100 people) in the United States of America (USA), India, France, Germany, Brazil, the Republic of Korea, Japan, Italy, and the United Kingdom (UK) for the period of 2020-2022. Data reveal a decrease in the CFR among newly confirmed cases since the beginning of 2022, when transmission of the Omicron variant predominates, and an increase in vaccination rates. The Republic of Korea had the lowest CFR among newly confirmed cases (0.093%) in 2022 and the highest vaccination rate (86.27%). Japan had the second highest vaccination rate (83.12%) and a decrease in the CFR among newly confirmed cases of 1.478% in 2020, 1.000% in 2021, and 0.148% in 2022; while the average estimated fatality ratio for seasonal influenza from 2015-2020 was 0.020%. Currently, most countries are now easing COVID-19-related restrictions and are exploring a shift in management of COVID-19 from an emerging infectious disease to a common respiratory infectious disease that can be treated as the equivalent of seasonal or regional influenza. However, compared to influenza, infection with the Omicron variant still has a higher fatality ratio, is more transmissible, and the size of future outbreaks cannot be accurately predicted due to the uncertainty of viral mutation. More importantly, as countries shift their response strategies to COVID-19, there is an urgent need at this time to clarify what the subsequent impacts on healthcare systems and new challenges will be, including the clinical response, the dissemination of scientific information, vaccination campaigns, the creation of future surveillance and response systems, the cost of treatments and vaccinations, and the flexible use of big data in healthcare systems.

Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic.

BACKGROUND: Currently, the knowledge of associations among newly recovered cases (NR), newly healed cases (NH), newly confirmed cases (NC), and newly dead cases (ND) can help to monitor, evaluate, predict, control, and curb the spreading of coronavirus disease 2019 (COVID-19). This study aimed to explore the panel associations of ND, NH, and NR with NC. METHODS: Data from China Data Lab in Harvard Dataverse with China (January 15, 2020 to January 14, 2021), the United States of America (the USA, January 21, 2020 to April 5, 2021), and the World (January 22, 2020 to March 20, 2021) had been analyzed. The main variables included in the present analysis were ND, NH, NR, and NC. Pooled regression, stacked within-transformed linear regression, quantile regression for panel data, random-effects negative binomial regression, and random-effects Poisson regression were conducted to reflect the associations of ND, NH, and NR with NC. Event study analyses were performed to explore how the key events influenced NC. RESULTS: Descriptive analyses showed that mean value of ND/NC ratio regarding China was more than those regarding the USA and the World. The results from tentative analysis reported the significant relationships among ND, NH, NR, and NC regarding China, the USA, and the World. Panel regressions confirmed associations of ND, NH, and NR with NC regarding China, the USA, and the World. Panel event study showed that key events influenced NC regarding USA and the World more greatly than that regarding China. CONCLUSION: The findings in this study confirmed the panel associations of ND, NH, and NR with NC in the three datasets. The efficiencies of various control strategies of COVID-19 pandemic across the globe were compared by the regression outcomes. Future direction of research work could explore the influencing mechanisms of the panel associations.

Long-term statistical assessment of meteorological indicators and COVID-19 outbreak in hot and arid climate, Bahrain.

The COVID-19 pandemic has significantly impacted the global lifestyle, and the spreading of the virus is unprecedented. This study is aimed at assessing the association between the meteorological indicators such as air temperature (°C), relative humidity (%), wind speed (w/s), solar radiation, and PM2.5 with the COVID-19 infected cases in the hot, arid climate of Bahrain. Kendall and Spearman rank correlation coefficients and quantile on quantile regression were used as main econometric analysis to determine the degree of the relationship between related variables. The dataset analysis was performed from 05 April 2020, to 10 January 2021. The empirical findings indicate that the air temperature, humidity, solar radiation, wind speed indicators, and PM2.5 have a significant association with the COVID-19 newly infected cases. The current study findings allow us to suggest that Bahrain's relatively successful response to neighboring GULF economies can be attributed to the successful environmental reforms and significant upgrades to the health care facilities. We further report that a long-term empirical analysis between meteorological factors and respiratory illness threats will provide useful policy measures against future outbreaks.

Does city lockdown prevent the spread of COVID-19? New evidence from the synthetic control method.

BACKGROUND: At 10 a.m. on January 23, 2020 Wuhan, China imposed a 76-day travel lockdown on its 11 million residents in order to stop the spread of COVID-19. This lockdown represented the largest quarantine in the history of public health and provides us with an opportunity to critically examine the relationship between a city lockdown on human mobility and controlling the spread of a viral epidemic, in this case COVID-19. This study aims to assess the causal impact of the Wuhan lockdown on population movement and the increase of newly confirmed COVID-19 cases. METHODS: Based on the daily panel data from 279 Chinese cities, our research is the first to apply the synthetic control approach to empirically analyze the causal relationship between the Wuhan lockdown of its population mobility and the progression of newly confirmed COVID-19 cases. By using a weighted average of available control cities to reproduce the counterfactual outcome trajectory that the treated city would have experienced in the absence of the lockdown, the synthetic control approach overcomes the sample selection bias and policy endogeneity problems that can arise from previous empirical methods in selecting control units. RESULTS: In our example, the lockdown of Wuhan reduced mobility inflow by approximately 60 % and outflow by about 50 %. A significant reduction of new cases was observed within four days of the lockdown. The increase in new cases declined by around 50% during this period. However, the suppression effect became less discernible after this initial period of time. A 2.25-fold surge was found for the increase in new cases on the fifth day following the lockdown, after which it died down rapidly. CONCLUSIONS: Our study provided urgently needed and reliable causal evidence that city lockdown can be an effective short-term tool in containing and delaying the spread of a viral epidemic. Further, the city lockdown strategy can buy time during which countries can mobilize an effective response in order to better prepare. Therefore, in spite of initial widespread skepticism, lockdowns are likely to be added to the response toolkit used for any future pandemic outbreak.