Bring the life stages into the domain of basic and clinical pharmacology.

Completely distinct physiological conditions and immune responses exist among different human life stages. Age is not always consistent with the life stage. We proposed to incorporate the concept of the life stages into basic and clinical pharmacology, including clinical trials, drug labels, and drug usage in clinical practice. Life-stage-based medical treatment is the application of medicine according to life stages such as prepuberty, reproductive, and aging. A large number of diseases are life-stage-dependent. Many medications and therapy have shown various age effects but not been recognized as life-stage-dependent. The same dosage and drug applications used in different life stages lead to divergent outcomes. Incorporating life stages in medicine and drug usage will enhance the efficacy and precision of the medication in disease treatment.

Association between population density and infection rate suggests the importance of social distancing and travel restriction in reducing the COVID-19 pandemic.

Currently, 2019-nCoV has spread to most countries of the world. Understanding the environmental factors that affect the spread of the disease COVID-19 infection is critical to stop the spread of the disease. The purpose of this study is to investigate whether population density is associated with the infection rate of the COVID-19. We collected data from official webpages of cities in China and in the USA. The data were organized on Excel spreadsheets for statistical analyses. We calculated the morbidity and population density of cities and regions in these two countries. We then examined the relationship between morbidity and other factors. Our analysis indicated that the population density in cities in Hubei province where the COVID-19 was severe was associated with a higher percentage of morbidity, with an r value of 0.62. Similarly, in the USA, the density of 51 states and territories is also associated with morbidity from COVID-19 with an r value of 0.55. In contrast, as a control group, there is no association between the morbidity and population density in 33 other regions of China, where the COVID-19 epidemic is well under control. Interestingly, our study also indicated that these associations were not influenced by the first case of COVID-19. The rate of morbidity and the number of days from the first case in the USA have no association, with an r value of - 0.1288. Population density is positively associated with the percentage of patients with COVID-19 infection in the population. Our data support the importance of such as social distancing and travel restriction in the prevention of COVID-19 spread.

An alternative approach-combination of lockdown and open in fighting COVID-19 pandemics.

As the COVID-19 pandemic enters its third year and the omicron variant becomes dominant, we propose an alternative strategy for dealing with COVID-19, called hybrid lockdown, that is, the combination of lockdown (the centralized and organized lockdown of the high-risk population) and free mobility (normal mobility) of the low-risk population. Such an approach will enable a country or region, especially with a high population density, to achieve significant prevention and control the effects of the COVID-19 pandemic at the least cost.

Mortality in Four Waves of COVID-19 Is Differently Associated with Healthcare Capacities Affected by Economic Disparities.

BACKGROUND: The greatest challenges are imposed on the overall capacity of disease management when the cases reach the maximum in each wave of the pandemic. METHODS: The cases and deaths for the four waves of COVID-19 in 119 countries and regions (CRs) were collected. We compared the mortality across CRs where populations experience different economic and healthcare disparities. FINDINGS: Among 119 CRs, 117, 112, 111, and 55 have experienced 1, 2, 3, and 4 waves of COVID-19 disease, respectively. The average mortality rates at the disease turning point were 0.036, 0.019. 0.017, and 0.015 for the waves 1, 2, 3, and 4, respectively. Among 49 potential factors, income level, gross national income (GNI) per capita, and school enrollment are positively correlated with the mortality rates in the first wave, but negatively correlated with the rates of the rest of the waves. Their values for the first wave are 0.253, 0.346 and 0.385, respectively. The r value for waves 2, 3, and 4 are -0.310, -0.293, -0.234; -0.263, -0.284, -0.282; and -0.330, -0.394, -0.048, respectively. In high-income CRs, the mortality rates in waves 2 and 3 were 29% and 28% of that in wave 1; while in upper-middle-income CRs, the rates for waves 2 and 3 were 76% and 79% of that in wave 1. The rates in waves 2 and 3 for lower-middle-income countries were 88% and 89% of that in wave 1, and for low-income countries were 135% and 135%. Furthermore, comparison among the largest case numbers through all waves indicated that the mortalities in upper- and lower-middle-income countries is 65% more than that of the high-income countries. INTERPRETATION: Conclusions from the first wave of the COVID-19 pandemic do not apply to the following waves. The clinical outcomes in developing countries become worse along with the expansion of the pandemic.

Variations of COVID-19 mortality are affected by economic disparities across countries.

BACKGROUND: When the COVID-19 case number reaches a maximum in a country, its capacity and management of health system face greatest challenge. METHODS: We performed a cross-sectional study on data of turning points for cases and deaths for the first three waves of COVID-19 in countries with more than 5000 cumulative cases, as reported by Worldometers and WHO Coronavirus (COVID-19) Dashboard. We compared the case fatality rates (CFRs) and time lags (in unit of day) between the turning points of cases and deaths among countries in different development stages and potential influence factors. As of May 10, 2021, 106 out of 222 countries or regions (56%) reported more than 5000 cases. Approximately half of them have experienced all the three waves of COVID-19 disease. The average mortality rate at the disease turning point was 0.038 for the first wave, 0.020 for the second wave, and 0.023 for wave 3. In high-income countries, the mortality rates during the first wave are higher than that of the other income levels. However, the mortality rates during the second and third waves of COVID-19 were much lower than those of the first wave, with a significant reduction from 5.7% to 1.7% approximately 70%. At the same time, high-income countries exhibited a 2-fold increase in time lags during the second and the third waves compared to the first wave, suggesting that the periods between the cases and deaths turning point extended. High rates in the first wave in developed countries are associated to multiple factors including transportation, population density, and aging populations. In upper middle- and lower middle-income countries, the decreasing of mortality rates in the second and third waves were subtle or even reversed, with increased mortality during the following waves. In the upper and lower middle-income countries, the time lags were about 50% of the durations observed from high-income countries. INTERPRETATION: Economy and medical resources affect the efficiency of COVID-19 mitigation and the clinical outcomes of the patients. The situation is likely to become even worse in the light of these countries' limited ability to combat COVID-19 and prevent severe outcomes or deaths as the new variant transmission becomes dominant.

The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19.

The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were chosen as representatives from the perspective of the coronavirus outbreak in China. Data were copied and pasted onto Excel spreadsheets to perform data analysis. A new methodology, Patient Information Based Algorithm (PIBA) [1], has been adapted to process the data and used to estimate the death rate of COVID-19 in real-time. Assumption is that the number of days from inpatients to death fall into a pattern of normal distribution and the scores in normal distribution can be obtained by observing 33 death cases and analysing the data [2]. We selected 5 scores in normal distribution of these durations as lagging days, which will be used in the following estimation of death rate. We calculated each death rate on accumulative confirmed cases with each lagging day from the current data and then weighted every death rate with its corresponding possibility to obtain the total death rate on each day. While the trendline of these death rate curves meet the curve of current ratio between accumulative death cases and confirmed cases at some points in the near future, we considered that these intersections are within the range of real death rates. Six tables were presented to illustrate the PIBA method using data from China and South Korea. One figure on estimated rate of infection and patients in serious condition and retrospective estimation of initially occurring time of CORID-19 based on PIBA.

A cost-effective plan for global testing - an infection rate stratified, algorithm guided, multiple-level, continuously pooled testing strategy.

The most effective measure to prevent or stop the spread of infectious diseases is the early identification and isolation of infected individuals through comprehensive screening. At present, in the COVID-19 pandemic, such screening is often limited to isolated regions as determined by local governments. Screening of potentially infectious individuals should be conducted through coordinated national or global unified actions. Our current research focuses on using resources to conduct comprehensive national and regional regular testing with a risk rate based, algorithmic guided, multiple-level, pooled testing strategy. Here, combining methodologies with mathematical logistic models, we present an analytic procedure of an overall plan for coordinating state, national, or global testing. The proposed plan includes three parts 1) organization, resource allocation, and distribution; 2) screening based on different risk levels and business types; and 3) algorithm guided, multiple level, continuously screening the entire population in a region. This strategy will overcome the false positive and negative results in the polymerase chain reaction (PCR) test and missing samples during initial tests. Based on our proposed protocol, the population screening of 300,000,000 in the US can be done weekly with between 15,000,000 and 6,000,000 test kits. The strategy can be used for population screening for current COVID-19 and any future severe infectious disease when drugs or vaccines are not available.

Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm.

The global COVID-19 outbreak is worrisome both for its high rate of spread, and the high case fatality rate reported by early studies and now in Italy. We report a new methodology, the Patient Information Based Algorithm (PIBA), for estimating the death rate of a disease in real-time using publicly available data collected during an outbreak. PIBA estimated the death rate based on data of the patients in Wuhan and then in other cities throughout China. The estimated days from hospital admission to death was 13 (standard deviation (SD), 6 days). The death rates based on PIBA were used to predict the daily numbers of deaths since the week of February 25, 2020, in China overall, Hubei province, Wuhan city, and the rest of the country except Hubei province. The death rate of COVID-19 ranges from 0.75% to 3% and may decrease in the future. The results showed that the real death numbers had fallen into the predicted ranges. In addition, using the preliminary data from China, the PIBA method was successfully used to estimate the death rate and predict the death numbers of the Korean population. In conclusion, PIBA can be used to efficiently estimate the death rate of a new infectious disease in real-time and to predict future deaths. The spread of 2019-nCoV and its case fatality rate may vary in regions with different climates and temperatures from Hubei and Wuhan. PIBA model can be built based on known information of early patients in different countries.