New Cases Of Coronavirus Disease 2019 Research Articles

The geographical distribution and socioeconomic risk factors of COVID-19, tuberculosis and leprosy in Fortaleza, Brazil

BackgroundFortaleza (Brazil) is high endemic for coronavirus disease 2019 (COVID-19), tuberculosis (TB) and leprosy. These three diseases share respiratory droplets through coughing or sneezing as the main mode of transmission but differ in incubation time, with COVID-19 having a short and leprosy a long incubation time. Consequently, contacts of a patient are at higher risk of infection and developing these diseases. There might be scope for combined preventive measures, but a better understanding of the geographical distribution and relevant socioeconomic risk factors of the three diseases is needed first. This study aims to describe the geographic distribution of COVID-19, TB and leprosy incidence and to identify common socioeconomic risk factors.MethodsThe total number of new cases of COVID-19, TB and leprosy, as well as socioeconomic and demographic variables, were retrieved from official registers. The geographical distribution of COVID-19, TB and leprosy rates per neighbourhood was visualised in Quantum GIS, and spatial autocorrelation was measured with Moran’s I in GeoDa. A spatial regression model was applied to understand the association between COVID-19, TB, leprosy rates, and socioeconomic factors.ResultsCOVID-19 and TB showed a more homogenous distribution, whereas leprosy is located more in the south and west of Fortaleza. One neighbourhood (Pedras) in the southeast was identified as high endemic for all three diseases. Literacy was a socioeconomic risk factor for all three diseases: a high literacy rate increases the risk of COVID-19, and a low literacy rate (i.e., illiteracy) increases the risk of TB and leprosy. In addition, high income was associated with COVID-19, while low income with TB.ConclusionsDespite the similar mode of transmission, COVID-19, TB and leprosy show a different distribution of cases in Fortaleza. In addition, associated risk factors are related to wealth in COVID-19 and to poverty in TB and leprosy. These findings may support policymakers in developing (partially combined) primary and secondary prevention considering the efficient use of resources.

Spatiotemporal risk assessment and COVID-19 trend estimation in a federative unit in northeastern Brazil.

Coronavirus disease 2019 (COVID-19) has spread worldwide, causing a high burden of morbidity and mortality, and has affected the various health service systems in the world, demanding disease monitoring and control strategies. The objective of this study was to identify risk areas using spatiotemporal models and determine the COVID-19 time trend in a federative unit of northeastern Brazil. An ecological study using spatial analysis techniques and time series was carried out in the state of Maranhão, Brazil. All new cases of COVID-19 registered in the state from March 2020 to August 2021 were included. Incidence rates were calculated and spatially distributed by area, while the spatiotemporal risk territories were identified using scan statistics. The COVID-19 time trend was determined using Prais-Winsten regressions. Four spatiotemporal clusters with high relative risks for the disease were identified in seven health regions located in the southwest/northwest, north and east of Maranhão. The COVID-19 time trend was stable during the analysed period, with higher rates in the regions of Santa Inês in the first and second waves and Balsas in the second wave. The heterogeneously distributed spatiotemporal risk areas and the stable COVID-19 time trend can assist in the management of health systems and services, facilitating the planning and implementation of actions toward the mitigation, surveillance and control of the disease.

The impact of the COVID-19 pandemic on perioperative chemotherapy for breast cancer.

Since it was first reported in December 2019, coronavirus disease 2019 (COVID-19) spread rapidly across the globe resulting in a pandemic. As of August 2022, seven outbreak peaks have been confirmed in Tokyo, and the numbers of new cases in the fifth and later outbreak periods have been far greater than in the preceding periods. This retrospective study examined the impact of the COVID-19 pandemic on perioperative chemotherapy for breast cancer. Patients with breast cancer who received perioperative chemotherapy at the National Cancer Center Hospital East were divided into 2 groups: 120 and 384 patients who started chemotherapy before and during the pandemic, respectively. The incidence of critical events that had potential detrimental effects on the prognosis, such as start of adjuvant chemotherapy ≥91 days after surgery and relative dose intensity of chemotherapy <85% were compared between groups. No significant difference in the incidence of critical events was found. When stratified by outbreak period, the incidence of critical events was positively correlated with the increasing number of new cases of COVID-19 (r = 0.83, p = 0.04). Moreover, 25/173 patients (14%) who started perioperative chemotherapy during the fifth and sixth outbreak periods developed COVID-19 infection, 80% of whom (20/25) had a delay or interruption to their surgery or other perioperative treatments. Although the impact of the COVID-19 pandemic on perioperative chemotherapy on whole groups of patients was not evident when comparing periods before and after the pandemic, the impact is becoming prominent in parallel with increasing numbers of new COVID-19 cases.

Intranasal COVID-19 vaccine: a modern vaccination approach.

Dear Editor, The emergence of severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2) which was originally reported to WHO in December 2019 resulted in the coronavirus illness and it was subsequently declared a global coronavirus disease 2019 (COVID-19) pandemic in March 2020. Recurring disease outbreaks repeatedly overloaded the public health sector, infecting over 613 million people and killing more than 6.5 million as of September 20221. As new strains emerge and natural immunity wanes, the population, particularly immunocompromised individuals, stand an increased risk of severe sickness and death due to this disease. Various research advances paved the way for developing multiple COVID-19 vaccines which have been approved to treat and prevent COVID-19. Currently, available COVID-19 vaccines including mRNA vaccines, recombinant adenovirus vaccines, and recombinant protein vaccines that have received Emergency Use Authorization (EUA) and have been subsequently used to immunize millions of people are all administered through intramuscular injection2. Intramuscular vaccines have been designed to induce antibody-mediated and cell-mediated immune responses and minimal to no detectable mucosal immunity at the site of infection. Since the nose and upper respiratory tract are the primary routes for inhalation pathogens like SARS-CoV-2 and are more susceptible to infection,3 the vaccine response generated after intramuscular immunization can leave the upper respiratory tract vulnerable to viral replication and dissemination because these vaccines are inefficient in stimulating immunoglobulin A secretion in mucosal cells2. Alternatively, it has been found recently that the intranasal (IN) vaccines for COVID-19 have shown a promising ability to induce sterilizing mucosal immunity against mucosal pathogens apart from inducing an antibody-mediated immune response and a robust cell-mediated immunity2. A single dose of effective SARS-CoV-2 vaccine via IN route boosts mucosal immunoglobulin A and T-cell responses in both upper and lower respiratory tract, providing almost complete prevention against viral infection, multiplication, and reducing virus shedding via nasal mucosa that acts as a first-line barrier to the virus entrance before spreading into the lungs2. This immediate protection at the site of infection by restricting the viral replication and clearance of SARS-CoV-2 infection reflects the potency of IN COVID-19 vaccines in the alleviation of seeding new cases of COVID-19 amidst the pandemic; hence, preventing even mild cases of illness and inhibiting the virus transmission to other people. In addition, these vaccines are minimally invasive, have a needle-free mode of administration, and are usually in the form of a nasal spray2. Owing to the easy transportation and storage, these vaccines are also more convenient to use2. However, an essential consideration for IN COVID-19 vaccine is its safety. An extremely rare risk of reversion of live attenuated virus into replicating state, regaining its ability to cause disease in the individual has been observed4. With that, the effectiveness of the vaccine is determined by the immunization type chosen and the addition of essential adjuvants. Phase I, II, and III clinical trials were performed to assess the efficacy of IN COVID-19 immunization by two companies; CanSino Biologics, a Chinese pharmaceutical company, and Bharat Biotech International, a biotechnology company headquartered in India, which received approval in their respective countries for its use after gaining successful results5. These companies have created ‘viral vector’ vaccines that use a harmless adenovirus to deliver the genetic material of the SARS-CoV-2 into host cells. Given the severity of the situation where the healthcare systems are challenged globally due to increased COVID-19-related hospitalizations, it is critical to continue developing effective and reliable COVID-19 vaccines in present times. In the context of respiratory pathogens like SARS-CoV-2, IN vaccines’ pros conceivably outweigh their cons, along with offering both localized protection at the site of infection and activating systemic responses as compared with intramuscular vaccines eliciting only systemic responses. Large-scale clinical studies and trials should be conducted to gather further evidence to prove its effectiveness so that the availability of IN COVID-19 vaccines may be made feasible. This vaccine might also serve as an alternative to limited and inadequate vaccine supplies as well as a booster to intramuscular vaccines. Immunization with IN COVID-19 vaccines will hopefully help deal with the persistent COVID-19 pandemic and potential viral contagious diseases that might arise in the future. Ethics approval Not applicable. Sources of funding None. Authors’ contribution All authors contributed equally. Conflicts of interest disclosure The authors declare that they have no financial conflict of interest with regard to the content of this report. Research registration Unique Identifying number (UIN) None. Guarantor Govinda Khatri.

Study of the Deep Processes of COVID-19 in Russia: Finding Ways to Identify Preventive Measures.

The novel coronavirus disease 2019 (COVID-19) pandemic has had a huge impact on all areas of human life. Since the risk of biological threats will persist in the future, it is very important to ensure mobilization readiness for a prompt response to the possible emergence of epidemics of infectious diseases. Therefore, from both a theoretical and practical standpoint, it is currently necessary to conduct a thorough examination of the COVID-19 epidemic. The goal of this research is to investigate the underlying processes that led to the COVID-19 pandemic in Russia and to identify ways to improve preventive measures and ensure mobilization readiness for a quick response to potential COVID-19-like pandemics. This research will analyze the daily dynamics of the number of infection cases and the number of new lethal cases of COVID-19. We analyzed the daily number of new cases of COVID-19 infection N(d), the daily number of new lethal cases L(d), their percentage ratio L(d)/N(d) 100% in Russia for 2 years of the pandemic (from the beginning of the pandemic to 23 March 2022), the rate of increase and decrease of these indicators (dN(d)/dd and dL(d)/dd), as well as their spectra created on the basis of wavelet analysis. Wavelet analysis of the deep structure of the N(d) and L(d) wavelet spectra made it possible to identify the presence of internal cycles, the study of which makes it possible to predict the presence of days with the maximum number of infections and new deaths in a pandemic similar to COVID-19 and outline ways and methods for improving preventive measures and measures to ensure mobilization readiness for a rapid response to the potential emergence of pandemics similar to COVID-19.

Assessment of Lockdown Effectiveness during COVID-19 Pandemic Using Air Pollution Data in Armenia in March–June 2019 and 2020: A Cross-Sectional Study

Various methods used by different countries’ governments to control the spread of coronavirus disease 2019 (COVID-19), the cause of pandemic in 2020, affected air quality. The aim of this study was to evaluate the effects of lockdown in Armenia on the content of the main air pollutants—dust, SO2 and NO2. This was a cross-sectional study. We analyzed data on the concentrations of SO2, NO2 and dust from March to June, 2019 and the same period in 2020 as well as data on positive COVID-19 cases from Yerevan, Vanadzor and Hrazdan. In 2020, dust was found to be lower in Yerevan and in Hrazdan and higher in Vanadzor than in the same period in 2019. The same pattern was present for SO2 concentrations: in Yerevan and Hrazdan there was a decrease, and there was an increase in Vanadzor. The concentrations of NO2 increased in Yerevan and Hrazdan, with a slight decrease in Vanadzor. New cases of COVID-19 had a negative correlation with dust and a positive correlation with SO2. The strict quarantine measures were effective in containing the spread of COVID-19.

A data-driven interpretable ensemble framework based on tree models for forecasting the occurrence of COVID-19 in the USA.

This prevalence of coronavirus disease 2019 (COVID-19) has become one of the most serious public health crises. Tree-based machine learning methods, with the advantages of high efficiency, and strong interpretability, have been widely used in predicting diseases. A data-driven interpretable ensemble framework based on tree models was designed to forecast daily new cases of COVID-19 in the USA and to determine the important factors related to COVID-19. Based on a hyperparametric optimization technique, we developed three machine learning algorithms based on decision trees, including random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), and three linear ensemble models were used to integrate these outcomes for better prediction accuracy. Finally, the SHapley Additive explanation (SHAP) value was used to obtain the feature importance ranking. Our outcomes demonstrated that, among the three basic machine learners, the prediction accuracy was the following in descending order: LightGBM, XGBoost, and RF. The optimized LAD ensemble was the most precise prediction model that reduced the prediction error of the best base learner (LightGBM) by approximately 3.111%, while vaccination, wearing masks, less mobility, and government interventions had positive effects on the control and prevention of COVID-19.

A problem of self-isolation in Japan: The relationship between self-isolation and COVID-19 community case.

Background: The Japanese government advised mild or asymptomatic coronavirus disease-2019 (COVID-19) cases to self-isolate at home, while more severe individuals were treated at health posts. Poor compliance with self-isolation could be a potential reason for the new outbreak. Our study aimed to find out the correlation between the rising new cases of COVID-19 and home-based patients in Japan. Methods: A secondary data analysis study was conducted with the data from COVID-19- involved databases collected from Johns Hopkins University, Japanese Ministry of Health, Labour and Welfare, and Community Mobility Reports of Google. New community cases, stringency index, number of tests, and active cases were analyzed. Using a linear regression model, an independent variable was utilized for a given date to predict the future number of community cases. Results: Research results show that outpatient cases, the stringency, and Google Mobility Trend were all significantly associated with the number of COVID-19 community cases from the sixth day to the ninth day. The model predicting community cases on the eighth day (R2=0.8906) was the most appropriate showing outpatients, residential index, grocery and pharmacy index, retail and recreation index, and workplaces index were positively related (β1=24.2, 95% CI: 20.3– 26.3, P<0.0001; β2=277.7, 95% CI: 171.8–408.2, P<0.0001; β3=112.4, 95% CI: 79.8–158.3, P<0.0001; β4=73.1, 95% CI: 53- 04.4, P<0.0001; β5=57.2, 95% CI: 25.2–96.8, P=0.001, respectively). In contrast, inpatients, park index, and adjusted stringency index were negatively related to the number of community cases (β6=-2.8, 95% CI: -3.9 – -1.6, P<0.0001; β7=-33, 95% CI: -43.6 – -27, P<0.0001; β8=-14.4, 95% CI: -20.1– -12, P<0.0001, respectively). Conclusion: Outpatient cases and indexes of Community Mobility Reports were associated with COVID-19 community cases.

Epidemiological Study of Infection and Death Due to COVID-19 in Fars Province, Iran, From February to September 2020.

Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has entered our lives with the fear of outbreak, death, and recurrence. Our objective in this study is to evaluate the epidemiological features of Coronavirus Disease 2019 (COVID-19) infection and death in Fars province, Iran.Methods: A cross-sectional study was conducted from February 18th to September 30th, 2020, where age, history of underlying diseases, sex, community-wide quarantine, nationality, close contact, pregnancy, medical staff job, traveling , and residency were compared between alive and deceased groups. Data were analyzed using IBM SPSS software, version 22.0, and the significance level was set at 0.05.Results: Regarding 57958 new cases of COVID-19, the basic reproduction number (R0) was estimated as 2.8, requiring a minimum of 65% immunization to reach herd immunity. Moreover, an R0=0.36 was required to reach the endemic state in the region. The incidence, mortality, fatality, and recurrence rates of COVID-19 were estimated as 1347.9 per 100,000 dwellers, 209.5 per 1000,000 dwellers, 1.6 %, and 3.1 per 100,000 dwellers, respectively. Age, history of underlying diseases, urban residency, and the male sex were significantly higher in the deceased group (OR=1.09, 5.48, 1.24, and 1.32; All Ps<0.001, <0.001, 0.005, and <0.001, respectively). In addition, the recurrence rate among positive cases was estimated as 0.23% with a median±inter-quartile range equal to 84±46.25 days. Community-wide quarantine was shown to be a protective factor for death due to COVID-19 (OR=0.58, P=0.005).Conclusion: Community-wide quarantine blocks the transmission of COVID-19 effectively. COVID-19 enjoys no solid immunity. History of underlying diseases, the male sex, urban residency, and age were among the most significant causes of death due to COVID-19. Further investigations are recommended on the genetic structure of SARS-CoV-2, treatments, and vaccination.

Analysis of the Influence of Social and Economic Factors on COVID-19 Cases with the Principal Component Analysis (PCA) Method

Coronavirus Disease 2019 (COVID-19) is a disease caused by the SARS-CoV-2 virus which infects the human respiratory. On March 11, 2019, World Health Organization (WHO) officially declared the COVID-19 outbreak as a global pandemic because in less than three months it had infected more than 120,000 people in 123 countries from Asia, Europe, United States, to South Africa. This rapid growth of cases is caused by many factors. Therefore, the author wants to examine social and economic factors in 29 countries regarding the COVID-19 case. In this study, the author uses 122 variables; consisting of 107 variables from social factors and 15 variables from economic factors. Because the number of variables used was quite large, the author reduced the data using the Principal Component Analysis (PCA). Then, 15 main components are formed from the results of the application of PCA carried out on 122 variables. It will then be used to form a regression equation for the COVID-19 case which consists of the average new cases of COVID-19 per day, the rate of infected population COVID-19, the percentage of deaths caused by COVID-19, and the percentile of the new cases of COVID-19 per day.

Low temperature, high relative humidity and higher precipitation are associated with a higher number of deaths from COVID-19

Background: Meteorological variables play a major role in the transmission of infectious diseases such as coronavirus disease 2019 (COVID-19). Objective: To analyze the correlation between climatic variables and COVID-19 deaths/cases. Methods: An exploratory-descriptive study based on secondary data on deaths, cases of COVID-19 and climatic variables from March 2020-May 2021 in Fortaleza, Brazil. Data from the COVID-19 surveillance system of the Ministry of Health were used. The climatic indicators were extracted from the National Institute of Meteorology. The variables under study were temperature (minimum, mean and maximum in °C), relative air humidity (%), total precipitation (mm) and total daily insolation (h). Pearson's correlation and the linear regression model were used for statistical analysis. Correlations were considered significant when P ≤ 0.05 and a 95% confidence interval was adopted. Results: All meteorologic variables were correlated with deaths from COVID-19, temperature minimum (r = -0,126; P &lt; 0,01), mean temperature (r = -0,146; P &lt; 0,05), maximum temperature (r = -0,190; P &lt; 0,001), insolation (r = -0,214; P &lt; 0,001), precipitation (r = 0,216; P &lt; 0,001) and relative humidity (r = 0,348; P &lt; 0,001). In relation to the new cases of COVID-19, only maximum temperature (r = -0,116; P &lt; 0,05), insolation (r = -0,141; P &lt; 0,01) and relative humidity (r = 0,231; P &lt; 0,001) were correlated are significantly. Conclusion: There were significant correlations between meteorological variables and COVID-19 deaths/cases. It was found that meteorologic variables had the most influence on COVID-19 deaths.

A Comprehensive Overview of the Paradigm of Laboratory Findings in the Diagnosis of the Pandemic Coronavirus Disease 2019 (COVID-19) in India

COVID-19 is a pandemic disease caused by 2019 Novel Coronavirus or SARS-CoV-2. As this disease is highly contagious and rapidly spreading, so all countries have to increase their level of preparedness, alert, and response to identify, manage, and care for new cases of COVID-19; laboratory testing is an integral part of this strategy. Accurate results by laboratory diagnosis are beneficial for public health.

Quantifying the impacts of human mobility restriction on the spread of coronavirus disease 2019: an empirical analysis from 344 cities of China.

Background:Since the outbreak of coronavirus disease 2019 (COVID-19), human mobility restriction measures have raised controversies, partly because of the inconsistent findings. An empirical study is promptly needed to reliably assess the causal effects of the mobility restriction. The purpose of this study was to quantify the causal effects of human mobility restriction on the spread of COVID-19.Methods:Our study applied the difference-in-difference (DID) model to assess the declines of population mobility at the city level, and used the log–log regression model to examine the effects of population mobility declines on the disease spread measured by cumulative or new cases of COVID-19 over time after adjusting for confounders.Results:The DID model showed that a continual expansion of the relative declines over time in 2020. After 4 weeks, population mobility declined by −54.81% (interquartile range, −65.50% to −43.56%). The accrued population mobility declines were associated with the significant reduction of cumulative COVID-19 cases throughout 6 weeks (ie, 1% decline of population mobility was associated with 0.72% [95% CI: 0.50%–0.93%] reduction of cumulative cases for 1 week, 1.42% 2 weeks, 1.69% 3 weeks, 1.72% 4 weeks, 1.64% 5 weeks, and 1.52% 6 weeks). The impact on the weekly new cases seemed greater in the first 4 weeks but faded thereafter. The effects on cumulative cases differed by cities of different population sizes, with greater effects seen in larger cities.Conclusions:Persistent population mobility restrictions are well deserved. Implementation of mobility restrictions in major cities with large population sizes may be even more important.

Early predicting indicators of conversion from mild to moderate in overseas-imported COVID-19 cases.

BackgroundDue to the ongoing pandemic of coronavirus disease 2019 (COVID-19) in foreign countries and regions, many overseas people arrive in China by air. Currently, most of the new cases of COVID-19 were imported from overseas. Here, we evaluated the predictive effect of the level of blood albumin (ALB) and serum prealbumin (PA) level in overseas-imported cases on the conversion of mild COVID-19 to moderate and its value in guiding nutritional support for these travelers.MethodsWe retrospectively analyzed serum levels of ALB and PA of 193 patients with imported COVID-19 admitted to the Shanghai Public Health Clinical Center at the time of admission on April 8, 2020.ResultsSince the first overseas-imported case was admitted to Shanghai on March 5, 2020, 195 overseas-imported cases have been treated in the Shanghai Public Health Clinical Center. The disease was mild or moderate. A total of 193 patients (111 males and 82 females) entered our analysis and the disease was moderate in 108 patients and mild in 85 patients. Patients were aged 6 to 66 years (mean: 28 years). There was a strong negative correlation between the proportion of moderate type and ALB (P=0.0073); thus, patients with a lower level of ALB were more likely to be diagnosed with moderate type. The correlation coefficient was close to 0 in the scatter plot, indicating that there was no linear correlation between PA and the diagnosis of moderate type (P>0.05). There was a strong negative correlation between age and ALB level (P<0.001), while length of hospital stay did not show a linear correlation with ALB or PB levels (both P>0.05). Therefore, older patients had lower levels of ALB and were more likely to develop moderate COVID-19.ConclusionsThe serum ALB level can be an early predictive indicator for the conversion of mild COVID-19 to moderate in cases imported overseas and may guide nutritional support.

Participatory Monitoring and Evaluation of the COVID-19 Response in a Local Public Health System.

The coronavirus disease 2019 (COVID-19) pandemic tested the capacity of local health systems to understand and respond to changing conditions. Although data on new cases of COVID-19 were widely shared in communities, there was less information on the multisector response activities and factors associated with implementation. To address this gap, this empirical case study examined (a) the pattern of implementation of COVID-19 response activities and (b) the factors and critical events associated with both the pattern of new cases and the implementation of the local COVID-19 response. We used a participatory monitoring and evaluation system to capture, code, characterize, and communicate 580 COVID-19 response activities implemented in the city of Lawrence and Douglas County, Kansas. Collaboration across sectors including public health, medical services, city/county government, businesses, social services, public schools, and universities enabled the local public health system’s response effort. Documentation results showed the varying pattern of new COVID-19 cases and response activities over time and the factors identified as enabling or impeding the response and related new cases. Similar participatory monitoring and evaluation methods can be used by local health systems to help understand and respond to the changing conditions of COVID-19 response and recovery.

Association between meteorological factors and daily new cases of COVID-19 in 188 countries: A time series analysis.

By 31 December 2020, Coronavirus disease 2019 (COVID-19) had been prevalent worldwide for one year, and most countries had experienced a complete seasonal cycle. The role of the climate and environment are essential factors to consider in transmission.We explored the association between global meteorological conditions (including mean temperature, wind speed, relative humidity and diurnal temperature range) and new cases of COVID-19 in the whole past year. We assessed the relative risk of meteorological factors to the onset of COVID-19 by using generalized additive models (GAM) and further analyzed the hysteresis effects of meteorological factors using the Distributed Lag Nonlinear Model (DLNM).Our findings revealed that the mean temperature, wind speed and relative humidity were negatively correlated with daily new cases of COVID-19, and the diurnal temperature range was positively correlated with daily new cases of COVID-19. These relationships were more apparent when the temperature and relative humidity were lower than their average value (21.07°Cand 66.83%). The wind speed and diurnal temperature range were higher than the average value(3.07 m/s and 9.53 °C). The maximum RR of mean temperature was 1.30 under −23 °C at lag ten days, the minimum RR of wind speed was 0.29 under 12 m/s at lag 24 days, the maximum RR of range of temperature was 2.21 under 28 °C at lag 24 days, the maximum RR of relative humidity was 1.35 under 4% at lag 0 days. After a subgroup analysis of the countries included in the study, the results were still robust.As the Northern Hemisphere enters winter, the risk of global covid-19 remains high. Some countries have ushered in a new round of COVID-19 epidemic. Thus, active measures must be taken to control the source of infection, block transmission and prevent further spread of COVID-19 in winter.

The successful development and implementation of an off campus triage system during the COVID-19 pandemic in Guangdong, China

To deal with the public health crisis caused by the coronavirus disease 2019 (COVID-19) outbreak, we developed an off campus triage system at entry points to the outpatient, emergency, and inpatient departments. To enhance this off campus triage system, we implemented intensive staff training and made detailed triage plans with a timely referral. Of the 85,414 patients/visitors who visited The Seventh Affiliated Hospital of Southern Medical University, one of the government-designated hospitals to triage-suspected COVID-19 patients between January 22 and March 10, 2020, 359 patients were triaged to the COVID-19 fever clinic and 1,218 were triaged to the general fever clinic; 187 were suspected of COVID-19 infection and quarantined; and four cases of COVID-19 were confirmed and referred. During the outbreak, no inhospital infection and no complaint from patients and their family members occurred, and up to September 10, 2020, no new cases of COVID-19 in this hospital or its catchment area were detected. The off campus triage system is an effective approach to improve the detection of COVID-19 infection and reduce inhospital cross infection.

Non-linear correlation between daily new cases of COVID-19 and meteorological factors in 127 countries

Meteorological parameters are the critical factors of affecting respiratory infectious disease such as Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS) and influenza, however, the effect of meteorological parameters on coronavirus disease 2019 (COVID-19) remains controversial. This study investigated the effects of meteorological factors on daily new cases of COVID-19 in 127 countries, as of August 31 2020. The log-linear generalized additive model (GAM) was used to analyze the effect of meteorological variables on daily new cases of COVID-19. Our findings revealed that temperature, relative humidity, and wind speed are nonlinearly correlated with daily new cases, and they may be negatively correlated with the daily new cases of COVID-19 over 127 countries when temperature, relative humidity and wind speed were below 20°C, 70% and 7 m/s respectively. Temperature(>20°C) was positively correlated with daily new cases. Wind speed (when>7 m/s) and relative humidity (>70%) was not statistically associated with transmission of COVID-19. The results of this research will be a useful supplement to help healthcare policymakers in the Belt and Road countries, the Centers for Disease Control (CDC) and the World Health Organization (WHO) to develop strategies to combat COVID-19.

Optimal models for estimating future infected cases of COVID-19 in Oman

The recent coronavirus disease 2019 (COVID-19) outbreak is of high importance in research topics due to its fast spreading and high rate of infections across the world. In this paper, we test certain optimal models of forecasting daily new cases of COVID-19 in Oman. It is based on solving a certain nonlinear least-squares optimization problem that determines some unknown parameters in fitting some mathematical models. We also consider extension to these models to predict the future number of infection cases in Oman. The modification technique introduces a simple ratio rate of changes in the daily infected cases. This average ratio is computed by employing the rule of Al-Baali [Numerical experience with a class of self-scaling quasi-Newton algorithms, JOTA, 96 (1998), pp. 533–553], in a sense to be defined, for measuring the infection changes.

Calling an Audible: COVID-19 and Competitive Sports.

Calling an Audible: COVID-19 and Competitive Sports.