HIV-related mortality in the United States during the COVID-19 pandemic: A population-based study.

BACKGROUND: US progress toward ending the HIV epidemic was disrupted during the COVID-19 pandemic. OBJECTIVES: To determine the impact of the pandemic on HIV-related mortality and potential disparities. METHODS: Using data from the Centers for Disease Control and Prevention and the United States (US) Census Bureau, HIV-related mortality data of decedents aged ≥25 years between 2012 and 2021 were analyzed. Excess HIV-related mortality rates were estimated by determining the difference between observed and projected mortality rates during the pandemic. The trends of mortality were quantified with joinpoint regression analysis. RESULTS: Of the 79,725 deaths documented in adults aged 25 years and older between 2012 and 2021, a significant downward trend was noted in HIV-related mortality rates before the pandemic, followed by a surge during the pandemic. The observed mortality rates were 18.8% (95% confidence interval [CI]: 13.1%-25.5%) and 25.4% (95%CI: 19.9%-30.4%) higher than the projected values in 2020 and 2021, respectively. Both of these percentages were higher than that in the general population in 2020 (16.4%, 95%CI: 14.9%-17.9%) and 2021 (19.8%, 95%CI: 18.0%-21.6%), respectively. Increased HIV-related mortality was observed across all age subgroups, but those aged 25-44 years demonstrated the greatest relative increase and the lowest COVID-19-related deaths when compared to middle- and old-aged decedents. Disparities were observed across racial/ethnic subgroups and geographic regions. CONCLUSIONS: The pandemic led to a reversal in the attainments made to reduce the prevalence of HIV. Individuals living with HIV were disproportionately affected during the pandemic. Thoughtful policies are needed to address the disparity in excess HIV-related mortality.

Trends of Cirrhosis-related Mortality in the USA during the COVID-19 Pandemic.

Immunocompromised status and interrupted routine care may render patients with cirrhosis vulnerable to the coronavirus disease 2019 (COVID-19) pandemic. A nationwide dataset that includes more than 99% of the decedents in the U.S. between April 2012 and September 2021 was used. Projected age-standardized mortality during the pandemic were estimated according to prepandemic mortality rates, stratified by season. Excess deaths were determined by estimating the difference between observed and projected mortality rates. A temporal trend analysis of observed mortality rates was also performed in 0.83 million decedents with cirrhosis between April 2012 and September 2021 was included. Following an increasing trend of cirrhosis-related mortality before the pandemic, with a semiannual percentage change (SAPC) of 0.54% [95% confidence interval (CI): (0.0-1.0%), p=0.036], a precipitous increase with seasonal variation occurred during the pandemic (SAPC 5.35, 95% CI: 1.9-8.9, p=0.005). Significantly increased mortality rates were observed in those with alcohol-associated liver disease (ALD), with a SAPC of 8.44 (95% CI: 4.3-12.8, p=0.001) during the pandemic. All-cause mortality of nonalcoholic fatty liver disease rose steadily across the entire study period with a SAPC of 6.79 (95% CI: 6.3-7.3, p<0.001). The decreasing trend of HCV-related mortality was reversed during the pandemic, while there was no significant change in HBV-related deaths. While there was significant increase in COVID-19-related deaths, more than 55% of the excess deaths were the indirect impact of the pandemic. We observed an alarming increase in cirrhosis-related deaths during the pandemic especially for ALD, with evidence in both direct and indirect impact. Our findings have implications on formulating policies for patients with cirrhosis.

Excess risk for acute myocardial infarction mortality during the COVID-19 pandemic.

The COVID-19 pandemic has had a detrimental impact on the healthcare system. Our study armed to assess the extent and the disparity in excess acute myocardial infarction (AMI)-associated mortality during the pandemic, through the recent Omicron outbreak. Using data from the CDC's National Vital Statistics System, we identified 1 522 669 AMI-associated deaths occurring between 4/1/2012 and 3/31/2022. Accounting for seasonality, we compared age-standardized mortality rate (ASMR) for AMI-associated deaths between prepandemic and pandemic periods, including observed versus predicted ASMR, and examined temporal trends by demographic groups and region. Before the pandemic, AMI-associated mortality rates decreased across all subgroups. These trends reversed during the pandemic, with significant rises seen for the youngest-aged females and males even through the most recent period of the Omicron surge (10/2021-3/2022). The SAPC in the youngest and middle-age group in AMI-associated mortality increased by 5.3% (95% confidence interval [CI]: 1.6%-9.1%) and 3.4% (95% CI: 0.1%-6.8%), respectively. The excess death, defined as the difference between the observed and the predicted mortality rates, was most pronounced for the youngest (25-44 years) aged decedents, ranging from 23% to 34% for the youngest compared to 13%-18% for the oldest age groups. The trend of mortality suggests that age and sex disparities have persisted even through the recent Omicron surge, with excess AMI-associated mortality being most pronounced in younger-aged adults.

Impact of the COVID-19 pandemic on liver disease-related mortality rates in the United States.

BACKGROUND: The pandemic has resulted in an increase of deaths not directly related to COVID-19 infection. We aimed to use a national death dataset to determine the impact of the pandemic on people with liver disease in the U.S, focusing on alcohol-associated liver disease (ALD) and non-alcoholic fatty liver disease (NAFLD). METHODS: Using data from the National Vital Statistic System from the CDC WONDER platform and ICD-10 codes, we identified deaths associated with liver disease. We evaluated observed versus predicted mortality for 2020-2021 based on trends from 2010-2019 with joinpoint and prediction modeling analysis. RESULTS: Among 626,090 chronic liver disease-related deaths between 2010 and 2021, Age-standardized mortality rates (ASMR) for ALD dramatically increased between 2010-2019 and 2020-2021 (annual percentage change [APC] 3.5% to 17.6%, P<0.01), leading to a higher observed ASMR (per 100,000 persons) than predicted for 2020 (15.67 vs.13.04) and 2021 (17.42 vs.13.41). ASMR for NAFLD also increased during the pandemic (APC:14.5%), while the rates for hepatitis B and C decreased. Notably, the ASMR rise for ALD was most pronounced in non-Hispanic Whites, Blacks, and Alaska Indians/Native Americans (APC: 11.7%, 10.8%, 18.0%, all P<0.05), with similar but less critical findings for NAFLD while rates were steady for non-Hispanic Asians throughout 2010-2021 (APC: 4.9%). The ASMR rise for ALD was particularly severe for the 25-44 age group (APC: 34.6%, versus 13.7% and 12.6% for 45-64 and ≥65, all P<0.01), which were also all higher than pre-COVID-19 rates (all P<0.01). CONCLUSIONS: ASMR for ALD and NAFLD increased at an alarming rate during the COVID-19 pandemic with the largest disparities among the young, non-Hispanic White, and Alaska Indian/Native American populations. LAY SUMMARY: The impact of the pandemic on people with liver disease in the U.S remains unclear. This study indicated that age-standardized mortality rates for alcohol associated liver disease and non-alcohol fatty liver disease greatly accelerated during the COVID-19 pandemic with the largest disparities among the young, non-Hispanic White, and Alaska Indian/Native American populations. Increasing awareness about the care importance of chronic liver disease in specific populations must be prioritized.

Excess diabetes mellitus-related deaths during the COVID-19 pandemic in the United States.

Background: Diabetes mellitus (DM) is a critical risk factor for severe SARS-CoV-2 infection, and SARS-CoV-2 infection contributes to worsening glycemic control. The COVID-19 pandemic profoundly disrupted the delivery of care for patients with diabetes. We aimed to determine the trend of DM-related deaths during the pandemic. Methods: In this serial population-based study between January 1, 2006 and December 31, 2021, mortality data of decedents aged ≥25 years from the National Vital Statistics System dataset was analyzed. Decedents with DM as the underlying or contributing cause of death on the death certificate were defined as DM-related deaths. Excess deaths were estimated by comparing observed versus expected age-standardized mortality rates derived from mortality during 2006-2019 with linear and polynomial regression models. The trends of mortality were quantified with joinpoint regression analysis. Subgroup analyses were performed by age, sex, race/ethnicity, and state. Findings: Among 4·25 million DM-related deaths during 2006-2021, there was a significant surge of more than 30% in mortality during the pandemic, from 106·8 (per 100,000 persons) in 2019 to 144·1 in 2020 and 148·3 in 2021. Adults aged 25-44 years had the most pronounced rise in mortality. Widened racial/ethnic disparity was observed, with Hispanics demonstrating the highest excess deaths (67·5%; 95% CI 60·9-74·7%), almost three times that of non-Hispanic whites (23·9%; 95% CI 21·2-26·7%). Interpretation: The United States saw an increase in DM-related mortality during the pandemic. The disproportionate rise in young adults and the widened racial/ethnic disparity warrant urgent preventative interventions from diverse stakeholders. Funding: National Natural Science Foundation of China.

COVID-19 epidemic in New York City: development of an age group-specific mathematical model to predict the outcome of various vaccination strategies.

BACKGROUND: Since December 14, 2020, New York City (NYC) has started the first batch of COVID-19 vaccines. However, the shortage of vaccines is currently an inevitable problem. Therefore, optimizing the age-specific COVID-19 vaccination is an important issue that needs to be addressed as a priority. OBJECTIVE: Combined with the reported COVID-19 data in NYC, this study aimed to construct a mathematical model with five age groups to estimate the impact of age-specific vaccination on reducing the prevalence of COVID-19. METHODS: We proposed an age-structured mathematical model and estimated the unknown parameters based on the method of Markov Chain Monte Carlo (MCMC). We also calibrated our model by using three different types of reported COVID-19 data in NYC. Moreover, we evaluated the reduced cumulative number of deaths and new infections with different vaccine allocation strategies. RESULTS: Compared with the current vaccination strategy in NYC, if we gradually increased the vaccination coverage rate for only one age groups from March 1, 2021 such that the vaccination coverage rate would reach to 40% by June 1, 2021, then as of June 1, 2021, the cumulative deaths in the 75-100 age group would be reduced the most, about 72 fewer deaths per increased 100,000 vaccinated individuals, and the cumulative new infections in the 0-17 age group would be reduced the most, about 21,591 fewer new infections per increased 100,000 vaccinated individuals. If we gradually increased the vaccination coverage rate for two age groups from March 1, 2021 such that the vaccination coverage rate would reach to 40% by June 1, 2021, then as of June 1, 2021, the cumulative deaths in the 65-100 age group would be reduced the most, about 36 fewer deaths per increased 100,000 vaccinated individuals, and the cumulative new infections in the 0-44 age group would be reduced the most, about 17,515 fewer new infections per increased 100,000 vaccinated individuals. In addition, if we had an additional 100,000 doses of vaccine for 0-17 and 75-100 age groups as of June 1, 2021, then the allocation of 80% to the 0-17 age group and 20% to the 75-100 age group would reduce the maximum numbers of new infections and deaths simultaneously in NYC. CONCLUSIONS: The COVID-19 burden including deaths and new infections would decrease with increasing vaccination coverage rate. Priority vaccination to the elderly and adolescents would minimize both deaths and new infections.

Modeling the Transmission Dynamics of COVID-19 Pandemic in Caputo Type Fractional Derivative

COVID-19 disease, a deadly pandemic ravaging virtually throughout the world today, is undoubtedly a great calamity to human existence. There exists no complete curative medicine or successful vaccines that could be used for the complete control of this deadly pandemic at the moment. Consequently, the study of the trends of this pandemic is critical and of great importance for disease control and risk management. Computation of the basic reproduction number by means of mathematical modeling can be helpful in estimating the potential and severity of an outbreak and providing insightful information which is useful to identify disease intensity and necessary interventions. Considering the enormity of the challenge and the burdens which the spread of this COVID-19 disease placed on healthcare system, the present paper attempts to study the pattern and the trend of spread of this disease and prescribes a mathematical model which governs COVID-19 pandemic using Caputo type derivative. Local stability of the equilibria is also discussed in the paper. Some numerical simulations are given to illustrate the analytical results. The obtained results shows that applied numerical technique is computationally strong for modeling COVID-19 pandemic.

Investigating the relationship between reopening the economy and implementing control measures during the COVID-19 pandemic.

OBJECTIVES: The COVID-19 pandemic has resulted in an enormous burden on population health and the economy around the world. Although most cities in the United States have reopened their economies from previous lockdowns, it was not clear how the magnitude of different control measures-such as face mask use and social distancing-may affect the timing of reopening the economy for a local region. This study aimed to investigate the relationship between reopening dates and control measures and identify the conditions under which a city can be reopened safely. STUDY DESIGN: This was a mathematical modeling study. METHODS: We developed a dynamic compartment model to capture the transmission dynamics of COVID-19 in New York City. We estimated model parameters from local COVID-19 data. We conducted three sets of policy simulations to investigate how different reopening dates and magnitudes of control measures would affect the COVID-19 epidemic. RESULTS: The model estimated that maintaining social contact at 80% of the prepandemic level and a 50% face mask usage would prevent a major surge of COVID-19 after reopening. If social distancing were completely relaxed after reopening, face mask usage would need to be maintained at nearly 80% to prevent a major surge. CONCLUSIONS: Adherence to social distancing and increased face mask usage are keys to prevent a major surge after a city reopens its economy. The findings from our study can help policymakers identify the conditions under which a city can be reopened safely.

How to Reduce the Transmission Risk of COVID-19 More Effectively in New York City: An Age-Structured Model Study.

Background: In face of the continuing worldwide COVID-19 epidemic, how to reduce the transmission risk of COVID-19 more effectively is still a major public health challenge that needs to be addressed urgently. Objective: This study aimed to develop an age-structured compartment model to evaluate the impact of all diagnosed and all hospitalized on the epidemic trend of COVID-19, and explore innovative and effective releasing strategies for different age groups to prevent the second wave of COVID-19. Methods: Based on three types of COVID-19 data in New York City (NYC), we calibrated the model and estimated the unknown parameters using the Markov Chain Monte Carlo (MCMC) method. Results: Compared with the current practice in NYC, we estimated that if all infected people were diagnosed from March 26, April 5 to April 15, 2020, respectively, then the number of new infections on April 22 was reduced by 98.02, 93.88, and 74.08%. If all confirmed cases were hospitalized from March 26, April 5, and April 15, 2020, respectively, then as of June 7, 2020, the total number of deaths in NYC was reduced by 67.24, 63.43, and 51.79%. When only the 0-17 age group in NYC was released from June 8, if the contact rate in this age group remained below 61% of the pre-pandemic level, then a second wave of COVID-19 could be prevented in NYC. When both the 0-17 and 18-44 age groups in NYC were released from June 8, if the contact rates in these two age groups maintained below 36% of the pre-pandemic level, then a second wave of COVID-19 could be prevented in NYC. Conclusions: If all infected people were diagnosed in time, the daily number of new infections could be significantly reduced in NYC. If all confirmed cases were hospitalized in time, the total number of deaths could be significantly reduced in NYC. Keeping a social distance and relaxing lockdown restrictions for people between the ages of 0 and 44 could not lead to a second wave of COVID-19 in NYC.

Modeling the effects of the contaminated environments on COVID-19 transmission in India.

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus that caused an outbreak of typical pneumonia first in Wuhan and then globally. Although researchers focus on the human-to-human transmission of this virus but not much research is done on the dynamics of the virus in the environment and the role humans play by releasing the virus into the environment. In this paper, a novel nonlinear mathematical model of the COVID-19 epidemic is proposed and analyzed under the effects of the environmental virus on the transmission patterns. The model consists of seven population compartments with the inclusion of contaminated environments means there is a chance to get infected by the virus in the environment. We also calculated the threshold quantity R0 to know the disease status and provide conditions that guarantee the local and global asymptotic stability of the equilibria using Volterra-type Lyapunov functions, LaSalle's invariance principle, and the Routh-Hurwitz criterion. Furthermore, the sensitivity analysis is performed for the proposed model that determines the relative importance of the disease transmission parameters. Numerical experiments are performed to illustrate the effectiveness of the obtained theoretical results.

Effects of New York's Executive Order on Face Mask Use on COVID-19 Infections and Mortality: A Modeling Study.

There is growing evidence on the effect of face mask use in controlling the spread of COVID-19. However, few studies have examined the effect of local face mask policies on the pandemic. In this study, we developed a dynamic compartmental model of COVID-19 transmission in New York City (NYC), which was the epicenter of the COVID-19 pandemic in the USA. We used data on daily and cumulative COVID-19 infections and deaths from the NYC Department of Health and Mental Hygiene to calibrate and validate our model. We then used the model to assess the effect of the executive order on face mask use on infections and deaths due to COVID-19 in NYC. Our results showed that the executive order on face mask use was estimated to avert 99,517 (95% CIs 72,723-126,312) COVID-19 infections and 7978 (5692-10,265) deaths in NYC. If the executive order was implemented 1 week earlier (on April 10), the averted infections and deaths would be 111,475 (81,593-141,356) and 9017 (6446-11,589), respectively. If the executive order was implemented 2 weeks earlier (on April 3 when the Centers for Disease Control and Prevention recommended face mask use), the averted infections and deaths would be 128,598 (94,373-162,824) and 10,515 (7540-13,489), respectively. Our study provides public health practitioners and policymakers with evidence on the importance of implementing face mask policies in local areas as early as possible to control the spread of COVID-19 and reduce mortality.

Projected COVID-19 epidemic in the United States in the context of the effectiveness of a potential vaccine and implications for social distancing and face mask use.

BACKGROUND: Multiple candidates of COVID-19 vaccines have entered Phase III clinical trials in the United States (US). There is growing optimism that social distancing restrictions and face mask requirements could be eased with widespread vaccine adoption soon. METHODS: We developed a dynamic compartmental model of COVID-19 transmission for the four most severely affected states (New York, Texas, Florida, and California). We evaluated the vaccine effectiveness and coverage required to suppress the COVID-19 epidemic in scenarios when social contact was to return to pre-pandemic levels and face mask use was reduced. Daily and cumulative COVID-19 infection and death cases from 26th January to 15th September 2020 were obtained from the Johns Hopkins University Coronavirus resource center and used for model calibration. RESULTS: Without a vaccine (scenario 1), the spread of COVID-19 could be suppressed in these states by maintaining strict social distancing measures and face mask use levels. But relaxing social distancing restrictions to the pre-pandemic level without changing the current face mask use would lead to a new COVID-19 outbreak, resulting in 0.8-4 million infections and 15,000-240,000 deaths across these four states over the next 12 months. Under this circumstance, introducing a vaccine (scenario 2) would partially offset this negative impact even if the vaccine effectiveness and coverage are relatively low. However, if face mask use is reduced by 50% (scenario 3), a vaccine that is only 50% effective (weak vaccine) would require coverage of 55-94% to suppress the epidemic in these states. A vaccine that is 80% effective (moderate vaccine) would only require 32-57% coverage to suppress the epidemic. In contrast, if face mask usage stops completely (scenario 4), a weak vaccine would not suppress the epidemic, and further major outbreaks would occur. A moderate vaccine with coverage of 48-78% or a strong vaccine (100% effective) with coverage of 33-58% would be required to suppress the epidemic. Delaying vaccination rollout for 1-2 months would not substantially alter the epidemic trend if the current non-pharmaceutical interventions are maintained. CONCLUSIONS: The degree to which the US population can relax social distancing restrictions and face mask use will depend greatly on the effectiveness and coverage of a potential COVID-19 vaccine if future epidemics are to be prevented. Only a highly effective vaccine will enable the US population to return to life as it was before the pandemic.

Modeling and analysis of COVID-19 epidemics with treatment in fractional derivatives using real data from Pakistan.

Coronaviruses are a large family of viruses that cause different symptoms, from mild cold to severe respiratory distress, and they can be seen in different types of animals such as camels, cattle, cats and bats. Novel coronavirus called COVID-19 is a newly emerged virus that appeared in many countries of the world, but the actual source of the virus is not yet known. The outbreak has caused pandemic with 26,622,706 confirmed infections and 874,708 reported deaths worldwide till August 31, 2020, with 17,717,911 recovered cases. Currently, there exist no vaccines officially approved for the prevention or management of the disease, but alternative drugs meant for HIV, HBV, malaria and some other flus are used to treat this virus. In the present paper, a fractional-order epidemic model with two different operators called the classical Caputo operator and the Atangana-Baleanu-Caputo operator for the transmission of COVID-19 epidemic is proposed and analyzed. The reproduction number R 0 is obtained for the prediction and persistence of the disease. The dynamic behavior of the equilibria is studied by using fractional Routh-Hurwitz stability criterion and fractional La Salle invariant principle. Special attention is given to the global dynamics of the equilibria. Moreover, the fitting of parameters through least squares curve fitting technique is performed, and the average absolute relative error between COVID-19 actual cases and the model's solution for the infectious class is tried to be reduced and the best fitted values of the relevant parameters are achieved. The numerical solution of the proposed COVID-19 fractional-order model under the Caputo operator is obtained by using generalized Adams-Bashforth-Moulton method, whereas for the Atangana-Baleanu-Caputo operator, we have used a new numerical scheme. Also, the treatment compartment is included in the population which determines the impact of alternative drugs applied for treating the infected individuals. Furthermore, numerical simulations of the model and their graphical presentations are performed to visualize the effectiveness of our theoretical results and to monitor the effect of arbitrary-order derivative.

Transmission patterns of COVID-19 in the mainland of China and the efficacy of different control strategies: a data- and model-driven study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak has seriously endangered the health and lives of Chinese people. In this study, we predicted the COVID-19 epidemic trend and estimated the efficacy of several intervention strategies in the mainland of China. METHODS: According to the COVID-19 epidemic status, we constructed a compartmental model. Based on reported data from the National Health Commission of People's Republic of China during January 10-February 17, 2020, we estimated the model parameters. We then predicted the epidemic trend and transmission risk of COVID-19. Using a sensitivity analysis method, we estimated the efficacy of several intervention strategies. RESULTS: The cumulative number of confirmed cases in the mainland of China will be 86 763 (95% CI: 86 067-87 460) on May 2, 2020. Up until March 15, 2020, the case fatality rate increased to 6.42% (95% CI: 6.16-6.68%). On February 23, 2020, the existing confirmed cases reached its peak, with 60 890 cases (95% CI: 60 350-61 431). On January 23, 2020, the effective reproduction number was 2.620 (95% CI: 2.567-2.676) and had dropped below 1.0 since February 5, 2020. Due to governmental intervention, the total number of confirmed cases was reduced by 99.85% on May 2, 2020. Had the isolation been relaxed from February 24, 2020, there might have been a second peak of infection. However, relaxing the isolation after March 16, 2020 greatly reduced the number of existing confirmed cases and deaths. The total number of confirmed cases and deaths would increase by 8.72 and 9.44%, respectively, due to a 1-day delayed diagnosis in non-isolated infected patients. Moreover, if the coverage of close contact tracing was increased to 100%, the cumulative number of confirmed cases would be decreased by 88.26% on May 2, 2020. CONCLUSIONS: The quarantine measures adopted by the Chinese government since January 23, 2020 were necessary and effective. Postponing the relaxation of isolation, early diagnosis, patient isolation, broad close-contact tracing, and strict monitoring of infected persons could effectively control the COVID-19 epidemic. April 1, 2020 would be a reasonable date to lift quarantine in Hubei and Wuhan.