Cumulative Cases Research Articles

Predictive potential of SARS-CoV-2 RNA concentration in wastewater to assess the dynamics of COVID-19 clinical outcomes and infections

Coronavirus disease 2019 - caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) -, has triggered a worldwide pandemic resulting in 665 million infections and over 6.5 million deaths as of December 15, 2022. The development of different epidemiological tools have helped predict new outbreaks and assess the behavior of clinical variables in different health contexts. In this study, we aimed to monitor concentrations of SARS-CoV-2 in wastewater as a tool to predict the progression of clinical variables during Waves 3, 5, and 6 of the pandemic in the Spanish city of Xátiva from September 2020 to March 2022.We estimated SARS-CoV-2 RNA concentrations in 195 wastewater samples using the RT-PCR Diagnostic Panel validated by the Center for Disease Control and Prevention. We also compared the trends of several clinical variables (14-day cumulative incidence, positive cases, hospital cases and stays, critical cases and stays, primary care visits, and deaths) for each study wave against wastewater SARS-CoV-2 RNA concentrations using Pearson's product-moment correlations, a two-sided Mann-Whitney U test, and a cross-correlation analysis.We found strong correlations between SARS-CoV-2 concentrations with 14-day cumulative incidence and positive cases over time. Wastewater RNA concentrations showed strong correlations with these variables one and two weeks in advance. There were significant correlations with hospitalizations and critical care during Wave 3 and Wave 6; cross-correlations were stronger for hospitalization stays one week before during Wave 6. No association between vaccination percentages and wastewater viral concentrations was observed.Our findings support wastewater SARS-CoV-2 concentrations as a potential surveillance tool to anticipate infection and epidemiological data such as 14-day cumulative incidence, hospitalizations, and critical care stays. Public health authorities could use this epidemiological tool on a similar population as an aid for health care decision-making during an epidemic outbreak.

Forecasting the Spread of COVID-19 Using Deep Learning and Big Data Analytics Methods.

To contain the spread of the COVID-19 pandemic, there is a need for cutting-edge approaches that make use of existing technology capabilities. Forecasting its spread in a single or multiple countries ahead of time is a common strategy in most research. There is, however, a need for all-inclusive studies that capitalize on the entire regions on the African continent. This study closes this gap by conducting a wide-ranging investigation and analysis to forecast COVID-19 cases and identify the most critical countries in terms of the COVID-19 pandemic in all five major African regions. The proposed approach leveraged both statistical and deep learning models that included the autoregressive integrated moving average (ARIMA) model with a seasonal perspective, the long-term memory (LSTM), and Prophet models. In this approach, the forecasting problem was considered as a univariate time series problem using confirmed cumulative COVID-19 cases. The model performance was evaluated using seven performance metrics that included the mean-squared error, root mean-square error, mean absolute percentage error, symmetric mean absolute percentage error, peak signal-to-noise ratio, normalized root mean-square error, and the R2 score. The best-performing model was selected and used to make future predictions for the next 61days. In this study, the long short-term memory model performed the best. Mali, Angola, Egypt, Somalia, and Gabon from the Western, Southern, Northern, Eastern, and Central African regions, with an expected increase of 22.77%, 18.97%, 11.83%, 10.72%, and 2.81%, respectively, were the most vulnerable countries with the highest expected increase in the number of cumulative positive cases.

Dynamic parameterization of a modified SEIRD model to analyze and forecast the dynamics of COVID-19 outbreaks in the United States

The rapid spread of the numerous outbreaks of the coronavirus disease 2019 (COVID-19) pandemic has fueled interest in mathematical models designed to understand and predict infectious disease spread, with the ultimate goal of contributing to the decision making of public health authorities. Here, we propose a computational pipeline that dynamically parameterizes a modified SEIRD (susceptible-exposed-infected-recovered-deceased) model using standard daily series of COVID-19 cases and deaths, along with isolated estimates of population-level seroprevalence. We test our pipeline in five heavily impacted states of the US (New York, California, Florida, Illinois, and Texas) between March and August 2020, considering two scenarios with different calibration time horizons to assess the update in model performance as new epidemiologic data become available. Our results show a median normalized root mean squared error (NRMSE) of 2.38% and 4.28% in calibrating cumulative cases and deaths in the first scenario, and 2.41% and 2.30% when new data are assimilated in the second scenario, respectively. Then, 2-week (4-week) forecasts of the calibrated model resulted in median NRMSE of cumulative cases and deaths of 5.85% and 4.68% (8.60% and 17.94%) in the first scenario, and 1.86% and 1.93% (2.21% and 1.45%) in the second. Additionally, we show that our method provides significantly more accurate predictions of cases and deaths than a constant parameterization in the second scenario (p < 0.05). Thus, we posit that our methodology is a promising approach to analyze the dynamics of infectious disease outbreaks, and that our forecasts could contribute to designing effective pandemic-arresting public health policies.

Analysis of disease burden of occupational pneumoconiosis in Gansu Province from 2010 to 2020

Objective: Through comparative analysis of the disease burden of occupational pneumoconiosis in Gansu Province from 2010 to 2020, the main influencing factors are screened, and scientific basis is provided for rational allocation of limited health resources, precise management and policy implementation. Methods: In August 2021, survey and collect information on surviving occupational pneumoconiosis patients and dead occupational pneumoconiosis patients diagnosed in Gansu Province from 2010 to 2020, and analyze and calculate indicators such as morbidity, mortality, and disability adjusted of life years (DALY). Analyzing the influencing factors of disease burden usirrg multiple linear regression. Results: From 2010 to 2020, the average annual incidence of occupational pneumoconiosis in Gansu Province was 0.9992/100000, the average annual mortality was 0.897/100000, the cumulative case fatality rate was 25.75%, and the cumulative DALY was 28932.96 person-years. The first stage of occupational pneumoconiosis was the highest among DALY loss (19920.14 person-years), and the DALY loss was positively correlated with the stage of occupational pneumoconiosis. Among occupational pneumoconiosis in Gansu Province, silicosis (13753.66 person-years) and coal worker's pneumoconiosis (13414.73 person-years) caused the highest disease burden, followed by cement pneumoconiosis and asbestos lung. Period, length of service, type of disease, and region are all influencing factors of DALY loss (P<0.05). Conclusion: From 2010 to 2020, the DALY losses caused by occupational pneumoconiosis in Gansu Province showed a fluctuating decrease, with the composition of DALY mainly changing from the loss of life years due to premature death to the loss of years due to injury and disability.

Estimation and sensitivity analysis of a COVID-19 model considering the use of face mask and vaccination

To model the COVID-19 infection and develop effective control measures, this paper proposes an SEIR-type epidemic model considering the impact of face-mask wearing and vaccination. Firstly, the effective reproduction number and the threshold conditions are obtained. Secondly, based on the data of South Korea from January 20, 2022 to March 21, 2022, the model parameters are estimated. Finally, a sensitivity analysis and the numerical study are conducted. The results show that the face-mask wearing is associated with 83% and 90% reductions in the numbers of cumulative cases and newly confirmed cases, respectively, after a period of 60 days, when the face mask wearing rate increases by 15%. Furthermore, the vaccination rate is associated with 75% and 80% reductions in the numbers of cumulative cases and the newly confirmed cases, respectively, after the same period of 60 days when the vaccination rate is increased by 15%. A combined measure involving face-mask wearing and vaccination may be more effective and reasonable in preventing and controlling this infection. It is also suggested that disease control departments should strongly recommended the wearing of face masks s as well as vaccination to prevent the unvaccinated people from becoming infected.

Assessing the Impact of Vaccination on the Dynamics of COVID-19 in Africa: A Mathematical Modeling Study.

Several effective COVID-19 vaccines are administered to combat the COVID-19 pandemic globally. In most African countries, there is a comparatively limited deployment of vaccination programs. In this work, we develop a mathematical compartmental model to assess the impact of vaccination programs on curtailing the burden of COVID-19 in eight African countries considering SARS-CoV-2 cumulative case data for each country for the third wave of the COVID-19 pandemic. The model stratifies the total population into two subgroups based on individual vaccination status. We use the detection and death rates ratios between vaccinated and unvaccinated individuals to quantify the vaccine's effectiveness in reducing new COVID-19 infections and death, respectively. Additionally, we perform a numerical sensitivity analysis to assess the combined impact of vaccination and reduction in the SARS-CoV-2 transmission due to control measures on the control reproduction number (Rc). Our results reveal that on average, at least 60% of the population in each considered African country should be vaccinated to curtail the pandemic (lower the Rc below one). Moreover, lower values of Rc are possible even when there is a low (10%) or moderate (30%) reduction in the SARS-CoV-2 transmission rate due to NPIs. Combining vaccination programs with various levels of reduction in the transmission rate due to NPI aids in curtailing the pandemic. Additionally, this study shows that vaccination significantly reduces the severity of the disease and death rates despite low efficacy against COVID-19 infections. The African governments need to design vaccination strategies that increase vaccine uptake, such as an incentive-based approach.

SARS-CoV-2 seroepidemiology in Mongolia, 2020–2021: a longitudinal national study

The COVID-19 pandemic has global impacts but is relatively understudied in developing countries. Mongolia, a lower-middle-income country, instituted strict control measures in early 2020 and avoided widespread transmission until vaccines became available in February, 2021. Mongolia achieved its 60% vaccination coverage goal by July 2021. We investigated the distribution and determinants of SARS-CoV-2 seroprevalence in Mongolia over 2020 and2021. We performed a longitudinal seroepidemiologic study aligned with WHO's Unity Studies protocols. We collected data from a panel of 5000 individuals in four rounds between October 2020 and December 2021. We selected participants through local health centres across Mongolia by age-stratified multi-stage cluster sampling. We tested serum for the presence of total antibodies against SARS-CoV-2 receptor-binding domain, and levels of anti-SARS-CoV-2 spike IgG and neutralising antibodies. We linked participant data with national mortality, COVID-19 case, and vaccination registries. We estimated population seroprevalence and vaccine uptake, as well as unvaccinated population prior-infection prevalence. At the final round in late 2021, 82% (n=4088) of participants completed follow-up. Estimated seroprevalence increased from 1.5% (95% CI: 1.2-2.0), to 82.3% (95% CI: 79.5-84.8) between late-2020 and late-2021. At the final round an estimated 62.4% (95% CI: 60.2-64.5) of the population were vaccinated, and of the unvaccinated population 64.5% (95% CI: 59.7-69.0) had been infected. Cumulative case ascertainment in the unvaccinated was 22.8% (95% CI: 19.1%-26.9%) and the overall infection-fatality ratio was 0.100% (95% CI: 0.088-0.124). Health workers had higher odds for being COVID-19 confirmed cases at all rounds. Males (1.72 (95% CI: 1.33-2.22)) and adults aged 20 and above (12.70 (95% CI: 8.14-20.26)) had higher odds for seroconverting by mid-2021. Among the seropositive, 87.1% (95% CI: 82.3%-90.8%) had SARS-CoV-2 neutralising antibodies by late2021. Our study enabled tracking of SARS-CoV-2 serological markers in the Mongolian population over one year. We found low SARS-CoV-2 seroprevalence in 2020 and early 2021, with seropositivity increasing over a 3-month interval in 2021 due to vaccine roll out and rapid infection of most of the unvaccinated population. Despite high seroprevalence in Mongolia amongst both vaccinated and unvaccinated individuals by end-2021, the SARS-CoV-2 Omicron immune escape variant caused a substantial epidemic. World Health Organization, WHO UNITY Studies initiative, with funding by the COVID-19 Solidarity Response Fund and the German Federal Ministry of Health (BMG) COVID-19 Research and development. The Ministry of Health, Mongolia partially funded this study.

Time series prediction for the epidemic trends of monkeypox using the ARIMA, exponential smoothing, GM (1, 1) and LSTM deep learning methods.

Monkeypox is a critical public health emergency with international implications. Few confirmed monkeypox cases had previously been reported outside endemic countries. However, since May 2022, the number of monkeypox infections has increased exponentially in non-endemic countries, especially in North America and Europe. The objective of this study was to develop optimal models for predicting daily cumulative confirmed monkeypox cases to help improve public health strategies. Autoregressive integrated moving average (ARIMA), exponential smoothing, long short-term memory (LSTM) and GM (1, 1) models were employed to fit the cumulative cases in the world, the USA, Spain, Germany, the UK and France. Performance was evaluated by minimum mean absolute percentage error (MAPE), among other metrics. The ARIMA (2, 2, 1) model performed best on the global monkeypox dataset, with a MAPE value of 0.040, while ARIMA (2, 2, 3) performed the best on the USA and French datasets, with MAPE values of 0.164 and 0.043, respectively. The exponential smoothing model showed superior performance on the Spanish, German and UK datasets, with MAPE values of 0.043, 0.015 and 0.021, respectively. In conclusion, an appropriate model should be selected according to the local epidemic characteristics, which is crucial for monitoring the monkeypox epidemic. Monkeypox epidemics remain severe, especially in North America and Europe, e.g. in the USA and Spain. The development of a comprehensive, evidence-based scientific programme at all levels is critical to controlling the spread of monkeypox infection.

Outbreak response strategies with type 2-containing oral poliovirus vaccines.

Despite exhaustive and fully-financed plans to manage the risks of globally coordinated cessation of oral poliovirus vaccine (OPV) containing type 2 (OPV2) prior to 2016, as of 2022, extensive, continued transmission of circulating vaccine-derived polioviruses (cVDPVs) type 2 (cVDPV2) remains. Notably, cumulative cases caused by cVDPV2 since 2016 now exceed 2,500. Earlier analyses explored the implications of using different vaccine formulations to respond to cVDPV2 outbreaks and demonstrated how different properties of novel OPV2 (nOPV2) might affect its performance compared to Sabin monovalent OPV2 (mOPV2). These prior analyses used fixed assumptions for how outbreak response would occur, but outbreak response implementation can change. We update an existing global poliovirus transmission model to explore different options for responding with different vaccines and assumptions about scope, delays, immunization intensity, target age groups, and number of rounds. Our findings suggest that in order to successfully stop all cVDPV2 transmission globally, countries and the Global Polio Eradication Initiative need to address the deficiencies in emergency outbreak response policy and implementation. The polio program must urgently act to substantially reduce response time, target larger populations - particularly in high transmission areas - and achieve high coverage with improved access to under-vaccinated subpopulations. Given the limited supplies of nOPV2 at the present, using mOPV2 intensively immediately, followed by nOPV2 intensively if needed and when sufficient quantities become available, substantially increases the probability of ending cVDPV2 transmission globally.

Investigating the relationship between cross-national suicide rates and COVID-19 first and second waves spread across the world: An exploratory study

IntroductionCOVID-19 spreads between people in close contact. Social isolation, which is linked with increased suicide risk, prevents COVID-19 from spreading. Suicide and COVID-19 may therefore represent two antagonistic phenomena. Specifically, we tested whether previous cross-national suicide rates inversely correlate with COVID-19 cases and deaths across countries. Material and methodsWe ran unadjusted bivariate correlations between the most updated (2016) cross-national Age-Standardised suicide rates and COVID-19 cumulative cases and deaths (as of: 30/08/2020, 11/10/2020 and 30/05/2021) across countries; and we controlled for WHO Income group, WHO region, suicide data quality, and urbanicity. ResultsSuicide rates negatively correlated with COVID-19 cumulative cases up to 30/08/2020 (r=−0.14, P=.064) and up to 11/10/2020 at an almost significant level (r=−0.149, P=.050) across 174 countries. As of 11/10/2020 this correlation became significant when controlling for WHO region (r=−0.17, P=.028), data quality (r=−0.181, P=.017) and urbanicity (r=−0.172, P=.039); and as of 30/08/2020 when adjusting for WHO region (r=−0.15, P=.047) and data quality a (r=−0.16, P=.036). No significant correlations between suicide rates and COVID-19 deaths were found. ConclusionsThere seems to be an inverse correlation between previous cross-national suicide rates and COVID-19 cumulative cases across countries. Suicide and COVID-19 appear to behave, to some degree, as antagonistic phenomena, which challenges their prevention.

LGBT‐Inclusive Education in Liberal Pluralist Societies

ABSTRACTWhat should be the aim of LGBT‐inclusive, state‐mandated curricula in liberal, pluralist societies? In this article, I identify two distinct aims that such curricula might have. The first, LGBT Respect, aims to teach that LGBT individuals have equal political status and rights. The second, LGBT Approval, aims to teach a positive attitude towards LGBT relationships, including that there is nothing wrongful about these forms of relationship. I examine what arguments in favour of these different aims are available to the liberal concerned with accommodating pluralism. To capture this concern, I adopt political liberalism's strict standard for legitimate political interventions that these must have justifications that all reasonable citizens can accept. This initially seems to recommend curricula that aim at LGBT Respect but stop short of LGBT Approval. Can the political liberal go any further? I propose and critically discuss the most promising arguments in favour of LGBT Approval, including the need to prevent harm to children, ensure political equality, and secure the social conditions needed for the development of the primary good of self‐respect. I tentatively conclude that there exists a cumulative case for state‐mandated curricula aiming at LGBT Approval, at least in the contingent, nonideal circumstances that currently obtain.

Mapping the long-term associations between air pollutants and COVID-19 risks and the attributable burdens in the continental United States

Numerous studies have investigated the associations between COVID-19 risks and long-term exposure to air pollutants, revealing considerable heterogeneity and even contradictory regional results. Studying the spatial heterogeneity of the associations is essential for developing region-specific and cost-effective air-pollutant-related public health policies for the prevention and control of COVID-19. However, few studies have investigated this issue. Using the USA as an example, we constructed single/two-pollutant conditional autoregressions with random coefficients and random intercepts to map the associations between five air pollutants (PM2.5, O3, SO2, NO2, and CO) and two COVID-19 outcomes (incidence and mortality) at the state level. The attributed cases and deaths were then mapped at the county level. This study included 3108 counties from 49 states within the continental USA. The county-level air pollutant concentrations from 2017 to 2019 were used as long-term exposures, and the county-level cumulative COVID-19 cases and deaths through May 13, 2022, were used as outcomes. Results showed that considerably heterogeneous associations and attributable COVID-19 burdens were found in the USA. The COVID-19 outcomes in the western and northeastern states appeared to be unaffected by any of the five pollutants. The east of the USA bore the greatest COVID-19 burdens attributable to air pollution because of its high pollutant concentrations and significantly positive associations. PM2.5 and CO were significantly positively associated with COVID-19 incidence in 49 states on average, whereas NO2 and SO2 were significantly positively associated with COVID-19 mortality. The remaining associations between air pollutants and COVID-19 outcomes were not statistically significant. Our study provided implications regarding where a major concern should be placed on a specific air pollutant for COVID-19 control and prevention, as well as where and how to conduct additional individual-based validation research in a cost-effective manner.

Autophagy in Inflammatory Response against SARS-CoV-2

The coronavirus disease pandemic, which profoundly reshaped the world in 2019 (COVID-19), and is currently ongoing, has affected over 200 countries, caused over 500 million cumulative cases, and claimed the lives of over 6.4 million people worldwide as of August 2022. The causative agent is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Depicting this virus' life cycle and pathogenic mechanisms, as well as the cellular host factors and pathways involved during infection, has great relevance for the development of therapeutic strategies. Autophagy is a catabolic process that sequesters damaged cell organelles, proteins, and external invading microbes, and delivers them to the lysosomes for degradation. Autophagy would be involved in the entry, endo, and release, as well as the transcription and translation, of the viral particles in the host cell. Secretory autophagy would also be involved in developing the thrombotic immune-inflammatory syndrome seen in a significant number of COVID-19 patients that can lead to severe illness and even death. This review aims to review the main aspects that characterize the complex and not yet fully elucidated relationship between SARS-CoV-2 infection and autophagy. It briefly describes the key concepts regarding autophagy and mentions its pro- and antiviral roles, while also noting the reciprocal effect of viral infection in autophagic pathways and their clinical aspects.

Modelling epidemic growth models for lumpy skin disease cases in Thailand using nationwide outbreak data, 2021-2022.

Lumpy skin disease (LSD) is a transboundary disease affecting cattle and has a detrimental effect on the cattle industries in numerous countries in Africa, Europe and Asia. In 2021, LSD outbreaks have been reported in almost all of Thailand's provinces. Indeed, fitting LSD occurrences using mathematical models provide important knowledge in the realm of animal disease modeling. Thus, the objective of this study is to fit the pattern of daily new LSD cases and daily cumulative LSD cases in Thailand using mathematical models. The first- and second-order models in the forms of Lorentzian, Gaussian and Pearson-type VII models are used to fit daily new LSD cases whereas Richard's growth, Boltzmann sigmoidal and Power-law growth models are utilized to fit the curve of cumulative LSD cases. Based on the root-mean-squared error (RMSE) and Akaike information criterion (AIC), results showed that both first and second orders of Pearson-type VII models and Richard's growth model (RGM) were fit to the data better than other models used in the present study. The obtained models and their parameters can be utilized to describe the LSD outbreak in Thailand. For disease preparedness purposes, we can use the first order of the Pearson-type VII model to estimate the time of maximum infected cases occurring when the growth rate of infected cases starts to slow down. Furthermore, the period when the growth rate changes at a slower rate, known as the inflection time, obtained from RGM allows us to anticipate when the pandemic has peaked and the situation has stabilized. This is the first study that utilizes mathematical methods to fit the LSD epidemics in Thailand. This study offers decision-makers and authorities with valuable information for establishing an effective disease control strategy.

Contact pattern, current immune barrier, and pathogen virulence determines the optimal strategy of further vaccination.

The current outbreak of novel coronavirus disease 2019 has caused a serious disease burden worldwide. Vaccines are an important factor to sustain the epidemic. Although with a relatively high-vaccination worldwide, the decay of vaccine efficacy and the arising of new variants lead us to the challenge of maintaining a sufficient immune barrier to protect the population. A case-contact tracking data in Hunan, China, is used to estimate the contact pattern of cases for scenarios including school, workspace, etc, rather than ordinary susceptible population. Based on the estimated vaccine coverage and efficacy, a multi-group vaccinated-exposed-presymptomatic-symptomatic-asymptomatic-removed model (VEFIAR) with 8 age groups, with each partitioned into 4 vaccination status groups is developed. The optimal dose-wise vaccinating strategy is optimized based on the currently estimated immunity barrier of coverage and efficacy, using the greedy algorithm that minimizes the cumulative cases, population size of hospitalization and fatality respectively in a certain future interval. Parameters of Delta and Omicron variants are used respectively in the optimization. The estimated contact matrices of cases showed a concentration on middle ages, and has compatible magnitudes compared to estimations from contact surveys in other studies. The VEFIAR model is numerically stable. The optimal controled vaccination strategy requires immediate vaccination on the un-vaccinated high-contact population of age 30-39 to reduce the cumulative cases, and is stable with different basic reproduction numbers ( ). As for minimizing hospitalization and fatality, the optimized strategy requires vaccination on the un-vaccinated of both aged 30-39 of high contact frequency and the vulnerable older. The objective of reducing transmission requires vaccination in age groups of the highest contact frequency, with more priority for un-vaccinated than un-fully or fully vaccinated. The objective of reducing total hospitalization and fatality requires not only to reduce transmission but also to protect the vulnerable older. The priority changes by vaccination progress. For any region, if the local contact pattern is available, then with the vaccination coverage, efficacy, and disease characteristics of relative risks in heterogeneous populations, the optimal dose-wise vaccinating process will be obtained and gives hints for decision-making.

The Case Against Race-Based Quotas in Pharmaceutical Trials.

This Article is the first to offer a comprehensive case against using racial quotas in pharmaceutical studies by providing a detailed examination of the arguments for and against the practice. It begins by discussing the current racial classification system, calls for racial quotas in pharmaceutical trials, and the troubling history of combining race and scientific investigation. It next examines the cautionary tale of BiDil, the first drug authorized by the U.S. Food and Drug Administration (FDA) for use in only Black people. The third part of the Article sets forth the arguments against racial quotas. The fourth part provides legal analysis of these arguments, concluding that racial quotas in pharmaceutical trials likely would not satisfy the strict scrutiny standard for two independent reasons. The fifth part evaluates the alleged benefits of racial quotas and demonstrates that when properly understood they are insignificant in comparison to the disadvantages. The final part weighs the evidence to arrive at a conclusion and considers future implications.Ultimately, this Article provides a valuable framework for assessing the legal and pragmatic implications not just for pharmaceutical trial quotas but also for other racial-classification issues in health care. For example, while it presents a cumulative case against the proposed practice of racial quotas in pharmaceutical trials, many of the same arguments presented are also applicable to the currently mandated practice of acquiring and reporting racial data of pharmaceutical trial participants. It will serve as a valuable resource not only for opponents of racial quotas but also for advocates. For example, this Article provides numerous race-neutral alternatives for consideration. And the strong case against racial quotas helps facilitate a refocus of efforts away from merely ameliorating the end results of health care disparities and instead targeting the root causes. Evidence suggests that this redirected focus on root causes is more effective at producing positive change. In this way, rejecting these quotas is not in conflict with addressing health disparities; rather, it is beneficial to it. This Article will hopefully serve as a catalyst for future research regarding best practices on how pragmatic; legal; and diversity, equity, and inclusion considerations can synergistically exist.

The Effects of the Government Policies on the Spread of the COVID-19 Pandemic

The study aims to determine whether government policies to control population mobility have been successful in the fight against the coronavirus disease 2019 (COVID-19) pandemic. Policies implemented by governments for controlling population mobility are identified with the Stringency Index prepared by Oxford University. Population mobility is observed through data provided by Google Community Mobility Report. The success of countries in the fight against the COVID-19 pandemic is measured by the Reproduction Rate. The intersection of valid data covering 104 countries is gathered from databases of relevant official websites for the period between the date of reaching the 100th cumulative case and the date 360 days later. The data is analyzed by conducting panel data analysis method to test the hypothesis. Results show that the Stringency Index demonstrating the stringency of government policies implemented by countries to prevent the spreading of pandemic affected human mobility dimensions significantly and reversely. Human mobility dimensions have a reverse and significant impact on staying at home at different levels at the 95% confidence interval. Furthermore, a significant relationship with a very small bi value (-0.00008) emerges between staying at home and the Reproduction Rate in the reverse direction.

Implicit feedback policies for COVID-19: why “zero-COVID” policies remain elusive

Successful epidemic modeling requires understanding the implicit feedback control strategies used by populations to modulate the spread of contagion. While such strategies can be replicated with intricate modeling assumptions, here we propose a simple model where infection dynamics are described by a three parameter feedback policy. Rather than model individuals as directly controlling the contact rate which governs the spread of disease, we model them as controlling the contact rate’s derivative, resulting in a dynamic rather than kinematic model. The feedback policy used by populations across the United States which best fits observations is proportional-derivative control, where learned parameters strongly correlate with observed interventions (e.g., vaccination rates and mobility restrictions). However, this results in a non-zero “steady-state” of case counts, implying current mitigation strategies cannot eradicate COVID-19. Hence, we suggest making implicit policies a function of cumulative cases, resulting in proportional-integral-derivative control with higher potential to eliminate COVID-19.

Modeling the impact of combined use of COVID Alert SA app and vaccination to curb COVID-19 infections in South Africa.

The unanticipated continued deep-rooted trend of the Severe Acute Respiratory Syndrome Corona-virus-2 the originator pathogen of the COVID-19 persists posing concurrent anxiety globally. More effort is affixed in the scientific arena via continuous investigations in a prolific effort to understand the transmission dynamics and control measures in eradication of the epidemic. Both pharmaceutical and non-pharmaceutical containment measure protocols have been assimilated in this effort. In this study, we develop a modified SEIR deterministic model that factors in alternative-amalgamation of use of COVID Alert SA app and vaccination against the COVID-19 to the Republic of South Africa's general public in an endeavor to discontinue the chain of spread for the pandemic. We analyze the key properties of the model not limited to positivity, boundedness, and stability. We authenticate the model by fitting it to the Republic of South Africa's cumulative COVID-19 cases reported data utilizing the Maximum Likelihood Estimation algorithm implemented in fitR package. Sensitivity analysis and simulations for the model reveal that simultaneously-gradually increased implementation of the COVID Alert SA app use and vaccination against COVID-19 to the public substantially accelerate reduction in the plateau number of COVID-19 infections across all the observed vaccine efficacy scenarios. More fundamentally, it is discovered that implementing at least 12% app use (mainly for the susceptible population not vaccinated) with simultaneous vaccination of over 12% of the susceptible population majorly not using the app using a vaccine of at least 50% efficacy would be sufficient in eradicating the pandemic over relatively shorter time span.

Effective pandemic policy design through feedback does not need accurate predictions.

The COVID-19 pandemic has had an enormous toll on human health and well-being and led to major social and economic disruptions. Public health interventions in response to burgeoning case numbers and hospitalizations have repeatedly bent down the epidemic curve, effectively creating a feedback control system. Worst case scenarios have been avoided in many places through this responsive feedback. We aim to formalize and illustrate how to incorporate principles of feedback control into pandemic projections and decision-making, and ultimately shift the focus from prediction to the design of interventions. Starting with an epidemiological model for COVID-19, we illustrate how feedback control can be incorporated into pandemic management using a simple design that couples recent changes in case numbers or hospital occupancy with explicit policy restrictions. We demonstrate robust ability to control a pandemic using a design that responds to hospital cases, despite simulating large uncertainty in reproduction number R0 (range: 1.04-5.18) and average time to hospital admission (range: 4-28 days). We show that shorter delays, responding to case counts versus hospital measured infections, reduce both the cumulative case count and the average level of interventions. Finally, we show that feedback is robust to changing compliance to public health directives and to systemic changes associated with variants of concern and with the introduction of a vaccination program. The negative impact of a pandemic on human health and societal disruption can be reduced by coupling models of disease propagation with models of the decision-making process. In contrast to highly varying open-loop projections, incorporating feedback explicitly in the decision-making process is more reflective of the real-world challenge facing public health decision makers. Using feedback principles, effective control strategies can be designed even if the pandemic characteristics are highly uncertain, encouraging earlier and smaller actions that reduce both case counts and the extent of interventions.