Epidemic Rates Research Articles

Data-driven estimation of the instantaneous reproduction number and growth rates for the 2022 monkeypox outbreak in Europe.

To estimate the instantaneous reproduction number Rt and the epidemic growth rates for the 2022 monkeypox outbreaks in the European region. We gathered daily laboratory-confirmed monkeypox cases in the most affected European countries from the beginning of the outbreak to September 23, 2022. A data-driven estimation of the instantaneous reproduction number is obtained using a novel filtering type Bayesian inference. A phenomenological growth model coupled with a Bayesian sequential approach to update forecasts over time is used to obtain time-dependent growth rates in several countries. The instantaneous reproduction number Rt for the laboratory-confirmed monkeypox cases in Spain, France, Germany, the UK, the Netherlands, Portugal, and Italy. At the early phase of the outbreak, our estimation for Rt, which can be used as a proxy for the basic reproduction number R0, was 2.06 (95% CI 1.63 - 2.54) for Spain, 2.62 (95% CI 2.23 - 3.17) for France, 2.81 (95% CI 2.51 - 3.09) for Germany, 1.82 (95% CI 1.52 - 2.18) for the UK, 2.84 (95% CI 2.07 - 3.91) for the Netherlands, 1.13 (95% CI 0.99 - 1.32) for Portugal, 3.06 (95% CI 2.48 - 3.62) for Italy. Cumulative cases for these countries present subexponential rather than exponential growth dynamics. Our findings suggest that the current monkeypox outbreaks present limited transmission chains of human-to-human secondary infection so the possibility of a huge pandemic is very low. Confirmed monkeypox cases are decreasing significantly in the European region, the decline might be attributed to public health interventions and behavioral changes in the population due to increased risk perception. Nevertheless, further strategies toward elimination are essential to avoid the subsequent evolution of the monkeypox virus that can result in new outbreaks.

Scoring epidemiological forecasts on transformed scales.

Forecast evaluation is essential for the development of predictive epidemic models and can inform their use for public health decision-making. Common scores to evaluate epidemiological forecasts are the Continuous Ranked Probability Score (CRPS) and the Weighted Interval Score (WIS), which can be seen as measures of the absolute distance between the forecast distribution and the observation. However, applying these scores directly to predicted and observed incidence counts may not be the most appropriate due to the exponential nature of epidemic processes and the varying magnitudes of observed values across space and time. In this paper, we argue that transforming counts before applying scores such as the CRPS or WIS can effectively mitigate these difficulties and yield epidemiologically meaningful and easily interpretable results. Using the CRPS on log-transformed values as an example, we list three attractive properties: Firstly, it can be interpreted as a probabilistic version of a relative error. Secondly, it reflects how well models predicted the time-varying epidemic growth rate. And lastly, using arguments on variance-stabilizing transformations, it can be shown that under the assumption of a quadratic mean-variance relationship, the logarithmic transformation leads to expected CRPS values which are independent of the order of magnitude of the predicted quantity. Applying a transformation of log(x + 1) to data and forecasts from the European COVID-19 Forecast Hub, we find that it changes model rankings regardless of stratification by forecast date, location or target types. Situations in which models missed the beginning of upward swings are more strongly emphasised while failing to predict a downturn following a peak is less severely penalised when scoring transformed forecasts as opposed to untransformed ones. We conclude that appropriate transformations, of which the natural logarithm is only one particularly attractive option, should be considered when assessing the performance of different models in the context of infectious disease incidence.

Elucidating Late Leaf Spot Disease Progression and Resistance Components in Different Groundnut (Arachis hypogaea L.) Cultivars towards Phaeoisariopsis personata

Late Leaf Spot (LLS) disease is caused by Phaeoisariopsis personata, a devastating disease that significantly affects groundnut (Arachis hypogaea L.) production worldwide. This research aimed to investigate the disease progress in resistant and susceptible groundnut genotypes under green house conditions with artificial inoculations. The four popularly growing groundnut cultivars in Andhra Pradesh i.e., Kadiri-6 (K6), Dharani, Harithandra and Lepakshi (K-1812) were evaluated in this study. The disease severity scale, percent disease index (PDI), Area under disease progression curve (AUDPC) and the epidemic rate (rate) were estimated by using the linear model. Highest disease severity was observed in K6 (88.19 %) and Dharani (85.19 %) with greater disease progression rate was observed in K6 (0.13) and Dharani (0.12) cultivars. However, the lowest disease progression was observed in Lepakshi (0.04) and Harithandhra (0.08) cultivars. Enhanced resistance to LLS was reported with Harithandhra and Lepakshi cultivars due to slower epidemic rate, longer incubation and latent periods with smaller lesions.

Diabetic Nephropathy: Significance of Determining Oxidative Stress and Opportunities for Antioxidant Therapies.

Diabetes mellitus (DM) belongs to the category of socially significant diseases with epidemic rates of increases in prevalence. Diabetic nephropathy (DN) is a specific kind of kidney damage that occurs in 40% of patients with DM and is considered a serious complication of DM. Most modern methods for treatments aimed at slowing down the progression of DN have side effects and do not produce unambiguous positive results in the long term. This fact has encouraged researchers to search for additional or alternative treatment methods. Hyperglycemia has a negative effect on renal structures due to a number of factors, including the activation of the polyol and hexosamine glucose metabolism pathways, the activation of the renin-angiotensin-aldosterone and sympathetic nervous systems, the accumulation of advanced glycation end products and increases in the insulin resistance and endothelial dysfunction of tissues. The above mechanisms cause the development of oxidative stress (OS) reactions and mitochondrial dysfunction, which in turn contribute to the development and progression of DN. Modern antioxidant therapies for DN involve various phytochemicals (food antioxidants, resveratrol, curcumin, alpha-lipoic acid preparations, etc.), which are widely used not only for the treatment of diabetes but also other systemic diseases. It has also been suggested that therapeutic approaches that target the source of reactive oxygen species in DN may have certain advantages in terms of nephroprotection from OS. This review describes the significance of studies on OS biomarkers in the pathogenesis of DN and analyzes various approaches to reducing the intensity of OS in the prevention and treatment of DN.

Effect of Rapid Urbanization in Mainland China on the Seasonal Influenza Epidemic: Spatiotemporal Analysis of Surveillance Data From 2010 to 2017.

The world is undergoing an unprecedented wave of urbanization. However, the effect of rapid urbanization during the early or middle stages of urbanization on seasonal influenza transmission remains unknown. Since about 70% of the world population live in low-income countries, exploring the impact of urbanization on influenza transmission in urbanized countries is significant for global infection prediction and prevention. The aim of this study was to explore the effect of rapid urbanization on influenza transmission in China. We performed spatiotemporal analyses of province-level influenza surveillance data collected in Mainland China from April 1, 2010, to March 31, 2017. An agent-based model based on hourly human contact-related behaviors was built to simulate the influenza transmission dynamics and to explore the potential mechanism of the impact of urbanization on influenza transmission. We observed persistent differences in the influenza epidemic attack rates among the provinces of Mainland China across the 7-year study period, and the attack rate in the winter waves exhibited a U-shaped relationship with the urbanization rates, with a turning point at 50%-60% urbanization across Mainland China. Rapid Chinese urbanization has led to increases in the urban population density and percentage of the workforce but decreases in household size and the percentage of student population. The net effect of increased influenza transmission in the community and workplaces but decreased transmission in households and schools yielded the observed U-shaped relationship. Our results highlight the complicated effects of urbanization on the seasonal influenza epidemic in China. As the current urbanization rate in China is approximately 59%, further urbanization with no relevant interventions suggests a worrisome increasing future trend in the influenza epidemic attack rate.

Bayesian estimation of real-time epidemic growth rates using Gaussian processes: local dynamics of SARS-CoV-2 in England

Abstract Quantitative assessments of the recent state of an epidemic and short-term projections for the near future are key public-health tools that have substantial policy impacts, helping to determine if existing control measures are sufficient or need to be strengthened. Key to these quantitative assessments is the ability to rapidly and robustly measure the speed with which an epidemic is growing or decaying. Frequently, epidemiological trends are addressed in terms of the (time-varying) reproductive number R. Here, we take a more parsimonious approach and calculate the exponential growth rate, r, using a Bayesian hierarchical model to fit a Gaussian process to the epidemiological data. We show how the method can be employed when only case data from positive tests are available, and the improvement gained by including the total number of tests as a measure of the heterogeneous testing effort. Although the methods are generic, we apply them to SARS-CoV-2 cases and testing in England, making use of the available high-resolution spatio-temporal data to determine long-term patterns of national growth, highlight regional growth, and spatial heterogeneity.

Gaidai reliability method for long-term coronavirus modelling.

Novel coronavirus disease has been recently a concern for worldwide public health. To determine epidemic rate probability at any time in any region of interest, one needs efficient bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the multi-dimensionality advantage, that suggested methodology offers, namely dealing efficiently with multiple regions at the same time and accounting for cross-correlations between different regional observations. Modern multi-dimensional novel statistical method was directly applied to raw clinical data, able to deal with territorial mapping. Novel reliability method based on statistical extreme value theory has been suggested to deal with challenging epidemic forecast. Authors used MATLAB optimization software. This paper described a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. Namely, accurate maximum recorded patient numbers are predicted for the years to come for the analyzed provinces. The suggested method performed well by supplying not only an estimate but 95% confidence interval as well. Note that suggested methodology is not limited to any specific epidemics or any specific terrain, namely its truly general. The only assumption and limitation is bio-system stationarity, alternatively trend analysis should be performed first. The suggested methodology can be used in various public health applications, based on their clinical survey data.

Impact of national-scale targeted point-of-care symptomatic lateral flow testing on trends in COVID-19 infections, hospitalisations and deaths during the second epidemic wave in Austria (REAP3)

BackgroundIn October 2020, amidst the second COVID-19 epidemic wave and before the second-national lockdown, Austria introduced a policy of population-wide point-of-care lateral flow antigen testing (POC-LFT). This study explores the impact of this policy by quantifying the association between trends in POC-LFT-activity with trends in PCR-positivity (as a proxy for symptomatic infection), hospitalisations and deaths related to COVID-19 between October 22 and December 06, 2020.MethodsWe stratified 94 Austrian districts according to POC-LFT-activity (number of POC-LFTs performed per 100,000 inhabitants over the study period), into three population cohorts: (i) high(N = 24), (ii) medium(N = 45) and (iii) low(N = 25). Across the cohorts we a) compared trends in POC-LFT-activity with PCR-positivity, hospital admissions and deaths related to COVD-19; b) compared the epidemic growth rate before and after the epidemic peak; and c) calculated the Pearson correlation coefficients between PCR-positivity with COVID-19 hospitalisations and with COVID -19 related deaths.ResultsThe trend in POC-LFT activity was similar to PCR-positivity and hospitalisations trends across high, medium and low POC-LFT activity cohorts, with association with deaths only present in cohorts with high POC-LFT activity. Compared to the low POC-LFT-activity cohort, the high-activity cohort had steeper pre-peak daily increase in PCR-positivity (2.24 more cases per day, per district and per 100,000 inhabitants; 95% CI: 2.0–2.7; p < 0.001) and hospitalisations (0.10; 95% CI: 0.02, 0.18; p = 0.014), and 6 days earlier peak of PCR-positivity. The high-activity cohort also had steeper daily reduction in the post-peak trend in PCR-positivity (-3.6; 95% CI: -4.8, -2.3; p < 0.001) and hospitalisations (-0.2; 95% CI: -0.32, -0.08; p = 0.001). PCR-positivity was positively correlated to both hospitalisations and deaths, but with lags of 6 and 14 days respectively.ConclusionsHigh POC-LFT-use was associated with increased and earlier case finding during the second Austrian COVID-19 epidemic wave, and early and significant reduction in cases and hospitalisations during the second national lockdown. A national policy promoting symptomatic POC-LFT in primary care, can capture trends in PCR-positivity and hospitalisations. Symptomatic POC-LFT delivered at scale and combined with immediate self-quarantining and contact tracing can thus be a proxy for epidemic status, and hence a useful tool that can replace large-scale PCR testing.

Population genetics and phylogeography of alfalfa mosaic virus in China and a comparison with other regional epidemics based on the cp gene.

Alfalfa mosaic virus (AMV) is the most pervasive epidemic virus affecting alfalfa production. However, detailed investigations on the molecular population genetics and evolutionary dynamics of AMV are scarce. This study aimed to report on a large-scale long-term survey of genetic variability in AMV populations from China and perform a comparative analysis of AMV population genetics in the three most thoroughly studied countries to date: China, Iran, and Spain. The study was based on the analysis of the coat protein gene (cp) using two analytical approaches: an analysis of molecular variance (AMOVA) and a Bayesian Markov Chain Monte Carlo approach that investigates the association between geographical origin and phylogeny. Both analytical approaches found significant genetic differentiation within localities, but not among localities nor among provinces. This observation might result from inappropriate agronomical practices involving extensive exchange of plant materials followed by rapid viral diversification within localities. In the Chinese population, both methods found that genetic diversification in AMV was strongly associated with different bioclimatic zones. Rates of molecular evolution were similar in the three countries. The estimated epidemic exponential population size and growth rate suggest that the epidemics grew faster and with higher incidence in Iran, followed by Spain and China. Estimates of the time to the most recent common ancestors suggest that AMV was first seen in Spain by the beginning of the twentieth century and later on in eastern and central Eurasia. After ruling out the existence of recombination breakpoints within the cp gene, a codon-based selection analysis per population was performed and identified many codons under significant negative selection and a few under significant positive selection; the latter varied among countries, suggesting regional differences in selective pressures.

Multiple Concurrent Causal Relationships and Multiple Governance Pathways for Non-Pharmaceutical Intervention Policies in Pandemics: A Fuzzy Set Qualitative Comparative Analysis Based on 102 Countries and Regions.

The global outbreak of COVID-19 has been wreaking havoc on all aspects of human societies. In addition to pharmaceutical interventions, non-pharmaceutical intervention policies have been proven to be crucial in slowing down the spread of the virus and reducing the impact of the outbreak on economic development, daily life, and social stability. However, no studies have focused on which non-pharmaceutical intervention policies are more effective; this is the focus of our study. We used data samples from 102 countries and regions around the world and selected seven categories of related policies, including work and school suspensions, assembly restrictions, movement restrictions, home isolation, international population movement restrictions, income subsidies, and testing and screening as the condition variables. A susceptible-exposed-infected-quarantined-recovered (SEIQR) model considering non-pharmaceutical intervention policies and latency with infectiousness was constructed to calculate the epidemic transmission rate as the outcome variable, and a fuzzy set qualitative comparative analysis (fsQCA) method was applied to explore the multiple concurrent causal relationships and multiple governance paths of non-pharmaceutical intervention policies for epidemics from the configuration perspective. We found a total of four non-pharmaceutical intervention policy pathways. Among them, L1 was highly suppressive, L2 was moderately suppressive, and L3 was externally suppressive. The results also showed that individual non-pharmaceutical intervention policy could not effectively suppress the spread of the pandemic. Moreover, three specific non-pharmaceutical intervention policies, including work stoppage and school closure, testing and screening, and economic subsidies, had a universal effect in the policies grouping for effective control of the pandemic transmission.

Mathematical Modeling and Dynamics of SARS-CoV-2 in Colombia

As COVID-19 continues to spread around the globe, it is critical to understand the true burden of a future outbreak in developing countries like Colombia where data may be limited. Here, we estimated the rate of the initial exponential growth of cases and the basic reproductive rate for the disease. We use models with different modeling assumptions to study the differences between five major Colombian cities and between selected Latin American countries. Using an ensemble modeling technique, we estimated that the reproduction number in Colombia varied from 1.10 in Cartagena to 1.75 in Medellin with Cali being 1.47. In Latin America, Ecuador has highest initial epidemic growth rate and Panama the lowest with Colombia in middle of the list. The choice of appropriate model and parameter estimates for a location provided different scenarios in outbreaks. This analysis provides a framework for the decision makers to be better prepared for an outbreak.

Inference from Open-Source Sequence Data on the Genetic Epidemiology of COVID-19 Infection in Africa

Between late December 2019 when it was first reported, to early September 2020, severe acute respiratory syndrome coronavirus-2 (SARSCoV- 2), the causative agent of coronavirus disease-2019 (COVID-19), reportedly infected over 10 million people globally. In Africa, more than 300,000 infections occurred within the period, from which several genetic sequences of the virus were generated. During disease outbreak, phylogenetic reconstruction of genomic data can assist in making epidemiological inferences about time of pathogen introduction, epidemic growth rate and temporal-spatial spread of the infection. In this work, we studied the genetic epidemiology of COVID-19 in Africa. Genetic sequence data of SARS-CoV-2 and metadata from African countries were obtained from publicly accessible sequence database hosted by the GISAID initiative. Whole genome sequences were subjected to multiple sequence alignment and the aligned sequences used to construct a Maximum Likelihood phylogenetic tree based on the general time reversible model. Of the 227 genetic sequences obtained for 9 African countries (DRC=133, Senegal=23, South Africa=20, Ghana=15, Tunisia=6, Algeria=3, Gambia=3, Egypt=2 and Nigeria=2), 220 were whole genome sequences while 7 were partial genome sequences of the surface glycoprotein S. Majority of the viruses (58.1%) belonged to the G clade of GISAID classification. Phylogenetic analysis confirmed multiple introductions of the virus to the continent from multiple external sources prior to local adaptation and spread. The very close alignment of three viruses - Ghana/1659_S14/2020|EPI_ISL_422405, DRC/KN0054/2020|EPI_ ISL_417437, and South_Africa/R05475/2020|EPI_ISL_435059 – to the reference Wuhan strain on the time tree, suggests possible introduction and circulation of the virus into the continent much earlier than when the first case in the continent was announced in February 15 2020. In conclusion, this study provided evidence to support multiple introductions of SARSCoV- 2 into Africa, and further suggests that the virus may have already been circulating in the continent prior to official reporting of the first case. Also, there is strong impression to infer likely genetic adaptation of the virus in the continent that may account for the close clustering of isolates from different countries of the continent.

A quasi-static biomechanical model of the human myocardium based on Cardiac Magnetic Resonance images

The left ventricle (LV) has an important mechanical part in pumping blood throughout the body and it is generally sensitive to the failure. Heart failure is one of the world's leading causes of death and has reached the epidemic rate. For the study of heart failure, there are several diagnostic methods, but computational biomechanical models of human myocardium would be a practical tool in diagnostics and patient specific treatment planning. These models help in classifying myocardium pathologies. In this paper, we developed a quasi-static biomechanical model of the LV for healthy subjects based on Cardiac Magnetic Resonance (CMR) acquisition. Estimation LV strain is done using Finite Element (FE) software Abaqus and clinical assessment measured from 20 healthy subjects, such as End Systole (ES) and End Diastole (ED) volumes, ED wall thickness and wall thickening. We acquired LV End Systole strain component is within one standard deviation of the measurements made in the equatorial region of LV. The results of this study suggest that the proposed FE model of LV describes the physiological function of the heart and can differentiate between normal and pathological cardiac function.

Gestational diabetes and its correlation with maternal socio-demographic characteristics

Background and objective: Gestational diabetes mellitus is the most common metabolic disorder of pregnancy with increasing prevalence at epidemic rates. This study aimed to determine the prevalence of gestational diabetes mellitus and related risk factors in a sample of pregnant women in Erbil city. Methods: A cross-sectional study was conducted on a convenience sample of 216 pregnant women at primary health care centers in Erbil city, Kurdistan Region, Iraq, from April 2019 to January 2021. The pregnant women were selected independent of having or not having risk factors. Pregnant women were screened for diabetes mellitus using 50 gm glucose challenge test in 24–28th weeks of gestational age. Women with positive glucose challenge test underwent 100 g glucose tolerance test. Results: The 50g glucose challenge test revealed that 54 women (25.0%) were positive for gestational diabetes mellitus. Glucose tolerance test (100 gm) revealed that the prevalence of diabetes among the sample was 11.9%. The association between the different characteristics and risk factors with the diagnosed diabetic status of the study participants showed that there was a significant association with the older age, obesity, higher parity, and history of recurrent miscarriage. Conclusion: Pregnancy is associated with a high prevalence of gestational diabetes mellitus in the current research. Pregnant women, particularly those in advanced maternal age, with pre-pregnancy high body mass index and having a history of recurrent miscarriages, should be investigated for the possibility of the presence of gestational diabetes mellitus.

Unveiling of the epidemiological patterns for caprine/ovine enterovirus infection in China.

Caprine/ovine enterovirus (CEV/OEV) infection is an emerging disease and remains largely unknown for its infection distribution, epidemic pattern, and the underlying contribution factors. Here, we report the investigation on CEV/OEV infection pattern and the underlying contribution factors by employing a sandwich ELISA kit for detection of CEV/OEV antigen. Epidemiological investigation revealed a wide range of infection rates of CEV/OEV from 19.80%-39.00% on goat/sheep farms in the major goat/sheep-raising provinces as such Henan, Shandong, Ningxia, Jilin, Inner Mongolia autonomous region, and Xinjiang autonomous region in China. Epidemic patterns and infection rates for CEV/OEV were affected by the breeds, raising mode, regions, and seasons. CEV/OEV infection rates were varied in different regions in China and significantly higher in the diarrheal herds (40.30%) than these in non-diarrheal herds (13.83%). Moreover, infection rate was higher in sheep (24.59%) than that in goats (9.84%), even dramatic difference among different breeds of goat or sheep. Out of different breeds of goat, Boer (20.13%) had the highest infection rate, followed by local breed (5.62%) and Saanen (2.61%). Among these breeds of sheep, higher infection rates were detected in local breed sheep (42.86%) and small-tailed Han sheep (35.91%) than these of Hu sheep (13.41%) and Dorper sheep (16.34%). Furthermore, raising modes were showed to contribute to the infection rate, where higher rates were detected among goats/sheep in captivity (27.10%) than these in free-range (12.27%) and semi-free range (19.24%). Additionally, CEV/OEV infection rate had obvious seasonality, while they increased from year 2015 to 2019. In summary, we investigated the CEV/OEV infection among the goat/sheep herds from different regions in China, revealed the epidemic pattern and the contribution factors to the infection, which provided the epidemiological data for future prevention and control of this emerging infection.

Survival and predictors of mortality among HIV-infected adults receiving ART in Hawassa comprehensive specialized hospital, Sidama regional state, Ethiopia

Background: Having claimed lives, HIV/AIDS is still a significant global public health concern. Antiretroviral therapy (ART) is now widely available, and this rapid expansion of access is dramatically improving HIV epidemic survival rates worldwide. Objectives: The aim of this study was to identify the mortality risk factors and survival status of ART patients attending Hawassa Comprehensive Specialized Hospital in 2020. Methods: In a five-year retrospective cohort research, all patients seen between January 2015 and December 2019 were analyzed. The data were analyzed with SPSS 25.0. The Kaplan-Meier Log-rank model was employed to gauge the survival time of ART patients based on explanatory variables. Both bivariate and multivariate Cox proportional hazards regression models were employed to identify the independent causes of mortality. Results: Patients on ART had a 74% overall survival probability. With a median survival of 34 months, there are 0.135 deaths for every 100 person-years. Hemoglobin level (HR = 2.38; 95% CI = 3.3-6.3), WHO clinical stage III and IV (HR = 3; 95% CI = 2.2-9.5, p = 0.04), Age &gt; = 60 (HR = 1.6; 95% CI = 1.3-2, p = 0.04) and Functional status bed ridden (HR = 3.1; 95% CI = 1.2-9.4, p = 0.04) were all independent predictors of death among RVI patients. Conclusion: In comparison to trials conducted in wealthy countries, the survival rate of ART patients in this study was low. Patients who are anemic; WHO advanced clinical stage; old age, and functional status bedridden should be closely followed and monitored.

A novel statistical method for long-term coronavirus modelling

Background: Novel coronavirus disease has been recently a concern for worldwide public health. To determine epidemic rate probability at any time in any region of interest, one needs efficient bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the multi-dimensionality advantage, that suggested methodology offers, namely dealing efficiently with multiple regions at the same time and accounting for cross-correlations between different regional observations. Methods: Modern multi-dimensional novel statistical method was directly applied to raw clinical data, able to deal with territorial mapping. Novel reliability method based on statistical extreme value theory has been suggested to deal with challenging epidemic forecast. Authors used MATLAB optimization software. Results: This paper described a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. Namely, accurate maximum recorded patient numbers are predicted for the years to come for the analyzed provinces. Conclusions: The suggested method performed well by supplying not only an estimate but 95% confidence interval as well. Note that suggested methodology is not limited to any specific epidemics or any specific terrain, namely its truly general. The only assumption and limitation is bio-system stationarity, alternatively trend analysis should be performed first. The suggested methodology can be used in various public health applications, based on their clinical survey data.

Authors’ Reply to the Discussion of ‘Are Epidemic Growth Rates More Informative than Reproduction Numbers?’ by Parag et al. in Session 1 of the Royal Statistical Society’s Special Topic Meeting on COVID-19 Transmission: 9 June 2021

Authors’ Reply to the Discussion of ‘Are Epidemic Growth Rates More Informative than Reproduction Numbers?’ by Parag et al. in Session 1 of the Royal Statistical Society’s Special Topic Meeting on COVID-19 Transmission: 9 June 2021

Survival and Predictors of Mortality among HIV-Infected Adults Receiving Art in Hawassa Comprehensive Specialized Hospital, Sidama Regional State, Ethiopia

Background: Having claimed lives, HIV/AIDS is still a significant global public health concern. Antiretroviral therapy (ART) is now widely available, and this rapid expansion of access is dramatically improving HIV epidemic survival rates worldwide.&#x0D; Objectives: The purpose of this study was to identify the mortality risk factors and survival status of ART patients attending Hawassa Comprehensive Specialized Hospital in 2020.&#x0D; Methods: All patients seen between January 2015 and December 2019 were examined in a five-year retrospective cohort study. SPSS 25.0 was used to analyze the data. Based on explanatory variables, the Kaplan-Meier Log-rank model was used to assess the survival time of ART patients. To find the independent determinants of mortality, bivariate, and multivariate Cox proportional hazards regression models were used.&#x0D; Result: Overall survival probability of ART patients was 74%. The death incidence rate of 0.135 per 100 person-years with medial survival of 34 months. Hemoglobin level (HR=2.38;95% CI= 3.3-6.3); WHO clinical stage III and IV (HR=3; 95% CI= 2.2-9.5, p=0.04); Age &gt;=60 (HR=1.6; 95% CI=1.3–2, p=0.04); Functional status bed ridden (HR=3.1; 95% CI=1.2-9.4, p=0.04) were independent predictors of mortality among RVI patients.&#x0D; Conclusion: In this study, the survival rate of ART patients was low in comparison to trials done in rich countries. Close follow-up and monitoring should be given to patients who are anemic; WHO advanced clinical stage; old age, functional status bedridden needs special attention.

Retrospective Analysis of COVID-19 Vaccination in a University Hospital and the Relationship with the Epidemic Rate

It was aimed to make a retrospective analysis of the vaccines administered in the vaccine unit of our hospital since the beginning of the COVID-19 pandemic. The study was carried out retrospectively from the vaccination records our hospital, 14 January 2021 to 01 July 2022. In the 18-month period, a total of 75079 (100%) vaccines were administered, 59386 (79%) were Biontech and 15693 (21%) were Sinovac vaccines. Only Sinovac was available until June, therefore, 66% of the Sinovac vaccines were administered in the first five months period. After the Biontech vaccine started to be applied in our hospital, the rates of Sinovac vaccination have declined drastically. When the epidemic and vaccination graphs were examined separately, inverse correlation was found between the peak points of the epidemic and vaccine graphics. Biontech was found to be the most preferred vaccine by individuals against COVID-19 in this study.