Actual Number Of Cases Research Articles

Early warning and predicting of COVID-19 using zero-inflated negative binomial regression model and negative binomial regression model

BackgroundIt is difficult to detect the outbreak of emergency infectious disease based on the exiting surveillance system. Here we investigate the utility of the Baidu Search Index, an indicator of how large of a keyword is in Baidu’s search volume, in the early warning and predicting the epidemic trend of COVID-19.MethodsThe daily number of cases and the Baidu Search Index of 8 keywords (weighted by population) from December 1, 2019 to March 15, 2020 were collected and analyzed with times series and Spearman correlation with different time lag. To predict the daily number of COVID-19 cases using the Baidu Search Index, Zero-inflated negative binomial regression was used in phase 1 and negative binomial regression model was used in phase 2 and phase 3 based on the characteristic of independent variable.ResultsThe Baidu Search Index of all keywords in Wuhan was significantly higher than Hubei (excluded Wuhan) and China (excluded Hubei). Before the causative pathogen was identified, the search volume of “Influenza” and “Pneumonia” in Wuhan increased with the number of new onset cases, their correlation coefficient was 0.69 and 0.59, respectively. After the pathogen was public but before COVID-19 was classified as a notifiable disease, the search volume of “SARS”, “Pneumonia”, “Coronavirus” in all study areas increased with the number of new onset cases with the correlation coefficient was 0.69 ~ 0.89, while “Influenza” changed to negative correlated (rs: -0.56 ~ -0.64). After COVID-19 was closely monitored, the Baidu Search Index of “COVID-19”, “Pneumonia”, “Coronavirus”, “SARS” and “Mask” could predict the epidemic trend with 15 days, 5 days and 6 days lead time, respectively in Wuhan, Hubei (excluded Wuhan) and China (excluded Hubei). The predicted number of cases would increase 1.84 and 4.81 folds, respectively than the actual number of cases in Wuhan and Hubei (excluded Wuhan) from 21 January to 9 February.ConclusionThe Baidu Search Index could be used in the early warning and predicting the epidemic trend of COVID-19, but the search keywords changed in different period. Considering the time lag from onset to diagnosis, especially in the areas with medical resources shortage, internet search data can be a highly effective supplement of the existing surveillance system.

Development and Comparison of Time Series Models in Predicting Severe Fever with Thrombocytopenia Syndrome Cases - Hubei Province, China, 2013-2020.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus, which has a high mortality rate. Predicting the number of SFTS cases is essential for early outbreak warning and can offer valuable insights for establishing prevention and control measures. In this study, data on monthly SFTS cases in Hubei Province, China, from 2013 to 2020 were collected. Various time series models based on seasonal auto-regressive integrated moving average (SARIMA), Prophet, eXtreme Gradient Boosting (XGBoost), and long short-term memory (LSTM) were developed using these historical data to predict SFTS cases. The established models were evaluated and compared using mean absolute error (MAE) and root mean squared error (RMSE). Four models were developed and performed well in predicting the trend of SFTS cases. The XGBoost model outperformed the others, yielding the closest fit to the actual case numbers and exhibiting the smallest MAE (2.54) and RMSE (2.89) in capturing the seasonal trend and predicting the monthly number of SFTS cases in Hubei Province. The developed XGBoost model represents a promising and valuable tool for SFTS prediction and early warning in Hubei Province, China.

The impact of COVID-19 and missed diagnosis of breast cancer.

1118 Background: To evaluate the impact of COVID-19 pandemic on breast cancer diagnosis based on age, race, and regions within the US. Methods: Data on breast cancer incidence were obtained from the United States Cancer Statistics (USCS) program from 2001-2020. SEER*Stat 8.4.1.2 and Joinpoint regression program 5.0.2 were used to calculate estimated and actual incidence rates per 100,000 females. Rates were age-adjusted as appropriate. We compared actual breast cancer incidence rates in the year 2020 to estimated rates based on trends from 2001-2019 with sub-analyses for age, race, and region. Results: From 2001 to 2019, there were 4,005,763 identified cases of breast cancer in the United States. In 2019, prior to the pandemic, the incidence rate of breast cancer was 162.87 per 100,000 in females in the US. Based on the data from 2001 to 2019, breast cancer increased by 1.32% per year (p<0.001). Using these prior trends, it was expected that the incidence of breast cancer to be 165.02 per 100,000 adults (265,206 total cases) in 2020. However, the reported incidence was only 147.77 per 100,000 (237,471). Thus, there were 10.46% (27,735) less breast cancer cases than expected. The missed diagnosis rate was more pronounced in older age groups with 15.89%, 14.75%, and 13.27% cases missed for the age groups 85+, 80-84, and 70-74 respectively. Based on racial and ethnic groups, we found that Hispanic females had the greatest number of missed cases at 13.04% compared to 12.68%, 10.57%, and 8.94% of Non-Hispanic Asians and Pacific Islander (NHAPI), Non-Hispanic White (NHW), and Non-Hispanic Black (NHB) females respectively. The discrepancy between expected and actual number of cases by U.S. region ranged from the highest in the Northeast (11.90%) followed by 11.83%, 10.60%, and 9.49% in the West, South, and Midwest regions respectively. An intersectionality analysis showed that 40–44-year-old Hispanic females residing in the Midwest region had the largest discrepancy at 27.01%. Conclusions: The reported breast cancer cases significantly declined during the pandemic. Older age, Hispanic race and ethnicity, and those residing in the Northeast region had the greatest number of missing cases. As breast cancer is the most common cancer in the United States, more mitigating factors are needed to improve the access to care for patients in these at-risk groups to improve detection of breast cancer and its associated mortality.

The impact of COVID-19 and missed diagnosis of colorectal cancer.

e15598 Background: To evaluate the impact of COVID-19 pandemic on colorectal cancer diagnosis based on sex, age, race, and regions within the US. Methods: Data on colorectal cancer incidence were obtained from the United States Cancer Statistics (USCS) program from 2001-2020. SEER*Stat 8.4.1.2 and Joinpoint regression program 5.0.2 were used to calculate estimated and actual incidence rates per 100,000 adults. Rates were age-adjusted as appropriate. We compared actual colorectal cancer incidence rates in the year 2020 to estimated rates based on trends from 2001-2019 with sub-analyses for sex, age, race, and region. Results: From 2001 to 2019, there were 2,627,565 identified cases of colorectal cancer in the United States. In 2019, prior to the pandemic, the incidence rate of colorectal cancer was the highest in males at 42.26 per 100,000 compared to 32.18 in females. The overall cancer incidence was 36.87 per 100,000 in both males and females in the US. Based on the data from 2001 to 2019, colorectal cancer decreased by 2.20% per year (p < 0.001). Using these prior trends, it was expected that the incidence of colorectal cancer to be 36.44 per 100,000 adults (115,392 total cases) in 2020. However, the reported incidence was only 32.47 per 100,000 (102,798). Thus, there were 10.9% (12,594) less colorectal cancer cases than expected. The missed diagnosis rate was more pronounced in males compared to females, where 11.90% and 10.84% less colorectal cancer cases were detected compared to expected in 2020, respectively. Based on age, the cases missed were the highest amongst middle aged adults, with 13.56%, 13.49%, and 13.05% cases missed for the age groups between 50-54, 65-69, and 70-74 respectively. Based on racial and ethnic groups, we found that Non-Hispanic Asians and Pacific Islander (NHAPI) had the greatest number of missed cases at 14.38% compared to 11.98%, 11.36%, and 11.27% of Non-Hispanic Black (NHB), Non-Hispanic White (NHW), and Hispanic adults, respectively. The discrepancy between expected and actual number of cases by U.S. region ranged from the highest in the Southern (11.64%) to the lowest in the Western (10.92%) region. An intersectionality analysis showed that 40–44-year-old NHAPI females residing in the Western region had the largest discrepancy at 47.96%. Conclusions: The reported colorectal cancer cases significantly declined during the pandemic. Males, middle age, NHAPI and NHB race and ethnicity, and those residing in the Southern region had the most number of missing cases. Due to the widespread prevalence of colorectal cancers, more mitigating factors are needed to improve the access to care for patients in these at-risk groups to improve diagnosis and detection of colorectal cancer and its associated mortality.

Mumps in Poland in 2021.

Mumps is a contagious viral disease occurring mainly in children, the source of infection being the sick/infected person. Since 2003, vaccination against mumps has been mandatory in Poland, performed according to a two-dose schedule. As part of the Public Health Immunization Program (PSO), the MMR combination vaccine (against measles, mumps and rubella) is used for the entire population of children. The aim of this study was to evaluate epidemiological indicators of mumps in Poland in 2021 compared to previous years, taking into account the impact of the COVID-19 pandemic. The analysis of the epidemiological situation of mumps in Poland in 2021 was based on the interpretation of data from the bulletin , "Infectious diseases and poisonings in Poland in 2021" and , "Immunization in Poland in 2021". 484 cases of mumps were registered in Poland in 2021. The total incidence was 1.3 per 100,000 residents, which was lower than in 2020. The highest incidence of 1.8 per 100,000 residents was registered in Pomorskie Province, and the lowest incidence of 0.7 in Lower Silesia Province. The highest incidence (6.4/100 thousand) was recorded in children aged 0-4 and 5-9. The incidence rate for men (1.4/100,000) was higher than for women (1.1). In 2021, there were 9 patients hospitalized due to mumps, this was more than in 2020. The decrease in the number of cases of mumps in 2021 remained related to the ongoing pandemic - the restrictions introduced during the pandemic period led to a decrease in the number of cases not only of COVID-19, but also of other diseases spread by the droplet route, including mumps. The number of registered cases based on the reports of diagnosing physicians may be underestimating the actual number of cases due to the continued difficult access of patients to primary care physicians.

An autoregressive integrated moving average and long short-term memory (ARIM-LSTM) hybrid model for multi-source epidemic data prediction

The COVID-19 pandemic has far-reaching impacts on the global economy and public health. To prevent the recurrence of pandemic outbreaks, the development of short-term prediction models is of paramount importance. We propose an ARIMA-LSTM (autoregressive integrated moving average and long short-term memory) model for predicting future cases and utilize multi-source data to enhance prediction performance. Firstly, we employ the ARIMA-LSTM model to forecast the developmental trends of multi-source data separately. Subsequently, we introduce a Bayes-Attention mechanism to integrate the prediction outcomes from auxiliary data sources into the case data. Finally, experiments are conducted based on real datasets. The results demonstrate a close correlation between predicted and actual case numbers, with superior prediction performance of this model compared to baseline and other state-of-the-art methods.

Clinical course, management and outcomes of COVID-19 in HIV-infected renal transplant recipients: A case series.

HIV-infected kidney transplant recipients with COVID-19 are at increased risk of acute illness and death owing to their underlying comorbidities and chronic immunosuppression. To describe the incidence, clinical presentation and course of COVID-19, vaccination status, and SARS-CoV-2 antibody positivity rate among HIV-infected-to-HIV-infected kidney transplant recipients in South Africa (SA). This retrospective study reports on rates of SARS-CoV-2 infection, COVID-19 and mortality among SA HIV-infected kidney transplant recipients who received organs from HIV-infected donors (HIV positive to HIV positive), before and after vaccination. Patient demographics, clinical presentation, course, management and disease outcomes were analysed. Antibody serology tests were performed between May and September 2022. Among 39 HIV-positive-to-HIV-positive transplant recipients, 11 cases of COVID-19 were diagnosed from March 2020 to September 2022. Six patients (55%) required hospitalisation, of whom 3 were admitted to a high-care unit or intensive care unit. Two patients required mechanical ventilation, and 2 received acute dialysis. One patient was declined access to intensive care. Four patients (10%) died of COVID-19 pneumonia. All the COVID-19-positive patients had at least one comorbidity. Vaccination data were available for 24 patients, of whom 5 had refused SARS-CoV-2 vaccination. SARS-CoV-2 antibody data were available for 20 patients; 4 vaccinated patients had a negative nucleocapsid protein antibody test and a positive spike protein antibody test, suggesting vaccination-acquired immunity. The remaining 16 patients demonstrated immunity that was probably due to COVID infection, and of these, 14 were also vaccinated. Of the 11 COVID-19 cases, only 1 was observed after vaccination. In our case series, ~10% of the HIV-positive-to-HIV-positive transplant recipients died of COVID-19 pneumonia. This mortality rate appears higher than figures reported in other transplant cohorts. However, it is likely that the actual number of cases of SARS-CoV-2 infection was much higher, as the study only included polymerase chain reaction-confirmed cases. It remains unclear whether HIV infection, transplant or the combination of the two drives poorer outcomes, and larger studies adjusting for important demographic and biological factors may isolate these effects.

Hematopoietic cell transplantation and cell therapy activity landscape survey in the Kingdom of Saudi Arabia; a report from the Saudi Society of Blood and Marrow Transplantation (SSBMT)

Hematopoietic Cell Transplantation (HCT) activity was surveyed in the Kingdom of Saudi Arabia (KSA). The overall rate of HCT per 10,000,000 inhabitants doubled every 10 years. 15,031 HCTs were reported by all the functional HCT centers in KSA since inception of HCT program. Out of total HCT 15,031; 10,232(68%) were reported in adults, and 4799(32%) in the pediatric population. Allogeneic HCT constituted 10,489(70%) of total HCT, with majority from Human Leukocyte Antigen matched identical sibling (85.4%). The autologous HCTs were 4542(30%). During the last five years 2018–2022; in total 5164 HCTs were performed, with the majority had allogeneic HCT 3,085(59.74%), followed by the autologous HCT 3085(40.2%). The top three main indications of the autologous HCT were Multiple Myeloma 299(28%), Hodgkin Lymphoma 293(27.8%), and Non-Hodgkin Lymphoma 212(20%). Hemoglobinopathies 615(27.6%) were mostly indicated for allogeneic HCT, followed by Acute Myeloid Leukemia 433(19.4%), and Precursors Lymphoid Neoplasms 322(14.4%). The HCT activity landscape survey provides the updated current state and trends for HCT in KSA. The reported HCT numbers differ than what was reported by international registries, since not all the cases have been reported. We urge to have a common data hub nationally in order to capture the actual number of cases.

Predicting COVID-19 Re-Positive Cases in Malnourished Older Adults: A Clinical Model Development and Validation.

Building and validating a clinical prediction model for novel coronavirus (COVID-19) re-positive cases in malnourished older adults. Malnourished older adults from January to May 2023 were retrospectively collected from the Department of Geriatrics of the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine. They were divided into a "non-re-positive" group and a "re-positive" group based on the number of COVID-19 infections, and into a training set and a validation set at a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to identify predictive factors for COVID-19 re-positivity in malnourished older adults, and a nomogram was constructed. Independent influencing factors were screened by multivariate logistic regression. The model's goodness-of-fit, discrimination, calibration, and clinical impact were assessed by Hosmer-Lemeshow test, area under the curve (AUC), calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CIC), respectively. We included 347 cases, 243 in the training set, and 104 in the validation set. We screened 10 variables as factors influencing the outcome. By multivariate logistic regression analysis, preliminary identified protective factors, risk factors, and independent influencing factors that affect the re-positive outcome. We constructed a clinical prediction model for COVID-19 re-positivity in malnourished older adults. The Hosmer-Lemeshow test yielded χ2 =5.916, P =0.657; the AUC was 0.881; when the threshold probability was >8%, using this model to predict whether malnourished older adults were re-positive for COVID-19 was more beneficial than implementing intervention programs for all patients; when the threshold was >80%, the positive estimated value was closer to the actual number of cases. This model can help identify the risk of COVID-19 re-positivity in malnourished older adults early, facilitate early clinical decision-making and intervention, and have important implications for improving patient outcomes. We also expect more large-scale, multicenter studies tofurther validate, refine, and update this model.

Analysis on Emergency Management of Urban Public Safety in the Era of Big Data

With the continuous development of computer technology and Internet technology, the world has entered the era of big data. The advent of the era of big data has brought opportunities and challenges to the development of all walks of life in China, and it is also true for urban emergency management, which not only helps the government to improve the efficiency of emergency management, but also effectively solves the adverse social consequences caused by the government’s lack of corresponding early warning capabilities. Of course, as the so-called opportunities and challenges coexist, the era of big data brings opportunities to the development of urban emergency management, but it also brings great challenges to relevant departments. This paper aims to comprehensively analyze the development of urban emergency management in the era of big data by consulting relevant literature and investigating a large number of actual cases of urban emergency management, so as to provide reference for relevant workers.

ОЦЕНКА ВЕРОЯТНОСТИ НЕСАНКЦИОНИРОВАННОЙ ПОДАЧИ НАПРЯЖЕНИЯ В СЕЛЬСКИХ ЭЛЕКТРИЧЕСКИХ СЕТЯХ 0,4-10 КВ

One of the causes of electrical injuries in rural power grids of 0.4-10 kilovolts is the unauthorized power supply, which is caused by various causes: the fall of a wire from one power line to another (disconnected), incorrect connection of an electric generator in a private household, etc. At the same time, the occurrence of a power supply in the 0.4 kilovolt grid due to reverse transformation at the 10/0.4 kilovolt transformer substation leads to the appearance of a voltage in the 10 kilovolt grid and poses a danger of electric shock both in 0.4 and 10 kilovolt grid. However, it is not possible to identify the number of cases of unauthorized power supply based on statistics or other data due to the fact that this information is not recorded until the accident occurs. Therefore, the definition of cases of unauthorized voltage supply is actual. (Research Purpose) The research purpose is estimating the probability of unauthorized power supply in electrical grids of 0.4-10 kilovolts. (Materials and methods) Methods of expert survey and processing of its results were applied. 74 experts serving rural power grids of 0.4-10 kilovolts were interviewed, who were asked questions about actual cases of unauthorized power supply and the expectation of meeting with such cases. Based on the results of the survey, the specific probabilities of the unauthorized power supply per kilometer of the line were determined (Results and discussion) The actual number of cases of unauthorized power supply recorded by experts has been determined, which amounted to 91 cases over five years, and the specific value is 0.011 cases per kilometer of lines per year. The assessment was carried out taking into account the total length of the grid serviced by experts - 1704 kilometers. The specific value of the probability of unauthorized power supply was determined according to experts (0.0056 cases per kilometer of lines per year). (Conclusions) The number of cases of unauthorized power supply predicted by experts is lower than the actual one, which confirms the need for additional training of personnel of power grid organizations on this issue.

EXPLORING THE DETERMINANTS OF COVID-19 MORTALITY RATE WORLDWIDE

SARS-CoV-2, also known as COVID-19, is a coronavirus that causes severe acute respiratory syndrome. The recent outbreak of COVID-19 had caught the world by surprise, resulting in millions of confirmed infected cases and 5,325,079 deaths documented worldwide. This study aims to explore the significant factors associated with COVID-19 mortality rate by focusing on the number of active cases, total cases, critical cases, vaccination doses, and screening tests. Secondary data from 219 countries was retrieved from CNN Health, Worldometer, and GitHub Our World in Data, which was later analysed using multiple linear regression. The results showed that total cases and vaccination doses of COVID-19 are the significant factors associated with COVID-19 mortality rate; active cases, critical cases, and screening tests were found to have insignificant relationships to COVID-19 mortality rate. This study offers further recommendations which can be accomplished using the updated data that include the patients’ common comorbidities, such as obesity, hypertension, and diabetes mellitus. Keyword(s) : COVID-19; mortality rate; multiple linear regression; vaccination doses

Tackling maternal mortality in Pakistan: A perspective and call for systemic improvements

Dear Madam, Pregnancy should be a time of immense hope and a positive experience for all women. This period necessitates significant management and care, as the woman’s body undergoes numerous physiological and psychological adaptations. Thus, establishing an effective management system and a proficient maternal healthcare service is of utmost importance for the mother and the children. However, in developing countries like Pakistan, it is still a shockingly scary experience for millions of pregnant women who lack access to high-quality healthcare systems. For many females, the miracle of childbirth is marred by the tragedy of maternal death. Maternal death is defined as the death of a woman while pregnant or within 42 days of termination of the pregnancy, irrespective of the duration and site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management but not from accidental or incidental causes [1]. Additionally, Pakistan has been ranked as one of the top ten countries expected to contribute 60% of all maternal deaths worldwide [2]. It continues to have the highest maternal deaths in Southeast Asia [3]. Despite Pakistan being officially categorized as a country with high maternal mortality rates, the actual number of cases could be even higher due to under-registration of vital events, particularly in the rural parts of the country [4]. Nevertheless, based on interview survey methods, the maternal mortality ratio in Pakistan is estimated to have declined from 430/10^5 live births in 1990 to 180/10^5 in 2015 [2]. However, the recent report, "Trends in Maternal Mortality", released by WHO, UNICEF, UNFPA, the world bank, and UN Population Division, reveals alarming setbacks for women's health over recent years, as maternal deaths either increased or remained stalled in nearly all regions of the world after 2015 [5]. The report statistics indicate a public health emergency that a woman dies every two minutes due to complications associated with pregnancy or childbirth. It emphasizes that the world must significantly accelerate progress in reducing maternal deathsor risk the lives of over one million more women by 2030 [5]. While the entire global community contends with the maternal mortality, Pakistan shoulders a disproportionate share of its consequences [2]. ---Continue

COVID-19 Regional Safety Assessment Using Evaluation Based on Distance from Average Solution (EDAS) Method

The process of assessing the safety and risk level of a particular region or area in respect to the COVID-19 pandemic is known as COVID-19 Regional Safety Assessment. It involves analyzing various factors, such as the number of active cases, testing and reporting capabilities, vaccination rates, healthcare system capacity, implementation of public health measures, travel restrictions, presence of variants of concern, and localized outbreaks. A complete evaluation of regional safety is necessary for public health professionals, legislators, and residents to successfully prevent the spread of COVID-19 and protect public health and wellbeing. Authorities may identify areas of concern, distribute resources wisely, and put targeted measures in place to restrict the virus's spread by performing a thorough examination. In order to restrict the virus's spread and protect the health and welfare of communities, it is crucial for guiding decision-making processes, identifying problem areas, and effectively allocating resources. The research carried out through regional safety assessments advances our knowledge of the pandemic, guides public health initiatives, and encourages the use of evidence-based decision-making in order to effectively battle COVID-19. Distance from Average Solution-Based Evaluation (EDAS)The evaluation based on distance from the average solution approach assesses the efficacy or quality of individual solutions or data points by comparing each solution or data point to the average or mean solution. This approach is commonly employed in various fields, including optimization, data analysis, and decision-making.In this evaluation method, the average solution serves as a reference point or baseline. It is crucial to remember that the evaluation's specific context and goals may influence the choice of the average solution and distance metric. Additionally, other evaluation criteria or metrics may be employed in conjunction with the distance-based evaluation to obtain a more comprehensive assessment of the solutions. China, Denmark, Germany, Hong Kong, Hungary, Israel, Australia, Austria, Canada, and Efficiency of the government, monitoring and detection, and quarantine Emergency Preparedness, regional resilience, and healthcare readiness .Ranking of the nation based on the Covid-19 Regional Safety Assessment survey. Hungary is shown as occupying the last slot, whereas China is listed as occupying the first spot. It has been noted that China has a significant influence on COVID-19.

Dynamics of the Main Indicators of Tuberculosis Epidemiology in Bukovina, the Influence of the COVID-19 Pandemic and Wartime Conditions

According to the WHO, Ukraine is one of the 27 countries in the world where 85 % of the incidence of tuberculosis (TB) is concentrated, and it ranks 4th in the world in terms of its prevalence. Objective — to analyze the regional epidemiological characteristics of TB in the Chernivtsi region under the influence of unfavorable socio-economic factors. Materials and methods. The medical records of all newly diagnosed tuberculosis cases registered at the Chernivtsi Regional Clinical Tuberculosis Dispensary from January 2021 to December 2022 were analyzed. Statistical processing of the data obtained during the research was conducted using the IBM SPSS Statistics v26.0 program. Results and discussion. Tuberculosis incidence among the adult population has significantly increased. The lockdown situation caused by the COVID-19 pandemic and the conditions of martial law have had a negative impact on TB prevalence. An analysis of TB prevalence in Bukovina revealed a decrease of this indicator by 25.9 % in 2020 compared to 2019, by 1.5 % in 2021 compared to 2020, but there was a substantial increase of 16.1 % in 2022. Conclusions. In the Chernivtsi region, from 2019 to 2022, there has been a noticeable trend of a significant decrease in the incidence rate of tuberculosis, both during this period and in the conditions of martial law, aligning with the national trend. Due to the onset of the COVID-19 pandemic caused by SARS-CoV-2, the number of newly diagnosed TB cases decreased in 2020 compared to 2019, which led to the accumulation of undiagnosed and latent TB cases, which were detected more, in late stages and in more severe forms and showed an indicative probable increase in the number of active cases in 2022. The mortality rate in Bukovina was 4.7 per 100,000 population in 2021, and 4.4 per 100,000 population in 2022, i. e. it shows a downward tendency under the negative influence of socio-economic factors.

COVID-19 Pandemic: Did Strict Mobility Restrictions Save Lives and Healthcare Costs in Maharashtra, India?

Maharashtra, India, remained a hotspot during the COVID-19 pandemic. After the initial complete lockdown, the state slowly relaxed restrictions. We aim to estimate the lockdown's impact on COVID-19 cases and associated healthcare costs. Using daily case data for 84 days (9 March-31 May 2020), we modeled the epidemic's trajectory and predicted new cases for different phases of lockdown. We fitted log-linear models to estimate the growth rate, basic (R0), daily reproduction number (Re), and case doubling time. Based on pre-restriction and Phase 1 R0, we predicted new cases for the rest of the restriction phases, and we compared them with the actual number of cases during each phase. Furthermore, using the published and gray literature, we estimated the costs and savings of implementing these restrictions for the projected period, and we performed a sensitivity analysis. The estimated median R0 during the different phases was 1.14 (95% CI: 0.85, 1.45) for pre-lockdown, 1.67 (95% CI: 1.50, 1.82) for phase 1 (strict mobility restrictions), 1.24 (95% CI: 1.12, 1.35) for phase 2 (extension of phase 1 with no restrictions on agricultural and essential services), 1.12 (95% CI: 1.01, 1.23) for phase 3 (extension of phase 2 with mobility relaxations in areas with few infections), and 1.05 (95% CI: 0.99, 1.123) for phase 4 (implementation of localized lockdowns in high-case-load areas with fewer restrictions on other areas), respectively. The corresponding doubling time rate for cases (in days) was 17.78 (95% CI: 5.61, -15.19), 3.87 (95% CI: 3.15, 5.00), 10.37 (95% CI: 7.10, 19.30), 20.31 (95% CI: 10.70, 212.50), and 45.56 (95% CI: 20.50, -204.52). For the projected period, the cases could have reached 631,819 without the lockdown, as the actual reported number of cases was 64,975. From a healthcare perspective, the estimated total value of averted cases was INR 194.73 billion (USD 2.60 billion), resulting in net cost savings of 84.05%. The Incremental Cost-Effectiveness Ratio (ICER) per Quality Adjusted Life Year (QALY) for implementing the lockdown, rather than observing the natural course of the pandemic, was INR 33,812.15 (USD 450.83). Maharashtra's early public health response delayed the pandemic and averted new cases and deaths during the first wave of the pandemic. However, we recommend that such restrictions be carefully used while considering the local socio-economic realities in countries like India.

Role of immigration and emigration on the spread of COVID-19 in a multipatch environment: a case study of India

Human mobility has played a critical role in the spread of COVID-19. The understanding of mobility helps in getting information on the acceleration or control of the spread of disease. The COVID-19 virus has been spreading among several locations despite all the best efforts related to its isolation. To comprehend this, a multi-patch mathematical model of COVID-19 is proposed and analysed in this work, where-in limited medical resources, quarantining, and inhibitory behaviour of healthy individuals are incorporated into the model. Furthermore, as an example, the impact of mobility in a three-patch model is studied considering the three worst-hit states of India, i.e. Kerala, Maharashtra and Tamil Nadu, as three patches. Key parameters and the basic reproduction number are estimated from the available data. Through results and analyses, it is seen that Kerala has a higher effective contact rate and has the highest prevalence. Moreover, if Kerala is isolated from Maharashtra or Tamil Nadu, the number of active cases will increase in Kerala but reduce in the other two states. Our findings indicate that the number of active cases will decrease in the high prevalence state and increase in the lower prevalence states if the emigration rate is higher than the immigration rate in the high prevalence state. Overall, proper travel restrictions are to be implemented to reduce or control the spread of disease from the high-prevalence state to other states with lower prevalence rates.

Vector Autoregression for Forecasting the Number of COVID-19 Cases and Analyzing Behavioral Indicators in the Philippines: Ecologic Time-Trend Study.

Traditional surveillance systems rely on routine collection of data. The inherent delay in retrieval and analysis of data leads to reactionary rather than preventive measures. Forecasting and analysis of behavior-related data can supplement the information from traditional surveillance systems. We assessed the use of behavioral indicators, such as the general public's interest in the risk of contracting SARS-CoV-2 and changes in their mobility, in building a vector autoregression model for forecasting and analysis of the relationships of these indicators with the number of COVID-19 cases in the National Capital Region. An etiologic, time-trend, ecologic study design was used to forecast the daily number of cases in 3 periods during the resurgence of COVID-19. We determined the lag length by combining knowledge on the epidemiology of SARS-CoV-2 and information criteria measures. We fitted 2 models to the training data set and computed their out-of-sample forecasts. Model 1 contains changes in mobility and number of cases with a dummy variable for the day of the week, while model 2 also includes the general public's interest. The forecast accuracy of the models was compared using mean absolute percentage error. Granger causality test was performed to determine whether changes in mobility and public's interest improved the prediction of cases. We tested the assumptions of the model through the Augmented Dickey-Fuller test, Lagrange multiplier test, and assessment of the moduli of eigenvalues. A vector autoregression (8) model was fitted to the training data as the information criteria measures suggest the appropriateness of 8. Both models generated forecasts with similar trends to the actual number of cases during the forecast period of August 11-18 and September 15-22. However, the difference in the performance of the 2 models became substantial from January 28 to February 4, as the accuracy of model 2 remained within reasonable limits (mean absolute percentage error [MAPE]=21.4%) while model 1 became inaccurate (MAPE=74.2%). The results of the Granger causality test suggest that the relationship of public interest with number of cases changed over time. During the forecast period of August 11-18, only change in mobility (P=.002) improved the forecasting of cases, while public interest was also found to Granger-cause the number of cases during September 15-22 (P=.001) and January 28 to February 4 (P=.003). To the best of our knowledge, this is the first study that forecasted the number of COVID-19 cases and explored the relationship of behavioral indicators with the number of COVID-19 cases in the Philippines. The resemblance of the forecasts from model 2 with the actual data suggests its potential in providing information about future contingencies. Granger causality also implies the importance of examining changes in mobility and public interest for surveillance purposes.

Ťahyňa virus-A widespread, but neglected mosquito-borne virus in Europe.

Ťahyňa virus (TAHV) is an orthobunyavirus and was the first arbovirus isolated from mosquitoes in Europe and is associated with floodplain areas as a characteristic biotope, hares as reservoir hosts and the mammal-feeding mosquitoes Aedes vexans as the main vector. The disease caused by TAHV ("Valtice fever") was detected in people with acute flu-like illness in the 1960s, and later the medical significance of TAHV became the subject of many studies. Although TAHV infections are widespread, the prevalence and number of actual cases, clinical manifestations in humans and animals and the ecology of transmission by mosquitoes and their vertebrate hosts are rarely reported. Despite its association with meningitis in humans, TAHV is a neglected human pathogen with unknown public health importance in Central Europe, and a potential emerging disease threat elsewhere in Europe due to extreme summer flooding events.

Population Genomics of SARS-CoV-2 in the Constituent Entities of Siberian and Far Eastern Federal Districts

The aim of the study was to analyze the genetic structure of populations and the patterns of evolutionary variability of the novel coronavirus infection in the Siberian and Far Eastern Federal Districts. Materials and methods. 1033 SARS-CoV-2 genomes from samples from individuals diagnosed with COVID-19 from eight entities of the Siberia and Far East between December 2020 and November 2021 were assessed. Sequencing was performed on the MinION Oxford Nanopore platform using the ARTIC v.3 protocol. The degree of SARS-CoV-2 genetic isolation was estimated applying the Fst criterion. Phylogenetic analysis was carried out using maximum likelihood method and Bayesian phylogenetic inference. A nonparametric Bayesian Skyline Plot (BSP) model was used to reconstruct population dynamics. Results and discussion. The original SARS-CoV-2 variant (B.1) was identified in 100 % of the cases at the initial stages. The Alpha variant was detected in March-June, 2021; Beta – in single samples in March-May, 2021. Delta was first identified in April, 2021. The maximum degree of SARS-CoV-2 genetic isolation (Fst=0.18) was established for the most remote territories (Altai Territory ↔ Republic of Buryatia and Altai Territory ↔ Irkutsk Region). A relatively free circulation of the virus was detected between Irkutsk Region, Republic of Buryatia and Krasnoyarsk Territory. According to the results of population genetic tests, a sharp increase in the effective virus population size was the determining mechanism of SARS-CoV-2 genetic diversity formation. Reconstruction of population dynamics in BEAST (BSP model) has revealed the consistency of trends in the genetic diversity of the virus and the number of active cases. Two subclusters have been identified in the Delta cluster, consisting predominantly of samples isolated in the Irkutsk Region and Krasnoyarsk Territory. Change in the dominant variant of SARS-CoV-2 has been traced in dynamics. Molecular-epidemiological data point to the multiple pathways of spatial expansion of different SARS-CoV-2 genotypes into the constituent entities with generation of individual monophyletic clusters and further intra- and extraterritorial spread of the decedents.