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The novel coronavirus, SARS-CoV-2, commonly known as COVID19 has become a global pandemic in early 2020. The world has mounted a global social distancing intervention on a scale thought unimaginable prior to this outbreak; however, the economic impact and sustainability limits of this policy create significant challenges for government leaders around the world. Understanding the future spread and growth of COVID19 is further complicated by data quality issues due to high numbers of asymptomatic patients who may transmit the disease yet show no symptoms; lack of testing resources; failure of recovered patients to be counted; delays in reporting hospitalizations and deaths; and the co-morbidity of other life-threatening illnesses. We propose a Monte Carlo method for inferring true case counts from observed deaths using clinical estimates of Infection Fatality Ratios and Time to Death. Findings indicate that current COVID19 confirmed positive counts represent a small fraction of actual cases, and that even relatively effective surveillance regimes fail to identify all infectious individuals. We further demonstrate that the miscount also distorts officials' ability to discern the peak of an epidemic, confounding efforts to assess the efficacy of various interventions.

In this paper, we continue development of the new epidemiological model [10], which is suitable for analyzing and predicting the propagation of COVID-19 epidemics. This is a discrete-time model allowing a reconstruction of the dynamics of asymptomatic virus holders using the available daily statistics on the number of new cases. We suggest to use a new indicator, the total inflection rate, to distinguish the propagation and recession modes of the epidemic. We check our indicator on the available data for eleven different countries and for the whole world. Our reconstructions are very precise. In several cases, we are able to detect the exact dates of the disastrous political decisions, ensuring the second wave of the epidemics. It appears that for all our examples the decisions made on the basis of the current number of new cases are wrong. Int his paper, we suggest a reasonable alternative. Our analysis shows that all tested countries are in a dangerous zone except Sweden.

In this paper, we continue development of the new epidemiological model [10], which is suitable for analyzing and predicting the propagation of COVID-19 epidemics. This is a discrete-time model allowing a reconstruction of the dynamics of asymptomatic virus holders using the available daily statistics on the number of new cases. We suggest to use a new indicator, the total inflection rate, to distinguish the propagation and recession modes of the epidemic. We check our indicator on the available data for eleven different countries and for the whole world. Our reconstructions are very precise. In several cases, we are able to detect the exact dates of the disastrous political decisions, ensuring the second wave of the epidemics. It appears that for all our examples the decisions made on the basis of the current number of new cases are wrong. Int his paper, we suggest a reasonable alternative. Our analysis shows that all tested countries are in a dangerous zone except Sweden.

It was observed that small children and pregnant women were affected to only a small extent by the H1N1 influenza outbreak of winter 1978-79. This supports earlier findings from the epidemic season of 1977-78 and demonstrates that the evolutionary changes in the epidemic virus were not reflected in any appreciable way in this curious phenomenon. The frequency of elderly subjects possessing antibodies against the epidemic H1N1 virus was low, and virtually equal in the pre-epidemic and post-epidemic sampling. This low attack rate contrasts with observations on young military servicemen, in whom the re-infection rate was high, thus indicating that the infection with the winter 1977-78 virus had conferred only modest protection against the closely related virus which caused the winter 1978-79 outbreak.

ObjectivesCOVID-19 vaccination uptake was lower in school-age children compared to older age groups. Our study aims to understand factors associated with uptake in school-age children and how vaccination programme affected infection rates in school settings.
ApproachThis study utilises existing administrative data sources to create a children's health data asset. The core study population includes over 8 million pupils registered in English state schools whose records have been linked to NHS Test and Trace data and National Immunisation Management Service (NIMS) data. Census 2021 data will also be included enriching the data on background characteristics of each pupil. Taking into account the school attended will allow for analysis into the clustering of infections and the impact of overall school vaccination rates on infections.
ResultsInitial analyses have provided detailed insights into vaccine uptake and its impact on infection. For example, vaccination uptake was found to vary by ethnic group, with Chinese (75%) and Indian (65.7%) 12–15-year-olds most likely to be vaccinated by January 2022. Those in receipt of free school meals (FSM) were less likely to have been vaccinated than non-FSM pupils (35.9% compared with 58.9%); However, there was a wide range in vaccination rates between different schools with similar levels of deprivation.
We also found that the rate of reporting a positive test in the first half of the Autumn 2021 term was estimated to be 38% higher for unvaccinated pupils in comparison to pupils vaccinated with one dose (following 14 days after vaccination).
ConclusionThe size and longitudinal nature of the dataset provides the potential to answer a wide variety of research questions. We must, however, be mindful of inherent biases that might occur when relying on using Test and Trace data to identify COVID-19 infections. Work is currently ongoing to expand this asset to include other data sources.

Objective Infection following surgery is a serious complication, especially in neurosurgery. The aim of the study is to report the change of incidence rates of infection in patients undergoing elective neurosurgical procedures at a university hospital in South China as well as the risk factors.
Material and Methods The medical records and postoperative courses for patients undergoing 1,033 neurosurgical procedures from 2008 to 2014 were reviewed retrospectively to determine the incidence of neurosurgical infection, the identity of the offending organisms, and the factors associated with infection.
Results A total of 33 patients (40 cases) experienced postoperative infection representing 3.19% of the study population. Twenty cases were incision infections (1.94%), and 20 were cranial/spinal infections (1.94%) including 15 intracranial infections and 5 intraspinal infections. The 2.4-fold greater incidence of postoperative infection in 2008 to 2010 was compared with that in 2011 to 2014 with perioperative antibiotic prophylaxis (p <0.01). The neurosurgical procedure associated with the highest rate of infection was cranioplasty (6.85%); the lowest rate of infection was associated with functional neurosurgery (1.08%). The most common offending organism was Staphylococcus aureus (27.5%). Foreign body implantation, operative time > 4 hours, and cerebrospinal fluid (CSF) leak (13 infections in 158 patients; p <0.001) were risk factors for infections (p <0.05).
Conclusion The neurosurgical infection rate is usually low with perioperative antibiotic prophylaxis even in developing countries. Less foreign body implantation, shorter operative times, and controlling CSF leak could reduce infection rates.

Introduction: Trypanosomiasis is a parasitic infection caused by the protozoa Trypanosoma. It is exclusively associated with Glossina species habitats and, therefore, restricted to specific geographical settings. It affects a wide range of hosts, including humans. Animals may carry different Trypanosoma spp. while being asymptomatic. They are, therefore, potentially important in unpremeditated disease transmission. Aim: The aim of this study was to study the potential impact of the government tsetse fly control program, and to elucidate the role of pigs in the Trypanosoma epidemiology in the West Nile region in Uganda. Methods: A historically important human African trypanosomiasis (HAT) hotspot was selected, with sampling in sites with and without a government tsetse fly control program. Pigs were screened for infection with Trypanosoma and tsetse traps were deployed to monitor vector occurrence, followed by tsetse fly dissection and microscopy to establish infection rates with Trypanosoma. Pig blood samples were further analyzed to identify possible Trypanosoma infections using internal transcribed spacer (ITS)-PCR. Results: Using microscopy, Trypanosoma was detected in 0.56% (7/1262) of the sampled pigs. Using ITS-PCR, 114 of 341 (33.4%) pig samples were shown to be Trypanosoma vivax positive. Of the 360 dissected tsetse flies, 13 (3.8%) were positive for Trypanosoma under the microscope. The difference in captured tsetse flies in the government intervention sites in comparison with the control sites was significant (p 0.05). Conclusion: This study illustrated the impact of a government control program with low vector abundance in a historical HAT hotspot in Uganda. The study could not verify that pigs in the area were carriers for the causative agent for HAT, but showed a high prevalence of the animal infectious agent T. vivax.

Many scholars have considered the relationship between the government response to COVID-19, an important social intervention strategy, and the COVID-19 infection rate. However, few have examined the sustained impact of an early government response on the COVID-19 infection rate. The current paper fills this gap by investigating a national survey performed in February 2020 and infection data from Chinese cities surveyed 1.5 years after the outbreak of COVID-19. The results suggest that the Chinese government&rsquo ; s early response to COVID-19 significantly and sustainedly reduced China&rsquo ; s COVID-19 infection rate, and that this impact worked through risk perception, the adoption of protective action recommendations (PARs), and the chain-mediating effects of risk perception and the adoption of PARs, respectively. These findings have important practical value. In demonstrating how government response and infection rate at the macro level are connected to the behaviour of individuals at the micro level, they suggest feasible directions for curbing the spread of diseases such as COVID-19. When facing such public health emergencies, the focus should be on increasing the public&rsquo ; s risk perception and adoption of PARs.