Number Of New Infections Research Articles

Into the Cauldron of the Variant Soup: Insights into the Molecular Epidemiology and Transition to Endemicity of SARS-CoV-2 in Cyprus (November 2022–February 2024)

The coronavirus disease 2019 (COVID-19) pandemic, driven by the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been characterized by the virus’s ongoing evolution, leading to the appearance of more transmissible variants that have often triggered infection surges. In this study, we analyzed the SARS-CoV-2 epidemic in Cyprus, utilizing 1627 viral sequences from infected individuals between November 2022 and February 2024. Over this period, 251 distinct lineages and sublineages were identified, predominantly categorized into three groups: Omicron 5, XBB, and JN.1 (parental lineage BA.2.86), all of which harbor S protein mutations linked to enhanced transmissibility and immune escape. Despite the relatively low numbers of new infections during this period, and the lack of any major waves, unlike earlier phases of the pandemic, these lineages demonstrated varying periods of dominance, with Omicron 5 prevailing from November 2022 to February 2023, XBB variants leading from March to November 2023, and JN.1 generating a wavelet from December 2023 to February 2024. These findings suggest that the SARS-CoV-2 epidemic in Cyprus has reached endemicity, with new variants gradually replacing previously circulating variants irrespective of seasonal patterns. This study highlights the critical importance of ongoing surveillance of SARS-CoV-2 evolution in Cyprus and emphasizes the role of preventive measures in limiting virus transmission, providing valuable insights for safeguarding public health.

Assessing vaccine strategies for mpox outbreak in New York City using an age-structure model

BackgroundSince May 7 2022, mpox has been endemic in many countries which has attracted the attention of health authorities in various countries and made control decisions, in which vaccination is the mainstream strategy. However, the shortage of vaccine doses and the reduction of protective efficacy have led to unresolved issues such as vaccine allocation decisions and evaluation of transmission scale.MethodsWe developed an epidemiological model to describe the prevalence of the mpox virus in New York City and calibrated the model to match surveillance data from May 19 to November 3, 2022. Finally, we adjusted the model to simulate and compare several scenarios of non-vaccination and pre-pandemic vaccination.ResultsRelative to the status quo, if vaccination is not carried out, the number of new infections increases to about 385%, and the transmission time will be extended to about 350%, while if vaccinated before the epidemic, the number of new infections decreases to 94.2-96%.ConclusionsThe mpox outbreak in New York City may be linked to the Pride event. However, with current vaccine coverage, there will be no more large-scale outbreaks of mpox, even if there is another similar activity. For areas with limited vaccines, priority is given to high-risk groups in the age group [34–45] years as soon as possible.

Infection epidemiological effects of school closures during the second COVID-19 pandemic wave-an exemplary analysis within alower Bavarian region

The rapid global spread of the SARS-CoV‑2 virus during the COVID-19 pandemic led to widespread non-pharmaceutical interventions, such as school closures, to curb infections. This study investigates the influence of school closures on the number of new infections to reduce the existing knowledge gap in this area. The effect of school closures starting on 16December 2020, was quantified using COVID-19 infection data from the district and city of Landshut in Bavaria between November 2020 and January 2021. An interrupted time series (ITS) analysis was performed to analyze daily new infections among 6-18 year olds and the entire population in the region before and after the school closures using anegative binomial regression model. The analysis showed that the trend of daily new COVID-19 infections among 6-18 year olds significantly decreased after the school closures (incidence rate ratio (IRR) 0.93; 95% confidence interval (CI): 0.89-0.96; p < 0.001). This was also true for the entire study population (IRR 0.95; 95% CI: 0.93-0.97; p < 0.001). In the context of various concurrent measures to contain the COVID-19 pandemic, the study provides evidence for asignificant association between school closures and reduced COVID-19 infections. By optimizing the existing incomplete evidence base on the role of school closures in pandemic control, this finding can support involved stakeholders and contribute to strengthening future approaches to pandemic prevention.

Improving adherence to a daily PrEP regimen is key when considering long-time partnerships

ABSTRACT A population model of HIV that includes susceptible individuals not taking the pre-exposure prophylaxis (PrEP), susceptible individuals taking daily PrEP, and infected individuals is developed for casual partnerships, as well as monogamous and non-monogamous long-term partnerships. Reflecting the reality of prescription availability and usage in the U.S., the PrEP taking susceptible population is a mix of individuals designated by the CDC as high and low risk for acquiring HIV. The rate of infection for non-monogamous long-term partnerships with differential susceptibility is challenging to calculate and requires Markov chain theory to represent the movement between susceptible populations before infection. The parameters associated with PrEP initiation, suspension and adherence impact both the reproduction number of the model and the elasticity indices of the reproduction model. A multi-parameter analysis reveals that increasing adherence has the largest effect on decreasing the number of new infections.

Updated vaccination and screening recommendations for hepatitis B: Implications for pharmacists

The number of new infections of hepatitis B is rising and a large number of cases are undiagnosed. These factors are contributing to hepatitis B-related liver complications, including liver cancer, and deaths that could be prevented. The Advisory Committee on Immunization Practices and the Centers for Disease Control and Prevention recently updated vaccination and screening/testing recommendations for hepatitis B. The updated recommendations remove the need for risk assessment before screening or vaccination. Pharmacists will play a key role in a concerted national effort to implement these updated recommendations. A multistakeholder advisory council convened by the Hepatitis B Foundation identified key barriers to screening and vaccination. The council also formulated strategies to optimize implementation of the updated recommendations. These strategies include educating pharmacists about the new recommendations and how they will help to reduce the burden of hepatitis B and liver cancer. Pharmacists could explore establishing pharmacy-provider collaborative practice agreements and potentially leverage capacity built with COVID-19 vaccine implementation. Hospital systems and other clinic settings could update their electronic health records to include prompts for hepatitis B vaccination and screening. Pharmacy systems can implement different reminder options to help patients complete the hepatitis B vaccine series. To address a lack of vaccine confidence, pharmacists can emphasize the cancer prevention benefit of hepatitis B screening and vaccination and engage with patients on an individual level to understand their concerns, assess vaccine status, and discuss vaccine recommendations. Effective implementation of the new recommendations will help achieve national and global goals of eliminating hepatitis B as a public health threat by 2030.

A Retrospective Study of COVID-19 Based on A Dual-Stage Model

This study proposed initial stage SEI1I2I3RD and final stage SEQ I1I2I3RD models to deeply analyze the transmission dynamics of COVID-19 at different stages and propose scientific prevention and control strategies. The initial stage SE I1I2I3RD model divides infected individuals into mild, moderate, and severe cases during the early stage of the epidemic, effectively predicting the number of new infections and cumulative recoveries. The final stage SEQ I1I2I3RD model builds on the initial stage SE I1I2I3RD model by adding a quarantine compartment, which reflects the impact of nucleic acid testing and quarantine measures on epidemic control. By introducing dynamic parameters and nonlinear processing, the final stage SEQ I1I2I3RD model significantly improves prediction accuracy. The study demonstrates that increasing the isolation rate, reducing the daily contact rate of infected persons, and improving the recovery rate of mildly infected persons are effective control strategies. Based on the model's predictions, policy recommendations such as enhancing public health education, strictly implementing isolation measures, restricting population movement, and optimizing medical resource allocation are proposed to effectively control the spread of the epidemic, reduce the burden on the healthcare system, and create favorable conditions for economic recovery.

Optimization of "vehicle-UAV" joint distribution routing for cold chain logistics considering risk of epidemic spreading and green cost.

To address the epidemic, such as COVID-19, the government may implement the home quarantine policy for the infected residents. The logistics company is required to control the risk of epidemic spreading while delivering goods to residents. In this case, the logistics company often uses vehicles and unmanned aerial vehicles (UAVs) for delivery. This paper studies the distribution issue of cold chain logistics by integrating UAV logistics with epidemic risk management innovatively. At first, a "vehicle-UAV" joint distribution mode including vehicles, small UAVs and large UAVs, is proposed. The green cost for vehicles and UAVs is calculated, respectively. The formula for infection risk due to large numbers of residents gathering at distribution centers to pick up goods is then derived. Furthermore, based on the control of infection risk, an optimization model is developed to minimize the total logistics cost. A modified ant colony algorithm is designed to solve the model. The numerical results show that the maximum acceptable risk and the crowd management level of distribution centers both have significant effects on the distribution network, logistics cost and number of new infections. Our study provides a new management method and technical idea for ensuring the needs of residents during the epidemic.

From multiplicity of infection to force of infection for sparsely sampled Plasmodium falciparum populations at high transmission.

High multiplicity of infection or MOI, the number of genetically distinct parasite strains co-infecting a single human host, characterizes infectious diseases including falciparum malaria at high transmission. It accompanies high asymptomatic Plasmodium falciparum prevalence despite high exposure, creating a large transmission reservoir challenging intervention. High MOI and asymptomatic prevalence are enabled by immune evasion of the parasite achieved via vast antigenic diversity. Force of infection or FOI, the number of new infections acquired by an individual host over a given time interval, is the dynamic sister quantity of MOI, and a key epidemiological parameter for monitoring the impact of antimalarial interventions and assessing vaccine or drug efficacy in clinical trials. FOI remains difficult, expensive, and labor-intensive to accurately measure, especially in high-transmission regions, whether directly via cohort studies or indirectly via the fitting of epidemiological models to repeated cross-sectional surveys. We propose here the application of queuing theory to obtain FOI on the basis of MOI, in the form of either a two-moment approximation method or Little's law. We illustrate these methods with MOI estimates obtained under sparse sampling schemes with the recently proposed " coding" method, based on sequences of the multigene family encoding for the major variant surface antigen of the blood stage of malaria infection. The methods are evaluated with simulation output from a stochastic agent-based model, and are applied to an interrupted time-series study from Bongo District in northern Ghana before and immediately after a three-round transient indoor residual spraying (IRS) intervention. We incorporate into the sampling of the simulation output, limitations representative of those encountered in the collection of field data, including under-sampling of genes, missing data, and usage of antimalarial drug treatment. We address these limitations in MOI estimates with a Bayesian framework and an imputation bootstrap approach. We demonstrate that both proposed methods give good and consistent FOI estimates across various simulated scenarios. Their application to the field surveys shows a pronounced reduction in annual FOI during intervention, of more than 70%. The proposed approach should be applicable to the many geographical locations where cohort or cross-sectional studies with regular and frequent sampling are lacking but single-time-point surveys under sparse sampling schemes are available, and for MOI estimates obtained in different ways. They should also be relevant to other pathogens of humans, wildlife and livestock whose immune evasion strategies are based on large antigenic variation resulting in high multiplicity of infection.

Epidemiological and clinical profile of HTLV-1 patients: a closer look at a reference center in Bahia, Brazil.

The human T-lymphotropic virus type 1 (HTLV-1) affects over 5 million people worldwide and is endemic in Brazil. Though HTLV-1 is a notifiable disease, the last epidemiological report regarding HTLV-1 infection covered the period from 2012 to 2019. To understand the specific challenges and to develop the best strategies for controlling HTLV-1 infection, it is important to know the characteristics of each region providing care to people living with this virus. This descriptive cross-sectional study evaluated patients treated at the HTLV reference center in Vitória da Conquista, Bahia, Brazil, between July 2021 and August 2022. The data were obtained through the analysis of medical records and routine clinical consultations. A total of 67 patients were evaluated, with 79.1% being female, 79.1% identifying as black, indigenous, and people of color, 37.31% being married, 80.6% identifying as heterosexual, and 59.7% reporting inconsistent condom use. Additionally, 37.3% of the patients were diagnosed with HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP), a chronic disease with a considerable effect on the quality of life. Furthermore, 53.7% of the patients had incomplete/complete elementary education, and 52.2% had an income of up to one minimum wage. The data highlight the necessity for more specific public policies (such as health education strategies, aimed at reducing the number of new infections) targeting the described at-risk population.

Opioid and other drug use and drug-related mortality as indicators of Hepatitis C and Human Immunodeficiency Virus in Oklahoma

Objectives Outbreaks of injection drug use (IDU)-associated infections have become major public health concerns in the era of the opioid epidemic. This study aimed to (1) identify county-level characteristics associated with acute HCV infection and newly diagnosed IDU-associated HIV in Oklahoma and (2) develop a vulnerability index using these metrics. Methods This study employs a county-level ecological design to examine those diagnosed with acute or chronic HCV or newly diagnosed IDU-associated HIV. Poisson regression was used to estimate the association between indicators and the number of new infections in each county. Primary outcomes were acute HCV and newly diagnosed IDU-associated HIV. A sensitivity analysis included all HCV (acute and chronic) cases. Three models were run using variations of these outcomes. Stepwise backward Poisson regression predicted new infection rates and 95% confidence intervals for each county from the final multivariable model, which served as the metric for vulnerability scores. Results Predictors for HIV-IDU cases and acute HCV cases differed. The percentage of the county population aged 18–24 years with less than a high school education and population density were predictive of new HIV-IDU cases, whereas the percentage of the population that was male, white, Pacific Islander, two or more races, and people aged 18–24 years with less than a high school education were predictors of acute HCV infection. Counties with the highest predicted rates of HIV-IDU tended to be located in central Oklahoma and have higher population density than the counties with the highest predicted rates of acute HCV infection. Conclusions There is high variability in county-level factors predictive of new IDU-associated HIV infection and acute HCV infection, suggesting that different public health interventions need to be tailored to these two case populations.

Genotypic analysis of RTS,S/AS01E malaria vaccine efficacy against parasite infection as a function of dosage regimen and baseline malaria infection status in children aged 5–17 months in Ghana and Kenya: a longitudinal phase 2b randomised controlled trial

The first licensed malaria vaccine, RTS,S/AS01E, confers moderate protection against symptomatic disease. Because many malaria infections are asymptomatic, we conducted a large-scale longitudinal parasite genotyping study of samples from a clinical trial exploring how vaccine dosing regimen affects vaccine efficacy. Between Sept 28, 2017, and Sept 25, 2018, 1500 children aged 5-17 months were randomly assigned (1:1:1:1:1) to receive four different RTS,S/AS01E regimens or a rabies control vaccine in a phase 2b open-label clinical trial in Ghana and Kenya. Participants in the four RTS,S groups received two full doses at month 0 and month 1 and either full doses at month 2 and month 20 (group R012-20); full doses at month 2, month 14, month 26, and month 38 (group R012-14); fractional doses at month 2, month 14, month 26, and month 38 (group Fx012-14; early fourth dose); or fractional doses at month 7, month 20, and month 32 (group Fx017-20; delayed third dose). We evaluated the time to the first new genotypically detected infection and the total number of new infections during two follow-up periods (12 months and 20 months) in more than 36 000 dried blood spot specimens from 1500 participants. To study vaccine effects on time to the first new infection, we defined vaccine efficacy as one minus the hazard ratio (HR; RTS,S vs control) of the first new infection. We performed a post-hoc analysis of vaccine efficacy based on malaria infection status at first vaccination and force of infection by month 2. This trial (MAL-095) is registered with ClinicalTrials.gov, NCT03281291. We observed significant and similar vaccine efficacy (25-43%; 95% CI union 9-53) against first new infection for all four RTS,S/AS01E regimens across both follow-up periods (12 months and 20 months). Each RTS,S/AS01E regimen significantly reduced the mean number of new infections in the 20-month follow-up period by 1·1-1·6 infections (95% CI union 0·6-2·1). Vaccine efficacy against first new infection was significantly higher in participants who were infected with malaria (68%; 95% CI 50-80) than in those who were uninfected (37%; 23-48) at the first vaccination (p=0·0053). All tested dosing regimens blocked some infections to a similar degree. Improved vaccine efficacy in participants infected during vaccination could suggest new strategies for highly efficacious malaria vaccine development and implementation. GlaxoSmithKline Biologicals SA, PATH, Bill & Melinda Gates Foundation, and the German Federal Ministry of Education and Research.

HIV infections and AIDS cases in Poland in 2021 year.

Despite a temporary decrease in the number of newly HIV infections in Poland in 2020, a return to the growing number of new infections was observed in 2021 in the surveillance. The aim of the study was to assess the epidemiological situation of newly HIV infections and AIDS cases and death among AIDS cases in Poland in 2021 in comparison to the changes in preceding years. Analysis of the epidemiological situation was based on reports of newly detected HIV cases and AIDS cases received from doctors and laboratories and the results of the annual survey of HIV testing conducted by laboratories throughout the country. A dataset on clients from Voluntary Testing and Counselling, coordinated by the National AIDS Center, which anonymously collects epidemiological and behaviour data on tested people was used. In 2021 there were 1 367 HIV cases newly diagnosed in Poland (diagnosis rate 3.58 per 100,000), including 236 among non-Polish citizens. The number of HIV infections increased by 43.3% compared to the previous year and was higher by 3.8% compared to the median in 2015-2019 years. Similarly, among VCT clients, number of HIV increased by 45%, from 309 in 2020 to 448 in 2021 year. The total number of AIDS cases reported to surveillance was 62 (incidence 0.16 per 100,000). The HIV infection was most often detected in the age group 30-39 year (35.8%) and among men (82.1%). Among cases with known transmission route, 68.7% concerned among MSM. The percentage of AIDS cases diagnosed at the same time with HIV increased by more than 30 percent points (from 63.5% to 95.1% of all AIDS cases). In 2021 the number of newly detected HIV infections increased compared to the previous year, probably due to a reduction in restrictions related to the COVID-19 pandemic and increased in HIV testing rate.

Burden of HIV on Society Current Management Options and Future Prospective

Nevertheless, there is still a disparity in the availability of antiretroviral therapy (ART), especially in low- and middle-income nations, due to obstacles such as financial constraints, inadequate infrastructure,and on going social stigma. The future outlook for HIV management is optimistic, as current research is dedicated to exploring innovative treatment methods such as long-acting injectable, gene editing medicines, and therapeutic vaccinations. Furthermore, progress in pre-exposure prophylaxis (PrEP) and other preventive measures provide optimism for decreasing the number of new infections. Furthermore, it is crucial to make endeavors towards tackling societal factors that influence health outcomes, such as poverty, education, and gender inequality, as they play a vital role in attaining long-lasting advancements in HIV prevention and care. Notwithstanding these progressions, there are still obstacles to overcome, such as the rise of drug-resistant variations, deficiencies in testing and diagnosis, and enduring social obstacles. To tackle these difficulties, a comprehensive strategy is needed that combines biological, behavioral, and structural interventions, with a focus on fairness and human rights. To reduce the impact of HIV/AIDS on society and achieve the objective of ending the epidemic, it is important to continue investing in research, improving health systems, and promoting collaboration across all sectors.

COVID-19 and Tuberculosis: Mathematical Modeling of Infection Spread Taking into Account Reduced Screening.

The COVID-19 pandemic resulted in the cessation of many tuberculosis (TB) support programs and reduced screening coverage for TB worldwide. We propose a model that demonstrates, among other things, how undetected cases of TB affect the number of future M. tuberculosis (M. tb) infections. The analysis of official statistics on the incidence of TB, preventive examination coverage of the population, and the number of patients with bacterial excretion of M. tb in the Russian Federation from 2008 to 2021 is carried out. The desired model can be obtained due to the fluctuation of these indicators in 2020, when the COVID-19 pandemic caused a dramatic reduction in TB interventions. Statistical analysis is carried out using R v.4.2.1. The resulting model describes the dependence of the detected incidence and prevalence of TB with bacterial excretion in the current year on the prevalence of TB with bacterial excretion in the previous year and on the coverage of preventive examinations in the current and previous years. The adjusted coefficient of model determination (adjusted R-squared) is 0.9969, indicating that the model contains almost no random component. It clearly shows that TB cases missed due to low screening coverage and left uncontrolled will lead to a significant increase in the number of new infections in the future. We may conclude that the obtained results clearly demonstrate the need for mass screening of the population in the context of the spread of TB infection, which makes it possible to timely identify patients with TB with bacterial excretion.

Assessing the dynamics and impact of COVID-19 vaccination on disease spread: A data-driven approach

The COVID-19 pandemic has significantly impacted global health, social, and economic situations since its emergence in December 2019. The primary focus of this study is to propose a distinct vaccination policy and assess its impact on controlling COVID-19 transmission in Malaysia using a Bayesian data-driven approach, concentrating on the year 2021. We employ a compartmental Susceptible-Exposed-Infected-Recovered-Vaccinated (SEIRV) model, incorporating a time-varying transmission rate and a data-driven method for its estimation through an Exploratory Data Analysis (EDA) approach. While no vaccine guarantees total immunity against the disease, and vaccine immunity wanes over time, it is critical to include and accurately estimate vaccine efficacy, as well as a constant vaccine immunity decay or wane factor, to better simulate the dynamics of vaccine-induced protection over time. Based on the distribution and effectiveness of vaccines, we integrated a data-driven estimation of vaccine efficacy, calculated at 75% for Malaysia, underscoring the model's realism and relevance to the specific context of the country. The Bayesian inference framework is used to assimilate various data sources and account for underlying uncertainties in model parameters. The model is fitted to real-world data from Malaysia to analyze disease spread trends and evaluate the effectiveness of our proposed vaccination policy. Our findings reveal that this distinct vaccination policy, which emphasizes an accelerated vaccination rate during the initial stages of the program, is highly effective in mitigating the spread of COVID-19 and substantially reducing the pandemic peak and new infections. The study found that vaccinating 57–66% of the population (as opposed to 76% in the real data) with a better vaccination policy such as proposed here is able to significantly reduce the number of new infections and ultimately reduce the costs associated with new infections. The study contributes to the development of a robust and informative representation of COVID-19 transmission and vaccination, offering valuable insights for policymakers on the potential benefits and limitations of different vaccination policies, particularly highlighting the importance of a well-planned and efficient vaccination rollout strategy. While the methodology used in this study is specifically applied to national data from Malaysia, its successful application to local regions within Malaysia, such as Selangor and Johor, indicates its adaptability and potential for broader application. This demonstrates the model's adaptability for policy assessment and improvement across various demographic and epidemiological landscapes, implying its usefulness for similar datasets from various geographical regions.

Impact of socioeconomic determinants on the speed of epidemic diseases: a comparative analysis

Abstract We study the impact of socioeconomic factors on two key parameters of epidemic dynamics. Specifically, we investigate a parameter capturing the rate of deceleration at the very start of an epidemic, and a parameter that reflects the pre-peak and post-peak dynamics at the turning point of an epidemic like coronavirus disease 2019 (COVID-19). We find two important results. The policies to fight COVID-19 (such as social distancing and containment) have been effective in reducing the overall number of new infections, because they influence not only the epidemic peaks, but also the speed of spread of the disease in its early stages. The second important result of our research concerns the role of healthcare infrastructure. They are just as effective as anti-COVID policies, not only in preventing an epidemic from spreading too quickly at the outset, but also in creating the desired dynamic around peaks: slow spreading, then rapid disappearance.

Differences between the true reproduction number and the apparent reproduction number of an epidemic time series

The time-varying reproduction number R(t) measures the number of new infections per infectious individual and is closely correlated with the time series of infection incidence by definition. The timings of actual infections are rarely known, and analysis of epidemics usually relies on time series data for other outcomes such as symptom onset. A common implicit assumption, when estimating R(t) from an epidemic time series, is that R(t) has the same relationship with these downstream outcomes as it does with the time series of incidence. However, this assumption is unlikely to be valid given that most epidemic time series are not perfect proxies of incidence. Rather they represent convolutions of incidence with uncertain delay distributions. Here we define the apparent time-varying reproduction number, RA(t), the reproduction number calculated from a downstream epidemic time series and demonstrate how differences between RA(t) and R(t) depend on the convolution function. The mean of the convolution function sets a time offset between the two signals, whilst the variance of the convolution function introduces a relative distortion between them. We present the convolution functions of epidemic time series that were available during the SARS-CoV-2 pandemic. Infection prevalence, measured by random sampling studies, presents fewer biases than other epidemic time series. Here we show that additionally the mean and variance of its convolution function were similar to that obtained from traditional surveillance based on mass-testing and could be reduced using more frequent testing, or by using stricter thresholds for positivity. Infection prevalence studies continue to be a versatile tool for tracking the temporal trends of R(t), and with additional refinements to their study protocol, will be of even greater utility during any future epidemics or pandemics.

Descrição epidemiológica das crianças expostas verticalmente ao HIV em hospital pediátrico terciário de Santa Catarina

INTRODUCTION: Acquired immunodeficiency syndrome was first recognized in the United States in 1981, with global spread in the last four decades. Although the number of new infections in children is decreasing, it still remains unacceptably high, causing severe immunosuppression if treatment is not carried out in a timely manner. OBJECTIVES: to describe the epidemiological profile of pediatric patients vertically exposed to HIV treated at a tertiary referral hospital in Santa Catarina. METHOD: an epidemiological, observational, analytical, cross-sectional study with retrospective data collection from January 2015 to December 2020 by CID Z 206 (contact with and exposure to the human immunodeficiency virus - HIV). The research was approved by the Ethics and Research Committee on Human Beings (CEP) of the institution. RESULTS: There were 678 medical records analyzed, with 579 (85.4%) white, 348 (51.0%) males, mean age of 324 days and maternal diagnosis of HIV prior to pregnancy in 434 (64.0%) cases. Most of these, 601 (88.6%), used antiretroviral therapy. Viral RNA (viral load) in 414 (61.0%) pregnant women was undetectable and 376 (55.4%) underwent intravenous zidovudine at delivery. The first viral load of those exposed was undetectable in 606 (89.3%) patients and 667 (98.3%) were not breastfed. Fourteen patients (2%) confirmed HIV infection, and all started antiretroviral therapy. CONCLUSION: Most pregnant women living with HIV had adequate prenatal care, however, with a high prevalence of syphilis. The rate of HIV-positive newborns was equivalent to the literature, but with incomplete prenatal care.

Analyzing factors of daily travel distances in Japan during the COVID-19 pandemic.

The global impact of the COVID-19 pandemic is widely recognized as a significant concern, with human flow playing a crucial role in its propagation. Consequently, recent research has focused on identifying and analyzing factors that can effectively regulate human flow. However, among the multiple factors that are expected to have an effect, few studies have investigated those that are particularly associated with human flow during the COVID-19 pandemic. In addition, few studies have investigated how regional characteristics and the number of vaccinations for these factors affect human flow. Furthermore, increasing the number of verified cases in countries and regions with insufficient reports is important to generalize conclusions. Therefore, in this study, a group-level analysis was conducted for Narashino City, Chiba Prefecture, Japan, using a human flow prediction model based on machine learning. High-importance groups were subdivided by regional characteristics and the number of vaccinations, and visual and correlation analyses were conducted at the factor level. The findings indicated that tree-based models, especially LightGBM, performed better in terms of prediction. In addition, the cumulative number of vaccinated individuals and the number of newly infected individuals are likely explanatory factors for changes in human flow. The analyses suggested a tendency to move with respect to the number of newly infected individuals in Japan or Tokyo, rather than the number of new infections in the area where they lived when vaccination had not started. With the implementation of vaccination, attention to the number of newly infected individuals in their residential areas may increase. However, after the spread of vaccination, the perception of infection risk may decrease. These findings can contribute to the proposal of new measures for efficiently controlling human flows and determining when to mitigate or reinforce specific measures.

Prediction of SARS-CoV-2 infection cases based on the meta-SEIRS model

Abstract Predicting epidemic trends of coronavirus disease 2019 (COVID-19) remains a key public health concern globally today. However, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection rate in previous studies of the transmission dynamics model was mostly a fixed value. Therefore, we proposed a meta-Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) model by adding a time-varying SARS-CoV-2 reinfection rate to the transmission dynamics model to more accurately characterize the changes in the number of infected persons. The time-varying reinfection rate was estimated using random-effect multivariate meta-regression based on published literature reports of SARS-CoV-2 reinfection rates. The meta-SEIRS model was constructed to predict the epidemic trend of COVID-19 from February to December 2023 in Sichuan province. Finally, according to the online questionnaire survey, the SARS-CoV-2 infection rate at the end of December 2022 in Sichuan province was 82.45%. The time-varying effective reproduction number in Sichuan province had two peaks from July to December 2022, with a maximum peak value of about 15. The prediction results based on the meta-SEIRS model showed that the highest peak of the second wave of COVID-19 in Sichuan province would be in late May 2023. The number of new infections per day at the peak would be up to 2.6 million. We constructed a meta-SEIRS model to predict the epidemic trend of COVID-19 in Sichuan province, which was consistent with the trend of SARS-CoV-2 positivity in China. Therefore, a meta-SEIRS model parameterized based on evidence-based data can be more relevant to the actual situation and thus more accurately predict future trends in the number of infections.