Search Results (195)

This study aims to develop and apply multistate models to estimate, forecast, and manage hospital length of stay during the COVID-19 epidemic without using any external packages. Data from Bellvitge University Hospital in Barcelona, Spain, were analyzed, involving 2285 hospitalized COVID-19 patients with moderate to severe conditions. The implemented multistate model includes transition probabilities and risk rates calculated from transitions between defined states, such as admission, ICU transfer, discharge, and death. In addition to examining key factors like age and gender, diabetes, lymphocyte count, comorbidity burden, symptom duration, and different COVID-19 waves were analyzed. Based on the model, patients hospitalized stay an average of 11.90 days before discharge, 2.84 days before moving to the ICU, or 34.21 days before death. ICU patients remain for about 24.08 days, with subsequent stays of 124.30 days before discharge and 35.44 days before death. These results highlight hospital stays’ varying durations and trajectories, providing critical insights into patient flow and healthcare resource utilization. Additionally, it can predict ICU peak loads for specific subgroups, aiding in preparedness. Future work will integrate the developed code into the hospital’s Health Information System (HIS) following ISO 13606 EHR standards and implement recursive methods to enhance the model’s efficiency and accuracy. Full article

Highly pathogenic avian influenza (HPAI) is a highly contagious viral disease that represents a significant threat to poultry production worldwide. Variants of the HPAI virus (HPAIV) H5A/Goose/GuangDong/1/96 (H5 Gs/GD/96) lineage have caused five intercontinental epizootic waves, with the most recent, clade 2.3.4.4b, reaching Argentina in February 2023. Initially detected in wild birds, the virus quickly spread to backyard and commercial poultry farms, leading to economic losses, including the loss of influenza-free status (IFS). By March/April 2023 the epidemic had peaked and vaccination was seriously considered. However, the success of strict stamping-out measures dissuaded the National Animal Health Authority (SENASA) from authorizing any vaccine. Suspected cases sharply declined by May, and the last detection in commercial poultry was reported in June. The effective control and potential eradication of HPAIV in Argentina were due to SENASA’s early detection and rapid response, supported by private companies, veterinarians, and other stakeholders. Stamping-out measures have been effective for virus elimination and reduced farm-to-farm transmission; however, as the virus of this clade may remain present in wild birds, the risk of reintroduction into poultry production is high. Therefore, maintaining continuous active surveillance will be crucial for promptly detecting any new HPAIV incursion and taking appropriate action to contain virus dissemination. Full article

In this work, the efficacy of fractional models under Atangana–Baleanu–Caputo, Caputo–Fabrizio, and Caputo is compared to the performance of integer-order models in the forecasting of weekly influenza cases using data from the Kingdom of Saudi Arabia. The suggested fractional influenza model was effectively verified using fractional calculus. Our investigation uncovered the topic’s essential properties and deepened our understanding of disease progression. Furthermore, we analyzed the numerical scheme’s positivity, limitations, and symmetry. The fractional-order models demonstrated superior accuracy, producing smaller root mean square error (RMSE) and mean absolute error (MAE) than the classical model. The novelty of this work lies in introducing the Atangana–Baleanu–Caputo fractional model to influenza forecasting to incorporate memory of an epidemic, which leads to higher accuracy than traditional models. These models effectively captured the peak and drop of influenza cases. Based on these findings, it can be concluded that fractional-order models perform better than typical integer-order models when predicting influenza dynamics. These insights should illuminate the importance of fractional calculus in addressing epidemic threats. Full article

Background: Florida, which led the country in terms of its number of opioid-prescribing physicians, was unique during the height of the opioid epidemic because of its lax prescribing laws and high number of unregulated pain clinics. Here, we address differences in the distribution rates of oxycodone and hydrocodone across Florida counties during the peak years of the opioid epidemic using an under-utilized database. Methods: The Washington Post and the United States Drug Enforcement Administration’s Automation of Reports and Consolidated Orders System (ARCOS) databases provided longitudinal oxycodone and hydrocodone distribution data in grams per county (2006–2014) and state (2006–2021). Grams of oxycodone and hydrocodone were converted into morphine milligram equivalents (MMEs). Results: There was a steep increase in oxycodone from 2006 to 2010, with a subsequent decline. In 2010, the average MME per person across Florida was 729.4, a 120.6% increase from 2006. The three counties with the highest MMEs per person in 2010 were Hillsborough (2271.3), Hernando (1915.3), and Broward (1726.9), and they were significantly (p < 0.05) elevated relative to the average county. Conclusions: The data demonstrated pronounced differences in opioid distribution, particularly oxycodone, between Florida counties during the height of the opioid epidemic. Legislative action taken between 2009 and 2011 aligns with the considerable decline in opioid distribution after 2010. Full article

SARS-CoV-2 infections elicit antibodies against the viral spike (S) and nucleocapsid (N) proteins; COVID-19 vaccines against the S-protein only. The BCG-Corona trial, initiated in March 2020 in SARS-CoV-2-naïve Dutch healthcare workers, captured several epidemic peaks and the introduction of COVID-19 vaccines during the one-year follow-up. We assessed determinants of systemic anti-S1 and anti-N immunoglobulin type G (IgG) responses using trial data. Participants were randomised to BCG or placebo vaccination, reported daily symptoms, SARS-CoV-2 test results, and COVID-19 vaccinations, and donated blood for SARS-CoV-2 serology at two time points. In the 970 participants, anti-S1 geometric mean antibody concentrations (GMCs) were much higher than anti-N GMCs. Anti-S1 GMCs significantly increased with increasing number of immune events (SARS-CoV-2 infection or COVID-19 vaccination): 104.7 international units (IU)/mL, 955.0 IU/mL, and 2290.9 IU/mL for one, two, and three immune events, respectively (p < 0.001). In adjusted multivariable linear regression models, anti-S1 and anti-N log₁₀ concentrations were significantly associated with infection severity, and anti-S1 log₁₀ concentration with COVID-19 vaccine type/dose. In univariable models, anti-N log₁₀ concentration was also significantly associated with acute infection duration, and severity and duration of individual symptoms. Antibody concentrations were not associated with long COVID or long-term loss of smell/taste. Full article

Computational simulation models have been widely used to study the dynamics of COVID-19. Among those, bottom-up approaches such as agent-based models (ABMs) can account for population heterogeneity. While many studies have addressed COVID-19 spread at various scales, insufficient studies have investigated the spread of COVID-19 within closed indoor settings. This study aims to develop an ABM to simulate the spread of COVID-19 in a closed indoor setting using three transmission sub-models. Moreover, a comprehensive sensitivity analysis encompassing 4374 scenarios is performed. The model is calibrated using data from Calabria, Italy. The results indicated a decent consistency between the observed and predicted number of infected people (MAPE = 27.94%, RMSE = 0.87 and ${\chi }^2(1,N=34)=(44.11,p=0.11)$). Notably, the transmission distance was identified as the most influential parameter in this model. In nearly all scenarios, this parameter had a significant impact on the outbreak dynamics (total cases and epidemic peak). Also, the calibration process showed that the movement of agents and the number of initial asymptomatic agents are vital model parameters to simulate COVID-19 spread accurately. The developed model may provide useful insights to investigate different scenarios and dynamics of other similar infectious diseases in closed indoor settings. Full article

Japanese encephalitis virus (JEV), a flavivirus transmitted by mosquitoes, has caused epidemics and severe neurological diseases in Asian countries. In this study, we developed a cDNA infectious clone, pBAC JYJEV3, of the JEV genotype 3 strain (EF571853.1) using a bacterial artificial chromosome (BAC) vector. The constructed infectious clone was transfected into Vero cells, where it exhibited infectivity and induced cytopathic effects akin to those of the parent virus. Confocal microscopy confirmed the expression of the JEV envelope protein. Comparative analysis of growth kinetics revealed similar replication dynamics between the parental and recombinant viruses, with peak titers observed 72 h post-infection (hpi). Furthermore, plaque assays demonstrated comparable plaque sizes and morphologies between the viruses. Cryo-electron microscopy confirmed the production of recombinant virus particles with a morphology identical to that of the parent virus. Immunization studies in mice using inactivated parental and recombinant viruses revealed robust IgG responses, with neutralizing antibody production increasing over time. These results showcase the successful generation and characterization of a recombinant JEV3 virus and provide a platform for further investigations into JEV pathogenesis and vaccine development. Full article

The coronavirus disease 2019 (COVID-19) pandemic disrupted public health and economies worldwide. In this paper, we investigate an optimal control problem to simultaneously minimize the epidemic size and control costs associated with intervention strategies based on official data. Considering people with undetected infections, we establish a control system of COVID-19 with time-varying parameters. To estimate these parameters, a parameter identification scheme is adopted and a mixed algorithm is constructed. Moreover, we present an optimal control problem with two objectives that involve the newly increased number of infected individuals and the control costs. A numerical scheme is conducted, simulating the epidemic data pertaining to Shanghai during the period of 2022, caused by the Omicron variant. Coefficient combinations of the objectives are obtained, and the optimal control measures for different infection peaks are indicated. The numerical results suggest that the identification variables obtained by using the constructed mixed algorithm to solve the parameter identification problem are feasible. Optimal control measures for different epidemic peaks can serve as references for decision-makers. Full article

Background: Cancer patients are at a high risk of complications in cases of infection, and head and neck cancers (HNC) are no exception. Since late 2019, SARS-CoV-2 has caused a global health crisis, with high rates and severe forms of the disease in cancer patients. Hospitalization, surgery and radiotherapy were rapidly described as increasing the risk of infection. Since March 2020, the Amiens University Hospital (France) has been taking care of COVID-19 patients while its maxillofacial surgery department managed HNC patients without interruption, even during lockdown periods. However, many questions concerning the impact on patient care were still pending. The aim of this study is to describe HNC management in our center during the first epidemic peak and to evaluate the impact of containment measures on patient treatment. Methods: We retrospectively included 44 HNC patients treated in our department between 1 March and 31 August 2020. Two groups were defined according to the period of care: lockdown (March to May) and lighter restrictions (June to August). Results: The results show typical epidemiological characteristics, maintained management times and non-downgraded procedures. Conclusions: Thus, during the first epidemic peak, continuity of care and patients’ safety could be ensured thanks to adequate means, adapted procedures and an experienced surgical team. Full article

Wastewater-based epidemiology (WBE) is currently used to monitor not only the spread of the viral SARS-CoV-2 pandemic but also that of other viruses in endemic conditions, particularly in the absence of syndromic surveillance. The continuous monitoring of sewage requires high expenditure and significant time investments, highlighting the need for standardized methods and structured monitoring strategies. In this context, we conducted weekly wastewater monitoring in northwestern Tuscany (Italy) and targeted human adenovirus (HAdV), norovirus genogroup II (NoVggII), enterovirus (EV), and SARS-CoV-2. Samples were collected at the entrances of treatment plants and concentrated using PEG/NaCl precipitation, and viral nucleic acids were extracted and detected through real-time reverse transcription qPCR. NoVggII was the most identified target (84.4%), followed by HAdV, SARS-CoV-2, and EV. Only HAdV and EV exhibited seasonal peaks in spring and summer. Compared with data that were previously collected in the same study area (from February 2021 to September 2021), the results for SARS-CoV-2 revealed a shift from an epidemic to an endemic pattern, at least in the region under investigation, which was likely due to viral mutations that led to the spreading of new variants with increased resistance to summer environmental conditions. In conclusion, using standardized methods and an efficient monitoring strategy, WBE proves valuable for viral surveillance in pandemic and epidemic scenarios, enabling the identification of temporal–local distribution patterns that are useful for making informed public health decisions. Full article

The current influenza season started in Italy in October 2023, approaching the epidemic peak in late December (52nd week of the year). We aimed to explore the mid-term virologic surveillance data of the 2023/2024 influenza season (from 16 October 2023 to 7 January 2024) in Sicily, the fourth most populous Italian region. A test-negative design was used to estimate the effectiveness of seasonal influenza vaccine (VE) against A(H1N1)pdm09 virus, the predominant subtype in Sicily (96.2% of laboratory-confirmed influenza cases). Overall, 29.2% (n = 359/1230) of oropharyngeal swabs collected from patients with influenza-like illness (ILI) were positive for influenza. Among the laboratory-confirmed influenza cases, 12.5% (n = 45/359) were vaccinated against influenza, with higher prevalence of laboratory-confirmed diagnosis of influenza A among subjects vaccinated with quadrivalent inactivated standard dose (29.4%), live attenuated intranasal (25.1%), and quadrivalent inactivated high-dose (23.8%) influenza vaccines. Comparing influenza vaccination status for the 2023/2024 season among laboratory-confirmed influenza-positive and -negative samples, higher vaccination rates in influenza-negative samples (vs. positive) were observed in all age groups, except for 45–64 years old, regardless of sex and comorbidities. The overall adjusted VE (adj-VE) was 41.4% [95%CI: 10.5–61.6%], whereas the adj-VE was 37.9% [95%CI: −0.7–61.7%] among children 7 months–14 years old and 52.7% [95%CI: −38.0–83.8%] among the elderly (≥65 years old). Full article

The aim of this study was to describe the 2022–2023 bronchiolitis epidemic season (the second after COVID-19 pandemic and the first without social restriction), focusing on patients discharged home from a pediatric emergency department (PED) and on those revisited within 72 h. We performed a retrospective observational study in an Italian tertiary care children’s hospital, reviewing PED accesses from 1 October 2022 to 31 March 2023. The number of hospitalizations for bronchiolitis was extracted from hospital discharge forms. A total of 512 patients diagnosed with bronchiolitis were admitted to PED (2.8% of total admissions). Accesses increased sharply from November to January, with a peak in December, in both admissions and hospitalizations. More than half of the patients (55.5%) were safely discharged home, while 38 (13.4%) came back to PED for a revisit. Overall PED accesses and hospitalizations for bronchiolitis increased since the previous epidemic season, and particularly compared to the pandemic and pre-pandemic eras. Empowering the collaboration between all healthcare provisioners is fundamental to suitable management of patients. Monitoring the epidemiology and seasonality of bronchiolitis is a starting point for an effective internal organization of pediatric departments and to further evaluate its socio-economic burden. Full article

Hemorrhagic fever with renal syndrome (HFRS) is caused by hantaviruses (HVs) and is endemic in Zhejiang Province, China. In this study, we aimed to explore the changing epidemiology of HFRS cases and the dynamics of hantavirus hosts in Zhejiang Province. Joinpoint regression was used to analyze long-term trends in the incidence of HFRS. The comparison of animal density at different stages was conducted using the Mann–Whitney Test. A comparison of HV carriage rates between stages and species was performed using the chi-square test. The incidence of HFRS shows a continuous downward trend. Cases are widely distributed in all counties of Zhejiang Province except Shengsi County. There was a high incidence belt from west to east, with low incidence in the south and north. The HFRS epidemic showed two seasonal peaks in Zhejiang Province, which were winter and summer. It showed a marked increase in the age of the incidence population. A total of 23,073 minibeasts from 21 species were captured. Positive results were detected in the lung tissues of 14 rodent species and 1 shrew species. A total of 80% of the positive results were from striped field mice and brown rats. No difference in HV carriage rates between striped field mice and brown rats was observed (χ² = 0.258, p = 0.611). Full article

The United States (US) opioid epidemic is a persistent and pervasive public health emergency that claims the lives of over 80,000 Americans per year as of 2021. There have been sustained efforts to reverse this crisis over the past decade, including a number of measures designed to decrease the use of prescription opioids for the treatment of pain. This study analyzed the changes in federal production quotas for prescription opioids and the distribution of prescription opioids for pain and identified state-level differences between 2010 and 2019. Data (in grams) on opioid production quotas and distribution (from manufacturer to hospitals, retail pharmacies, practitioners, and teaching institutions) of 10 prescription opioids (codeine, fentanyl, hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, and tapentadol) for 2010 to 2019 were obtained from the US Drug Enforcement Administration. Amounts of each opioid were converted from grams to morphine milligram equivalent (MME), and the per capita distribution by state was calculated using population estimates. Total opioid production quotas increased substantially from 2010 to 2013 before decreasing by 41.5% from 2013 (87.6 MME metric tons) to 2019 (51.3). The peak year for distribution of all 10 prescription opioids was between 2010 and 2013, except for codeine (2015). The largest quantities of opioid distribution were observed in Tennessee (520.70 MME per person) and Delaware (251.45) in 2011 and 2019. There was a 52.0% overall decrease in opioid distribution per capita from 2010 to 2019, with the largest decrease in Florida (−61.6%) and the smallest in Texas (−18.6%). Southern states had the highest per capita distribution for eight of the ten opioids in 2019. The highest to lowest state ratio of total opioid distribution, corrected for population, decreased from 5.25 in 2011 to 2.78 in 2019. The mean 95th/5th ratio was relatively consistent in 2011 (4.78 ± 0.70) relative to 2019 (5.64 ± 0.98). This study found a sustained decline in the distribution of ten prescription opioids during the last five years. Distribution was non-homogeneous at the state level. Analysis of state-level differences revealed a fivefold difference in the 95th:5th percentile ratio between states, which has remained unchanged over the past decade. Production quotas did not correspond with the distribution, particularly in the 2010–2016 period. Future research, focused on identifying factors contributing to the observed regional variability in opioid distribution, could prove valuable to understanding and potentially remediating the pronounced disparities in prescription opioid-related harms in the US. Full article

This work is a response to the appeal of various international health organizations and the Automatic Control Community for collaboration in addressing Coronavirus/COVID-19 challenges during the initial stages of the pandemic. Specifically, this study presents scientific evidence supporting the efficacy of three primary non-pharmacological strategies for pandemic mitigation. We propose a control system to aid in formulating a public decision policy aimed at managing the spread of COVID-19 caused by the SARS-CoV-2 virus, commonly known as coronavirus. The primary objective is to prevent overwhelming healthcare systems by averting the saturation of intensive care units (ICUs). In the context of COVID-19, understanding the peak infection rate and its time delay is crucial for preparing healthcare infrastructure and ensuring an adequate supply of intensive care units equipped with automatic ventilators. While it is widely recognized that public policies encompassing confinement and social distancing can flatten the epidemiological curve and provide time to bolster healthcare resources, there is a dearth of studies examining this pivotal issue from the perspective of control system theory. In this study, we introduce a control system founded on three prevailing non-pharmacological tools for epidemic and pandemic mitigation: social distancing, confinement, and population-wide testing and isolation in regions experiencing community transmission. Our analysis and control system design rely on the susceptible-exposed–infected–recovered–deceased (SEIRD) mathematical model, which describes the temporal dynamics of a pandemic, tailored in this research to account for the temporal and spatial characteristics of SARS-CoV-2 behavior. This model incorporates the influence of conducting tests with subsequent population isolation. An On–off control strategy is analyzed, and a proportional–integral–derivative (PID) controller is proposed to generate a sequence of public policy decisions. The proposed control system employs the required number of critical beds and ICUs as feedback signals and compares these with the available bed capacity to generate an error signal, which is utilized as input for the PID controller. The control actions outlined involve five phases of “Social Distancing and Confinement” (SD&C) to be implemented by governmental authorities. Consequently, the control system generates a policy sequence for SD&C, with applications occurring on a weekly or biweekly basis. The simulation results underscore the favorable impact of these three mitigation strategies against the coronavirus, illustrating their efficacy in controlling the outbreak and thereby mitigating the risk of healthcare system collapse. Full article