ABSTRACT
This paper presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location-allocation mixed-integer programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Objective: This study aims at comparing the patients with spinal trauma in the Coronavirus disease-2019 pandemic era and pre-pandemic era. Materials and Methods: Patient records for a 9-month period of pandemic (April 1, 2020 - December 31, 2020) and the same period of the previous year (April 1, 2019 - December 31, 2019) were retrospectively collected. These 2 periods were compared in terms of the total number of patients with spinal trauma, the type of injuries, the level of injuries in the spine, the treatment methods applied, and whether there was a neurological deficit. The first group was called as pandemic group (PG) and the latter as control group (CG). The differences between them were statistically examined. Results: The study sampled 278 patients (CG: 203 patients, PG: 75 patients). It was detected that the number of patients with spinal trauma in the PG dropped by 60% compared to the CG. The most frequent cause of spinal trauma for both groups was traffic accidents. No statistically significant difference was detected in terms of the type, level and severity of injuries, neurological examination findings and method of treatment (p>0.05). However, the rate of indoor or outdoor falls were significantly different between the two groups (p=0.002). Conclusion: It has been determined that the pandemic-induced social isolation and lockdown process is an important factor in the primordial prevention of spinal trauma. With the result obtained, we think that if adequate and correct measures are taken, the number of spinal traumas will continue to remain low in the post-pandemic period as well. © Copyright 2022 by the Turkish Spine Society/The Journal of Turkish Spinal Surgery published by Galenos Publishing House.
ABSTRACT
Covid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations. Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
OBJECTIVE: To characterize clinical and epidemiologic features of SARS-CoV-2 in companion animals detected through both passive and active surveillance in the US. ANIMALS: 204 companion animals (109 cats, 95 dogs) across 33 states with confirmed SARS-CoV-2 infections between March 2020 and December 2021. PROCEDURES: Public health officials, animal health officials, and academic researchers investigating zoonotic SARS-CoV-2 transmission events reported clinical, laboratory, and epidemiologic information through a standardized One Health surveillance process developed by the CDC and partners. RESULTS: Among dogs and cats identified through passive surveillance, 94% (n = 87) had reported exposure to a person with COVlD-19 before infection. Clinical signs of illness were present in 74% of pets identified through passive surveillance and 27% of pets identified through active surveillance. Duration of illness in pets averaged 15 days in cats and 12 days in dogs. The average time between human and pet onset of illness was 10 days. Viral nucleic acid was first detected at 3 days after exposure in both cats and dogs. Antibodies were detected starting 5 days after exposure, and titers were highest at 9 days in cats and 14 days in dogs. CLINICAL RELEVANCE: Results of the present study supported that cats and dogs primarily become infected with SARS-CoV-2 following expo- sure to a person with COVID-19, most often their owners. Case investigation and surveillance that include both people and animals are necessary to understand transmission dynamics and viral evolution of zoonotic diseases like SARS-CoV-2.
ABSTRACT
Objectives: The COVID19 pandemic caused over six million deaths worldwide as of 2022 and made necessary the rapid development of vaccines. The objective of this Systematic Literature Review is to summarise the main evidence from economic evaluations of vaccines against COVID19. Method(s): Searches were conducted on PubMed on July 13th 2022. The selected papers considered COVID19 vaccination scenarios without population limits. The types of study design examined were cost-benefit and cost-effectiveness analyses. Result(s): Overall, 16 articles from an initial list of 1842 were included in this review. Out of the 16 models, there were five Markov cohort models (three of them were combined with a decision tree model), four dynamic transmission models, three microsimulation models, three epidemiological models (without further information on the model structure) and one decision tree model. Model characteristics were considerably consistent between high-, middle- or low-income countries. Five studies considered both the healthcare and societal perspective, while seven studies reported only the former, and one only the latter. Two studied did not specify the study perspective. Ten of the studies did not consider any level of herd immunity, and no study considered cross-protection. Although eight studies used "naive" comparisons between vaccines, none of the studies conducted thorough indirect treatment comparison. All the models suggest that vaccines are cost-effective as they prevent death and transmission, and reduce the severity of cases. Although the sources of effectiveness estimates were always stated, the details of those studies were rarely reported. Nevertheless, the outcome measures and the key parameters used in the models were generally clearly stated and justified. Conclusion(s): This SLR highlights several challenges for conducting Health Economic evaluations of COVID19 vaccines. The quality of the models and their estimates suffered from the very fast pace of COVID19 research. Therefore, economic evidence on vaccination programs requires additional rigorous research.Copyright © 2023
ABSTRACT
Against the background of the global spread of the new SARS-CoV-2 coronavirus, the prevention of infections with airborne mechanisms of transmission has become a priority in the Armed Forces. The development of effective COVID-19 prevention measures requires consideration of the peculiarities of military service and everyday life due to the inability of organized military collectives to comply with the requirements of the lockdown regime introduced at the peak of morbidity by the civilian health system. The patterns of incidence of COVID-19 in military personnel of the Western Military District in organized military collectives were studied in relation to the conditions of training and combat activities and the characteristics of military service. It was found that the dynamics of the incidence of COVID-19 among military personnel of the Western Military District in 2020–2021 exhibited a wave-like character and included four epidemic rises that coincided with epidemic waves among the civilian population. At the same time, from April to December 2020, the morbidity rate in military personnel was significantly higher than that in the general population, and from January to December 2021 against the background of mass vaccination of military personnel against COVID-19, the incidence rate in military personnel decreased by 50% relative to that in the general population. The effectiveness of anti-epidemic measures has increased significantly in recent months. The average number of patients in the epidemic outbreak decreased by 46.3%, the average duration of the outbreak decreased by 12.4%, and the proportion of group morbidity in the structure of the overall incidence of COVID-19 decreased by 19.8%. It is shown that the incidence of COVID-19 in various types of military collectives depends on the conditions of military service and the specifics of daily activities. The highest epidemiological significance of COVID-19 was detected in military units of constant readiness, as well as in medical and military educational organizations. © 2023 Nutritec. All rights reserved.
ABSTRACT
In this article, we present a epidemiological model to analyze the impact of the emerging disease COVID-19. When an infectious disease like coronavirus suddenly emerges out of the blue, little is known about it. As time passes we get equipped with better information and knowledge. Some of the common tactics generally adopted to fight off the disease include awareness, isolation, lockdown, treatment and vaccination. Media also plays a pivotal role in spreading these information to general population. Here, we consider a changing population with immigration during an outbreak. We apply some of the above said measures to the population and study the effect of them in combating the disease. The effect of media is also examined. Both analytical and numerical simulations help us in establishing our findings. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
In Morocco, 966,777 confirmed cases and 14,851 confirmed deaths because of COVID-19 were recorded as of January 1, 2022. Recently, a new strain of COVID-19, the so-called Omicron variant, was reported in Morocco, which is considered to be more dangerous than the existing COVID-19 virus. To end this ongoing global COVID-19 pandemic and Omicron variant, there is an urgent need to implement multiple population-wide policies like vaccination, testing more people, and contact tracing. To forecast the pandemic's progress and put together a strategy to effectively contain it, we propose a new hybrid mathematical model that predicts the dynamics of COVID-19 in Morocco, considering the difference between COVID-19 and the Omicron variant, and investigate the impact of some control strategies on their spread. The proposed model monitors the dynamics of eight compartments, namely susceptible (S)$$ (S) $$, exposed (E)$$ (E) $$, infected with COVID-19 (I)$$ (I) $$, infected with Omicron (IO)$$ \left({I}_O\right) $$, hospitalized (H)$$ (H) $$, people in intensive care units (U)$$ (U) $$, quarantined (Q)$$ (Q) $$, and recovered (R)$$ (R) $$, collectively expressed as SEIIOHUQR$$ SEI{I}_O HUQR $$. We calculate the basic reproduction number Script capital R0$$ {\mathcal{R}}_0 $$, studying the local and global infection-free equilibrium stability, a sensitivity analysis is conducted to determine the robustness of model predictions to parameter values, and the sensitive parameters are estimated from the real data on the COVID-19 pandemic in Morocco. We incorporate two control variables that represent vaccination and diagnosis of infected individuals and we propose an optimal strategy for an awareness program that will help to decrease the rate of the virus' spread. Pontryagin's maximum principle is used to characterize the optimal controls, and the optimality system is solved by an iterative method. Finally, extensive numerical simulations are employed with and without controls to illustrate our results using MATLAB software. Our results reveal that achieving a reduction in the contact rate between uninfected and infected individuals by vaccinating and diagnosing the susceptible individuals, can effectively reduce the basic reproduction number and tends to decrease the intensity of epidemic peaks, spreading the maximal impact of an epidemic over an extended period of time. The model simulations demonstrate that the elimination of the ongoing SARS-COV-2 pandemic and its variant Omicron in Morocco is possible by implementing, at the start of the pandemic, a strategy that combines the two variables of control mentioned above. Our predictions are based on real data with reasonable assumptions.
ABSTRACT
BACKGROUND: Seasonal influenza activity showed a sharp decline in activity at the beginning of the emergence of COVID-19. Whether there is an epidemiological correlation between the dynamic of these 2 respiratory infectious diseases and their future trends needs to be explored. OBJECTIVE: We aimed to assess the correlation between COVID-19 and influenza activity and estimate later epidemiological trends. METHODS: We retrospectively described the dynamics of COVID-19 and influenza in 6 World Health Organization (WHO) regions from January 2020 to March 2023 and used the long short-term memory machine learning model to learn potential patterns in previously observed activity and predict trends for the following 16 weeks. Finally, we used Spearman correlation coefficients to assess the past and future epidemiological correlation between these 2 respiratory infectious diseases. RESULTS: With the emergence of the original strain of SARS-CoV-2 and other variants, influenza activity stayed below 10% for more than 1 year in the 6 WHO regions. Subsequently, it gradually rose as Delta activity dropped, but still peaked below Delta. During the Omicron pandemic and the following period, the activity of each disease increased as the other decreased, alternating in dominance more than once, with each alternation lasting for 3 to 4 months. Correlation analysis showed that COVID-19 and influenza activity presented a predominantly negative correlation, with coefficients above -0.3 in WHO regions, especially during the Omicron pandemic and the following estimated period. The diseases had a transient positive correlation in the European region of the WHO and the Western Pacific region of the WHO when multiple dominant strains created a mixed pandemic. CONCLUSIONS: Influenza activity and past seasonal epidemiological patterns were shaken by the COVID-19 pandemic. The activity of these diseases was moderately or greater than moderately inversely correlated, and they suppressed and competed with each other, showing a seesaw effect. In the postpandemic era, this seesaw trend may be more prominent, suggesting the possibility of using one disease as an early warning signal for the other when making future estimates and conducting optimized annual vaccine campaigns.
Subject(s)
COVID-19 , Influenza Vaccines , Influenza, Human , Humans , Influenza, Human/epidemiology , SARS-CoV-2 , Pandemics , Retrospective Studies , World Health OrganizationABSTRACT
Breakthrough infection (BI) after coronavirus disease 2019 (COVID-19) vaccination has exploded owing to the emergence of various SARS-CoV-2 variants and has become a major problem at present. In this study, we analyzed the epidemiological information and possession status of neutralizing antibodies in patients with BI using SARS-CoV-2 pseudotyped viruses (SARS-CoV-2pv). Analysis of 44 specimens diagnosed with COVID-19 after two or more vaccinations showed high inhibition of infection by 90% or more against the Wuhan strain and the Alpha and Delta variants of pseudotyped viruses in 40 specimens. In contrast, almost no neutralizing activity was observed against the Omicron BA.1 variant. Many cases without neutralizing activity or BI were immunosuppressed individuals. The results of this study show that contact with an infected person can result in BI even when there are sufficient neutralizing antibodies in the blood. Thus, even after vaccination, sufficient precautions must be taken to prevent infection.
ABSTRACT
BACKGROUND: Many SARS-CoV-2 seroprevalence surveys since the end of 2020 have disqualified the first misconception that Africa had been spared by the pandemic. Through the analysis of three SARS-CoV-2 seroprevalence surveys carried out in Benin as part of the ARIACOV project, we argue that the integration of epidemiological serosurveillance of the SARS-CoV-2 infection in the national surveillance packages would be of great use to refine the understanding of the COVID-19 pandemic in Africa. METHODS: We carried out three repeated cross-sectional surveys in Benin: two in Cotonou, the economic capital in March and May 2021, and one in Natitingou, a semi-rural city in the north of the country in August 2021. Total and weighted-by-age-group seroprevalences were estimated and the risk factors for SARS-CoV-2 infection were assessed by multivariate logistic regression. RESULTS: In Cotonou, a slight increase in overall age-standardised SARS-CoV-2 seroprevalence from 29.77% (95% CI: 23.12%-37.41%) at the first survey to 34.86% (95% CI: 31.57%-38.30%) at the second survey was observed. In Natitingou, the globally adjusted seroprevalence was 33.34% (95% CI: 27.75%-39.44%). A trend of high risk for SARS-CoV 2 seropositivity was observed in adults over 40 versus the young (less than 18 years old) during the first survey in Cotonou but no longer in the second survey. CONCLUSIONS: Our results show that, however, rapid organisation of preventive measures aimed at breaking the chains of transmission, they were ultimately unable to prevent a wide spread of the virus in the population. Routine serological surveillance on strategic sentinel sites and/or populations could constitute a cost-effective compromise to better anticipate the onset of new waves and define public health strategies.
Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Adolescent , Benin/epidemiology , COVID-19/epidemiology , Pandemics , Cross-Sectional Studies , Seroepidemiologic Studies , Antibodies, ViralABSTRACT
The COVID-19 pandemic resulted in the collapse of healthcare systems and led to the development and application of several approaches of wastewater-based epidemiology to monitor infected populations. The main objective of this study was to carry out a SARS-CoV-2 wastewater based surveillance in Curitiba, Southern Brazil Sewage samples were collected weekly for 20 months at the entrance of five treatment plants representing the entire city and quantified by qPCR using the N1 marker. The viral loads were correlated with epidemiological data. The correlation by sampling points showed that the relationship between the viral loads and the number of reported cases was best described by a cross-correlation function, indicating a lag between 7 and 14 days amidst the variables, whereas the data for the entire city presented a higher correlation (0.84) with the number of positive tests at lag 0 (sampling day). The results also suggest that the Omicron VOC resulted in higher titers than the Delta VOC. Overall, our results showed that the approach used was robust as an early warning system, even with the use of different epidemiological indicators or changes in the virus variants in circulation. Therefore, it can contribute to public decision-makers and health interventions, especially in vulnerable and low-income regions with limited clinical testing capacity. Looking toward the future, this approach will contribute to a new look at environmental sanitation and should even induce an increase in sewage coverage rates in emerging countries.
Subject(s)
COVID-19 , Myrtaceae , Humans , Wastewater , SARS-CoV-2 , Sewage , COVID-19/epidemiology , Brazil/epidemiology , PandemicsABSTRACT
During the COVID-19 pandemic, excess mortality has been reported worldwide, but its magnitude has varied depending on methodological differences that hinder between-study comparability. Our aim was to estimate variability attributable to different methods, focusing on specific causes of death with different pre-pandemic trends. Monthly mortality figures observed in 2020 in the Veneto Region (Italy) were compared with those forecasted using: (1) 2018-2019 monthly average number of deaths; (2) 2015-2019 monthly average age-standardized mortality rates; (3) Seasonal Autoregressive Integrated Moving Average (SARIMA) models; (4) Generalized Estimating Equations (GEE) models. We analyzed deaths due to all-causes, circulatory diseases, cancer, and neurologic/mental disorders. Excess all-cause mortality estimates in 2020 across the four approaches were: +17.2% (2018-2019 average number of deaths), +9.5% (five-year average age-standardized rates), +15.2% (SARIMA), and +15.7% (GEE). For circulatory diseases (strong pre-pandemic decreasing trend), estimates were +7.1%, -4.4%, +8.4%, and +7.2%, respectively. Cancer mortality showed no relevant variations (ranging from -1.6% to -0.1%), except for the simple comparison of age-standardized mortality rates (-5.5%). The neurologic/mental disorders (with a pre-pandemic growing trend) estimated excess corresponded to +4.0%/+5.1% based on the first two approaches, while no major change could be detected based on the SARIMA and GEE models (-1.3%/+0.3%). The magnitude of excess mortality varied largely based on the methods applied to forecast mortality figures. The comparison with average age-standardized mortality rates in the previous five years diverged from the other approaches due to the lack of control over pre-existing trends. Differences across other methods were more limited, with GEE models probably representing the most versatile option.
Subject(s)
COVID-19 , Cardiovascular Diseases , Neoplasms , Humans , Child, Preschool , Pandemics , Italy/epidemiology , Neoplasms/epidemiology , MortalityABSTRACT
The effect of environmental and socioeconomic conditions on the global pandemic of COVID-19 had been widely studied, yet their influence during the early outbreak remains less explored. Unraveling these relationships represents a key knowledge to prevent potential outbreaks of similar pathogens in the future. This study aims to determine the influence of socioeconomic, infrastructure, air pollution, and weather variables on the relative risk of infection in the initial phase of the COVID-19 pandemic in China. A spatio-temporal Bayesian zero-inflated Poisson model is used to test for the effect of 13 socioeconomic, urban infrastructure, air pollution, and weather variables on the relative risk of COVID-19 disease in 122 cities of China. The results show that socioeconomic and urban infrastructure variables did not have a significant effect on the relative risk of COVID-19. Meanwhile, COVID-19 relative risk was negatively associated with temperature, wind speed, and carbon monoxide, while nitrous dioxide and the human modification index presented a positive effect. Pollution gases presented a marked variability during the study period, showing a decrease of CO. These findings suggest that controlling and monitoring urban emissions of pollutant gases is a key factor for the reduction of risk derived from COVID-19.
Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Humans , COVID-19/epidemiology , Air Pollutants/analysis , Pandemics , Bayes Theorem , Particulate Matter/analysis , Air Pollution/analysis , Carbon Monoxide/analysis , China/epidemiology , Environmental MonitoringABSTRACT
The COVID-19 virus's high transmissibility has resulted in the virus's rapid spread throughout the world, which has brought several repercussions, ranging from a lack of sanitary and medical products to the collapse of medical systems. Hence, governments attempt to re-plan the production of medical products and reallocate limited health resources to combat the pandemic. This paper addresses a multi-period production-inventory-sharing problem (PISP) to overcome such a circumstance, considering two consumable and reusable products. We introduce a new formulation to decide on production, inventory, delivery, and sharing quantities. The sharing will depend on net supply balance, allowable demand overload, unmet demand, and the reuse cycle of reusable products. Undeniably, the dynamic demand for products during pandemic situations must be reflected effectively in addressing the multi-period PISP. A bespoke compartmental susceptible-exposed-infectious-hospitalized-recovered-susceptible (SEIHRS) epidemiological model with a control policy is proposed, which also accounts for the influence of people's behavioral response as a result of the knowledge of adequate precautions. An accelerated Benders decomposition-based algorithm with tailored valid inequalities is offered to solve the model. Finally, we consider a realistic case study - the COVID-19 pandemic in France - to examine the computational proficiency of the decomposition method. The computational results reveal that the proposed decomposition method coupled with effective valid inequalities can solve large-sized test problems in a reasonable computational time and 9.88 times faster than the commercial Gurobi solver. Moreover, the sharing mechanism reduces the total cost of the system and the unmet demand on the average up to 32.98% and 20.96%, respectively.
ABSTRACT
BACKGROUND: We provide a compartmental model for the transmission of some contagious illnesses in a population. The model is based on partial differential equations, and takes into account seven sub-populations which are, concretely, susceptible, exposed, infected (asymptomatic or symptomatic), quarantined, recovered and vaccinated individuals along with migration. The goal is to propose and analyze an efficient computer method which resembles the dynamical properties of the epidemiological model. MATERIALS AND METHODS: A non-local approach is utilized for finding approximate solutions for the mathematical model. To that end, a non-standard finite-difference technique is introduced. The finite-difference scheme is a linearly implicit model which may be rewritten using a suitable matrix. Under suitable circumstances, the matrices representing the methodology are M-matrices. RESULTS: Analytically, the local asymptotic stability of the constant solutions is investigated and the next generation matrix technique is employed to calculate the reproduction number. Computationally, the dynamical consistency of the method and the numerical efficiency are investigated rigorously. The method is thoroughly examined for its convergence, stability, and consistency. CONCLUSIONS: The theoretical analysis of the method shows that it is able to maintain the positivity of its solutions and identify equilibria. The method's local asymptotic stability properties are similar to those of the continuous system. The analysis concludes that the numerical model is convergent, stable and consistent, with linear order of convergence in the temporal domain and quadratic order of convergence in the spatial variables. A computer implementation is used to confirm the mathematical properties, and it confirms the ability in our scheme to preserve positivity, and identify equilibrium solutions and their local asymptotic stability.
Subject(s)
Models, Theoretical , Quarantine , Humans , Computer Simulation , VaccinationABSTRACT
This study explores the dynamic transmission of infectious particles due to COVID-19 in the environment using a spatiotemporal epidemiological approach. We proposed a novel multi-agent model to simulate the spread of COVID-19 by considering several influencing factors. The model divides the population into susceptible and infected and analyzes the impact of different prevention and control measures, such as limiting the number of people and wearing masks on the spread of COVID-19. The findings suggest that reducing population density and wearing masks can significantly reduce the likelihood of virus transmission. Specifically, the research shows that if the population moves within a fixed range, almost everyone will eventually be infected within 1 h. When the population density is 50%, the infection rate is as high as 96%. If everyone does not wear a mask, nearly 72.33% of the people will be infected after 1 h. However, when people wear masks, the infection rate is consistently lower than when they do not wear masks. Even if only 25% of people wear masks, the infection rate with masks is 27.67% lower than without masks, which is strong evidence of the importance of wearing a mask. As people's daily activities are mostly carried out indoors, and many super-spreading events of the new crown epidemic also originated from indoor gatherings, the research on indoor epidemic prevention and control is essential. This study provides decision-making support for epidemic preventions and controls and the proposed methodology can be used in other regions and future epidemics.