scholarly journals Modeling SARS-CoV-2 spread with dynamic isolation

2021 ◽  
Author(s):  
Md. Azmir Ibne Islam ◽  
Sharmin Sultana Shanta ◽  
Ashrafur Rahman
Keyword(s):  
Public Health ◽  
New Jersey ◽  
Screening Program ◽  
Vaccine Coverage ◽  
Wide Spectrum ◽  
Demographic Data ◽  
Model Parameters ◽  
Third Wave ◽  
Susceptible Population ◽  
The Impact

Background: The SARS-CoV-2 pandemic is spreading with a greater intensity across the globe. The synchrony of public health interventions and epidemic waves signify the importance of evaluation of the underline interventions. Method: We developed a mathematical model to present the transmission dynamics of SARS-CoV-2 and to analyze the impact of key nonpharmaceutical interventions such as isolation and screening program on the disease outcomes to the people of New Jersey, USA. We introduced a dynamic isolation of susceptible population with a constant (imposed) and infection oriented interventions. Epidemiological and demographic data are used to estimate the model parameters. The baseline case was explored further to showcase several critical and predictive scenarios. Results and analysis: The model simulations are in good agreement with the infection data for the period of 5 March 2020 to 31 January 2021. Dynamic isolation and screening program are found to be potential measures that can alter the course of epidemic. A  7% increase in isolation rate may result in a 31% reduction of epidemic peak whereas a 3 times increase in screening rate may reduce the epidemic peak by 35%. The model predicts that nearly 9.7% to 12% of the total population of New Jersey may become infected within the middle of July 2021 along with 24.6 to 27.3 thousand cumulative deaths. Within a wide spectrum of probable scenarios, there is a possibility of third wave Conclusion: Our findings could be informative to the public health community to contain the pandemic in the case of economy reopening under a limited or no vaccine coverage. Additional epidemic waves can be avoided by appropriate screening and isolation plans. 

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

scholarly journals Developing fit-for-purpose self-report instruments for assessing consumer responses to tobacco and nicotine products: the ABOUT™ Toolbox initiative

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1878 ◽  
Author(s):  
Christelle Chrea ◽  
Catherine Acquadro ◽  
Esther F. Afolalu ◽  
Erica Spies ◽  
Thomas Salzberger ◽  
...  
Keyword(s):  
Public Health ◽  
Wide Spectrum ◽  
Medical Product ◽  
Consumer Perception ◽  
Self Report ◽  
Measurement Instruments ◽  
Patient Reported ◽  
Fit For Purpose ◽  
And Behavior ◽  
The Impact

Background. Determining the public health impact of tobacco harm reduction strategies requires the assessment of consumer perception and behavior associated with tobacco and nicotine products (TNPs) with different exposure and risk profiles. In this context, rigorous methods to develop and validate psychometrically sound self-report instruments to measure consumers’ responses to TNPs are needed. Methods. Consistent with best practice guidelines, including the U.S. Food and Drug Administration’s “Guidance for Industry Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims,” scientifically designed, fit-for-purpose, reliable, and valid instruments are now being applied to tobacco regulatory research. Results. This brief report presents the ABOUT™ Toolbox (Assessment of Behavioral OUtcomes related to Tobacco and nicotine products) initiative. This communication: (1) describes the methodological steps followed for the development and validation of the measurement instruments included in the ABOUT™ Toolbox, (2) presents a summary of the high-priority tobacco-related domains that are currently covered in the ABOUT™ Toolbox (i.e., risk perception, dependence, product experience, health and functioning, and use history), and (3) details how the measurement instruments are made accessible to the scientific community. Conclusions. By making the ABOUT™ Toolbox available to the tobacco research and public health community, we envision a rapidly expanding knowledge base, with the goals of (1) supporting consumer perception and behavior research to allow comparisons across a wide spectrum of TNPs, (2) enabling public health and regulatory communities to make better-informed decisions for future regulation of TNPs, and (3) enhancing surveillance activities associated with the impact of TNPs on population health.

scholarly journals A prototype vaccination model for endemic Covid-19 under waning immunity and imperfect vaccine take-up

2021 ◽  
Author(s):  
John S Dagpunar ◽  
ChenChen Wu
Keyword(s):  
Basic Reproduction Number ◽  
Vaccine Efficacy ◽  
Reproduction Number ◽  
Vaccine Coverage ◽  
Infection Prevalence ◽  
State Variables ◽  
Susceptible Population ◽  
Waning Immunity ◽  
The Impact ◽  
The Basic Reproduction Number

In this paper, for an infectious disease such as Covid-19, we present a SIR model which examines the impact of waning immunity, vaccination rates, vaccine efficacy, and the proportion of the susceptible population who aspire to be vaccinated. Under an assumed constant control reproduction number, we provide simple conditions for the disease to be eliminated, and conversely for it to exhibit the more likely endemic behaviour. With regard to Covid-19, it is shown that if the control reproduction number is set to the basic reproduction number (say 6) of the dominant delta (B1.617.2) variant, vaccination alone, even under the most optimistic of assumptions about vaccine efficacy and high vaccine coverage, is very unlikely to lead to elimination of the disease. The model is not intended to be predictive but more an aid to understanding the relative importance of various biological and control parameters. For example, from a long-term perspective, it may be found that in the UK, through changes in societal behaviour (such as mask use, ventilation, and level of homeworking), without formal government interventions such as on-off lockdowns, the control reproduction number can still be maintained at a level significantly below the basic reproduction number. Even so, our simulations show that endemic behaviour ensues. The model obtains equilibrium values of the state variables such as the infection prevalence and mortality rate under various scenarios.

scholarly journals Brazil Needs Organized Breast Cancer Screening: Pilot Project in Rio De Janeiro

2018 ◽  
Vol 4 (Supplement 2) ◽  
pp. 31s-31s
Author(s):  
S. Gioia ◽  
C. Torres ◽  
J. Cavalcanti ◽  
A. Heringer
Keyword(s):  
Public Health ◽  
Breast Cancer ◽  
Digital Mammography ◽  
Rio De Janeiro ◽  
Screening Program ◽  
Private Insurance ◽  
Breast Cancer Mortality ◽  
Adequate Treatment ◽  
Screening Programs ◽  
The Impact

Background: In Rio de Janeiro there is only the opportunistic screening program for women with breast cancer who arrive at health facilities and with a 14% rate of mammography coverage. In countries that have implemented effective screening programs, with coverage of the target population, quality of screening, and adequate treatment, breast cancer mortality has declined. Evidence of the impact of screening on mortality by this neoplasm justifies its adoption as a public health policy, as recommended by WHO. 80% of the population use the public health system (Sistema Unico de Saude - SUS), provided by the government. This system mainly provides conventional mammography. The private insurance system covers the remaining 20%, who have access to modern technologies such as digital mammography or MRI. Aim: The breast cancer organized screening program in the community of the Andaraí, RJ is committed in assisting women asymptomatic 50-69 years from SUS. Methods: The program foresees the participation of these women for an indefinite period, free of charge, and the accomplishment of biennial digital mammography, going through the stages of early detection and diagnosis. In case of positivity for malignant disease, it will be treated properly. Results: Since April 2014 have been 350 women with an average age of 54 years. 100% of them were asymptomatic and 49% had never done before mammography. Only 1 woman presented clinical suspect aged 44 years. The screening program organized by breast cancer in the community of Andaraí, RJ presented a mammographic coverage rate of 70%. The program is contemplated in the healthcare plan of the SUS. Conclusion: Preliminary results of the study suggest that population based organized screening are feasible and age of onset mammography screening should be 50 years in Rio de Janeiro.

scholarly journals Modeling Tuberculosis Among Healthcare Workers

2019 ◽  
Vol 5 (2) ◽  
pp. 186-196
Author(s):  
T.S. Faniran ◽  
A.O. Falade ◽  
T.O. Alakija
Keyword(s):  
Sensitivity Analysis ◽  
Equilibrium Point ◽  
Healthcare Workers ◽  
Compliance Rate ◽  
Transmission Dynamics ◽  
Infectious Period ◽  
Model Parameters ◽  
Asymptotically Stable ◽  
Susceptible Population ◽  
The Impact

AbstractA mathematical model for transmission dynamics of tuberculosis among healthcare workers is formulated. Tuberculosis is an airborne disease caused by Mycobacterium tuberculosis bacteria that affect the lungs of a host. Previous research had concentrated on mathematical modeling of transmission dynamics of tuberculosis without considering the impact of compliance rate to particulate respirator by healthcare workers on the transmission. Therefore, how compliance rate to particulate respirator reduces the transmission of tuberculosis is an active question, and we develop a new system of ordinary differential equations that explicitly explores the impact of compliance rate to particulate respirator by healthcare workers upon transmission. Rigorous analysis of the model shows that the disease-free equilibrium point is locally asymptotically stable when the basic reproduction number, Ro < 1. This is established through the analysis of characteristic equation. Basic reproduction, Ro is the number of new cases that an existing case generates on average over the infectious period in a susceptible population. We also show that the endemic equilibrium point is locally asymptotically stable for Ro > 1, by using Routh-Hurwitz criteria for stability. Sensitivity analysis is carried out to determine the relative importance of the model parameters to the disease transmission. The result of the sensitivity analysis shows that the most sensitive parameter is β (Human-to-human transmission rate), followed by Λ (Human recruitment rate). Also, the result shows that increase in ψ (compliance rate to particulate respirator by healthcare workers) leads to decrease in Ro which reduces tuberculosis spread among healthcare workers.

scholarly journals A Mathematical Model of COVID-19 with Vaccination and Treatment

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
M. L. Diagne ◽  
H. Rwezaura ◽  
S. Y. Tchoumi ◽  
J. M. Tchuenche
Keyword(s):  
Public Health ◽  
Mathematical Model ◽  
Disease Transmission ◽  
Vaccine Efficacy ◽  
Reproduction Number ◽  
Vaccine Coverage ◽  
Public Health Intervention ◽  
Rank Correlation ◽  
Latin Hypercube Sampling ◽  
Model Parameters

We formulate and theoretically analyze a mathematical model of COVID-19 transmission mechanism incorporating vital dynamics of the disease and two key therapeutic measures—vaccination of susceptible individuals and recovery/treatment of infected individuals. Both the disease-free and endemic equilibrium are globally asymptotically stable when the effective reproduction number R 0 v is, respectively, less or greater than unity. The derived critical vaccination threshold is dependent on the vaccine efficacy for disease eradication whenever R 0 v > 1 , even if vaccine coverage is high. Pontryagin’s maximum principle is applied to establish the existence of the optimal control problem and to derive the necessary conditions to optimally mitigate the spread of the disease. The model is fitted with cumulative daily Senegal data, with a basic reproduction number R 0 = 1.31 at the onset of the epidemic. Simulation results suggest that despite the effectiveness of COVID-19 vaccination and treatment to mitigate the spread of COVID-19, when R 0 v > 1 , additional efforts such as nonpharmaceutical public health interventions should continue to be implemented. Using partial rank correlation coefficients and Latin hypercube sampling, sensitivity analysis is carried out to determine the relative importance of model parameters to disease transmission. Results shown graphically could help to inform the process of prioritizing public health intervention measures to be implemented and which model parameter to focus on in order to mitigate the spread of the disease. The effective contact rate b , the vaccine efficacy ε , the vaccination rate v , the fraction of exposed individuals who develop symptoms, and, respectively, the exit rates from the exposed and the asymptomatic classes σ and ϕ are the most impactful parameters.

scholarly journals When time-dependence in disease outcome risk is not captured by impact evaluation modeling studies: a measles vaccination case study

2021 ◽  
Author(s):  
Allison Portnoy ◽  
Yuli Lily Hsieh ◽  
Kaja Abbas ◽  
Petra Klepac ◽  
Heather Santos ◽  
...  
Keyword(s):  
Vaccine Coverage ◽  
Disease Incidence ◽  
Estimation Methods ◽  
Model Parameters ◽  
Time Varying ◽  
Modeling Studies ◽  
Measles Vaccination ◽  
Secondary Outcomes ◽  
The Impact

Background: In modeling studies that evaluate the effects of health programs, the risk of secondary outcomes attributable to infection can vary with underlying disease incidence. Consequently, the impact of interventions on secondary outcomes would not be proportional to incidence reduction. Here we use a case study on measles vaccine program to demonstrate how failure to capture this non-linear relationship can lead to over- or under-estimation. Methods: We used a published model of measles CFR that depends on incidence and vaccine coverage to illustrate the effects of: (1) assuming higher CFR in 'no-vaccination' scenarios; (2) time-varying CFRs over the past; and (3) time-varying CFRs in future projections on measles impact estimation. We evaluated how different assumptions on vaccine coverage, measles incidence, and CFR levels in 'no-vaccination' scenarios affect estimation of future deaths averted by measles vaccination. Results: Compared to constant CFRs, aligning both 'vaccination' and 'no-vaccination' scenarios with time variant measles CFR estimates led to larger differences in mortality in historical years and lower in future years. Conclusions: To assess consequences of interventions, impact estimates should consider the effect of 'no-intervention' scenario assumptions on model parameters to project estimated impact for alternative scenarios according to intervention strategies and investment decisions.

scholarly journals Cohort profile: Actionable Register of Geneva Outpatients with SARS-CoV-2 (ARGOS)

2021 ◽  
Author(s):  
Camille Genecand ◽  
Flora Koegler ◽  
Dan Lebowitz ◽  
Denis Mongin ◽  
Simon Regard ◽  
...  
Keyword(s):  
Public Health ◽  
Disease Burden ◽  
Demographic Data ◽  
Participation Rate ◽  
Health Policies ◽  
Contact Tracing ◽  
Valuable Resource ◽  
Disease Evolution ◽  
Public Health Policies ◽  
The Impact

Purpose The Actionable Register of Geneva Outpatients with SARS-CoV-2 (ARGOS) is an ongoing prospective cohort created by the Geneva Directorate of Health (GDH). It consists of an operational database compiling all SARS-CoV-2 test results conducted in the Geneva area since late February 2020. While the disease evolution of patients hospitalized with SARS-CoV-2 are now relatively numerous, the same cannot be said for outpatients. This article aims at presenting a comprehensive outpatient cohort in light of the varying public health measures in Geneva, Switzerland, since March 2020. Participants As of July 28, 2020, the database included 58 226 patients, among which 6848 had at least one positive test result for SARS-CoV-2. Among all positive patients, 66.8% were contacted once, and 21% of participants had 3 or more follow-up calls. Participation rate is 96.9%. Data collection is ongoing. Findings to date ARGOS data illustrates the magnitude of COVID-19 pandemic in Geneva, Switzerland, and details a variety of population factors and outcomes. The content of the cohort includes demographic data, comorbidities and risk factors for poor clinical outcome, COVID-19 symptoms, environmental and socio-economic factors, contact tracing data, hospitalizations and deaths. Future plans: The data of this large real-world registry provides a valuable resource for various types of research, such as epidemiological research or policy assessment as it illustrates the impact of public health policies and overall disease burden of COVID-19. STRENGTHS AND LIMITATIONS OF THIS STUDY - ARGOS main strength consists of its large number of cases, representative of all diagnosed cases on a regional level with the primary aim of assessing all cases. - ARGOS involves every tested individual and is not limited to hospitalized patients, thus providing a valuable resource to assess the impact of public health policies and overall disease burden of COVID-19 in a geographically defined population. - To mitigate confounding effects and improve data analysis and interpretation, we present the data according to four policy periods. - This cohort is multicentric as it includes all tests performed in Geneva's hospitals (both public and private), private practices and medical centers. - Due to operational needs, symptoms and comorbidities are self-reported, which may lead to measurement error or misclassification.

scholarly journals Vaccination and three non-pharmaceutical interventions determine the end of COVID-19 at 381 metropolitan statistical areas in the US

2021 ◽  
Author(s):  
LU ZHONG ◽  
Mamadou Diagne ◽  
Qi Wang ◽  
Jianxi Gao
Keyword(s):  
Vaccine Coverage ◽  
Face Mask ◽  
Analytical Framework ◽  
Model Parameters ◽  
Metropolitan Statistical Areas ◽  
Input Signals ◽  
The Us ◽  
Manageable Level ◽  
The Impact ◽  
Pharmaceutical Interventions

The rapid rollout of the COVID-19 vaccine global raises the question of whether and when the ongoing pandemic could be eliminated with vaccination and non-pharmaceutical interventions (NPIs). Despite advances in the impact of NPIs and the conceptual belief that NPIs and vaccination control COVID-19 infections, we lack evidence to employ control theory in real-world social human dynamics in the context of disease spreading. We bridge the gap by developing a new analytical framework that treats COVID-19 as a feedback control system with the NPIs and vaccination as the controllers and a computational and mathematical model that maps human social behaviors to input signals. This approach enables us to effectively predict the epidemic spreading in 381 Metropolitan statistical areas (MSAs) in the US by learning our model parameters utilizing the time series NPIs (i.e., the stay-at-home order, face-mask wearing, and testing) data. This model allows us to optimally identify three NPIs to predict infections actually in 381 MSAs and avoid overfitting. Our numerical results universally demonstrate our approach's excellent predictive power with R2>0.9 of all the MSAs regardless of their sizes, locations, and demographic status. Our methodology allows us to estimate the needed vaccine coverage and NPIs for achieving Re to the manageable level and the required days for disease elimination at each location. Our analytical results provide insights into the debates on the aims for eliminating COVID-19. NPIs, if tailored to the MSAs, can drive the pandemic to an easily containable level and suppress future recurrences of epidemic cycles.

scholarly journals Protecting the vaccinating population in the face of a measles epidemic: assessing the impact of adjusted vaccination schedules

2002 ◽  
Vol 128 (1) ◽  
pp. 47-57 ◽  
Author(s):  
S. van den HOF ◽  
J. WALLINGA ◽  
M.-A. WIDDOWSON ◽  
M. A. E. CONYN-VAN SPAENDONCK
Keyword(s):  
Vaccine Coverage ◽  
Population Based ◽  
Vaccination Schedule ◽  
Model Parameters ◽  
Serological Survey ◽  
Measles Antibodies ◽  
The Face ◽  
Measles Vaccination ◽  
Antibody Levels ◽  
The Impact

We investigated which vaccination schedule gives best protection to the vaccinating population, in case there is a measles epidemic in an area with low vaccine coverage. We considered combinations of an early measles vaccination (none, at 6 months or at 9 months), a measles–mumps–rubella (MMR) vaccination around the first birthday (at either 11 or 14 months), and MMR vaccination at an older age (at either 4 or 9 years). The different estimates on measures of protection (percentage of susceptibles, number of reported cases in an epidemic year, percentage of lifetime spent susceptible) relied on a mathematical model of decline of maternal antibody levels with age, and the impact of that antibody level on seroconversion and immunity. Model parameters were estimated from a Dutch population-based serological survey on measles antibodies. Different measures of protection favoured different vaccination schedules, but dropping the age of second MMR vaccination prevents considerably more cases than an extra early measles vaccination or dropping the age of first MMR vaccination.

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