scholarly journals The impact of surveillance and control on highly pathogenic avian inuenza outbreaks in poultry in Dhaka division, Bangladesh

2017 ◽  
Author(s):  
Edward M. Hill ◽  
Thomas House ◽  
Madhur S. Dhingra ◽  
Wantanee Kalpravidh ◽  
Subhash Morzaria ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Control Measure ◽  
Control Policy ◽  
Transmission Model ◽  
Model Framework ◽  
Highly Pathogenic ◽  
Management Actions ◽  
H5n1 Influenza ◽  
And Control ◽  
The Impact

AbstractIn Bangladesh, the poultry industry is an economically and socially important sector, but it is persistently threatened by the effects of H5N1 highly pathogenic avian influenza. Thus, identifying the optimal control policy in response to an emerging disease outbreak is a key challenge for policy-makers. To inform this aim, a common approach is to carry out simulation studies comparing plausible strategies, while accounting for known capacity restrictions. In this study we perform simulations of a previously developed H5N1 influenza transmission model framework, fitted to two separate historical outbreaks, to assess specific control objectives related to the burden or duration of H5N1 outbreaks among poultry farms in the Dhaka division of Bangladesh. In particular, we explore the optimal implementation of ring culling, ring vaccination and active surveillance measures when presuming disease transmission predominately occurs from premises-to-premises, versus a setting requiring the inclusion of external factors. Additionally, we determine the sensitivity of the management actions under consideration to differing levels of capacity constraints and outbreaks with disparate transmission dynamics. While we find that reactive culling and vaccination policies should pay close attention to these factors to ensure intervention targeting is optimised, across multiple settings the top performing control action amongst those under consideration were targeted proactive surveillance schemes. Our findings may advise the type of control measure, plus its intensity, that could potentially be applied in the event of a developing outbreak of H5N1 amongst originally H5N1 virus-free commercially-reared poultry in the Dhaka division of Bangladesh.

scholarly journals The exacerbation of Ebola outbreaks by conflict in the Democratic Republic of the Congo

2019 ◽  
Vol 116 (48) ◽  
pp. 24366-24372 ◽  
Author(s):  
Chad R. Wells ◽  
Abhishek Pandey ◽  
Martial L. Ndeffo Mbah ◽  
Bernard-A. Gaüzère ◽  
Denis Malvy ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Democratic Republic ◽  
Population Level ◽  
Model Framework ◽  
Public Health Response ◽  
Local Conditions ◽  
Health Response ◽  
Republic Of The Congo ◽  
And Control ◽  
The Impact

The interplay between civil unrest and disease transmission is not well understood. Violence targeting healthcare workers and Ebola treatment centers in the Democratic Republic of the Congo (DRC) has been thwarting the case isolation, treatment, and vaccination efforts. The extent to which conflict impedes public health response and contributes to incidence has not previously been evaluated. We construct a timeline of conflict events throughout the course of the epidemic and provide an ethnographic appraisal of the local conditions that preceded and followed conflict events. Informed by temporal incidence and conflict data as well as the ethnographic evidence, we developed a model of Ebola transmission and control to assess the impact of conflict on the epidemic in the eastern DRC from April 30, 2018, to June 23, 2019. We found that both the rapidity of case isolation and the population-level effectiveness of vaccination varied notably as a result of preceding unrest and subsequent impact of conflict events. Furthermore, conflict events were found to reverse an otherwise declining phase of the epidemic trajectory. Our model framework can be extended to other infectious diseases in the same and other regions of the world experiencing conflict and violence.

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 The effects of non-pharmaceutical interventions on SARS-CoV-2 transmission in different socioeconomic populations in Kuwait: a modeling study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fatima Khadadah ◽  
Abdullah A. Al-Shammari ◽  
Ahmad Alhashemi ◽  
Dari Alhuwail ◽  
Bader Al-Saif ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Model ◽  
Socioeconomic Groups ◽  
Before And After ◽  
Cross Transmission ◽  
Community Transmission ◽  
The Impact ◽  
Pharmaceutical Interventions ◽  
The Basic Reproduction Number

Abstract Background Aggressive non-pharmaceutical interventions (NPIs) may reduce transmission of SARS-CoV-2. The extent to which these interventions are successful in stopping the spread have not been characterized in countries with distinct socioeconomic groups. We compared the effects of a partial lockdown on disease transmission among Kuwaitis (P1) and non-Kuwaitis (P2) living in Kuwait. Methods We fit a modified metapopulation SEIR transmission model to reported cases stratified by two groups to estimate the impact of a partial lockdown on the effective reproduction number ($$ {\mathcal{R}}_e $$ R e ). We estimated the basic reproduction number ($$ {\mathcal{R}}_0 $$ R 0 ) for the transmission in each group and simulated the potential trajectories of an outbreak from the first recorded case of community transmission until 12 days after the partial lockdown. We estimated $$ {\mathcal{R}}_e $$ R e values of both groups before and after the partial curfew, simulated the effect of these values on the epidemic curves and explored a range of cross-transmission scenarios. Results We estimate $$ {\mathcal{R}}_e $$ R e at 1·08 (95% CI: 1·00–1·26) for P1 and 2·36 (2·03–2·71) for P2. On March 22nd, $$ {\mathcal{R}}_e $$ R e for P1 and P2 are estimated at 1·19 (1·04–1·34) and 1·75 (1·26–2·11) respectively. After the partial curfew had taken effect, $$ {\mathcal{R}}_e $$ R e for P1 dropped modestly to 1·05 (0·82–1·26) but almost doubled for P2 to 2·89 (2·30–3·70). Our simulated epidemic trajectories show that the partial curfew measure greatly reduced and delayed the height of the peak in P1, yet significantly elevated and hastened the peak in P2. Modest cross-transmission between P1 and P2 greatly elevated the height of the peak in P1 and brought it forward in time closer to the peak of P2. Conclusion Our results indicate and quantify how the same lockdown intervention can accentuate disease transmission in some subpopulations while potentially controlling it in others. Any such control may further become compromised in the presence of cross-transmission between subpopulations. Future interventions and policies need to be sensitive to socioeconomic and health disparities.

scholarly journals Analysis of Linkages between Environmental Policy Instruments and Innovation: A Case Study of End-of-Life Vehicles Technologies in Japan

2016 ◽  
Vol 9 (2) ◽  
pp. 181 ◽  
Author(s):  
Rajeev Kumar Singh ◽  
Helmut Yabar ◽  
Rie Murakami-Suzuki ◽  
Noriko Nozaki ◽  
Randeep Rakwal
Keyword(s):  
Environmental Policy ◽  
End Of Life ◽  
Environmental Regulations ◽  
Policy Instruments ◽  
Control Policy ◽  
Before And After ◽  
Exogenous Factors ◽  
And Control ◽  
The Impact

<p>Environmental policies are designed to deal with externalities either by internalizing environmental costs or imposing specific standards for environmental pollution. This study aims to examine the impact of environmental regulations related to End-of-Life Vehicles (ELV) on innovation in Japan. We determined whether there is any statistical difference in patent activity comparing the periods before and after the regulations were enacted. In order to control for exogenous factors such as business cycles, we also analyzed the ratios of ELV and total environmental patents during the same periods. Results showed that environmental regulations drive innovations and the number of ELV-related patents were larger even after controlling for such exogenous factors. We concluded that environmental policy for ELV in Japan was effective in inducing innovation. However, we also found that the weakness in these types of command and control policy is the lack of incentives for further innovation.</p>

scholarly journals Vaccinating Adolescents and Children Significantly Reduces COVID-19 Morbidity and Mortality across All Ages: A Popula-Tion-Based Modeling Study Using the UK as an Example

Vaccines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1180
Author(s):  
Tinevimbo Shiri ◽  
Marc Evans ◽  
Carla A. Talarico ◽  
Angharad R. Morgan ◽  
Maaz Mussad ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Age Groups ◽  
Population Level ◽  
Vital Role ◽  
Morbidity And Mortality ◽  
Transmission Model ◽  
Base Case ◽  
Global Public Health ◽  
The Uk ◽  
The Impact

Debate persists around the risk–benefit balance of vaccinating adolescents and children against COVID-19. Central to this debate is quantifying the contribution of adolescents and children to the transmission of SARS-CoV-2, and the potential impact of vaccinating these age groups. In this study, we present a novel SEIR mathematical disease transmission model that quantifies the impact of different vaccination strategies on population-level SARS-CoV-2 infections and clinical outcomes. The model employs both age- and time-dependent social mixing patterns to capture the impact of changes in restrictions. The model was used to assess the impact of vaccinating adolescents and children on the natural history of the COVID-19 pandemic across all age groups, using the UK as an example. The base case model demonstrates significant increases in COVID-19 disease burden in the UK following a relaxation of restrictions, if vaccines are limited to those ≥18 years and vulnerable adolescents (≥12 years). Including adolescents and children in the vaccination program could reduce overall COVID-related mortality by 57%, and reduce cases of long COVID by 75%. This study demonstrates that vaccinating adolescents and children has the potential to play a vital role in reducing SARS-CoV-2 infections, and subsequent COVID-19 morbidity and mortality, across all ages. Our results have major global public health implications and provide valuable information to inform a potential pandemic exit strategy.

scholarly journals The attainment of herd immunity with a reducing risk of contact infection in modelling 

2021 ◽  
Author(s):  
Kian Boon Law ◽  
Kalaiarasu M Peariasamy ◽  
Hishamshah Ibrahim ◽  
Noor Hisham Abdullah
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Herd Immunity ◽  
Sir Model ◽  
Mixed Population ◽  
Transmission Model ◽  
Force Of Infection ◽  
Initial Stage ◽  
And Control ◽  
Model Features

Abstract The risk of contact infection among susceptible individuals in a randomly mixed population can be reduced by the presence of immune individuals and this principle forms the fundamental of herd immunity. The conventional susceptible-infectious-recovered (SIR) model features an infection-induced herd immunity model, but does not include the reducing risk of contact infection among susceptible individuals in the transmission model, therefore tends to overestimate the transmission dynamics of infectious diseases. Here we show that the reducing risk of contact infection among susceptible individuals can be achieved by incorporating the proportion of susceptible individuals (model A) or the inverse of proportion of recovered individuals (model B) in the force of infection of the SIR model. We numerically simulated the conventional SIR model and both new SIR models A and B under the exact condition with a basic reproduction number of 3·0. Prior to the numerical simulation, the threshold for the eradication of infectious disease through herd immunity was expected to be 0·667 (66·7%) for all three models. All three models performed likewise at the initial stage of disease transmission. In the conventional SIR model, the infectious disease subsided when 94·0 % of the population had been infected and recovered, way above the expected threshold for eradication and control of the infectious disease. Both models A and B simulated the infectious disease to diminish when 66·7% and 75·6% of the population had been infected, showing herd immunity might protect more susceptible individuals from the infectious disease as compared to the projection generated by the conventional SIR. Our study shows that model A provides a better framework for modelling herd immunity through vaccination, while model B provides a better framework for modelling herd immunity through infection. Both models overcome the insufficiency of the conventional SIR model in attaining the effect of herd immunity in modelling outputs, which is important and relevant for modelling infectious disease, such as the COVID-19 in a randomly mixed population.

scholarly journals Natural Variation Can Significantly Alter the Sensitivity of Influenza A (H5N1) Viruses to Oseltamivir

2006 ◽  
Vol 50 (11) ◽  
pp. 3809-3815 ◽  
Author(s):  
M. A. Rameix-Welti ◽  
F. Agou ◽  
P. Buchy ◽  
S. Mardy ◽  
J. T. Aubin ◽  
...  
Keyword(s):  
Influenza A ◽  
Influenza Pandemic ◽  
Fold Increase ◽  
Influenza Viruses ◽  
Control Measures ◽  
Inhibition Assay ◽  
Highly Pathogenic ◽  
H5n1 Influenza ◽  
The Impact

ABSTRACT Geographic spread of highly pathogenic avian H5N1 influenza viruses may give rise to an influenza pandemic. During the first months of a pandemic, control measures would rely mainly on antiviral drugs, such as the neuraminidase (NA) inhibitors oseltamivir and zanamivir. In this study, we compare the sensitivities to oseltamivir of the NAs of several highly pathogenic H5N1 viruses isolated in Asia from 1997 to 2005. The corresponding 50% inhibitory concentrations were determined using a standard in vitro NA inhibition assay. The Km for the substrate and the affinity for the inhibitor (Ki ) of NA were determined for a 1997 and a 2005 virus, using an NA inhibition assay on cells transiently expressing the viral enzyme. Our data show that the sensitivities of the NAs of H5N1 viruses isolated in 2004 and 2005 to oseltamivir are about 10-fold higher than those of earlier H5N1 viruses or currently circulating H1N1 viruses. Three-dimensional modeling of the N1 protein predicted that Glu248Gly and Tyr252His changes could account for increased sensitivity. Our data indicate that genetic variation in the absence of any drug-selective pressure may result in significant variations in sensitivity to anti-NA drugs. Although the clinical relevance of a 10-fold increase in the sensitivity of NA to oseltamivir needs to be investigated further, the possibility that sensitivity to anti-NA drugs could increase (or possibly decrease) significantly, even in the absence of treatment, underscores the need for continuous evaluation of the impact of genetic drift on this parameter, especially for influenza viruses with pandemic potential.

scholarly journals Optima TB: A tool to help optimally allocate tuberculosis spending

2021 ◽  
Vol 17 (9) ◽  
pp. e1009255
Author(s):  
Lara Goscé ◽  
Gerard J. Abou Jaoude ◽  
David J. Kedziora ◽  
Clemens Benedikt ◽  
Azfar Hussain ◽  
...  
Keyword(s):  
Priority Setting ◽  
Disease Transmission ◽  
Technical Assistance ◽  
Transmission Model ◽  
People Living With Hiv ◽  
Evidence Based ◽  
Middle Income ◽  
Substantial Impact ◽  
Local Stakeholders ◽  
The Impact

Approximately 85% of tuberculosis (TB) related deaths occur in low- and middle-income countries where health resources are scarce. Effective priority setting is required to maximise the impact of limited budgets. The Optima TB tool has been developed to support analytical capacity and inform evidence-based priority setting processes for TB health benefits package design. This paper outlines the Optima TB framework and how it was applied in Belarus, an upper-middle income country in Eastern Europe with a relatively high burden of TB. Optima TB is a population-based disease transmission model, with programmatic cost functions and an optimisation algorithm. Modelled populations include age-differentiated general populations and higher-risk populations such as people living with HIV. Populations and prospective interventions are defined in consultation with local stakeholders. In partnership with the latter, demographic, epidemiological, programmatic, as well as cost and spending data for these populations and interventions are then collated. An optimisation analysis of TB spending was conducted in Belarus, using program objectives and constraints defined in collaboration with local stakeholders, which included experts, decision makers, funders and organisations involved in service delivery, support and technical assistance. These analyses show that it is possible to improve health impact by redistributing current TB spending in Belarus. Specifically, shifting funding from inpatient- to outpatient-focused care models, and from mass screening to active case finding strategies, could reduce TB prevalence and mortality by up to 45% and 50%, respectively, by 2035. In addition, an optimised allocation of TB spending could lead to a reduction in drug-resistant TB infections by 40% over this period. This would support progress towards national TB targets without additional financial resources. The case study in Belarus demonstrates how reallocations of spending across existing and new interventions could have a substantial impact on TB outcomes. This highlights the potential for Optima TB and similar modelling tools to support evidence-based priority setting.

scholarly journals Clumpiness: Modeling the Impact of Social Dynamics on COVID-19 Spread

2021 ◽  
Author(s):  
Ben Goertzel ◽  
Cassio Pennachin ◽  
Deborah Duong ◽  
Matthew Iklé ◽  
Michael Duncan ◽  
...  
Keyword(s):  
Social Networks ◽  
Disease Transmission ◽  
Social Dynamics ◽  
Demographic Data ◽  
Herd Immunity ◽  
Control Policy ◽  
Policy Formation ◽  
Packet Transmission ◽  
Physical Proximity ◽  
The Impact

We present an agent based simulation supplemented with two novel social network interconnectivity measures, `clumpiness' and `hoprank,' that are the same concept defined at global and local levels, respectively. The measures may be computed from samples of readily available demographic data, and are useful for measuring probabilistic packet transmission through social networks. For simplicity, agents in our simulation group together through homophily, the principle of `like attracts like'. In three studies we apply clumpiness to measure the effects, on disease transmission, caused by social networks of both homophilic physical proximity and homophilic information replication. The particular characteristic we are interested in about disease transmission is herd immunity, the percentage of a population that has to be immune in order to prevent infection from spreading to those who are not. Two studies demonstrate innovations measuring herd immunity levels and predicting future outbreak locations, procedures relevant to epidemiological control policy. In the first study, we look at how homophilic physical proximity networks form natural bubbles that act as frictive surfaces that affect the speed of transmission of packets and influence herd immunity levels. In the second study, we test clumpiness in homophilic proximity social networks as a predictor of future infection outbreaks at the level of individual schools, restaurants, and workplaces. Our third study demonstrates that protective social bubbles form naturally from homophilic information replication networks, and enhance the natural bubbles that come from the homophilic physical proximity networks. Accurate description of this information environment lays the foundation for epidemiological messaging policy formation.

scholarly journals Evolution of disease transmission rate during the course of SARS-COV-2: Patterns and determinants

2020 ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Region Of Interest ◽  
Health Interventions ◽  
Transmission Model ◽  
Public Health Interventions ◽  
Containment Measures ◽  
The Impact

Abstract To date, many studies have argued the potential impact of public health interventions on flattening the epidemic curve of SARS-CoV-2. Most of them have focused on simulating the impact of interventions in a region of interest by manipulating contact patterns and key transmission parameters to reflect different scenarios. Our study looks into the evolution of the daily effective reproduction number during the epidemic via a stochastic transmission model. We found this measure (although model-dependent) provides an early signal of the efficacy of containment measures. This epidemiological parameter when updated in real-time can also provide better predictions of future outbreaks. Our results found a substantial variation in the effect of public health interventions on the dynamic of SARS-CoV-2 transmission over time and across countries, that could not be explained solely by the timing and number of the adopted interventions. This suggests that further knowledge about the idiosyncrasy of their implementation and effectiveness is required. Although sustained containment measures have successfully lowered growth in disease transmission, more than half of the 101 studied countries failed to maintain the effective reproduction number close to or below 1. This resulted in continued growth in reported cases. Finally, we were able to predict with reasonable accuracy which countries would experience outbreaks in the next 30 days.

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