scholarly journals Estimating dynamic transmission model parameters for seasonal influenza by fitting to age and season-specific influenza-like illness incidence

Epidemics ◽  
2015 ◽  
Vol 13 ◽  
pp. 1-9 ◽  
Author(s):  
Nele Goeyvaerts ◽  
Lander Willem ◽  
Kim Van Kerckhove ◽  
Yannick Vandendijck ◽  
Germaine Hanquet ◽  
...  
Keyword(s):  
Seasonal Influenza ◽  
Transmission Model ◽  
Model Parameters ◽  
Influenza Like Illness ◽  
Dynamic Transmission Model

Cost-effectiveness of influenza control measures: a dynamic transmission model-based analysis

2013 ◽  
Vol 141 (12) ◽  
pp. 2581-2594 ◽  
Author(s):  
S.-C. CHEN ◽  
C.-M. LIAO
Keyword(s):  
Cost Effectiveness ◽  
Natural Ventilation ◽  
Seasonal Influenza ◽  
School Setting ◽  
Control Measures ◽  
Transmission Model ◽  
Model Based ◽  
Dynamic Transmission Model ◽  
Influenza Transmission ◽  
The Cost

SUMMARYWe investigated the cost-effectiveness of different influenza control strategies in a school setting in Taiwan. A susceptible-exposure-infected-recovery (SEIR) model was used to simulate influenza transmission and we used a basic reproduction number (R0)–asymptomatic proportion (θ) control scheme to develop a cost-effectiveness model. Based on our dynamic transmission model and economic evaluation, this study indicated that the optimal cost-effective strategy for all modelling scenarios was a combination of natural ventilation and respiratory masking. The estimated costs were US$10/year per person in winter for one kindergarten student. The cost for hand washing was estimated to be US$32/year per person, which was much lower than that of isolation (US$55/year per person) and vaccination (US$86/year per person) in containing seasonal influenza. Transmission model-based, cost-effectiveness analysis can be a useful tool for providing insight into the impacts of economic factors and health benefits on certain strategies for controlling seasonal influenza.

scholarly journals Dependence of COVID-19 Policies on End-of-Year Holiday Contacts in Mexico City Metropolitan Area: A Modeling Study

2021 ◽  
Vol 6 (2) ◽  
pp. 238146832110492
Author(s):  
Fernando Alarid-Escudero ◽  
Valeria Gracia ◽  
Andrea Luviano ◽  
Jorge Roa ◽  
Yadira Peralta ◽  
...  
Keyword(s):  
Mexico City ◽  
Metropolitan Area ◽  
Prediction Interval ◽  
Specific Capacity ◽  
Transmission Model ◽  
Primary Data ◽  
Model Parameters ◽  
Hospital Capacity ◽  
Dynamic Transmission Model ◽  
The City

Background. Mexico City Metropolitan Area (MCMA) has the largest number of COVID-19 (coronavirus disease 2019) cases in Mexico and is at risk of exceeding its hospital capacity in early 2021. Methods. We used the Stanford-CIDE Coronavirus Simulation Model (SC-COSMO), a dynamic transmission model of COVID-19, to evaluate the effect of policies considering increased contacts during the end-of-year holidays, intensification of physical distancing, and school reopening on projected confirmed cases and deaths, hospital demand, and hospital capacity exceedance. Model parameters were derived from primary data, literature, and calibrated. Results. Following high levels of holiday contacts even with no in-person schooling, MCMA will have 0.9 million (95% prediction interval 0.3–1.6) additional COVID-19 cases between December 7, 2020, and March 7, 2021, and hospitalizations will peak at 26,000 (8,300–54,500) on January 25, 2021, with a 97% chance of exceeding COVID-19-specific capacity (9,667 beds). If MCMA were to control holiday contacts, the city could reopen in-person schools, provided they increase physical distancing with 0.5 million (0.2–0.9) additional cases and hospitalizations peaking at 12,000 (3,700–27,000) on January 19, 2021 (60% chance of exceedance). Conclusion. MCMA must increase COVID-19 hospital capacity under all scenarios considered. MCMA’s ability to reopen schools in early 2021 depends on sustaining physical distancing and on controlling contacts during the end-of-year holiday.

scholarly journals Estimating the Burden of Dengue in the Philippines Using a Dynamic Transmission Model

2018 ◽  
Vol 52 (2) ◽  
Author(s):  
Kent Jason G. Cheng ◽  
Hilton Y. Lam ◽  
Adovich S. Rivera ◽  
Bernadette A. Tumanan-Mendoza ◽  
Marissa M. Alejandria ◽  
...  
Keyword(s):  
The Philippines ◽  
Sensitivity Analyses ◽  
Transmission Model ◽  
Model Parameters ◽  
Government Interventions ◽  
Productivity Losses ◽  
Annual Productivity ◽  
Dynamic Transmission Model ◽  
Public Payer ◽  
The Cost

Objective. This study aimed to describe dengue burden in the Philippines. Specifically, health and economic costs of the disease were estimated. Methods. A published serotype-specific and age-stratified dengue dynamic transmission model was populated with Philippine-specific dengue epidemiology and cost data. Data were gathered from literature and record reviews. Dengue experts were consulted to validate the model parameters. Sensitivity analyses were performed to test the uncertainty of input parameters on model outcomes. Results. By 2016 to 2020, it is estimated that annually, average hospitalized cases will amount to 401,191 and ambulatory cases will amount to 239,497; resulting to USD 139 million (PhP 5.9 billion) and USD 19 million (PhP 827 million) worth of aggregate costs shouldered by the public payer for hospitalized and ambulatory cases, respectively. Average annual productivity losses may amount to USD 19 million (PhP 821 million) and DALY lost is expected to be 50,622. Conclusion. The cost of dengue is high especially since the Philippines is an endemic country. Thus, there is a need to optimize government interventions such as vector control and vaccination that aim to prevent dengue infections.

scholarly journals Analysis and Evaluation of Non-Pharmaceutical Interventions on Prevention and Control of COVID-19: A Case Study of Wuhan City

2021 ◽  
Vol 10 (7) ◽  
pp. 480
Author(s):  
Wen Cao ◽  
Haoran Dai ◽  
Jingwen Zhu ◽  
Yuzhen Tian ◽  
Feilin Peng
Keyword(s):  
Prevention And Control ◽  
Transmission Model ◽  
Model Parameters ◽  
Protection Measures ◽  
Dynamic Transmission Model ◽  
Sufficient Intensity ◽  
And Control ◽  
Self Protection ◽  
The Impact ◽  
Pharmaceutical Interventions

As the threat of COVID-19 increases, many countries have carried out various non-pharmaceutical interventions. Although many studies have evaluated the impact of these interventions, there is a lack of mapping between model parameters and actual geographic areas. In this study, a non-pharmaceutical intervention model of COVID-19 based on a discrete grid is proposed from the perspective of geography. This model can provide more direct and effective information for the formulation of prevention and control policies. First, a multi-level grid was introduced to divide the geographical space, and the properties of the grid boundary were used to describe the quarantine status and intensity in these different spaces; this was also combined with the model of hospital isolation and self-protection. Then, a process for the spatiotemporal evolution of the early COVID-19 spread is proposed that integrated the characteristics of residents’ daily activities. Finally, the effect of the interventions was quantitatively analyzed by the dynamic transmission model of COVID-19. The results showed that quarantining is the most effective intervention, especially for infectious diseases with a high infectivity. The introduction of a quarantine could effectively reduce the number of infected humans, advance the peak of the maximum infected number of people, and shorten the duration of the pandemic. However, quarantines only function properly when employed at sufficient intensity; hospital isolation and self-protection measures can effectively slow the spread of COVID-19, thus providing more time for the relevant departments to prepare, but an outbreak will occur again when the hospital reaches full capacity. Moreover, medical resources should be concentrated in places where there is the most urgent need under a strict quarantine measure.

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 High COVID-19 transmission potential associated with re-opening universities can be mitigated with layered interventions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ellen Brooks-Pollock ◽  
Hannah Christensen ◽  
Adam Trickey ◽  
Gibran Hemani ◽  
Emily Nixon ◽  
...  
Keyword(s):  
Prediction Interval ◽  
Mitigation Strategies ◽  
Control Measures ◽  
Transmission Model ◽  
Model Parameters ◽  
Infection Rates ◽  
First Year Students ◽  
Transmission Potential ◽  
Plausible Model ◽  
Effective Option

AbstractControlling COVID-19 transmission in universities poses challenges due to the complex social networks and potential for asymptomatic spread. We developed a stochastic transmission model based on realistic mixing patterns and evaluated alternative mitigation strategies. We predict, for plausible model parameters, that if asymptomatic cases are half as infectious as symptomatic cases, then 15% (98% Prediction Interval: 6–35%) of students could be infected during the first term without additional control measures. First year students are the main drivers of transmission with the highest infection rates, largely due to communal residences. In isolation, reducing face-to-face teaching is the most effective intervention considered, however layering multiple interventions could reduce infection rates by 75%. Fortnightly or more frequent mass testing is required to impact transmission and was not the most effective option considered. Our findings suggest that additional outbreak control measures should be considered for university settings.

The Effect of Thermo-Hydrodynamics on Manual Automotive Transmissions Gear Rattle

2009 ◽  
Author(s):  
Miguel De la Cruz ◽  
Stephanos Theodossiades ◽  
Homer Rahnejat ◽  
Patrick Kelly
Keyword(s):  
Contact Stiffness ◽  
Hertzian Contact ◽  
Transmission Model ◽  
System Response ◽  
Common Denominator ◽  
Gear Teeth ◽  
Lubricated Contacts ◽  
Engine Order ◽  
Dynamic Transmission Model ◽  
Gear Rattle

Manual transmission gear rattle is the result of repetitive impacts of gear meshing teeth within their backlash. This NVH phenomenon is a major industrial concern and can occur under various loaded or unloaded conditions. It fundamentally differs from other transient NVH phenomena, such as clonk or thud, which are due to impulsive actions. However, they all have their lowest common denominator in the action of contact/impact forces through lubricated contacts. Various forms of rattle have, therefore, been defined: idle rattle, drive rattle, creep rattle and over-run rattle. This paper presents a dynamic transmission model for creep rattle conditions (engaged gear at low engine RPM). The model takes into account the lubricated impact force between a gear teeth pair during a meshing cycle as well as the friction between their flanks. Hertzian contact conditions are applied to the gear pair along the torque path. Additionally, isoviscous hydrodynamic regime of lubrication is assumed for unselected (loose gear pairs) with lightly loaded impact conditions. The highly non-linear impacts induce a range of system response frequencies. These include engine order harmonics, harmonics of meshing frequency and natural frequencies related to contact stiffness. The last of these are dependent on the contact geometry and lubricant rheology. The analysis includes lubricant viscosity variation due to generated contact pressures as well as temperature. For loose gears, subject to oscillations on their retaining bearings, bearing friction is also considered.

scholarly journals Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England

Science ◽  
2021 ◽  
Vol 372 (6538) ◽  
pp. eabg3055 ◽  
Author(s):  
Nicholas G. Davies ◽  
Sam Abbott ◽  
Rosanna C. Barnard ◽  
Christopher I. Jarvis ◽  
Adam J. Kucharski ◽  
...  
Keyword(s):  
United States ◽  
Severe Acute Respiratory Syndrome ◽  
Reproduction Number ◽  
The United States ◽  
Control Measures ◽  
Transmission Model ◽  
Educational Institutions ◽  
Stringent Control ◽  
Dynamic Transmission Model ◽  
Credible Intervals

A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in September 2020 and is rapidly spreading toward fixation. Using a variety of statistical and dynamic modeling approaches, we estimate that this variant has a 43 to 90% (range of 95% credible intervals, 38 to 130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine rollout, COVID-19 hospitalizations and deaths across England in the first 6 months of 2021 were projected to exceed those in 2020. VOC 202012/01 has spread globally and exhibits a similar transmission increase (59 to 74%) in Denmark, Switzerland, and the United States.

Sign in / Sign up

Export Citation Format

Share Document